From f9197f6aeefdef5b7d195fe5435aaa9d2037673d Mon Sep 17 00:00:00 2001 From: mbeach-aws <85963088+mbeach-aws@users.noreply.github.com> Date: Thu, 12 Oct 2023 12:22:10 -0400 Subject: [PATCH 01/24] Update PennyLane Example Notebook 0 to use Hybrid Jobs Decorators (#393) --- .../0_Getting_started/0_Getting_started.ipynb | 337 +++++++++++++++--- 1 file changed, 279 insertions(+), 58 deletions(-) diff --git a/examples/pennylane/0_Getting_started/0_Getting_started.ipynb b/examples/pennylane/0_Getting_started/0_Getting_started.ipynb index a5065df03..597f221d7 100644 --- a/examples/pennylane/0_Getting_started/0_Getting_started.ipynb +++ b/examples/pennylane/0_Getting_started/0_Getting_started.ipynb @@ -7,17 +7,6 @@ "# Combining PennyLane with Amazon Braket" ] }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Use Braket SDK Cost Tracking to estimate the cost to run this example\n", - "from braket.tracking import Tracker\n", - "t = Tracker().start()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -28,11 +17,6 @@ ] }, { - "attachments": { - "pl-braket.png": { - "image/png": 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" - } - }, "cell_type": "markdown", "metadata": {}, "source": [ @@ -62,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -79,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -111,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -156,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -172,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -189,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -198,8 +182,8 @@ "text": [ "Drawing of circuit:\n", "\n", - " 0: ──RX(0.1)──╭C──┤ \n", - " 1: ──RY(0.2)──╰X──┤ ⟨Z⟩ \n" + "0: ──RX(0.10)─╭●─┤ \n", + "1: ──RY(0.20)─╰X─┤ \n" ] } ], @@ -217,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -233,13 +217,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([-0.0978434 , -0.19767681]),)" + "array([-0.0978434 , -0.19767681])" ] }, "execution_count": 8, @@ -269,11 +253,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "opt = qml.GradientDescentOptimizer(stepsize=0.2)" + "opt = qml.GradientDescentOptimizer(stepsize=0.1)" ] }, { @@ -285,32 +269,30 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Minimized circuit output: -0.9999996577749632\n", - "Optimized parameters: [4.19618503e-04 3.14087965e+00]\n" + "Minimized circuit output: 0.372610706471266\n", + "Optimized parameters: [0.4839502 1.13630274]\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "iterations = 50\n", + "iterations = 20\n", "\n", "costs = []\n", "\n", @@ -319,9 +301,10 @@ " costs.append(cost)\n", "\n", "# Visualize results\n", - "import matplotlib.pyplot as plt \n", + "import matplotlib.pyplot as plt\n", + "\n", "costs.append(circuit(params))\n", - "plt.plot(costs)\n", + "plt.plot(costs, \"-o\")\n", "plt.xlabel(\"Iterations\")\n", "plt.ylabel(\"Cost\")\n", "\n", @@ -346,11 +329,6 @@ ] }, { - "attachments": { - "remote-single-job.png": { - "image/png": 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" - } - }, "cell_type": "markdown", "metadata": {}, "source": [ @@ -358,15 +336,27 @@ "\n", "Amazon Braket also provides access to on-demand, high-performance simulators and quantum processing units (QPUs) from different [providers](https://aws.amazon.com/braket/hardware-providers/). These devices can be accessed through PennyLane by changing a single line of code, unlocking the potential for machine learning and optimization on quantum hardware and high performance simulators!\n", "\n", - "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Use Braket SDK Cost Tracking to estimate the cost to run this example\n", + "from braket.tracking import Tracker\n", "\n", - "Each remote Braket device can be selected through its [ARN](https://docs.aws.amazon.com/general/latest/gr/aws-arns-and-namespaces.html). The supported devices on Braket are listed [here](https://docs.aws.amazon.com/braket/latest/developerguide/braket-devices.html). For now, we will pick the on-demand SV1 simulator." + "braket_tasks_cost = Tracker().start()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "Each remote Braket device can be selected through its [ARN](https://docs.aws.amazon.com/general/latest/gr/aws-arns-and-namespaces.html). The supported devices on Braket are listed [here](https://docs.aws.amazon.com/braket/latest/developerguide/braket-devices.html). For now, we will pick the on-demand SV1 simulator.\n", + "\n", "
\n", "Caution: Running hybrid algorithms on a QPU can take a long time and incur high usage fees charged to your AWS account.\n", "
" @@ -394,7 +384,7 @@ "metadata": {}, "outputs": [], "source": [ - "dev = qml.device('braket.aws.qubit', device_arn=device_arn, wires=2)" + "dev = qml.device(\"braket.aws.qubit\", device_arn=device_arn, wires=2)" ] }, { @@ -420,8 +410,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Result of circuit run on SV1: -0.9999996577749632\n", - "Result of gradient calculation on SV1: (array([ 0.00041962, -0.000713 ]),)\n" + "Result of circuit run on SV1: 0.37261070647126604\n", + "Result of gradient calculation on SV1: [-0.19585986 -0.80291741]\n" ] } ], @@ -451,25 +441,256 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Task Summary\n", - "{'arn:aws:braket:::device/quantum-simulator/amazon/sv1': {'shots': 0, 'tasks': {'COMPLETED': 6}, 'execution_duration': datetime.timedelta(microseconds=264000), 'billed_execution_duration': datetime.timedelta(seconds=18)}}\n", + "Quantum Task Summary\n", + "{'arn:aws:braket:::device/quantum-simulator/amazon/sv1': {'shots': 0, 'tasks': {'COMPLETED': 2}, 'execution_duration': datetime.timedelta(microseconds=10000), 'billed_execution_duration': datetime.timedelta(seconds=6)}}\n", "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run this example: 0.022 USD\n" + "Estimated cost to run this example: 0.008 USD\n" ] } ], "source": [ "print(\"Quantum Task Summary\")\n", - "print(t.quantum_tasks_statistics())\n", - "print('Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).')\n", - "print(f\"Estimated cost to run this example: {t.qpu_tasks_cost() + t.simulator_tasks_cost():.3f} USD\")" + "print(braket_tasks_cost.quantum_tasks_statistics())\n", + "print(\n", + " \"Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\"\n", + ")\n", + "print(\n", + " f\"Estimated cost to run this example: {braket_tasks_cost.qpu_tasks_cost() + braket_tasks_cost.simulator_tasks_cost():.3f} USD\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running on a QPU with Amazon Braket Hybrid Jobs\n", + "\n", + "In this notebook, the classical part of the algorithm was running locally. For longer-running algorithms or those requiring more intensive compute resources, it's recommended to dispatch the algorithm to Amazon Braket Hybrid Jobs, which fully manages the classical infrastructure, allowing you to focus on the algorithm. For example, you can train a larger circuit or increase the number of iterations.\n", + "\n", + "The second benefit of running the algorithm as a hybrid job is that for iterative algorithms that require repeated calls to a QPU, you retain priority for that QPU. Once your quantum tasks are created in the hybrid job, they run ahead of other tasks waiting in the regular quantum task queue. This is because hybrid jobs have a separate queue from standalone tasks, ensuring that only a single hybrid job can run on a QPU at a time. This means your algorithm will not be interrupted by other quantum tasks, so it will run more efficiently and predictably. However, hybrid jobs have a separate queue from standalone tasks, so only a single hybrid job can run on a QPU at a time. For a single quantum circuit or a batch of circuits, it's recommended to create quantum tasks instead of hybrid jobs. Only iterative algorithms benefit from QPU priority queuing.\n", + "\n", + "Note that hybrid jobs have at least a one-minute startup time since they create a containerized environment on Amazon EC2. So for very short workloads, there is likely no need to create a hybrid job.\n", + "\n", + "You can run your local Python code as an Amazon Braket hybrid job by annotating your code with the `@hybrid_job`` decorator, as shown in the following code example. Only Python 3.10 is supported by default. For custom Python versions, you can choose to use a custom container from [Amazon Elastic Container Registry (ECR)](https://aws.amazon.com/ecr/) (see [BYOC](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-byoc.html)).\n", + "\n", + "\n", + "In the following code, we create a hybrid job for 10 iterations targeting Rigetti Aspen-M-3. Since we specified Aspen-M-3 as the device, this job will run once Aspen-M-3 is available and has no jobs running ahead of it." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from braket.jobs.decorator import hybrid_job\n", + "from braket.devices import Devices\n", + "from braket.jobs import log_metric\n", + "\n", + "device_arn = Devices.Amazon.SV1\n", + "# device_arn = Devices.Rigetti.AspenM3\n", + "\n", + "\n", + "@hybrid_job(device=device_arn) # set priority QPU\n", + "def qubit_rotation(stepsize=0.1, iterations=5):\n", + " task_tracker = Tracker().start() # track Braket quantum tasks costs\n", + "\n", + " dev = qml.device(\"braket.aws.qubit\", device_arn=device_arn.value, wires=2, shots=1_000)\n", + "\n", + " params = np.array([0.1, 0.2])\n", + "\n", + " @qml.qnode(dev)\n", + " def circuit(params):\n", + " qml.RX(params[0], wires=0)\n", + " qml.RY(params[1], wires=1)\n", + " qml.CNOT(wires=[0, 1])\n", + " return qml.expval(qml.PauliZ(1))\n", + "\n", + " opt = qml.GradientDescentOptimizer(stepsize)\n", + "\n", + " costs = []\n", + " for i in range(iterations):\n", + " params, cost = opt.step_and_cost(circuit, params)\n", + " costs.append(cost)\n", + "\n", + " # Record the value of the cost function with each iteration\n", + " log_metric(metric_name=\"cost_function\", value=cost, iteration_number=i)\n", + "\n", + " # Additionally, keep track of cost in USD for Braket tasks\n", + " braket_task_cost = float(\n", + " task_tracker.qpu_tasks_cost() + task_tracker.simulator_tasks_cost()\n", + " )\n", + " log_metric(metric_name=\"braket_cost\", value=braket_task_cost, iteration_number=i)\n", + "\n", + " return {\"params\": params, \"costs\": costs, \"braket_tasks_cost\": braket_task_cost}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How long will it take the hybrid job to run? \n", + "Let's first check if the device is currently available with `AwsDevice(device_arn).is_available()`. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from braket.aws import AwsDevice\n", + "from braket.devices import Devices\n", + "\n", + "AwsDevice(device_arn).is_available" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we check the hybrid job queue depth with `AwsDevice(device_arn).queue_depth().job`. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "AwsDevice(device_arn).queue_depth().job" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the device is available and there are no hybrid jobs currently running, then it should take about 5 minutes to complete. \n", + "\n", + "
\n", + "Caution: Running the following cell will only run once the QPU is available. This may take a long time and will result in usage fees charged to your AWS account. Only uncomment the cell if you are comfortable with the potential wait-time and costs. We recommend monitoring the Billing & Cost Management Dashboard on the AWS console and being aware that hybrid jobs involving a large number of qubits can be costly.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AwsQuantumJob('arn':'arn:aws:braket:us-west-1:961591465522:job/qubit-rotation-1696513018')\n" + ] + } + ], + "source": [ + "job = qubit_rotation(stepsize=0.2, iterations=20)\n", + "print(job)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the hybrid job has completed, we can retrieve the results with `job.results()`." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'params': [0.32900000000000007, 2.5835999999999997], 'costs': [0.974, 0.958, 0.932, 0.936, 0.894, 0.838, 0.794, 0.73, 0.7, 0.544, 0.426, 0.284, 0.156, 0.056, -0.124, -0.24, -0.374, -0.558, -0.69, -0.748], 'braket_tasks_cost': 0.375}\n", + "CPU times: user 259 ms, sys: 15.9 ms, total: 275 ms\n", + "Wall time: 6min 35s\n" + ] + } + ], + "source": [ + "%%time\n", + "results = job.result()\n", + "print(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minimized circuit output: 0.37261070647126604\n", + "Optimized parameters: [0.4839502 1.13630274]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(results[\"costs\"], \"o-\")\n", + "plt.xlabel(\"Iterations\")\n", + "plt.ylabel(\"Cost\")\n", + "\n", + "print(\"Minimized circuit output:\", circuit(params))\n", + "print(\"Optimized parameters:\", params)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", + "Estimated cost to run this example: 0.398 USD\n" + ] + } + ], + "source": [ + "job_cost = job.result()[\"braket_tasks_cost\"]\n", + "sv1_cost = float(braket_tasks_cost.simulator_tasks_cost())\n", + "\n", + "print(\n", + " \"Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\"\n", + ")\n", + "print(f\"Estimated cost to run this example: {job_cost + sv1_cost :.3f} USD\")" ] } ], @@ -489,7 +710,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" }, "vscode": { "interpreter": { From 00ab585a87293f6db6ab318a8b1ac678c504e423 Mon Sep 17 00:00:00 2001 From: mbeach-aws <85963088+mbeach-aws@users.noreply.github.com> Date: Fri, 13 Oct 2023 08:54:49 -0400 Subject: [PATCH 02/24] PennyLane notebook w jobs decorator 2 (#397) --- .../2_Graph_optimization_with_QAOA.ipynb | 1871 +++++++++-------- 1 file changed, 1039 insertions(+), 832 deletions(-) diff --git a/examples/pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb b/examples/pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb index 831d4a697..06530b640 100644 --- a/examples/pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb +++ b/examples/pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb @@ -1,833 +1,1040 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Graph optimization with QAOA" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Use Braket SDK Cost Tracking to estimate the cost to run this example\n", - "from braket.tracking import Tracker\n", - "t = Tracker().start()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One application area where near-term quantum hardware is expected to shine is in graph optimization. Graph-based problems are interesting to explore because they have both strong links to practical use-cases (such as logistics and social networks) and are also often hard to solve." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Graphs are composed of a collection of interconnected nodes. For example, here is a six-node graph:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import networkx as nx\n", - "\n", - "n_nodes = 6\n", - "p = 0.5 # probability of an edge\n", - "seed = 1967\n", - "\n", - "g = nx.erdos_renyi_graph(n_nodes, p=p, seed=seed)\n", - "positions = nx.spring_layout(g, seed=seed)\n", - "\n", - "nx.draw(g, with_labels=True, pos=positions, node_size=600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Many practical use-cases can be mapped to a graph structure. In a social network, the nodes of a graph can represent users and the edges can represent connections between the users.\n", - "\n", - "We often need to solve optimization problems to identify important properties of the graph. These problems can include:\n", - "\n", - "- finding large clusters of fully connected nodes (known as [maximum clique](https://en.wikipedia.org/wiki/Clique_problem))\n", - "- finding a minimum number of nodes that connect to every edge in the graph (known as [minimum vertex cover](https://en.wikipedia.org/wiki/Vertex_cover))\n", - "- finding a partition of the nodes into two subsets so that the greatest number of edges are intersected (known as [maximum cut](https://en.wikipedia.org/wiki/Maximum_cut))\n", - "\n", - "This tutorial shows how a quantum algorithm called QAOA can be run using PennyLane and Braket to solve graph-based optimization problems. We begin with a small 6-node graph and then push the limits to run a 20-node graph using parallel executions on SV1." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Note This notebook requires PennyLane version 0.17 or above.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## QAOA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The quantum approximate optimization algorithm (QAOA) is an algorithm designed for near-term hardware. It can find approximate solutions to combinatorial optimization problems such as graph-based problems.\n", - "\n", - "QAOA is covered in more depth in the [QAOA_braket](../../hybrid_quantum_algorithms/QAOA/QAOA_braket.ipynb) notebook as well as in PennyLane [tutorials](https://pennylane.ai/qml/demos/tutorial_qaoa_intro.html). The following is a short summary to refresh the key concepts.\n", - "\n", - "\n", - "QAOA begins by associating the optimization problem with a cost Hamiltonian $H_C$ and choosing a mixer Hamiltonian $H_{M}$. It proceeds by repetitively applying multiple layers of the unitaries $\\exp{(-i \\gamma_i H_C)}$ and $\\exp{(-i \\alpha_i H_M)}$ with controllable parameters $\\gamma_i$ and $\\alpha_i$, as shown in the diagram below." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The algorithm then measures the cost Hamiltonian $H_C$. By varying the controllable parameters $\\gamma_i$ and $\\alpha_i$, the expectation value of the cost Hamiltonian is minimized. Applying the optimized unitaries prepares a quantum state that contains information about the optimal configuration for the problem. Sampling from the state will give a candidate solution.\n", - "\n", - "
\n", - "Summary If you are less familiar with QAOA and quantum algorithms, the key takeaway message is that the algorithm involves an optimization of the controllable parameters $\\gamma_i$ and $\\alpha_i$ that the quantum circuit depends on. This can be tackled naturally using the PennyLane/Braket pipeline.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing the problem" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's consider the graph above and aim to find the maximum clique, i.e., the largest set of nodes that are fully connected.\n", - "\n", - "To solve this using QAOA in PennyLane and Braket, we first calculate the cost Hamiltonian $H_C$ and corresponding mixer Hamiltonian $H_M$" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cost Hamiltonian:\n", - " (-0.5) [Z1]\n", - "+ (-0.5) [Z5]\n", - "+ (0.25) [Z0]\n", - "+ (0.25) [Z4]\n", - "+ (0.25) [Z2]\n", - "+ (0.25) [Z3]\n", - "+ (0.75) [Z0 Z1]\n", - "+ (0.75) [Z1 Z4]\n", - "+ (0.75) [Z2 Z5]\n", - "+ (0.75) [Z3 Z5]\n", - "Mixer Hamiltonian:\n", - " (1) [X0]\n", - "+ (1) [X1]\n", - "+ (1) [X2]\n", - "+ (1) [X3]\n", - "+ (1) [X4]\n", - "+ (1) [X5]\n" - ] - } - ], - "source": [ - "import pennylane as qml\n", - "from pennylane import numpy as np\n", - "\n", - "cost_h, mixer_h = qml.qaoa.max_clique(g, constrained=False)\n", - "# constrained=True results in greater circuit depth but potentially better solutions\n", - "\n", - "print(\"Cost Hamiltonian:\\n\", cost_h)\n", - "print(\"Mixer Hamiltonian:\\n\", mixer_h)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up the algorithm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We begin by setting up a single QAOA layer" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This layer contains the controllable parameters $\\gamma_i$ and $\\alpha_i$." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def qaoa_layer(gamma, alpha):\n", - " qml.qaoa.cost_layer(gamma, cost_h)\n", - " qml.qaoa.mixer_layer(alpha, mixer_h)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The full QAOA circuit is then given by:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "n_layers = 4\n", - "wires = n_nodes\n", - "\n", - "def circuit(params, **kwargs):\n", - " for i in range(wires): # Prepare an equal superposition over all qubits\n", - " qml.Hadamard(wires=i)\n", - " \n", - " qml.layer(qaoa_layer, n_layers, params[0], params[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Note We have chosen to use 4 QAOA layers. The choice of depth is a tradeoff between improved solutions (for greater depth) and increasing runtime.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are overall eight controllable parameters: the first four are for $\\gamma_i$ of the cost Hamiltonian and the second four are for $\\alpha_i$ of the mixer Hamiltonian:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[0.72511958, 0.57312068, 0.6448612 , 0.55801009],\n", - " [0.94368854, 0.93863944, 0.52819152, 0.5817428 ]], requires_grad=True)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.random.seed(1967)\n", - "params = np.random.uniform(size=[2, n_layers])\n", - "params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this part of the tutorial, we will use the local Braket simulator (see the [introduction tutorial](./0_Getting_started.ipynb) for further details):" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "dev = qml.device(\"braket.local.qubit\", wires=wires)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The final step is to define the cost function. In QAOA, the output cost function is given by the expectation value of the cost Hamiltonian $H_C$, i.e.," - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "@qml.qnode(dev, diff_method='parameter-shift')\n", - "def cost_function(params, **kwargs):\n", - " circuit(params)\n", - " return qml.expval(cost_h)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running the algorithm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have set up the cost function, we just need to pick an optimizer and run the standard optimization loop." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = qml.GradientDescentOptimizer()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial cost: 0.6516478992954955\n", - "Completed iteration 1, cost function: -0.6687410127547244\n", - "Completed iteration 2, cost function: -1.7159072783906053\n", - "Completed iteration 3, cost function: -1.950115458213473\n", - "Completed iteration 4, cost function: -2.033092951409877\n", - "Completed iteration 5, cost function: -2.105710086578066\n", - "Completed iteration 6, cost function: -2.1748317555275776\n", - "Completed iteration 7, cost function: -2.242888962573281\n", - "Completed iteration 8, cost function: -2.311283045926767\n", - "Completed iteration 9, cost function: -2.3807887017553835\n", - "Completed iteration 10, cost function: -2.451785569648757\n" - ] - } - ], - "source": [ - "print(\"Initial cost:\", cost_function(params))\n", - "\n", - "for i in range(10):\n", - " params = optimizer.step(cost_function, params)\n", - " cost_eval = cost_function(params)\n", - " print(f\"Completed iteration {i + 1}, cost function:\", cost_eval)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Investigating the result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How do we know how well the algorithm has performed? To do this, we can sample from the circuit using the optimized parameters. This will give us binary samples that allow us to select which nodes of the graph to use as part of our clique, e.g., either by simply selecting the most common sample or selecting the sample with the lowest corresponding energy.\n", - "\n", - "Let's take some samples and see which ones occur most frequently. To start, we'll create a QNode designed for sampling." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "shots = 100000\n", - "dev = qml.device(\"braket.local.qubit\", wires=wires, shots=shots)\n", - "\n", - "@qml.qnode(dev, diff_method='parameter-shift')\n", - "def samples(params):\n", - " circuit(params)\n", - " return np.array([qml.sample(qml.PauliZ(i)) for i in range(wires)])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Samples can now be generated and converted into probabilities:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from collections import Counter\n", - "\n", - "s = samples(params).T\n", - "s = (1 - s.numpy()) / 2\n", - "s = map(tuple, s)\n", - "\n", - "counts = Counter(s)\n", - "indx = np.ndindex(*[2] * wires)\n", - "\n", - "probs = {p: counts.get(p, 0) / shots for p in indx}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now plot the probability distribution over all possible samples:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "plt.style.use(\"seaborn-v0_8-colorblind\")\n", - "labels = [\"{0:{fill}6b}\".format(i, fill='0') for i in range(len(probs))]\n", - "\n", - "plt.bar(range(2 ** wires), probs.values())\n", - "plt.xticks([i for i in range(len(probs))], labels, rotation='vertical', size=12)\n", - "plt.yticks(size=12)\n", - "\n", - "plt.xlabel(\"Sample\", size=20)\n", - "plt.ylabel(\"Probability\", size=20)\n", - "\n", - "fig = plt.gcf()\n", - "fig.set_size_inches(16, 8)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the plot, it is clear that the sample ``101110`` has the greatest probability. Since each qubit corresponds to a node, this sample selects the nodes ``[0, 2, 3, 4]`` to form a subgraph. Let's check if this is a clique, i.e., if all of the nodes are connected:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sub = g.subgraph([0, 2, 3, 4])\n", - "nx.draw(g, pos=positions, with_labels=True)\n", - "nx.draw(sub, pos=positions, node_color=\"r\", edge_color=\"r\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great, this is a clique! Moreover, it is the *largest* clique in this six-node graph. QAOA, using PennyLane and Braket, has helped us to solve the maximum clique problem!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Scaling-up QAOA for larger graphs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have seen how we can use PennyLane on Braket to solve graph optimization problems with QAOA. However, we have so far restricted to a simple six-node graph and used the local Braket device. Let's now be more ambitious and try to solve an optimization problem on a twenty-node graph!" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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AgcjJyRHK/4O8vDxMmTIFsrKyOH36NKndRRAtRBIngmgDLBYL8fHx7LlKL1++xLBhw8BgMECn06Grq8tVOzeTsuAR+pQ9PEdRFJg3dkKyhw7kzCcDAEqe3kBpYgRYNZXorNobioxfQZOQhBgN2DvDDHQjNUE9zXapbtl+UFAQ39qkKArS0tIoKCiAjIwM+35tbW3cuXMH+vr6fLsWL6qrq7F48WL873//w9WrV4UWB0G0Z2SojiAEJC8vDxEREQgLC0NERASUlJTAYDCwceNGDB06tEWrq64lZrKTpur8dDAj/kRVZjLkeugAAMqSo1Ecfx2qrlshJtUF+Ze3oPjJFchZTgGLAq4+yySJ01fevHmDs2fP4vXr13xtt26j3/z8fGhra7Pvr9u3TlgJi6SkJIKDg7F//34MHToUJ0+exJgxY4QSC0G0VyRxIgg+qa2txZMnT9i9SsnJyRg+fDgYDAb8/Pygo6PTqvazisoRk/LfEF3J078g238UKrr1YN9X+uIuug1xhLj0l2KXCvRFoGqr2Y/HvGciq6iclCr4f8uWLYOvry+UlJT43nZDiVNdIcxZs2bx/Xq8mD9/PgwNDeHs7IzffvsNixcvJkO4BMElkjgRRCvk5OQgPDwcYWFhuH37NtTU1ECn07FlyxYMHToUnTp14tu1EjOK8Kn8vyRIYcwCAEDFh0T2fTUFH8H6/ANyzq5FbWkBOmv1Q3e7/2oSFZVV4/nHTyRxAvD333/j1atXuHDhgkDaV1JSApPJORfNysoK+/btE8j1eGVra4vY2Fg4ODggMTER+/bta5MaUwTR3okJOwCCaE9qamoQFRWF1atXY9CgQTAwMMCVK1cwYsQIJCQkICkpCVu3bsWIESP4mjQBQMHnqmaPoVg1KP+QgB6TfKE2awdY5SUoenCC53Y6utraWixduhQBAQECq6zd0Mo6ExMTpKWloaCgQCDX5JWOjg6ioqJQVlYGOzs7ZGVlCTskghB5pMeJaDeyisqRmFGEgrLqNq2OnZWVxdGrpKWlBQaDge3bt8PKygqSkpJ8uxZFUcjPz8fbt2/x77//cvz3fbUcZBlLmjxfXFYRMj9YQqzzlwnJXfoNx6eoUI5jFLrwN6Frj44fP46uXbvCyclJYNdoKHGSkJDA4MGDERsbi3Hjxgns2rzo0qULzp49Cz8/PwwZMgSXLl3C4MGDhR0WQYgskjgRIo2iKIS9yMa1xEzEpDA5hqoEVR27uroasbGx7LlKHz58wKhRo8BgMBAYGNiqzV+BxpOjup9pNBr09fXZt/Hjx0NfXx9dlbUw/eQLjtfgWzIG1ih78xCyJmNBk+iE8n9j0Fntv4nI8jKS6K8h16r427vS0lKsXr0aV65cEei8noYSJ+C/QpiikjgBXyazr169GsbGxhg/fjwCAwPh6uoq7LAIQiSRxIkQWf9Vx2YKvDr2x48f2Zvl3rlzB7169QKDwcDu3bthYWHBc69SXXL0ba9R3X/FxMSgr6+P3r17cyRHvXv3hqKiYqPtWupm4dbL7EYf72o2DqyKEmQfWwyKYqGTih4U6XP/O7+X4nc/v+mPP/7AiBEjMGTIEIFeR1FREW/evKl3v7W1NQICAgR67ZZycHCAnp4ee97Tli1bSLFMgvgGqeNEiCReqmPXMVLvxnV17OrqakRFRbGrdaenp2P06NFgMBgYO3Ys1NSaX7L/dXL0bWL0dXL0dYJU918FBQWun9fXvq3jxBNWLeYYUFgze+J3u4IqIyMDJiYmSEhI4FjtJgihoaG4cuUKzp49y3F/UVERtLS0UFBQwNdhXn5iMpmYOnUqOnXqhNDQUMjLyws7JIIQGSRxIkSOoKpjp6ensxOlu3fvonfv3mAwGGAwGBgyZAgkJOp3wFIUhby8vAZ7jf79919ISEjUS4pamxw1haIouBx6hOgU3l+bPvI0fDi2FD11dLBr1y6+VcluT2bOnAlNTU34+fkJ/Fq3b9/Gli1bcOfOnXqPGRsb4+jRoxg0aJDA42ipmpoaLF26FLdu3cK1a9e+y78XgmgIGaojRE7Yi2zEfpUYfFsdm1XxGcywXahmZgAUhS7GIyFn8SMAICaFifCX2aAbqaGyshKRkZHsIbjs7GyMGTMGjo6O2LdvH5SVldntN5Qc1f38dXKkr68Pe3t7doIkiOSoKTQaDTucTeB+PA4veOiN0+oCHF8wHN2XJCIoKAjW1tZwd3fH6tWr0bVrVwFGLDri4uIQERGBf/75p02u11A5gjp1hTBFOXGSkJDArl27cPjwYdjY2ODYsWMiNS+LIISF9DgRIueXU/HseTwc1bFtXCBnPhkFt/cDNBoURs0Dq6oCmYcWoofDMnTWMAQAGMh8hlRcCO7du4c+ffqwtzXR0dFBSkpKg71HEhISDfYaCSM54kZOcTmWnEtE9Lt8AI0Pu4nRAENFCTzZtQg3L51hf1BnZWXB19cXd+7cQUBAAGbMmNGhh+8oioKdnR1cXV3x888/t8k109PTYWlpiYyMjHqPnThxAn/99RfOnTvXJrG0VlRUFKZMmYLFixdj2bJlHfpvhSCaQxInQqRkFZWDvvshe+VYQcSf6KRugIoPieweJ4qiAIoFmpg4qgszkXNqOZSn+6GT0pc5K2I15bApeQAFaXFkZmay5xx9mxzV/SyqyVFzqquroTdsMqxmLMabwloUlXGuOLTSVcREE3WM7aeKa9euYcGCBYiMjOTYFy86OhoeHh7o0qULgoKCYGpqKoynInCXL1/G2rVr8ezZszab7FxWVgYFBQWUl5fXSzTevn2LYcOGISMjo90kIenp6Zg0aRIMDQ1x8ODBRjeiJoiOjgzVESKFm+rYNBoNoIkj//o2fH4TBZkfLCGp8F+JAJaENHJrpaErL4PBgwe36+SoKdeuXYO2WCHOeIxEVlE5thw8jZSMXHjMm43+GnIcq+ccHByQmZkJBoOBqKgo9hYjVlZWePLkCQ4dOoSxY8fCyckJmzZt6lCvVVVVFX777TcEBwe36QoxGRkZiIuL4/Pnz5CVleV4TE9PDzU1NUhLS2v1VjxtRUtLCw8fPoS7uztsbW1x+fJlaGpqCjssgmhzpHI4IVIKyhqvUfQtJXsfaP16GqyKEnyKOsPx2E8/L8Tvv/+OGTNmYMiQIR0qEagTFBQET09PAICavDR6S5WhJysTY/upNlhyYMGCBXBycsLEiRNRVlbGvl9cXBzz58/H69evQaPRYGhoiH379qG2trbNnosg7d27F/r6+kLZzLaxWk40Go29b117IiMjg9OnT8PJyQnm5uaIiYkRdkgE0eZI4kSIFAWZ5pdnl6fEo6bky6RbsU7S6GI4DFXZbzmO8Zg7C/369cO4ceOwYMECbNmyBaGhoYiOjsbHjx/BYrEEEn9bef78Od6+fYvJkyez72OxWM0O+/j5+aF3795wcXGplxgpKCggODgY4eHhCAkJweDBg9vdB/u3CgoK4O/vj23btgnl+oqKig0mTsB/E8TbGxqNBl9fX+zfvx8ODg44evSosEMiiDZFhuoIkWKiKQ85ackmq2N/fhMJ2j8xUBi7CKitwec3DyHdcwD7cXkZSYTfOIvKwmykpqayb0+fPmX/XFhYCE1NTejo6EBbWxs6OjocNy0tLYHtYcYPQUFB+OWXXzjqAFEUBTGxpr8L0Wg0HDp0COPHj4eXlxf27NlTL9kyNTXFgwcPEBoaiilTpmDkyJEICAjgqraVqNmwYQOcnJzQt29foVy/sR4n4EshzNOnT7dxRPwzYcIE3L9/n10sc9u2bQ2W9CCIjob8lRMiRU1eGpa6ik1Wx1YY4Q5meDCyDi8CaDTI6Fug6+CJ7McteynCQEsZ0FJG//79G2yjoqIC6enpHInV/fv32T9nZmZCUVGxXkL1dZLVrVs3vj9/bhQUFODChQtITk7muJ+bHicA6NSpEy5evAhbW1sEBATA19e33jE0Gg0zZsyAvb09Nm3aBGNjY/j6+sLLy4vvmxcLyj///INTp07h1atXQouhqcTJzMwMycnJKCkpabclIQwNDfHo0SNMmzYNDAYDZ8+e7ZDD4gTxNZI4ESJnook6Il5lc1THVprw3+a2YlKy6OGwvMFzxWiAg6l6s9eQkpJir6xrSG1tLTIzM9mJVFpaGp4/f47r16+z7+vUqVO9xOrrW48ePQSyYurw4cOwt7dn16Gqw02PU51u3brh5s2bsLKygoaGBn766acGj+vatSsCAgIwZ84cLF68GIcPH8bu3bsxevToVj8PQVu+fDmWLVtW73VqS03VcurcuTMGDBiAR48eYdSoUW0cGf90794dN27cwPLlyzFkyBBcvXoV/fr1E3ZYBCEwJHEiRA7DSBU/yNPwppD3ShkWvRQxtp9qq2MQFxeHlpYWtLS0MHTo0HqPUxQFJpPJ0WOVmpqKyMhIpKWlITU1FZ8/f25wGLDupqGhwfPQRm1tLYKDg3H+/Pl6j3Hb41RHXV0dN2/exPDhw6Gmptbkh7eBgQFu3ryJ69evY/78+TA1NUVgYCB69uzJU/xt5f79+3j27BlCQ0OFGkdTPU7Al3lO0dHR7TpxAr4Uy9y+fTtMTExgZ2eHI0eOwN7eXthhEYRAkMSJEDmnTp1CQvAG9Jm/C6kl3CdPRurdsMPZpE3q4tBoNCgpKUFJSQkDBw5s8JjS0lJ2ElV3CwsLY/+cm5sLNTW1RudZaWtrQ0ZGhqPN69evQ01NDYMHD653PV56nOr07dsXFy5cgJOTEyIiIpqs40Sj0TBx4kSMGTMG27Ztw8CBA+Hp6Ynly5eLVE0fFosFb29vbN68GVJSUkKNRUlJCUlJSY0+bm1tjb1797ZhRILl5uYGAwMDODk5ISkpCStWrGg3daoIglskcSJEBkVR8Pf3x8GDB/G/sJtQ1OyFJecSEZvCbHJTW4pVC2t9ZeycagrlbsL9oPyarKws+vbt2+jE5KqqKmRkZHAkV48ePcK5c+eQmpqK9PR0dOvWjSOZun79OiZMmICEhATo6Oige/fu7A8mXnuc6tjY2GDv3r2YMGECoqKimq0rJCUlhdWrV8PNzQ1Lly6FoaEhAgMD4ejoKBIfkqdOnUKnTp3g7Ows7FC46nFyc3NDbW1tm9aYEiRzc3M8fvwYkyZNwvPnz3HkyJF6XwAIoj0jlcMJkVBTU4OFCxfiyZMnuHHjBtTVv8xToigK4S+zcfVZJmLeM+tVxzbq0QlXdq3GLp85cHefI6zwBYLFYiEnJ4cjqdq/fz/s7OzYE9tZLBY7qWIymZCWlsb8+fPZ96mqqnLdC7Vr1y7s378fkZGRPE3wvXPnDry8vKChoYHdu3ejT58+LX3Krfb582f06dMH586dg6WlpdDiqHP37l1s3LgR//vf/xo95ocffsCFCxcaXcjQXlVUVGDevHl48eIFrly5Am1tbWGHRBB8QRInQuhKS0vh7OyM2tpanD9/vtEVRplFZTC0Hovd+49AtXtXdnVsBoOBmJgY5OTkiHQJgdb65ZdfoKamht9//519X1FRETuxOnDgAPLy8qCtrc2+r6ioiF12oaGblpYWxyo5Hx8fPH78GBERETwNc1VXVyM4OBibNm3CrFmzsHbtWqGsOtywYQNevXqFM2fONH9wG0hMTISrq2uTw3WzZ8+Gubk5fvnllzaMrG1QFIXAwEBs374d586da3C+IEG0NyRxIoQqOzsbEyZMgImJCfbt28dRl+hbxcXF0NDQQElJCcf979+/R58+fbBq1SqsXbtW0CELRWFhIXR1dfH69WuoqjY8+X3Tpk2oqKjApk2b2PeVl5fXK7vw9S0zMxM9evRgz7HS1tbG3bt3ISUlhb1796JXr148LZXPycmBr68vwsPDsWXLFri6uvI876qlMjMzYWxsjLi4OPTq1atNrtmcjx8/YtCgQcjKymr0mIMHD+LBgwc4efJkG0bWtm7dugU3Nzf4+fm12SbLBCEoJHEihObNmzdgMBjsHorm5se8ffsWY8eOxbt37+o95uzsjOvXryM9PR2KioqCClloAgMDER8fj5CQkEaP2bBhA6qrq7Fx40au262pqUFmZibHPKuUlBRcunQJwJfES1pausl6Vg2VXYiNjYWnpyckJSWxZ88emCZrPxsAACAASURBVJmZteyJ88Dd3R1KSkoICAgQ+LW4VVFRgW7duqGysrLRv+9Xr15hwoQJSElJaePo2lZycjIcHBwwatQo7Nixo8kvSQQhykjiRAjFw4cP8eOPP2LLli2YPXs2V+dER0fD29sbsbGx9R5LT0+Hvr4+fvrpJxw8eJDf4QpVbW0t9PX1ERoaCnNz80aPW79+PVgsFtavX9/qaxYVFcHGxgYzZ87EzJkz6/VUfZ1olZeXN1p9PTY2Fjt27MCkSZPg5+cnsKT22bNnoNPpSE5OhpycnECu0VJdu3bFx48fGx26ZLFYUFJSwsuXL9tldXZefPr0CdOnT0dFRQXOnTvH3myaINoTsqqOaHPnz5/HwoULERISwtPGq3l5eY0WM9TS0oKrqytOnTqF3377rdHClu3RzZs3oaSk1GTSBLR8VV1D5OXlcfPmTVhbW0NTUxPTpk3DoEGDGjy2pKSkXtmFGzdusH9mMpkICQnBkSNHYGpqilGjRqFXr14cvVetKWdAURSWLl2KtWvXilzSBPy3sq6xxElMTAyWlpaIjo6Gk5NTG0fXtuTk5HD9+nWsXLmSXSzT2NhY2GERBE9I4kS0GYqisGPHDgQGBuL27dtN1gxqSG5ubpNVoDdu3IjTp09j8eLFuHHjRmvDFRlBQUHw9PRs9jiKovi6pF1LSws3btzAqFGjoKqqCjs7uwaP69q1K/r169dotei6sgt3797Ftm3bcOTIEZiZmaGyshKpqanIyMiAnJxco/WsdHR0IC8v32hSeOPGDWRlZWHevHn8eup8VZc46erqNnqMtbX1d5E4AV+KywYEBKB///4YMWIEDhw4AEdHR2GHRRBcI4kT0SZqa2vh7e2Nv//+G9HR0S1ampybm4sePXo0+riamhrmzZuHw4cPIyoqCtbW1q0JWSS8fv0az58/x9SpU5s9lp89TnWMjY1x5swZODs74++//25R70CnTp2gq6sLXV1duLu74+zZs1i2bBlsbW1x8uRJqKqqIjs7m2MYMDk5GREREez7ADQ4x0pDQwOLFy/Gzp07RXaD2eZqOQFf6jk1tGdgR+bi4gIDAwM4Ojri+fPnWLNmTZstJCCI1hDNdxqiQykvL4eLiwsKCwsRGRmJ7t27t6id3NzcZrf4WLVqFQ4dOgQPDw88ffpUJAoytsaePXswb948rsostKRyODeGDx+OXbt2Yfz48YiOjoampmaL26LRaJg2bRomTJgAf39/9O/fH7/99hsWL14MdXX1BmsvURTFUXah7vbkyRM8efIEWVlZcHJygpaWVoO9Vdra2vXKLrQlbhKnIUOGICkpiT0Z/3sxaNAgPH78GJMnT0ZSUhKOHTsGWVlZYYdFEE0iiRMhUPn5+Zg4cSJ69eqF0NDQVtVZysvLw5AhQ5o8pkePHvDy8sKBAwdw/vx5rnpqRNWnT58QGhqKFy9ecHU8i8US2Df2adOmISMjAwwGAw8fPoS8vHyr2pOVlYW/vz/H5sG7du0CnU6vdyyNRkP37t3RvXt3juHdoqIiGBgYIC4uDvr6+vXmWd25c4f9c1ZWFnr06NHkpsyC+sBWVFRsNnGSkZFB3759ERcXBxsbG4HEIarU1NRw7949LFiwANbW1rh69arI7oFIEABJnAgBevfuHRgMBpycnODn59fqD/Xm5jjV8fHxQXBwMJYuXQoHB4d2WxTz2LFjGDNmDLuKenMEMVT3taVLlyI9PR2Ojo64desWX17X3r1746+//sJff/0FDw8P9OvXDzt27GhyPlAdPz8/TJw4kV1x28DAAAYGBg0eW1NTg48fP3IkVwkJCbhy5Qp7eLCu7EJj86yUlJRa9Ppy0+MEfJnnFBUV9d0lTgDQuXNnHD58GLt374aFhQXOnDnT6Jw6ghA2kjgRAlG3V9WaNWuwYMECvrTZ3BynOgoKCvD29sahQ4cQHBwMb29vvly/LbFYLOzZswfHjx/n+hxBDdXVodFoCAwMhLOzM2bNmoWQkBC+XW/ChAkYNWoUAgMDMXjwYCxcuBArVqxodI+zd+/e4ejRo1z3xklISLAToIYSE4qikJeXV2848MGDB+yfKysrOZKqbxMsdXX1BudZKSkpISEhodkYrayscOrUKa6eT0dEo9Hw66+/ol+/fnB2dsa6dev49t5BEPxE6jgRfHf9+nXMmTMHhw8fxsSJE/nWrpqaGuLj47nqgfn06RN0dXVBURTevn3L095rouDmzZtYs2YN4uLiuO7l8PHxgYqKCpYtWybQ2CoqKjB69GhYWFhg69atfG8/PT0dy5YtQ0xMDLZv3w4nJ6d6r8GUKVNgamqKVatW8f36jSkpKWm0Antqairy8/Ohrq5er6eqbp/Bq1evNjl/6ePHjzAxMUFeXl67n5vXWm/fvoWDgwNsbGywe/duoc1PI4iGkMSJ4Ks///wTGzZswNWrV5udj8QLFouFzp074/Pnz1y/iW7ZsgWHDx+Gvb09AgMD+RZLW2AwGOyeHW4tXboU6urqWLp0qeAC+38FBQUYOnQofvnlF3h5eQnkGvfu3YOnpyeUlZWxe/dudrmDqKgoTJ8+HW/evGm0R0oYKisrkZGRUS+hevbsGd68eQMWiwV5efkm51mZmpoiPDy80SHH70lxcTFcXV1RVFSECxcucDVMTxBtgSROBF+wWCysWrUKFy9eRFhYGPT09PjaPpPJhL6+PgoKCrg+p7S0FLq6uqiqqkJ8fDzfYxKU5ORk2NraIjU1laeNdpcsWQItLa02G5pMTU2FtbU1du3aJbD6QzU1Ndi7dy82btwIV1dXrF27FnQ6HZ6ennB1dRXINfntxYsXcHZ2RlJSErKysupVXv/6VlFRARUVFZiamjaYWKmoqHxXS/ZZLBbWrl2LU6dO4cqVKzzXfiMIQSBznIhWq6ysxJw5c/D+/XtER0cLZBsFbuc3fU1WVha+vr44fPgwVqxYgXPnzvE9LkEIDg7G3LlzeUqaAMHPcfqWjo4O/vrrL4wZMwbKysoCmdQsISEBLy8vTJs2DStXroSuri7k5eUxbdo0vl9LUOomh4uJiUFDQwMaGhqwsrKqdxxFUdi6dStiYmLY29ykpaXh8ePH7MSquLiYo+zCt/OsNDU1O9SwlpiYGDZt2gRjY2OMHj0ae/fuxZQpUxo9PquoHIkZRSgoq4aCjCRMNOWhJv/9lHcg2gbpcSJapaioCI6OjujevTtCQkIEVoPmwYMHWLVqFR4+fMjTeeXl5dDT0wOLxcKlS5ca/MASJSUlJdDR0cHz5895rpfk5eUFPT09/PrrrwKKrmG3b9+Gq6sr7t27B0NDQ4Fdp7y8HL169YKCggK6deuGPXv2NLoNjCipqqqCjIwMqqqqmk1sExISMGPGDLx+/brBx8vKyhrtrUpNTUV2djaUlZUbrWclyLILgpaQkIBJkybBzc0N69evZ7+WFEUh7EU2riVmIiaFiU/l1exz5GUkYdlLERNN1UHvp/rdzx0j+IMkTkSLpaWlYdy4cRg5ciQCAwP5ut3Hty5cuIDQ0FBcvHiR53ODg4Nx8OBBSEtLIzo6WqTfPPfs2YMHDx60qHfM09MTP/zwA1fbs/DbyZMnsWbNGkRHR3NdPoFXmzdvRlxcHM6fP4/jx49j5cqV7EKavPZGtjU5OTl8+PCh2eKvNTU1UFBQwPv371u0IXJd2YXGEqu0tDTIyMg0Oc9KUVFRZP+N5OTk4Mcff4SioiJOnjyJMkoCS84mIvY9E6wmPsnEaIBFL0XscDaBSjfSA0W0DhmqI1rk2bNnsLe3x5IlS7BkyRKBv9G2ZKiuzty5c/HHH3+grKwMFy5caLKrX5hYLBaCgoJw6NChFp8vrA+8n376CRkZGRg3bhwePHjQ6Ia2LZWTk4Pt27cjNjYWYmJimD17NhwdHbF+/Xr07dsXa9euxYIFC0R62xUmk9ls4iQhIYEhQ4YgJiYGEyZM4Pk6X5ddaAhFUcjNzeWYZ/X+/Xvcu3ePnVxVVVU1WsuqruyCIL8kNUVFRQV37tyBh4cHzO3GQH3aRrwtqGz2PBYFRKcw4X48DodnDiLJE9EqpMeJ4Nnt27fh4uKCPXv2tFll7nXr1oHFYmHDhg0tOv/gwYPYt28fioqK8OrVK5EsihkeHo7ly5cjISGhRQnQwoULYWRkhIULFwoguuZRFIVFixbh33//xY0bN/g612b+/PmQlZXF9u3b6z324sULeHl5IT8/H0FBQRg2bBjfrssv5ubm2LVrFywsLJo99vfff0dVVRU2b97cBpHVV1xc3GhvVWpqKphMJkfZhW+TLG1tbZ7n5/GKxWLBbv0lpFXxngBZ6ynilLu5yPaqEaJPNL+eESLr2LFjWL58OS5evNimFY7z8vJaNX9m1qxZ2LJlC5SVlbF3714sWbKEj9HxR1BQEDw9PVv8hi7MHifgSwHDoKAgODk5Ye7cuTh+/Dhf4nnx4gUuX76M5OTkBh83MjLCnTt3cOHCBfz000+wtrbG1q1bW7WnHr9xWz0c+FJB3M/PT8ARNa5bt24wNjZudEPnyspKpKencyRVkZGRCAkJQWpqKj5+/Iju3bs3OsdKR0en1Vv23HqZg4zq+klT6Yv/ofjRRYBGg5hkZ3QfNR+d1fQ5jolJYSL8ZTboRmqtioH4fpHEieAKRVHYuHEjjh49KvBJwA3Jzc1tVU+CpKQk1q5di6CgIPj7+2PmzJkiVRTz7du3ePToEc6fP9/iNtp6VV1DxMXFcfr0aYwcORKrVq2Cv79/q9v08fHB6tWrmxzmotFomDJlCsaNG4fNmzfDxMQEPj4+8Pb2FoneRV4SJ3Nzc8THx6OqqkokV8h17twZvXv3Ru/evRt8vLa2FtnZ2RyJ1cuXL3Hz5k327+Li4k3Os1JWVm7yb/laYma9OU3VzAwU/e8IVGfvgoSsAsrfPUHeZX9oLjzKcRyLAq4+yySJE9FiJHEimlVdXY0FCxYgISEBMTExUFVVbfMYWjPHqY6Liws2b94Mc3Nz+Pn5NTjsIyzBwcFwd3dv1apEYfc41ZGRkcH169dhbW0NLS2tVm2bcevWLbx//57rNrp06YJNmzZh9uzZWLJkCYyMjLBz506MHz++xTHwAy+Jk5ycHPT09PDs2TO+FpFtK+Li4s2WXSgoKKg3DBgbG8v+vaSkhKPswtc3aUU1xKTUfy1p4pJQYHhBQvbLF6JOqvqoLS0EVVsNmrgkx7Ex75nIKionpQqIFiGJE9GkkpISTJ06FTQaDffv3xfaUmZuN/htioSEBNatW8eulbNo0SKuNpMVtNLSUpw4cQJPnz5tVTui0ONUR0lJCWFhYbCxsYG6ujocHBx4bqOmpgZLly7F1q1bISkp2fwJX9HT08O1a9cQFhaGX3/9FX/++Sd27tzZaC+JoPGSOAFf9q2Liopql4lTc2g0GhQVFaGoqAgzM7MGj/n8+XO9sgsRERFfkixWd4gN+6XeORLyKpCQVwHw5d9C4d1DkNEfUi9pAoCismo8//iJJE5Ei4jGuywhkrKysjBs2DBoamri2rVrQq3/kpeXx5ctF6ZOnYqqqiqMHz8eK1as4ENkrXfixAnY2dk1uhKKW6LS41RHV1cX165dw88//4yYmBiezz906BBUVFRgb2/f4hgYDAaSkpJgY2MDCwsLrFq1Cp8/f25xey2lqKjIU+JkbW2NqKgoAUYk2rp06QJDQ0PQ6XTMnz8f/v7+CAkJQWRkJPwDdzd5LquqAvlXtqC6MAuKjMa3Ayr4XMXvsInvBEmciAa9evUKlpaWmDx5Mg4cOCDUZd41NTX49OkTX+YkiYmJYf369UhKSkJUVFSLPtD5iaIo7Nmzhy+1l0Spx6nOwIEDcfz4cTg6OuKff/7h+rzi4mKsW7cO27dvb3Uy2LlzZyxfvhyJiYn48OEDDA0NcfbsWbTlguKW9jh9b4ueKYpCYWEhnj9/jr/++gt79+6Fr68vZsyYgaFDh0JbWxsLZrs1en7Np1xkn/QBxMSgMt0fYlKNf9lT6CJ688eI9oEM1RH13L9/H1OnTsXWrVvh5tb4m1Rbyc/PR/fu3flWO8bR0RGbNm3C5MmT4ePjg8jISKH11Ny5cwfi4uJ8WUIvaj1OdRgMBvz9/UGn0xEdHc3VHLnNmzeDwWBgwIABfItDQ0MDISEhePDgATw9PbFv3z7s3r270dVj/FRXx4lbvXr1AgB8+PCB/XNHUFVVhYyMDKSlpSE9PR1paWn1bgDYq/DqbgwGg/2zmKwiJv4Zy1EhHABqy0uQfXoFZI1HQn7ojCbjkJeRRH8NOYE9T6JjI4kTweHMmTPw8vLC6dOnMWrUKGGHA4A/85u+RqPRsHHjRvj6+kJcXByXLl0S2Ca1zWltCYKviWKPU505c+YgPT0dEyZMwL1795oc9v3w4QMOHDiA58+fCyQWW1tbxMfHY//+/Rg5ciSmTZuGDRs2tHqJfFN47XGi0WiwtrZGdHR0u0mcKIpCfn5+vUTo6wQpPz8fampqHEmRqakpJk6cCC0tLWhra0NOTq7Zfw+Wuoq49TKb477ShJuoLc5D2T8xKPvnv55klel+EJfmLMiqJ1sLVTnB1poiOi5SAJMA8OVNb9u2bdi9ezdu3LiB/v37Czsktjt37sDPzw93797lW5sURcHS0hKjRo3CmTNn8OrVqzZf+p2SkoIhQ4YgNTUVXbp0aXV7rq6uGDt2LH766Sc+RMd/FEVh3rx5yMjIwLVr1xqd8D1jxgwYGBjg999/F3hM+fn5WLVqFa5evQo/Pz/Mnj1bIMlnbm4u+vXrh7y8PK7PCQwMxNu3b7F3716+x9MS5eXljfYS1d0vIyMDbW1tdhL07U1VVZUvw/43k7LgEfq0yW1WGkMDBYnYY1Asz2Bv2yOqXzgI0UQSJwK1tbX49ddfcf/+fYSFhYlU4UAACA0NxZUrV3D27Fm+tnv79m14enqiV69eGDt2LBYvXszX9pvj4+MDGo2GrVu38qU9FxcXjBs3Di4uLnxpTxBqamrg4OAAFRUVHD58uF7PQmxsLH788UckJyfzJZnkVnx8PDw9PVFTU4M9e/bwfTVbTU0NpKSkUFlZyfWQ86NHjzB//nw8e/aMr7E0hMViITs7u9HEKC0tDSUlJdDU1ORIhL5OkLS0tNpsAQlFUXA59AjRKdwPf9ax0lXEidmDcOXKFfj7+6O6uhorV67ElClTRHbLHkK0kMTpO1dWVoYZM2agpKQEly5dgpyc6I3779q1C2/fvkVQUBBf26UoCsOGDQOdTsfOnTuRnJzc7F5i/PL582fo6OjgyZMnfBuKmT59Ouzt7TFjRtPzO4SttLQUw4cPx/jx47Fu3Tr2/RRFwdraGvPmzcOsWbPaPC4Wi4VTp07B19cXdDodmzdvhoqKCt/aV1BQwL///sv15r1VVVVQUFBAZmZmq/f+KykpaXT4LC0tDR8/foS8vHyDvUR1yVFzRSnbWk5xOdyPx+FFZjHX5xipd+PYq46iKISHh8PPzw9ZWVlYvnw53NzcRKJoKiG6SHr9HcvLy4O9vT309fVx7tw5kaxSDPB/jlMdGo3GLpbo4OAAPz8/bNu2je/XacipU6cwdOhQvs5fEeU5Tl+TlZXFjRs3YGVlBU1NTcydOxcAcP78eVRUVAhtQYKYmBjc3Nzg4OCADRs2wMjICKtXr8bChQt5riPVkLp5TtwmTp06dYKZmRliY2MxZsyYRo+rqalBZmZmk3OL6jbu/bqHaMSIEeyfNTU1Bb6/HL+pdJPG4ZmDsORcImJTmE0O24nRvsyL2jHVFMrd/nueNBoNdDoddDodDx8+hL+/P9avXw8fHx/8/PPPbdrrSbQfJHH6Tr19+xYMBgNTp07Fpk2bRHI1Vp28vDwMHDhQIG3b2tpCV1cXBgYG2Lx5MxYuXCjwopgURSEoKAi7du3ia7uiuqquIcrKyggLC4OtrS3U1dUxYsQI+Pr64vDhw0JP/uTk5LB9+3bMnTsXXl5eOHjwIIKCgjB8+PBWtVtXy8nAwIDrc6ysrHDnzh2oqKg0OrcoOzsbysrKHD1EhoaGGDt2LPs+BQWFdvO3wQuVbtIIcTdH+MtsXH2WiQfJ2fhc89/jtKoyMMx0MdFEHWP7qTb5GtjY2CAsLAxPnz6Fv78//P394eXlBQ8PD4EuHCDaH5I4fYdiY2Ph6OiI9evXY968ecIOp1n82G6lKRs3boSzszM8PDywcuVKnDlzRmDXAoB79+6BoiiMGDGCr+22lx6nOvr6+rhy5Qrs7e0xbdo0GBsbtzo54SdDQ0NERETg0qVLmD17NoYMGYJt27ZBW1u7Re01VJKgsrISGRkZjc4tSklJQU1NDW7evMkxfNa/f39275GGhgZfesTaKxqNBrqRGuhGati65yAevEzF9Flz8ctsV1D5H7DD/x1PQ29mZma4cOECXr9+jYCAAOjp6WHevHlYvHgxX4duifaLJE7fmatXr2Lu3Lk4duyY0Pfv4paghurqWFhYwMjICHJycoiMjERsbCwsLCwEdr2goCB4eHjwvQegPfU41TE3N0dgYCDc3NwQHh4u7HDqodFocHJyAoPBQEBAAAYMGIAlS5bAx8en2aEtiqKQl5fHToJyc3MRHByMkydPsu9jMplQV1fnSIrMzMwwadIkaGtrQ0ZGBqampnj27Bnf6ph1ZNnv38CmlwpmmPfE2qpsSCp2RUJCQov+PRsaGuLYsWP48OEDtm7dCkNDQ7i4uGDZsmUtTp6JjoEkTt+R4OBg+Pn5ISwsDIMGDRJ2OFwTdOIEABs2bMDEiRPx+++/w8fHBw8fPhRIEpKamor79+/jxIkTfG+7vfU41YmJiYGdnR0WLlyI6OhogfYutpSMjAzWr1+PWbNmwdvbG/369UNAQACMjIzq9RbV/Z6eno4uXbqwE6Lq6mpISEjgxx9/5Fie31xCpK6ujqSkJJiamrbRs22/kpOTYWtrCwDQ1tZGly5dEBMT06ovQj179kRwcDDWrFmDHTt2YMCAAXBwcMDy5ct5GnYlOg6SOH0HWCwWfH19cfXqVURGRorExra8yMvLE/iH6cCBA2Fubo7i4mKUlpbi8uXLmDx5Mt+vs3fvXsycOVMgy7ZZLFa7S5xev36Nc+fO4c2bN9ixYwfs7e1x9+5dyMjICDWu2tpa5OTkNLo0Pz8/H1OnToW0tDT69++PPn36QFtbG9bW1hxzjb6eXBwQEAAmkwlnZ2eeYqkrhEkSp+YlJyfjhx9+APAlcRIXF0dsbCxf2lZVVUVAQAB8fX0RFBQEGxsb2NnZYeXKleT/zXeGJE4dXGVlJWbNmoX09HRER0dzvaJHVFRUVKC8vLxNJmeuX78eo0ePxsGDB+Ht7Y0JEybwdaVhWVkZjhw5wrc38m+1x6G6ZcuWYcWKFVBUVMTGjRuRkZGBadOm4dKlSwKtqVNcXNzo0vy0tDRkZmaie/fuHENourq6sLOz46hwvWfPHmzZsgXDhg3DsmXLmkyIlZSUkJyczHOsVlZWuHv3LhYuXNiap9zhVVVVIT09HXp6egC+JE4FBQW4ffs2X6/TvXt3rF27Ft7e3jhw4ADGjx8PU1NTrFy5EtbW1ny9FiGaSOLUgRUWFmLSpEno0aMHbt++DWlpaWGHxLO63qa2SAiMjY1hZ2eHly9fQl9fH/v27YOXV+O7q/Pq9OnTsLCwYL+x81t7G6q7ffs23rx5g4sXLwL4Mp/o4MGDmDBhAjw8PPDnn3+26P97dXV1veX53yZH1dXV7P3Q6hKhUaNGcSzP52ZCsY+PD2bMmIHly5ejT58++OOPPzB9+vQG4+Z125U61tbW2LhxI8/nfW9SUlKgpaXF/rKjra2NtLQ0lJeX4+PHj9DQ0ODr9WRlZeHt7Y2FCxfi+PHj+Omnn6ClpYVVq1Zh9OjR7e5LDME9kjh1UKmpqWAwGKDT6di2bVu7+kD9Wl5ensDnN31t3bp1sLW1xdWrVzFp0iS4ubnxpberrgRBa6uEZxWVIzGjCAVl1VCQkYSJpjzU5L8kxO2px6m2thZLly7FH3/8wZGgSEpK4sKFCxg2bBj8/f2xatUqjvMoikJhYWGT237k5ORARUWFo7eob9++oNPp7N+7d+/Ot9dKXV0dJ0+eRFRUFDw8PLB//37s3r0bJiYmHMe1NHEyMDBASUmJQD78O5Kvh+mAL4lTeno6LCwsEBMTgx9//FEg15WSksL8+fPh7u6Os2fPYsmSJZCWlsbKlSsxadKkdvveSzSOJE4dUEJCAuzt7eHj49Pm24jwm6BLEXyrT58+YDAYiIiIgIODA/z9/fHHH3+0ut2HDx+isrKyRRsnUxSFsBfZuJaYiZgUJseu8PIykrDspYiJpupgtaMep2PHjkFeXh6Ojo4c91dUVCAnJwcrV67EggULEB8fD0VFRY7kSEJCol51a1NTU3bvkbq6ulCW51tbWyMuLg4HDx7EmDFjMGXKFGzYsAEKCgoA/qvjxCsajQYrKytER0djypQp/A67w0hOTuaYrF2XOM2aNUugiVMdCQkJuLi4YPr06bh27Rr8/PywevVqrFixAtOmTfuuS0Z0NGTLlQ4mPDwcrq6u2LdvH5ycnIQdTqudOHECEREROHXqVJtd8927dzA3N8eDBw9ga2uLuLg49OzZs1Vt/vjjjxg+fDgWLVrE03k5xeVYcjYRse+br4zc+VM61o7WwvRJollmgqIo5Obm4vXr15g8eTJ7W5Wve40KCwuhoaEBLS0tyMnJ4e7du3B3d2f3FmlpabV6+5G2wGQysWbNGly8eBEbN26Eu7s7CgsLYWBgUK+WEze2bNmCnJwc7NixQwDRdgzu7u4YMmQI5s+fD+DLtkZKSkq4ceMGVq9ejejo6DaNh6Io9gblHz58wG+//YbZs2e3uwrtRH0kcepAjhw5gpUrV+LixYsdZpLitm3bkJmZicDAwDa97s8//wxlZWV07twZN0bx6wAAIABJREFUr1+/RmhoaIvbSk9Ph6mpKT58+ICuXbtyfV5OcTnmHI/DSx724tKSBS54jmDvxdWWPn/+3OjS/Lqfu3btCgkJCUhISGDy5MkcW4Boa2tDRUWFY3l+VFQUHB0dcevWLZiZmbX5c2qthIQEeHp6oqKiAjt37oSdnR0qKip4nvj+4MED+Pj44PHjxwKKtP2zsbHBxo0bYWdnx75PSUkJT548gZGREQoKCoS2B110dDQ2b96M+Ph4eHt7Y/78+Ty9FxCihQzVdQAURWH9+vU4ceIE7t+/36Fqi7T1HKc6q1evhpmZGeLj42FtbY1Hjx7B3Ny8RW39+eefcHV15emNkqIoLDmX2GDSVBx/HaUJYQAACXk1KDI8Id7lyzys9FLA+1wiTrmb83W+U21tLbKzs5ucW/T58+d6iZCNjQ27p0hLSwtMJhOmpqZ4/PgxtLS0mr2utbU19u/fD3t7e0RFRbW656+tDRgwAA8fPkRISAicnZ0hISGBN2/ewMjIiKd2Bg8ejJcvX6KsrEzopRpE1bdznIAvw3VMJhM//PBDiwth8oOVlRWuX7+OxMREbN68GQEBAVi0aBG8vLzYQ7lE+0F6nNq56upqzJ8/H0lJSfjrr786zJYAdZOgA/cehImBLrxnOrEnQbcVDw8PSElJoW/fvjh69CgePHjAczJSXl4OHR0dREZG1ntTb8rNpCx4hD6tNzxXmf0WeZf8oT4nCGJSXVB49zBYVeVQpHuwjxGjAXtnmIFupMb19T59+tTo0vy0tDRkZWVBUVGxXmL09U1JSanZ18fNzQ3a2trYtGkT17EBwJ49e7Bnzx5ERUW1u5IadUpKStCzZ0/U1tZi7dq18PT05Gnei7m5Of744w8MGzZMgFG2T4WFhdDR0cGnT584/gbrFnj8/fff0NfXx5IlS4QY5X/++ecf/PHHH7h8+TLmzJkDb29vqKlx/++VEC6SOLVjxcXFmDJlCiQlJXH27Nl2v5M3t5Og6c1s1skvmZmZMDY2RmJiIsaNG4f169fXm8zcnKNHj+L8+fO4efMmT+f9cioet15mN/gYVVsDmrgEqJoq5N/YCQl5FXQfNpPjGEY/Vfzp+mVj5Orqanz8+LHJukW1tbXs5flfL9Gvu2loaLR6mCMuLg4TJ05EcnJyi4Ypli9fjsjISPz999/tsrQGAAwdOhS//PILTp48ifT0dOzevZvrBQPe3t6Q6q4KW8efGlxV+T179OgRFi1ahLi4OI77vby8oKurC0VFRVy/fh3nzp0TUoQNS09Px7Zt23Dy5Ek4Ozvjt99+Q69evYQdFtEMkji1U5mZmRg3bhwsLCywZ88egRYLbAu8TIK26KWIHc4mbTKPx9vbGzU1NZgwYQIWLVqEly9fcl0Uk6IoDBw4EH5+fmAwGFxfM6uoHPTdDzkSx2+V/RMDZlgQaOISUJmxGZIKnMvUxWsqoPhkPzLfvkJubi5UVVXr9RB9nRzJy8sLNBmlKArDhg2Dm5sb5s6d26I2WCwW3NzcUFZWhvPnz7fLvdsmTZqEmTNnYtKkSbh69SqWLFkCMzMzBAYGQkdHp8Fz6r5Q/HnzCV7lV6NW4r/JxcL4QiGKTpw4gfDwcISEhHDcXzdHcuHChRg+fDjS09OFFGHTcnNzsWvXLuzfvx/jxo2Dr68v+vbtK+ywiEa0j7XLBIeXL1/C0tIS06ZNw59//tkhkqY5x+MQndJ00gQALAqITmHC/XgccorLBR6br68vQkJCYGhoCD09Pezfv5/rc6Ojo1FaWoqxY8fydM0n7/OaTJoAQOYHS2j9ehpyQ2cg9+xaUBSL4/FaCSmY06di7969ePr0KWJiYnDz5k2cOnUKW7ZswcKFC2Fvbw8TExO+1jRqzJUrV1BUVITZs2e3uA0xMTEcOXIEnz59wuLFi9Eev/PV1XKi0WiYNGkSXr16BRMTE5iZmWHDhg0oL+f8m84pLofLoUfwCH2KpCJxjqQJAIrKqhH2MhuLTj+Fy6FHbfJvQhQ1NL8J+K8Ipp6eHioqKpCRkSGE6JqnrKwMPz8/vH37Fn369MHw4cMxefLkej1ohGggPU7tzP/+9z9MmzYNgYGBcHFxEXY4rUZRFFwOP0L0O96XaFvrKfJ9EnRDfH19UVRUBA8PD4wcORLJyclcFcV0dnaGubk5pk2bhvz8fPaNyWQ2+buEwTDIj2l4e43qwkzUlhZC6v/YO/O4mrP/j7+KpChatZeKKCpaEJImVCi0WCL7GoaQhClLG7KOKLKMrIWxVsaSIdmKKNIiokXat1vduu/fH37ud5puy21nej4e9/Hons9Z3p/bvZ/zOue8z/vIawAAiFWF1J2TIbcyEJ0Eqm/TF0sOA8/7RygqKmK/SktLISAgACEhoWZ5CQgI1Pv5V1RUQF1dHYcOHcKYMWMa+KnXTkFBAQwNDWFnZwcnJ6cm19earF+/HiIiInB2dq6W/vHjR6xZswbR0dHYvXs3LC0tkVVUxvWuygEywgiYrdsmuyrbEmtra1hbW2PatGnV0h8/foyVK1fi6dOnsLCwwKxZs36IWFilpaU4evQodu7cCXV1dbi4uMDQ0PA/O6PY3ugQTj8QZ86cwapVq3Du3DkYGxu3tTnNwr+doIkIOTf2gk9CET2GfDtk99O+Gegk9D+HYOEhU9BdY3SjnKAbQ05ODtTU1PDgwQNs374d/Pz8mDVrVp1CKDMzE58/f0bnzp0hISEBMTExiIuLs191vY/4WIwlp6M52lL2KRbZV3dCeu5+dBLsgeLXd1D49DJk5v9eI6/fTB2M05CqlsZisVBSUlJNTHF6FRYW1punqKgIlZWV6N69e53iKj4+Hp8/f4aDgwM7TVhYmGPehjpLp6WlwcDAAJ6enpgxYwb3/9Q2YufOnfjy5Qt27drF8frt27excuVKyMnLQ8DcCTGZZVy30VoDivaEpqYmTp48iUGDBlVLT09Px+DBg5GZmQlPT098/fq11UObNIWKigqcOnUKXl5e6NWrF1xcXGBmZvaf+t+2RzqE0w8AEcHb2xu+vr64efMm11uZ2zP/dIJmZn9Czq1DqEh/hx4j7dBjyBQwcz4jK3grZBf7cyz/TydobqioqGALnvpmgHJycpCWlgYmkwlxcXHk5uZCV1cXcnJytQqhkydPoqKiAn5+fg1+yKWkpCA4OBhnr4QiW28ReLtyPjC2KPomiqKvA7yd0Km7KETHLgVfz+oCqacgH0JWjGxxx2Emk1mnsMrMzMS2bdswY8YM8PPz1yvEOnfu3ODZrqKiIuzfvx9r1qzBiBEjalzv3r17u4ukfvz4cdy/fx8nTpyoNQ+TycTKncdxs0AKPLzf/Lj+PaBgMcuRe+sQKjITASJ0ke4L0bFLwcvH32oDivYCi8VC9+7dkZWVVeOQZRaLBQEBARQUFODx48fYsGEDIiMj28jSxlNVVYWgoCB4eHiAl5cXLi4usLKy+iH9/H4GfmznmP8AlZWVWLlyJSIiIhAZGflTnVWVkc9A5Pv/LdEVRV9Hd00TlAn/74iV8rS3AC8vMs9sAItRBEG14ehhYMvuUCJTcpCaXYQulSUNXgrLyclBaWlpNbHzz78VFBQwaNCgakKoU6dO0NXVxcOHD3H27FkkJCTUcERl21xejuDgYISHh9crmpKTkxEUFITg4GCkpqZi8uTJ2OG2ARfSe+DW2yyOZYQGm0NosHmd9TI/xyE5VhDSI0bUma+p8PHxQVRUtNZYNKtWrcKcOXNw8ODBeusiIpSVlTVoJiwrKwtFRUXQ09ODu7s7Bg4cCCKqsSwpKCjI1dJjbTNhQkJC6Nq1a5NH+g05r46Pjw+koAMeTgMKiW8O5AWRFwBWFaTnHQCIkH3NB4WRQehpOBMsAq68TP/PCKfU1FSIiorWEE3AN784WVlZfP78GXp6enj16hXKy8vbLBBmY+nUqROmTZuGqVOn4saNG3B3d8fmzZvh7OwMOzu7Bm9Y6aB56BBO7ZiSkhJMnz4dDAYDDx48+CGOmuCGmM/51ZygRccuBQCUfYhhpxGrCgJKgyAyeh6oshxZQVvAyy8IYT1LAN+cYzVGmEIwL4mjCJKXl4e2tnaNZbEePXpw3QmuWrUKW7Zsgb+/P/r27Ytnz55BT0+vRr7z589DS0sL/fr141hPYmIigoKCEBQUhIyMDEyZMgU7duyAoaEh29G/4nUGbsdn1esszwleHsBMXQKzZs2CqqoqtmzZAgMDA+4raiIJCQkIDAzE27dvG5Sfh4cHAgICEBAQ4Cro6YULF7BmzRpERERAQUGBnc5isVBcXNygJcfU1NQGLUs21SeMyWQiMzMTTCaz1mXJhgwousproPMAY/Dw8AI8QJdeymBmp7KvR6bkICOf8Z8IVfDvM+r+zXcHcVVV1TYPhNlUeHh4MGHCBIwfPx7h4eHw8PCAq6sr1q1bhwULFvywYTp+NDqEUzslKysLEyZMQP/+/REcHPxTjihyS+veOQYAQtqm7L95OvNBSG8SiqKusYUTAPidPI0ZQ5RawsRqrFy5EqqqqkhJScHWrVuxdu3aGrNKRIQDBw7A1dW1Wtn4+HgEBwcjKCgIWVlZsLKywp49ezBy5EiO0+1mA6QwtLcYHr3n3ml+aG8x7FxgDo+ltvjjjz8wY8YMqKmpwc3NDcOGDeP+xhuJk5MTnJycWvyQZltbW6SlpcHMzAwPHz6EiIgIgG+zDcLCws024KioqKhViP3TJ6ywsBBpaWkc8+Xm5uLr16/o2rUr+Pj4OIurXuookP6fDyOnAYVA7/8dP1NZkIWi51ch+o8gqPmlTLxKK/hPCKeEhIQGCScAGDZsGCIjI39Y4fQdHh4ejB49GqNHj8bTp0/h6ekJd3d3rFq1CkuXLkWPHj3a2sSfmg7h1A5JSEiAmZkZ7OzssGXLlp/WEVBUsH5H4OLYu+gi2RtdJL8HhSP2Mt13kuJeIk2Or8WXMYWEhLBu3Tq4ubnh/Pnz2Lt3L65evQpLy/+JuCdPniA3NxdmZmZ48+YNexkuNzcXVlZW+P3332FgYFCvbwIPDw/2TNXC/JPPEcvFrqrePTphz1Qt8PDwoEuXLliwYAHs7e1x4sQJTJs2Df3794ebm1uLdxzh4eGIiYnBuXPnWrSd76xevRqfPn3CpEmTEBYW1iIHqXbp0qXOZcmGkJeXh969eyMvLw8MBoOjuLr9gYGkBu6a/xZJ3h1CgydAUFW/2rXckopG2/kjUVsogu/8Wzhdu3at3UQQbw709fVx+fJlxMbGwsvLCyoqKli6dCl+/fVXiIuLt7V5PyUdzuHtjEePHmHKlCnYvn17owMF/ijUFugx+/oethNs3r3jYOamQWLyBlBVJbIuuKKbhhF7JqqKUYjKa1tR9OXbAbLDhg3DkCFDoK+vD11d3WY/SLO0tBSqqqq4efMmvnz5gpUrVyI2NvabXwoRxo8fj6qqKnz69AlFRUXsbdLDhg1rlKPyl0IGVl+IweN6Ylzx8gC82ck4NGsIxozkfKZeRUUFjh8/Dnd3d2hoaGDLli3Q19fnmLcpsFgs6OrqwtnZGba2ts1ef13tTp8+HQBw9uzZducYDnyzkZ+fH6WlpbUu1YXGZnDcVfnP3wUAlLy5j9xbhyA6Zgm6aRjVyM+8+ztU+Eugqqpa4/UzLfuPGTMGjo6OtQaZ9ff3x9OnT3H06FEkJSW160CYzUFycjJ27tyJCxcuYPbs2VizZg3k5OTa2qyfig7h1I64fPkyFi1ahD/++IOrSNM/MpyOFvlnB8FiliH31mFUpL8DsSoh2G8Eehras2fhjFR6wqhzIkJDQxEWFgYhISFISkqCwWAgJSUFysrK0NfXZ4upAQMGcHU+GCf279+P27dv4+rVqxg3bhwGDx6Mzp074+zZs3j//j2WLl2KmTNnYsiQIc3SeRMRwuIyceVlOiJTcpD/jyVOKivGeF1VWGjJYIP9eAQcPYrBgwfXUds35/Vjx47Bw8MDmpqacHNz4+ir1VhOnjwJPz8/REREtPpsaVlZGcaNGwcdHZ12u+1cUlISr1+/rvVcyYYMKEriHyL31iFI2riBX7pPjTp6CvAhwFoFhV9SkZycjKSkpGovQUHBGmJKRUUFqqqqEBUV/aFmuRUVFXHv3j0oKytzvB4SEoI9e/bg1q1bICJISkrixYsXP72YSEtLw+7du3H8+HFYW1vDyckJqqqqbW3WT0GHcGon7N+/H97e3rh69Sp0dLjfXv+jUtthtg3h39uuq6qq8PTpU4SGhiI0NBTx8fHQ1taGjIwMmEwm3rx5g9TUVGhra7OFlL6+PpSUlLjqKBgMBnr37o1x48YhPDycHaOIwWCgU6dOOHz4MPc300Ay8hl4lVaA3JIKiAjywX7CKEQ/vAs5OTloa2vj+PHjNWLZ1EZ5eTkCAgLg6ekJLS0tuLm5QVdXt0n2lZSUQE1NDcHBwW3mR5KXl4cRI0ZgwYIF7XJJRl1dHUFBQdDQ0Kg1j73/3/g7paha2j+FU5rfQrDKSqrFN+OXU4fY//tD1RWmg4jw5cuXGmLq+4uHh4fjLJWqqiokJSXblaj6vju2uLi41uXvuLg4WFlZIT4+HgB+qECYzUF2djb2798PX19fjB07Fhs2bMDAgQPb2qwfmg7h1MawWCysW7cON2/eREhICJSUlNrapFaFiGB39EmjnKAHyQjg0vLRtT7Iv379ir/++gshISEICwuDqKgojI2NoaCggIqKCkRHR+PJkyeoqqqqNiulr6/PdjD+p53R0dFsn6WCggJ0794dQUFB8PX1hZiYGAIDA3H79u06O8Tm5p+dgJaWFv744w9oaWlxVUdZWRmOHj0KLy8vDB48GK6uro0W71u2bEF8fDzOnj3bqPLNRWpqKoYPHw4fH59WXS5sCIaGhti2bRtGjRpV41phYSE8PDxw7K8XEBy7AgD3IoUXgK9d4+I4ERFycnKQlJTEcaaqrKys1pkqGRmZVl8ejYmJwYwZMxAXF1drnsLCQkhLS6O4uBg8PDw/ZCDM5qCwsBCHDx/Gnj17oK+vDxcXFwwZwnlZv4O66RBObUhZWRns7e2RmZmJP//8s0lOpz8yXwoZXDtB9+KrQOopZ1w6c6JBO8VYLBZevHiBkJAQhIaGIiYmBiNHjsS4ceMwaNAgZGVl4enTp3j69CmioqIgLS0NfX19SElJITMzExEREejcuTNsbGxgbW0NDQ0N9OvXD6dOnULv3r3Rr18/aGpqIiIioikfBdd4enoiOzsbPj4+GDhwIE6fPg1NTc1G1VVWVoYjR47Ay8sLurq6cHNza/DsFfAtSvPAgQMRFRXVLgYAMTExGDNmDIKCgjiKlLZiypQpsLOzg5WVFTutsrISAQEBcHNzg5mZGbZt24Z1Nz81akBRlf4GByb3xaRJlvVn5pL8/HyOgiopKQkFBQVQVlbmOFMlLy/fIsEaL1y4gHPnzuHSpUt15hMREUFSUhLExMQQHh7+wwbCbA4YDAaOHTuGHTt2QFVVFS4uLjA2Nm5XM4ntnQ7h1Ebk5ubC0tISMjIyOHnyZIvsAvqR4MYJepiyGPbYauP5w7uYPXs2Dh8+XK0Tagh5eXm4ffs2e1mva9euMDU1hampKQQEBHDq1Clcv34dVVVV4OfnR0FBAQYOHIihQ4eyZ6cePHiAwMBA3Lt3D3JyclBVVUV4eHjTPgguuXfvHjZu3IhHjx5hwIABOHfuXJMjyzMYDPj7+8Pb2xtDhgyBq6srtLW16y03b948SEpKwsvLq0ntNyd37tzBjBkzcPfu3VadCayLRYsWQUdHB4sXLwYAhIWFYc2aNZCQkMDu3bvZYjU5PRsm2y6CejbcF0e5Jx8cdbpi5aI5MDExwZ49e9CtW7cWuY9/U1xcjPfv33MUVVlZWVBSUqo2Q/X9paSk1Gi/w+3bt6OkpASenp515tPS0sKJEycwaNAglJSUQFJSErm5uT9cIMzmhMlk4syZM/D09ETPnj3h4uKCCRMmtMtNFe2NDuHUBnz48AFmZmYYP348duzY0fFF/X/qcoLuKcgHA2UxWGjJYJyGFHt0FB0dDQsLCzg6OmL16tWNGjVVVVXh9OnTOHLkCJ49ewYmkwlFRUVMmTIFc+bMgYaGBkpLS9lLe99npvLz88FkMjFy5EhERUWhc+fOuHbtWpP9hLihuLgYvXr1Qm5uLgYNGlSv7ww3MBgM+Pn5wdvbG8OGDYOrq2uty4AvX76Eqakp3r171+5iyJw+fRouLi549OhRu4i87+Ligm7dumHy5MlYu3YtEhMTsWvXLlhYWLC/v1VVVbCwsEAvpb5gaNviSUpuvQMK5e5ViD+2HhF3QiEiIoIVK1YgMjISp0+fblbn/8bwfbMGJ1GVlpbGHnj8+9W7d+86B5WzZs2CsbEx5s6dW2f7EydOxIIFC9ihQwYPHoyDBw+2alyz9kpVVRUuX74MDw8PMJlMuLi4wMbGhh2MtwMOUAetyvPnz0lGRob279/f1qa0a9LzSik0NoPOPPlIobEZlJ5XWmvejx8/0oABA2j58uVUWVnZoPqrqqro77//ppUrV5KsrCxpaGiQm5sbxcXFUWFhIf3555+0ePFiUlBQIHl5eVq4cCFdvHiRCgoK2HVkZGTQmjVrSEBAgFRVVUlAQID4+fnJxsaGdu3aRQ8ePKCSkpImfxb1oaWlRY8fPyY1NTV68+ZNs9dfUlJCPj4+JCUlRVOmTKGYmJhq11ksFhkbG9OhQ4eave3mwsvLiwYOHEj5+fltbQpt2bKFNDU1SVxcnPbs2UPl5eU18jg6OtIvv/xCFRUVxGKxKOR1Og1dG0B9nf8kRefr7JfW1jBaGvicQl6nE4vFoj179lD//v0pNzeXiIjOnz9PEhIStH379gb/Nlqb8vJySkhIoJs3b9L+/ftp5cqVZG5uTn379iV+fn5SUFAgY2NjWrhwIXl7e9PFixcpJiaGiouLSU9PjyIiIuptY9myZdWeucuWLSMfH5+WvK0fDhaLRSEhITRixAhSUVEhf39/Kisra2uz2iUdwqkVuXHjBomLi9OlS5fa2pSfjvz8fDIxMaGJEydScXExxzyVlZUUHh5ODg4OJC0tTZqamrR169Y6xQaLxaK3b9/S7t27aezYsdS9e3cyNDQkDw8PevHiBX3+/Jl4eXnp/PnzVFFRQX369KFVq1aRg4MD6enpkYCAAGlpadGiRYvo6NGj9Pr162bvwJYsWUJ79+6lvn37Unx8fLPW/U9KSkpo165d1KtXL7K2tqZXr14REdHVq1dJXV2dmExmi7XdVFgsFjk4OJCxsTFHodIalJWV0Y4dO6hbt27Ur18/ys7O5pjv6NGj1KdPH7b4+Y62tjZdu/Og3gHF6tWrydDQkBgMBhERpaamkpGREY0cOZI+fPjQ/DfWgjCZTHr//j3dunWLfH19ydHRkSwsLEhdXZ26du1KPDw8NHToUJo7dy65u7vT+fPnKSoqqoZA9vLyorVr17Lfnzp1iqytrVv7dn4Y/v77bzI1NSVZWVnas2dPrc/U/yodwqmV8Pf3p169etGjR4/a2pSflvLycpozZw7p6upSRkYGEX178N65c4eWLl1KvXr1Im1tbXJ3d6d37941qo3i4mK6ceMGrVixglRVVal79+4kLS1NysrKlJ2dTSEhIaSmpkYVFRVERMRgMOjx48e0b98+srOzI1VVVRISEiIjIyNycnKi4OBg+vTpE7FYrEbf94kTJ2jq1Kmkqqra6PvihuLiYtq5cyf16tWLrKysSElJiW7evNni7TaVyspKmjx5Ms2YMYOqqqparV0Wi0VBQUHUu3dvsrCwoMOHD5OZmRnHvOHh4SQpKVlDAOfm5pKQkBD7e1UXVVVVZGNjQ7a2tuz7rKqqoh07dpCEhAQFBgY2/abaAenp6dSzZ0+6d+8eHTlyhNavX09WVlakpaVF3bp1IwkJCRo6dCjNnDmTrKysaMiQIfT48WPKzs6mxMREkpWVbetbaPdERUWRlZUVSUhI0LZt2ygvL6+tTWoXdAinFobFYtGmTZtIRUWFEhIS2tqcnx4Wi0Wurq4kJSVFtra2JCkpSYMHDyZPT09KTExs1rbKy8tJQkKCnJ2dSUhIiAQEBGjYsGGkrKxM69atq7VzzsnJodDQUNqyZQuNHz+exMXFSVpamiZNmkQeHh50586dakuC9REfH0+KioqkoqLS7PdYF8XFxWRhYUFdunQhW1tbiouLa7W2G0tpaSkZGBiQk5NTq7T39OlTGjFiBGlpadHt27eJiOjx48ekp6dXI29SUhL16tWL/vrrrxrXrl69SiYmJg1ul8Fg0IgRI6rNshARRUdHU//+/Wn69Ok/fCd4//59GjZsGMdrLBaLMjIy6MGDB3T8+HGyt7cnMTEx0tHRoR49elDPnj2pc+fOZGFhQZs2baITJ07Qw4cPKSMjo0mDmJ+VN2/e0OzZs0lUVJScnZ0pMzOzrU1qUzqEUwtSXl5O9vb2pK+vT1++fGlrc35qKioqKCwsjBYsWEDi4uLUu3dv6t69O50+fbrF2jx79iwZGRkREdGVK1dIQ0ODwsLCaNasWdSpUycSExOjGTNm0KlTp+r8/7NYLHr//j2dPXuWVq9eTcOHDydBQUHq378/zZkzh3x9fSkqKqrW2QYWi0WioqKkoKBASUlJLXKvnMjNzSVJSUl6/PgxeXl5kaSkJE2bNq1F/Kyak+zsbFJTU6MDBw60WBupqak0c+ZMkpaWpoCAgGrLs0lJSdS7d+9q+fPz86l///7k6+vLsb61a9fS1q1bubIhJyeH+vXrR/v27auWXlJSQsuWLSNFRUW6f/8+V3W2J/z9/WnOnDkNypuamkoyMjJE9O33kp2dTSNHjqTly5eTm5sbzZw5k4YOHUri4uLUrVs30tLSIisrK1q/fj0dOXKE7t27R58+fWrVmcr2SEpKCi1btoxERERo+fKJsg0JAAAgAElEQVTl9PHjx7Y2qU3oEE4txHefGwsLi1ZxEP4vUl5eTiEhITRv3jwSExOjIUOG0K5duyglJYWIiO7cuUOSkpJ06tSpFmnfwMCALl68SETfHsZ6enp0/vx5IiKaO3cuLVu2jPz8/Gjy5MnUo0cP0tHRoU2bNtHDhw/r9QeqqKig6OhoOnToEM2dO5fU1dVJUFCQDAwMaNWqVXT27FlKTk5mj47NzMxIUlKS3r9/3yL3yok1a9bQwoUL2e8LCwvJw8ODJCQkaMaMGfT27dtWs4Vb3r9/TzIyMuz/X3NRVFREmzdvJlFRUdq0aRMVFhbWyJOfn09CQkLs90wmk0xNTcnBwaHWevX09BolclJSUkhWVpbjfV6/fp2kpKTI2dm5zfy+msLatWvJ09OzQXmZTCbx8fFVu09PT09atWpVjbx5eXn0/PlzOnfuHG3fvp3mzJlDI0aMICkpKRIQECANDQ2ytLSkNWvW0KFDh+ivv/6i9+/ft1vn+5YgIyODnJycSFRUlObOnduivpXtkQ7h1AJ8/vyZNDU1admyZf+pH1NrUF5eTtevX6c5c+aQqKgoDRs2jHbv3l3ryCc2NpYUFRVp69atzToF//z5c1JQUKgmgEJDQ6l///5UWVlJnz9/JlFRUbZd5eXlFB4eTs7OzqSlpUUiIiJkY2NDAQEB9Pnz5wa1WVBQQHfv3iVPT0+aPHkyycjIkLi4OJmbm9Po0aNJUFCQoqOjm+0e6yIpKYnExMTYvmT/pLCwkNzd3UlcXJzs7Oza7UM1KiqKJCQk6OHDh02uq7KykgICAkhGRobs7OwoNTW11rwsFov4+PjYO5ZWrVpFJiYmtYrpwsJC6tatG9vZm1uio6Nrvc/MzEwaP3486ejotNv/U21MnDiRK+GroKBQbWARHh5OQ4cO5arNoqIiiomJoYsXL5K3tzctXLiQRo8eTfLy8sTPz099+/Ylc3NzWrlyJe3fv59u3rxJCQkJDfJN+xHJzc2lLVu2kISEBNnY2NCLFy/a2qRWoUM4NTOvXr0ieXl58vb27lgrbyYYDAZdvXqVZs2aRSIiIjRixAjau3cvffr0qUHl09PTafDgwTRv3rxme4DNnj2bvLy8qqWxWCwaPnw4e4Zr8+bNNHPmTI7l09LS6Pjx42Rra0siIiKkqalJTk5OdPfuXa5G/58/f6aLFy+Sra0t8fDwULdu3UhVVZVmzJhB+/bto8jIyEZ3uHVhZWVF7u7udeYpKCigbdu2kbi4OM2cObNVHNe5JTQ0lHr16tWk2bG7d++StrY2DR8+nJ48edKgMlJSUpSWlkb+/v6kpqZWYwfdPwkJCaFRo0Y12j6i/90nJ3HEYrHI19eXxMXF6fDhwz/Mc6tv374UGxvb4PwjRoyg8PBw9vvi4mISFBRsti33paWlFBcXR1euXCEfHx9aunQpjRkzhpSVlalLly6krKxMY8eOZYdCuHLlCsXFxbXI77O1KSoqIh8fH5KRkSFzc/NmGYy0ZzqEUzNy584dkpCQoDNnzrS1KT88DAaD/vzzT7Kzs6OePXuSoaEhHThwgNLS0hpVX1FREY0fP57GjBnDleM1J7Kysqhnz54ct5PfvXuXVFVVqaKiggoLC0lKSoqeP39eZ31MJpMiIiJo8+bNpKenR8LCwmRpaUmHDx9u8Pbx/Px84uHhoYSEBIqNjaWAgABavHgxaWtrk4CAAOnq6tKyZcvo5MmT9Pbt2yb5ajx48IAUFBSotLT22Fr/tm3r1q0kLi5O9vb27W6TxPHjx0lJSYnj7FldvHv3jiwsLKh379504cIFrgTHgAEDKCAggCQlJev9PDZs2ECbN2/myjZOHDt2jHr37l3rfb5584YGDRpEFhYWlJWV1eT2WpKKigri5+fnSvTMmDGD/vjjj2ppgwYNapWdzvXFqpKXl6fRo0dzjFX1I8FgMOjw4cPUu3dvMjQ0pLCwsB9GiHNDh3BqJv744w+SlJSsNqLpgDtKS0vp4sWLNH36dOrRowcZGRnRwYMHKT09vVnqZzKZtGzZMho4cGCdSyn14e7uTvPmzav1urGxMR09epSIiPz8/MjIyIirh0dWVhYFBgbSzJkzSUJCgvr160erV6+msLCwOkennTt35hgWoKSkhB4+fEg+Pj40depUUlJSoh49epCJiQm5uLjQlStXGiwaqqqqSE9Pr1Fb2vPy8mjLli0kJiZGs2fPblVH9vrYtm0bDRo0iKNP0r/JycmhX3/9lcTExMjb27tRMwb6+vokIiJCd+7cqTevgYEBe0deU9myZQvp6OhQUVERx+vl5eW0fv16kpaWbtchJt69e0fKyspclXF2dqbt27dXS2sPgTBri1WloaFBAgICJC0tTSNHjqS5c+fS9u3b6dy5c/T8+fN2Ecy1NphMJgUGBpK6ujrp6OjQxYsXfyrH+g7h1ERYLBa5u7uTgoICV9PGHXyjpKSEgoKCyNbWlnr06EG//PILHTp0qMW2u7JYLNq5cyfJyck1aj2+oqKCZGVl6ywbERFBioqKVF5eTkwmk9TV1enq1auNsreqqoqePXtG27ZtIwMDAxISEiJzc3Pav39/jdADAgICNTqG2vjy5Qtdu3aNNm/eTOPGjSMRERFSUFAga2tr2rlzJ92/f5/jaDcwMJD09PSa9BDMy8sjV1dXEhMTo7lz51JycnKj62ouWCwWLVq0iMaNG1frcm55eTnt3buXJCQkaOnSpY3eKZuXl0dCQkLVHOtro6SkhLp169ZsG0xYLBbNnz+fzMzM6tygcO/ePZKXl6fly5c3eGaxNbl69SqZmppyVcbX15cWLVpULa29B8KsqqqiT58+1RqrSlxcnB2rys3NjQIDA9mxqtrDTE9VVRVdvnyZdHV1qX///vTHH3802l0iPa+UQl6n0+knHynkdXqdp0m0NB3CqQkwmUxatGgRaWtrN3oJ6b9IUVERnT9/nqytrUlYWJjGjBlD/v7+rbo8cOHCBRIXF6eQkBCuy40cObLefKampuyt5Tdu3KgWFLMp5Obm0vnz52nu3LkkLS1NKioq5ODgQNevXychISGaMmVKo+plsViUkJBAp06dohUrVpC+vj4JCAiQpqYmLViwgI4cOUJPnjwheXl5evDgQZPv4/u9/PbbbyQmJkbz5s1r1R2BnGAymTRx4kSaPXt2tU6HxWLRlStXqE+fPmRqatqkARKTyaSxY8fSgAEDag098E/u3LlTa6yixlJRUUFmZma0YMGCOjvX3Nxcmjp1Kqmrq7c7p9+dO3fSr7/+ylWZ69ev1xBbSUlJP2wgzH/Hqtq4cSNNnTqVdHV12bGqdHR0aOrUqbRx40Y6fvw4PXjwoE1iVbFYLPrrr7/IyMiIlJSUyNfXt0EztSwWi268SqfFp56T5pawGscNLTn1nG7+/3FDrUnHIb+NpLi4GNOmTQOTyURwcDCEhITa2qR2TVFREa5fv47g4GDcvn0bBgYGsLa2xqRJkyAmJtYmNj169AhWVlbYunUrFi5c2KAyhoaGWLFiBWxsbOrM9+zZM0yePBmJiYno2rUrxo4diylTpmDp0qXNYTqAb4civ3r1CqGhoQgNDUV4eDi6du0Kd3d3mJqaon///o069Pg75eXliImJYR9qfPPmTeTn52P48OEYMmQI9PX1MWTIEMjJyTWpndzcXOzZswe+vr6YMmUKNm7cCCUlpUbX1xRKSkpgbGyMsWPHYtu2bXj58iUcHR3x5csX+Pj4wNTUtEn1//rrr4iPj4eOjg4EBASwefPmOvO7urqioqICnp6eTWr33xQXF2PUqFGYNGlSnTYQEQIDA+Ho6Ij169fD0dGxXRxKvmjRIgwaNIir39Pr168xbdo0xMXFsdOICL169UJUVBTk5eVbwtQ2gYiQm5vL8VDlpKQkMBgMjocqq6qqQkZGpkX/x48ePYKnpyeioqLg6OiIxYsXc+w/vxQysPp8DB6n5NR7wPXQ3mLYM1ULvYQFWszuf9IhnBrBly9fMH78eGhqasLPzw98fHxtbVK7pLCwENeuXUNQUBDu3buHESNGwNraGpaWlhAVFW1r8wAAiYmJMDc3h42NDbZv317nA+Ply5eYMGECUlJSGvQ/t7S0xOjRo7Fq1Sq8fPkSpqamSEhIgLCwcHPeAhsxMTGUlpbCxsYG9+/fBwCYmprC1NQUv/zyS5PazczMxIABA3Dr1i1kZ2fj6dOnePLkCZ48eYJOnTqxRZS+vj709PTQo0cPrtvIzc3F7t27cejQIVhZWcHFxaVNBNTXr18xZMgQSEtLIzk5GW5ubliwYEGTT4s/fPgw9u3bh8jISJw4cQIpKSnYt29fnWWMjIzg7OzcZMHGiczMTBgYGOC3337DnDlz6sybkpKCWbNmoWvXrjh58iRkZWWb3R5uGDVqFFxdXWFsbNzgMgUFBZCTk0NhYWE1oW9paQk7OzvY2tq2hKntkvz8fCQnJ1cTU9/f5+XlQVlZmaOokpeXb/Lv4DsxMTHw9PTEnTt34ODggJUrV7L7hS+FDMw7+Rxx6YUNrm+AjDACZuu2injqEE5c8u7dO5iZmWH27Nn47bffmjTS/hnJz89ni6Xw8HCMGjUKNjY2mDhxIkRERNraPI5kZ2fDwsICioqKOHHiBPj5+Tnmmz9/PlRUVODi4tKgemNiYmBqaoqkpCR069YNc+fOhYyMDNzd3ZvTfDZiYmLQ1NSEo6MjJkyYgPj4eISEhCA0NBSRkZHQ0dGBqakpzMzMoKmpydV3d9GiRRAWFsauXbuqpRMRUlNT8eTJE7aYevHiBeTl5dlCSl9fH5qamujSpUuD2srJyYGPjw/8/PxgbW2NjRs3QkFBgavPorGUlpbCx8cHPj4+qKysxNGjRzFt2rQm13v37l3MmDEDDx8+hKqqKgIDAxESEoLTp0/XWqasrAzi4uJIT09vMbH97t07jBo1CidPnsS4cePqzFtZWQkvLy8cOHAABw8ehLW1dYvY1BCkpKTw/PlzyMnJcVWuR48e+PDhQ7VnkZeXF758+YI9e/Y0t5k/JMXFxXj//j3HmaqsrCwoKirWEFQqKipQUlJq8G/8nyQkJGDHjh24fPky5s2bh9WrV2PtzVQ8Ss7hmL80IRLZ13dDwTGoxrXhKmIInD+kxfvlDuHEBREREbCysoKHhwfmzZvX1ua0G/Ly8nDlyhUEBwfjwYMHMDIyYoulxsw8tAUMBgP29vb48uUL/vzzzxozYjk5OVBVVUVCQgIkJCQaXK+trS10dXXh5OSEz58/Q0tLCy9fvmyRZQFRUVH2zIiHh0e1ayUlJQgPD0doaChCQkJQWlrKno0aM2ZMnaL29evXMDExwbt379CzZ8967aisrERsbCx7ie/Jkyd4//49NDU1qy3xKSsr1/mAy87Oho+PD/z9/WFra4sNGza0mIBisVg4c+YMXFxcMGzYMHh5eSE7Oxvjx4/HtWvXMGTIkEbXnZiYiBEjRuDcuXMYPXo0ACA0NBR79uxBWFhYreUePHgAR0dHPHv2rNFtN4SIiAhMnjwZYWFhGDRoUL35nzx5gpkzZ2LEiBHYv39/q7spFBQUQFZWFkVFRVx3kAMHDsTp06ehqanJTrt//z7Wr1+Px48fN7epPx0MBgMpKSk1ZqmSkpLw+fNnyMrKcpypUlZWRteuXeus+9OnT9i1axfOPngLwbErAJ6as//M3DRkXXBDVUkeFNYE17jOywP4zhgM0wHSzXbPHGlVj6ofmKCgIJKQkKDQ0NC2NqVdkJ2dTQEBAWRqakrCwsI0efJkOn36dJNjJLUlVVVVtG7dOlJTU6ux08vLy4tmz57NdZ1xcXEkISHB/lw2bdpE9vb2zWFuDXr06EHnz5+n0aNH15s3MTGRDhw4QObm5iQkJEQGBga0detWevr0abUdcywWi8aMGdPkc90KCwvp3r175O3tTVOmTCE5OTkSFRUlU1NTcnV1pRs3btDXr185lv369SutX7+eREVFaenSpQ0OfNpQHjx4QHp6eqSnp1cjcN/3Y0kaG3sqNzeX1NTUyN/fv1r6s2fPSEdHp86y27ZtozVr1jSqXW4JDg4mWVnZBscNKyoqovnz55OysnKrxEH6J0+fPqVBgwY1qqy5uTldu3atWtr3QJg/QyDKtqQxsapevnxZIzTGnKMPqzmBf3/JrwmmLtJ9ScJqM/HwdeWYR9H5Oi05VXfcvOagQzg1gN27d5OsrGyrHWfRXvn69Sv5+/vT2LFjSVhYmKysrOjcuXMNin3zI3Hw4EGSlpamx48fE9G3nVDy8vL1BrKsDTs7O/YBrd+DYkZFRTWbvd8RFhamlJQUEhIS4uqoHwaDQbdu3aLVq1dT//79SUJCguzs7OjUqVN09uzZZtsR+G/S0tLo8uXLtGHDBjI2NiZhYWFSVlam6dOn0549e+jRo0fVtsJnZWXRunXrSEREhBwcHBp8VE1tJCcnk7W1NcnLy9Pp06drDbFw5MgRUlFR4Tr8AJPJJBMTE467v96/f0+Kiop1ljcxMWl0GIvGsG/fPurfvz/l5OQ0uMylS5dIUlKSXF1d6z1/sbk4deoUTZ06tVFlFy9eTAcPHqyRPnjwYIqIiGiqaR3Uwr9jVa1Zs4YsLS1JQ0ODunbtSlJSUjRixAiaNm8J9XH+k6Mg6qYxmsTMVpLskoA6hZPW1rAWD1XQsVRXBywWC2vWrEFYWBhCQkKgqKjY1ia1OllZWbh8+TKCg4Px9OlTjBs3DjY2NjA3N0e3bt3a2rwW4/r165g7dy78/f1BRPDx8UFERESj6kpMTMSwYcOQmJgIERER+Pn54fz587hz506zrsULCQkhLS0N+vr6OH/+PLS0tBpVz8ePH9lLeteuXYOysjKmT58OU1NT6OvrN5tz6L9hsViIj4+vtsT39u1b9O/fv9oSn4iICHx8fBAQEAA7Ozts2LABMjIyDW4nPz8f7u7uOHbsGBwdHbF69WoICgrWWcbV1RUhISG4d+9eg7/3K1asQFJSEq5du1bjMysqKoK0tDSKi4s5lmUymRAVFUVqamqr+gauXbsWT58+xa1bt+pdWvlOeno65syZg6KiIgQGBkJFRaVFbdy8eTN4eXmxZcsWrst6eHigsLAQXl5e1dKXL1+O3r17Y82aNc1lZgcNhMViIT09HUlJSbj+8hOCv9TcOFQUfQPlGYkQH78KlflfkB7gwHGp7jt+M3UwTkOqxWxu+32l7RQGgwFbW1u8ePECERER/ynRlJmZiUOHDsHY2Bh9+/ZFeHg4lixZgoyMDFy4cAE2NjY/tWgCgAkTJiA0NBTLly+Hk5MTVqxY0ei6+vTpA0tLS+zevRvANyfzzMxM3Lx5s7nMBfDNUZuXlxfDhg1rkr+GoqIiFi9eDFNTUxgaGsLf3x8VFRVYtmwZJCUlMXXqVBw/fhwZGRnNaD3Ay8sLdXV1zJkzB76+voiKikJOTg4OHDiAvn37IiwsDBMmTEC/fv3w4sULzJo1C6mpqdDQ0MCvv/6K9PT0OuuvrKyEr68v1NTUkJeXh9jYWGzcuLFe0QQAbm5uGDhwIGxtbVFZWVlvfl9fX9y5cwfnzp3jKDS7d+8OJpMJBoPBsXxUVBRUVFRafUPFjh07IC0tDXt7e7BYrAaVkZGRQWhoKKZOnYqhQ4fixIkTaMnx+Lt376CmptaosgoKCkhNTa2RPmzYMERGRjbVtA4aAYPBQFFREcrKylBcyXkgWfz6NioyEpB+bAWygtxAlRVIP7YClUWcHchzSypa0uQO53BO5OTkwMLCAgoKCnXusvqZyMjIwKVLlxAUFISXL19i/PjxsLGxwbhx4yAg0DqxMdojoaGhmDhxIpYsWYK9e/eiU6dOjarnw4cP0NHRwbt37yAuLo4bN25g3bp1ePXqVbPN4AgKCuLr168IDAxkb3lvLAUFBVBTU0NoaCi0tbXZ6enp6QgLC0NoaCj++usvKCgosHfqGRgYtEpoju/hEL7PSj1+/BiVlZUoLy+Hvr4+HB0dMXbsWHTv3h3AN0EZGhqKNWvWQFpaGrt3727UbByTyYSFhQXk5OTg7+9f62zh7du3MXPmTERERNQ5+yIrK4snT55w3Bnm7e2N9PT0esMVtARlZWUYN24cdHV14ePjw1XZV69ewc7ODv369YOfn1+LhB3R1tZGQEAAdHR0uC77999/w8XFBQ8fPqyW/v79e4wcORKfP3/u2CndjBARcnJykJqaio8fP9Z4paamori4GPLy8lBUVAS/qj5iexrUWWd7mHHqEE7/4v379zAzM8OkSZPg6enZLoK9tRRpaWm4ePEigoOD8fr1a0yYMAE2NjYYO3Zsg6fpf3YWLVoEcXFxPH78GD169MDp06cbNEPBiaVLl0JYWBje3t4gIpiYmMDGxgZLlixpFlsFBASQm5uLxMRE2NraIj4+vtF1OTs7IysrC8eOHas1T2VlJZ4+fcoOeZCYmIjRo0fDzMwMpqamrRZCgIiQnJyMsLAwHD16FK9fvwYPDw9UVFSgrq6Od+/eoaSkBHv37oWlpWWTOsbi4mIYGRnBwsICv/32W43rCQkJGDlyJC5cuIBRo0bVWZeWlhZOnjxZTZh+x9zcHPPnz4eVlVWjbW0KeXl5GD58OBYtWoRVq1ZxVbasrAzOzs64ePEiTp48yVWspfpgsVgQEhJCRkZGo0I0fPjwAYaGhjVmnYgIUlJSePbsWat9b38GqqqqkJ6eXkMM/fNvPj4+KCoqsl8KCgrV3nfu3BlhYWG4ceMGbj14gm42HkCX2lc06hNOPQX5ELJiJKR7ttyAv0M4/YNnz57B0tISGzduhIODQ1ub0yJ8+vQJFy9eRFBQEN6+fQsLCwtYW1tjzJgx/4mZNW7Izc2FiooK4uPjISIiggULFiA+Ph7Xrl1Dr169uK7v8+fP0NTUxJs3byAlJYUXL17A3Nwc7969a5Y4Pfz8/CgoKAAfHx9ERETw4cOHRo34v8+OvX79mivfoaysLNy6dQuhoaEICwuDpKQkO+TByJEjW02Mp6enY9OmTQgMDAQAaGhogMFgIC0tDYMGDaoWrFNBQYFrIfXlyxcYGBhg48aN1cKS5OXlYciQIVi/fj3mz59fbz2//PILNmzYABMTk2rpVVVVEBMTQ2JiIlehL5qbjx8/Yvjw4di7d2+jYjbdunULc+fOxfTp0+Hu7t4sz5fU1FQMHTq03mXZ2mAymejWrRtKS0trzPROmjQJ06dPx9SpU5ts588Cg8GoIYT+KZIyMjIgLi7OURB9T/v3s42IEBsbixs3buDGjRuIiYmBkZERxo8fD3Nzc7jfz0JoXGajbTbTkMKhmdzPRnJDh3D6f65fv4558+bh6NGjsLCwaGtzmpWPHz+yxVJCQgIsLS1hY2ODX375pVEBy/4r7Nq1CzExMTh16hSAbz94Nzc3nDp1Cjdv3kS/fv24rvP76H3v3r0AgNmzZ0NeXh7bt29vsr1dunRBcXExunTpgl9++QVr166FmZkZ1/VMnz4d/fv35zij0lBYLBaioqLYx8G8fv0ahoaG7GW9lnIgLisrw969e7Fr1y5MmTIFABAcHIx58+Zh8eLF+PjxY7Wo5wDYQTqHDBkCPT29BsWqSkhIwKhRo3Ds2DGYmZmByWSyA4t+92Wrj6lTp2LKlCk1OuqoqCjY29tXOxqkrXj58iXGjh2LS5cuYcSIEVyXz87OxsKFC5GSkoLTp09DQ0OjSfbcvn0b7u7uuHfvXqPrkJOTw6NHj2rMLHl7eyMzM/M/EwiTiJCXl8dREH1P+x5tnZMgUlRUhJycXIMEMYPBwN27d9liiZeXF+PHj8eECRNgZGRUbVB183UGlp+JRsM87KrTWnGcOoQTAD8/P7i5ueHPP/9sUqC79kRKSgqCg4MRHByM5ORkTJo0CTY2NjA2Nu44IqYBVFVVQVVVFRcuXICenl61aydOnMD69esRFBQEQ0NDrurNzMyEhoYGYmJiICcnh0+fPkFbW5v9vil07twZDAYDfHx82LhxIzp16oStW7dyVUdkZCRsbGzw7t27Zt0AkJubi9u3b7OX9bp3785e0jMyMmr08ud3iAgXLlyAs7MztLW1sWPHDvTp0wfAtyVpT09PnDlzBgsWLMC6desgISEBIsKnT5/YQurp06eIjo6GrKxstVkpLS0tjgOMyMhIWFpa4ubNmzh+/Dg+fPiAq1evNtgPzsHBAerq6jVmt3fv3o2kpCT4+vo26TNpLm7dugV7e3uEh4c3arBARAgICICzszPc3Nzg4ODQ6OXSgwcP4tWrV/Dz82tUeQAwMDDAjh07agjBv//+G05OTj9NIMyqqipkZmZyFETf/+bl5a1zGa1Xr16NdldJTU1lC6W///4bgwYNYoulus7RzMzMxIjfglAppsx1mwbKYji9oCNyeItCRNi4cSOCg4MREhLS4ttoW5rk5GQEBwcjKCgIqampmDx5MqytrWFkZNQhlrjkypUr8PT0rPUhevv2bcyYMQN79+7FjBkzuKrbyckJxcXF7I5x48aNSEtLa5IzNwB06tQJFRUV6NSpE65fv479+/fj1q1bDS5PRDAwMMCSJUswe/bsJtlSXzuvXr1ii6ioqCgYGBiwl/X69evH1YPvyZMnWL16NcrKyrB7924YGRlxzPf582d4enri7NmzWLhwIdatWwdxcfFqeSorK/HmzZtqYiopKQkDBw6sJqZUVVXBw8ODK1euwN7eHpKSkoiKiuJqyfW3335Dp06d4OrqWi3d0tIS06dPb5ajXpqLkydPws3NDZGRkZCSapzTbWJiIuzs7CAuLo5jx441qp6VK1dCSUkJjo6OjbIBAKZNmwYLC4sav9uSkhJISkoiJyfnh/DxLCsrw6dPn2pdRktLS4OoqGidy2gNmV1tKJWVlXj8+DFu3LiB69evIzMzE6amppgwYQLGjh3boB2iCQkJMDMzg5XdXMRJjDgn2gAAACAASURBVOo4q669UVFRgXnz5iE5ORlXr15tU1+CppCYmMgWS2lpaZgyZQpsbGxgaGjYYvF2/guYmJhg7ty5sLOzqzXPd4f6xYsXY8OGDQ3u7LOzs6GmpoaoqCgoKSmhqKgIffv2xc2bNxt05EVt8PDwgMVigYeHB1+/fkWfPn2Qm5vb4BHj+fPnsWPHDjx79qxVN0UUFhbizp077NhRvLy81Q4nru1Ij48fP2LDhg24f/8+3N3dMWvWrAbN9nz69AkeHh64cOECFi1ahDVr1tQQUP+kuLgY0dHR1cRUUVER9PX1IS4ujkuXLkFcXBzR0dF11vNv9u/fj8TERBw4cICdxmKxIC4ujri4OEhLt/CxEVyyfft2XL58GeHh4Y0+ZoXJZGLLli0ICAiAv78/Jk6cyFX5cePGYeXKlRg/fnyj2ge+DVxERUXh7Oxc45qOjg4OHDgAA4O6d3a1Bvn5+XUuo+Xm5kJOTq6GKPr+Xl5evsUFYE5ODkJDQ3Hjxg2EhYVBXl4eEyZMwPjx46Gvr8/VLuTIyEhMmTIF27dvx/z58/GlkIHVF2Lw+H0OWHWoFF4eYJiyGPbYakNSuJUEb4uG12yn5Ofnk7GxMU2aNIlKSkra2hyuiY+Pp23btpGWlhZJSUmRg4MD3bt3j6to0R3UTmxsLElJSVF5eXm9edPS0khbW5sWLFjAVXTtjRs30rx589jvDx06RMbGxsRisRplM4vFon//nFVUVCg2NrZB5RkMBikpKdG9e/ca1X5zwWKxKC4ujnx8fMjExIS6d+9ORkZG5O3tTTExMcRisaiwsJBcXFxIVFSUfvvttxpHNjSUDx8+0OLFi0lUVJQ2bNhA2dnZDS6bnp5OBw8eJEFBQdLR0SF+fn7i5+cnKysr2r17Nz18+LDeZ8vp06dp2rRp1dJiYmKoT58+jbqflobFYtHChQvJ1NS0yZHkHzx4QEpKSrR48WIqLi5ucDklJSVKTExsUtsHDhygpUuXcrzm4OBAu3btalL9DaGqqorS09MpMjKSzp07R97e3rRs2TKaMGECDRw4kISFhal79+6koaFB5ubmtHTpUvLy8qIzZ85QREQEff78uU2e9ywWi2JiYsjDw4OGDx9OQkJCZGFhQX5+fk06Cuny5cskISFBN2/erNFeyOt0WnLqOWltDasRIXxp4HMKeZ3e6OdmY/nPCafU1FQaMGAArVix4ocSGm/evKEtW7bQgAEDSEZGhlasWEH379//oe7hR2HJkiXk6ura4PyFhYVkbm5O48aNa/DxM7m5uSQmJsbuBJhMJvXr149u3LjRGJOpqqqKeHh4qqXNnDmTjhw50qDy3t7eZGlp2ai2W5Li4mK6fv06OTg4kLKyMvXs2ZMEBATI0NCQXr9+3SxtfPjwgRYuXEiioqLk4uLSoCNHcnJySFVVlQICAoiIqLKykiZOnEiDBw+mJUuWkK6uLgkICNCgQYNo8eLFdOzYMYqNja32ew0LCyMTE5Nq9e7fv5/mz5/fLPfVEjCZTDI3N6d58+Y1ubPKz8+nmTNnkpqaWoOOMyotLSV+fv4mH+1y5coVGj9+PMdrgYGBZGVl1aT6ib6d25aUlER37tyhY8eOkZubG82dO5eMjY1JRUWF+Pn5SVJSkvT09Mja2pocHR1p37599Oeff9KLFy8oNze31cVAbZSUlNC1a9doyZIlJC8vT71796bly5dTSEhIs5zv9/vvv5OMjEy934H0vFIKjc2gM08+UmhsRosfq1IX/ynhFBMTQ3JycrRr165286WsDRaLRa9fvyZXV1dSV1cnWVlZ+vXXX+nhw4e1nqnVQdPJy8ujnj17Unp6OlflmEwmLV68mLS0tBo88tq6dSvNnDmT/f7atWukrq7eqI6ByWQSLy9vtbTff/+9QZ1wVlYWiYmJ0bt377hut7X466+/aODAgaSrq0tr165lH048fPhw2rZtGz179qzJv4uUlBRasGABiYqK0qZNmyg3N5djvoqKCjI2Nqa1a9dWSy8vLycTExNaunQpsVgsYjAYFBkZSfv27aMZM2aQqqoqCQsL0+jRo8nZ2Zl27txJGhoa1eqwtramP/74o0n30dIUFRWRrq4uubm5NUt9Z8+eJQkJCfL09KxzIPjq1Svq169fk9t78eIFDRw4kOO15ORkkpGRqbd/KCgooFevXtH169fp4MGD5OTkRFOnTqWhQ4eSjIwMdenShZSUlMjQ0JBmzZpFmzZtoiNHjtCtW7coPj6+2hmM7ZEPHz7QwYMH2b+zUaNG0Y4dOyguLq7Z+s6qqipycnIiNTU1ev/+fbPU2Vr8Z4TTX3/9RRISEnTu3Lm2NqVWvk+Dbtq0ifr160cKCgrk6OhIjx496hBLrYSPjw9Nnz69UWVZLBZ5e3uTvLw8xcTE1Ju/oKCAJCQkKC4ujl1+9OjR5Ofnx3XbFRUV1Llz52ppUVFRNTpmTixbtozjQbTtgbdv39KECRNIWVmZgoODqz20S0tLKSwsjFavXk39+vUjCQkJmjlzJp0+fZq+fv3a6Dbfv39P8+fPJzExMdq8eXM1AcVisWjx4sU0YcIEjp18QUEBaWlpkYeHB8e6s7OzKSQkhNzc3Gj06NHEy8tLsrKyNHnyZPL09CQRERH296E9k5mZScrKyuwZt6by8eNHGjVqFBkaGtLHjx855gkODm6WWdGcnBzq0aMHx2ssFoskJCTo2bNn9OTJE7pw4QLt3LmTli9fThYWFqSlpUU9e/YkQUFB6t+/P5mamtLixYvJw8ODAgMD6cGDB5SamvrDrQQwmUz6+++/af369TRgwAASFxenWbNm0blz5ygvL6/Z2ysrK6Pp06fT8OHDuVoiby/8J4TTiRMnSFJSku7fv9/WptSAxWJRdHQ0ubi4UJ8+fUhRUZHWrl1Ljx8/bvezYj8blZWVpKysTJGRkU2q59y5cyQhIUFhYWH15vXy8iIbGxv2+6ioKJKWlm7wkt93ysrKiI+Pr1paRUUFde/enfLz82stFxcXRxISEg1anmpNsrOzafny5SQuLk67du2isrKyesukpKTQ4cOHydLSkoSFhUlPT49+++03evToUaM6suTkZJo7dy6JiYmRq6sr5eXl0f79+2nAgAFUUFBQa7m0tDRSVFSsd+aopKSEBAQEKDk5mc6ePUv29vbEz89PgoKCpKGhQfPmzaPDhw9TdHR0k5enWoJ3795Rr169KCQkpFnqq6ysJC8vL5KQkKAzZ87UuO7u7k5OTk5Nbqe8vJwEBATo2rVrdOLECdqyZQvNnz+fTExMqE+fPsTLy0tCQkKko6NDU6ZMoVWrVtGePXvo0qVLFBUVRV+/fv0pns3Z2dkUGBhI06dPJ1FRUdLW1qaNGzc2+vfSUPLy8sjIyIisrKza/cxbbfzUwonFYtHWrVtJSUmJ3rx509bmsGGxWPT8+XNav349qaioUO/evcnJyYmePXv2U/wgf1SuXbtGurq6zfI/ePDgAfXq1aveEXlxcTFJSUnRy5cv2WmzZs2izZs3c9Ueg8Egfn7+GumGhoZ069atWsuZm5vT7t27uWqrJSkvLycfHx8SFxcnBwcHysrKanQ99+7dIycnJ9LU1CRRUVGaOnUqHT9+nDIyMriqKzExkebMmUNCQkLUvXv3Bs0mxsXFkaSkZJ2fPRGRgIAA24n80KFDZG9vTxUVFRQVFUW+vr40Z84cUldXp27dutHw4cPJ0dGRzp07RykpKe3iWREREUESEhIUFRXVbHU+f/6c1NTUyM7Orprot7e3p6NHj9ZbvqioiGJjY+nGjRt06NAhcnZ2punTp5OBgQHJysoSHx8fde7cmQYPHkx2dnbk4uJCfn5+FBoaSm/evKGtW7e22xnYpvB/7J13PJX9/8dfNO5SMk/2dlckhZASpWVFREOa2stdGlJ3S3vX3d57GZVCKQ1RRtppKRUlRWSGc6737w9f55ccMs6x8nw8/HGu8fm8z+U61/X+vOfPgd3du3fnW2B3Zfjw4QN17NiR3N3d651V7mcarOJUUFBA48ePJ319/UrHqwgChmEoOjqa5s6dS2pqaqShoUGenp4UGxtbJx6AjRD179+fjhw5wrfxXr58SRoaGrRw4cJy/8ebN28u4YJ4//49SUpKVuphlpOTQy1atCi1fd68ebRs2TKe54SEhJCmpmaFsgcFDcMw5O/vTxoaGmRtbc33hU5SUhIdOHCAnJ2dSUJCgrp06UKenp5069atCmWIxcXFkaSkJA0YMICkpaVp+fLl5VryiIjCwsKIxWLRgwcPyjxGUVGR65oaPnx4mYp2RkYGhYaG0qpVq2jQoEEkJydHLBaLbGxsaPny5XT58uUyY7IEjb+/P8nLy1NCQgLfxszJyaHJkyeTqqoqhYWFERGRsbExhYWFUUpKCsXExJCvry9t3LiRZs6cSfb29qSnp0eSkpLUsmVLat++PfXv358mTJhAK1asoKNHj9KtW7fo3bt3VFhYSAMGDCgzEePWrVtkZGTEt+9Sm5QV2H358mW+BHZXhuIY47q0UKsqDVJxysrKIktLS7K0tKxyqjI/YBiG7t69Sx4eHqSiokLt2rWjhQsX0oMHDxqVpTrG8+fPSUZGpkIuocrw5csX6tatG7m4uJQ5dl5eHikoKFBMTAx324IFC2jMmDEVnic7O5tERERKbT937hxZWVmV2s5ms6lTp07k7+9f4TkExb1798jMzIx0dHQq5N6sLoWFhRQeHk6LFi0iAwMDEhMTIwcHB9q7dy99+PCh1PGpqamkoaFBhw4dIqIihXjkyJEkLS1N3t7e5brtfHx8SEFBgd69e8dzf5cuXbiLJ3l5eYqPj6/Qd2AYhhITE8nPz4/mzZtHvXr1IlFRUfr777/J1dWVtm3bRpGRkXy/n8viv//+o/bt21fb5VtYWEjv3r2jW7du0dGjR2nEiBHUsmVLUlNTI2FhYWrZsiVJSkqSnp4eDRo0iGbOnEkbN24kX19fiomJoS9fvlTo2TphwgTavXs3z305OTkkIiJS44oFvygO7LaysuIGdq9fv57i4uJq7b1z7do1YrFYdObMmVqZn980OMUpOTmZ9PX1yc3Nrdq1RqoCh8OhiIgImjVrFikpKVGHDh3o33//pcePHzcqS3WYadOm0aJFiwQydm5uLjk6OpK5uXmZVoEdO3aQpaUl9/P3799JRkamXGvFz2RmZlKrVq1KbU9OTiYJCYlSyQX79u0jMzOzWr0nk5KSaPTo0SQrK0t79uyptTielJQUOnbsGLm4uJC0tDRpa2uTh4cHXb16lTIzM8nc3JxnbM2LFy9oxIgRJC0tTStWrCgzLm3Lli3UoUOHUkrFp/RcMh48iRYeDKJDV++TvKZWtf4fbDabnjx5Qvv376eJEydSly5dSEREhAwNDWn69Ol09OhRevnypcASTebOnUs9evQoV+HIycmhuLg4Cg4Opj179pCXlxeNGDGCTE1NSVlZmZo3b06KiorUvXt3Gj58OHl6etLatWupc+fOJCwszDeXoLe3N3l5eZW538DAgMLDw/kyl6ApDuyeN28edezYkaSlpWnUqFF05swZgQR2V5ajR4/W2RjjqtKgKoc/f/4c1tbWGDduHBYtWiTwfjXFMAyDO3fuwMfHB35+fhAXF4eTkxOcnZ2r3dSyEcHz/ft3qKmp4cmTJ1BQUBDIHBwOB/PmzUNQUBCCgoKgpqZWYn9+fj7atWuHU6dOcasW79y5E/7+/rh69epv7+XMzEwoKioiM7N0iwI1NTVcvnwZ7du3BwBkZWWhffv2CAgIQNeuXfn0DStOTk4O1q9fj//++w+TJk2Cp6dnpVqVCBIOh1OiOfG9e/cgKSmJhQsXwsbGBurqpftnvXjxAt7e3rh69SpmzZqF6dOnl6qsPWfOHERFRSEkJAQ34jMQ8OgT7r5Nw/e8Qu4xTdj56N9ZBXZd5GHZUZYvz6+cnBw8ePCAW/E8KioKmZmZMDQ0LNHcuG3bttWei8PhwNnZGZmZmZgyZQrPdiDZ2dlQUlIqsz+aoqIiz/ZQt2/fxqhRo5CVlYXVq1dj/Pjx1bo+R48exdWrV7kNvH9lxowZUFFRwZw5c6o8hyBJTU3lVuwOCQmBsrIyt2K3oaFhpSp2Cwoiwpo1a7Bnzx4EBQVBW1u7tkXiGw1Gcbp9+zacnJywbt06gfbZKobD4SA8PBy+vr7w8/ODtLQ0nJ2d4eTkBC0tLYHP3wj/2Lp1K+7cuYMzZ84IfK7t27dj1apVuHDhQqnmwQcOHMDJkycRGhoKoKg9RadOnbB582ZYWVmVO25GRgZUVFTw/fv3UvuGDx+OAQMGYMyYMQCARYsW4cOHDzh69Ch/vlQFYRgGx44dw8KFC9GzZ0+sXr0aqqqqNSpDZdi6dSv27NmDefPm4datW7h8+TJERUVhaWkJKysrmJubl2hO/Pz5c3h7e+PatWuYPXs2pk+fjtatWwMo+u7OI93wSro7clrJ/7aFRDc1KWwe2lkgfbdSUlIQExPDVaaio6MhJibG7cNnbGwMfX39Uo2XORwOPn36VGbD2A8fPqBp06Zgs9mQkZGBtbV1qf5oLBarSu18Dhw4gLCwMMyfPx8uLi5QVVXF/v37K9Xi5mdu3ryJJUuW4NatWzz3nzx5En5+fvDz86vS+PyG/tffsbhp7tOnT9G7d2/Y2NjA2tpaYAu+qsJmszFjxgxu7zp5efnaFomvNAjF6ezZs5g+fTpOnDiBfv36CWweDoeDsLAw+Pr6wt/fHzIyMlxlqXg130j9gmEYtG/fHocPH0aPHj1qZM6AgAC4ublh//79sLe3524vLCyElpYW9u/fz21UGxAQgAULFuDRo0fl9h5MT0+Huro60tPTS+3btm0b4uLisHv3biQmJqJLly54+PAhlJSU+P7dyuLWrVuYPXs2mjVrhs2bN8PExKTG5q4KwcHBcHNzw927d6GiogKg6F55/Pgxt6fe/fv30aNHD25fvfbt20NISAhxcXFYvnw5bty4AQ8PD0ybNg3ZHGGMPRSDuM9ZFZahppqWMgyD+Ph43L59Gzdu3MC9e/eQkJAAcXFxiImJQUhICNnZ2UhNTYW0tHSZDWNVVFTQpk0bZGRkwNTUFG5ubpg1axZfZJw/fz7ExMTg5eWF/Px8LFq0CCdPnsTBgwcxYMCASo/39u1b9OnTBwkJCTz3JyQkoEePHvj48WONeS5+JTc3F6GhoVxlqXnz5rCxsYGNjQ3Mzc3rbCPinJwcDB8+HPn5+fD19a1yX8M6Te15CasPwzC0YcMGUlRULJHOzU8KCwvp2rVrNGnSJGrbti3p6+vT6tWr6dWrVwKZr5GaJSgoiPT09Go81ic6Oprk5ORo27ZtJbYfPXqUTE1NufIwDEO9evWivXv3ljteamoqSUhI8NwXFRVFurq6RFTUhqWypQ6qw+vXr8nBwYFUVFTo1KlT9SLOr7i2VURERLnHZWRkkJ+fH02YMIEUFRVJVVWVJk+eTBcuXKDMzEx6+vQpDRkyhGRkZKj7otPcHlvK8y9SK50+JN57HKl4XiJF91Mk0qEnNZVUoOYyGiTRdxL3WJd9d/lyzRiGobS0NHrw4AGdP3+etm7dSrNnz6bBgweToaEhtW3blv766y/S0NAgCwsLGjVqFE2YMIFcXFyob9++pKKiwu0bOG/ePPLz8ys36/P9+/ekqKjIt2Bge3t78vHxKbEtNDSUFBUVaebMmZUO5P7x4wc1b968zJR4hmFIRkamzGKcgiIhIYG2b99OVlZW3Ou9fv16ev78eb347aSkpJCRkRGNGTOmVmKMa4p6a3HicDiYNWsWbty4gaCgIL6ungsLC3Hz5k34+Pjg/PnzUFFR4VqWeMU4NFJ/sba2hrOzM8aOHVvjcyckJMDGxgaWlpbYsGEDhIWFweFwoKOjgy1btnBX0rGxsRg4cCBevXrFdf38SmpqKjp06IDU1NRS+woKCiAhIYHAwEC4uLiUOw6/SE9Px4oVK3DkyBF4eHjgn3/+QcuWgrWc8IPU1FQYGxtjyZIlGDVqVIXPIyLExcVxY6MiIyNhaGgIKysrZEto4kh8U0BIGIWpiUgL2YWCTy8h1nMExIwdkXppEyDcBFKW0wFi8MVvBUT1bSCiaQRhIWCniz4sdeTKnZ9hGCQnJ5fpRnv//j2EhYXLjC1SUVGBjIxMuW60b9++ISYmhuvei4qKQrNmzUrESnXt2pUbr/bo0SP069cPvr6+MDMzq/C15IWWlhbOnj2LTp06lZJp8uTJeP78OU6cOAFdXd0KjyknJ4d79+6V6eZycHDAsGHDMHTo0GrJXh5sNht37tzhWpW+fPkCKysr2Nraon///hATExPY3Pzm9evXsLKywogRI7B06dJas9TVBPVSccrLy8OIESOQkZEBf39/iIuLV3vMwsJChIaGwtfXFxcuXICGhgacnJzg5ORUp+MwGqk6r169gqmpKT58+FBrZu/09HQ4ODhASkoKx44dg4iICM6ePYsNGzYgKiqK+/AZOXIk1NXVsWzZMp7jfPnyBR07dsTXr1957u/evTuys7Ph7u4ONzc3gX2fwsJC7NmzB97e3hg0aBCWL18OGRkZgc3HTwoKCtCvXz90794dq1evrtZY2dnZuHnzJi5fvoxLGXKAUhcAwLeQXWgu3x4/3j1CM5YKxIwd8Wn/VEj2m4wWKkUv/azYS8hPfg1p2yI3l1VHWWx26sgNtv5VIXr//j0+fvwISUlJngpR8TZ+PCd/hojw/v37EoHnDx8+hIqKCleRYhgGy5Ytw40bN6ocHMxms9G6dWukp6fzVL6JCEePHsWcOXPg5eUFd3f3CsVRGRsbY8uWLWW6jdetW4dPnz5hy5YtVZK7LIoDuy9duoSQkBCoqqrCxsYGtra26Nq1a50I7K4skZGRcHBwgLe3N8aPH1/b4giceqc4paamYuDAgdDQ0MDBgwfRvHnzKo9VUFCAa9euwcfHBwEBAWjfvj2cnZ0xePBgKCsr81HqRuoi7u7uaNWqFVatWlWrcuTn58PNzQ3x8fEICAiAtLQ09PT0sGLFCgwcOBAA8OHDB+jp6eHx48c8V8gpKSnQ1dVFSkoKzzns7OwQExODpKQkgTyYiQiBgYGYM2cOlJWVsXHjxlLWgboMEWHChAlITU2Fv79/lQKYeZGckQfLbWH4nscusT310mau4pQatBUAIDVgGohTiC++3hBq0gQyQ72LZPuRhS9HZ0FOrGWZsUVKSkp1IualsLAQz549K6FMvX79GkJCQhg5ciT69OkDIyMjqKmpVdgiER8fj379+pUZj1TM27dv4erqilatWuHIkSO/DUi2GzYanXsPhJaeESRFmqGzojjkxP9fMbt9+zY3G7I60P8Cuy9duoTAwEA8e/YMvXv3hq2tLaytret94PSFCxcwYcIEHD58GNbW1rUtTo1QrxSnN2/ewMrKCk5OTli5cmWVTIH5+fkICQmBr68vLl68CG1tbTg7O8PR0bFGg2UbqV2ysrKgoqKCR48e1Yn/OxFh8eLFOHXqFIKCghAXF4dly5YhNjaW+xJfsGABvnz5ggMHDpQ6Pzk5GXp6evj8+XOpffn5+VBRUYGamhru3r3Ld9kfP34MDw8PJCUlYePGjbCysqp3ZvrNmzfjyJEjCA8P56sb8/LTZEw+cb/U9p8VJ+ZHNtKvH0T+pxdo0loSLVR0kZ/0HG2dl3CP3+2iB8tO9fMFm5WVhX/++QfBwcEwMDDAgwcPkJ+fX8LFZ2hoCCkpKZ7nBwYGYtu2bbhy5cpv52Kz2Vi5ciV27tyJXbt2wdHRscR+IkLw088IePQJoU8/oFDo/xfe4iLNYKImxS0HkZeXBxaLhbS0tEorpTk5OdzA7qCgIDRv3pxbLsDc3Bx//fVXpcarq+zcuRMrVqyotdImtUXZaTp1jOjoaNjb22Pp0qWYNGlSpc798eMHrly5Ah8fHwQGBkJXVxdOTk5YtWpVnUvjbKRmOHLkCCwsLOqE0gQAQkJC8Pb2hqqqKszMzODj44MmTZrA398fTk5OAABPT0+0b98ejx8/LhXLQURlWkl27NgBHR0dPHr0CETEN6Xm8+fP+PfffxEQEIDFixdj4sSJPGvw1HUCAwOxfv16REZG8j3261tu4W+PYfJzId57LJq0LMo++h7pi6YSJZWk9F8sVvUJUVFR7N+/H5MnT8a7d++QkJCAL1++cGOl1q1bh3v37kFGRoarSBkZGaFLly5o0aIFXr58WeGs5aZNm2LJkiUYMGAAXF1dERgYiK1bt6J169ZIyczDrDOPEJmQVlQOQqiktyIjtxDBzz7jStxnbjkILS0txMbGVijjNiEhgRurFB4eDkNDQ9jY2GDWrFncjMuGAsMw8PLywrlz5xAeHv7Hxf7WqsUpOSMPj5Iy8C23kKeptJiAgACMHz8eBw8ehK2tbYXGzsvLw+XLl+Hj44OgoCDo6enB2dkZDg4OkJMrP9CykYYNwzDQ1tbG3r17qx20KghCQkLg6uqKcePG4eLFi3j8+DHXvbZjxw4EBASUWn0nJSXB2NgYHz9+LLE9LS0NHTp0QFhYGPr374+bN29CQ0OjWvLl5eVh8+bN2LRpE8aMGYNFixbxPX6mpih2m1y4cEEgJRLOxbzFLP/npbb/bHFKv3UElJ8Lyf5TwMlJx+dj8yBtPxd/ybXjHr/H1QADOsryXb6ahM1mY9CgQWCxWDh48GAJRYLD4eDFixdc9150dDRevnwJbW1t5ObmwsDAAF5eXmjXrl2F3ahZWVlwd3fH7du38d++I/jvCQfPPpUuEFsWOvJtIPvqPDqoyPEshFlYWFgisPvr16+wtraGjY1NvQvsrgz5+fkYN24c3r17h4CAgDIthQ2ZGlecfjaV/lo591dTqZCQENcUyKtg4K/k5uYiKCgIvr6+uHz5MgwMDLjKUn0JUG1E8ISEhGDu3Ll4dzqT7wAAIABJREFU+PBhnV0FPn78GDY2NhAWFsaqVaswYsQIAEUPax0dHWzduhWWlpbc4xMTE9G9e3ckJiaWGMfd3R0cDgfbt2+Hs7Mz7O3t4erqWiWZiAinT5+Gp6cnDA0NsXbt2morYbXJ169fYWxsDG9vb+715QcZGRkICAiAr68vbsU8grTrJnCalnT1lHDV5eci9dImsNM/AQDadHNGa53e3GPFRZoheEZPnovK+kZOTg569eoFa2vrMhMdisnNzcWDBw8wevRoKCoq4sOHD/j27RsMDQ25VikjIyPIypavUPr6+mHWxbdoIl8yOD03PgYZt46AOIVozlKFlLU7hP8qWfRTo1UhxB4cg///CmGmpqYiODiYW7FbTU2NW1vJ0NCQb7FxdZWMjAw4OjpCXFwcJ06cqBeZsoKgRhWnUqbSMhAWAozVJMF6E4Qr584iODi4TFNgTk4OAgMD4ePjg5CQEBgbG8PJyQkODg5gsVgC+iaN1GdsbW0xaNCgOp/98fHjR5iZmSEtLQ2fP3/mxllcuHABCxcuxMOHD7lFMd+/f4+ePXviw4cP3PNfvnyJHj164Pnz52CxWNi0aRPevn2L7du3V1qWu3fvYtasWWCz2di0aVOdtNRVhvz8fPTt2xdmZmZYuXJltcf79u0bLly4AF9fX9y+fRsWFhZwcnLCwIEDMf9iPC4/Kx17VlE6S3Bwfu7AOqvkV5aUlBR0794dCxYsqNBvUF5eHpGRkVBWVsaXL1+4JRGKLVOioqIlXHwGBgZo1aoV9/ygJ8mYfvI+mJ/G5OR+x6f9UyHrug7NJBWQfuMQmII8SA2YWmJuIQB5V7dh+qCeCAoKwrNnz2BhYcGt2F3fA7srQ2JiIqytrdG7d29s3ry5Xmb/8YsaU5xSMvMw7si9SplKm2Ul4+JcG3RQKelay8rK4ipL165dg4mJCZycnDBo0KAql+Bv5M/gzZs36NatG96/f1+qpURdpLiPnqKiIiIiIiAqKgoiQq9evTBy5EiMHz8eyRl5uPrgFRZ7r8XubRu5Lm97e3uYmppi7ty5AIA7d+5gxowZiI2NrfD87969g6enJyIiIriWr/q+qiYiuLm5ISMjA76+vlX+PqmpqTh//jx8fHwQGRmJvn37wtnZGTY2NiWqJfvHvMVsv2eAUOXnEQLQLPoIlIW+YcuWLdDR0amSrHWNV69ewczMDIcOHSq3nVBWVhZkZWWRlZXF8/9ERIiPjy+hSD158gSamppcRep6gTruJOaVOC/72Q3kxt1CW+elAAB2Rgo+HZoBpX/OlFJQc19EwEY8GcOHD29Qgd2V4cmTJ7CxsYG7uztmz57dYJT4qlIjihMRYcSBKNx5k1ZqX+7LO8gIPwkICUG4RWtIWc1EM4n/V5R6aEjhuJsxsrKycPHiRfj6+uL69evo0aMH1/UgKSkp6K/QSAOhuO3H2rVra1uUCnPjxg3Y2dlBQ0MDQUFBkJeXR3R0NBxmLoPV5MWI+fC9lMtbozUHsf578TzkJNec/uPHD0hJSeHr16+/VRozMzOxevVq7N27F+7u7vDw8Cixiq/PbNy4EcePH0d4eHilv1NKSgrOnTsHX19fxMTEwNLSEk5OTrCysuIZWP7ixQs4ODqihdVcpDevfCPd7uqSODKmK3bv3g1vb28MHToUy5YtaxDPvLt378LOzo4bVsGL2NhYuLm54eHDhxUet6CgAI8ePUJ0dDTCYh4jSrIP8FfJ//P3uz5gf08pKjoKgBgOPqyzh9Kss6XcdU3YPzC3ExuTRwmuEGZd5vr16xg2bBi2bduGYcOG1bY4dYIaWToGP/2MyLellSamMB+plzaC5egF+XH/QUTTGOnX9pQ45s6bVJi7ukNRURGnT5/GoEGD8O7dOwQFBWHs2LEN4gHSSM2QnZ2NI0eOYOrUqb8/uA7Ru3dvdO/eHaqqqjAxMcGtqAfY8ohBs16Tce1lagmlCSjKDor9wkDI1A1uxx8hJbNotd2iRQvo6Ojg3r17Zc7FZrOxZ88etG/fHp8/f8aTJ0+wePHiBqM0Xbp0CZs2bUJAQECFv1NycjJ27NiB3r17o3379ggLC8PUqVORnJyMM2fOwNnZmafS5O/vDzMzM3jMno3LS4ZBR75NpWRtlp2MFo980LRpU8yYMQNxcXFgGAYdOnTAjh07wGbX30w7ADAxMcG+fftgZ2dXZo2mly9fol27djz3lUXz5s1haGiIadOmYeycpaWUJgBAWfYCHlZBTtMWCH3wulIyNBROnDiB4cOHw8fHp1Fp+okaUZwCHn3iHdNEDEBF6bgAwBTmQahJyRRRghBad+yFxMREXLx4EaNHj4aEhEQNSN1IQ+PYsWMwNzfnNm2tT3h7eyM2NhYei5Zj5P47uPM27beuH4IQ7rxNg9uRe1zlqVu3boiMjOR5fEhICPT09HDq1CkEBgbi0KFDDSqG48mTJxg3bhz8/f1/W4YiKSkJ27Ztg5mZGbS1tREZGYl//vkHnz9/xsmTJ+Ho6Fim1Y7NZmP+/PmYPXs2goKCMH78eMi0aYkDo7uiu4YUhH/j5RAWKrK0B3va49HdW1i0aBEAQFpaGjt27EBoaCj8/Pygp6eH69evV+la1BUGDRoELy8vWFlZIS2t9OK6MqUIeFFWOYgmbVjgZP9/Q2xOVhqEW7SGcHPe9ZqeJyTy3N5QISKsWbMGXl5euH79OszNzWtbpDqFwOs4JWfk4S4PaxMACDdvCckBU/H52Bw0adkGxDCQHbmu1HGfOK2RS83RMJM7G6kJiAjbt2+vUmB0XcDIyAidu3SBT5IIhP8Xx0dESAvcws3OIoaD9ND9yEt4ADActDF2gKieNZ5+ysTss49w3M0YJiYmOHPmTImx4+LiMGfOHLx+/Rrr16+Hvb19g4th+PLlC+zs7LBlyxYYGxvzPObDhw/w8/ODj48PXrx4ATs7O8yfPx99+/atcFzLly9fMHz4cDRp0gT37t0rEXMp06YlTrgZ48qzz7jw8BPuJqQhI7eki7W7uhTsOstjwP+yiq9cuQIzMzOIiYlh3rx5AIBOnTohNDQU58+fx/jx49GlSxds2LCh3tbSmTZtGj58+AA7Oztcu3atRKbWq1evYGNjU+WxJUV41xVrqaaH9OsHUPjtI5pJKiDrQRBa/t2tzHGS4l8gLy/vj8gi43A4mDFjBu7cuYO7d+82qMUTvxC4xelRUkYpV0IxBV/e4XvEaciP3wXF6Uch1n0Ivvqvwq9hVxm5hXj88bugRW2kAXP9+nUICwujV69etS1KlbGasAAffhS9wAtTE5FyaiFyX4Rz92c/vIzC9E+QH78DsmM2IzMmAPmfXgIA7r5Nw5Vnn7kWJyLC169fMW3aNPTq1Qv9+/fHs2fPMGjQoAanNOXn58PR0RGurq5wcXEpsS8hIQHr16+HsbEx9PX18ezZMyxevBifP3/G4cOHYWNjU2GlKSoqCl27doWJiQmCg4N5JqoICQnBUkcOu1wNEDyjJ/a4GmC1Qyfs+d/nnSMMYKkjx/0fSEtL4+rVq9i1axf27t1bYhwHBwfExcWha9euMDIywsKFC5GdnV2NK1V7rF69GioqKnB1dQWHw+Fur4qrjohw7949zJ8/H5OHWgP5OaWOadJKHNI27vh6bjU+7puMwq/vIWHBu4ejuEgzqIs1qVRSRX0lNzcXjo6OeP36NcLCwhqVpjIQuOJUXuXcvIT7+EtRixsMLqpvg8LUD2DySmfefcspEJiMjTR8tm3bhunTp9drpeBJ5l8QEi5KAc66fwmtdftCpIMpd3/uq7tordsPQsJN0KRFa7TS6omcZzcBAAwBFx5+goqKCogIixYtgra2Npo2bYrnz5/jn3/+qVbfx7oKEWHixImQlZXl1g2Kj4/HmjVr0LVrVxgbGyM+Ph4rVqxAcnIy9u/fD0tLy0pdCyLC7t27MXDgQGzfvh0rVqyoUKq2nHhLDOgoi+FGyhjQUbbMOk0KCgq4evUqli1bhtOnT5fY16JFC3h5eeHRo0dITExEhw4dcOzYMTAMw3OsuoqwsDAOHTqEb9++Yfbs2SAiEBFevXpVIVfdz8qShoYG1+p3/sQhWOrztsS11DCEvNt2KEzYjbbOS7iV23/FRE0KpgY6AmlXVJf4+vUrLCwsIC4ujsDAQLRpU7mYvD8JgbvqyjKVAkBzGQ1kxV4CJycdTVpJIPdVJJqKyaCJSGmnnGSrhvdQb6RmSEhIQEREBE6ePFnbolSZX13ekv2nAAB+vHvE3cbOTEVT0f+3cjRpI42Cr++4n+8mpOHAKT9kZWXh8uXLCA8Pr1b8SH1g/fr1ePLkCQ4cOIDVq1fDx8cHnz9/hqOjI9atWwczMzNuLayqkJeXhylTpiA2NhYRERH4+++/+Sj9/6OpqYnLly+jb9++EBUVLeW+UlBQwNGjR3H37l24u7tjx44d2Lp1a5luybrIX3/9hXPnzsHU1BSbN2/GkCFD0Lp16zIrcBMRYmNj4ePjw21R5OzsDH9/f3Tu3Jm7SLJrkoyQuJRyaweWhbAQYN9FHulNTODr61udr1eniY+Ph5WVFYYNG4bly5fX6wVmTSBwxamzojjEWjbj6a5rqdoZYsaO+HxyAYSEm0K4pShYgxeVHiQ/B/evnYexwrB629qhkdpj586dGDNmTL3ODCvP5c2FSlsZhH4KIM/ILcTGQ74YMWIEREREGrzSVGz9kZeXh42NDQYPHoxt27ahR48efCne9/btWwwePBhaWlqIjIwU+P3VqVMnBAQEYODAgfDx8eEZsGtiYoLIyEgcO3YMDg4O6N+/P1avXl1v2kyJi4sjODgY3bt3R3p6eql7tKLK0s9Y6ciim5pUUUJFJemmJoUBHWXxvrUJPDw8+Nrrsa4QFRWFQYMGYdmyZZg4cWJti1MvELirTk68JUzUy+5lI2pgC4UJuyHvth2yLqvRnFU648lAURQP79yAqqoqRo8ejYiIiFJxUI00woucnBwcOnSo3pUg+JWKNItt2oYFTvY37mdOVhqaiJaMs1nkvQaurq4N0u1ARHj8+DEWL14MdXV1uLu7w8bGBgcOHEBSUhL+++8/mJmZ8UVpCg4OhomJCcaOHYsTJ07UmFJubGyM06dPw9nZGTExMTyPERYWxujRo/Hy5UvIysqiU6dOWLNmDX78+FEjMlYXJSUlBAYGYsuWLdyCr7zccP7+/nj16hVWrVqFLl26lKnQCAkJYfPQzpUuB6Ej3wabhxYpY8Uu7p8r8zcEAgICYGtri3379jUqTZWgRsoR2HWW/20KblkICwET+nfBmTNn8Pr1a+jq6sLNzQ0dO3bE5s2beaawNtJIMSdOnECPHj3qbcZRMeW5vItp+Xc3ZD++CmI4YH5kI+d5GETalcwUkhZtga5du+Lp06f15kVaHkSEBw8eYOHChejQoQPs7Ozw9etX5OXl4dixYzh16hR69OjBt2rnDMNg2bJlmDBhAvz8/DBz5swat0BYWFhg//79GDhwIJ49e1bmcaKiolizZg0iIyMRGRmJjh074sKFC/Vi0dmpUyd07doVISEhUFJSqrSy9CtVKQdxcLQhZNoUxZ0JCQnBxMSkQS04du3ahcmTJyMoKAi2tra1LU69okYUp2JTaVXoIteK2xWcxWLBw8MDz58/x+7du3H//n3uCuT69ev1LiCyEcFCRPjvv/8wY8aM2hal2hS7vMtDVN8aTSXkkHxwBpKPzEJr3f5oodyJu19cpBl0FcQgIiKCDh064P79+4IWWyD8bIHQ1NSEk5MT2Gw2jh8/jufPn+Px48eYOHFiqQy66pKeno6BAwciNDQUMTExMDU1/f1JAsLOzg4bN26EpaUl3r59W+6xmpqaOH/+PHbv3g0vLy9uBmVd41fLUnR0NLp06QKGYXDz5s1KK0u/UlwOYqeLPqw6ykL8l8VIMyqAilAadrro47ibMdq2KVnTqaEoTgzDYMGCBdi8eTNu374NQ0PD2hap3lGjvercjtzD00r0qmvb9Ae++C1H2OWLUFZW5nlMeno6jh8/jn379iE3NxcTJkzAmDFjICMjwy/RG6mn3Lx5E1OnTsWzZ88aRFzC5OOx1WoWq9E8E4ELHNCiRQtMnz4d6urqmD17Nh8lFBxEhKioKPj6+sLX1xfNmjWDs7MznJ2duS9TIsLo0aPx48cPnD59mq899R4+fIjBgwfD3t4ea9euRbNmv7cA1gQ7d+7Exo0bcfv27QqljhcWFtap9i1lxSwNGTIEgwcPRnBwMPz8/HDmzBmEhYXxNdMrOSMPjz9+x7ecAki2ag7h9ERMHDkEb9684XnvhIeHY/bs2YiOjuabDDVNQUEBxo0bhzdv3uDixYuNvV2rSI1166yKqfTSXBu4TxgDCwsLJCUl8TxWQkICM2bMwKNHj3DixAm8fv0aHTp0wODBg3H58uUSNUEa+bNoCCUIfqY6Lm8hAOz4SGhpaeHkyZMwMjKq86tnhmEQERGBWbNmQUVFBWPHjoWIiAgCAgK47ho9PT3u/3ft2rWIi4vD4cOH+ao0HT16FP369cPKlSuxadOmOqM0AcDUqVMxfvx49O/fv0JhC82aNeO2b+FwONDS0sLOnTtrtH1LRWKWtLS08PHjR6ipqcHT05PbyL2w8PexfhXl13IQfXt0RevWrREREcHzeAMDAzx79gx5eXk899d1vn//DisrK+Tk5CA0NLRRaaoGNWZxKoaIKlU5FwA2bNiAPXv24ObNm1BQUPjtHJmZmTh16hT27duHr1+/ws3NDePGjYOioqLAvlcjdYv3799DX18f79+/59lHrD5CRBixP6pK2UHd1aVwYrwxwsLCMHfuXOTl5eHz58/4+vWrACStOhwOBxEREfD19YWfnx8kJSXh5OQEJycndOzYsczzzp8/j+nTpyMqKqpCz4iKUFBQgFmzZuHq1as4d+5cufPXJkQET09PXL9+HaGhoZWyyjx+/Bju7u5ITU3F1q1bYWFhITAZy7Is8cqGe/bsGRwdHfHyZVEBVzabDUdHR0hKSuLQoUMCWwytW7cOr1+/xr59+3juNzQ0xObNm2vVTVsVkpKSYG1tDTMzM2zdupUvCRJ/MjWuOP3Mr6ZSXQWxMovArV27FgcPHsTNmzcrlVr74MED7Nu3D6dPn0aPHj0wYcIEWFtbV6t2SyN1H09PT+Tn52Pz5s21LQpfScnMw9jDMYhLzqrwOTrybXBgdFduoCvDMDhz5gxcXV1hYWGBrVu3QltbW1Ai/xY2m43bt2/Dx8cH/v7+kJWV5SpLHTp0+O35Dx8+RL9+/RAcHIyuXbvyRaakpCQ4OztDVlYWhw8fLrOWUF2BiDBlyhS8ePECwcHBlWoNQkQ4d+4cPDw8oK+vjw0bNkBNTY0vMlVGWfqZc+fO4dChQwgICOBuy8nJgYWFBQYMGIDly5dXWz5efPz4EZ06dcLHjx95XsOZM2dCSUkJc+fOFcj8guDJkyewsbHBjBkzMGfOnAZjga9VqB6xcuVK6tChAyUnJ1f63OzsbDp48CCZmJiQvLw8LVy4kN6+fSsAKRupbXJzc0laWppev35d26LwFYZh6MKFC6TZyYC0pu4ilXkXSMXzUtl/8wPIZd9dSvmex3M8KysrGjVqFLFYLJo4cSJ9+vSpxr5LQUEBhYSE0MSJE4nFYpG+vj6tXr2aXr16ValxkpOTSVlZmc6cOcM32a5fv06ysrK0Zs0aYhiGb+MKGjabTcOHDydbW1sqKCio9Pm5ubm0YsUKkpSUJC8vL8rKyqr0GAzDUExMDM2bN4/U1NRIU1OTFixYQA8ePKjUtVy9ejV5eHiU2p6SkkIaGhq0d+/eSstWUfr161fm/XTy5ElycHAQ2Nz8JjQ0lFgsFp08ebK2RWlQ1CvFiYho+fLlpK2tTSkpKVUe48mTJ+Tu7k5SUlLUr18/Onv2LOXn5/NRykZqk/3795ONjU1ti8FXHjx4QBYWFqStrU3BwcF06dIl0uo/jCYdu0edlgaXUJg6L79CxrP3URvtnpSenl7mmCtXriQPDw9KS0sjDw8PkpSUpCVLllTphVkR8vPzKTg4mNzc3EhKSoqMjIxo3bp19ObNmyqNl5eXR926daOlS5fyRT6GYWjdunUkIyNDV69e5cuYNU1BQQHZ2NjQ8OHDic1mV2mMpKQkGjFiBCkoKNCxY8eIw+GUezy/lKWfGTNmTJnK0evXr0lWVpYuXbpUpbF/x7Fjx8p8fiQkJJCsrGy9UKhPnDhBLBaLrl+/XtuiNDjqneJERLR48WLq2LEjffnypVrj5OXl0YkTJ8jc3Jzatm1Lc+fOpZcvX/JJykZqA4ZhqHPnznT58uXaFoUvfPr0icaNG0cyMjK0c+dOKiwsJCKiXr160fHjx4uOSc8lV8/1ZDnNmy4/TaZP6bmUkJBAzZs3J29v7zLHDg0Npe7du3M/v337loYPH05ycnK0Z88e7lzV4cePH3Tp0iUaPXo0SUpKkomJCW3cuJHevXtXrXEZhqERI0bQ0KFD+fISy8zMpMGDB5OhoSG9f/++2uPVJrm5uWRubk6TJ0+u1rWJiIigrl27Urdu3SgqKqrEPkEoSz9jYmJCt27dKnN/ZGQksVgsiomJqfZcv5KdnU3i4uL0+fPnUvsYhiFZWVlKSEjg+7z8gmEYWrNmDSkpKdGTJ09qW5wGSb1UnBiGoYULF1KnTp3o69evfBnz5cuXNHfuXGrbti2Zm5vT8ePHKS+Pt4ujkbpLWFgYtWvX7rer5LpOTk4OLV++nCQlJWnevHmUkZHB3RcdHU3Kysol3DFpaWkkJSVF8fHx3G1mZmYkJiZGP3784DlHZmYmiYiIlLK2RkdHk7m5OWlra9PFixcr/SLMy8uj8+fPk6urK4mLi5OpqSlt2bKFEhMTKzVOeaxcuZIMDQ0pNze32mPFxcVRhw4daOLEiQ3mN//9+3fq2rUrzZ8/v1rjcDgcOnToEMnJydHo0aMpODhYYMrSz0hJSfFUXH7mwoULJCcnV2WLZXmMGjWKNm/ezHOfg4NDnXV9sdlsmjp1Kunq6lJSUlJti9NgqZeKE1GR8uTp6UmdO3em1NRUvo2bn59PPj4+1L9/f5KWliZ3d3d6+vQp38ZvRLA4OTnRtm3baluMKsPhcOjYsWOkpKREzs7OPF8Kzs7OtGnTplLbly5dSqNGjeJ+Dg4OJlFRUdq3b1+Z8+nq6payJhAV/b4CAgKoQ4cO1KtXL7p37165cufk5JCfnx8NHz6cxMTEqFevXrR9+3b6+PFjuedVBT8/P1JUVOTL2GfPniVpaWk6ePAgHySrW6SmppK2tjatXr26ymMUW5bc3d1JTEyMhIWFqVevXhQVFSUwd1Vqaiq1adOmQuPv3LmT2rVrx7cFdDHXrl0jPT09nvvWrVtHM2bM4Ot8/CAnJ4fs7e2pT58+JRZajfCfeqs4ERX9qOfOnUt6enqUlpbG9/Hfvn1LixYtInl5eTIxMaGDBw9SdnY23+dphD98+PCBJCQk6Pv377UtSpW4ffs2GRoakqGhIYWHh/M85s2bNyQlJUWZmZml9mVkZBCLxaLnz58TUZESJi8vT8rKymVa4CZNmkRbt24tU6bCwkLavXs3ycrKkouLSwkXRXZ2Np09e5aGDBlCbdq0oT59+tCuXbt+aymoDvfv3ydpaenfKnK/o7CwkDw8PEhVVZViY2P5JF3d4+PHj6Surk47d+6s8DnlueFevXpFdnZ2pKGhQefPnxeI8hQREUGGhoYVPt7T05NMTEz4Yn0shs1mk6KiIk9X1+3bt6lr1658m4sffPnyhYyNjcnV1bUxXrcGqNeKE1HRj3z27NlkYGBA3759E8gchYWFdOHCBbK1tSUJCQmaPHlyg37Y1le8vLzq5Erwd7x584acnJxISUmJjh8/Xq6bcdq0aeTp6Vnm/lWrVtHQoUO5n9evX0+SkpLk7+/P8/hDhw7RsGHDfitjZmYmLV68mCQkJMjW1pZsbW2pTZs21L9/f9q7d2+14w0rwqdPn0hJSYl8fHyqNc7nz5/J3NycBgwYwFdrdV3lzZs3pKCgwI2J40VlY5auXLlCWlpa1K9fP75b5A8dOkSurq4VPp7D4dCIESPIwcGhygHxvPD09KR58+aV2p6bm0siIiJ8VdSqQ3x8PGlqapKXl1e9CFpvCNR7xYmo6Efv7u5OhoaGAjdRJiYm0vLly0lZWZkMDAxo9+7d9dbC0ZDIy8sjFotVr4L7MzIyaO7cuSQpKUne3t6Uk5NT7vFfv34lcXHxcssGZGVlkYyMDD1+/JiIitweIiIipK+vz/Oh+uLFC1JVVf2tnMeOHSN7e3tq1aoVKSoqkqioKHl7e5cZP8VvcnNzycjIiJYvX16tce7cuUOKior077//8vUlW9d5+vQpycjIUEBAAHcbwzB07969KscsFRQU0NatW0laWppmzJjBN6u/p6dnuUkNvMjPzycLCwuaPn0635SHZ8+ekby8PM/7xNDQkMLCwvgyT3WIiooiWVlZ2rVrV22L8kfRIBQnoqKHwPTp06lbt241osiw2WwKDg4mR0dHEhcXp3HjxtHdu3cbNf5a4tChQ2RpaVnbYlSIwsJC2rFjB7Vt25bc3NwqXD9p2bJlNG7cuN8et2HDhhK1ZkaPHk1SUlI8s5Q4HA5JSEiUqo327ds3Onz4MNna2pKoqCgNHDiQjhw5wrXqPn36lKytrUldXZ1Onz4t0PueYRgaPnw4DRs2rMrzMAxDO3bsIBaLRRcvXuSzhPWD6OhoYrFYtHv3br4GeH/9+pWmTJlCbdu2LZH5WVUcHByqVJcrIyODOnXqROvXr6/W/D9jYGBAISEhpbbPnDmT1q1bx7d5qkJAQABJS0uXUIYbqRkajOJEVPRwnDJlCnXv3p1nDIigSE5OpjVr1pCmpibp6OjQ1q1bBeY2bKQ0DMOQnp4eBQYG1rYovyUoKIjunpsrAAAgAElEQVS0tLSod+/e9ODBgwqfl5ubS23btqW4uLjfHpuTk0Py8vJcd3JMTAxJSUmRlZUVz+OtrKzo3LlzlJqaSgcOHCBLS0sSFRWlQYMG0fHjx8u14oaGhpK+vj4ZGRkJbAXu7e1NRkZGVXaN5OTk0MiRI0lXV7fBFUWtCD9blmRlZalJkyY0atQovmfDPXr0iHr16kWdOnWqVu0gbW1tevjwYZXOTUxMJCUlJTp16lSV5/+ZrVu38nQbnjp1qlYLYRbHHfJK7GhE8DQoxYmoaAU9ceJEMjU1FVghv/LmDg0NpWHDhpGYmBi5urrSrVu3Gq1QAiY8PJw0NTXrdAmCJ0+e0IABA6hdu3YUEBBQ6Xti165dNHDgwAof/99//5Uo4mdgYEDi4uJcF14xX758oYEDB5Kqqiq1adOGBg8eTKdPn67UwoPD4dDx48dJWVmZ7O3t6cWLFxU+93f4+vqSkpJSlauax8fHk66uLrm6uv7WFdqQKM8NFxAQQDIyMgKp8cMwDPn6+pKqqio5OjpWujsDm82mFi1aVOt/9fjxY2KxWHTjxo0qj1FMSkoKiYmJlXqXvHv3rlYKYTIMQ15eXqSpqflHLgLqCg1OcSIqepCPHz+ezMzMai0L7uvXr7Rp0ybS0tKi9u3b04YNG2okgPZPZOjQobRly5baFoMnKSkpNGnSJGKxWLR169YqZbyw2WzS1NSslEXnx48fpKSkRHfu3CEiosOHD1O7du1o5MiRlJycTDt37iQLCwsSExMjc3Nz0tLSqvZvJS8vj9auXUtSUlI0ZcqUamfXxcbGkrS0NN2/f79K51+8eJFYLBZt3779j1i8VCZm6eTJk6SgoFCi7hc/yc3NJW9vb5KUlKSFCxdWeBH75s0bUlJSqvb8oaGh1LZtW74Erhe7qX+GYRiSk5Or0UKY+fn5NHLkSDI2Nm58l9QyDVJxIipSnsaOHUu9evWq1ZUmwzAUHh5Oo0ePJnFxcRoyZAhdvXq1TltH6hMfP34kCQmJOle3JC8vj9asWUNSUlL0zz//VCtw1s/Pj4yNjSv98t+zZw/17duXiIosL61ataImTZpQmzZtyMXFhfz9/SknJ4cyMjKoVatWVepvxovU1FRyd3evcNA7Lz5+/EiKiork5+dX6XPZbDYtXryYFBUVKSIiotLn1yeqE+C9e/duUlNTE2ihxMTExBLtW34nU1BQEPeerS4nTpwgZWXlan8/Hx8f6tOnT6ntjo6ONVYIMyMjg/r06UN2dnZ/lOW0rtJgFSeiogfoqFGjyMLCok7cbOnp6bR9+3bq3Lkzqaur08qVK2u0sWpD5N9//6WpU6fWthhcGIahM2fOkKqqKtnb21c7y49hGDI2NiZfX99Kn1tc80lHR4fExcVJW1ubNDQ0aNq0aaWO1dbW5nuJjfj4eHJ2diYFBQU6cOBAhbPYcnNzydDQkFasWFHpOdPS0sjS0pLMzc0FWk+qNqluNtzPrF27lrS0tPheQPJXitu3mJiYUHR0dJnHbdmyhef9WVXWrFlDurq61UoYysvLI0lJSfrw4UOJ7evXr6+R8idJSUmkq6tLU6ZM+aMyQesyDVpxIipSnlxdXalv3751pu4GwzAUHR1NEyZMIHFxcbK3t6fAwMDGH0Ul+fHjB8nIyFQoYLomiIyMJBMTE+rSpQvfGmuGhYWRpqZmhe+NhIQE2rBhA3Xr1o0kJSXJ1NSUOnbsSHl5eRQfH08SEhIkLi5eqn6Rm5sb7dixgy8y/0pkZCSZmpqSjo4OBQUFlftyZxiGhg4dSi4uLpVWAmJjY0lVVZU8PDz40mevLsFPZelXFixYQAYGBgLPRuZwOHTw4EGSk5OjMWPG8Fw0Tpkyha+V/xmGoalTp1Lfvn2rVRhy4sSJpSqwh4eHC7wQ5pMnT0hZWZnWrFnzR7ib6wsNXnEiKlKehg8fTgMGDKhzvagyMzNp3759ZGRkREpKSrRkyZJ632S0pjh69Cj169evtsWg9+/fk4uLC8nLy9PBgwf5qgAPHDjwt1Wf37x5Q2vXriVDQ0OSkpIiNzc3unz5MuXn51NhYSG1a9eOm1JtZWVFpqampeoh7du3r1JFBysLwzB07tw5ateuHfXp06fMuKVly5aRsbFxpX+nBw8eJGlpaTp79iw/xK0TCFJZ+nWeKVOmkJmZWY1Y5r9//07z5s0jKSkpWrNmTYlaYBYWFnxv0M1ms8ne3p5GjhxZ5esWHh5O2traJc4XdCHMGzduEIvFKrdwaSO1wx+hOBEV1c4ZMmQIWVtb11jRvsry8OFDmjZtGklKSpKVlRX5+/vzLe6kocEwDHXt2rVWa5hkZmaSl5cXSUpK0uLFi/mexRkXF0dt27bl+WB+9eoVrVq1ivT09IjFYtGkSZPo6tWrPO+XkydPcmOkLl26RDo6OtS2bdsSL8mnT5+SpqYmX+XnRUFBAe3YsYNkZGRo5MiRJRYJZ8+eJWVl5VI1pcrjx48fNHHiRGrfvj09e/ZMECLXKDWlLP1KcfVtKyurGmvZ8erVKxo4cCBpaGjQhQsXiGEYUlBQEEjAdU5ODhkbG9PChQurdD7DMKSurl6q1Y+gCmGePHmSWCwWhYaG8n3sRqrPH6M4ERU9tAcPHky2trZ1VnkiKvqRHzlyhExNTUlWVpYWLFggsOyX+srdu3dJTU2tVtybbDab9u3bR3JycjRy5MhSsQ/8ws3NjZYuXcr9/Pz5c1q+fDnp6uqSrKwsTZ06la5fv/5btxSHw6GOHTvSpUuXiM1mk6qqKvXs2bOEa47D4ZCYmFiNZet8//6dFi5cSJKSkuTp6Uk3btwgaWnpStW2+vDhAxkaGtLgwYPrdfX+2lKWfqWgoIDs7Oxo6NChNfq7unz5MmlpaZGFhQU1b95cYIkzX758IU1NTdq9e3eVzl+6dCnNnDmzxLaZM2fS2rVr+SEeERXdC+vWrSMlJaVSpUMaqTv8UYoTUdHDwcHBgezs7OpFM8Rnz57RrFmzSFpamvr06UOnT5+u00pfTeHi4kIbN26s8XmvXbtGurq6ZGpqSjExMQKb59OnTyQuLk5hYWG0ZMkS6tixI8nLy9OMGTMoLCys0i82Pz8/btuVNWvWkLW1NampqZVQuvr161fjVbUTExNpyJAhJCwsTOPHj6/wb/LatWskKytL69atq5exH2UpS/fv36/V75OXl0e9e/emCRMm1LjSNmfOHGrSpAnNnDlTYAWE4+PjSVZWtkr3eXx8PLFYrBJW3VOnTtGgQYP4Ihubzabp06eTjo4OJSYm8mXMRgTDH6c4ERXVw7C3tycHB4d64wr78eMHnTp1iiwsLIjFYpGHhwc9f/68tsWqFZKTk0lcXJzS09NrbM7nz5+Tra0tqaurk6+vr8BeKgzD0MOHD6l79+4kLi5OSkpKNGvWLIqIiKjWSry4urq/vz+3552xsXGJCsv//vsveXl58eNrVJicnBwyMDCgmTNn0oABA0hTU5N8fHzKvL7Fip+srGy9c2PUVWXpVzIzM8nIyIjmzJlTo3KdOnWKbG1tafLkyXxr38KLqKgoYrFYVaq63aNHjxLhAe/evSMZGZlqX6fc3FwaNGgQ9e7du86VVmmkNH+k4kRUpDzZ2trS4MGD643yVMzr16/J09OTZGRkqGfPnnT06NE6kzFYEyxdupQmTZpUI3OlpqbSjBkzSEpKitavXy8Qax/DMBQbG0uenp6kqalJysrK1LJlS/L19eWr2+LixYuko6NDHA6HRo4cSWPHjqUuXbpwH/pBQUFkYWHBt/l+B4fDIWdn5xJBuyEhIdS5c2cyMTEpVYPp+/fv5ODgQMbGxvVmRV5flKVfSUtLIx0dnSqVhKgqS5cu5SruDx48IHNzc9LV1eVbhurPBAQEkJycXKVDIPbs2UPOzs7cz8WFMCtbIf1nvn79SiYmJjRixIh64QVp5A9WnIiKrDjW1tY0ZMiQepm+XFBQQH5+fmRpaUmSkpI0ffp0evToUW2LJVDy8/NJVlZWIO0ifp1n06ZNJC0tTVOnTuV77A/DMBQVFUVz584lNTU1UldXp/nz51NMTAxt2rSpxMOZn3MWW5kiIyNJXV2dtLS0uBl3aWlpJCoqWmPxLUuWLCETE5NSGXRsNpsOHz5MioqK5OjoSK9evaKnT59Su3btaMqUKXXeVV1flaVf+fTpE2loaNB///1XI/O5uLiUqNDNMAz5+PiQiooKDR48uFrKCS92795Nf//9d6V+29++fSMxMbESrkRHR0c6ceJElWR48+YN/f333+Tp6dlYFLke8UcrTkRFPn1LS0saPnx4vVSeinn37h23WrKxsTHt37+/xnv11QQnTpwQqFWEYRjy9/cnTU1Nsra25mumFofDoTt37tDs2bNJWVmZ/v77b/Ly8ioRBFxQUEDKysrlFgmsDiEhIdS+fXsqKCggfX19mj17domqyO3bt68R5fv06dOkoqJSbpHK3NxcWrVqFbVu3ZpatGjB1/o+/KahKEu/kpCQQIqKiqVajggCAwMDunv3bqntv7Zv4WcbLS8vL+rWrVulyjA4OzvTnj17uJ/Xr19P06dPr/Tc0dHRJCcnJ7D6aY0Ijj9ecSIq+mH269ePRowYUe+LULLZbLp06RLZ2dmRhIQETZw4kWJiYur1w/tnunXrRufOnRPI2LGxsWRubk4dO3akK1eu8GVMDodDt2/fJnd3d1JUVCQtLS1avHgxPX78mOf/5MSJE2Rubs6XuXnBMAz17NmTjhw5Qvv37ydra2tSVFTkplmPHj26yllHFSU6OppYLNZvFbSCggKaNWsWqaio0NChQ0lKSopWrVpVZ9zSDVVZ+pW4uDiSlZUV2O+OqOhaioqKlhsUnpiYSC4uLqSoqEjHjx/nyzVmGIZGjhxJ9vb2FX72BwQEUI8ePbifw8PDycDAoFLzXrp0iaSlpen8+fOVOq+RukGj4vQ/cnJyyMLCgkaNGlXvladiPn78SCtWrCBVVVXq0qUL7dixo14HHkZHR5OKigrf/z9JSUk0evRokpWVpT179lTb8shms+nmzZs0bdo0kpOTo06dOtGyZct+a71iGIY6d+5Mly5dqtb8v+PmzZukrq5OGRkZJCUlRV5eXlzX4K5du2jMmDECmzsxMZEUFBTowoUL5R6XnJxMPXv2JGtra+7L9OXLl+To6EhKSkp0+PDhWvmd/inK0q/cu3ePWCwWXbt2TSDjf/r0iVgsVoWOLVZUTExM+JLZmp+fT3369KGpU6dW6H9YUFBALBaLGx+Vl5dHIiIiFbZa7dmzh2RlZXla1xqpHzQqTj+Rk5NDvXr1ojFjxjQofzOHw6GQkBBycnIiMTExGjNmDEVERNS7B/3IkSNp3bp1fBsvOzubli5dyq0lVJ1aQIWFhXTt2jWaPHkyycjIUJcuXWjFihX04sWLCo8REhJC2traNXLv9enTh/bu3UuzZs2iWbNmkZSUFL1+/ZoePnxIHTp0EMic2dnZpK+v/9u6N+Hh4aSgoEBLly7leS3Cw8PJxMSEOnfuzI3PEiR/qrL0K7du3SIWiyWQF/6NGzfI1NS0wsdzOBw6cOAAycrK0tixYytVNJUXGRkZ1KlTpwrXZJo5cyYtWbKE+9nIyOi3hTAZhqFFixaRhoYGvXr1qjriNlLLNCpOv5CdnU1mZmbk5ubWoJSnYlJSUmjdunX0999/k7a2Nm3evLlU37K6yOfPn0lcXJzS0tKqPRaHw6EjR46QoqIiDRs2rMqVigsKCujKlSs0YcIEYrFYZGBgQGvWrKHXr19Xabx+/frRwYMHq3RuZblz5w4pKyvT06dPicVi0fz582ny5MlUWFhIoqKifLnOP8PhcMjJyYlGjRpVbqmBrVu3Utu2bSkwMLDc8YoDhzU0NGjAgAF8j8tqVJZ4ExgYSG3btuX79d69ezeNGzeu0ud9//6d5s6dS1JSUrR27dpqJQ4kJiaSkpJShQK97927R+rq6tx7wd3dvVylKz8/n0aNGkVGRkaUkpJSZRkbqRs0Kk48yMrKIlNTU5o4cWKDVJ6Iil4MN2/epBEjRpCYmBi5uLjQjRs36uxLYfny5TR+/Phqj3Pr1i3S19cnY2NjunPnTqXPz8/Pp6CgIBo7dixJSUmRsbExrV+/vtoZPw8ePCB5efkazRiztram7du3/x975x3VRPq98Yuo61oQIQkdAUFRFBAsYFmlSBEFQRRFsSF2Ze0F144Nu2LvYMeOgg1Q7IrYUewNUER6hzy/P/zJd1lAkpAwAeZzjmfPZt6595mQTJ55y31hY2ODTZs2QV5eHgkJCbCwsEBISIhYc/3zzz/o3LlzmdeXkZEBd3d3GBsb482bNwLHzc3NLTJbw4cPx+fPn0XW+MsszZw5Ezo6OqxZKoPDhw9DVVVVrL0mkydPrlAF7tjYWPTq1avY9i2i8OTJE3C53HJLIPD5fLRq1QrXr18H8PM9KasQZmpqKnr06IHevXuLdWI7C3OwxqkM0tLS0KlTJ4wdO7ba3zSTkpKwbt06GBgYQE9PDytWrJCqp6K8vDyoqqpW6Cn31atXcHFxQdOmTXHo0CGh/qY5OTk4c+YMhgwZgiZNmqBTp05Ys2aNWDdjHjRoEJYvXy62eIJw//59qKqq4siRIzAzM8OYMWPg4+OD2bNnY968eWLLc+DAAWhpaZX5mXr16hXatGmDIUOGiLzJbEpKCmbNmlW08krQYVfWLInGjh070LRpU7FtN+Tg4CCWidIhISHQ19eHjY2NyCtiw8LCwOPxyi15smzZMowaNQrAz42+SyuE+eXLFxgZGRX15rJUD1jj9BtSU1NhZmaGCRMm1IibKJ/Px82bNzF8+HDIy8ujb9++uHDhAuO9bocPHxZ5pVlycjKmTJki9IqsrKwsnDx5EoMGDYK8vDy6du2K9evXV6hHoyw+fPgABQWFSq2E/os+ffrAz88PmpqaOHXqFBQVFXHkyBHY2NiIJf7t27fB5XLL3Hfr9OnT4HK52LJli1i+Yx8+fMCQIUOgpKQEf3//UovbsmZJPKxatQotWrQQS40zXV1dPH/+XAyqfj5orVu3DhwOR+TtWw4cOABNTc3fft8/fvwIBQUFZGdng8/nQ1VVtVjP89OnT9G0aVMsW7aM/VxVM1jjVA4pKSlo3749vL29a9SHPyUlBVu2bIGJiQm0tLSwePFiiZgGQejcuTOCgoKEOicvLw8bN24Ej8eDl5fXb+sF/SIzMxNBQUEYMGAAGjduDAsLC/j7+yMuLk5U6QIxefJkTJ06VaI5yuLRo0dQUlLCvHnz4Onpif79+2PBggVo3LhxhQ3zx48foaqqWmyLil8UFBTAx8cHGhoauH37doXylMaDBw9gbW2N5s2b4+TJkygsLGTNkgSYO3cu2rZtW6HVurm5ufjjjz/EXjX727dvGD16NHg8HrZs2SL0KswVK1bA0NDwt9dmZWWFo0ePAgD69u1bND8qIiICPB4PAQEBol8Ai9TCGicBSE5OhqmpKaZMmVIjb7L379/H6NGj0aRJE/Tu3RtnzpyptG7nqKgoaGhoCJyPz+cjODgY+vr6sLa2Lnd4Lz09HUeOHIGrqyvk5ORgbW2NrVu3CmS0xEFycjKaNGkitiEPUXBzc4OPjw/k5eURFhYGdXV16OjoVKj4Z0ZGBoyNjeHn51fi2Pfv32FjYwMLCwuJDgkXFhZiw4YN4HA4qFevHtTV1VmzJGb4fD4mTJiALl26iDzM+vz5c+jq6opZ2f+Ijo7GX3/9BUNDQ4SHhwt8Hp/Px/jx42FlZVWmqdu3bx969+4N4GcP3IQJE3D48GGJlm5gYR7WOAlIUlIS2rZti+nTp9fYm256ejp27doFMzMzqKmpYe7cuSKvSBOUYcOGYdmyZQK1ffz4MaytraGvr4/g4OAy/06pqak4cOAAnJ2d0ahRI9ja2mLHjh1ITEwUp3SBWLZsGTw8PCo977+JiYkBl8uFq6sr1qxZAysrK5ibm2Pnzp0ixSssLISzszOGDx9e4m9w7949NG3aFDNmzJCI+S5tGG7mzJmYN28eVFVV0b9/f6H3J2P5Pb/2PrSzsxOp1+jUqVNwcHCQgLL/wefzcfToUTRt2hSurq4C37cKCgrg5OSEwYMHl3o/SU9PR+PGjfH161dERkZCXV0d6urq1X7rq5oOa5yE4Pv37zAyMsKsWbNqrHn6xePHj4s2v7W1tUVQUFCFN0uOS85CyJM4HLjzASFP4vDkzSfIy8uXa2ji4+Ph5eUFHo+HTZs2laojOTkZ+/fvh6OjIxo1aoSePXtiz549Yl92Lww5OTlQUVGRipush4cHRowYAT09PYSGhkJZWRmenp4ixfLx8UGXLl1KrKDbuXMnuFyu0MOu5SHonKWMjIyirTu8vb2rRBmOqkJ+fj769OkDV1dXkYbEJk+eLCFlxcnKysKiRYugoKCAuXPnCrTKLTMzE2ZmZkUbEP8XDw8PrF27FuPGjYOMjAxevnwpbtksUgZrnIQkMTERbdq0gY+PT403T8DPG1FAQAD++usvKCkpYcaMGUItU+bz+Tj3OA6jA+7DcOEFNJ0VXPRPb9YptPPeivNP4kp9r3/tZaaoqIipU6eWmASalJSEPXv2oGfPnmjUqBEcHR2xf/9+RiZhl8auXbtga2vLtAwAP1e2KSoqwsDAAKGhoWjRogU0NTWFjhMYGAhtbe1iE4azs7MxcuRItGzZEjExMWLRW5EJ3gkJCRg7diw4HA5WrFhRYpNhFtHIycmBtbU1RowYIdS9ccSIERLf5ue/fPz4EQMHDoS6ujoOHDhQrt7ExETo6elhy5YtJY6dPXsW8vLy6N69O0xMTHD16lVJyWaREljjJALfvn1D69atxbpkuzrw4sULTJs2DVwuFxYWFjh48OBvf5QSUrMwcPstaM8OLmaY/vtPe3YwBm6/hYTUnyvi+Hw+Dh48CE1NTbi4uBQbeklMTMSOHTtga2uLRo0awdnZGQcOHKhQVXBJUFhYCH19famaB+Hp6Ql7e3s4OTkhICAAtWrVEmrS761bt8Dlcost437//j1MTU3Rr18/pKWlVUifuFfDxcTEwMnJCZqamggICGB89Wh1ID09HWZmZpg8ebLAf5MuXboINfdInERGRsLExASdOnUqd/uW169fQ0VFpdhih+/fv8Pc3Bx//vknoqOj4e3tXellRVgqHxkAIBah+fbtG1lYWJCbmxvNmzePaTlSRW5uLp0+fZp27NhBDx8+pMGDB5OXlxe1atWqqM3XtGwase8+PYtLEzhua1U5GmcgQ4t9plN+fj6tWbOG/vrrL/r27RudPHmSgoKC6O7du2RjY0Ourq7Us2dPatSokSQuscKcPXuW5s+fT1FRUSQjI8O0HCIi+vDhA5mYmBCfz6eoqChq1aoVLV++nP7+++9yz/348SOZm5vT9u3bycHBgYiILl26RB4eHjRjxgyaPHmySNcJgB48eEDHjh2jY8eOUa1atahfv37Ur18/MjY2Fst7d+3aNZo+fToVFBSQn58fWVpaVjhmTSY5OZm6detG/fr1o3/++afc9jwejx4+fEiqqqqVoK4khYWFtHfvXpo7dy7Z29vT0qVLSVlZudS29+7dIwcHBwoODiYul0t2dnbk7OxMfD6fZGVlydjYmA4dOkSnTp2q5KtgqVQYNm5VmoSEBOjr68PX15dpKVLLmzdvMGfOHKioqKBz587Yu3cvMjIyMHDHrRI9S43a94GsHBd1eNqow9NGff2uJdpoDPHD3r378PnzZ/j7+8PCwgJycnJwc3PDsWPHqkxl3q5du+LgwYNMyyjBuHHj0LZtW8yZMwfW1tZo0aJFueekp6fDyMgIq1evBvCzN83X1xcqKiqIiIgQWgMTdZb4fD4OHz4MbW1t9OzZE0+fPpVInppCfHw8dHV1sW7dut+2+/HjBxo1aiQV0x5SUlIwbdo0KCoqYuXKlWVWuT979iwUFRXB5XKxadMmAD8rjqurq+Pt27fg8XhScT0skoPtcaog8fHx1L17dxoxYgTNnDmTaTlSS0FBAZ07d4527NhBd+PzqX6PCUQytYq1id8/lZpYjqR66i1/EwmkGHOS3l49QQ4ODuTq6kq2trb0559/SvYCxMidO3fIzc2NXr9+TbVr12ZaTjG+fPlCBgYGVKdOHVqzZg2NHDmSoqOji/UW/hs+n099+/YlBQUF2rlzJ6WlpdHQoUPp27dvdOzYMVJTUxMoLyqhZ0kQcnNzafPmzbRs2TJycnKiRYsWkYqKSqXkrm58+PCBunbtSosWLaJhw4aV2ub27ds0YcIEun//fuWK+w2xsbE0ZcoUevnyJa1Zs4Z69epV7PN3/vx56t+/P8nJydGjR4+Iy+USEZGpqSmtWLGChg4dSpGRkaSjo8PUJbBIGNkFCxYsYFpEVaZRo0bk4uJCkyZNory8POrUqRPTkqSSWrVqkb6+Prm7u9PTWlr0ISWv2HEU5NOPy9uJn51KqTePUs77h/SHaguqVa/BfyLJUPMW+nT76Gbq378/6evrU506dSrvQsTA33//TQMGDCBzc3OmpZRATk6Ovn79SrGxsdShQwcKu/WAYtNrkQxPj74kZ1GDurWpUb3/vd8+Pj4UGxtLR44coZiYGLK2tiYTExM6dOgQNWnS5Le5fpmlDRs20JgxY+j48ePUunVrWrhwIfn6+pK1tTWpqKhU6lBm7dq1ydzcnEaOHEn37t0jLy8vysrKovbt21PdunUrTUd1QF5enuzt7cnDw4N0dHSoZcuSD0RhYWGUkpJCffv2ZUBh6SgqKpK7uzvp6enR1KlTKTg4mExNTYnL5dLOnTvJ29ubzp07Rw0bNiRfX19yd3enOnXqUHZ2Nl24cIGUlJSosG4jSqrDobvvk0v93rBUbdgeJzHx+fNn6t69O40fP54mT57MtBypJT4lm+w2RFJqdn6x1/NTEujHxXG4uMQAACAASURBVC2kYOVFtRXUKO3uCcp8dpVUhq8v8cMpX78OhUzsSiryVaeX6RevX78mc3NzevfuHTVs2JBpOaWSkJBABraDSNHUjnLlNUnmj//plK9fh8y1FcnRWJUSH1ymhQsX0J07d+jSpUs0adIkWrt2LQ0ePLjM2NLSsyQo79+/Jx8fHwoPD6f58+eTp6en1PUSSjvR0dFka2tLgYGBZGNjU+yYj48P1a1bl+bPn8+Qut+Tn59P/v7+5OvrS82aNaOEhAS6ePEiNW/enADQsGHDKCUlhU6cOEGJiYnU2t6D2vQaRp/z6lN+rf8Z7X9/b+wMlKXuc84iHKxxEiOfPn2i7t2706RJk8jb25tpOVJJ6NN4GnPgQbntANCntf1JZcRGqiNfcqLmtsGmZGtQ+gROaWbcuHGkoKBAS5YsYVpKqXxNy6bJRx7RzTffiX5zc5chovwvzylgrCWdOryfzp07R8ePHycjI6MSbauaWSqN+/fv0/Tp0+nr16+0YsWKEsM3LL/n+vXr5OzsTKdPny7WK+/q6kqurq40YMAABtX9nvz8fBoyZAhduXKFANDixYvJy8uLZGVlKS8vjxwcHEijeWvKNu5Pt958LzEF4d/UkiEy01aktW5GpCRX9R78WH5S9l+YRWg0NDQoLCyM1q1bR5s2bWJajlTyIyu/1Nfzvr2jjKdhJV6XqVX60/2PzLxSX5dmEhMT6fDhwzRx4kSmpZTKr5WON98m/dY0ERGBiGqrtaJRB6Lp5Yd4unfvXjHTBICioqJo1qxZpKurSwMGDKBatWpRUFAQxcbG0tKlS6lt27ZVxny0a9eOwsLCaOXKlTRz5kyysLCQqnk50k6XLl0oICCAnJ2d6dGjR0Wvx8bGUosWLRhU9nvS09OpV69elJGRQe/evaNLly7RoUOHyMTEhCIiIqhu3bq0dd8hikBLuvX2x29NExERH0Q33yaR57779DUtu5KugkXcsMZJzDRt2pTCw8Np1apVtGXLFqblSB0K9csY55eRoeRL2yg/JYGIiDKiz1NdrhbVluOUHqdB1Ztv4u/vT66urqSkpMS0lBIAoMlHHxWVhwBA34PXUuqdE0Vt+DkZFLdrAuXGvyp6Lb+RCik4TCZ5eflqZ5b+i4yMDPXq1YseP35M7u7u5OjoSO7u7vT+/XumpVUJ7OzsaNOmTWRvb0+xsbHE5/Pp9evXpKenx7S0UomPj6e//vqLtLS06OTJk9SgQQMyNjamiIgImjt3Lg0dOpRc+/Wj6cefEL+xYIsgfvE0Lo2mHH1E7IBP1YQdrJcAWlpaFBYWRt27dydZWVkaNWoU05KkBiN1eWr8Z50Sc5zqcrWoSY8xlBi0iAA+1W7EIY7j9FJjyNevQ4ZqjStDrtjIysqizZs3U2RkJNNSSiXkaQLdfptERET53z9R0sUtlBf3khpzmxIRUfabe/Tj8g4qSP1W4txbb3/Q4Nl+dPvYtqJhuKCgoCozDCcstWvXplGjRpG7uzutWrWKTE1Nafjw4eTj41PuhPiaTr9+/Sg9PZ169OhBBw8eJAUFBamc6xcTE0P29vY0atQomj17drHPsYyMDPXr148cHBxo/PIdFPY5nWRqyRYdz3hyhdLu/a+OEz83iwrTv5P6+L0k2+B/n49bb5PowrMEsmvNrtqsarDGSULo6OhQWFgYWVhYkKysLHl6ejItSSpQkf+TzHUUKfRZQoljDVtbUMPWFuXGMNdWrHITw/fs2UOdOnWS2mGJM4/iiP//D7/pD4KpoaE15chxi46n3T9LnF6TKfG0X4lzQUQfZZSqtVkqjYYNG9KCBQto9OjRNH/+fGrRogXNmjWLxo8fT3/88QfT8qSWESNGUFpaGrm5uZG2tjbTckoQGRlJrq6u5OfnR0OGDCmzXf369emP5l1I5j/3soZtrKhhGysiIkJhASUcmEmNzVyLmSain8N2px/GscapCsIO1UkQXV1dCgsLo/nz59PevXuZliM1OBqpUi2Rf1tBTsbMVBgWlcLCQlqzZg1Nn156DxrTxKdk063/720iIlKwGUsNWxevnq3ktoj+UCu7vlZ6fRVS1tavMabp36ioqND27dspPDycwsPDSV9fnw4dOkR8Pp9paVLL33//TW3atKGYmBhKSUlhWk4Rx44do759+1JgYOBvTRNRye9NaaTeDiLZ+vLUqK19qcdvvUui+BR2rlNVg+1xkjB6enp05coVsrS0pFq1apX7ZawJ2LdWJjNtxZ+TkIVE5tsrClp/mrpt2FBlil6eOHGClJSUqHPnzkxLKZVHn1NKDJ0KS0pWPjl7/k2N0t4SERUZqLL+K0gbYdqKO56obblcLunr69PEiRNp0qRJ1KFDhxK1qKRBpzS89z9+/KCGDRuSkZERDRs2rKiXjimdYWFhFB4eTmPGjKHY2Fh69erVb895mfknpWYrUlkUZqVS+t2TpDx8fZltUrLy6fGX1CrXg17TYY1TJdCiRQu6fPkyWVtbk6ysLA0aNIhpSYwiIyNDa92MyGFFMH3n1xf4vNaqcrR+kgfN+Xs8derUiYKCgqhZs2YSVFpxAJCfnx/NmTOHaSllUtZKR2Hp0duZ/lKTLZrwWtZ/f3dMlLbijieOti4uLnT37l06efIkqaurk4uLC6moqEidzoq2La/Nr3+ltf327Rt17NiRXrx4QQEBAeTq6kqysrICxRWnTj6fT9evX6dPnz5R7969KS0tjZ49e1Zu3E91NYkalW2cMh5eoD/1zEotp/JvquIK4ZoOa5wqiZYtW9KlS5fI2tqaatWqRQMHDmRaEqN8eR1DHwNnU5fpO+lhXGbR/JrSqCVDZK6jSGv7GxNPrh4dPnyYNm3aRObm5rRjxw5ycnKqPOFCcu3aNUpNTSVHR0empZRJmSsdhaRLe2PqUQVra0kKLy8v2rhxI23atIlWrFhBrq6utGDBAqlcVckE+/fvp2XLlpGmpia5ubnRmzdv6MiRI5VaYDQ7O5s8PDyoSZMmFBERIdTk/vJq0mXGXCOFHqPLjVMVVwjXdNg5TpVIq1at6OLFizRlyhQ6evQo03IYIysri9zd3Wn98sV0fHw32uxuQvYGyiT/nx9w+fp1qGdrZdrsbkKBnh2JJ1ePiH72WE2cOJHOnDlDkyZNohkzZlBBQQETl1Iufn5+NHXqVKpVS3q/ar9WOlaEqrjSsTKoV68eTZs2jV68eEF//vknGRgY0OLFiykzM5NpaYySlZVFiYmJ1LRpU6pduzYdPHiQMjIyyMvLq9LmhiUlJVGPHj2oTp06dOHCBaFXRP7ue1OYk0EFKfG/nRdIxH5vqips5XAGePz4Mdna2tKmTZukao+mymLs2LGUnp5OgYGBxV6PT8mmx19S6UdmHik0qEuGao3LHfv//v07DRo0iLKzs+nIkSNStSHr8+fPydLSkt6/f0/16tVjWs5vGRN4n0KffRX5fHsDZdoy2FSMiqonb9++pTlz5lBkZCQtXLiQhg8fXmx4qqbw6NEjcnd3LxoSIyLKzMwkGxsbateuHa1bt06iCw3evXtH9vb25OjoSMuXLxf5wWZMYFSpK4Rz42Pp+2k/Uhuz47fns9+bqon0PgZXYwwNDSkkJITGjx9PJ0+eZFpOpXL27FkKCQkhf3//EsdU5P8kWwNlGthBk2wNlAWaMMnhcOj8+fNkZWVFpqamFBERIQHVorFq1SqaMGGC1Jum9+/fU/TpnUQiPunXkqEqt9KRKXR0dOjw4cN04sQJ2r9/PxkZGdH58+drXCHEly9flijN0aBBAzp37hxdu3aNFi5cKLHcUVFR1KVLFxo/fjytXLmyQr3BXTXrEaHk9+YPleblmib2e1N1YY0TQxgbG9P58+dpzJgxdObMGablVApfv36lUaNGUUBAADVuLL7uaVlZ2aKSDwMGDKDly5czvhQ8Li6OTp06RWPHjmVUx+/g8/m0efNmat++PTmbalPzJqLdDsy0FavkvoFM0rFjR7p69Sr5+vrSlClTyNramh48KH8Px+pCWVutyMvL04ULF+jQoUO0du1asecNCQkpqmBe0a2Prly5QtMH9CAV2QyRzme/N1UX1jgxiImJCZ07d468vLwoODiYaTkSBQCNGDGCRowYQV27dpVIDhsbG7p37x6dPn2a+vTpQ8nJyRLJIwgbNmygwYMHk6Ji2atumOTNmzdkZWVFAQEBdO3aNTI2NqKHW7xJS064YSO5wlRa62ZUI+s3VRQZGRlycnKiJ0+ekKurKzk4OJCHhwd9/PiRaWkSp7Qep1/weDy6dOkSrVu3jnbv3i22nLt27aLhw4fT6dOnydnZWeQ4hYWFtGjRIvLw8KCAgAA6NbMPtVaVEypGa1U59ntThWGNE8O0a9eOzp49SyNGjKDz588zLUdibN68mb59+0YLFiyQaB4NDQ26evUq6ejokKmpKSNP8WlpabRjxw6aPHlypecuDz6fTxs2bKCOHTtSr1696Pr16/T+/XsaMmQInT4cQEfGd6NOzRTLLVBaS4aonXoj+hI4h2rlivbEzfKTOnXq0NixYyk2Npa0tLSobdu2NHPmTKkqDCluXr58Sc2bNy/zuKamJl26dInmzp1Lx44dq1AuALRgwQLy9fWlq1evUqdOnUSO9e3bN7K3t6crV65QVFQUWVlZkZLcn7RraDuBvzedmynS7qHtSUmOrd1UVZFdIOlfMpZyUVNTo65du5KbmxsZGhqSrq4u05LEyvPnz2no0KF0/vx54vF4Es8nKytLdnZ2pKKiQgMHDiR5eXkyMTGptKe7TZs2Ub169cjLy6tS8gnKq1evyNnZmZ4/f05nzpyh3r17U2hoKA0bNozOnDlD5ubm1PCPOuTSVo30lRtRIR/0PTOXcvL/N+wpX78OWbbg0ZQezWm2gwG9iY2hmJgYsrAof6sclt/zxx9/kKWlJQ0ePJjOnTtHkyZNorp165KJiUm1mkAOgKZPn06LFy+m+vXLruOmqKhIVlZW5O7uTm3atBHpvpifn09eXl4UGRlJYWFh1LRpU5F1R0ZGkq2tLdnb29PevXuLTTco73tDuRlkb6RJU3s0pxm2+tSwnnhKgLAwBFikhhs3boDL5eLSpUtMSxEbOTk5MDY2xrZt2xjJHxMTAwMDA3h4eCAjI0Pi+fLy8qCuro779+9LPJegFBQUYNWqVVBUVMT69etRWFgIAAgODgaXy8Xt27fLPDcuOQuhT+Nx8M4HhD6NR1xyVrHjL1++BIfDqZT3tqbx+PFj2NnZoVmzZjh69Cj4fD7TksRCQkICFBUVBW5/48YNcDgcREZGCpUnLS0Ntra26NmzJ9LT04WVWURhYSFWrFgBHo+Hc+fOCXTOv783IU/i0NyoA27evCmyBhbpgjVOUkZkZCS4XC6uXLnCtBSxMH36dDg5OTF608/IyMDgwYPRunVrvHjxQqK59u/fD0tLS4nmEIaYmBiYmZmhW7dueP36ddHrZ8+eLdc0CYqLiwvWr19f4TgspXPp0iUYGxvDzMwM169fZ1pOhbl69SrMzc2FOufixYvgcrmIiooSqH1cXBzatm0LLy8v5OfniyITAJCUlITevXvDzMwMHz58EDnO0qVLMWbMGJHPZ5EuWOMkhVy9ehUcDgfh4eFMS6kQYWFhUFVVxbdv35iWAj6fj61bt4LD4eDo0aMSy9GmTRuEhIRIJL4w5OfnY/ny5VBUVIS/v39RLxPw0zTxeDzcuXNHLLlu374NTU1N5OXliSUeS0kKCwuxb98+aGhowNnZGS9fvmRakshs374dw4YNE/q848ePQ1lZGTExMb9t9/z5c2hpaWHx4sUVemC7c+cOtLS0MHnyZOTm5oocBwA+fPgABQUF5OTkVCgOi3TAGicpJSwsDBwOB1evXmVaikj8+PEDGhoaUmEi/s39+/ehpaUFb2/vCt8M/0toaCjatGnD+JDKkydP0L59e1hZWeHt27fFjp05cwY8Hg93794Va85u3bohMDBQrDFZSpKVlVVkiMePHy8VDyXCMm3aNCxbtkykc/fs2QMNDQ28f/++1OORkZHg8XjYs2ePyPr4fD42bNgALpeLEydOiBznv1hYWCAoKEhs8ViYgzVOUszly5dFGttnGj6fj/79+2PixIlMSymVpKQkODg4wNzcHJ8+fRJbXCsrK+zbt09s8YQlLy8PS5YsAYfDwbZt20oYuNOnT0vENAHA+fPnYWhoyLhprCkkJiZi4sSJUFRUhK+vLzIzM5mWJDC9e/eukCFZv349dHV1ER8fX+z1Y8eOgcvl4sKFCyLHTklJgaurK9q2bVtsaFsc7NmzB46OjmKNycIMrHGSci5cuAAul4sbN24wLUVg9u3bBwMDA2RlZZXfmCEKCwuxdOlSKCsr4+LFixWOFxUVBXV1dbH3YgnKo0eP0LZtW9ja2pY6F+PUqVPg8Xi4d++eRPLz+XwYGhri/PnzEonPUjqxsbHo27cv1NXVsWfPHhQUFDAtqVyaN2+Op0+fVijGokWL0KZNG/z48QMAsHbtWqipqSE6OlrkmNHR0dDV1cWYMWOQnZ1dIX2lkZaWhsaNGyMxMVHssVkqF9Y4VQFCQkLA5XJx69YtpqWUy9u3b8HhcPDw4UOmpQhEWFgYVFRUsHDhwmLzgIRl4MCB8PPzE6MywcjNzcX8+fPB4XCwa9euUnt8Tp48CR6PJ/GVfoGBgejWrZtEc7CUzo0bN2Bubg5DQ8MK9bhImry8PPzxxx8VnuvD5/MxZcoUmJmZYfz48WjZsmWZw3eCxNq+fTs4HA4OHDhQIV3lMWjQIGzcuFGiOVgkD2ucqgjnzp0Dl8sV24ReSZCfn49OnTph1apVTEsRii9fvqBLly6wtbUV6Wnw/fv3UFBQQGpqqgTUlU1UVBQMDQ3h4OCAz58/l9rmxIkT4PF4Aq9Gqgh5eXlo2rSpWFbqsQgPn89HUFAQdHV1YWNjI5UPLy9fvoSOjo5YYmVlZUFbWxvy8vKIi4sTKUZGRgY8PDxgYGBQ7qRzcRAaGor27dtLPA+LZGErh1cRevbsSbt376bevXtTVFQU03JKZdmyZfTnn39KZcXs36GqqkphYWFkaGhIpqamdOfOHaHOX7t2LXl6epKcnHDbLohKbm4uzZ07l+zs7GjatGl09uxZUlNTK9HuxIkTNHbsWAoJCSETExOJ66pTpw5NmTKFVq5cKfFcLCWRkZGhvn370vPnz6l3795kY2NDw4YNo8+fPzMtrYjyKoYLyo8fP8jGxoZMTU3J0tKSxo0bRwUFBULFeP78OXXo0IFq1apFd+/eJX19/QrrKg8rKyv6/PkzxcTESDwXiwRh2rmxCMevuSoPHjxgWkoxbt++DR6PV2bPR1Xh5MmT4HK52Lhxo0ATnZOSktCkSZNKu+67d+/CwMAATk5Ov33KDgoKgpKSUqV/TjIyMsDlciVeL4ulfFJSUjB79mwoKChg9uzZld4jWhp+fn7w9vauUIx3795BX18fU6dORWFhIXJzc2FnZwcPDw+Bh9sDAgLA4XCwe/fuCmkRhWnTpmH27NmVnpdFfLDGqQpy/PhxKCkpSU1XfHp6OnR1davNUttXr17ByMgIAwYMKLfi8JIlS0SqSSMs2dnZmDlzJng8Hg4ePPhbU/fLNFVkomxFmD9/Pry8vBjJzVKSjx8/YujQoVBSUsLGjRsZrbc1cuRIbN68WeTzHzx4AFVVVaxbt67Y65mZmejSpQsmTJjw2+9GVlYWvLy80Lx5czx69EhkHRXh8ePH0NDQqNCcShZmYY1TFeXYsWNQUlJi7Mv/bzw9PTF8+HCmZYiVrKwsjBgxAvr6+nj27FmpbbKzs6GsrFzhFULlcfPmTejr66Nv375ISEj4bdtfnwumTBPwc6l8kyZNRJ53wiIZoqOj0aNHD+jp6eHEiROMlI7466+/RN4VITQ0FBwOp8wHtJSUFLRt2xZz584t9XhsbCyMjIzg5ubGeO+bsbExwsLCGNXAIjqscarCHD58GMrKynjy5AljGk6cOIFmzZohLS2NMQ2SZNeuXWWuttm+fTt69uwpsdxZWVmYOnUqlJWVBap2fvToUanpiZwwYQJmzpzJtAyWUvhVqLVz586VvlJXSUlJpNppu3fvhpKSUrlbznz79g36+volVrj+qvHk7+8vFbXG1qxZUyk91SySgTVOVZyDBw9CRUWlzF4RSfLlyxcoKSlViTIJFeHhw4fQ1dXF2LFji5ZRFxYWokWLFhLbFicyMhJ6enoYMGCAQNWhjxw5AmVlZakwTcDPeSgKCgpISUlhWgpLKRQUFGD37t1QU1ODq6srXr16JfGcKSkpaNCggVDGhc/nY+HChdDW1hZ41dunT5+gpaWF7du3Izc3F5MmTYK2trbEapiJQnx8POTl5dnNsasorHGqBgQEBEBVVbVSltP+orCwED169MCCBQsqLSeTpKSkwNnZGe3atcO7d+9w6tQptGvXTuxPrxkZGZg0aRJUVFQErq78q+dRGoZt/83AgQOxcuVKpmWw/IbMzEwsWbIEioqKmDRpkkSLM969exdt27YVuH1eXh48PT1hYmJSokp4ecTGxkJJSQnNmjWDk5NTUaFMacLe3p7dpqiKwhqnasK+ffugpqZWaauZ1q5dC3Nz8wrtPF7V4PP5WLVqFXg8Hlq1aoUjR46INX54eDh0dHQwePBgfP/+XaBzfpmmx48fi1WLOIiOjoaqqiq7sWkV4OvXrxg3bhwUFRWxfPlyiVT9DwgIwIABAwRqm56eDnt7e9jZ2ZW7QKM0zp49CwUFBTRs2BDBwcFCn18ZHDp0CLa2tkzLYBEB1jhVI3bv3g11dXXExsZKNM+jR4/A4XDw5s0bieaRVrZs2QJZWVnMnj1bLFtcpKenY9y4cVBTU8OZM2cEPu/gwYNSa5p+YWtri507dzItg0VAXrx4gT59+kBTUxP79+8X68qvuXPnYt68eeW2i4+Ph4mJCTw9PYVeAZiXl4cZM2ZAQ0MDN27cwK1bt6R2s/SsrCw0adIEX758YVoKi5CwxqmasWPHDmhoaIh9g8pfZGdno3Xr1hXafbyq4+zsDF9fX1hYWMDS0hJfv34VOdbly5ehpaWFYcOGCTWccODAAaioqDC6MEAQwsLC0KJFC3bpdRXj2rVr6NixI9q2bYvLly+LJWa/fv3K3dLkxYsX0NbWxsKFC4UeBv/8+TO6dOkCOzu7YkOOly9fBpfLlfiWQ6Lg6enJyFZNLBWDNU7VkK1bt0JTU1MiPULe3t5wdXWVipUpTPDy5UtwuVxkZGSgoKAAPj4+UFdXL3e1z39JTU3FqFGjoKGhIfTGuIGBgVBRUZF4GQRxwOfz0a5dO5w8eZJpKSxCwufzceTIEejo6MDe3r7CJt3IyOi35uX69etQUlISqSjlxYsXoaysjCVLlpRq0k+ePAklJSVGFtH8joiICLRu3brG3k+rKqxxqqb4+/ujadOmePfundhiXrhwAerq6khKShJbzKrGqFGjSgw3BAcHg8fjYfXq1QLdAENDQ6GpqYmRI0cKveosICCAsVWUonLs2DGYmZmxPw5VlJycHKxduxZcLheenp5CDS3FJWch5EkcAm+9Q+PW3fDyU+krRIOCgsDhcBAaGiqUtoKCAsybNw+qqqrl1kXav38/1NXV8fbtW6FySJLCwkI0bdqU0bprLMLDGqdqzMaNG6GtrS3yruH/JjExEaqqqmLrtq+KJCQkoEmTJqWWB3j37h3atWsHFxeXMs1QcnIyhg8fjqZNm+LixYtC5/+1erIqmSbg54+bnp6eVM4zYRGc5ORkTJ8+HQoKCvjnn3/KrN3G5/Nx7nEcRgfch+HCC2g6K7jon9GiCxgTcB/nn8QVGen169dDVVVV6I2oExISYGVlhe7duwu86m7jxo1o1qyZVBVnnTt3LqZMmcK0DBYhYDf5rcZMmDCBvL29ydLSkj59+iRyHADk5eVFAwcOJCsrKzEqrFps2rSJ3NzciMvlljimpaVF169fJx6PR+3ataNHjx4VO37u3Dlq06YN1atXj548eUI9evQQKndAQADNnDmTLl++TK1atarQdVQ2srKyNG3aNHbz3yqOvLw8rVy5kh48eEDv3r2j5s2b09atW4ttrvs1LZsG7bxDEw49oNBnCZSanV8sRkpWPoU8S6DxBx+Q+87bNH7aHNqyZQvduHFDqI2or127RqampmRubk6XLl0iZWVlgc6bMGECDR8+nGxsbOjHjx8C55MkHh4edPDgQaE3KWZhDhkAYFoEi2RZs2YNbdmyhSIiIkhNTU3o83fu3EmbNm2iO3fu0B9//CEBhdJPZmYmaWlp0c2bN0lPT++3bQMDA2ny5Mnk5+dHjo6O9Pfff9P169dp165dZGFhIXTu/fv30+zZs+ny5cvUsmVLUS+BUXJyckhbW5suXbpErVu3ZloOixh48OABTZ8+neLi4mjFihXUoVsP8tx/n57FpQkco056PAXP6EUtNAUzPnw+n1auXEnr1q2jvXv3kp2dndC6AdCMGTPo2rVrdPnyZWrUqJHQMcSNubk5zZs3j+zt7ZmWwiIArHGqIfj5+dGOHTsoIiKCVFVVBT7v1atX1KlTJ4qIiCADAwMJKpRuNm7cSBEREXT8+HGB2j979oxsbGwoJSWFhg4dSitXrqSGDRsKnXffvn00Z84cunLlCunr6wt9vjSxbNkyiomJof379zMthUVMAKCQkBCaPmMGFXQdS7nyWkWvJ51bR3W4TalxRxdKPLmU8pPji84rSP1K9TRaE891HnVupkiBnh1JRkbmt7mSkpJoyJAhlJKSQocPHyYNDY0K6R49ejS9fv2azp8/T/Xq1RM5ljjYvHkzRUZG0qFDhxjVwSIYrHGqQSxfvpz27t1L4eHhpKKiUm77/Px86ty5M3l4eNDEiRMrQaF0UlBQQHp6enTo0CEyMzMrt/33799p0qRJdOfOHdLSbBM4cQAAIABJREFU0qIfP35QUFAQNWvWTKi8e/fupblz59Lly5ervGkiIkpJSSEdHR16+PAhaWpqMi2HRYycffSZJh5+SEQylP/9EyVd3EJ5cS+pcddB1LijS7G2ufGxlHhyGSkPXkm15bhUS4Zos7sJ2bUu+550+/ZtcnNzo379+tGyZcuoTp06FdZcWFhIgwYNouzsbAoKChJLTFFJSkoiHR0d+vjxIzVu3JgxHSyCwc5xqkHMmjWLBg8eTJaWlvT169dy2y9atIgUFRVpwoQJlaBOejl+/Dipq6sLZJqCgoKoTZs2pKKiQk+ePKHLly+Tp6cnmZub06lTpwTOuWfPHpo7d2616Gn6hby8PI0YMYLWrl3LtBQWMXPuyVci+tljlP4gmBoaWlN9/S4l2qEwn5KC15KClRfVlvs5V5APotMP40qNC4DWrVtHjo6OtGHDBlq1apXYDI6srCzt37+fCgoKaPjw4cTn88USVxQUFRXJyspK4B5tFmZhe5xqIAsXLqSjR49SeHg48Xi8Uttcv36dXF1d6eHDhwJPvKyOAKD27dvTvHnzyNHRscx23759o/Hjx9OTJ09o9+7d1KlTp2LHfz0xu7m50dKlS6l27dplxtq9ezfNmzePrly5Qi1atBDbtUgDX758oTZt2tDr169JQUGBaTksYiA+JZvsNkSWmAj+PXht0VDdL9IfnKeslzdIaaBvsbby9etQyMSupCL/Z9FrqampNGLECPrw4QMdPXqUdHR0JKI/KyuL7O3tycDAgPz9/csdMpQUp06donXr1lFERAQj+VkEh+1xqoHMnz+f+vbtS1ZWVpSYmFjieGpqKnl4eND27dtrtGkiIoqIiKDMzEzq1atXqccB0OHDh8nQ0JB0dHQoOjq6hGkiIjIzM6OoqCh69OgRWVpaUnx8fCnRiHbt2kXz58+nsLCwameaiIjU1NSoT58+5O/vz7QUFjHx6HNKCdNUFmn3TlHjTm4lXk/JyqfHX1KL/j86OppMTU1JRUWFbty4ITHTRERUv359Onv2LN29e5d8fHwklqc8evbsSc+ePaP3798zpoFFMFjjVENZuHAhOTo6krW1NX3//r3YsYkTJ5KNjc1ve1hqCn5+fjR16lSqVavkVyUhIYFcXFxo8eLFdPr0aVqxYgX9+eefpUT5CYfDofPnz5O1tTW1a9euxJPlzp07acGCBRQWFkbNmzcX96VIDdOnT6dNmzZRdnY201JYxEBiWpZA7fIS3hDxC+kPzTalHv+RmUcAaNu2bWRjY0O+vr60adOmSlnJKycnR6GhoXTq1ClasWKFxPOVRt26dal///4UGBjISH4WwWGNUw1FRkaGlixZQvb29tSjR4+imiZHjhyhO3fu0Jo1axhWyDxPnjyhhw8f0uDBg4u9DoACAwPJyMiIWrVqRQ8ePKCOHTsKFFNWVpbmzZtHe/bsoQEDBtDy5cuJz+fTjh07aOHChRQWFlZuuYOqTsuWLcnMzIz27NnDtBQWIQFAnz59omPHjtGUKVOoU6dONHG0p0Dn5nx6SvWaGpU5FFZflk+DBw8mf39/un79Orm5leyZkiQcDocuXbpE27Zto23btlVq7l8MGTKE9u/fT+wMGumm7IkWLNUeGRkZWrZsGRUWFlKPHj1o7969NHHiRDp//jw1aNCAaXmMs2rVKpo4cWKxpcpfvnyhMWPG0IcPH+j8+fNkamoqUmwbGxu6d+8e9e/fnw4dOkRJSUkUERFBurq64pIv1cycOZMGDx5Mo0aN+u18LxZmyc7OpqioKLp9+zbdvn2bbt26Rfn5+WRubk5mZma0dOlSUm/emvrufFDucF1+chzJNi59TmWjurVohqcbdTFtTbdv36b69etL4nLKRU1NjS5dukTdunUjOTk5GjhwYKXm79ChA8nIyNDdu3cFfhhjqXzYyeEsBIAmT55Mu3fvJm9vb1q8eDHTkhjn8+fPZGhoSG/evKEmTZoQANq7dy/NnDmTxo4dSz4+PlS3bt0K5/H396fZs2dT48aN6dSpUyIbsapI165dafz48TRgwACmpbDQz/vAu3fvipmk58+fU6tWrcjMzKzILGlra5foNRoTGEWhzxJEzp3/5i759tSmYcOGVfAqxMPTp0/J2tqadu7cWeb8Rknh6+tLcXFx7DxAKYZ91GMhGRkZUlFRoUaNGtGFCxdo2rRpNb6WyPr162no0KHUpEkT+vTpE40aNYoSEhLo4sWLZGxsLJYcW7dupZUrV1J0dDRFRUWRnZ0d+fr6kpeXF2MreyqTGTNm0Pz588nNza1GXK+0kZGRQffv36dbt24VmaXatWsXGaT+/fuTqanpb+ft/cLRSJUuPk8gvgiP4eAXUt6rm/ThQy3Kzs4WKJ+kad26NZ05c4Z69epFR48epe7du1da7sGDB5OpqSmtWbOmxu7UIO2wPU4s9ODBA7Kzs6O7d+/SqlWrKCoqii5cuEBycnJMS2OE1NRU0tHRoaioKLp06RLNmTOHvL29aebMmWKrIbNlyxZavnw5hYeHF60YevnyJfXt25dMTExoy5Yt1X64lM/nU5s2bWjdunVC793HIhwA6NWrV8VMUmxsLBkZGZGZmVlRj5K6urpIJhYADdp5h26+TRL63I5N5WmlvTpNmzaNoqKiaMOGDZXey1MW4eHh5ObmRufOnaP27dtXWt7u3buTt7c3OTs7V1pOFsFhjVMNJysri0xNTemff/4hd3d3AkDjxo2jx48fU2hoqFTs41TZ+Pn50Y0bNygzM5OSk5Npz5491KZN6SuBRGHz5s20cuVKCgsLK7HMOjMzk8aOHUvR0dEUFBRULUsS/Jt9+/ZRQEAAXb58mWkp1YrU1FS6e/du0ZDbnTt3qFGjRsWG3IyNjcXao/E1LZs8992np0LsVWegKke7h7YjJbmfvUwXL16kiRMnUosWLWj9+vWkra0tNn2icvbsWfLy8qIrV65U2rZTu3fvprNnz9LJkycrJR+LcLDGqYYzfvx4SklJoQMHDhS9xufzacyYMRQTE0MhISEi7bFWVcnJySEVFRUCQLNmzaJp06aJdfKyv78/+fn5UXh4eJk/CgBox44d5OPjQ5s3b6Z+/fqJLb+0kZeXR82aNatx87vECZ/Pp5iYmCKTdPv2bXr//j2ZmJgUmSQzMzOBtlmqKF/Tsmny0Ud0+23Sb4ftZIioUzNFWtvfmHhyxfeJy83NpbVr1xYtzpgxYwbjw3cHDx4s2hhYkjWlfpGWlkaampr05s0bUlRUlHg+FuFgjVMN5ty5czR+/Hh6+PAhycvLFzvG5/Np1KhR9OrVqxqzyu7t27fk4OBAcXFxdOvWLWrVqpVY4wtimv5NVFQUubq6kpOTE61cuVIsk9GlkTVr1tCdO3foyJEjTEupEvz48YPu3LlTZJLu3r1LHA6nmEkyNDRkbO81AHThWQKdfhhHt94lUUrW/1bbyRbkkIWBKrm20yJbA+XfDgt+/PiRpkyZQtHR0bRhwwZycHCoDPllsmXLFlq1ahVFRkYKtVG6qLi7u1OXLl1o3LhxEs/FIiRgqZF8/foVKioquHr1apltCgsLMWzYMFhYWCAzM7MS1VUuhYWF2LBhAxQUFKCsrIzz58+LPcfGjRuhpaWFd+/eCXVeUlISevXqBXNzc3z69EnsuqSBtLQ0cDgcvH79mmkpUkd+fj6io6OxZcsWDB06FM2bN0ejRo1gaWmJOXPm4OzZs/j27RvTMsskLjkLi3afhnLnvhi/bDu+JAt/HwkNDYWenh4cHR3x9u1bCagUnGXLlqFVq1b4/v27xHOFhISgY8eOEs/DIjyscaqB8Pl8ODg4YPbs2eW2LSgogIeHB6ytrZGVlVUJ6iqX2NhYdO3aFZ07d8b27dthZGQEPp8v1hzr168XyTT9orCwEEuXLoWSkhIuXrwoVm3Sgo+PD8aMGcO0DMb5+vUrTp8+jdmzZ6N79+5o2LAh9PX1MWzYMGzbtg2PHj1CQUEB0zIFIi8vD9OmTYOmpiZu3bpVoVg5OTlYunQpFBUVsXDhQmRnZ4tJpfDMnDkT7dq1Q2pqqkTz5OfnQ1lZGS9evJBoHhbhYY1TDWTz5s0wNTVFbm6uQO0LCgrg7u4OGxsbRm9Y4qSgoACrV6+GoqIi1q5di4KCAlhYWCAwMFCsedatWwdtbW28f/++wrHCwsKgoqKChQsXorCwUAzqpIevX79CXl4eCQkJTEupNPLy8nDv3j1s2LAB7u7u0NHRQePGjWFjY4P58+cjJCQEP378YFqmSHz69AmdOnVCz549xdo78+HDB7i4uKBZs2YIDg4WW1xh4PP5GDNmDLp16ybxh8kpU6bAx8dHojlYhIc1TjWM58+fg8PhCP0Uk5+fjwEDBsDOzq7Km6eYmBiYm5vjr7/+wqtXrwAA9+7dg6amJvLy8sSWZ+3atWIzTb/48uULunbtCltbWyQmJootrjQwZsyYav0j8eXLFwQFBWHatGno0qULGjRogDZt2sDLywu7du3C8+fPq4UhDg0NhZKSEpYuXSqx62F6+K6wsBDu7u5wcHAQ6z3jvzx8+BCamprV4nNRnWCNUw0iNzcXbdu2xdatW0U6Pz8/H/369UPPnj2Rk5MjZnWSJz8/HytWrICioiI2btxY7GbUv39/rFmzRmy51qxZAx0dHXz48EFsMX+Rn5+P6dOnQ1NTE7dv3xZ7fKZ4/fo1FBUVkZaWxrSUCpOTk4ObN29izZo16NevHzQ0NKCoqAgHBwcsXrwYly9flvhQT2VTUFCAuXPnQlVVFRERERLPl5OTA19fXygqKmLRokWV/kCXl5eHXr16YcCAARIdPjU0NKyU95NFcFjjVIOYMWMGHB0dKzSHJy8vD3379kXv3r0FHuqTBp4+fYr27dvD0tKyxBPqmzdvxPqDvXr1aomZpn9z8uRJcLlcbNy4UezzsphC3Aa2MuDz+Xj//j0OHz4Mb29vdOzYEfXr10fbtm0xbtw47N+/H7GxsdXmb1Qa8fHxsLCwgJWVVaUPt75//x7Ozs5o1qyZRBZ2/I6srCx0794do0ePltjfd9WqVRgxYoREYrOIBmucagjh4eFQUVERywqcvLw89OnTB05OTlJvnvLy8rBkyRJwOBxs3bq11JvbhAkTMGvWLLHkW7VqFZo1a4aPHz+KJV55vH79GkZGRhgwYEC16Km5f/8+1NXVpfpzlZmZiWvXrmHFihVwdnaGiooKlJSU0KdPHyxfvhxXr15FRkYG0zIrjfDwcKiqqmLevHmMTlwPCQmBrq4unJycRF6IIQppaWlo3749ZsyYIRHzFBcXB3l5+Wq9srmqwRqnGsCPHz+goaEh1qex3NxcODo6wsXFRaJj/BXh0aNHMDExQY8ePcqcZ/T9+3c0adIEcXFxFc7n5+cHXV3dSi8bkJWVBU9PT+jr6+Pp06eVmlsSWFlZYe/evUzLAPCzN+nVq1cICAjA+PHjYWJigvr166NDhw6YNGkSDh06hHfv3lXr3qSyKCwshK+vL5SVlXHhwgWm5QD4OXy3ZMkSKCoqYvHixZU2fPf9+3e0atUKS5culUh8W1tbHDx4UCKxWYSHNU7VHD6fDzc3N0yYMEHssXNycuDg4IB+/fohPz9f7PFFJTc3FwsWLACHw8HOnTt/+6O2aNEisXSDr1y5khHT9G92794NDoeDAwcOMKZBHFy8eBGtWrViZEJsWloarly5Al9fX/Tq1QscDgfq6upwdXXF6tWrcePGjSq/OEIcJCYmws7ODp07d8bnz5+ZllMCJobvvnz5Ah0dHfj7+4s99oEDB2BnZyf2uCyiwRqnas7+/fvRqlUriS2bzc7Ohp2dHdzc3KTCPD148ABGRkbo2bNnuSYmKysLSkpKeP78eYVyrlixArq6ulLxA/Lw4UPo6upi7NixVXICP/DT7Ldt2xZnz56VaJ7CwkLExMRg9+7dGDVqFAwNDdGgQQN07twZU6dOxbFjx6pt0dGKcPPmTWhoaGDGjBlS29v8i/Pnz0NXVxd9+vSplOG7t2/fQl1dHQEBAWKNm5mZCXl5ecTHx4s1LotosMapGvP27VtwOBxER0dLNE92djZsbGzg7u7O2ByHnJwczJ07F1wuF/v27RNo6GTr1q3o3bt3hfIuX74cenp6UmGafpGSkgIXFxe0a9euUud6iJNDhw6hS5cuYo2ZnJyM0NBQLFiwAHZ2dmjSpAm0tbUxcOBArF+/Hnfv3pXquVVMw+fzsWbNGvB4PJw5c4ZpOQKTnZ1dqcN3z549g5KSEk6fPi3WuMOHD8fq1avFGpNFNFjjVE3Jz89H586d4efnVyn5srKyYG1tjcGDB1e6ebp37x4MDAzg6OiIL1++CHROQUEB9PT0cO3aNZHzLlu2DM2bN5cq0/QLPp+P1atXg8fjMVYosCLk5+dDW1sbN27cEOn8goICPH78GNu3b8fw4cPRsmVLNGzYEN26dcOsWbNw6tQp9uldCJKTk9GnTx+0b9++yprxd+/eoU+fPtDV1UVISIhEc927dw9cLhdXrlwRW8zw8HAYGRmJLR6L6LDGqZqyePFiWFpaVuo8kczMTFhaWmLo0KGVYp6ys7Mxa9Ys8Hg8HDhwQKgJuidOnEDHjh1FntS7dOlSNG/eXGCjxhSRkZFQU1ODj49Pldmq4xf+/v5wcnISqG1iYiKCg4Ph4+MDKysryMnJQU9PD0OGDMHmzZvx4MEDqRhKrorcv38fOjo6mDhxYpUd/v03v4bvnJ2dxVqc9r9ERESAw+GIrdZaYWEhNDU18fDhQ7HEYxEd1jhVQ+7cuQMej8fI/IyMjAx0794dw4cPl6hpu3XrFlq2bAkXFxeh68bw+XyYmZkhKChIpNxLlixBixYtpN40/SIhIQGWlpawtLTE169fmZYjMFlZWeDxeCXmoOXn5+PBgwfw9/eHh4cHdHV1IScnB2tra8ydOxfBwcHVrqo6E/D5fGzevBkcDgdHjx5lWo5Yyc7OxuLFi6GoqIglS5ZIzBAGBweDx+Ph8ePHYok3Z84cTJ06VSyxWESHNU7VjPT0dOjq6jJ6o8vIyEDXrl0xcuRIsZunrKwsTJ06FUpKSjhy5IhIPUaRkZHQ1dUVqQdm8eLFaNGihVjKF1QmBQUF8PHxgZqaGiIjI5mWIzCLFi2Cm5sbTp48iZkzZ+Kvv/5Cw4YN0apVK4wYMQI7duzAkydPqlxvmrSTlpaGAQMGwMjICLGxsUzLkRjv3r2Dk5MT9PT0JDZ8d+jQIaiqqhZt71QRYmJioKKiwvaeMgxrnKoZI0eOxNChQ5mWgbS0NHTu3BljxowRW42b69evo3nz5ujfv3+FCnk6Ojpi8+bNQp+3aNEi6OvrVznT9G9+PQGvXr1aKmsP5ebm4s6dO1i/fj0GDBgATU1NyMjIoHv37li4cCEuXLiA5ORkpmVWax4/fowWLVrAy8tL4pvYSgvnzp1Ds2bNJDZ8t23bNmhpaYllFKBDhw4IDQ0VgyoWUWGNUzXixIkT0NHRkZo9sFJTU2Fubo5x48ZV6Ec6IyMD3t7eUFFRwfHjxyukKSYmBjweT+gfhIULF0JfX79aTCh+9+4d2rVrBxcXF6SkpDCq5dOnTzh69CimTJmCTp06oX79+jAyMsLo0aOxZ88evHjxAt7e3pg2bRqjOmsKv2qB7d+/n2kplU52djYWLVoERUVF+Pr6in34buXKldDX16/w7g2bNm2Cu7u7mFSxiAJrnKoJX758gZKSksirkCRFSkoKOnTogIkTJ4pkniIiItCsWTMMGjQI379/r7CekSNHYsGCBUKds2DBArRs2bJamKZf5OTkYOzYsdDV1a20yabZ2dm4fv06Vq1aBVdXV6ipqYHD4aB3797w9fVFWFhYqdvGfPjwAQoKCmxPkwTJzMzEsGHD0LJly2pRfb4ivH37tmj4Ttw9O3PmzIGJiUmFHlgSExPRuHFjqXlAromwxqkaUFhYCBsbG8ybN49pKaWSnJyMdu3a4e+//xbYPKWnp2P8+PFQVVUVWz2U+Ph4NGnSRKiJw/Pnz0erVq0qfePSyiIwMBAcDgd79uwRa1w+n4+3b9/i4MGDmDhxItq3b4/69evD1NQUEyZMQGBgIF6/fi3w58HDw0Ni21nUdF68eIHWrVtj0KBBSE9PZ1qO1BAcHAwdHR24uLiIbcNuPp+PcePGoWvXrhXae87JyQm7d+8WiyYW4WGNUzVg3bp1MDMzk+oJgz9+/ICJiQmmTp1a7o/l5cuXoaWlhaFDh+LHjx9i0zBnzhyMGzdO4PbV3TT94unTp2jRogU8PT1FntOSkZGBiIgILFu2DE5OTlBSUoKKigpcXFywcuVKXLt2rUI/FE+ePIGysjK73YmYOXToEDgcDrZv3y6Vc96YJjs7GwsXLoSioiKWLl0qluG7wsJCDB48GPb29iIXXD1+/DgsLCwqrIVFNFjjVMV5/PgxOBwOXr9+zbSUcklKSoKxsXGZu4inpqZi9OjRUFdXx7lz58SaOz09XeD3ic/nY968eTAwMKhSy/crQlpaGtzc3GBsbFzue8Tn8xEbG4t9+/Zh7Nix+L/27jsqymvrA/APg1HxCkqTLoiCClLUKGhMNMYSe8XeYDDqNRYSjagk1oudWBJRhqARiNiiGDGi0Vgi5WoEVGIBREQQkd7bu78/cuUTR5Aywwywn7VYLmfe95yDwsyec87ex9bWllRUVMjOzo6WLl1KAQEB9OTJE6m/EY8YMYL2798v1TabqoKCgvKlWlmfLNAYxMXF0ejRo6lz585SOdC4pKSExowZQw4ODrXKCC0sLCQNDQ2Z1qFilePAqQErKCig7t27N6gp25cvX5K1tTW5urpWeGP97bffyMjIiJycnGSyYfm7776jSZMmvfM6QRDIzc2NLC0tm0zQ9IogCLRnzx7S0tKiX375pfzxrKwsunDhAm3YsIGGDx9O6urqZGRkRA4ODuTh4UEhISH1UhjxypUrtS4jwf5fbGws9ejRgyZMmCD35ICG5syZM9SxY0eaMGFCnZfvCgoK6JNPPiGRSFSrDxkLFiygTZs21WkMrHY4cGrAli1bRhMmTGhwU+ypqanUvXt3cnNzo4yMDHJ0dCQjIyOpfJJ7m5KSEurQoQOFh4dXeZ0gCLRmzRqytLSsc+ZLQ1VWVkb+/v6krq5OVlZWZGFhQa1bt6b+/fvT8uXL6cSJE3Ir/PmqcOmxY8fk0n9jcPLkSdLS0qLdu3c3uNcNRSHN5bvs7Gzq06dPtbYwvOnGjRtkbm7O/49ywIFTAxUcHEwGBgZSyTSThxcvXpCRkRGpqqrS/PnzZZoh4u/vTx9//HGV1wiCQKtXr6bu3bs3qaApLS2NgoKC6JtvvqEhQ4aQmpoamZqa0sSJE6lLly5kY2MjtY2x0vDLL7/QBx98wG8WNVRcXEwuLi7UoUMHqR0B0tTFxsbSqFGjyMzMrE4f+tLS0sjS0pI2btxYo/sEQaBOnTpRWFhYrftmtcOBUwP08uVL0tfXpwsXLsh7KLWSnp5Os2bNIiMjIzIyMqINGzbIrC9BEMjGxqbKg24FQSBXV9dGHzSVlpZSREQEeXp60pw5c8jc3JzatGlDAwcOJFdXVwoMDKywPFlWVkbr1q0jXV1dunz5svwG/pqysjIyNzenS5cuyXsoDUZCQgLZ29vTyJEjKS0tTd7DaXReLd9NnDiREhISatVGUlISmZqa0u7du2t03/r162nRokW16pPVHgdODYwgCDR+/HhycXGR91Bq5dSpU6Snp0eLFi2inJwcSk5OJnNzc5mlml+4cIG6detW6dEvgiDQypUrycrKqtGdb/bixQsKDAwkV1dXGjhwILVp04bMzc1pzpw55OnpSREREdXaLxQcHEw6Ojrk7u5er4dGV0YsFtPQoUPlPYwGISgoiNq3b09btmxRiP+7xio/P5/Wrl1LGhoa5O7uXqtsucePH5OhoSEdOnSo2vfExcWRpqZmrbPzWO1w4NTAeHt7k5WVVYM7pTw1NZWmTp1KpqamdOXKlQrPPXv2jDp37kxbtmyRer9DhgypdPO8IAj09ddfk7W1dYMPmoqLi+nmzZu0Z88emj59OpmampKamlp5fa+goKA6zTYkJCSQnZ0djRw5UqolImqjsLCQ9PT0OBusCiUlJbRq1SrS19enq1evyns4TUZsbCyNHDmSzM3NKTg4uMb3R0dHk46ODp08ebLa93z00Ud06tSpGvfFao8Dpwbk0aNHpKmp2eAq+x4/fpx0dHRo6dKlldbySUxMpE6dOtH27dul1m9ERATp6em9NcgUBIFWrFhB1tbWDXKfWFJSEp04cYKWL19OH374IbVu3ZosLS1JJBKRt7c33bt3T+ozDEVFRbRkyRIyMTGhmzdvSrXtmtq6dSsfO1GJpKQk+vjjj+nTTz9tcpmhiiIwMJBMTExqtXx369Yt0tLSqvZWDC8vLxo/fnxthslqiQOnBqK4uJh69+5Nu3btkvdQqi0lJYUmTZpEZmZmdP369Xde//TpUzI1NSUPDw+p9D9jxgzavHmzxOOCINDy5cvJxsamQQRNhYWFFBISQh4eHuTg4EBGRkakrq5Ow4cPpw0bNtCFCxfqNa386NGjpKmpSfv375fbJu3MzExSV1enx48fy6V/RfX777+Trq4urV27lss2yFl+fj59++23pKGhQZs3b67RctrVq1dJU1OTbty48c5rMzMzSVVVlfev1SMOnBoINzc3Gjp0aIPIJhIEgY4cOULt27enr776qkbVqJ88eUImJiY13iT5poSEhLeebyYIAn311Vdka2urkEGTIAj05MkTCggIoKVLl5KdnR2pqKiQjY0NLViwgA4dOkQPHz6U+8/B/fv3ycLCgmbOnEm5ublyGcPXX3/NG2P/p6ysjNavX086OjoNNmmksYqJiaERI0aQubl5jf5vgoKCSFtbu1pnSU6ePJn27dvsjsTxAAAgAElEQVRXl2GyGuDAqQG4fv06tW/fnpKSkuQ9lHdKTk6mcePGUdeuXWud9hwfH0/Gxsb0/fff13ocLi4u9OWXX1Z4TBAE+vLLL8nW1lZhPp3l5+fT1atXaevWrTR+/HjS1dUlbW1tGjNmDLm7u9Mff/yhsOeH5ebm0syZM8nS0pLu379f7/0nJSXV+OzBxujFixc0dOhQ+uijj+RWY4u9W2BgIBkbG9OkSZPo6dOn1bonICCAdHV16eHDh1Ved/bsWbK3t5fGMFk1cOCk4LKyssjExEThN/8JgkC+vr6kra1Nrq6udT5TLC4ujoyMjMjT07PG92ZkZJC6unqFvQWCIJCLiwv16NFDbkGTIAgUExNDvr6+tGjRIurZsyepqKjQBx98QF988QX5+/tTXFyc3GeTakIQBNq/fz9pamrS0aNH671/Z2dn+vbbb+u9X0Vx/fp1MjAwoJUrVyr0WZXsH/n5+fTNN9+QhoYGbdmypVrLd2KxmDp06FDlXqmSkhJq3779OwMsJh0cOCm4WbNmkbOzs7yHUaVnz57RqFGjyNLSkv773/9Krd2YmBgyNDSkAwcO1Oi+zZs308yZM8v/LggCLVu2jHr27FmvGWE5OTl06dIl2rRpE40aNYq0tLRIX1+fJk6cSNu3b6fr16/X+lBdRXPz5k0yMTGhJUuW1Gtq9IMHD0hLS0tuy4XyIggCbdu2jbS1tausUcYUU0xMDA0fPpy6dOlCFy9efOf1O3bsIHNz8yo3+y9dupTc3NykOUxWCQ6cFFhAQAB17txZYd8UBEEgHx8f0tLSIjc3N5mUSHj06BEZGBiQt7f3W59Pysinc3eSyC/sCZ27k0TxKZmkp6dHkZGR5WNcunQp9erVS6ZBU1lZGd2/f598fHzo888/J2tra1JRUaG+ffuSi4sLHT16tNrT8w1Veno6jRw5kuzt7ev1ex0/fnyDSpqoq/T0dBo9ejT16dOHD3ltwARBoNOnT5OxsTE5ODi883fGzc2NbGxsJPZtvvLXX3+RsbEx1+uqBxw4KaiEhATS0tJ65/lq8pKQkECfffYZWVtby7yezoMHD0hfX58OHjxIRP+84JyNSqLPD98kq3XnqcPKX8u/uqw6TRafe1DQnSQqKyujJUuWUK9evSp9samtzMxMOn/+PK1bt44+++wzateuHRkbG9OUKVNo165dFBYW1uBqbUlDWVkZubu7U/v27WtVx6Y2QkNDqUOHDlRcXFwv/clTeHg4GRsb09KlS7noYSNR3eU7QRDoiy++oH79+r21rIsgCGRpacl1u+qBEhERmEIRBAGffvopBg0ahNWrV8t7OBUQEby9veHq6oovvvgCrq6uaN68ucz7vX//PgYNGoRVG7bgz7JOCH2cBqGKn9xmSkC7kjQgxAe///oL2rZtW+u+BUFAdHQ0QkNDERISgtDQUDx58gQ9e/aEvb097OzsYGdnBx0dnVr30dj88ccfmDZtGubPn481a9agWbNmMu1vwIABcHZ2xvTp02Xaj7wQEX744QesW7cO+/btw4QJE+Q9JCZlMTExWLx4MR4/foy9e/di0KBBEtcIgoC5c+ciJSUFp0+fRosWLSo8v23bNjx8+BBeXl71NewmiQMnBbRt2zYEBgbijz/+wHvvvSfv4ZR78uQJnJ2dkZaWhoMHD6J79+712v+ft+5g6v5raKbRodr3dG3fGgcd+6C9aqtq35OWloawsLDyICk8PBzt27cvD5Ds7e1haWlZLwFjQ5acnIzJkydDRUUFvr6+0NTUlFlf586dw8qVKxEREQElJSWZ9SMP2dnZcHZ2xqNHj3Ds2DGYmprKe0hMRogIgYGBWLp0Kfr06YMdO3ZAX1+/wjWlpaVwcHBAs2bNcOTIESgrK5c/l5SUBEtLSzx79gytWlX/NY/VjGw/BrIau337NrZu3YrDhw8rTNAkCAL27duHXr16YeDAgQgLC6v3oImI8P3tPDTT6AAiwstfPZAVdlLiuhcnNyE9eF/53/9OyYPL0UhU9vmgtLQUt2/fxr59+zB79myYmZnBxMQEO3fuBBFh6dKliI2NxcOHD/HTTz9h4cKFsLW15aCpGnR1dXHp0iVYWVmhZ8+eCAsLk1lfw4YNAwCcP39eZn3IQ2RkJHr16oV27drhxo0bHDQ1ckpKShgzZgzu3bsHMzMzWFtbY9u2bSguLi6/RllZGT///DOys7Mxb948CIJQ/pyenh569eqFwMBAeQy/yeAZJwWSn5+PXr16YdWqVZgxY4a8hwMAiIuLg0gkQl5eHnx8fNCtWze5jCPoTjIW/fwXilKfIi14H4qTHkCt/3So9Rlffk1W6HFkh51E6679oT5kQfnjzZSAH6b1wDBLXaSkpCA0NLR82e3WrVswNDQsn0mys7NDt27dFCZobSxOnTqFefPmwc3NDYsWLZLJrJCfnx/EYjEuX74s9bbrGxHhxx9/xMqVK/Hdd9812iVIVrVXy3fx8fHYu3cvPvnkk/Ln8vLyMHjwYPTp0wc7d+4s/53y9fXFzz//jLNnz8pr2I0eB04KZNGiRUhLS4O/v7/clxsEQcD333+PdevW4euvv8ayZcsqTAnXt/m+t/DbvedID96H9/XMURgfieZaHcoDp8InUcj88whaGnSFUJhbIXACAPW8J8j8dTsyMjLKl9zs7OzQp0+fOu1/YtUXGxuLiRMnokuXLjhw4ADatGkj1fZLSkrQuXNnHD16FL1795Zq2/UpLy8PCxcuxK1bt3Ds2DF07dpV3kNicvRq+W7JkiWws7OrsHyXkZGBAQMGYMKECfjmm28A/PPzY2BggPv370NooYrIxEyk55dAXaU5rA3aQrctL+HVlfzeCVkFQUFBOHPmDCIjI+UeNMXExMDJyQklJSW4fv06unTpItfxJGcWICQuDQDKA6LC+Mjy50tz0pB+8QC0J69H7u1zb22joI0BDh09jX49LGS+UZm9nampKW7cuIHFixejd+/eOH78OCwsLKTWfvPmzeHi4oItW7bgxIkTUmu3Pv3999+YNGlS+dJm69at5T0kJmevlu8GDx4Md3d3WFtbY+XKlViyZAnatWuH4OBg9O/fH2pqaliyZAlUVFRg77AQ0/ddRmozdWQVlJS31ValOexNNDDaRg/DLHTk/l7TUPE7iAJ48eIFRCIRfvrpJ7nOfpSVlcHDwwN2dnYYO3Ysrl27JvegCQAiEzMr/PK/jspK8fL0VrQb5Azlf6lX2kaB8B7yW2lx0CRnrVq1gpeXF77++msMGDAA/v7+Um3fyckJ165dw8OHD6Xabn3w9/fHRx99BBcXFxw8eJCDJlaBiooKNmzYgJCQEPz++++wsbHB5cuX0b59e1y4cAE7duzA7gMHMV0chr/V+yKmqI3E62ZmfgnO3XuOf/v/heniMKRkF8jpu2nYeMZJzogITk5OmDVrFj7++GO5jePBgweYO3culJWVERoaik6dOsltLG9Kz3970AQAxc8foTQrBRmXxMgAUJaXAQgCqLQEGsMXV7g2lV8kFMacOXNga2uLiRMn4vr16/Dw8JBIra6N1q1bY+HChdi+fTsOHDgghZHKXmFhIZYuXYpLly7h4sWLsLa2lveQmALr3LkzgoKCcPr0acydOxf29vbYvn07Ak6fw+R9V9BMI+2dbQgE3IhLg9Ohm/Ce3atGWceM9zjJnaenJ7y8vBASEoL333+/3vsvKyvDzp07sWXLFqxduxYLFy5UuFmZ3+4mY77fXxUee/mrR4U9Tq9kXvODUJAtsccJADLPbIFJ8xxYWVnB2tq6/E8tLS2Zjp9VLisrC46OjkhISMCxY8dgbGxc5zZfvnwJMzMz3Lt3D7q6unUfpAzFxMRg0qRJ6Ny5M8RiMVRVVeU9JNaA5Ofn4z//+Q88PT3RZeEPSCypOEtZ/CIe6Rc8IRTlQ6lZM6gPW4QWOhU/FPcz1YCvUx9etqsBxXqHbGLu378PNzc3+Pn5ySVoio6ORt++fXHu3DmEh4dj0aJFChc0JSQk4PdjPqCi3Dq101alOW5d+AX79+9H//79ER8fjw0bNsDMzAy6uroYOnQoli9fDl9fX0RFRVVI/2Wyo6amhuPHj2Pq1Kno06ePVDKBNDU1MX36dOzevVsKI5SdEydOoG/fvhCJRAgICOCgidWYiooKNm7ciC3+wUgsrjhrJJQU4kWAG9TsJkDPcTfU+k7By8DtEm2ExKXh/L3n9TXkRoFnnOSkuLgY9vb2cHZ2xvz58+u179LSUmzduhU7d+7Exo0bMW/ePIUKmIqLi3HmzBmIxWKEh4dj6tSpSOs6DiGJhbVu8zMLHeyb0VPicSJCYmIiIiMjERUVhaioKERGRiI+Ph5mZmawsrKqMEPF1cFl5/r165g6dSpmz56NdevW1akkRHx8PHr16oW4uDiFC0iKi4uxYsUKBAYGIiAgAB988IG8h8QauFdZx6/LfxiC7PBfoDNjK4B/XutKUuPxvraJxP2VvT6yt+PASU5cXV1x7949nD59ul6nSKOiojB37lxoaGjAy8sLHTpUvwq3rD148ADe3t746aefYG5uDmdnZ0yYMAGtWrUqr+NU1TErlXm9jlN1FRQUIDo6WiKgUlZWlgimunbtKpX9OeyfRImpU6cCAH7++Wdoa2vXuq1p06bB1tYWy5cvl9bw6iwhIQEODg5o3749Dh48iHbt2sl7SKyBS84swLDd1yQ2gmeFHkdx8kMotWiNkheP0axFa7QdOFdiqQ74Z0b+3Bf9uVRBNXHgJAdXrlzB1KlTERERUac3hpooKSmBu7s79uzZA3d3dzg5OSnEmnZ+fj5OnDgBLy8vPHz4ELNnz4aTkxPMzMwqXEdEmC4Ow424d298fFPfjhrwE9V9DZ+IkJSUJBFMxcXFoVOnThIBla6urkL8Gzc0ZWVlWLt2LXx8fHDkyBF8+OGHtWonIiICI0aMQFxcnEIEtmfPnoWTkxOWL18OFxcX/tlgUvG2PaAAkHUjAFk3jqL9tP+ghZ458h+GIu38Xhgs8IGSsuTJB/tn9MRQC55Rrw4OnOpZZmYmrK2tsW/fPgwfPrxe+oyIiMCcOXOgp6eH/fv3w9DQsF76fdeYvLy8cOTIEfTp0wcikQijRo2q8iiTlOwCOB26ibtJ2dXux1JPVeZZI4WFhfj7778lAioiKg+iXgVU3bp1Q8uWLWU2lsYkKCgIc+fOLS/AWptAY9iwYXBwcICjo6MMRlg9paWlcHNzg6+vL44cOYJ+/frJbSys4SEipKWlITEx8a1f8e/po7SHg8R9uVEXkHPrV+jO3VX+2NNd06AzfQuaa0q+B7iP646pvY1k+r00Fhw41bNp06ZBXV0de/fulXlfxcXF2LhxIzw9PbF161bMnj1brp9ys7Oz4e/vD7FYjNTUVDg5OWHOnDkwMqr+L2tKdgGWHY1EaFxalct2zZQA+44a8HCwgbZq/QcqRITnz5+XB1GvAqpHjx7BxMREIqDS19fnGYi3iI+Px6RJk2BoaAgfHx+oqanV6P7Lly9jwYIFiI6Olss+vqSkJEyZMgWtWrWCr68vZ3CyCgRBQEpKChITE/Hs2bO3BkavDuw1MDCo8KWvrw8DAwM8EdrhP1dTJdouy81AkngBtKdsRAudTihMuIvUU+4wWOgDJWXJZCSecao+DpzqkZ+fHzZt2oRbt27J/OTqmzdvYu7cuTAxMYGnpyf09PRk2l9liAg3btyAWCzGqVOn8Omnn0IkEuHTTz+t9eZfIsL5e89xOiIJIY/TkJlfsTJu344aGG2th6EKWBm3qKgI9+/flwioiouLKyzzWVlZwcLCAioqKvIestwVFRXBxcUFwcHBOH78eI3qHBERevfujdWrV2Ps2LEyHKWkixcvYtasWVi4cCFWrVqlUAkYTPZKS0uRnJxc6UxRYmIikpOT0bZtW4mg6M0AqapiqJXtcQKAwoS7yLj8I6ikEErvNUe7T+ehpaFktX7e41QzHDjVk/j4ePTu3RvBwcGwsbGRWT+FhYVYv349vL29sXPnTkybNk0uwUNqaioOHz4MsVgMQRAgEokwa9Ysqe/pSs4sQNSzLKTnFUO99fuw0ldrkL/8KSkpFZb5oqKi8ODBA3To0EEioDIyMlK4gLA++Pv7Y8mSJdi2bRvmzJlT7fuOHz+OHTt24MaNG/Xy71ZWVoaNGzdi//798PX1rXAwK2scioqK3jpD9Ppjqamp0NLSqjIo0tPTk8r+u7dl1dUEZ9XVDAdO9aCsrAwDBgzA6NGjZZrhExYWhrlz56JLly744Ycf6j11XhAE/P777/Dy8kJwcDDGjBkDZ2dn9OvXr0m+0ddVSUlJ+ezU6wFVfn6+xEZ0S0vLJnFER3R0NCZMmIB+/fphz5491Zq5LSsrQ9euXeHt7Y3+/fvLdHwvXrzAjBkzUFxcjJ9//lnhC3AySXl5eZUum736yszMhJ6e3luXzl596ejoVLlnU5rqO+u4qePAqR5s2rQJv//+Oy5evCiT6fqCggJ88803OHz4MHbt2gUHB4d6DVQSExPh4+MDb29vqKurw9nZGVOnTpXruXuNWWpqankw9Sqgun//PgwMDCQCKmNj40YXtObk5MDZ2RkPHjzA8ePHYWpq+s57Dhw4gMDAQPz6668yG9e1a9cwbdo0zJo1C+vWrYOyMp9opUiICNnZ2VUGRImJiSgoKKhylsjAwADa2toKtfSqCFnHTQkHTjL23//+FyNGjMCtW7dkks32559/wtHREdbW1ti7d2+9ljc4e/Zs+XExU6ZMgUgkQo8ePeqlf1ZRSUkJHj16JJHZl5OTg+7du1cIqCwtLdGmTRt5D7lOiAjff/891q9fjwMHDrxz/1JhYSFMTExw4cIFWFpaSnUsgiBg+/bt2LlzJ3x8fPDZZ59JtX32bpVlnr05c0REMDQ0rHI/kYaGRoMMIhQ167gx4sBJhnJzc9GjRw9s3LgRDg6S6aJ1kZ+fj9WrV+PIkSPYu3cvJkyYINX2K/Po0SN4e3vj0KFD6NSpE0QiESZOnNgklokaorS0NNy5c6c8oIqMjER0dDT09PQkZqdMTEwU6lN0dYSFhcHBwQEODg5wd3evcpbH3d0d9+/fx6FDh6TWf3p6OmbPno20tDQEBAQoRKmPxkYQBLx48aLKWaI3M8/eXDZ79aWqqtogg6LqaihZxw0dB04yNG/ePBQVFUn1hRoArl69CkdHR/Tp0we7du2CpqamVNt/U2FhIU6cOAGxWIx79+5h1qxZEIlE6NKli0z7ZbJRWlqKmJgYidmpjIwMidmp7t27K9yRJW96+fIlZsyYgfz8fAQEBFS6rygzMxOmpqa4fft2jUpgVCY8PBwODg6YMGECNm/eXG/7WRqT+so8a0oactZxQ8GBk4ycOnUKLi4uiIiIkNobT25uLlxdXXHy5En88MMPGDNmjFTarUxUVBTEYjH8/f3Rq1cviEQijB49Wi4HEjPZy8jIqDA7FRUVhbt370JbW1ui7lTHjh3rdJactAmCgE2bNmHfvn3w9/fHgAED3nrdV199hbKyMnh4eNS6LyLCnj17yjPnxo0bV+u2GrM3M8/etuH6XZln+vr60NPT46KxtdRYso4VDQdOMpCcnAxbW1ucOHFCalWCL126BJFIhP79+8PDwwPq6upSafdNOTk5OHLkCLy8vJCcnAxHR0fMnTsXxsbGMumPKbaysjLExsZK1J1KTU2FhYVFhYDKyspK7gkBFy5cwKxZs7BkyRKsWLFCYunx2bNn6N69O2JiYmr1O5SVlQWRSIS4uDgcO3YMHTt2lNbQG5SaZp5VtnRWn5lnjEkLB05SJggChg8fjt69e2P9+vV1bi87OxsrVqzA2bNn4enpiREjRkhhlBUREUJDQyEWi3Hy5EkMHDgQzs7OGDJkiELNKjDFkZWVhTt37lQIqO7evQt1dXWJulOdO3eu15+jp0+fYvLkydDQ0MBPP/0kcZCuo6MjOnbsiDVr1iA5swCRiZlIzy+BukpzWBu0rfQTeUREBCZNmoTBgwdj586djXIWpDqZZ8+ePUN+fn6DyzxjTFo4cJKy3bt3w8/PD9evX6/zJ6ng4GDMmzcPgwYNwo4dO6T+aT4tLa28SGVxcXF5kcr6rv/EGgdBEBAXFydRd+r58+fo1q2bREAlq1lT4J/jhlasWIHAwEAcO3YMPXv+f3G/6OhoDJr7FUYu/BbhT7IqVFxuq9Ic9iYaGG2jh2H/2wNCRBCLxVi1ahX27NmDKVOmyGzcsvR65llVs0WVZZ69PmvUUDPPGJMGDpyk6O7duxg4cCBCQkLQqVOnWreTlZWFL7/8EhcuXMCBAwcwdOhQqY1REARcvnwZYrEY586dw6hRoyASifDRRx/xCyGTiZycnPLZqVcB1Z07d6CmpiaR2WdmZibV+kfHjh3DwoULsXHjRsybNw8vcgqxLCASN2JTAaXKZ0OaKQF2JhrYOKozvl2+FBERETh+/DjMzc2lNjZpqm7mWcuWLd85U9TYM88YqysOnKSkqKgIvXv3xuLFi+Hk5FTrdoKCgvD5559jxIgR2Lp1q9Q2liclJZUXqWzTpg2cnZ0xffp0iWUMxuqDIAh48uSJRGbfs2fP0LVrV4mAqi6Zow8ePMDEiRPRrac9sm2n4+/nudW+VynjKexLIiHes0Nu5wZWlXn2auYoKSmJM88YqyccOEnJl19+icePH+PEiRO1+rSWkZGBZcuW4cqVKxCLxRg0aFCdx1RaWoqgoCCIxWJcu3YNDg4OcHZ2Rs+ePfkTJVNIubm5uHfvXoWAKioqCioqKuVB1Ks/zc3Nq70cnpubi35uAchqKbkMTURIO/sdmmt1gFqf8RLP9zPVgK+TbCorFxUVISkpqcqZordlnr252ZozzxirPxw4ScHFixcxZ84cREZGQkNDo8b3BwYGYsGCBRg3bhw2b96Mf/3rX3UaT2xsLH788UccPHgQxsbGEIlEmDRpUp3bZUweiAgJCQkSs1NPnz6Fubm5xOzU26rnV3aWV8nLp0gL3ofipAdQ6z/9rYFTbc/yqm7mma6ubpUzRZx5xphi4cCpjtLS0mBjY4Mff/wRgwcPrvG9ixcvRmhoKLy9vSutPVMdhYWFOHXqFMRiMSIjIzFz5kw4OTnBwsKi1m0ypsjy8/MlZqciIyPRokULibpTeyOKEPz3C4k20oP34X09cxTGR1Y64wRUPD2+ssyzN4Okd2We6evrQ1tbmzNXGWtgOHCqAyLCpEmTYGhoWOOCeidPnsS///1vTJ48GZs2bar13oO7d+9CLBbDz88PNjY2EIlEGDt2LFq0aFGr9hhryIgIiYmJFbL6Ih7EI+/jJXivVeX7BV/+6lFl4KRcVgSDuz8h5fEDicyzymoUceYZY40TH99dBwcPHsTDhw/h6+tb7XtSU1OxaNEi3L59G8eOHcOHH35Y435zc3MREBAAsViMhIQEODo6Ijw8HCYmJjVui7HGRElJCYaGhjA0NCyvefbb3WTM9/urTu2WvtcCn037HCNtjTjzjLEmjquT1VJsbCxWrFgBf3//am3KJCIcPXoU3bt3h5GRESIjI2sUNBERwsPDMW/ePBgaGuLMmTNYvXo1njx5gg0bNnDQxFgl0l87q6sujMwsYGFhATU1NQ6aGGvCeMapFkpLSzFjxgysWbMGlpaW77w+JSUFCxcuRHR0NE6dOgU7O7tq95Weng4/Pz94eXkhLy8PIpEI0dHRlR5kyhirSF1FOhur1VvzGY2MMZ5xqpWNGzdCVVUVX3zxRZXXERH8/PzKC/vdvn27WkETEeHy5cuYPn06OnbsiJCQEOzatQuPHj2Cq6srB02M1YC1QVuotapb8NRWpTms9NWkNCLGWEPGM041FBISAk9PT9y+fbvKc5iSk5Mxf/58xMbG4tdff8UHH3zwzraTk5Nx6NAheHt7o2XLlnB2dsbu3btrVeKAMfYP3batYN9RA7/de17pNZojl1XZhr2JBp8qzxgDwDNONZKTk4MZM2bA09Oz0lkfIsKhQ4fK06Fv3bpVZdBUWlqKs2fPYuzYsejWrRtiY2Ph6+uLqKgoLF68mIMmxqRgtLUemtVyW1IzJWCMjZ50B8QYa7B4xqkGFi9ejE8++QRjx4596/OJiYn4/PPP8ezZM5w/fx62traVthUfHw9vb2/4+PjAwMAAIpEIhw8fRps2bWQ1fMaarM8sdWBnooEbcWk1vtfORANDLfjga8bYP3jGqZqOHz+OP//88631mogI3t7esLW1Re/evREeHv7WoKmoqAhHjx7FkCFD0KtXL+Tk5ODcuXMIDQ2FSCTioIkxGVFSUoLHZGtY6tXs7EdLPVV4TLbmLDrGWDkugFkNiYmJ6NmzJ86cOYPevXtXeC4hIQHOzs54+fIlfHx8YGVlJXF/dHQ0vL29cfjwYXTv3h0ikQjjxo3js6UYq2cp2QVYdjQSoXFpEsevvK6ZEmDfUQMeDjbQVuXfU8bY/+PA6X+SMwsQmZiJ9PwSqKs0h7VBW+i2bQVBEDB48GAMHDgQa9asKb9eEAQcOHAAa9asgYuLC5YvX17hPKm8vDwcO3YMXl5eePz4MebMmQNHR0d06tRJHt8eY+x/iAjn7z3H6YgkhDxOQ+ZrdZ7aqjRH344aGG2th6EWOjzTxBiT0KQDJyLCubvPERiZhJC4NGQVVHwBtTfRAJ78F1HnfHH1ypXyM6UeP34MJycn5ObmwsfHp/w8OCLCX3/9BS8vLxw9ehT9+vWDs7Mzhg8fDmVl3k7GmKJJzixA1LMspOcVQ731+7DSV+PsOcZYlZps4JSSXYBlAZEIfVz1lD0JZehh0Aaes+2g9a8W+OGHH7B27VqsWLECLi4uUFZWRmZmJvz8/CAWi5GVlQUnJyfMmTMH+vr69fcNMcYYY0zmmmTglJJdAMdDN3EvKbva93TWaIH8czsg5GfCx8cH5ubmuAG2QMAAAANLSURBVHbtGsRiMQIDAzFs2DCIRCJ88sknVdZ3YowxxljD1eQCJyLCdO8w3IitmJacffMMcv76FUrK76O5hiHUhyzAe60qZrkZNM9DwOf94OfnB29vbygrK0MkEmHmzJnQ1NSsz2+DMcYYY3LQ5DbenLv7HKFv1HIpfBKF7LDj0Jm5A8qqmsi9ewnpv+2B1rhVFa5LLG4F65GzMdrWCAcPHoSdnR1vHmWMMcaakCYXOAVGJknsaSp6HoOWHWygrPrPrJGKWV+kndsNKiuB0nuvnXGl1Axjl2yEeE71D+lljDHGWOPRpDbjJGcWIOQtlYNb6JqhMCEKpVkvAAC5dy4AZaUoK8iRuPbm02wkZxbIfKyMMcYYUzxNasYpMjGzQsmBV1oaWUKt31SkntwEKCnhX1aD0axlGyi9J/nPk5lfgqhnWZyyzBhjjDVBTSpwSs+XDJoAQCjKR0sjS7SxHgIAKMvLQOZVXzRr+fYjUNLzimU2RsYYY4wpria1VKeu0vytj5flpiPF3xVCUT4AIOvPI2jd7aNKN36rt35fZmNkjDHGmOJqUjNO1gZtodaqucRyXXMNA6jaTUTyTy4AEVoYdIP64PlvbaOtSnNY6avVx3AZY4wxpmCaVOCk27YV7Dtq4Ld7zyWeU+05Cqo9R72zDXsTDd7fxBhjjDVRTWqpDgBGW+uhWS1LLzVTAsbY6El3QIwxxhhrMJpc4PSZpQ7sTDRqda+diQaGWuhIeUSMMcYYayiaXOCkpKQEj8nWsNRTrdF9lnqq8JhszZXCGWOMsSasyZ1V90pKdgGWHY1EaFyaRCXx1zVTAuw7asDDwQbaqi3rb4CMMcYYUzhNNnAC/jnw9/y95zgdkYSQx2nIfK3OU1uV5ujbUQOjrfUw1EKHZ5oYY4wx1rQDp9clZxYg6lkW0vOKod76fVjpq3H2HGOMMcYq4MCJMcYYY6yamtzmcMYYY4yx2uLAiTHGGGOsmjhwYowxxhirJg6cGGOMMcaqiQMnxhhjjLFq4sCJMcYYY6yaOHBijDHGGKsmDpwYY4wxxqqJAyfGGGOMsWriwIkxxhhjrJo4cGKMMcYYqyYOnBhjjDHGqokDJ8YYY4yxauLAiTHGGGOsmv4PDUop2kkmkTMAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import networkx as nx\n", - "\n", - "nodes = wires = 20\n", - "edges = 60\n", - "seed = 1967\n", - "\n", - "g = nx.gnm_random_graph(nodes, edges, seed=seed)\n", - "positions = nx.spring_layout(g, seed=seed)\n", - "\n", - "nx.draw(g, with_labels=True, pos=positions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A twenty-node graph (which maps to the same number of qubits) definitely puts us in a regime where the local simulator will be slow to execute. As we have discussed in the [parallelization tutorial](../1_Parallelized_optimization_of_quantum_circuits/1_Parallelized_optimization_of_quantum_circuits.ipynb), this slowness will be compounded when it comes to training the circuit, with each optimization step resulting in multiple device executions due to calculation of the gradient. Thankfully, the remote SV1 simulator is highly suited to speeding up gradient calculations through parallelization. We now show that this makes training the circuit for QAOA solvable within a reasonable time.\n", - "\n", - "Let's first load a new device:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "device_arn = \"arn:aws:braket:::device/quantum-simulator/amazon/sv1\"" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "dev = qml.device(\n", - " \"braket.aws.qubit\",\n", - " device_arn=device_arn,\n", - " wires=wires,\n", - " parallel=True,\n", - " max_parallel=20,\n", - " poll_timeout_seconds=30,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note the specification of ``max_parallel=20``. This means that up to ``20`` circuits will be executed in parallel on SV1 (the default value is ``10``).\n", - "\n", - "
\n", - "Caution: Increasing the maximum number of parallel executions can result in a greater rate of spending on simulation fees. The value must also be set bearing in mind your\n", - " service quota, which can be found here. Similarly, if you instead choose to run the QAOA problem on a QPU, it can take a long time and incur high usage fees. \n", - "
\n", - "\n", - "We now just need to set up the QAOA circuit and optimization problem in the same way as before. However, we will switch to a new optimization problem to keep things interesting: aiming to solve maximum cut, with the objective of partitioning the graph's nodes into two groups so that the greatest number of edges are shared between the groups (see the image below). This problem is NP-hard, so we expect it to be tough as we increase the number of graph nodes." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "cost_h, mixer_h = qml.qaoa.maxcut(g)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "def qaoa_layer(gamma, alpha):\n", - " qml.qaoa.cost_layer(gamma, cost_h)\n", - " qml.qaoa.mixer_layer(alpha, mixer_h)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "n_layers = 2\n", - "\n", - "@qml.qnode(dev, diff_method='parameter-shift')\n", - "def cost_function(params, **kwargs):\n", - " for i in range(wires): # Prepare an equal superposition over all qubits\n", - " qml.Hadamard(wires=i)\n", - " \n", - " qml.layer(qaoa_layer, n_layers, params[0], params[1])\n", - " return qml.expval(cost_h)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(1967)\n", - "params = 0.01 * np.random.uniform(size=[2, n_layers])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A variety of [optimizers](https://pennylane.readthedocs.io/en/stable/introduction/optimizers.html) are available in PennyLane. Let's choose ``AdagradOptimizer``:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = qml.AdagradOptimizer(stepsize=0.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We're now set up to train the circuit! Note, if you are training this circuit yourself, you may want to increase the number of iterations in the optimization loop and also investigate changing the number of QAOA layers.\n", - "\n", - "
\n", - "Caution: Running the following cell will take a long time and will result in usage fees charged to your AWS account. Only uncomment the cell if you are comfortable with the potential wait-time and costs. We recommend monitoring the Billing & Cost Management Dashboard on the AWS console and being aware that hybrid jobs involving a large number of qubits can be costly.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# import time\n", - "\n", - "# iterations = 10\n", - "\n", - "# for i in range(iterations): \n", - "# t0 = time.time()\n", - " \n", - "# params, cost_before = optimizer.step_and_cost(cost_function, params) \n", - "\n", - "# t1 = time.time()\n", - " \n", - "# if i == 0:\n", - "# print(\"Initial cost:\", cost_before)\n", - "# else:\n", - "# print(f\"Cost at step {i}:\", cost_before)\n", - "\n", - "# print(f\"Completed iteration {i + 1}\")\n", - "# print(f\"Time to complete iteration: {t1 - t0} seconds\")\n", - "\n", - "# print(f\"Cost at step {iterations}:\", cost_function(params))\n", - "\n", - "# np.save(\"params.npy\", params)\n", - "# print(\"Parameters saved to params.npy\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Initial cost: -29.98570234095951\n", - "Completed iteration 1\n", - "Time to complete iteration: 93.96246099472046 seconds\n", - "Cost at step 1: -27.154071768632154\n", - "Completed iteration 2\n", - "Time to complete iteration: 84.80994844436646 seconds\n", - "Cost at step 2: -29.98726230006233\n", - "Completed iteration 3\n", - "Time to complete iteration: 83.13504934310913 seconds\n", - "Cost at step 3: -29.999163153600062\n", - "Completed iteration 4\n", - "Time to complete iteration: 85.61391234397888 seconds\n", - "Cost at step 4: -30.002158646044307\n", - "Completed iteration 5\n", - "Time to complete iteration: 86.70688223838806 seconds\n", - "Cost at step 5: -30.012058444011906\n", - "Completed iteration 6\n", - "Time to complete iteration: 83.26341080665588 seconds\n", - "Cost at step 6: -30.063709712612443\n", - "Completed iteration 7\n", - "Time to complete iteration: 85.25566911697388 seconds\n", - "Cost at step 7: -30.32522304705352\n", - "Completed iteration 8\n", - "Time to complete iteration: 83.55433392524719 seconds\n", - "Cost at step 8: -31.411030331978186\n", - "Completed iteration 9\n", - "Time to complete iteration: 84.08745908737183 seconds\n", - "Cost at step 9: -33.87153965616938\n", - "Completed iteration 10\n", - "Time to complete iteration: 87.4032838344574 seconds\n", - "Cost at step 10: -36.05424874438809\n", - "Parameters saved to params.npy\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example shows us that a 20-qubit QAOA problem can be trained within around 1-2 minutes per iteration by using parallel executions on the Amazon Braket SV1 device to speed up gradient calculations. If this problem were run on the local Braket simulator without parallelization, we would expect for training to take much longer.\n", - "\n", - "Pre-optimized parameters for the above 2-layer QAOA circuit after 30 iterations can be loaded with:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "params_30 = np.load(\"params_30.npy\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "What's next? See if you can analyze the trained QAOA circuit for the 20-node graph by adapting the earlier analysis. Also, check out the followup tutorial on quantum chemistry.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Task Summary\n", - "{}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run this example: 0.000 USD\n" - ] - } - ], - "source": [ - "print(\"Quantum Task Summary\")\n", - "print(t.quantum_tasks_statistics())\n", - "print('Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).')\n", - "print(f\"Estimated cost to run this example: {t.qpu_tasks_cost() + t.simulator_tasks_cost():.3f} USD\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.15" - }, - "vscode": { - "interpreter": { - "hash": "590fab68195cf107911461461f81d5c472d3d6127f579badfcfad30f03e5cab2" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Graph optimization with QAOA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One application area where near-term quantum hardware is expected to shine is in graph optimization. Graph-based problems are interesting to explore because they have both strong links to practical use-cases (such as logistics and social networks) and are also often hard to solve." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Graphs are composed of a collection of interconnected nodes. For example, here is a six-node graph:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import networkx as nx\n", + "\n", + "n_nodes = 6\n", + "p = 0.5 # probability of an edge\n", + "seed = 1967\n", + "\n", + "g = nx.erdos_renyi_graph(n_nodes, p=p, seed=seed)\n", + "positions = nx.spring_layout(g, seed=seed)\n", + "\n", + "nx.draw(g, with_labels=True, pos=positions, node_size=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many practical use-cases can be mapped to a graph structure. In a social network, the nodes of a graph can represent users and the edges can represent connections between the users.\n", + "\n", + "We often need to solve optimization problems to identify important properties of the graph. These problems can include:\n", + "\n", + "- finding large clusters of fully connected nodes (known as [maximum clique](https://en.wikipedia.org/wiki/Clique_problem))\n", + "- finding a minimum number of nodes that connect to every edge in the graph (known as [minimum vertex cover](https://en.wikipedia.org/wiki/Vertex_cover))\n", + "- finding a partition of the nodes into two subsets so that the greatest number of edges are intersected (known as [maximum cut](https://en.wikipedia.org/wiki/Maximum_cut))\n", + "\n", + "This tutorial shows how a quantum algorithm called QAOA can be run using PennyLane and Braket to solve graph-based optimization problems. We begin with a small 6-node graph and then push the limits to run a 20-node graph using parallel executions on SV1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Note This notebook requires PennyLane version 0.17 or above.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## QAOA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The quantum approximate optimization algorithm (QAOA) is an algorithm designed for near-term hardware. It can find approximate solutions to combinatorial optimization problems such as graph-based problems.\n", + "\n", + "QAOA is covered in more depth in the [QAOA_braket](../../hybrid_quantum_algorithms/QAOA/QAOA_braket.ipynb) notebook as well as in PennyLane [tutorials](https://pennylane.ai/qml/demos/tutorial_qaoa_intro.html). The following is a short summary to refresh the key concepts.\n", + "\n", + "\n", + "QAOA begins by associating the optimization problem with a cost Hamiltonian $H_C$ and choosing a mixer Hamiltonian $H_{M}$. It proceeds by repetitively applying multiple layers of the unitaries $\\exp{(-i \\gamma_i H_C)}$ and $\\exp{(-i \\alpha_i H_M)}$ with controllable parameters $\\gamma_i$ and $\\alpha_i$, as shown in the diagram below." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The algorithm then measures the cost Hamiltonian $H_C$. By varying the controllable parameters $\\gamma_i$ and $\\alpha_i$, the expectation value of the cost Hamiltonian is minimized. Applying the optimized unitaries prepares a quantum state that contains information about the optimal configuration for the problem. Sampling from the state will give a candidate solution.\n", + "\n", + "
\n", + "Summary If you are less familiar with QAOA and quantum algorithms, the key takeaway message is that the algorithm involves an optimization of the controllable parameters $\\gamma_i$ and $\\alpha_i$ that the quantum circuit depends on. This can be tackled naturally using the PennyLane/Braket pipeline.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing the problem" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's consider the graph above and aim to find the maximum clique, i.e., the largest set of nodes that are fully connected.\n", + "\n", + "To solve this using QAOA in PennyLane and Braket, we first calculate the cost Hamiltonian $H_C$ and corresponding mixer Hamiltonian $H_M$" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost Hamiltonian:\n", + " (-0.5) [Z1]\n", + "+ (-0.5) [Z5]\n", + "+ (0.25) [Z0]\n", + "+ (0.25) [Z4]\n", + "+ (0.25) [Z2]\n", + "+ (0.25) [Z3]\n", + "+ (0.75) [Z0 Z1]\n", + "+ (0.75) [Z1 Z4]\n", + "+ (0.75) [Z2 Z5]\n", + "+ (0.75) [Z3 Z5]\n", + "Mixer Hamiltonian:\n", + " (1) [X0]\n", + "+ (1) [X1]\n", + "+ (1) [X2]\n", + "+ (1) [X3]\n", + "+ (1) [X4]\n", + "+ (1) [X5]\n" + ] + } + ], + "source": [ + "import pennylane as qml\n", + "from pennylane import numpy as np\n", + "\n", + "cost_h, mixer_h = qml.qaoa.max_clique(g, constrained=False)\n", + "# constrained=True results in greater circuit depth but potentially better solutions\n", + "\n", + "print(\"Cost Hamiltonian:\\n\", cost_h)\n", + "print(\"Mixer Hamiltonian:\\n\", mixer_h)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up the algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We begin by setting up a single QAOA layer" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This layer contains the controllable parameters $\\gamma_i$ and $\\alpha_i$." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def qaoa_layer(gamma, alpha):\n", + " qml.qaoa.cost_layer(gamma, cost_h)\n", + " qml.qaoa.mixer_layer(alpha, mixer_h)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The full QAOA circuit is then given by:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "n_layers = 1\n", + "wires = n_nodes\n", + "\n", + "\n", + "def circuit(params, **kwargs):\n", + " for i in range(wires): # Prepare an equal superposition over all qubits\n", + " qml.Hadamard(wires=i)\n", + "\n", + " qml.layer(qaoa_layer, n_layers, params[0], params[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Note We have chosen to use a single QAOA layer. The choice of depth is a tradeoff between improved solutions (for greater depth) and increasing runtime.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are overall two controllable parameters: the first one is $\\gamma_i$ of the cost Hamiltonian and the one is $\\alpha_i$ of the mixer Hamiltonian:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0.72511958],\n", + " [0.57312068]], requires_grad=True)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(1967)\n", + "params = np.random.uniform(size=[2, n_layers])\n", + "params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this part of the tutorial, we will use the local Braket simulator (see the [introduction tutorial](./0_Getting_started.ipynb) for further details):" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "dev = qml.device(\"braket.local.qubit\", wires=wires)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The final step is to define the cost function. In QAOA, the output cost function is given by the expectation value of the cost Hamiltonian $H_C$, i.e.," + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "@qml.qnode(dev, diff_method=\"parameter-shift\")\n", + "def cost_function(params, **kwargs):\n", + " circuit(params)\n", + " return qml.expval(cost_h)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have set up the cost function, we just need to pick an optimizer and run the standard optimization loop." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = qml.GradientDescentOptimizer()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 1.2873831170401528\n", + "Completed iteration 1, cost function: 1.0283347453164076\n", + "Completed iteration 2, cost function: 0.6850453114142451\n", + "Completed iteration 3, cost function: 0.26816570914692445\n", + "Completed iteration 4, cost function: -0.18397174104834577\n", + "Completed iteration 5, cost function: -0.6134512514217036\n", + "CPU times: user 9.66 s, sys: 3.35 ms, total: 9.66 s\n", + "Wall time: 9.66 s\n" + ] + } + ], + "source": [ + "%%time\n", + "print(\"Initial cost:\", cost_function(params))\n", + "\n", + "for i in range(5):\n", + " params = optimizer.step(cost_function, params)\n", + " cost_eval = cost_function(params)\n", + " print(f\"Completed iteration {i + 1}, cost function:\", cost_eval)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Investigating the result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How do we know how well the algorithm has performed? To do this, we can sample from the circuit using the optimized parameters. This will give us binary samples that allow us to select which nodes of the graph to use as part of our clique, e.g., either by simply selecting the most common sample or selecting the sample with the lowest corresponding energy.\n", + "\n", + "Let's take some samples and see which ones occur most frequently. To start, we'll create a QNode designed for sampling." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "shots = 10_000\n", + "dev = qml.device(\"braket.local.qubit\", wires=wires, shots=shots)\n", + "\n", + "\n", + "@qml.qnode(dev, diff_method=\"parameter-shift\")\n", + "def samples(params):\n", + " circuit(params)\n", + " return np.array([qml.sample(qml.PauliZ(i)) for i in range(wires)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Samples can now be generated and converted into probabilities:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import Counter\n", + "\n", + "s = samples(params).T\n", + "s = (1 - s.numpy()) / 2\n", + "s = map(tuple, s)\n", + "\n", + "counts = Counter(s)\n", + "indx = np.ndindex(*[2] * wires)\n", + "\n", + "probs = {p: counts.get(p, 0) / shots for p in indx}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now plot the probability distribution over all possible samples:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "plt.style.use(\"seaborn-v0_8-colorblind\")\n", + "labels = [\"{0:{fill}6b}\".format(i, fill='0') for i in range(len(probs))]\n", + "\n", + "plt.bar(range(2**wires), probs.values())\n", + "plt.xticks([i for i in range(len(probs))], labels, rotation=\"vertical\", size=12)\n", + "plt.yticks(size=12)\n", + "\n", + "plt.xlabel(\"Sample\", size=20)\n", + "plt.ylabel(\"Probability\", size=20)\n", + "\n", + "fig = plt.gcf()\n", + "fig.set_size_inches(16, 8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the plot, it is clear that the sample ``101110`` has the greatest probability. Since each qubit corresponds to a node, this sample selects the nodes ``[0, 2, 3, 4]`` to form a subgraph. Let's check if this is a clique, i.e., if all of the nodes are connected:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OnVRUVNCjRw/Gjx9PZGQkkZGRDBo0SOmYBin3f/+j9803k2tnx7DCQswVOj7ywPr1DLnvPrK+/BKvu+5q8/VSX3+dES++SMyECUQoVJKF0FdSKIUQBuHs2bPs3LmzeU/I/Px8TE1NCQoKat4T0t/fHzOZWWpXJXv20BQYSLW5OY65uYput1NRWEgPFxfiH3mE4Pff18k1o8LDCY2JYd+yZYx47jmdXFMIQyCFUgjRKTU2NpKUlNRcIJOTk9FoNHh4eDQXyIiICLp37650VKNRVVzMcTc3ujc0YJKYiKO/v9KRKDU15WBoKBFRUTq5XlNDA3sdHel/5gxNqak4dMAWSEJ0BlIohRCdglarJTc3t7lA7tq1i6qqKmxtbZkwYULzbWwXFxeloxqlxtpa9rq44HHqFCf16HSZfT16UNOzJ2MKCnR2zVNZWTQOHUqpjQ3eJ04odktfCH0ihVIIobdOnz7Njh07mtdCFhYWYm5uTnBwcPMspJ+fH6ampkpHNWpajYZYb2+CcnLIePNNRsydq3SkZjGentgXFuJVW6vT6+7/8EO8H3mEuIAAIpKTdXptITojKZRCCL3R0NDA7t27m2ch09LS0Gq1eHt7NxfIsLAwbGxslI4q/iZq4kQitm0j7v77CVm7Vuk4/xA1eTL+v/yCdVOTzk+6iZoyhYiffiJp3jxGLVmi02sL0dlIoRRCKEar1ZKVldVcIKOioqitrcXe3p4JEyYwceJEJkyYgLOco6y34h95hOAPPyQqIoKIXbuUjvMfyQsXErh4MSeSk3EMCNDptbUaDUnOznidOEHFrl30j4jQ6fWF6EzkcUchRIcqLS1l+/btzZuKHz9+HEtLS0JCQli0aBGRkZEMGzYMlZybrPfSV64k4MMPiXVzI1yBjcuvhH1ICAAlMTE6L5QmKhVeCQmcdXOj9tprqSsuxkr2MhVGSmYohRDtqq6ujri4uOZ1kHv37gXAx8en+VSa0NBQusqDDZ1K3o8/Yn/jjeTZ2uJTUICFni5DaKytBWtrEm6/nbBvv22XMXI2bsTl9ttJ9vJq07nhQnRmUiiFEDql1WrJyMhoLpAxMTHU1dXh4ODQ/CT2hAkTcHR0VDqqaKWTe/fS6O9Prbk5fXJy6NG/v9KRLumIhQWFQ4YQnp7ebmPE3nMPoV98QfxDDxH84YftNo4Q+koKpRCizU6cOMH27dv5448/2L59OyUlJVhZWREeHt78MM3QoUMxMTFROqpoo+qSEo65utKzvh4SE3V+G7k9JDk4oGpqIqCsrN3G0Go0xLu745efz4nNm3GbMqXdxhJCH0mhFEK0WG1tLbGxsc0P02RkZADg5+fXXCCDg4OxsrJSOKnQJXVdHen9+zO4rIwTGzcy+NZblY50RaICAnDdu5f+jY3tOk5NaSnF/fuj0mrpU1CAjYNDu44nhD6Rh3KEEJel0WjYt29fc4GMjY2loaEBJycnJk6cyLx58xg/fjy9e/dWOqpoJ1qNht0jRjCmrIx9S5YwspOUSQCVlxfOqanUV1Zi2Y4nJ1n37o3p//6H/bXXsn/UKIKOHNH5VkVC6CuZoRRCXFBRUVHzk9jbt2+nrKwMa2trIiIimmchPT095Ta2kYiaNImI334j9t57CV2/Xuk4LbJv9WqGPfEEeR10K3r3k08y5t13ib7tNsI3bGj38YTQB1IohRAAVFdXEx0d3TwLmZWVhYmJCf7+/s0FMigoCAsLC6Wjig62+/HHGfPee0SFhhIRE6N0nBYry8zE3seHxOeeY/SyZR0yZrSvL6MzMjj82Wd433NPh4wphJKkUAphpJqamtizZ09zgdy9ezeNjY3079+fiRMnMnHiRMaNG4ednZ3SUYWC9q1ejecTT5Di6kpwbm6nvIWr1WioNDUlfeJEIn7/vUPGrK+sJLdvX3rW1WGdk8NVgwZ1yLhCKEUKpRBGpKCgoLlA7tixg/Lycrp168bYsWOb94R0d3eX29gCgMNbtmB3ww3k9+zJ0MJCvd1r8kpk2thwxtGR0NzcDhuzKD6erqGh5Nnb43/8OCozeWxBGC4plEIYsMrKSnbt2tW8J2Rubi4qlYrAwMDmAjlq1CjMzc2Vjir0TOn+/dSPHEmdqSm9c3Lo4eKidKQ2iXN1pUdZGT5VVR06bsqrrxKwaBFRV19NxG+/dejYQnQkKZRCGBC1Wk1KSkpzgUxMTKSpqYlBgwY1r4McO3YsPeV4OHEJNaWlFAwciG1dHU1xcTgFBSkdqc2iJkzAd+dObDWajh87JITQ+HgyVq1i+FNPdfj4QnQEKZRCdHKHDx9uLpA7d+6koqKCHj16MH78+OZZSFdXV6Vjik5CXVfHHhcXvEpLOf7NN3hOn650JJ1ImDOHoFWrOJ2djd3gwR06dlNDA/v79MGpshLS0+nt69uh4wvREWRBhxCdzNmzZ9m5c2fzWsj8/HzMzMwYPXo0zzzzDJGRkfj7+2Mm67VEC2k1Gnb7+zOmtJS9r72Gv4GUSQC7MWNg1SqO79zZ4YXS1MICp+hoNH5+nAgPx/bECcxk039hYGSGUgg919jYSFJSUnOBTE5ORqPR4OHh0TwDGRERQfd23LBZGIeo664j4tdfibn7bsK++ELpODp1rrycLnZ2xM6cSeinnyqSYd/q1Qx54gnigoKI2L1bkQxCtBcplELoGa1WS25ubnOB3LVrF1VVVdja2jJhwgQiIyOJjIzEpZM/JCH0y1+bcUcFBxMRF6d0nHZRZGZGnr8/EYmJimWIuvZaIrZuJXnhQgJffVWxHELomhRKIfTA6dOn2bFjR3OJLCwsxNzcnODg4OaHafz8/DA1NVU6qjBA+957D8/HHyd1wADGHD7cKfeavBJpdnY0WloyurhYsQwatZoUZ2c8SkupiYnBOSREsSxC6JIUSiEUUF9fT0JCQnOBTEtLQ6vV4u3t3Vwgw8LCsOnE+/6JziF/61auuu46jvbogXdBQbueda20aF9fnHNyGFRfr2iOs0eOUOXhQZWFBYNOnDDoP3NhPKRQCtEBtFotWVlZzQUyKiqK2tpa7O3tmTBhAhMnTmTChAk4OzsrHVUYkbLMTM75+dFgakqvrCx6DhyodKR2FT1tGkGbNqE6d07xh2KyvvoK17vvJtHHh/D9+xXNIoQuSKEUop2Ulpayffv25i19iouLsbS0JCQkpPlhmmHDhqEy0NuLQr/VlJZy1NWVXufO0RgTg3NwsNKR2l3aG28wct48CnbswGXcOKXjEHPHHYR9++35s9LffVfpOEK0iewrIoSO1NXVERcX11wg9+7dC4Cvry/Tp08nMjKS0NBQunbtqmxQYfSaGhrIHDaMITU1HPvqK7yMoEwC9AkNBaA0NlYvCmXoV18Rn5DAsNWrOTxxIoMmT1Y6khCtJjOUQrSSVqslIyOjuUDGxMRQV1eHg4ND8zrICRMm4ODgoHRUIZppNRpihg8nJCODPS+/TMCiRUpH6jAatZo6c3OSp0whYvNmpeMAUF1SwkkXF7QmJjgWFmLdu7fSkYRoFSmUQrTAiRMn2LZtW/M/J0+epEuXLoSFhTXfxh46dCgmJiZKRxXigqKmTCHip5+IufNOwr76Suk4HS6nSxdODhxI2MGDSkdpdnjLFvpcfz17Bw4kOC/PYJ+yF4ZNCqUQl1BbW0tMTEzzLGRmZiYAfn5+zQUyODgYKzn1QnQCfx0/GDV6NBEJCUrHUURCv35YVVfjd+aM0lH+If7RRwn+4AOjLfqi85NCKcTfaDQa9u7d21wg4+LiaGhowMnJqblAjh8/nt5yW0p0Mvs//BCPRx4hzcWFoLw8VEZ6NGdUSAiDExJwbGpSOsp/xAwdSuCBAxz56iu87rxT6ThCtIgUSmH0ioqKmgvk9u3bOXXqFNbW1kRERDSvhfT09JTb2KLTOvL773SfNIlj3bvjVVho1Psexj30ECFr1lB1/Djd+vZVOs4/1FdWctjREZuGBrofOmTw2zgJwyKFUhid6upqoqOjm/eEzMrKwsTEBH9//+YCGRQUhIWFhdJRhWizsgMHqB0+HLVKhV12ttGXlAPr1zPkvvvI+vJLvO66S+k4/3EsJoZuERHk9OlD4PHjsp5SdBpSKIXBa2pqYs+ePc0Fcvfu3TQ2NuLi4tJ8G3vcuHHY2dkpHVUInao9dYr8AQPofe4c9bt20S8sTOlIiqsoLKSHiwvxjzxC8PvvKx3ngpIXLiRw8WKirruOiC1blI4jxBWRQikM0tGjR5ufxN6+fTtnzpyhW7dujBs3rnkW0s3NTW5jC4PV1NBAyoAB+Jw4QcHnn+M9Y4bSkfRGqakpB0NDiYiKUjrKRUWNHk1IUhIHVq9m2GOPKR1HiMuSQikMQmVlJbt27WpeC5mbm4tKpWLUqFHNBTIwMBBzc3OlowrRIaL9/AjZu5e0hQsJfPVVpePolX09elDTsydjCgqUjnJR6ro6Mh0ccKiqwnTfPuyHDlU6khCXJIVSdEpqtZqUlJTmApmYmEhTUxODBg1qLpBjx46lZ8+eSkcVosNF33QT4Zs3E33bbYRv2KB0HL0T4+mJfWEhXrW1Ske5pJN792IyciRFPXowrKQEU1nXLfSYFErRaRw+fLh5HeTOnTupqKigZ8+ejBs3rnktpKurq9IxhVBUwnPPMWr5cmICA4lISlI6jl6KmjwZ/19+wbqpSe8feklfuRLfZ54hNjSUiJgYpeMIcVFSKIXeOnPmDDt37myehTxy5AhmZmaMHj26uUD6+/tjZqT76Qnxbxkff4zbgw+S3q8fo/PzjXavycv566GXE8nJOAYEKB3nsqImTiRi2zZSX3sN/xdfVDqOEBckhVLojcbGRhITE5sLZEpKChqNBg8Pj+YCGRERQXcj3kNPiIs5um0b3a6+mqJu3RhcUICVLPe4qCO//87Aa64hffly/J55Ruk4l6VRq0nr2xfXU6eoi4/HKShI6UhC/IcUSqEYrVbLoUOHmgtkVFQUVVVV2NraMmHCBCIjI4mMjMTFxUXpqELotdM5OVT5+KAxMeGqgwe5atAgpSPptcbaWrC2JuH22wn79lul41yR8txcar28OGtlhUdJCRY2NkpHEuIfpFCKDnX69Gl27NjRvBaysLAQc3NzgoODmx+m8fPzw9TUVOmoQnQK58rLyXNxwaGmhnM7d9I/IkLpSJ3CEQsLCocMITw9XekoV+zA+vW433cfCcOHd6rcwjhIoRTtqr6+nt27dzfvCZmWloZWq8Xb27v5NnZYWBg28tu2EC3W1NBAysCB+BYXc/Szz/C+5x6lI3UaSQ4OqJqaCCgrUzpKi0RPm0b4pk0kPP00QStXKh1HiGZSKIVOabVaDh482HwbOzo6mtraWuzt7ZtvYUdGRuLk5KR0VCE6vaiRIwnds4fU+fMZ9frrSsfpVKICAnDdu5f+jY1KR2kRrUZDwoABDD12jNO//cbAq69WOpIQgBRKoQOlpaVs3769+TZ2cXExlpaWhIaGNt/G9vX1RaXn23MI0ZlET51K+A8/nJ+x+u47peN0OjEzZhDy5Zc0VlRg2cke9KsqLqZs4EDUKhXOx47RtVcvpSMJgewpIVqsrq6OuLi45gK5d+9eAHx9fZk+fToTJ04kJCSErl27KhtUCAOVNG8eoT/8QNTIkURImWyVHgEBqL78kmO7duE2ZYrScVqkW9++lGzYgPPNN5MWFERobq7SkYSQGUpxeVqtloyMjOYCGRMTQ11dHQ4ODs0zkBMmTMDBwUHpqEIYvMx16xg0axZ7nZwYdfSo7DXZSmWZmdj7+JD43HOMXrZM6TitEvfAA4R88gmx995L6Pr1SscRRk4KpbigEydOND9Is23bNk6ePEmXLl0ICwtrfphm6NChmJiYKB1VCKNRsHMnXSdM4ISNDR6FhbLXZBtoNRoqTU1JnziRiN9/VzpOq8V4eRGQnU3hxo0MvvVWpeMIIyaFUgBQW1tLTExM88M0mZmZAPj5+TUXyODgYKysrBROKoRxOp2TQ6WPDwA9DhzA1t1d4USdX6aNDWccHTv1LeO6s2c52rcvVmo1V+Xl0aN/f6UjCSMlhdJIaTQa9u7d21wg4+LiaGhowMnJqblATpgwAXt7e6WjCmH06s6e5VD//jhWV1O7fTsu48YpHckgxLm60qOsDJ+qKqWjtEnBzp30HD+erL59GXXsmN6fTy4Mkyy+MSJFRUXNBXL79u2cOnUKa2trIiIiWLZsGRMnTsTT01NuYwuhRzRqNek+PgyrqiJ/7VqGSpnUGbWrK05Hjyodo81cxo0j6YUXGP3GG+ef/v/f/5SOJIyQzFAasOrqaqKjo5sfpsnKysLExAR/f//mWcigoCAsLCyUjiqEuIiogADCUlNJeeEFRi1dqnQcg5IwZw5Bq1ZxOjsbu8GDlY7TZlH+/gSnpZH90Uf4PPig0nGEkZFCaUCamppIS0trnoVMSEigsbERFxeX5gI5btw47OzslI4qhLgCf52KEn3zzYR//73ScQzOoU2b8Jg2jf0ffIDvww8rHafNGmtryXJwwL6mBvPMTHp5eSkdSRgRKZSd3NGjR5sL5I4dOzhz5gzdunVj3LhxzVv6uLm5yW1sITqZpAUL8F+yhNgRI4hIS1M6jkE6V15OFzs7YmfOJPTTT5WOoxMnUlMxCwykwNYWv+JiTOUOlOggRl8oa+rVHD1dQ4Nag4WZigF21lhb6u/S0srKSnbt2tV8Gzs3NxeVSsWoUaOaC2RgYCDm5uZKRxVCtNLBzz9nwL33sr9vXwILCmSvyXZUZGZGnr8/EYmJSkfRmT3LljH8+eeJiYggYtcupeMII2GUhTL3ZBVfJxWyK6eUwvJa/v4HYAL0t+3K2MG9uXNUf9z7dFMqJgBqtZqUlJTmApmYmEhTUxODBg1qLpBjx46lp+xHJ4RBKIyKosu4cZRYW+NWUEAXW1ulIxm0NDs7Gi0tGV1crHQUnYoaN46wXbtIX7qUkS+8oHQcYQSMqlAeK69l/v8yiM07hanKhCbNxb/1v14PdevFkpt86GfbcccIHj58uLlA7ty5k4qKCnr27Mm4ceOa10K6urp2WB4hRMc4c/gwZ7y9UWm12OzbJ2vgOkC0ry/OOTkMqq9XOopOadRq9jg4MKC8nMakJBwDApSOJAyc0RTKDSmFLPrpAGqN9pJF8t9MVSaYqUx45YYh3B7QPhvGnjlzhp07dzavhTxy5AhmZmYEBQU1z0KOHDkSM7ntJYTBqjt7lhwXF5yrqqjetg2X8eOVjmQUoqdNI2jTJlTnzmFmYAc3nM7JoX7IEE517YpXSQnmXTtuYkQYH6NoKO/tymX5H4da9dmmPwvoCz9kcKq6nsfGtv10isbGRhITE5tnIVNSUtBoNAwePJjrrruOiRMnEhERQbduyt5uF0J0DI1azR5fX/wqK8lbswYfKZMdxmbECCw2baJg926D2zDebvBgMj74AK8HHyQ+LIyI1FSlIwkDZvCFckNKYavL5L8t/+MQ9jaW3NbCmUqtVsuhQ4eaC+SuXbuorq7G1taWCRMmMHv2bCIjI+kvR2YJYZRigoMJO3aMpGefJeiBB5SOY1T6hIUBUBoba3CFEsDngQeI3rqViM2bSZw7l9HLlikdSRgog77lfay8lgmroqlXa/7zmqbhHJVJP1BfnEPDiUNo6qqxu/YpbHwnXPKalmYqtj8dftk1ladOnWLHjh3Nt7GPHTuGubk5wcHBzesg/fz8MDU1bdP3KITo3GKmTydswwaib7xRTjhRgEatps7cnOQpU4jYvFnpOO1Cq9GQ2L8/3sePc3b7dllOIdqFQc9Qzv9fBuqLrJfU1FZSEf8tpt3tMe89kPrCjCu6plqjZf7/Mvjy/lH/+Hp9fT27d+9uLpB79uxBq9Xi7e3N1KlTiYyMJCwsDBsbmzZ/X0IIw5D80ksEb9hA9LBhUiYVojIz45iVFapDurmTpY9MVCqGJCZy2tWV+uuv51xRkeweIHTOYAtl7skqYvNOXfR1UxtbnB/7ElObq6g/kUvJ509f0XWbNFpi806Re7KKhlOFzQUyOjqa2tpa7O3tiYyM5LHHHiMyMhInJyddfUtCCANy8IsvGPLaa6Q4OhKSnKx0HKNW3qsX3U6cUDpGu+ru7EzJN9/gMm0aKWPGEJadrXQkYWAMtlB+nVR4ya2BTMzMMbW5qnUX12oY9+Aiin5chaWlJaGhoSxatIiJEyfi6+uLSqVqQ3IhhKEriouj18yZHLG2xnf/fjnNRGH1Li4MSEhQOka787jlFmJnziRs/XriZs8m5JNPlI4kDIjBFspdOaUt2h6oRUxUdPcM4vdHriE0NJQuXbq0zzhCCINz9sgR6sePp0mlok9SEl179VI6ktEzGzoUx/h4qoqL6da3r9Jx2lXop58SGxfHyLVrOXTNNXhMnap0JGEgDHIqrbpeTWF5bbuOUWvSleDwcVImhRBXrL6ykqN+flzV2IhmyxbshwxROpIArhp1fk180c6dCifpGCN37+aYlRXm06dTWVSkdBxhIAyyUBacrqG9H13XAkdP17TzKEIIQ6FRq0n19cWzooKi999n4NVXKx1J/Mn5z6eey43gtjdA1169sPzpJ+waG8kcMwat5r87oQjRUgZZKBsusE1QZx5HCNH5xYSFEVxQQPrTT+P78MNKxxF/06N/f0pVKhoPHFA6SocZEBnJgTlzGHPsGDG33qp0HGEADLJQWph1zLf165afiI6O5vjx42jkNzwhxEXE3HUXEQkJRE2eTNDKlUrHERdwwsYGiyNHlI7RoYJWrCB6+HCCvv+ezHXrlI4jOjmDfChngJ01JtC+t721Wl56+mE0DecA6NKlC4MGDcLNzQ13d3fc3Nya/3F2dpYnv4UwUimvvMKYr78m2seH8B9/VDqOuIgKR0fsCwuVjtHhgmJjyXF0xPbBBykPC8PWve3HCwvjZJCF0trSjP62XSloxwdzXHpZk1N5hiNHjpCXl/ePf77//nuOHj3aPGtpaWmJq6vrBctm//795bQcIQxU1tdf4/Xyy6Q5OBCSmoqJ/GKptzRubvTLyUGr0RjV/04WNjbYbd+OZVAQh4OD6VlcjMrMIKuBaGcG+7dm7ODefJlUcMmtgyrTfkZTV0NTdTkA5/KSUVed3wy9+8jrUVlZX/BzpioTxnr0xtLSEk9PTzw9Pf/znoaGBgoKCv5TNn/++WeOHDmCWq0GwNzcnIEDB16wbLq4uGBubt7WPwohhAKK4uOxnTGDAmtrhu7bJ3tN6rmufn7Y/PILJ9LScAwIUDpOh+o7ahSpr77KyIULiZk0iYht25SOJDohgz3LO/dkFZFvx1zyPUUf3EdTZekFX3N6aB1mPftc9LPbnw7DrXe3VmVTq9UUFhb+p2zm5uaSn59PQ0MDAKampgwYMOCCZXPgwIFYyA8oIfRSRUEBZYMHY9HURJf0dOyHDlU6kriM/K1bcb32WtKXL8fvmWeUjqOIqLAwQmNj2W/Efwai9Qy2UALcvS6J3fmndbrBuanKhDGudv85y1tXmpqaKCoqumDZPHz4MHV1dQCoVCr69+/fXDD/XjhdXV2xsrJql3xCiEurr6zkoIsLAyoqOPPLL7hOmqR0JHEFGmtrwdqahNtvJ+zbb5WOo4imhgb2OTjQ7+xZmlJTcRgxQulIohMx6EJ5rLyWCauiqdfh9j6WZiq2Px1OP9uuOrvmldJoNBQXF1+wbObl5VFbe37NqImJCc7Ozhec2Rw0aBBdu3Z8diGMgVajYfegQfgfPUr26tUMe+wxpSOJFjhiYUHhkCGEp6crHUUxZQcOoPb1pdTGhiEnT2ImkxPiChl0oQTYkFLICz9k6Ox6b97sw20B/XV2PV3RarWUlJRctGxWVVU1v7dv374XnNkcNGgQ3bq17ja+EAKiQkKIiI9n9xNPMOadd5SOI1ooycEBVVMTAWVlSkdR1P4PPsD70UeJCwwkIilJ6TiikzD4Qgnw3q5clv9xqM3XeW7iYB4d66aDRB1Lq9VSVlZ2wbKZm5tLRUVF83v79OlzwbLp5uZGjx49FPwuhNBvsffcQ+gXXxB17bVE/PKL0nFEK0QFBDBw715cGhuVjqK4qOuvJ2LLFpLmz2fU668rHUd0AkZRKOH8TOWinw6g1mhbtKbS1ATMTFW8esMQvZyZbCutVkt5eflFZzZPnz7d/N5evXpdtGza2toq+F0IoazUxYsZvnAhu4cMIXT/fqPadsaQxMyYQfCXX6KuqMCye3el4yhKq9GQ5OyMZ0kJlTt30j8iQulIQs8ZTaGE82sq5/8vg9i8U5iqTC5ZLE1NoEkL3WuL+WXRnYqsmdQHZ86c4fDhwxcsm6Wl//8J+auuuuqiZbNXr16YmJgo+F0I0X5yNm6k7+23k9W7NyMKCmTNWSe2b/Vqhj3xBHmbN+M2ZYrScRRXUVDAWTc3as3NGVhcjFXPnkpHEnrMqArlX3JPVvF1UiG7DpVSeLr2HyfqmAD97boy1qM3NiXpPPfg3cTFxREcHKxUXL1VWVl50bJ54sSJ5vd17979omWzT58+UjZFp3U8IQHTkBDKraxwOXIE6969lY4k2qAsMxN7Hx8Sn3uO0cuWKR1HL2R/+y0D7riDZG9vwozorHPRckZZKP+upl7N0dM1NKg1WJipGGBnjbXl+f3eNRoNgYGBqFQqEhMT5fjEFqipqblo2SwqKmp+n7W19UXLpqOjo/yZC71VUVBA6eDBdFGrsdizh96+vkpHEm2k1WioNDUlfeJEIn7/Xek4eiNmxgzCvvyS+IcfJviDD5SOI/SU0RfKy4mNjSUsLIwvvviCu+++W+k4BuHcuXPk5+dfsGwWFhby119JOR9d6KuG6moy+/fH9exZTv/0E4MmT1Y6ktCRTBsbzjg6Epqbq3QUvaHVaIh3d8cvP58TP/6I2w03KB1J6CEplFdg2rRpJCQkkJOTg7X1hY9jFLpRX1//n/PR/yqbcj660Ad//XANyM8n+913Gfb440pHEjoU5+pKj7IyfP621ZqAmtJSivv3R6XV0qegABsHB6UjCT0jhfIK5Ofn4+Xlxfz581m0aJHScYzWhc5H/6tsyvnooqNEhYcTERPD7sceY8zq1UrHEToWNWECvjt3YqvR3YEYhiL/11+xv+469ru4MCY/X3YzEP8ghfIKPf/887z33nscOnQIJycnpeOIf7nQ+eh/lU05H13oSuzMmYR+9hlRV19NxG+/KR1HtIOEOXMIWrWK09nZ2A0erHQcvbP7iScYs3o1MUZ8RKW4MCmUV6iyshI3NzcmTZrE559/rnQc0QIXOh/9r7J5sfPR/1025Xx0kbZ0Kb7z55Pg5UVoZqbMzhioQ5s24TFtGvs/+ADfhx9WOo5eivb1ZXRGBoc//xzvGTOUjiP0hBTKFlizZg0PPfQQycnJBAQEKB1H6MCFzkf/q2zK+ejiLznffYfjbbeRY2+PX2Gh7DVpwGpPnaKrvf352ehPP1U6jl6qr6wkr29fetTVYZ2Tw1WDBikdSegBKZQtoFarGTFiBN27dyc2Nlb2TzRwrTkf/UJlU85H79xOpKTA6NGctbSkX36+PIxgBIrMzMjz9yciMVHpKHqrKD4e69BQcu3t8T9+HJWZmdKRhMKkULbQ9u3biYyM5LvvvmPatGlKxxEKac356P8um3I+uv6rKCzk5ODBdG1sxDw1lT7DhysdSXSANDs7Gi0tGV1crHQUvZby8ssEvPIKUddcQ8TWrUrHEQqTQtkK119/PZmZmWRlZcm6OvEfFzof/e+30eV89M6hsbaW/f364VZeTpkcxWdUon19cc7JYVB9vdJR9F5UcDChu3eT8fbbDH/ySaXjCAVJoWyFnJwchg4dyuLFi3n++eeVjiM6mX+fj/73snmh89EvNLMp56O3XM3ps5SkZaA+V4dZFyscRvpgbdfzgu/VajTEDR7MqLw8DqxYgd+cOR0bVigqeto0gjZtQnXunKyXvQx1XR0Zjo70razEJD1dTowyYlIoW+nJJ59k/fr15Obm0qdPH6XjCAMh56PrVkFMCiXL3sY5MQrH08X8/blsDXDCri9FoyNwmPsULmH//0G7qLFjiYiKkqPmjFTa0qWMnD+fgh07cBk3Tuk4eq90/360fn4Ud++Oz4kTUsKNlBTKViovL8fNzY1bbrmFjz/+WOk4wgjI+ehXrjj9IKfvmonPwWTUJirMtBffpPqv1zO8A7H7aj35H6wiZO1aoiIjifjjjw5MLfRFUXw8ziEh59cIymEWV2TvO+/g89RTxI4ZQ0R8vNJxhAKkULbBu+++y9NPP016ejq+Ms0vFPTv89H/fhvd2M5HT17wJr7LXsKsSX3JIvlvahMValMz9vZxo8GmidCDB2WvSSOlUaupMzcnecoUIjZvVjpOpxE1aRIRv/1GyqJFBLz8stJxRAeTQtkGjY2N+Pj44OzszLZt2+Q2o9BLxnQ+esL9cwj6dBVaoDX/Nv71ud33PMGYz97RbTjRqeR06cLJgQMJO3hQ6SidhkatJtXJCfeyMmpiY3EODlY6kuhAUijbaMuWLVx//fX89NNPXH/99UrHEaJFDOl89OQFbxK45AXdXm/xXJ1dT3QuCf36YVVdjd+ZM0pH6VTOHD5MtacnlZaWuBUXY9m9u9KRRAeRQtlGWq2WiRMnUlhYSEZGhpwFLQxGZzofvTj9ILaBfliqG/4zM3kAeBlIA0qAroA38BxwsV8BtUC9mQXlyen09fNup9RCn0WFhDA4IQHHpialo3Q6B7/4gkH33EOijw/h+/crHUd0ECmUOpCRkcHw4cNZsWIFTz31lNJxhGh3/z4f/e9rNpU4Hz1jyCi8slIvuGbyV+BdIAjoC9QC3wOxwBrggYtcU22iIsvLH58DSTrJKDqXuIceImTNGqqOH6db375Kx+l0YqZPJ2zDBnY//jhj3n1X6TiiA0ih1JGHHnqIjRs3kpeXh52dndJxhFBMR5+PXhCTgkt4YIsyNgEjgTog+3LXj03BJcS/RdcXnd+B9esZct99ZH35JV533aV0nE5Hq9Gw29WVYQUFlP7yC67XXqt0JNHOpFDqSGlpKe7u7txzzz28K7+NCXFBFzof/e9lszXnoydNvpORv25o0RPdcP52dwrnb4NfjNpERdq1tzNqy9ct+0ZFp1dRUECPAQOIf+QRgt9/X+k4nVJ1SQknXVzQmJjQt7AQ6969lY4k2pEUSh1atmwZ8+fPJzMzE09PT6XjCNGpXOh89L/K5qXOR39l81b6nz152evXAOeACuAnzq+hvA24XFUssnPC+VTRZd4lDFGZSsWBsDAioqKUjtJp5f30E45TppDu6kpwbq5sxWXApFDqUH19PV5eXnh7e7Nlyxal4whhMP59Pnrzhu55+exMSuBKfkQ9xPk1kwAq4GbgY+Cqy3xOA5w7deaixzQKw7WvRw9qevZkTEGB0lE6tfhHHiH4ww+Juftuwr74Quk4op1IodSxTZs2MW3aNH7//XcmTpyodBwhDNrhP2IZdHXYFb03GygCioHvAAvgQ+BKDk49/HsMgyaGtjam6KRiPD2xLyzE6891v6L1YoYMIfDgQY589RVed96pdBzRDmTuWcemTp1KaGgoc+bMad7DTwjRPtTn6q74vZ7ABGAGsAWo5vw6yiv5jbol4wjDoXFzo9+5c2g1LVufK/4rMD6eI1260PXee6mQGV+DJIVSx0xMTFi1ahUHDx5k7dq1SscRwqCZdWn9tkO3cP6hnEPtPI7ovLr6+WEDlKSlKR2l07Pq2ROb336jR1MT2aNHS0k3QFIo28HIkSOZMWMGL7300j8eJBBC6JbDSB9a+2Pp3J//93L/hmqA/CUvs++992iorm7laKIz6jVmDAAlMTEKJzEM/cLCyJk3j1ElJUTfcIPScYSOyRrKdlJcXIy7uzuPPvooy5YtUzqOEAbreC8nnE4XX/T1UuDfm5U0AqOBrD9ft7nE9Y/16E33ilJ6cH5T9CxbW6pGjqTXrbcy+I47ML/C/TJF59NYWwvW1iTcfjth336rdByDETVqFCHJyRx8/318H3lE6ThCR2SGsp307duX559/nrfffpvDhw8rHUcIg1U0OgK1ycX/U/YgMB54BVgLLAZ8gT1//v+XKpNqExXFIROwqa8n68svSb7uOjSmpozcto2hs2dTZ21NSu/eRF13HQe/+IKmP4+jFIbBvGtXiszN0WZfbvt70RIh0dEc6N4d+8cfp+zAAaXjCB2RGcp2VFtby+DBgwkMDOT7779XOo4QBulyJ+VsANYBGcBpoBvnT8l5HLiSm24XOimnsbaWnG++4dT//R/dUlPxKi+nK+dvn2c7OFA3ejQO06fjfvPNqMzMWveNCb2Q5OCAqqmJgLIypaMYlJI9ezD19+dYz54MKynB1MJC6UiijaRQtrOvv/6au+66i6ioKMLDw5WOI4RButRZ3q3VkrO8G6qryf7iC8o3baJHejpeZ89iBZw2MSHX0ZH6MWNwuvtuBk2eLBs7dzJRAQEM3LsXl8ZGpaMYnPSVK/F95hliQ0OJkHWqnZ4Uynam0WgICgpCrVaTkpKCSn6YCKFzxekHsQ30w1LdgIkOrqcF6s0sKE9Op6+fd4s/X3f2LFmffkrF5s3Y7t2LV1UV5kCpSkWekxPq0FD6zZjBgMhIKZh6LmbGDIK//BJ1RQWW3bsrHcfgREVGErZ9O3teew3/F19UOo5oA/kvWTtTqVSsWrWKPXv28IWcECBEu+jr583+ua/qpEwCmAD7n3+tVWUSzm+R4jdnDhExMfhWVtJw8iRpS5ZwMDCQ7uXljPnmGwZecw0l5ubEuboSO3Mmx2SGRi/1CAjAFDi2a5fSUQxS2NatpNnbM/CllyhOuvzdAKG/ZIayg9x+++3ExMRw6NAhbGwu9RiAEKK1Eu6fQ9Cnq9BCq8rlX5/bfu10JvzyjW7D/U1lURE569ZR8/PP9Dl4kMHnzqECikxNOTJwICZjx+J6//30HTWq3TKIK1OWmYm9jw+Jzz3HaNmxo12U5+ZS6+XFmS5dGHziBBbyM7JTkkLZQQoKChg8eDDPPfccr732mtJxhDBYyQvexHfZS5g1qVu0plJtokJtakaMsydDjx3AbM8eevv6tmPS/+/skSPkfPIJdVu34pidjUfd+ZN5jpqbUzhoEGYTJuA2e3aH5RH/n1ajodLUlPSJE4n4/Xel4xiszHXr8Jg1i4ThwwlPT1c6jmgFKZQdaMGCBaxcuZKcnBz69++vdBwhDFZx+kFO3zUTn4PJqE1UlyyWf72e4R2I3VfrUWnrICCAUmtrBhcVKbJu7lRWFrlr19Lwxx84HzrEoD+3IzpsYUGRhwcWEyfi8cAD2A0e3OHZjFGmjQ1nHB0Jzc1VOopBi77lFsK//56EOXMIWrFC6TiihaRQdqCqqio8PDwYN24cX3/9tdJxhDB4BTEplCx7G6fEaPqePv6PReMaoNjOieOjw3F44el/bA2UuXYt7rNnkzR4MKEHDyr+4MzJvXs5vG4d6u3b6X/4MAP+fOL4kJUVJwYPxmrSJAY/8AA9Bw5UNKehinN1pUdZGT5VVUpHMWhajYaEAQMYeuwYp3/7jYFXX610JNECUig72Lp165g1axaJiYmMkvVRQnSYmtNnKUnLQH2uDrMuVjiM9MHarudF3x83axYh69YRPW0a4d9913FBr0BxUhL569ej3bmTgfn5ODc1oQFyunThpLc31pMnM3jWLLo7Oysd1SBETZiA786d2Mr50+2uqriYUwMG0GhqivOxY3Tt1UvpSOIKSaHsYE1NTfj7+2NlZcXu3bsxMdHVc6lCCF2LHjGC4PR0MletYvhTTykd56KOxcRwdP16TKKjGVRQgKNGgxrItrbmlI8P3W64Ac/778e6978PoRRXImHOHIJWreJ0drYsM+gAh77/HudbbiHN3Z3QQ4eUjiOukBRKBezatYtx48bxzTffMH36dKXjCCEuQl1Xxz4nJwacOUNtVBT9wsKUjnRZWo2Ggh07KPzsM8xiY3EvKsJeq6UByO7WjfJhw+hx00143XcfVj17Kh23Uzi0aRMe06ax/4MP8H34YaXjGIW42bMJWbuW2HvvJXT9eqXjiCsghVIhN910E2lpaeTk5NClSxel4wghLuLM4cNUenpSZ2aG05Ej2Dg4KB2pRbQaDYe3bOH4V19hsXs3g4uLsdVqqQOyevakws+Pq26+Ga9775XtWi6i9tQputrbEztzJqGffqp0HKMR4+lJQE4OhRs3MvjWW5WOIy5DCqVC8vLy8Pb2ZtGiRSxYsEDpOEKIS8j78Uf63HgjmU5OjDp6tFOfz61Rq8n94QdKvv0Wq8REPEtK6AHUAlm2tlT5+9Nr2jQ877oLMysrpePqjSIzM/L8/YlITFQ6itE4V15OgbMzVmo1V+Xl0UN2R9FrUigV9Mwzz7BmzRpyc3NxdHRUOo4Q4hKS5s9n1NKlRI0bR8SOHUrH0ZmmhgYOffcdJ7/9FuuUFDzLyugGVAHZ9vbUBATQZ/p0PG69FVMLC6XjKibNzo5GS0tGFxcrHcWoFOzcSc/x4zno5MTowkLFd1wQFyeFUkFnz57Fzc2NKVOmsG7dOqXjCCEuI2r8eCJ27iTphRcYtXSp0nHaRWNtLTnffMOp//s/uqWm4lVeTlfgrIkJOX36UDd6NA7Tp+N+882deqa2paJ9fXHOyWFQfb3SUYxO4vPPM3rZMqJvvJHw//1P6TjiIqRQKuz999/n8ccfJzU1lREjRigdRwhxCRq1mqQBAxh6/DglP/yA+003KR2p3dVXVpL9xRec+eEHeqSn43X2LFbAaRMTDvXtS8OYMTjddReDJk826Nmj6GnTCNq0CdP6eqOeqVVKlL8/wWlpZK9Zg88DDygdR1yAFEqFqdVqhg0bhr29Pbt27ZJthITQc9UlJRwfOBArtZru2dlcNWiQ0pE61LnycrI/+4yK//0P23378KqqwhwoVanIc3JCHRZG/3vuwWX8eIMqmGlLlzJy/nwKduzAZdw4peMYncbaWrIcHOhVW4vlgQOyfZMeMpx/2zspMzMzVqxYQXR0NJs3b1Y6jhDiMmwcHOj6++/YNDWRHxiI+s9zt41FF1tb/ObMISI2Ft/KShpOniRtyRIOBgbSvbycMV9/zYCJEykxNyfO1ZXYmTM5FhOjdOw26/PnllGlsbEKJzFO5l27Yr9jBxYaDUeCg2n68zhSoT9khlJPTJo0idzcXA4cOIClpaXScYQQl5G+ciU+zzxDvJ8f4Xv2KB1Hb1QWFZGzdi01W7bQ5+BBBp87hwooMjXliKsrJuPGMej++3EMCFA6aoto1GrqzM1JnjKFCPnlXzFpb7yB37x5xIwdS8TOnUrHEX8jhVJPHDx4EF9fX958802eeeYZpeMIIa5A9K23Ev5//0fc/fcTsnat0nH00tkjR8j5+GPqtm7FMScHjz9ndI+am1M4aBBmkZG4zZpFb19fhZNeXk6XLpwcOJCwgweVjmLUosaOJSwqivSlSxn5wgtKxxF/kkKpRx599FG+/vprcnNzsbe3VzqOEOIytBoNsd7ejMrJIfeTTxg6a5bSkfTeqawscteupeGPP3A+dIhBf966PGxhQZGHBxYTJ+LxwAN6uUYuoV8/rKqr8TtzRukoRq2poYH0vn0ZUF5OY1JSp5vtNlRSKPXIqVOncHNz44477uCDDz5QOo4Q4go0VFeT3bcvvWtqICUFB9mtoUVO7t3L4XXrUG/fTv/DhxnQ2AjAISsrTnh6YjVpEoNnz6bnwIEKJ4WokBAGJyTg2NSkdBSjdyori8ahQymztsarpATzrl2VjmT0pFDqmZUrV/Lcc8+xf/9+hgwZonQcIcQVKMvMpHH4cMqtrHArKpIzstugOCmJ/HXr0O7axcAjR3BuakLDn7ebvb2xvv56PGfPplvfvh2eLe6hhwhZs4aq48cVGV/8U8aaNXg+9BDxI0cSkZqqdByjJ4VSzzQ0NDBkyBAGDRrEb7/9pnQcIcQVyvrqKwbcfTdprq4E5+Ya1JY5SjoWE8PR9esxiY5mUEEBjhoNaiDb2ppTPj50u+EGPO+/H+vevds9y4H16xly331kffklXnfd1e7jicuLvukmwjdvJnHuXEa/+abScYyaFEo9tHnzZm666SZ+/fVXJk2apHQcIcQVin/0UYI/+ICoG24g4scflY5jcLQaDUe3bePYF19gFhuLe1ER9lotDUBW9+6cGTaMHjfeiNd997XLLHFFQQE9Bgwg/pFHCH7/fZ1fX7ScVqMhsX9/vI8f56zsEaooKZR6SKvVMm7cOE6ePMm+ffswNzdXOpIQ4gpFjRpFaHIye5csYeS8eUrHMWhajYbDW7Zw/Msvsdy9G48TJ7DVaqkDsnr2pMLPD9tbbsFzxgwsbGx0MmaZSsWBsDAioqJ0cj3RdhWFhZxxc6POzAyXoiK62NoqHckoSaHUU3v37mXEiBG8++67PPbYY0rHEUJcoaaGBvY4O+N+6hQV27bhMn680pGMhkatJveHHyj59lusEhPxLCmhB1ADZNvaUuXvT69p0/C86y7MrKxaNca+Hj2o6dmTMQUFOs0u2ibnu+/of9ttpHh6EpaVpXQcoySFUo/NmjWL//3vf+Tl5XHVVVcpHUcIcYUqCgood3dHY2KC/eHDdHd2VjqSUWpqaCBnwwZKN27EOiUFz7IyugGVQI69PTWBgfS5/XY8br31is/njvH0xL6wEK/a2nbNLlouduZMQj/7jLjZswn5+GOl4xgdKZR6rKSkBHd3d2bPns3KlSuVjiOEaIH8rVuxu/Zasvv0IaCoCJWZmdKRjF5jbS0533zDqe++o1taGl7l5XQFzpqYkNOnD3WjR+N455243XjjRf/3ipo8Gf9ffsG6qUkevNJDse7ujMzLo2jTJjymTlU6jlGRfxv0mIODA/PmzeO9994jNzdX6ThCiBZwnTSJQy+/TMDJk8RERCgdR3D+POihs2YR8ccfjDx9GtOKCvatXs3eiAgs6uoYvXkzHtOmccbCggRnZ6JvvZW8n35Cq9E0X6Ornx82QIkct6mXRiYkUGRlhfn06VQVFysdx6jIDKWeO3fuHJ6envj5+bFZzo8VotOJmjSJiN9+Y/dTTzFm1Sql44hLOFdeTvb69VRs3oztvn14VVVhDpSqVOQ5O6MODaWrtzf+CxaQvnw5fnJMrl468vvv2F1zDZn9+hF09KjMJHcQKZSdwIYNG5g+fTo7duxgnGyJIESnotVo2O3qyvCCAoo2bGDwbbcpHUlcoZrSUrLXraPqxx/plZmJV00NpoAWOGhtTfmttzLwvvtwDglROqr4l4Q5cwhatYroW24h/P/+T+k4RkEKZSeg1WoJDg6mpqaGPXv2YGpqqnQkIUQL1J46RUH//nRraMAqI4NeXl5KRxKtUFlURM7atbi9+ipqwE6rRQUcMzPj6MCBmIwbx6D775ezpfVE9PDhBO3bx6G1axl6//1KxzF4Uig7ieTkZEaNGsUnn3zCrFmzlI4jhGih4qQkzIOCON69O0OKi+Xs4U4sycEBVVMTbomJHFq7lrqtW3HMycGjrg6Ao+bmFA4ahFlkJG6zZtHb11fhxMapobqaQw4O9Kyro2tWFrbu7kpHMmhSKDuRu+66i+3bt5Obm0u3bt2UjiOEaKH9H3yA56OPkjhkCGGZmUrHEa0UFRDAwL17cWls/MfXT2Vlkbt2LY2//45Tbi6DGhoAOGxhQZGHBxbXXIPHrFnYDR6sRGyjdDwhgS7BwRzu1YuRxcWy20I7kpWqncjSpUuprKxk6dKlSkcRQrSC7yOPkHj33YQdOEDMnXcqHUe0ksrLC2e1mvrKyn98vZeXF0ErVhCWmcmg+npOpqez+7HHOO7qiktODkHLl2Pn6UlOly5E+/mRNH8+FbJBertyCgoi/9VXGVlWRsy11yodx6DJDGUn89JLL7Fs2TKys7MZMGCA0nGEEK0Q7evLmIwMDq5ezTA5CavT2bd6NcOeeIK8zZtxmzLlij9XnJRE/rp1aHftYuCRIzg3NaEBcrp04eSQIVhPnozn7Nl069u3/cIbqaiwMEJjY9m/YgV+c+YoHccgSaHsZKqrq/Hw8CA0NJSNGzcqHUcI0QqNtbVkOjnhXFFBQ3w8TkFBSkcSLVCWmYm9jw+Jzz3H6GXLWn2dYzExHF2/HlVUFK6FhThqNKiBbGtrTvn40G3KFLxmzaJrr166C2+kmhoa2OfgQL+zZ2lKTcVhxAilIxkcKZSd0Oeff869995LXFwcwcHBSscRQrTC6Zwczg0ZQpWFBS6FhVIaOhGtRkOlqSnpEycS8fvvOrvm0W3bOPbFF5jFxuJeVIS9VksDkNW9O2eGDaPHjTfidd99WPXsqZMxjU3ZgQOofX0ptbFhyMmTrT7PXVyYFMpOSKPREBgYiEqlIjExEZVs2ipEp5Tz3Xc433Yb+2QD5k7ngI0N5Y6OhLbTKWZajYbDP//M8a++wnL3bjxOnMBWq6UOyOrZk4oRI7CdOhXPGTOwsLFplwyGaP8HH+D96KPEBQYSkZSkdByDIoWyk4qNjSUsLIwvv/ySu+66S+k4QohWSnjmGYJWriTq6quJ+O03peOIKxTn6kqPsjJ8qqo6ZDyNWk3uDz9Q8u23WCUk4HnyJD2AGiDb1pYqf3963XornnfeKTNvlxE1eTIRv/xC0vz5jHr9daXjGAwplJ3YLbfcQmJiIjk5OVhbWysdRwjRSlFhYYTFxpK6cCGBr76qdBxxBaImTMB3505s/3bOd0dqamggZ8MGSjduxDolBa+yMmyASiDH3p6awED63H47HrfeiqmFhSIZ9ZVWoyHZyYnBJ09SFRVFv7AwpSMZBCmUnVh+fj5eXl7Mnz+fRYsWKR1HCNFKGrWalH798Copoeznnxk0ebLSkcRl/HW03+nsbL3YV7Kxtpbsr77i9KZNdEtLw6u8nK7AWRMTcvr0oS4oCMc77sDtxhtlL0agoqCACjc3aszNGVhcLOtSdUAKZSf3/PPP895773Ho0CGcnJyUjiOEaKWq4mJKXF0x02iwzc2lh4uL0pHEJRzatAmPadPY/8EH+D78sNJx/qO+spLsL77gzA8/0HPPHrwqKrAETpmYkNu3Lw1jxuA8Ywau115rtGt3s77+moF33UWytzdhBw4oHafTk0LZyVVWVuLm5sakSZP4/PPPlY4jhGiDgp076T5hAnl2dow4flxuVeqx2lOn6GpvT+zMmYR++qnScS7rXHk52evXU7F5M7b79uFVVYU5cFKl4rCzM+rQUPrfcw8u48cbVcGMuesuwr7+mviHHyb4gw+UjtOpSaE0AGvWrOGhhx4iJSUFf39/peMIIdog7Y03GD5vHrEBAUQkJysdR1xCkZkZef7+RCQmKh2lxapLSshet47qn3+mV2YmXjU1mALFpqbku7igDQ9n4H334RwSonTUdqXVaIh3c8PvyBFO/PgjbjfcoHSkTksKpQFQq9X4+fnRs2dPYmJiMDExUTqSEKINom+6ifDNm4l/6CGCP/xQ6TjiItLs7Gi0tGR0cbHSUdqssqiInLVrqdmyhT4HDzL43DlUwDEzM44OHIjJuHEMuv9+HAMClI6qczWlpRT3749Kq6VPQQE2Dg5KR+qUpFAaiG3btjFx4kS+++47pk2bpnQcIUQbaDUa4j088D98mPzPP8d7xgylI4kLiPb1xTknh0H19UpH0bkzhw9zaO1a6rZuxTEnB4+6OgCOmJtzbNAgzCIjcX/gAeyHDlU4qW7k//orva+7jn0uLozJzzeq2/66IoXSgFx//fVkZmaSlZWFlexDJkSnVnf2LHnOztidO4dpejq9fX2VjiT+JXraNII2bcK0vt7g17ueysoi95NPaPzjD5xycxnU0ABAnqUlxz08sLj6ajxmzdKLJ95ba/cTTzBm9Wpipk8n7JtvlI7T6UihNCA5OTkMHTqUxYsX8/zzzysdRwjRRiV79kBAAGVdu+Jx/DiW3bsrHUn8TdrSpYycP5+CHTtwGTdO6Tgd6uTeveR98glNO3bgcvgwLmo1ADlWVpR4emI1aRKeDz7Y6XYriPb1ZXRGBoflzkCLSaE0ME8++STr168nNzeXPn36KB1HCNFGmevW4TZrFikeHoRkZcmtOD1SFB+Pc0gIKS+/TICR7wVcnJRE/rp1aHftYuCRIzg3NdEE5HTtSqm3N9aTJ+M5ezbd+vZVOuol1VdWkte3L93r6+l26BA9Bw5UOlKnIYXSwJSXl+Pm5sa0adNYs2aN0nGEEDoQ98ADhHzyCdFTpxK+aZPSccSfNGo1debmJE+ZQsTmzUrH0RtajYZjMTEUfPYZquhoXAsLcdRoUANZNjac9vGh2w034DVrFl179VI67n8UxcVhHRZGrr09/sePy0bwV0gKpQF65513mDNnDunp6fjKuishDELUyJGE7NlDxvLl+D3zjNJxxJ9yunTh5MCBhB08qHQUvaXVaDi6bRvHvvgCs9hY3IuKsNdqaQCyunfnzLBh9Lz5ZjzvvVdvTqxJefllAl55hahrriFi69YLvqfm9FlK0jJQn6vDrIsVDiN9sLbr2bFB9YgUSgPU2NjI0KFD6devH9u2bZNthIQwAOq6OvY5OTHwzBlq5PxhvZHg7IxVTQ1+Z84oHaXT0Go0HP75Z45/9RWWu3fjceIEtlotdUBWz55UjBiB7dSpeM6YgYWNjWI5o8aMISQhgcy332b4k08CUBCTQsmyt3FOjMLxdDF/X4CiAU7Y9aVodAQOc5/CJczwtli6FCmUBmrLli1cf/31/PTTT1x//fVKxxFC6MCZw4ep9PSk3syMvkeOyH55eiAqJITBCQk4NjUpHaXT0qjV5H7/PSe+/ZYuiYl4njxJD6AGyLKzo9rfn17TpuF5552YdeAOJuq6OjIdHHCsquLUxs1oFi3G52AyahMVZlrNxT/35+sZ3oHYfbWevn7eHZZZSVIoDZRWq2XixIkUFhaSkZGBhYFvaSGEscj78Uf63HgjBxwdCSwslPVdCot76CFC1qyh6vhxvX/gpLNoamggZ8MGSjduxDo5Ga9Tp7ABKoEce3tqAgPpM306HtOmtft2TaX795M1+Q5GncjFrEl9ySL5b2oTFWpTM/bPfZXA1w1/5xV5XNBAmZiYsHLlSvLy8vhQTtoQwmC4TZnCwfnzGX3iBDGRkUrHMXpXjRoFQNHOnQonMRymFhZ4z5hBxC+/EFBWhmVNDRlr1rAnMhJVUxOBv/yC1113UWVlRZKjI9E338yhTZvQ/Ll1kS4dfuczwo8dwFLd0KIyCWCm1WCpbiBwyQsk3D9H59n0jcxQGrgHH3yQ//u//yM3Nxc7Ozul4wghdCRqwgQiduwgce5cRr/5ptJxjFZFQQE9Bgwg/pFHCH7/faXjGIX6ykqyP/+cMz/8QM/0dLwqKrAETpmYkNu3Lw0hITjfdReu117bpm22khe8SeCSF3SWO3nBmwQunquz6+kbKZQG7uTJk7i7u3Pvvffy7rvvKh1HCKEjWo2GRBcXfIqKKN60CY+pU5WOZLTKVCoOhIURERWldBSjdK68nOz166nYvBnbffvwqqrCHDipUnHY2Rl1aCguM2fSf+zYKy6YxekHsQ30w1LdwIUea60HXgK+BM4AvsBi4GL3DLRAvZkF5cnpBrumUgqlEVi2bBnz588nMzMTT09PpeMIIXSkprSUIhcXuqjV2Bw8iK27u9KRjNK+Hj2o6dmTMQUFSkcRQHVJCdnr1lH988/0yszEq6YGU6DY1JR8Fxe04eEMvO8+nENCLnqNjCGj8MpKveht7unAJuApwB34DEgBdgEXu6raREWWlz8+B5Ja/b3pMymURqC+vh4vLy+8vb3ZsmWL0nGEEDpUFBeHVVgYhT174ltc3KFPwYrzYjw9sS8sxKu2Vuko4gIqCgvJWbuW2l9/pc/Bgww+dw4VcMzMjKMDB6IaPx7X++/H0d8fOL81kEt44EWvlwyMAt4Cnv3za3XAUKA3sPsyeQpiU3AJ8W/jd6V/pFAaiU2bNjFt2jT++OMPImUhvxAGZe877zDkqafYPWwY4Xv3Kh3H6ERNnoz/L79g3dQkR2N2AmcOH+bQ2rXUbd1K3+xs3OvrAThibs6xQYNQd+lD2N7Yi85OzgVWAuVA9799fSkwHygE+l1kbLWJirRrb2fUlq919e3oDSmURkKr1RIeHs6ZM2dIT0/HTLYaEcKgRN9+O+EbNxJ7772Erl+vdByjkrxwIYGLF3MiJaV5lkt0Hqeyssj95BMa//gDp9xcrKx64FRZdtH3RwLHgX+fjbQDmAD8BFxq9+ciOyecTxW1NbbekV+ljMRf2whlZmaydu1apeMIIXQs7JtviPH0JPCzz8j4+GOl4xiVXmPGAFASHa1wEtEavby8CFq5krDMTPocL8HxEmUS4ATgeIGv//W14suM1/f0cWpOn215UD0nhdKI+Pv7c8899/DSSy9RUVGhdBwhhA6ZqFSMTkkhp1s3ej/8MCdSU5WOZDT6hYfTCFTJn3mnd3JP5mWL0TnA8gJft/rb65eiAkrSMloaTe9JoTQyS5Ysoaamhtdff13pKEIIHbOwscFx927UJiacCQ/nXHm50pGMgnnXrhSZm6PNzlY6imgj9bm6y76nC+e3Dfq3ur+9rotxOhsplEamb9++PP/887zzzjvk5+crHUcIoWP2Q4dS+fnnDKytJc3fH62mZad7iNYptbWla5HhrYszNmZdLr9LgiPnb3v/219fu5IDOK9knM5GCqURevbZZ+nduzdz5xrujv1CGDOvO+8k/bHHCDlyhOgpU5SOYxTOOTvT++xZpWOINnIY6cPlfgUbDhzi/Nnif5f0t9cvRfPnOIZGCqUR6tq1K0uXLuX7778nWhaRC2GQxqxeTdTo0YRu2UKqLHFpdypvb5zVauor/10zRGdibdeTE3aXnmO8BWgC/v7oWz2wnvP7U15sy6C/FNs5YW3Xsw0p9ZMUSiN1xx13EBAQwJw5c9DILTEhDFJodDRpvXvjtnAhR7dtUzqOQesREIApcGzXLqWjiDYqGh2B2uTi9WgUMA2Yx/k9KT8GxgFHgWWXubbaRMXx0eG6CapnpFAaKZVKxdtvv82ePXv44osvlI4jhGgHphYWDE5JodzcnKbJk6mUNX7tpu/YsQCcio9XOIloK7P777ropuZ/+YLzxy5+CTwBNAJbgLDLXVurweGFp3WQUv9IoTRiY8aM4bbbbmP+/PlUV1crHUcI0Q569O8PmzfTq6GB7JEjaWpoUDqSQerl7U0FULdvn9JRRCudOnWKZ599luDpNxHt6HbJWUorzh+9eILzT3cnA1df5vpqExUZ3oEGeewiSKE0em+++Sbl5eUsW3a5iXohRGflOmkSh155Bf/SUmL/nEkTumWiUlFkbY2p7J7R6VRVVfHqq6/i6urKmjVreOGFF+j73TeoTc3Q1VGCWkBtaobdV4Z7ipUUSiPn4uLCnDlzeOuttygsLFQ6jhCinQS89BIx115LxO7d7H7ySaXjGKQzvXvTs6RE6RjiCtXV1bFq1SpcXV1ZsmQJs2fPJj8/n5dffhn3kAD2z30VEx2NZQLsf/41+vp56+iK+kfO8hZUVVXh7u7O+PHj+fprwzuwXghxnlajYfegQfgdPUrhN9/gOX260pEMStSECfju3ImtPOio19RqNZ999hmvvPIKJ06c4L777mPhwoX06/ff57MT7p9D0Ker0EKryuVfn0u8fw6j165oY3L9JjOUgm7duvH666/zzTffkJSUdPkPCCE6JROVihFpaRzt2hWbu++m7MABpSMZFEtfX2y1Wk7n5CgdRVyARqPhu+++Y8iQIcyePZvg4GAOHjzIxx9/fMEyCeD+7Gyi+w2h3szikmsqL0RtoqLezILkBW8afJkEKZTiT/feey/Dhw/n6aefRiathTBcXWxtuSoqCgutluIxY2iQB/J0xm7MGACKo6KUDSL+QavVsnXrVvz9/bnttttwc3Njz549bNiwAQ8Pj4t+TqNWczQ0FO+ig+R+uYEsr/MP01yuWP71epaXP+XJ6QQuNo5DRKRQCgBMTU1ZuXIlCQkJbNy4Uek4Qoh25BgQQMl77+FVWUniqFFKxzEYzhERAJyVOz16Iy4ujrCwMK699lqsra2JiYnhl19+wc/P77KfjbnuOkacPk3h66/jc/tN+BxIoiA6mbRrb6fIzuk/J+pogCI7J9KuvZ2C2BR8DiQZ9JrJf5M1lOIfbrzxRtLT08nOzqZLlys54l4I0VnF3nsvoZ9/Tsz06YR9843ScQxCkZkZef7+RCQmKh3FqO3du5cFCxbw66+/Mnz4cJYsWcI111yDicmVrYTct3o1Q594gtjgYCLi4i74nprTZylJy0B9rg6zLlY4jPQxyBNwrpQUSvEPubm5DBkyhEWLFrFgwQKl4wgh2lm0ry9jMjI4+O67DHv8caXjdHppdnY0WloyurhY6ShG6dChQ7z00kts3LgRd3d3XnvtNaZNm4ZKdeU3ZMsOHEDt68vJbt0YWlKCmZVVOyY2HHLLW/yDu7s7jz/+OEuXLuXEiRNKxxFCtLMxiYlk9uxJ3yefpEhOeWmzaicn7E+fVjqG0SkqKmL27Nl4e3sTHx/PJ598wsGDB7nttttaVCY1ajUF4eGYabU47NwpZbIFpFCK/1i4cCFWVla8+OKLSkcRQrQz865d6Z+YSJ2pKdWRkdSUliodqXPz8KBfQ4OcSNRBysrKeOaZZ3Bzc2Pz5s289dZb5ObmMmvWLMzMzFp8vZhrr21eN+kwYkQ7JDZcUijFf/Ts2ZNXXnmF9evXk56ernQcIUQ7sxs8mHPffovzuXPs8/dHK/sotprNyJFYAEUXWXcndKOyspKXX34ZV1dXPvnkE+bPn8/hw4d5+umnsWrlrOK+1asJ3baNmJAQRs6bp+PEhk8KpbigBx98EE9PT9lGSAgj4XHLLWQ8+yxjjh0j+pprlI7TafUJCwOgNDZW4SSG6dy5c6xYsQJXV1feeOMNHnzwQfLz83nppZfo3r17q69bduAAfZ56iowePQjZtk2HiY2HFEpxQWZmZqxYsYLo6Gg2b96sdBwhRAcIeustosLCCNu2jeSFC5WO0yn1HTWKWqBG7u7oVGNjI5988gnu7u48//zzTJ06lby8PJYvX06vXr3adG2NWk1hWBimsm6yTaRQiouaNGkSV199Nc899xz19fVKxxFCdICwHTtIdnTEc/FiDm/ZonScTkdlZsYxKytUhw4pHcUgaDQaNmzYwJAhQ3jggQcICwsjKyuLNWvW4OzsrJMxYiZNwq+8XNZNtpEUSnFJK1as4OjRo7z33ntKRxFCdACVmRlDUlM5aWmJ6c03c/bIEaUjdTrldnZ0k10y2kSr1fLLL78wYsQIpk+fjoeHB3v37uWbb77B3d1dZ+PsfecdQrdvl3WTOiCFUlzSkCFDePDBB3nttdcoKytTOo4QogN069sXi19/pbtaTV5AAOq6OqUjdSr1AwbgUFmpdIxOKzY2lrCwMCZPnkyPHj2Ii4tjy5YtDBs2TKfjlB04gMOcOezv2ZPQHTt0em1jJIVSXNYrr7wCwMsvv6xsECFEh3EZN46jb77J8NOniQsJUTpOp2I2dCiOGg1Vsrl5i6SnpzNp0iTCwsKoqalh69atREVFERwcrPOx/r5usu+uXZhaWOh8DGMjhVJcVq9evVi4cCEfffQRBw4cUDqOEKKDjHjuOeJvuomItDTiHnpI6TidxlV/no9etHOnwkk6h5ycHG677TZGjBjBkSNH+O6770hNTW3RUYktFXPNNefXTS5ZQp/hw9tlDGMjhVJckccee4yBAwfyzDPPKB1FCNGBwjZtItbdHf81azj4+edKx+kUnMeNA+BMUpLCSfTbsWPHmDVrFkOGDCEhIYF169aRmZnZ4qMSW2rv228TumMHMaGhjHzhhXYbx9jIWd7iim3evJmbbrqJX3/9lUmTJikdRwjRQeorKznk5ESv2lpUaWkyo3MFylQqDoSFEREVpXQUvVNWVsaSJUv44IMP6N69OwsWLOChhx5q9YbkLRo7MxPNsGEUd++O78mTcqtbh6RQiium1WoZN24cJ0+eZN++fZibmysdSQjRQU7u3Ytm5EhOde2Kx/HjWLZhE2ljsK9HD2p69mRMQYHSUfRGRUUFK1euZOXKlahUKp599lmeeuopunXr1iHjNzU0sNfRkf5nzqDZs0d+MdIxueUtrpiJiQmrVq0iOzubjz/+WOk4QogO1Gf4cMrXrsW9uppkOZ7xsiocHblKdsYAzp9us3z5clxdXVm2bBkPP/ww+fn5LFy4sMPKJEDstdfiV17OsTfekDLZDqRQihYZPnw4M2fOZNGiRZw5c0bpOEKIDjRk5kxSH3yQ0NxcYqZNUzqOXtO4udHv3DmjLt6NjY2sWbMGNzc35s2bx7Rp08jLy2PZsmXY2dl1aJb0lSvPr5sMC2PE3LkdOraxkEIpWmzx4sXU1dXx2muvKR1FCNHBQj76iKiRIwn+4Qf2vPWW0nH0Vlc/P2yAkj17lI7S4TQaDd9++y3e3t48/PDDREREkJWVxUcffYSTk1OH5ynLzKTvc8+d329SzuluN1IoRYs5Ojoyf/583nvvPXJzc5WOI4ToYCFxcey1s2PA889TKA+dXFCvMWMAKImOVjhJx9FqtWzZsgU/Pz/uuOMOvLy82Lt3L19//TVubm6KZGpqaKAwPBwTkP0m25kUStEqTz/9NI6Ojjz33HNKRxFCdDAzKyvcUlKoNDOj/pprZAPvC+gXHk4jUJWaqnSUDhEdHU1ISAjXX389V111FfHx8fz000/4+voqmiv2z3O6i2TdZLuTQilapUuXLrz55pv8+OOP7Nq1S+k4QogO1nPgQNSbNtGnvp4D/v5o1GqlI+kV865dKTI3R5udrXSUdrVnzx6uueYaIiIiqK+v5/fff2fXrl2M+XOGVknpK1cSunMnMeHhjJDJj3Yn2waJVtNqtQQHB1NbW0taWhqmpqZKRxJCdLDkhQvxX7yYmPBw2XPxX5IcHFA1NRFggE97Z2dns3DhQjZt2sTgwYNZvHgxU6dObbeTbVpK9pvseDJDKVrtr22E9u3bx/r165WOI4RQQOBrrxETGUlEdDSJ8vTsP5xzdqb32bNKx9CpwsJC7r//foYMGUJycjKffvopmZmZ3HLLLXpTJpsaGjgWFoYJ4BQdLWWyg0ihFG0yatQo7rjjDl588UWqqqqUjiOEUED4b7+xu18/hr71Foe+/17pOHpD5e2Ns1pNfWWl0lHarLS0lKeeegp3d3d+/vlnVq1axaFDh5g5cyZmZmZKx/uH2GuuYfiZMxS98Qa9FV7DaUykUIo2e+ONN6isrGTp0qVKRxFCKMBEpWJYaipFXbrQ5fbbOZ2To3QkvdAjIABT4FgnXmdeUVHBwoULcXV1Zf369SxcuJD8/HyeeOIJLC0tlY73H+krVhC2axcxERGybrKDSaEUbdavXz+effZZVq5cydGjR5WOI4RQgHXv3ths20aXpiYKR4+msbZW6UiK6zt2LACn4uMVTtJytbW1vPXWW7i6urJ8+XIeffRRjhw5wosvvoiNjY3S8S6odP9+nObOZe9VVxH6++9KxzE6UiiFTsydOxdbW1teeOEFpaMIIRTiHBzM8XfeYejZs+wOClI6juJ6eXtTAdTt26d0lCvW2NjIRx99hJubG/Pnz+e2227j8OHDvPnmm9ja2iod76KaGhooiogAwDkqStZNKkAKpdAJGxsblixZwsaNG4nvhL+NCyF0Y9jjj5MwfTrh+/cTe++9SsdRlIlKRZG1Nab5+UpHuSyNRsPXX3+Np6cnjzzyCOPGjSM7O5sPPviAvn37Kh3vsmKvvprhZ85wfNkyWTepENk2SOiMRqMhICAAU1NTEhMTUank9xUhjFWMtzejs7LI+egjfB58UOk4iolzdaVHWRk+evrQolar5eeff+bFF18kIyODG264gcWLF+Pj46N0tCuWvmIFw559lpiICCI68XrVzk5+4gudUalUrFq1ipSUFL755hul4wghFDQ6OZms7t3p88gjnEhJUTqOYtSurjjV1Cgd44KioqIIDg5mypQp2NnZsXv3bn788cdOVSZl3aT+kEIpdCosLIypU6cyb948amVRvhBGy8LGhr67d9NoYsKZiAjOlZcrHUkRlr6+2Gq1evXke2pqKldffTVjx46lsbGRP/74g507dxLUyda9/rVuUgv0i4mRdZMKk0IpdG7ZsmWUlpayfPlypaMIIRRkP2QIVV9+yYDaWvaMHIlWo1E6Uoez+/MIwmI9OEUoKyuLW265hYCAAI4dO8b3339PcnIykZGRerMpeUvEXn01w86cofitt7AfOlTpOEZPCqXQOVdXV5588knefPNNjh8/rnQcIYSCPKdPZ+8TTxB89CjR11+vdJwO5/znk8dnk5IUy1BQUMDMmTMZOnQoqampfPbZZ2RkZHDzzTd3yiIJsOettwiLiiJ27Fj85sxROo5AHsoR7aSiogJ3d3cmTZrE559/rnQcIYTCosaMISwhgT2vvYb/iy8qHadDFZmZkefvT0RiYoeOe/LkSZYsWcJHH31Ez549efHFF3nggQf0ckPylijdvx/8/Cjq0YNhJSVyq1tPSKEU7WbNmjU89NBDpKSk4O/vr3QcIYSCNGo1qU5ODC4tpfy33xh49dVKR+owaXZ2NFpaMrq4uEPGO3v2LMuXL+ftt9/GzMyMuXPn8sQTT+jthuQt0dTQwP4+fehbWYlq3z651a1HpFCKdqNWq/Hz86Nnz57ExMR02lsrQgjdqCgs5JS7OyaAXW4uPfr3VzpSh4j29cU5J4dB9fXtOk5tbS2rV6/mzTffpK6ujieffJLnnntOrzckb6moiAhCo6PZv2KF3OrWM7KGUrQbMzMzVq5cSVxcHN9//73ScYQQCuvRvz+qn37CrqGBnIAAmhoalI7UMTw86NfQ0G7fb0NDAx9++CFubm68+OKLTJ8+ncOHD7N06VKDKpN73nqLsOhoYseNkzKph6RQinYVGRnJddddx9y5c6mrq1M6jhBCYQOvvprcxYsZWVpKbHi40nE6hM3IkVgARXFxOr1uU1MTX331FV5eXjz66KNMmDCBnJwc3n//fRwdHXU6ltJO7t1Lv+efJ93WltCtW5WOIy5ACqVod8uXL+fYsWO88847SkcRQugB/wULiL3uOiISE9n9+ONKx2l3fcLCACiNjdXJ9bRaLT/++CPDhw/n7rvvxtfXl/379/PFF1/g6uqqkzH0SVNDA8Vjx6IxMaF/dLQ8hKOnpFCKdufp6cnDDz/M66+/zsmTJ5WOI4TQA+E//UTcwIEMf+89sr7+Wuk47arvqFHUAjXp6W2+1q5duxgzZgw33ngjvXv3JjExkf/9738MNeCHU2IjI/E9e5YTK1bIQzh6TAql6BCLFi3CzMyMl156SekoQgg9YKJSMTI1laNdu9L9nnsoy8xUOlK7UZmZcczKCtWhQ62+RkpKCpGRkYwbN46mpia2bdvGjh07GDVqlA6T6p89y5YRFhND7PjxDH/qKaXjiEuQQik6hJ2dHYsWLWLt2rVkZGQoHUcIoQe62NpyVVQU5lotxcHBNFRXKx2p3ZTb2dHtxIkWf+7gwYNMnTqVwMBAiouL+eGHH0hKSmLChAntkFK/nNy7l34vvHB+3eSvvyodR1yGFErRYR555BHc3Nx4+umnkd2qhBAAjgEBnPzwQzwrK0kMCDDY4xnrBwzAobLyit9/9OhR7r33Xnx8fNizZw+ff/45+/fv56abbjKKLdj+WjfZZGJCfzmnu1OQQik6jLm5OcuXL2fHjh1s2bJF6ThCCD3h88ADJN97L2HZ2cTeeafScdqF2dChOGo0VF1mc/OTJ0/y+OOP4+HhwW+//cY777xDdnY2M2bMwNTUtIPSKi92wgR8z56lZMUK7IcMUTqOuAJSKEWHmjx5MuPHj+fZZ5+lwVj2oBNCXFbo+vVEDxtG0IYN7DXAHSGu+nOtY9HOnRd8/ezZsyxYsABXV1e++uorXnnlFQ4fPsxjjz3W6Y9KbKm0N94gLDZW1k12MnJSjuhw+/fvx8/Pj5UrV/Lkk08qHUcIoScaa2vJcHam/9mz1MXE4BwSonQknakoKKDHgAHsfuwxxqxe3fz1mpqa5tNtGhoamk+3ueqqqxRMq5ySPXsw9fen8Kqr8Dt5EpWZmdKRxBWSQikU8eCDD/J///d/5OXlGdRJDkKItinPzaXa25tzZmY4FxRg3bu30pF0pkyl4kBYGBFRUTQ0NPDJJ5+wePFiTp8+zYMPPsiCBQtwcHBQOqZi1HV1ZDo44FBVhen+/XKru5ORW95CEa+++ipqtZpXXnlF6ShCCD1i6+5O3YYNONXVsX/kSIN6SKe4WzfMjxzhyy+/xNPTk8cff5yJEyeSk5PD6tWrjbpMAsRNnIhPRQUlK1dKmeyEpFAKRfTp04cFCxbw/vvvk52drXQcIYQe8Zg6lcy5cwkqKiL66quVjqMTWq2W4zY2dD12jBkzZjB8+HAyMjL4/PPPGThwoNLxFJe2dOn5dZMTJjBclkJ1SnLLWyimrq4OLy8vhgwZIk99CyH+I2rsWCKiokiaP59Rr7+udJxW27FjB/Pnzyc0OZmXgQMJCYwaPVrpWHrjr3WTBba2jCgpkXWTnZTMUArFWFlZ8dZbb/HLL7+wbds2peMIIfRM2LZtJDo64rVkCXk//aR0nBZLTk5mwoQJzZuQB9xxBzZAfylMzdR1dZSMG4faxASX6Ggpk52YFEqhqKlTpxIaGsqcOXNQq9VKxxFC6BGVmRlD9+zhpKUl5lOncubwYaUjXZEDBw5w0003MWrUKEpKSti8eTOJiYkE3HUXACXR0Qon1B9xkZH4VFRQ+vbbsm6yk5NCKRRlYmLCypUryczMZN26dUrHEULoGRsHByx/+41uTU3kBwairqtTOtJFHTlyhBkzZuDj48O+ffv44osv2LdvH1OmTMHExIR+4eE0AlWpqUpH1QtpS5cSFhdHbGQkwx5/XOk4oo1kDaXQC/fccw9bt24lNzeXHj16KB1HCKFn0leswOfZZ4kbOZIIPStkJSUlLF68mI8//hg7OzsWLlzIrFmzsLjAcYFHLCwoHDKE8PR0BZLqD1k3aXhkhlLohSVLllBTU8PrnXjhvRCi/fg98wzxU6cSkZZG3AMPKB0HgDNnzjBv3jxcXV35+uuvee2118jLy+ORRx65YJkEKLW1pWtRUQcn1S9/Xzc5IDZWyqSBkEIp9IKTkxNz587lnXfeIT8/X+k4Qgg9FPbdd8S6u+P/ySccWL9esRw1NTUsWbKEgQMH8u677/L0009z5MgRnn/+eaytrS/52XPOzvQ+e7ZjguqpuAkTzq+bfOcdenl5KR1H6Ijc8hZ6o7a2lsGDBzNq1Cg2bdqkdBwhhB6qr6zkkJMT9rW1kJKCw4gRHTZ2Q0MDH3/8MYsXL6a8vJyHHnqIBQsW0KdPnyu+RsyMGQR/+SXqigosu3dvx7T6KfX11xnx4ovETJxIxO+/Kx1H6JDMUAq90bVrV5YuXcr3339PTEyM0nGEEHrIsnt3esfGogVOhYVR1wGzfU1NTXz++ecMHjyYJ598kmuuuYZDhw7x7rvvtqhMAvQICMAUOLZrV/uE1WMnUlMZsHAhe+zsCPvlF6XjCB2TQin0yh133EFAQABPP/00GgM6ck0IoTt9hg/nzKef4lZTQ6q/f7sdz6jVavnhhx/w8fHh3nvvZcSIEWRkZPDZZ58xYMCAVl2z79ixAJyKj9dhUv2nrqvj5IQJNMq6SYMlhVLoFZVKxapVq9izZw9ffPGF0nGEEHrK+557SHvoIUIOHyZm6lSdX3/79u2MGjWKqVOn4uzsTHJyMt9//z3e3t5tum4vb28qgLr9+3UTtJOIGz+eobJu0qBJoRR6Jzg4mFtvvZX58+dTXV2tdBwhhJ4K/vBDovz9Cdm8mT3LlunkmomJiYwfP57IyEhUKhU7d+7kjz/+ICAgQCfXN1GpKLK2xrSTbNKuC6mvv07E7t3EXX01wx57TOk4op1IoRR66c0336S8vJxlOvohIYQwTKHx8ezp1YuBL7xAwc6drb5OZmYmN954I0FBQZSWlvLjjz+SkJDA2D9vUevSmd696VlSovPr6qO/1k2m9OpF2JYtSscR7UgKpdBLAwYMYM6cOSxfvpxjx44pHUcIoadMLSxwT07mrJkZDddeS1VxcYs+n5+fz913342vry8ZGRl89dVX7N27lxtuuAETE5N2yax2dcWppqZdrq1P1HV1lI4fT6OJCQNjYmTdpIGTQin01rx58+jevTvz5s1TOooQQo/1HDgQzQ8/0Ke+noMjR6JRqy/7mRMnTvDII48wePBgduzYwfvvv09WVhZ33nknpqam7ZrX0tcXW62W0zk57TqO0uLGj2dIZSVlq1fLukkjIIVS6K1u3bqxePFivv76a5KSkpSOI4TQY4MmTyZ74UICSkqIGTfuou8rLy/nhRdeYNCgQWzYsIHXX3+dvLw8Hn744YuebqNrtqNHA1AcFdUh4ykhdfHi5nWTvo88onQc0QFkY3Oh15qamhg5ciRdu3YlPj6+3W5BCSEMQ9TVVxPxxx8kzJlD0IoVzV+vrq7mnXfe4a233kKtVvP000/zzDPP0LNnzw7PWHvqFF3t7YmdOZPQTz/t8PHb24mUFMxHjeJIr16MLC6WW91GQgql0Hu7du1i3LhxfPvtt9x+++1KxxFC6DGtRkPCgAEMO3aM4//3f7hcfz1r1qzh9ddf5+zZszz00EPMnz+/xRuS61qRmRl5/v5EJCYqmkPX1HV1HOjTh97V1VgcPIjd4MFKRxIdRAql6BRuvPFG0tPTyc7OpkuXLkrHEULosdpTpyjo35+u9fVc36cPB06e5J577mHRokW4uLgoHQ+ANDs7Gi0tGd3Ch4j0XdSYMYQkJHDw/fflVreRkTWUolN46623OHHiBKtWrVI6ihBCj2m1WrZGR/Ng79500Wj44NQp0pOT+fTTT/WmTAJUOzlhf/q00jF0KnXxYiISEoi75hopk0ZICqXoFNzd3XnsscdYsmQJJ06cUDqOEELPaLXa5g3Ib7nlFroMHkzSc88xqrGRMzNnKh3vvzw86NfQQFNDg9JJdOJESgoDX3qJFHt7wn7+Wek4QgFSKEWnsXDhQqysrHjxxReVjiKE0COJiYmMGzeOq6++GgsLC3bt2sXvv//O9cuWkXDHHYRnZBAzY4bSMf/BZuRILICiuDilo7SZuq6OsvHjqVepcJVzuo2WFErRaVx11VW8/PLLrF+/nvT0dKXjCCEUlpGRwZQpUwgKCuL06dP89NNPxMfHExER0fyesK+/JmbIEEZ/+SX7P/xQubD/0icsDIDS2FiFk7Rd3NixeFdVcWr1ankIx4jJQzmiU2lsbGTYsGH06dOHnTt3yjZCQhihw4cPs2jRIr755htcXV159dVXuf3221GpLjxH0lBdzUFnZ5wqK2lMSKDvqFEdnPi/NGo1debmJE+ZQsTmzUrHabWUV18lYNEioiZNIuLXX5WOIxQkM5SiUzE3N2fFihVERUXx448/Kh1HCNGBiouLefjhh/H09GTXrl18+OGHZGVlcccdd1y0TAJY2NjgFB9PvUpFxdix1J461YGpL0xlZsYxKytUublKR2m1EykpuL788vl1kz/9pHQcoTCZoRSd0jXXXENeXh4HDhzA0tJS6ThCiHZ0+vRpli1bxurVq+nSpQvz5s3j0UcfbfEWYjkbN9Lv9ttJd3FhTH4+JpcooR0hwdkZq5oa/M6cUTRHa6jr6jjYuze9amuxPHBAbnULmaEUndOKFSs4evQo7733ntJRhBDtpLq6msWLF+Pq6sr777/Ps88+S35+Ps8++2yr9qMdfNtt7H3qKYILCoiePLkdErdM/YABOFRWKh2jVeIiImTdpPgHKZSiUxoyZAgPPPAAr732GmVlZUrHEULoUH19Pe+88w6urq689tpr3HfffeTn5/Pqq6/So0ePNl17zKpVRAUHE7Z1KymvvKKjxK1jNnQojhoNVZ1sc/OUV14hIimJuGuvxffhh5WOI/SEFErRab3y5w+Dl19+WdkgQgidUKvVfPrpp3h4eDBnzhxuuOEGcnNzWbVqFb1799bZOGFRUaT06YPHyy+Tv3Wrzq7bUlf9+XBQ0c6dimVoqeKkJFxfeYXk3r0Jk3Xs4m+kUIpOy97enoULF7JmzRoOHjyodBwhRCtpNBo2bdqEj48P999/P6NGjeLgwYOsXbuW/v3763w8lZkZXqmpnLKwwGTKFCoKCnQ+xpVwHjcOgDNJSYqM31KNtbWcioykXqXCLS5O9psU/yCFUnRqjz32GAMGDOCZZ55ROooQooW0Wi2///47AQEBTJs2jQEDBpCamsp3333H4HZel9fd2RnTLVu4Sq3mUECAIifW9HBxoczEhIaMjA4fuzXix43Du6qK0++/j627u9JxhJ6RQik6NUtLS9566y1+++03tip460oI0TK7d+9m7NixXHPNNVhZWREdHc3WrVsZOXJkh2UYEBnJ4ddfZ0RZGbF/bjTe0Yq7dcPiyBFFxm6JlJdfbl436fPgg0rHEXpItg0SnZ5Wq2Xs2LGUlpayb98+zM3NlY4khLiI/fv3s2DBArZs2YKvry9Llizh2muvVfSQgqgpU4j46Sd2P/YYY1av7tCxYzw9sS8sxKu2tkPHbYnipCSsgoLIs7fH//hxudUtLkhmKEWnZ2JiwqpVq8jOzubjjz9WOo4Q4gLy8vK44447GD58ONnZ2XzzzTekp6dz3XXXKX7iVfj//kecqyt+771H1ldfdejYGjc3+p07h1aj6dBxr9Rf6ybPybpJcRlSKIVB8PPzY+bMmSxatIgznXCTYCEM1fHjx3nooYfw8vIiJiaGjz76iIMHDzJ9+vRLnm7TkUxUKvzT0si3tqbHvfdSlpnZYWN3GTYMG6Bkz54OG7Ml4seOxauqivIPPpB1k+KS9OPfZiF0YPHixdTV1bF48WKlowhh9E6fPs1zzz2Hm5sbmzZt4o033iA3N5cHHnhAL5elWPXsiW1UFKZaLSfGjKG+gzYctw8JAaAkOrpDxmuJlJdfJiI5mfjrrsPngQeUjiP0nBRKYTAcHR2ZN28eq1evJrcTn48rRGdWVVXFq6++ysCBA/noo4+YO3cu+fn5PPPMM6063aYjOfr7U7ZmDYOrqkgKDOyQ29D9wsNpBKpSU9t9rJY4npDAoFdfPb/f5ObNSscRnYA8lCMMyrlz5/D09MTPz4/N8h9BITpMXV0dH330EUuWLKGyspJHHnmEefPmYW9vr3S0Fou97z5C168n+rbbCN+wod3HO2JhQeGQIYSnp7f7WFeisbaW7D59uOrcOaxzcrhq0CClI4lOQGYohUHp0qULb7zxBj/++CO7du1SOo4QBk+tVrNu3To8PDx49tlnmTJlCrm5uaxcubJTlkmA0E8/JXr4cMZs3Mjet99u9/FKbW3pWlTU7uNcqfiICDyrqznzwQdSJsUVkxlKYXC0Wi1jxozh3LlzpKWlYWpqqnQkIQzOX6fbLFy4kEOHDnHrrbfy6quvtvuG5B1FXVfH/r59cTl7lnMxMTj/udaxPUT5+zNw3z5cGhvbbYwrlfzSSwS+9hpRkycT8fPPSscRnYjMUAqDY2Jiwttvv82+ffv47LPPlI4jhEHRarX89ttv+Pv7c9tttzFo0CD27NnDxo0bDaZMAphZWTEgKYlqU1NqIiOpLilpt7FU3t44q9Ud9iDQxRxPSMBt8WKS+vQhXM7pFi0khVIYpFGjRnHHHXewYMECqqqqlI4jhEGIj48nPDycSZMm0bVrV2JiYvj111/x8/NTOlq7sHV3p+G77+hbV0eGv3+7PaTTIyAAU+CYgst0GmtrKZ84kVqVCo/4eEz0ZEsn0XnI3xhhsJYuXUpFRQVLly5VOooQndrevXu57rrrCAkJobKykl9++YXY2FhCQ0OVjtbu3G+6iYMvvEDQ8eNER0a2yxh9x44F4FR8fLtc/0rEh4fjWV3N2Y8+knWTolWkUAqD1b9/f5599llWrlzJ0aNHlY4jRKeTm5vL9OnT8fPzIzc3lw0bNrBnzx7Fj0rsaKOWLiVq7Fgidu4kaf58nV+/l7c3FUDd/v06v/aVSF64kIjUVOJvuIGhs2YpkkF0fvJQjjBo1dXVeHh4EBYWxoYO2P5DCENQVFTEq6++yqeffoqDgwOLFi3i3nvv1csNyTuKRq0macAAhh4/zsnNm3GbMkWn1z9gY0O5oyOhHbyH7vGEBLoGB3Ood28Ci4vlVrdoNfmbIwyajY0NS5YsYePGjezevVvpOELotVOnTvHMM8/g5ubGDz/8wLJly8jLy2P27NlGXSYBVGZm+KSmcsLKCvNbbuHM4cM6vf6Z3r3p2Y4P/lzIX+sma2TdpNAB+dsjDN6MGTMYMWIETz/9NJoOOPlCiM6mqqqKV155BVdXVz755BPmzZtHfn4+c+bMwcrKSul4esPGwYEuv/+OTVMT+YGBqOvqdHZttasrTjU1OrvelZB1k0KXpFAKg6dSqVi1ahXJycl88803SscRQm/U1dWxcuVKXF1dWbp0KbNnzyY/P59FixbRvXt3pePppX5hYRQuX86w8nLix4zR2XUtfX2x1Wo5nZOjs2teSvO6ySlTZN2k0AkplMIohIWFcfPNNzNv3jxqa2uVjiOEotRqNWvXrsXd3Z25c+dy0003kZeXx4oVK+jVq5fS8fSe35w5xN9yC+Hp6cTpqIzZjh4NQHFUlE6udylF8fG4v/46SQ4OhP/wQ7uPJ4yDFEphNJYtW0ZpaSnLly9XOooQitBoNGzcuBFvb29mz55NSEgIWVlZfPzxxzg7Oysdr1MJ27iRmMGDCVi3jsx169p8vX7jxgFwNimpzde6lIbqas5effX5dZNxcbJuUuiM/E0SRmPQoEE8+eSTvPnmmxw/flzpOEJ0GK1Wy6+//srIkSO5/fbbcXd3Jz09nW+//RZ3d3el43VKJioVo5KTyenWjV4PPEDJnj1tul7XXr0oMjWl6eBBHSW8sN0REQyuqeHsmjWyblLolBRKYVQWLFiAtbU1CxYsUDqKEB0iLi6OsLAwrrvuOrp160ZsbCy//PILw4cPVzpap2fZvTsOcXFoTEw4FRZG3dmzbbreyR49sCos1E24C0hasICItLTz6ybvv7/dxhHGSQqlMCo9evTg1Vdf5fPPPyc1NVXpOEK0m/T0dK699lpCQ0Opqalh69atREdHExISonQ0g9Lb15ezn37KoJoaUkeObNPxjNVOTtifPq3DdP9fUXw8HkuXyrpJ0W6kUAqjM2vWLIYMGcLTTz+N7OsvDM2hQ4e4/fbbGTFiBIcPH2bjxo2kpqZyzTXXGNXpNh3Je8YM9jzyCCH5+UTffHPrL+ThQb+GBpoaGnQXjn+umxy8e7esmxTtQv5WCaNjZmbGypUriYuL4/vvv1c6jhA6cezYMWbPno23tzfx8fGsXbuWAwcOcOutt6KSAtHugt9/n6jAQEJ//JG0N95o1TVsRo7EAiiKi9Nptt0REXjU1FDxySf0HDhQp9cW4i/yXxlhlCZOnMh1113H3LlzqdPh5sRCdLSysjLmzJmDu7s7mzdvZvny5eTm5nL//fdjZmamdDyjEhoby55evXCdP5+CHTta/Pk+YWEAlMbG6ixT0vz5RKSlkXDjjQyZOVNn1xXi3+Qsb2G0srOz8fHx4fXXX2fu3LlKxxGiRSorK1mxYgUrV67ExMSEZ599lqeffppu3bopHc2oVRQUUO7ujsbEBPvDh+negu2YNGo1debmJE+ZQsTmzW3OUhQXh01YGDl9+hB4/Ljc6hbtSgqlMGpPPPEEn332Gbm5ufTp00fpOEJc1rlz5/jggw9YunQpNTU1PPbYYzz//POyIbkeyf/1V+yuu47sPn0IKCpC1YKZ4pwuXTjp6krYgQNtytBQXc0hBwd61NfT7dAhudUt2p38uiKM2qJFizAzM+Oll15SOooQl9TY2MjHH3+Mu7s7zz//PLfccgt5eXm89dZbUib1jOu113Lo5ZcJOHmSmLFjW/TZcjs7uhUXtznD7vBwPGpqqPz4YymTokNIoRRGzc7Ojpdeeom1a9eSkZGhdBwh/kOj0fDtt9/i7e3Ngw8+SFhYGNnZ2Xz00Uc4OTkpHU9cRMCiRcRccw0RcXEkzJlzxZ+rHzAAh8rKNo2dNH8+EXv2kHDTTbJuUnQYueUtjF5DQwM+Pj7079+fP/74Q7ZWEXrhr9NtFixYwL59+5g8eTKLFy9m2P9r796joqwTN4A/MwwgoJGghqAi4YDcYbyhmZcsrV23zHbVxPy1umabuaZbWlxEQDBRV13L3VZzdxPTs1m4VtvaGqZlCsT9LhcFQRDkKndm5v39UXCaEByYYV4uz+ecjh1m5p2HYg6P3/d78fISOxppSVCrccXBAV5FRSg+dQrOy5ff9zXfvvwyZr/3Hu6WlGCErW2P37N93mS2jQ1mFBdz3iQZDH/SaMgzMTHB3r17cf78eXz++edixyHCpUuX8Oijj2Lx4sWwtLTEt99+i08//ZRlcoCRSKXwTkhAkZkZLPz8cCcr676vGTljBgCgOCamx+/XWl+PmiefxF0jI0y+fJllkgyKP21EABYvXowFCxbgj3/8I9ra2sSOQ0NUYmIinnrqKcydOxdNTU3473//i6+//hqPPPKI2NGol8xHjcIDX30FU7UaxTNnoq2xsdvnj3vsMQBAdWxsj9+rfd7k3aNHOW+SDI6FkgiARCLBn/70J+Tl5eHw4cNix6EhJicnB8uWLcOUKVNw/fp1fPTRR4iPj8eiRYs4BWMQsJs5E6XvvAPX2lp89+MIZFcs7e1RIZGgtYdzumPfeuuHeZNLl8L1//5Pl7hEvcI5lEQ/8dJLL+H06dPIy8uDlZWV2HFokCsqKkJoaCj+8Y9/wNbWFjt27MDq1au5IfkgdWn1asw5fhyX/PwwJyqqy+elWFqi4cEHMauwUKvr3rx0CSPmzeO8SRIVf+qIfiIsLAxKpRIhISFiR6FBrLy8HJs3b4ZcLsfZs2exb98+5ObmYs2aNSyTg9icDz7ARXd3+J44gdRu7oTUjh2LkRUVWl2ztb4edb/4Be4aGcHlyhWWSRINf/KIfuKhhx5CQEAADh8+jJycHLHj0CBTW1uL7du3w9HREceOHUNQUBDy8/OxadMmmJqaih2PDGBWbCwyLS0x9tVXUXLlyj2fo540CaPa1Mg/9w1y/v0/5H/5DRoqa+753Ctz5kD+47xJS3v7PkxO1D3e8ib6mebmZri4uMDd3R2ffvqp2HFoEGhqasK7776LXbt2obGxERs3bsS2bdtgbW0tdjQSwZ2sLDR7eOCuiQnsi4pg/uPG9IWX4lEWeQC2l7+CXc1tjREfNYBSa1sU+86DzdbXYD9nGmLfegsz3n4bF5cuxdyPPxbleyFqx0JJdA8fffQRli1bhi+//BJPPPGE2HFogGpra8OxY8cQGhqK8vJy/O53v0NQUBBse7G/IA0uOf/6F8YtX46UCRNg/8nnqFq9Fh6ZcVBKpJAJ6i5f1/54itwHZmU3UTXCBDNu3uStbhIdCyXRPQiCgDlz5qCmpgZJSUmc10Y9olarcerUKWzfvh0FBQVYuXIlduzYgUmTJokdjfqRK1u2wOhf5+B5Ow8ylbLbIvlzSokUSiMZvt+wDbMPhPZhSiLt8K80RPfQvo1Qeno63n//fbHj0AAhCAI+++wz+Pj4wM/PD25ubkhOTkZUVBTLJHVWC0wvyYSpsrVHZRIAZIIapspWzD4YhitrtT/akaivsFASdWHatGlYvXo1goKCUFtbK3Yc6ucuXryI2bNn41e/+hWsrKzw3Xff4d///jc8PT3Fjkb9UFzAbsw8th8A0NudRttfN/PYfsQFRuolF1FvsVASdSMiIgINDQ2IiIgQOwr1UwkJCVi0aBHmzZuHlpYWnDt3DjExMZg5c6bY0aifupWUCc/I7dB2vlk4fiiP7l08LgDw3B2EW0mZeslH1BsslETdsLOzw9atW3HgwAEUFBSIHYf6kezsbPzmN7/B1KlTUVRUhNOnTyM+Ph4LFy7k6TbUrcpVv4VMpdRqZLIYQAQAi26eIwEgUylRueq3eslH1BtclEN0Hw0NDXB2doavry9Onz4tdhwSWWFhIUJCQvDPf/4T48aNQ0hICFatWsWFW6SVwkvxsJ87XevnrwBQAUAF4A6A9Ptd/5t42M+e2vuARL3EEUqi+7CwsMCuXbvw8ccf49KlS2LHIZGUl5dj06ZNcHJywueff479+/fj2rVrePHFF1kmSWtlkQeglGj3q/cSgNMADmh5baVEirK39/cyGZFuOEJJpAW1Wg1fX1+o1WrExcVByj3fhoza2lrs3bsX+/fvh0wmwxtvvIFNmzZh+PDhYkejAahklB3sKm/d93kqAAoAMwH8FcA8aDdCWWxth3F3inVMSdRz/K1IpAWpVIr9+/cjISEBx48fFzsOGUBjYyMiIyPh4OCAffv24dVXX0VBQQECAgJYJqlX6u9UY6wWZRL4oUQWAgjr4XvYVpZ0eUwjUV9ioSTS0iOPPIJly5bB398f9fX1YsehPtLW1oa//OUvmDRpEgICArBixQrk5eXh7bffhpWVldjxaAC7nZiu1S/dSgDbAQQBGN3D95ACKEtI62k0Ip2xUBL1wO7du1FZWYnISO75NtioVCqcOHECkydPxoYNG7BgwQLk5OTg8OHDPCqR9ELZ1KzV8wIBWAHY2MfvQ6RPLJREPTBx4kRs3rwZe/fuxc2bN8WOQ3ogCALOnj0Lb29vrFq1Ch4eHkhJScHx48fx8MMPix2PBjhBEFBYWIjo6Gh88tmn931+LoC/AfgDgFsAbvz4TzOAth//veo+15CZDet9YKJe4qIcoh6qq6uDk5MTHn/8cURFRYkdh3Rw4cIF+Pv74+rVq5g/fz4iIiLg6+srdiwaoNRqNXJzc5GYmIikpKSOP6uqfqiAE23skF9W0u1IztcA5t/nfTah65XfagBNd6phYf1gz8IT6YiFkqgXjh49inXr1iE2NhbTp2u/pxz1D99//z38/f3xv//9D9OmTUNERAQWLFjADclJa21tbcjMzOwojomJiUhJSemYX21vbw8fHx8oFIqOP8eOHYtbo8d1u8r7DoBv7/H1QAB3ARwE4AjAo4vXc5U3iYWFkqgXVCoVpkyZAnNzc1y+fJlFZIDIyspCYGAgPvnkE7i4uGDnzp149tln+f+PutXU1IS0tLSO4piUlIS0tDS0tLRAIpFALpdDoVB0lEcfHx9YW1vf81qxi/0w5T+nIBPUPcowD/ffNkgpkSLhFysw47MTPbo2kT6wUBL1UkxMDBYsWICTJ09ixYoVYsehbty4cQMhISH44IMPMH78+I7TbYyMjMSORv1MXV0dkpOTNW5bZ2VlQaVSwcjICG5ubhqjjl5eXhgxYoTW1+/pSTnt5oEn5VD/xkJJpINnnnkGycnJyM7OhpmZmdhx6Gdu376N8PBw/PWvf4WVlRUCAwOxbt06mJqaih2N+oGKigqNuY6JiYnIy8sDAJiamsLLy6ujOCoUCri7u2PYMN0XvKS5zYBL1vc9HqXsjlIiRZbLVHhkxOrtmkQ9wUJJpINr167Bzc0NISEh8Pf3FzsO/aimpgZ79uzBgQMHYGxsjK1bt2LTpk2wsLAQOxqJQBAElJSUaBTHxMREFBf/MNdwxIgRHbeq28vj5MmT++xIzVtJmbCa7gNTZSv0MdlCANAiM0FVXBJsfVz1cEWinmOhJNLRli1bcOTIEeTm5sLGxkbsOENaY2MjDh06hN27d6O5uRmbNm3C1q1bMXLkSLGjkYGo1WoUFBRoFMekpCRUVFQAAKytrTtKY/uta0dHR4MfpxoXsBvTI97U7/V2btXb9Yh6ioWSSEfV1dWQy+VYsmQJjh49KnacIam1tRVHjx5FWFgYKisr8dJLLyEgIABjx44VOxr1IaVSiezsbI3b1klJSairqwMA2NnZaRRHhUKBcePG9ZtFWFfWbsHMY/shAL0aqWx/3dW1W+B7dJ9+wxH1EAslkR688847+MMf/oCEhAT4+PiIHWfIUKlU+PDDDxEcHIwbN25g1apVCAkJgYODg9jRSM9aWlqQnp6uMeqYkpKC5uYfToVxdHTUKI4+Pj4YM2aMyKnvLy5gNzwjt0OmUvZoTqVSIoXSSIbUbWEcmaR+gYWSSA/a2trg6ekJGxsbxMTE9JsRkMGq/XSbgIAAZGRkYMmSJdi5cyfc3NzEjkZ6UF9fj5SUFI05jxkZGVAqlZBKpXBxcdEoj97e3rC0tBQ7dq/dSspE5arfwiMzDkqJtNti2f54mut0WEf9nXMmqd9goSTSk//85z/45S9/iejoaCxZskTsOINWTEwM/P39ERsbiwULFiAiIoKbyw9gVVVVHbeq20cfr127BkEQYGJiAg8PD43FMh4eHjA3Nxc7dp8ovBSPssgDsLt6EbaVmifqqAHcsrZDie9c2Ly5mVsDUb/DQkmkJ4Ig4KmnnkJ+fj4yMjJgYmIidqRBJS4uDgEBATh//jymT5/ecboNDRylpaWdVloXFhYCACwsLODl5aUx59HV1XXIfo4aKmtQlpAGZVMzZGbDYDPFg8cpUr/GQkmkRxkZGfDy8kJkZCS2bNkidpxBITMzE4GBgYiOjoarqyvCw8PxzDPPcFpBPyYIAm7cuNFppXVZWRkAYOTIkRqjjj4+PpDL5dxonmgAY6Ek0rNXXnkFH374IXJzczF69Gix4wxYN27cQHBwMKKiojBhwgSEhoZi5cqVLB39jEqlQm5urkZxTExMRE1NDQDAxsam00pre3t7/oWAaJBhoSTSs4qKCsjlcvj5+eHdd98VO86AU1ZWhvDwcLz33nuwsrJCUFAQ1q1bN2RvffYnra2tyMzM1CiOycnJaGxsBABMnDix00prbt1ENDSwUBL1gb179+LNN99EamoqXF25ClMb1dXV2LNnDw4ePAgTExNs27YNGzdu5Ok2ImlsbERqaqpGeUxPT0drayskEgmcnZ07rbS2srISOzYRiYSFkqgPtLS0wM3NDXK5HF988YXYcfq1hoYG/PnPf0ZkZCRaW1vx2muv4fXXX+fpNgZUU1OD5ORkjTmP2dnZUKvVkMlkcHd315jz6OnpieHDh4sdm4j6ERZKoj4SHR2NpUuX4osvvsCTTz4pdpx+p7W1FUeOHEFYWBiqqqqwfv16BAQE8PjKPlZeXt5psUx+fj4AwMzMDJ6enhpzHt3d3WFqaipyaiLq71goifqIIAiYP38+ysvLkZqaCplMJnakfkGlUuHEiRMIDg5GUVERXnjhBQQHB/N0Gz0TBAE3b97sVB5LSkoAAA888IDGXEeFQgFnZ2f+nBJRr7BQEvWhpKQkTJkyBYcOHcKGDRvEjiMqQRBw5swZBAYGIjMzE0uXLkVYWBjnmOqBWq1Gfn5+p5XWlZWVAIDRo0d3Wmnt4OAAqVR6nysTEWmHhZKoj61ZswZnz55Fbm7ukJ0XeP78efj7+yM+Ph6PP/44IiIiMG3aNLFjDUhKpRJZWVmdVlrfvXsXADB+/HiN4qhQKGBra8tteoioT7FQEvWx0tJSyOVyrF+/Hvv27RM7jkHFxsbC398fMTExmDFjBiIiIvDYY4+JHWvAaG5uRlpamsZt67S0NDQ3NwMA5HJ5pw3CR40aJXJqIhqKWCiJDCA8PBwhISHIyMiAXC4XO06fS09PR1BQEM6cOQM3NzeEh4fj6aef5ihZN+7evdtppXVmZiZUKhWMjIzg6uqqMefR29sbDzzwgNixiYgAsFASGURTUxMmT54MhUKB6OhoseP0mYKCAuzYsQNRUVGYOHEiQkND8fzzz/N0m5+prKzstFgmNzcXgiDA1NQUHh4eGqOOHh4eMDMzEzs2EVGXWCiJDOTkyZNYuXIlYmJiMH/+fLHj6FVpaSl27tyJI0eOwNraGtu3b8fatWuH/Ok2giCgtLS002KZoqIiAMDw4cPh7e2tMefRxcUFxsbGIicnIuoZFkoiAxEEAbNmzUJTUxMSEhIGxahddXU1IiMjcfDgQQwbNqzjdBtzc3OxoxmcIAi4fv16p/JYXl4OALCysuq0WGbSpElcaU1EgwILJZEBXb16FTNnzsTRo0exdu1aseP0WkNDAw4ePIjIyEi0tbVh8+bNeP311/Hggw+KHc0gVCoVcnJyNIpjUlISamtrAQC2tradzrSeMGEC55AS0aDFQklkYH5+fvjqq6+Qm5uLESNGiB2nR1paWvC3v/0N4eHhqK6uxssvvwx/f3889NBDYkfrMy0tLcjIyNCY85iamorGxkYAwMMPP9xppfVg/u9BRHQvLJREBlZUVARnZ2ds2bIF4eHhYsfRikqlQlRUFIKDg3Hz5k2sXr0aO3bsgL29vdjR9KqhoQGpqakat63T09PR1tYGqVQKZ2dnjeLo7e09ZPcWJSL6KRZKIhEEBQVhz549yM7OxsSJE8WO0yVBEBAdHY3AwEBkZWXhueeeQ1hYGFxcXMSOprPq6mokJydrlMecnByo1WoYGxvD3d1d47a1p6cnLCwsxI5NRNQvsVASiaC+vh5OTk6YM2cOTp06JXacTgRB6Djd5vvvv8fChQsRHh6OqVOnih2tV27fvt1pscz169cBAGZmZp1WWru5uQ35FepERD3BQkkkkr///e9Ys2YNLl++jFmzZokdp8PVq1fh7++PCxcuwNfXF7t27cK8efPEjqUVQRBQVFSkURwTExNRWloKALC0tOy00trJyWlQrLgnIhITCyWRSNRqNaZOnQpjY2NcuXJF9O1j0tPTERAQgLNnz8LDwwPh4eFYvHhxv12ZrFarkZub22mldVVVFQBgzJgxHaWxvUQ6ODj02++HiGggY6EkEtHFixcxb948REVFwc/PT5QMBQUFCA4OxokTJ+Dg4IDQ0FCsWLGiX43atbW1ITMzU2PUMSUlBfX19QAAe3t7jS16FAoFxo4dy/JIRGQgLJREInvuuecQFxeHnJwcjQ3BG1qUuFHZgFalGiYyKSZaW8DCVKa3971161bH6TajR4/uON1G7FNampqakJaWpjHnMS0tDS0tLZBIJJDL5Rqjjj4+PrC2thY1MxHRUMdCSSSy/Px8uLq6IiAgAM+v34wTsUW4kFOOoqpG/PTDKQEwwcoc853HwG/GBMgf6t0ellVVVdi9ezcOHTqEYcOG4a233sKGDRtEOd2mrq6uY6V1++hjVlYWVCoVjIyM4ObmpjHq6OXlNeD27iQiGgpYKIn6gVfeCMKZEjOY2HvBSCqBSt31x7L98UcnjULEsx4Yb6VdEayvr8fBgwexZ88eKJXKjtNtLC0t9fVtdKuiokJjrmNiYiLy8vIAAKampvDy8tJYLOPu7o5hw4YZJBsREemGhZJIZKfiixB8NgPNrW2QSLWft2gklUAmlSDkaTesmDahy+e1tLTgvffeQ3h4OGpqavD73/8e/v7+GDNmjD7idyIIAkpKSjqttC4uLgYAjBgxouNWdXt5nDx5MmQy/d3OJyIiw2KhJBLROxdysffLazpf5/WFTnh1vlzja0qlEsePH8eOHTtQXFyMF198Edu3b9fr6TZqtRoFBQUaxTEpKQkVFRUAAGtr604rrR0dHUVf0U5ERPrFIQEikZyKL9JLmQSAvV9ew+jhplg+bQIEQcAnn3yCwMBAZGdn49e//jXOnTuHyZMn6/QeSqUS2dnZGretk5KSUFdXBwCws7ODQqHAK6+80jH6OG7cOK60JiIaAjhCSSSCm1WNeHz/RbQo1Z0eay5Mxe2T/vd8nc0Le2Fqd+9iaCqTIniqFH8KC0BCQgIWLVqE8PBwTJkypcf5WlpakJ6erjHqmJKSgubmZgCAo6OjxmIZHx+fPruFTkRE/R9HKIlE4B+dBmU3C28AYMSUX8FkrJPG12Qjx3b5/JY2JTafTIajiQm+/vprzJ07V6ss9fX1SElJ0ZjzmJGRAaVSCalUChcXFygUCixfvhwKhQLe3t4GW8hDREQDAwslkYHl3r6Lb/Lu3Pd5puPdYDF5tvYXlkhh5qDAPw++1uWWQlVVVR23qttHH69duwZBEGBiYgIPDw9MmzYN69evh0KhgIeHhyjbCRER0cDCQklkYCdii+67NVA7dUsjJMamWq/+NpJKcCK2CDuedkNpaWmnldaFhYUAAAsLC3h5eeGJJ57Atm3b4OPjA1dXV5iYmOj0vRER0dDEOZREBjZ3zwUUVjV2+Xj7HEqJiRmE1iZAIoXpeDeMnL8GpmPlXb6unXFLDepPvo6ysjIAwMiRIzW26PHx8YFcLu9XRysSEdHAxkJJZED1LUp47DiH7j50zcVZuBsfDbOHp0Jqbom2O0Woi4uG0NYMm1V7YGLj2P2bCAJWyOIxXeEFhUIBe3t7rrQmIqI+xUJJZEAZt2rxy0Pf9vh1bdW3UPr+RpiOd8NDy0Pv+/zPN86Gmy0XzhARkWFwd2EiA2q9xzZB2jAeaQsz+Qw0F6VCUKv67H2IiIh6g4WSyIBMZL3/yMkeGAWolBDaWvr0fYiIiHqKv3WIDGiitQV6O5tRWVMGicwEEpNh3T5P8uP7EBERGQoLJZEBWZjKMMGq+30dVY21nb7WersAjblxGDbRBxJJ9x/bCdbmsDDljmBERGQ4/K1DZGDzncfgeGxhl/tQVpzZDamxCUztXH5c5X0T9Sn/hcTYFCPnvdjttY2kEsx34hGIRERkWCyURAbmN2MC/nHlRpePmzv5oiHja9TFnYG6tRFG5pYwd5oFy9nPw3ikbbfXVqkFrPKdoOfERERE3eO2QUQieOH9WHxXUKnVaTnaMpJKMOthaxxfO0Nv1yQiItIG51ASiSDiWQ/IpPrdbFwmlSDiWQ+9XpOIiEgbLJREIhhvZY6Qp930es3Qp90w/j4LfoiIiPoCCyWRSFZMm4DXFzrp5VpvLHTG8mmcO0lEROLgHEoikZ2KL0Lw2Qwo1UKP5lQaSSWQSSUIfdqNZZKIiETFQknUD9ysaoR/dBq+ybsDI6mk22LZ/vijk0Yh4lkP3uYmIiLRsVAS9SO5t+/iRGwRLlwrR1FlI3764ZTgh03L5zuNwSrfCZg0ZoRYMYmIiDSwUBL1Uw0tStyobECrUg0TmRQTrS14Ag4REfVLLJREREREpBOu8iYiIiIinbBQEhEREZFOWCiJiIiISCcslERERESkExZKIiIiItIJCyURERER6YSFkoiIiIh0wkJJRERERDphoSQiIiIinbBQEhEREZFOWCiJiIiISCcslERERESkExZKIiIiItIJCyURERER6YSFkoiIiIh0wkJJRERERDphoSQiIiIinbBQEhEREZFOWCiJiIiISCcslERERESkExZKIiIiItIJCyURERER6YSFkoiIiIh0wkJJRERERDphoSQiIiIinbBQEhEREZFOWCiJiIiISCcslERERESkExZKIiIiItIJCyURERER6YSFkoiIiIh0wkJJRERERDphoSQiIiIinbBQEhEREZFOWCiJiIiISCcslERERESkExZKIiIiItLJ/wM5MfRv1JtE4gAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sub = g.subgraph([0, 2, 3, 4])\n", + "nx.draw(g, pos=positions, with_labels=True)\n", + "nx.draw(sub, pos=positions, node_color=\"r\", edge_color=\"r\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great, this is a clique! Moreover, it is the *largest* clique in this six-node graph. QAOA, using PennyLane and Braket, has helped us to solve the maximum clique problem!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scaling up QAOA for larger graphs with hybrid jobs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have seen how we can use PennyLane on Braket to solve graph optimization problems with QAOA. However, we have so far restricted to a simple six-node graph and used the local Braket device. Let's now be more ambitious and try to solve an optimization problem on a 18 node graph! We will use [Amazon Braket Hybrid Jobs](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html) to scale up the classical resources, and run the entire algorithm asynchronously. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Use Braket SDK Cost Tracking to estimate the cost to run this example\n", + "from braket.tracking import Tracker\n", + "\n", + "task_tracker = Tracker().start() # track Braket tasks costs" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import networkx as nx\n", + "\n", + "nodes = wires = 18\n", + "edges = 60\n", + "seed = 1967\n", + "\n", + "g = nx.gnm_random_graph(nodes, edges, seed=seed)\n", + "positions = nx.spring_layout(g, seed=seed)\n", + "\n", + "nx.draw(g, with_labels=True, pos=positions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A 18 node graph (which maps to the same number of qubits) definitely puts us in a regime where the local simulator will be slow to execute. As we have discussed in the [parallelization tutorial](../1_Parallelized_optimization_of_quantum_circuits/1_Parallelized_optimization_of_quantum_circuits.ipynb), this slowness will be compounded when it comes to training the circuit, with each optimization step resulting in multiple device executions due to calculation of the gradient. Thankfully, the remote SV1 simulator is highly suited to speeding up gradient calculations through parallelization or adjoint differentiation. We now show that this makes training the circuit for QAOA solvable within a reasonable time.\n", + "\n", + "Let's first load a new device:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from braket.devices import Devices\n", + "\n", + "device_arn = Devices.Amazon.SV1\n", + "# device_arn = \"arn:aws:braket:::device/quantum-simulator/amazon/sv1\" # alternatively use the device ARN" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "dev = qml.device(\"lightning.qubit\", wires=wires)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now just need to set up the QAOA circuit and optimization problem in the same way as before. However, we will switch to a new optimization problem to keep things interesting: aiming to solve maximum cut, with the objective of partitioning the graph's nodes into two groups so that the greatest number of edges are shared between the groups (see the image below). This problem is NP-hard, so we expect it to be tough as we increase the number of graph nodes." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "cost_h, mixer_h = qml.qaoa.maxcut(g)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def qaoa_layer(gamma, alpha):\n", + " qml.qaoa.cost_layer(gamma, cost_h)\n", + " qml.qaoa.mixer_layer(alpha, mixer_h)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "n_layers = 2\n", + "\n", + "\n", + "@qml.qnode(dev)\n", + "def cost_function(params, **kwargs):\n", + " for i in range(wires): # Prepare an equal superposition over all qubits\n", + " qml.Hadamard(wires=i)\n", + "\n", + " qml.layer(qaoa_layer, n_layers, params[0], params[1])\n", + " return qml.expval(cost_h)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(1967)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A variety of [optimizers](https://pennylane.readthedocs.io/en/stable/introduction/optimizers.html) are available in PennyLane. Let's choose ``AdagradOptimizer``:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = qml.AdagradOptimizer(stepsize=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're now set up to train the circuit! Note, if you are training this circuit yourself, you may want to increase the number of iterations in the optimization loop and also investigate changing the number of QAOA layers.\n", + "\n", + "\n", + "We create a hybrid job by annotating our main function with `@hybrid_job`. \n", + "This allows us to choose a target QPU for priority queueing, and additional arguments such as the type of classical instances to use. \n", + "In this example, we use an \"ml.c5.xlarge\" instance. \n", + "\n", + "Note that creating hybrid jobs is only supported on Python 3.10. For other versions, you may use [scripts](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html) or a [custom container image](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-byoc.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "from braket.jobs import hybrid_job, InstanceConfig\n", + "from braket.jobs.metrics import log_metric\n", + "\n", + "large_instance = InstanceConfig(instanceType=\"ml.c5.xlarge\")\n", + "\n", + "\n", + "@hybrid_job(device=\"local:pennylane/lightning.qubit\", instance_config=large_instance)\n", + "def qaoa_training(iterations, n_layers=2):\n", + " task_tracker = Tracker().start() # track Braket tasks costs\n", + "\n", + " dev = qml.device(\"lightning.qubit\", wires=wires)\n", + "\n", + " @qml.qnode(dev)\n", + " def cost_function(params, **kwargs):\n", + " for i in range(wires): # Prepare an equal superposition over all qubits\n", + " qml.Hadamard(wires=i)\n", + "\n", + " qml.layer(qaoa_layer, n_layers, params[0], params[1])\n", + " return qml.expval(cost_h)\n", + "\n", + " params = 0.01 * np.random.uniform(size=[2, n_layers])\n", + "\n", + " for i in range(iterations):\n", + " params, cost = optimizer.step_and_cost(cost_function, params)\n", + "\n", + " # Record the value of the cost function with each iteration\n", + " log_metric(metric_name=\"cost\", value=cost, iteration_number=i)\n", + "\n", + " # Additionally, keep track of cost in USD for Braket tasks\n", + " braket_task_cost = float(\n", + " task_tracker.qpu_tasks_cost() + task_tracker.simulator_tasks_cost()\n", + " )\n", + " log_metric(metric_name=\"braket_cost\", value=braket_task_cost, iteration_number=i)\n", + "\n", + " return {\n", + " \"parameters\": params,\n", + " \"final_cost\": cost_function(params),\n", + " \"braket_tasks_cost\": task_tracker.qpu_tasks_cost() + task_tracker.simulator_tasks_cost(),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, we create the hybrid job by calling the function as usual. The function arguments are logged as hyperparamters for the hybrid job. \n", + "\n", + "
\n", + "Caution: Running the following cell will take a long time and will result in usage fees charged to your AWS account. Only uncomment the cell if you are comfortable with the potential wait-time and costs. We recommend monitoring the Billing & Cost Management Dashboard on the AWS console and being aware that hybrid jobs involving a large number of qubits can be costly.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AwsQuantumJob('arn':'arn:aws:braket:us-west-1:961591465522:job/qaoa-training-1697141494101')\n" + ] + } + ], + "source": [ + "job = qaoa_training(iterations=5, n_layers=2)\n", + "print(job)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The hybrid job will be scheduled to run and will appear in the \"QUEUED\" state. \n", + "If the target is a QPU, the hybrid job will be queued with other hybrid jobs. \n", + "If the target device is not a QPU, the hybrid job should start immediately. \n", + "\n", + "Note that since the algorithm code is run in a containerized environment, it takes approximately 1 minute to start running your algorithm. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'QUEUED'" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "job.state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the state is \"COMPLETED\", we retrieve the results with " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 337 ms, sys: 18 ms, total: 355 ms\n", + "Wall time: 8min 54s\n" + ] + }, + { + "data": { + "text/plain": [ + "{'parameters': tensor([[-0.01460419, 0.00094966],\n", + " [ 0.0187241 , 0.00412996]], requires_grad=True),\n", + " 'final_cost': array(-30.03947181),\n", + " 'braket_tasks_cost': Decimal('0')}" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "\n", + "job.result()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results included the three values from the return statement of our function.\n", + "\n", + "Additionally, we can retrieve the metrics recorded during the training with:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " iteration_number timestamp cost braket_cost\n", + "0 0.0 3.394283e+09 -29.992536 0.0\n", + "1 1.0 3.394284e+09 -27.007548 0.0\n", + "2 2.0 3.394284e+09 -29.993231 0.0\n", + "3 3.0 3.394284e+09 -30.002011 0.0\n", + "4 4.0 3.394284e+09 -30.009532 0.0" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics = job.metrics()\n", + "df = pd.DataFrame(metrics)\n", + "df = df.groupby(\"iteration_number\").sum().reset_index()\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The metrics are plotted below. " + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'cost function')" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting the convergence of the loss function metric\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "df.sort_values(by=[\"iteration_number\"]).plot(x=\"iteration_number\", y=\"cost\", marker=\"o\")\n", + "plt.xlim(1, 5)\n", + "plt.ylabel(\"cost function\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example shows us that a 18-qubit QAOA problem can be trained using the \"lightning.qubit\" with adjoint gradients by using hybrid jobs. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "What's next? See if you can analyze the trained QAOA circuit for the 18-node graph by adapting the earlier analysis. Also, check out the followup tutorial on quantum chemistry.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", + "Estimated cost to run this example: 0.000 USD\n" + ] + } + ], + "source": [ + "job_cost = job.result()[\"braket_tasks_cost\"]\n", + "\n", + "print(\n", + " \"Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\"\n", + ")\n", + "print(f\"Estimated cost to run this example: {job_cost :.3f} USD\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "vscode": { + "interpreter": { + "hash": "590fab68195cf107911461461f81d5c472d3d6127f579badfcfad30f03e5cab2" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file From 92d3f0cf27f665e5803d368424d8e1f1598fb534 Mon Sep 17 00:00:00 2001 From: mbeach-aws <85963088+mbeach-aws@users.noreply.github.com> Date: Fri, 13 Oct 2023 08:58:53 -0400 Subject: [PATCH 03/24] PennyLane notebook w jobs decorator 3 (#398) --- ..._Hydrogen_Molecule_geometry_with_VQE.ipynb | 396 ++++++++++++++---- .../requirements..txt | 1 + .../requirements.txt | 1 + 3 files changed, 322 insertions(+), 76 deletions(-) create mode 100644 examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/requirements..txt create mode 100644 examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/requirements.txt diff --git a/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/3_Hydrogen_Molecule_geometry_with_VQE.ipynb b/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/3_Hydrogen_Molecule_geometry_with_VQE.ipynb index c3a7ed033..c5f0b7a0f 100644 --- a/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/3_Hydrogen_Molecule_geometry_with_VQE.ipynb +++ b/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/3_Hydrogen_Molecule_geometry_with_VQE.ipynb @@ -19,7 +19,7 @@ "metadata": {}, "source": [ "
\n", - "Note This notebook requires pennylane>=0.18 and amazon-braket-pennylane-plugin>=1.5.0\n", + "Note This notebook requires pennylane>=0.32 and amazon-braket-pennylane-plugin>=1.5.0\n", "
" ] }, @@ -45,8 +45,7 @@ "source": [ "import pennylane as qml\n", "from pennylane import qchem\n", - "from pennylane import numpy as np\n", - "import time" + "from pennylane import numpy as np" ] }, { @@ -65,26 +64,26 @@ "name": "stdout", "output_type": "stream", "text": [ - " (-0.24274280046635355) [Z2]\n", - "+ (-0.24274280046635355) [Z3]\n", - "+ (-0.04207898539323007) [I0]\n", - "+ (0.17771287502652944) [Z0]\n", - "+ (0.17771287502652955) [Z1]\n", - "+ (0.12293305045330435) [Z0 Z2]\n", - "+ (0.12293305045330435) [Z1 Z3]\n", - "+ (0.16768319431907505) [Z0 Z3]\n", - "+ (0.16768319431907505) [Z1 Z2]\n", - "+ (0.17059738365083382) [Z0 Z1]\n", - "+ (0.17627640722442822) [Z2 Z3]\n", - "+ (-0.0447501438657707) [Y0 Y1 X2 X3]\n", - "+ (-0.0447501438657707) [X0 X1 Y2 Y3]\n", - "+ (0.0447501438657707) [Y0 X1 X2 Y3]\n", - "+ (0.0447501438657707) [X0 Y1 Y2 X3]\n" + " (-0.24274280046588792) [Z2]\n", + "+ (-0.24274280046588792) [Z3]\n", + "+ (-0.04207898539364302) [I0]\n", + "+ (0.17771287502681438) [Z0]\n", + "+ (0.1777128750268144) [Z1]\n", + "+ (0.12293305045316086) [Z0 Z2]\n", + "+ (0.12293305045316086) [Z1 Z3]\n", + "+ (0.16768319431887935) [Z0 Z3]\n", + "+ (0.16768319431887935) [Z1 Z2]\n", + "+ (0.1705973836507714) [Z0 Z1]\n", + "+ (0.1762764072240811) [Z2 Z3]\n", + "+ (-0.044750143865718496) [Y0 Y1 X2 X3]\n", + "+ (-0.044750143865718496) [X0 X1 Y2 Y3]\n", + "+ (0.044750143865718496) [Y0 X1 X2 Y3]\n", + "+ (0.044750143865718496) [X0 Y1 Y2 X3]\n" ] } ], "source": [ - "symbols, coordinates = qchem.read_structure('hydrogen_molecule/h2.xyz')\n", + "symbols, coordinates = qchem.read_structure(\"hydrogen_molecule/h2.xyz\")\n", "h, qubits = qchem.molecular_hamiltonian(symbols, coordinates, name=\"h2\")\n", "print(h)" ] @@ -217,7 +216,7 @@ "metadata": {}, "outputs": [], "source": [ - "dev = qml.device(\"braket.local.qubit\", wires=qubits)" + "dev = qml.device(\"lightning.qubit\", wires=qubits)" ] }, { @@ -235,11 +234,13 @@ "source": [ "wires = dev.wires.tolist()\n", "\n", + "\n", "@qml.qnode(dev)\n", "def energy_expval(params):\n", " circuit(params, wires)\n", " return qml.expval(h)\n", "\n", + "\n", "@qml.qnode(dev)\n", "def S2_expval(params):\n", " circuit(params, wires)\n", @@ -291,14 +292,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "Energy: -0.2730496738440533\n", - "Spin: (0.11000908988780544+0j)\n" + "Energy: -0.2730496738441154\n", + "Spin: 0.11000908988780544\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/drbeach/miniconda3/envs/decorator/lib/python3.10/site-packages/pennylane_lightning/core/_serialize.py:133: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " coeffs = np.array(unwrap(observable.coeffs)).astype(self.rtype)\n" ] } ], "source": [ - "print(\"Energy:\", energy_expval(params))\n", - "print(\"Spin: \", spin(params))" + "print(\"Energy:\", float(energy_expval(params)))\n", + "print(\"Spin: \", float(spin(params)))" ] }, { @@ -348,26 +357,32 @@ "metadata": {}, "outputs": [], "source": [ + "import time\n", + "from braket.jobs.metrics import log_metric\n", + "\n", + "\n", "def run_vqe(energy_expval, spin, opt, initial_params, iterations):\n", " energies = []\n", " spins = []\n", " params = initial_params\n", - " \n", + "\n", " start = time.time()\n", " for i in range(iterations):\n", " params = opt.step(energy_expval, params)\n", - " \n", + "\n", " e = energy_expval(params)\n", " s = spin(params)\n", - " \n", + "\n", " energies.append(e)\n", " spins.append(s)\n", - " \n", + "\n", + " log_metric(metric_name=\"energy\", value=e, iteration_number=i)\n", + "\n", " print(f\"Completed iteration {i + 1}\")\n", " print(\"Energy:\", e)\n", " print(\"Total spin:\", s)\n", " print(\"----------------\")\n", - " \n", + "\n", " print(f\"Optimized energy: {e} Ha\")\n", " print(f\"Corresponding total spin: {s}\")\n", " print(f\"Elapsed: {time.time()-start} s\")\n", @@ -379,53 +394,71 @@ "execution_count": 14, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/drbeach/miniconda3/envs/decorator/lib/python3.10/site-packages/pennylane_lightning/core/_serialize.py:133: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " coeffs = np.array(unwrap(observable.coeffs)).astype(self.rtype)\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ + "Metrics - timestamp=1697139811.2718408; energy=-0.5602851198326602; iteration_number=0;\n", "Completed iteration 1\n", - "Energy: -0.5602851198327653\n", - "Total spin: (0.13287789321454424+0j)\n", + "Energy: -0.5602851198326602\n", + "Total spin: 0.13287789321454435\n", "----------------\n", + "Metrics - timestamp=1697139811.3152246; energy=-0.7773309017116151; iteration_number=1;\n", "Completed iteration 2\n", - "Energy: -0.7773309017117552\n", - "Total spin: (0.09488198120584446+0j)\n", + "Energy: -0.7773309017116151\n", + "Total spin: 0.09488198120583491\n", "----------------\n", + "Metrics - timestamp=1697139811.3421786; energy=-1.0083677187505569; iteration_number=2;\n", "Completed iteration 3\n", - "Energy: -1.0083677187507512\n", - "Total spin: (0.04940760029257241+0j)\n", + "Energy: -1.0083677187505569\n", + "Total spin: 0.04940760029256386\n", "----------------\n", + "Metrics - timestamp=1697139811.369656; energy=-1.1060574430621748; iteration_number=3;\n", "Completed iteration 4\n", - "Energy: -1.1060574430624097\n", - "Total spin: (0.01902625475224662+0j)\n", + "Energy: -1.1060574430621748\n", + "Total spin: 0.01902625475224251\n", "----------------\n", + "Metrics - timestamp=1697139811.3969185; energy=-1.1301112509281241; iteration_number=4;\n", "Completed iteration 5\n", - "Energy: -1.130111250928373\n", - "Total spin: (0.005634781963640423+0j)\n", + "Energy: -1.1301112509281241\n", + "Total spin: 0.0056347819636390906\n", "----------------\n", + "Metrics - timestamp=1697139811.424453; energy=-1.1351064397742514; iteration_number=5;\n", "Completed iteration 6\n", - "Energy: -1.1351064397745017\n", - "Total spin: (0.0012625902560896574+0j)\n", + "Energy: -1.1351064397742514\n", + "Total spin: 0.0012625902560892133\n", "----------------\n", + "Metrics - timestamp=1697139811.4511507; energy=-1.136021579371058; iteration_number=6;\n", "Completed iteration 7\n", - "Energy: -1.1360215793713093\n", - "Total spin: (0.00022566866798257035+0j)\n", + "Energy: -1.136021579371058\n", + "Total spin: 0.00022566866798245933\n", "----------------\n", + "Metrics - timestamp=1697139811.4915578; energy=-1.1361670884772965; iteration_number=7;\n", "Completed iteration 8\n", - "Energy: -1.1361670884775477\n", - "Total spin: (3.2154663963668284e-05+0j)\n", + "Energy: -1.1361670884772965\n", + "Total spin: 3.215466396355726e-05\n", "----------------\n", + "Metrics - timestamp=1697139811.5190527; energy=-1.1361867905470242; iteration_number=8;\n", "Completed iteration 9\n", - "Energy: -1.1361867905472756\n", - "Total spin: (3.626608394258213e-06+0j)\n", + "Energy: -1.1361867905470242\n", + "Total spin: 3.626608394258213e-06\n", "----------------\n", + "Metrics - timestamp=1697139811.5578496; energy=-1.1361890094090557; iteration_number=9;\n", "Completed iteration 10\n", - "Energy: -1.1361890094093072\n", - "Total spin: (3.158279300308209e-07+0j)\n", + "Energy: -1.1361890094090557\n", + "Total spin: 3.158279299197986e-07\n", "----------------\n", - "Optimized energy: -1.1361890094093072 Ha\n", - "Corresponding total spin: (3.158279300308209e-07+0j)\n", - "Elapsed: 11.235898971557617 s\n" + "Optimized energy: -1.1361890094090557 Ha\n", + "Corresponding total spin: 3.158279299197986e-07\n", + "Elapsed: 0.35576891899108887 s\n" ] } ], @@ -444,14 +477,14 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -460,23 +493,16 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n", "\n", "theory_energy = -1.136189454088\n", "theory_spin = 0\n", "\n", - "plt.hlines(theory_energy, 0, iterations, linestyles=\"dashed\", colors=\"black\")\n", - "plt.plot(energies)\n", + "plt.hlines(theory_energy, 0, iterations, colors=\"black\")\n", + "plt.plot(energies, \"o-\")\n", "plt.xlabel(\"Steps\")\n", "plt.ylabel(\"Energy\")\n", "\n", - "axs = plt.gca()\n", - "\n", - "inset = inset_axes(axs, width=\"50%\", height=\"50%\", borderpad=1)\n", - "inset.hlines(theory_spin, 0, iterations, linestyles=\"dashed\", colors=\"black\")\n", - "inset.plot(spins, \"r\")\n", - "inset.set_xlabel(\"Steps\")\n", - "inset.set_ylabel(\"Total spin\");" + "axs = plt.gca()" ] }, { @@ -486,6 +512,157 @@ "We have learned how to efficiently find the ground state energy of a molecule using the PennyLane/Braket pipeline!" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scaling up with hybrid jobs\n", + "\n", + "For long-running VQE algorithms, you can run the entire algorithm on Amazon Braket by using [Hybrid Jobs](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html). As a fully managed solution, hybrid jobs give you access to monitor near-real time metrics like the energy during the training phase. \n", + "\n", + "Amazon Braket Hybrid Jobs provides fully managed orchestration of hybrid quantum-classical algorithms, combining Amazon EC2 compute resources with Amazon Braket QPU access. Quantum tasks created in a hybrid job have priority queueing over individual quantum tasks so that your algorithms will not be interrupted by fluctuations in the quantum task queue. Each QPU maintains a separate hybrid jobs queue, ensuring that only one hybrid job can run at any given time.\n", + "\n", + "\n", + "You can run your local Python code as an Amazon Braket hybrid job. You can do this by annotating your code with an @hybrid_job decorator, as shown in the following code example. Only Python 3.10 is supported by default with hybrid job decorators. For custom environments, you can opt to use hybrid job scripts, or specify a custom container from Amazon Elastic Container Registry (ECR) (see [BYOC](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-byoc.html)). \n", + "\n", + "The device argument in the @hybrid_job decorator specifies the device that the hybrid job will have priority access to. \n", + "In this case, we run with a simulator, so we don't need to target a QPU. \n", + "If you want to run a large number of circuits, consider using built-in MPI support to run local simulators on multiple instances within a single hybrid job. \n", + "See [embedded simulators](https://docs.aws.amazon.com/braket/latest/developerguide/pennylane-embedded-simulators.html) for further information. \n", + "\n", + "In this example set set `device=None` since we are using the lighning.qubit simulator. We also change the classical instance to a large instance called \"ml.c5.xlarge\"." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from braket.jobs import hybrid_job, InstanceConfig\n", + "from braket.tracking import Tracker\n", + "\n", + "large_instance = InstanceConfig(instanceType=\"ml.c5.xlarge\")\n", + "\n", + "\n", + "@hybrid_job(device=None, dependencies=\"requirements.txt\", instance_config=large_instance)\n", + "def run_large_vqe(iterations):\n", + " task_tracker = Tracker().start() # track Braket quantum tasks costs\n", + "\n", + " energies, spins = run_vqe(energy_expval, spin, opt, params, iterations)\n", + "\n", + " return {\n", + " \"energies\": energies,\n", + " \"spins\": spins,\n", + " \"braket_tasks_cost\": task_tracker.qpu_tasks_cost() + task_tracker.simulator_tasks_cost(),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following cell, we create the hybrid job by calling the function normally. \n", + "Note that the function returns the handle to the hybrid job which runs asynchronously. \n", + "Once the hybrid job has completed, we retrieve the results. \n", + "\n", + "
\n", + "Caution: Running the following cell will result in usage fees charged to your AWS account. We recommend monitoring the Billing & Cost Management Dashboard on the AWS console.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AwsQuantumJob('arn':'arn:aws:braket:us-west-1:961591465522:job/run-large-vqe-1697139811768')\n" + ] + } + ], + "source": [ + "job = run_large_vqe(iterations=100)\n", + "print(job)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You retrieve the results when the algorithm is complete with" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'energies': [array(-0.55187225), array(-0.70005221), array(-0.89729232), array(-1.03687194), array(-1.09926479), array(-1.1235433), array(-1.13217906), array(-1.1350177), array(-1.13587664), array(-1.13611398), array(-1.13617316), array(-1.13618628), array(-1.1361888), array(-1.1361891), array(-1.13618883), array(-1.13618784), array(-1.13618452), array(-1.13617163), array(-1.13611418), array(-1.13582078), array(-1.13416153), array(-1.12555889), array(-1.10835438), array(-1.11654572), array(-1.12851297), array(-1.13326765), array(-1.13489124), array(-1.13548314), array(-1.13571512), array(-1.13579802), array(-1.13579607), array(-1.13571334), array(-1.13550525), array(-1.13504536), array(-1.13403895), array(-1.1319386), array(-1.12839979), array(-1.12513719), array(-1.12533626), array(-1.12823079), array(-1.1310321), array(-1.13281667), array(-1.13379207), array(-1.13427242), array(-1.13444455), array(-1.13438512), array(-1.13409575), array(-1.13352525), array(-1.13260457), array(-1.13134793), array(-1.13003231), array(-1.12921109), array(-1.1288403), array(-1.11658034), array(-1.11047216), array(-1.12219718), array(-1.10627973), array(-1.1236975), array(-1.13135412), array(-1.13243005), array(-1.13244886), array(-1.13202399), array(-1.13149092), array(-1.13098935), array(-1.13070568), array(-1.13068291), array(-1.13087401), array(-1.13099708), array(-1.13071549), array(-1.12901891), array(-1.1259372), array(-1.12274542), array(-1.12239518), array(-1.12201351), array(-1.12577647), array(-1.12791115), array(-1.12943676), array(-1.12956896), array(-1.12972941), array(-1.12965116), array(-1.12982715), array(-1.12971169), array(-1.12954916), array(-1.12859317), array(-1.12765628), array(-1.12607814), array(-1.12582959), array(-1.12546759), array(-1.12678979), array(-1.12732392), array(-1.12836158), array(-1.12843569), array(-1.1288221), array(-1.12868107), array(-1.12889865), array(-1.12861641), array(-1.12861102), array(-1.12794607), array(-1.12775452), array(-1.1269936)], 'spins': [0.15969152963337319, 0.14300644899470527, 0.10138139829601989, 0.05841186821393585, 0.029397735445742645, 0.012683239937044788, 0.004709419412889271, 0.0015356565172549574, 0.0004406833589031267, 0.00011150185434460891, 2.4696881266605963e-05, 4.754753258340294e-06, 7.841747289294432e-07, 1.0911400150082073e-07, 1.2487133216332325e-08, 1.1431044999454798e-09, 7.994138684352947e-11, 4.0460967909439205e-12, 1.354472090042691e-13, 2.55351295663786e-15, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1102230246251565e-16, 1.2212453270876722e-15, 1.2989609388114332e-14, 2.1038726316646716e-13, 2.903122187092322e-12, 6.226463789005265e-11, 1.0713994136324345e-09, 3.111918500664501e-08, 6.453371255155105e-07, 2.5295425650662118e-05, 0.0006400722119676017, 0.022025029044442035, 0.023385561885357342, 0.00014311106120790118, 0.021705432372581934, 0.008924076514024493, 0.0011652488654905202, 0.0002831588044178712, 4.200450060132255e-05, 9.251351257621998e-06, 1.8141004576310849e-07, 1.6834871601201229e-06, 7.732131861448721e-06, 3.198347546606861e-05, 7.046684828326821e-05, 0.00026530689107850947, 0.0006129610537283225, 0.002087209960787595, 0.00290374071321875, 0.0020472894698817523, 2.360806709422736e-05, 0.0017449222730436809, 0.0014809373925386282, 0.0009302646937726644, 0.00023724624727050614, 8.150100063286647e-05, 4.984330999269204e-06, 4.925328396909734e-06, 3.6265129713664024e-05, 0.00013657677273337665, 0.00022008908595805288, 0.00044302657230532727, 0.0003533976575068598, 0.00020351535710572133, 5.43610939485184e-07, 0.00011382311021634894, 0.00019422317002082412, 0.00021497283602989192, 8.70192302054873e-05, 4.1234804362910715e-05, 4.038649503712577e-06, 1.124619311898556e-06, 1.3053006908458897e-05, 4.337371918849975e-05, 5.271085643610007e-05, 7.287264124733461e-05, 3.572624691072779e-05, 1.0876786015745488e-05], 'braket_tasks_cost': Decimal('0')}\n", + "CPU times: user 118 ms, sys: 8.17 ms, total: 126 ms\n", + "Wall time: 1min 55s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "result = job.result()\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we plot the results from the hybrid job" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Energy')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "theory_energy = -1.136189454088\n", + "theory_spin = 0\n", + "\n", + "plt.hlines(theory_energy, 0, iterations, colors=\"black\")\n", + "plt.plot(result[\"energies\"], \"o-\")\n", + "plt.xlabel(\"Steps\")\n", + "plt.ylabel(\"Energy\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -499,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -523,7 +700,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -564,7 +741,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -581,7 +758,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -597,22 +774,25 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "wires = dev.wires.tolist()\n", "\n", + "\n", "@qml.qnode(dev)\n", "def energy_expval(params):\n", " circuit(params, wires)\n", " return qml.expval(h)\n", "\n", + "\n", "@qml.qnode(dev)\n", "def S2_expval(params):\n", " circuit(params, wires)\n", " return qml.expval(S2)\n", "\n", + "\n", "def spin(params):\n", " return -0.5 + np.sqrt(1 / 4 + S2_expval(params))" ] @@ -626,25 +806,66 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Metrics - timestamp=1697139937.3019145; energy=-0.5412825955158931; iteration_number=0;\n", "Completed iteration 1\n", - "Energy: -0.5412825955159788\n", + "Energy: -0.5412825955158931\n", "Total spin: (0.15326105042318272+0j)\n", "----------------\n", + "Metrics - timestamp=1697139944.412684; energy=-0.6936120312135796; iteration_number=1;\n", "Completed iteration 2\n", - "Energy: -0.6936120312137185\n", + "Energy: -0.6936120312135796\n", "Total spin: (0.1396092557178953+0j)\n", "----------------\n", + "Metrics - timestamp=1697139951.4129083; energy=-0.8906199472711986; iteration_number=2;\n", "Completed iteration 3\n", - "Energy: -0.8906199472713872\n", + "Energy: -0.8906199472711986\n", "Total spin: (0.09703433737097567+0j)\n", - "----------------\n" + "----------------\n", + "Metrics - timestamp=1697139958.4950173; energy=-1.0433105151800557; iteration_number=3;\n", + "Completed iteration 4\n", + "Energy: -1.0433105151800557\n", + "Total spin: (0.05785302723925401+0j)\n", + "----------------\n", + "Metrics - timestamp=1697139965.4518325; energy=-1.0992473165501542; iteration_number=4;\n", + "Completed iteration 5\n", + "Energy: -1.0992473165501542\n", + "Total spin: (0.024499761677734266+0j)\n", + "----------------\n", + "Metrics - timestamp=1697139972.4262433; energy=-1.1281011541233785; iteration_number=5;\n", + "Completed iteration 6\n", + "Energy: -1.1281011541233785\n", + "Total spin: (0.012347538297979854+0j)\n", + "----------------\n", + "Metrics - timestamp=1697139979.4071403; energy=-1.132303797965168; iteration_number=6;\n", + "Completed iteration 7\n", + "Energy: -1.132303797965168\n", + "Total spin: (-0.007658654996353209+0j)\n", + "----------------\n", + "Metrics - timestamp=1697139986.409697; energy=-1.1349500459612611; iteration_number=7;\n", + "Completed iteration 8\n", + "Energy: -1.1349500459612611\n", + "Total spin: (0.0019462122578475238+0j)\n", + "----------------\n", + "Metrics - timestamp=1697139993.3897896; energy=-1.1356074477133202; iteration_number=8;\n", + "Completed iteration 9\n", + "Energy: -1.1356074477133202\n", + "Total spin: (0.0023445033042561736+0j)\n", + "----------------\n", + "Metrics - timestamp=1697140000.283897; energy=-1.1373271109993635; iteration_number=9;\n", + "Completed iteration 10\n", + "Energy: -1.1373271109993635\n", + "Total spin: (-0.0016527315214822647+0j)\n", + "----------------\n", + "Optimized energy: -1.1373271109993635 Ha\n", + "Corresponding total spin: (-0.0016527315214822647+0j)\n", + "Elapsed: 69.98771715164185 s\n" ] } ], @@ -662,6 +883,29 @@ "What's next? The hydrogen_molecule folder contains additional molecular structure files for different atomic separations of molecular hydrogen. Pick one of the separations and find the ground state energy. How does the ground state energy change with atomic separation? \n", "" ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", + "Estimated cost to run this example: 0.000 USD\n" + ] + } + ], + "source": [ + "job_cost = job.result()[\"braket_tasks_cost\"]\n", + "\n", + "print(\n", + " \"Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\"\n", + ")\n", + "print(f\"Estimated cost to run this example: {job_cost:.3f} USD\")" + ] } ], "metadata": { @@ -683,7 +927,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/requirements..txt b/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/requirements..txt new file mode 100644 index 000000000..69c65e72a --- /dev/null +++ b/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/requirements..txt @@ -0,0 +1 @@ +pennylane>=0.32 \ No newline at end of file diff --git a/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/requirements.txt b/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/requirements.txt new file mode 100644 index 000000000..69c65e72a --- /dev/null +++ b/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/requirements.txt @@ -0,0 +1 @@ +pennylane>=0.32 \ No newline at end of file From 547e14540e17cbdcb3b23bdd54c345e20d7ca47c Mon Sep 17 00:00:00 2001 From: mbeach-aws <85963088+mbeach-aws@users.noreply.github.com> Date: Fri, 13 Oct 2023 18:02:00 -0400 Subject: [PATCH 04/24] Getting started with job decorators (#396) --- .../0_Creating_your_first_Hybrid_Job.ipynb | 593 ++++++++++++++++++ .../console_figures/expval.png | Bin 0 -> 48742 bytes .../console_figures/queue_viz.png | Bin 0 -> 138714 bytes .../requirements.txt | 1 + .../Creating_your_first_Hybrid_Job.ipynb | 0 .../algorithm_script.py | 0 .../algorithm_script_parametrized_circuit.py | 0 .../console_figures/1-create.png | Bin .../console_figures/2-algorithm.png | Bin .../console_figures/3-container.png | Bin .../console_figures/4-execution.png | Bin .../testJob/results.json | 0 12 files changed, 594 insertions(+) create mode 100644 examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/0_Creating_your_first_Hybrid_Job.ipynb create mode 100644 examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/console_figures/expval.png create mode 100644 examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/console_figures/queue_viz.png create mode 100644 examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/requirements.txt rename examples/hybrid_jobs/{0_Creating_your_first_Hybrid_Job => 8_Creating_Hybrid_Job_Scripts}/Creating_your_first_Hybrid_Job.ipynb (100%) rename examples/hybrid_jobs/{0_Creating_your_first_Hybrid_Job => 8_Creating_Hybrid_Job_Scripts}/algorithm_script.py (100%) rename examples/hybrid_jobs/{0_Creating_your_first_Hybrid_Job => 8_Creating_Hybrid_Job_Scripts}/algorithm_script_parametrized_circuit.py (100%) rename examples/hybrid_jobs/{0_Creating_your_first_Hybrid_Job => 8_Creating_Hybrid_Job_Scripts}/console_figures/1-create.png (100%) rename examples/hybrid_jobs/{0_Creating_your_first_Hybrid_Job => 8_Creating_Hybrid_Job_Scripts}/console_figures/2-algorithm.png (100%) rename examples/hybrid_jobs/{0_Creating_your_first_Hybrid_Job => 8_Creating_Hybrid_Job_Scripts}/console_figures/3-container.png (100%) rename examples/hybrid_jobs/{0_Creating_your_first_Hybrid_Job => 8_Creating_Hybrid_Job_Scripts}/console_figures/4-execution.png (100%) rename examples/hybrid_jobs/{0_Creating_your_first_Hybrid_Job => 8_Creating_Hybrid_Job_Scripts}/testJob/results.json (100%) diff --git a/examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/0_Creating_your_first_Hybrid_Job.ipynb b/examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/0_Creating_your_first_Hybrid_Job.ipynb new file mode 100644 index 000000000..eb567753a --- /dev/null +++ b/examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/0_Creating_your_first_Hybrid_Job.ipynb @@ -0,0 +1,593 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating your first Hybrid Job\n", + "\n", + "\n", + "This tutorial provides an introduction to running hybrid quantum-classical algorithms using\n", + "PennyLane on Amazon Braket . With Amazon Braket, you gain access to both real quantum devices and\n", + "scalable classical compute, enabling you to push the boundaries of your algorithm.\n", + "\n", + "In this tutorial, we'll walk through how to create your first hybrid quantum-classical algorithms on AWS.\n", + "With a single line of code, we'll see how to scale from PennyLane simulators on your laptop to running full-scale experiments on AWS that leverage both powerful classical compute and quantum devices.\n", + "You'll gain understanding of the hybrid jobs queue, including QPU priority queuing, and learn how to scale classical resources for resource-intensive tasks. \n", + "We hope these tools will empower you to start experimenting today with hybrid quantum algorithms!\n", + "\n", + "\n", + "\n", + "## Amazon Braket Hybrid Jobs\n", + "\n", + "Amazon Braket Hybrid Jobs offers a way for you to run hybrid quantum-classical algorithms that\n", + "require both classical resources and quantum processing units (QPUs). Hybrid Jobs is designed to\n", + "spin up the requested classical compute, run your algorithm, and release the instances after\n", + "completion so you only pay for what you use. This workflow is ideal for long-running iterative\n", + "algorithms involving both classical and quantum resources. Simply package up your code into a single\n", + "function, create a hybrid job with a single line of code, and Braket will schedule it to run as soon\n", + "as possible without interruption.\n", + "\n", + "Hybrid jobs have a separate queue from quantum tasks, so once your algorithm starts running, it will\n", + "not be interrupted by variations in the quantum task queue. This helps your long-running algorithms\n", + "run efficiently and predictably. Any quantum tasks created from a running hybrid job will be run\n", + "before any other quantum tasks in the queue. This is particularly beneficial for iterative hybrid\n", + "algorithms where subsequent tasks depend on the outcomes of prior quantum tasks. Examples of such\n", + "algorithms include the Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum\n", + "Eigensolver (VQE), or Quantum Machine Learning (QML). You can also monitor your algorithm's progress in near-real\n", + "time, enabling you to keep track of costs, budget, or custom metrics such as training loss or\n", + "expectation values.\n", + "\n", + "Importantly, on specific QPUs, running your algorithm in Hybrid Jobs benefits from [parametric compilation](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-parametric-compilation.html). \n", + "This reduces the overhead associated with the computationally expensive compilation step by compiling a circuit only once and not for every iteration in your hybrid algorithm. \n", + "This reduces the total runtime for many variational algorithms by up to 10x.\n", + "For long-running hybrid jobs, Braket automatically uses the updated calibration data from the hardware provider when compiling your circuit to ensure the highest quality results.\n", + "\n", + "## Getting started with PennyLane\n", + "\n", + "\n", + "Let’s setup an algorithm that makes use of both classical and quantum resources. We adapt the [PennyLane qubit rotation tutorial](https://pennylane.ai/qml/demos/tutorial_qubit_rotation).\n", + "\n", + "First, we define a quantum simulator to run the algorithm on. In this example, we will use the Braket local simulator before moving onto a QPU." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pennylane as qml\n", + "from pennylane import numpy as np\n", + "\n", + "\n", + "device = qml.device(\"braket.local.qubit\", wires=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we define a circuit with two rotation gates and measure the expectation value in the $Z$-basis" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "@qml.qnode(device)\n", + "def circuit(params):\n", + " qml.RX(params[0], wires=0)\n", + " qml.RY(params[1], wires=0)\n", + " return qml.expval(qml.PauliZ(0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we create a classical-quantum loop that uses gradient descent to minimize the expectation value.\n", + "\n", + "We add the ``log_metric`` function from Braket to record the training progress (see [metrics\n", + "documentation](https://amazon-braket-sdk-python.readthedocs.io/en/stable/_apidoc/braket.jobs.metrics.html)).\n", + "When running on AWS, ``log_metric`` records the metrics in [Amazon CloudWatch](https://aws.amazon.com/cloudwatch/), which is accessible\n", + "through the Braket console page or the Braket SDK. When running locally on your laptop,\n", + "``log_metric`` prints the iteration numbers.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from braket.jobs.metrics import log_metric\n", + "\n", + "\n", + "def qubit_rotation(num_steps=10, stepsize=0.5):\n", + " opt = qml.GradientDescentOptimizer(stepsize=stepsize)\n", + " params = np.array([0.5, 0.75])\n", + "\n", + " for i in range(num_steps):\n", + " # update the circuit parameters\n", + " params = opt.step(circuit, params)\n", + " expval = circuit(params)\n", + "\n", + " log_metric(metric_name=\"expval\", iteration_number=i, value=expval)\n", + "\n", + " return params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To run the entire algorithm, we call the `qubit_rotation`` function to see that it runs correctly." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Metrics - timestamp=1696957901.4336345; expval=0.38894534132396147; iteration_number=0;\n", + "Metrics - timestamp=1696957901.480698; expval=0.12290715413453956; iteration_number=1;\n", + "Metrics - timestamp=1696957901.5271728; expval=-0.09181374013482171; iteration_number=2;\n", + "Metrics - timestamp=1696957901.5738564; expval=-0.2936094099948542; iteration_number=3;\n", + "Metrics - timestamp=1696957901.6199787; expval=-0.5344079938678078; iteration_number=4;\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.6767967215302757, 2.3260934173312653]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qubit_rotation(5, stepsize=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! We see the expectation value change with each iteration number and the final parameters were returned as a list. Now, instead of running on our laptop, let’s submit this same function to be run on AWS." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running as a hybrid job\n", + "\n", + "You can execute long-running algorithms asynchronously with Amazon Braket Hybrid Jobs, \n", + "which fully manages the classical infrastructure so you can focus on the algorithm. For example, you\n", + "can train a larger circuit, or increase the number of iterations. Note that each hybrid job has\n", + "at least a one minute startup time since it creates a containerized environment on Amazon EC2. So\n", + "for very short workloads, such as a single circuit or a batch of circuits, it may suffice for you to\n", + "use quantum tasks.\n", + "\n", + "We now show how you can go from running your local Python function to running it as a hybrid job.\n", + "\n", + "
\n", + " Note: Only Python 3.10 is supported by default. For other versions, you may use hybrid job scripts, or specify a custom container image from Amazon Elastic Container Registry (ECR) (see \n", + "containers documentation).\n", + "
\n", + "\n", + "\n", + "The first step to creating a hybrid job is to annotate which function you want to run with the\n", + "`@hybrid_job` decorator. Then you create the job by invoking the function as you would for normal\n", + "Python functions. However, the decorated function returns the hybrid job handle rather than the\n", + "result of the function. To retrieve the results after it has been completed, use `job.result()`.\n", + "\n", + "For algorithms that do not need priority queueing or scheduling for on-demand QPUs, you may specify the device as `local:/` or simply `None`. For example, when using a simulator that runs on the same classical host alongside the rest of your algorithm, such as the PennyLane Lightning CPU/GPU simulators, you may set `device=\"local:pennylane/lightning.qubit\"`. \n", + "\n", + "\n", + "The required device argument in the `@hybrid_job` decorator specifies the QPU that the hybrid job\n", + "will have priority access to.\n", + "The device string you give is accessible in the hybrid job instance as the environment variable ``\"AMZN_BRAKET_DEVICE_ARN\"``.\n", + "In the following code, we annotate the `qubit_rotation` function from above.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from braket.jobs import hybrid_job\n", + "from braket.jobs import save_job_result\n", + "\n", + "@hybrid_job(device=\"local:pennylane/lightning.qubit\", dependencies=\"requirements.txt\")\n", + "def qubit_rotation_hybrid_job(num_steps=1, stepsize=0.5):\n", + " opt = qml.GradientDescentOptimizer(stepsize=stepsize)\n", + " params = np.array([0.5, 0.75])\n", + "\n", + " for i in range(num_steps):\n", + " # update the circuit parameters\n", + " params = opt.step(circuit, params)\n", + " expval = circuit(params)\n", + "\n", + " log_metric(metric_name=\"expval\", iteration_number=i, value=expval)\n", + "\n", + " return params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we create a hybrid job by calling the function as usual. This returns an `AwsQuantumJob` object that contains the device ARN, region, and job name." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AwsQuantumJob('arn':'arn:aws:braket:us-west-1:961591465522:job/qubit-rotation-hybrid-job-1696957901652')\n" + ] + } + ], + "source": [ + "job = qubit_rotation_hybrid_job(num_steps=10, stepsize=0.5)\n", + "print(job)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The hybrid job automatically captures the function arguments as [hyperparameters](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-hyperparameters.html).\n", + "In this case, we set `num_steps = 10` and `stepsize = 0.5` as the hyperparameters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can check the status with:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'QUEUED'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "job.state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the hybrid job starts, it will change the status to `RUNNING`. We can also check the hybrid job status in the Braket console.\n", + "\n", + "We can monitor the metrics in near-real time in the [Braket console page](https://console.aws.amazon.com/braket/) as shown below. \n", + "\n", + "![Training to minimize an expectation value.](console_figures/expval.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the hybrid job completes, we can get the results with `job.result()`. For this example, it should take approximately 2 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'result': tensor([0.03642036, 3.10081929], requires_grad=True)}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "job.result()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Any objects in the return statement are automatically captured by Braket. Note that the objects returned by the function must be a tuple with each element being serializable as text. \n", + "\n", + "Additionally, we can plot the metrics recording during the algorithm. Below we show the expectation value decreases with each iteration as expected." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df = pd.DataFrame(job.metrics())\n", + "df.sort_values(by=[\"iteration_number\"], inplace=True)\n", + "\n", + "plt.plot(df[\"iteration_number\"], df[\"expval\"], \"-o\", color=\"orange\")\n", + "plt.xlabel(\"iteration number\")\n", + "plt.ylabel(\"expectation value\")\n", + "plt.title(\"Simulator results\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running on a QPU with priority\n", + "\n", + "The next step is to see how well the qubit rotation works on a real QPU. We\n", + "create a hybrid job with the Rigetti device as the priority QPU. We also increase the number of\n", + "steps to 10.\n", + "\n", + "Using hybrid jobs for iterative algorithms is very beneficial because you retain priority access to the\n", + "target QPU. So once your quantum tasks are created in the hybrid job, they run ahead of other tasks\n", + "waiting in the *quantum task queue*. This means your\n", + "algorithm will not be interrupted by other quantum tasks, so it will run more efficiently and\n", + "predictably. Quantum tasks submitted as part of a hybrid job have priority, and are aggregated in the *priority task queue*.\n", + "\n", + "Hybrid jobs have their own *hybrid jobs queue* so that only a single\n", + "hybrid job can run on a QPU at a time. Each QPU has its own hybrid jobs queue. Note that this is a different queue from the quantum tasks. For a single quantum circuit, or a batch of circuit, it’s\n", + "recommended to create quantum tasks instead of hybrid jobs. For more information on quantum tasks and hybrid jobs queue see the [Amazon Braket documentation](https://docs.aws.amazon.com/braket/latest/developerguide/braket-task-when.html).\n", + "\n", + "To get QPU priority, you must ensure that the device ARN used within the function matches that\n", + "specified in the decorator. For convenience, you can use the helper function ``get_device_arn()`` to\n", + "automatically capture the device ARN declared in ``@hybrid_job``.\n", + "\n", + "
\n", + " Note: Only hybrid jobs running on AWS receive priority. Hybrid jobs running locally, or with a mismatched device ARN do not get priority task queueing. \n", + "
\n", + "\n", + "In the previous example, we declared the local simulator outside the decorated function scope.\n", + "However, for AWS devices such as QPUs or on-demand simulators, the device must be declared within the function scope. \n", + "\n", + "
\n", + " Note: AWS devices must be declared within the body of the decorated function.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from braket.devices import Devices\n", + "\n", + "# device_arn = Devices.Amazon.SV1\n", + "device_arn = Devices.Rigetti.AspenM3\n", + "\n", + "@hybrid_job(device=device_arn, dependencies=\"requirements.txt\") # set priority QPU\n", + "def qpu_qubit_rotation_hybrid_job(num_steps=10, stepsize=0.5):\n", + " # AWS devices must be declared within the decorated function.\n", + " device = qml.device(\n", + " \"braket.aws.qubit\",\n", + " device_arn=device_arn.value, # Make sure the device ARN matches the hybrid job device ARN\n", + " wires=2,\n", + " shots=1_000\n", + " )\n", + "\n", + " @qml.qnode(device)\n", + " def circuit(params):\n", + " qml.RX(params[0], wires=0)\n", + " qml.RY(params[1], wires=0)\n", + " return qml.expval(qml.PauliZ(0))\n", + " \n", + " opt = qml.GradientDescentOptimizer(stepsize=stepsize)\n", + " params = np.array([0.5, 0.75])\n", + "\n", + " for i in range(num_steps):\n", + " # update the circuit parameters\n", + " params = opt.step(circuit, params)\n", + " expval = circuit(params)\n", + "\n", + " log_metric(metric_name=\"expval\", iteration_number=i, value=expval)\n", + "\n", + " return params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get a sense of how long we will wait before the hybrid job runs, we can check the hybrid job\n", + "queue depth with `AwsDevice(device_arn).queue_depth().jobs`. We can also check if the device is\n", + "currently available with `AwsDevice(device_arn).is_available()`.\n", + "\n", + "You can check the queue depth on the devices page of the [Amazon Braket Console](https://console.aws.amazon.com/braket/home). Below, we show the devices page for Rigetti Aspen-M-3.\n", + "\n", + "![Rigetti Aspen-M-3 showing the queue depths.](console_figures/queue_viz.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "When there are no other hybrid jobs in the queue ahead of you, and the device is available, the hybrid job will start running.\n", + "\n", + "
\n", + " Note: Running the following cell will only run once the QPU is available. This may take a long time and will result in usage fees charged to your AWS account. Only run the cell if you are comfortable with the potential wait-time and costs. We recommend monitoring the Billing & Cost Management Dashboard on the AWS console.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AwsQuantumJob('arn':'arn:aws:braket:us-west-1:961591465522:job/qpu-qubit-rotation-hybrid-job-1696958403615')\n" + ] + } + ], + "source": [ + "qpu_job = qpu_qubit_rotation_hybrid_job(num_steps=10, stepsize=0.5)\n", + "print(qpu_job)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we wait for the algorithm to complete and download the result. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'result': [0.13750000000000018, 2.9715000000000003]}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qpu_job.result()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we plot the expectation value per iteration number below. We see that on a real QPU, the data is not as smooth as the simulator, but the minimum still is detected correctly!" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.DataFrame(qpu_job.metrics())\n", + "df.sort_values(by=[\"iteration_number\"], inplace=True)\n", + "\n", + "plt.plot(df[\"iteration_number\"], df[\"expval\"], \"-o\", color=\"teal\")\n", + "plt.xlabel(\"iteration number\")\n", + "plt.ylabel(\"expectation value\")\n", + "plt.title(\"QPU results\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. \n", + " Estimated charges shown may differ from your actual charges. \n", + " Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2)\n", + " Estimated cost to run this example is $39 USD.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this tutorial, we showed how to migrate from local Python functions to running algorithms on simulators and QPUs on Amazon Braket.\n", + "We adapted the simple example of rotating a qubit using gradient descent, running this on both a\n", + "local simulator and a real QPU. \n", + "Using Amazon Braket Hybrid Jobs allowed us to run algorithms asynchronously, scale classical compute using AWS, and obtain priority access to the selected QPU for the duration of our algorithm." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "decorator", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "orig_nbformat": 4 + }, + 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z{6|Ikk8=K>Y{74?`^WbFR!0AkrAR96{|DP!1H{`ym9AO4g`PpVd+vU@2IVF~_MnhlaJ+7f{X6$sFcM;h(O{7>Qwo%SfM~FqUiI|!BvX?(-EW=PEXyEahqw2RbNfm zJ4MZLQ!eNV)AQ5RuaB?-(_0tucrB&M3!qEtz>XP7wXdVaY|Ni}?8lPtZF3#)aQ6X3@xK?Or~gp(KM?01&iN0-`G<4<19AT0oc} Date: Sat, 14 Oct 2023 15:51:47 -0400 Subject: [PATCH 05/24] jobs decorator QCBM example (#406) --- ...earning_in_Amazon_Braket_Hybrid_Jobs.ipynb | 1696 ++++++++--------- .../console_figures/hp_job_console.png | Bin 129672 -> 115621 bytes .../console_figures/running_job.png | Bin 46796 -> 56824 bytes .../data.npy | Bin 384 -> 0 bytes .../qcbm/qcbm.py | 1 - .../qcbm/qcbm_job.py | 98 - 6 files changed, 797 insertions(+), 998 deletions(-) delete mode 100644 examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/data.npy delete mode 100644 examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/qcbm/qcbm_job.py diff --git a/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb b/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb index 6e71c4e92..6b05099a1 100644 --- a/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb +++ b/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb @@ -1,901 +1,799 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Quantum machine learning in Amazon Braket Hybrid Jobs\n", - "\n", - "This notebook demonstrates a typical quantum machine learning workflow, including uploading data, monitoring training, and tuning hyperparameters. We focus on training a parameterized quantum circuit for an unsupervised generative modelling task.\n", - "\n", - "\n", - "## Learning outcomes\n", - "\n", - "* Set input data \n", - "* Set hyperparameters \n", - "* Submit multiple hybrid jobs asynchronously \n", - "* Monitor hybrid job progress via the AWS Console \n", - "* Download and plot results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Background: Generative modelling \n", - "\n", - "Generative modelling is an unsupervised learning task where the goal is to generate new synthetic samples from an unknown target probability distribution. We denote the target probability distribution as $p(x)$, and the estimated distribution as $p_{\\theta}(x)$. The goal is to learn $p_{\\theta}(x)$ that closely resembles the target $p(x)$. One metric to quantify the difference between probability distributions is the maximum mean discrepancy (MMD) loss . \n", - "\n", - "$$MMD(x, y) = \\sum_{j=1}^N \\sum_{j'=1}^N k(y_j, y_{j'}) + \\sum_{i=1}^N \\sum_{i'=1}^N k(x_i, x_{i'}) - 2 \\sum_{j=1}^N \\sum_{i=1}^N k(y_j, x_i)$$\n", - "where $x$ is a sample from the target data $p(x)$, $y$ is a sample from the generative model $p_{\\theta}(x)$, and $k$ is a Gaussian kernel\n", - "\n", - "$$ k(x,y)= \\sum_{\\sigma} e^{-(x-y)^2/(2 \\sigma^2))}$$\n", - "\n", - "The MMD loss is zero if and only if $p(x)=p_{\\theta}(x)$ for all $x$. \n", - "\n", - "Learning a good approximation $p_{\\theta}$ depends on the expressibility of the model, the effectiveness of the training algorithm, and the ability to sample the circuit efficiently. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quantum Circuit Born Machine \n", - "\n", - "Quantum circuits are a natural fit for generative modelling because they are inherently probabilistic; the wavefunction encodes a probability according to the Born rule:\n", - "\n", - "$$p(x)=|\\langle x|\\psi\\rangle|^2$$\n", - "\n", - "In quantum mechanics, we do not have access to $p(x)$ directly, but we can efficiently sample using projective measurements [1]. This is an implicit generative model similar to generative adversarial networks (GANs). Quantum circuits allow fast sampling from a high-dimension distribution, and have large expressive power. \n", - "\n", - "The QCBM in this tutorial consists of alternating layers of single qubit rotations ($RX, RZ, RX$), followed by an entangling layer of CNot gates on each neighboring qubits. The final measurement layer computes the bit string samples of each outcome. Run the cell below to print a circuit diagram of a QCBM with randomly initialized parameters.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "T : | 0 | 1 | 2 |3|4|5|Result Types|\n", - " \n", - "q0 : -Rx(0.875)---Rz(0.321)-Rx(0.449)--C---X-Probability--\n", - " | | | \n", - "q1 : -Rx(0.872)---Rz(0.615)-Rx(0.0769)-X-C-|-Probability--\n", - " | | | \n", - "q2 : -Rx(0.00923)-Rz(0.352)-Rx(0.942)----X-C-Probability--\n", - "\n", - "T : | 0 | 1 | 2 |3|4|5|Result Types|\n" - ] - } - ], - "source": [ - "from braket.devices import LocalSimulator\n", - "from qcbm.qcbm import QCBM\n", - "import numpy as np\n", - "\n", - "n_qubits = 3\n", - "n_layers = 1\n", - "init_params = np.random.rand(3 * n_layers * n_qubits)\n", - "device = LocalSimulator()\n", - "qcbm = QCBM(device, n_qubits, n_layers, np.random.rand(1))\n", - "print(qcbm.create_circuit(init_params))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Problem setup\n", - "\n", - "This notebook demonstrates training a QCBM on a toy data set using Amazon Braket Hybrid Jobs. The code for the QCBM is in `qcbm` directory. The `qcbm_job.py` contains the code that will run when we create a Braket Hybrid Job. The other file (`qcbm.py`) contain the source code for the QCBM. \n", - "\n", - "In this tutorial, we use a small number of qubits to make it quick to test the algorithm. We use the on-demand simulator SV1 to run our circuits and gradient calculations in parallel (up to 35 concurrent tasks). \n", - "\n", - "We first set the number of qubits we want to use in our problem:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "n_qubits = 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate data\n", - "\n", - "As an example, we consider the toy example of learning a mixture of Gaussian distributions. We set a numpy random seed to produce the same data each time, but try experimenting with the number of peaks and number of qubits to produce harder or easier data sets. For this example, the target distribution $p(x)$ is a Gaussian on 5 bits (so $2^5$ possible values), with peaks at $\\mu_1=7$ and $\\mu_2=20$, with standard deviations $\\sigma_1=1$, $\\sigma_2 = 2$. We generate and plot the data as a probability density function in the cell below." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline \n", - "\n", - "def gaussian(n_qubits, mu, sigma=1):\n", - " x = np.arange(2 ** n_qubits)\n", - " gaussian = (\n", - " 1.0\n", - " / np.sqrt(2 * np.pi * sigma ** 2)\n", - " * np.exp(-((x - mu) ** 2) / (2 * sigma ** 2))\n", - " )\n", - " return gaussian / sum(gaussian)\n", - "\n", - "\n", - "data = gaussian(n_qubits, mu=4, sigma=5) + gaussian(n_qubits, mu=20, sigma=2)\n", - "data = data / sum(data)\n", - "\n", - "\n", - "labels = [\"{0:{fill}6b}\".format(i, fill='0') for i in range(len(data))]\n", - "\n", - "plt.bar(range(2 ** n_qubits), data)\n", - "plt.xticks([i for i in range(len(data))], labels, rotation='vertical', size=12)\n", - "plt.yticks(size=12)\n", - "\n", - "plt.xlabel(\"Sample\", size=20)\n", - "plt.ylabel(\"Probability\", size=20)\n", - "plt.title(\"Two-peak Gaussian distribution\")\n", - "\n", - "fig = plt.gcf()\n", - "fig.set_size_inches(16, 8)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "np.save(\"data.npy\", data) # save data to file" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Upload data\n", - "\n", - "To run our algorithm as a Hybrid Job, we need to input the data to the job. There are two primary ways to pass data to a Braket Hybrid Job.\n", - "\n", - "1. Firstly, we could pass the name of a local file as a string. In this example, it would be \"data.npy\". In the algorithm script (`qcbm_job.py`), we would then load the file with `f\"{input_dir}/input/data.npy\"` since `\"input\"` is the default S3 channel used by Braket Hybrid Jobs. We create the hybrid job with \n", - "\n", - "```\n", - " AwsQuantumJob(input_data=\"data.npy\",...)\n", - "```\n", - "\n", - "\n", - "2. Secondly, we could directly upload the data to S3 and point the hybrid job to that bucket. In the cell below, we provide a utility function that uploads the data to S3 with the name `filename` under the directory `dir_name` in the default bucket. Optionally, we can pass a specific AwsSession to customize where the data is saved. The S3 path would be passed to create a hybrid job similarly:\n", - "\n", - "```\n", - " AwsQuantumJob(input_data=s3_path,...)\n", - "```\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Upload dataset to S3\n", - "from braket.aws import AwsSession\n", - "\n", - "def setup_input_stream(data, filename, dir_name=\"job-data\", aws_session=AwsSession()):\n", - " stream_s3_uri = aws_session.construct_s3_uri(aws_session.default_bucket(), dir_name)\n", - " np.save(filename, data)\n", - " path = f\"{stream_s3_uri}/\" + filename\n", - " aws_session.upload_to_s3(filename, path)\n", - " return path" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "s3://amazon-braket--/job-data/data.npy\n" - ] - } - ], - "source": [ - "s3_path = setup_input_stream(data, \"data.npy\")\n", - "print(s3_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As a double check, try navigating to this S3 bucket using the AWS Console to verify the data was successfully uploaded." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Hyperparameters \n", - "\n", - "Next, we set the hyperparameters for our training hybrid job. To keep it simple, we only consider the following hyperparameters: number of qubits `n_qubits`, number of layers in the QCBM `n_layers`, and the number of iterations in the optimization algorithm.\n", - "\n", - "The number of layers determines how many parameters are in the quantum circuit. For the QCBM, we need `n_params = 3 * n_layers * n_qubits`.\n", - "\n", - "Note that the hyperparameters are defined in a Python dictionary of strings. Within the `qcbm_job.py` script, we will need to convert from strings back into integers.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Declare hyperparameters for QCBM\n", - "\n", - "n_iterations = 10\n", - "n_layers = 3\n", - "\n", - "hyperparams = {\n", - " \"n_qubits\": n_qubits,\n", - " \"n_layers\": n_layers,\n", - " \"n_iterations\": n_iterations\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now prepared to run a fully-managed Hybrid Job to keep track of our experiments and scale up to larger models. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating a Braket Job\n", - "\n", - "The `AwsQuantumJob.create()` function requires a few arguments. \n", - "\n", - "\n", - "* `device`: Specifies the device we wish to run on. In this tutorial, we will use the Amazon Braket SV1 simulator. SV1 uses the full state-vector and supports up to 34 qubits. \n", - "* `source_module`: The directory or single file containing the code to run. This tutorial uses a directory qcbm that contains the script `qcbm_job.py`.\n", - "* `entry_point`: is the main script or function the hybrid job will run. This tutorial uses the function main() within `qcbm_job.py`\n", - "* `job_name` (optional): A unique string to identify the hybrid job. We use the prefix `f\"qcbm-gaussian-training-{n_qubits}-{n_layers}\"`. We also append a time stamp to give the job a unique name. \n", - "* `hyperparameters` (optional): The Python dictionary containing the hyperparameter names and values (as strings). \n", - "* `input_data` (optional): The path to Amazon S3 where our data was uploaded, or a local path that will be automatically upload data to S3.\n", - "* `wait_until_complete` (optional) : Optionally, we monitor the hybrid job inline (note this will prevent using the Jupyter notebook while the hybrid job is running). Set to `False` to do an async hybrid job. Defaults to `False`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a Amazon Braket Hybrid Job\n", - "\n", - "import time \n", - "from braket.aws import AwsQuantumJob\n", - "\n", - "job_name = f\"qcbm-gaussian-training-{n_qubits}-{n_layers}-\" + str(int(time.time()))\n", - "\n", - "job = AwsQuantumJob.create(\n", - " device=\"arn:aws:braket:::device/quantum-simulator/amazon/sv1\",\n", - " source_module=\"qcbm\",\n", - " entry_point=\"qcbm.qcbm_job:main\",\n", - " job_name=job_name,\n", - " hyperparameters=hyperparams,\n", - " input_data=\"data.npy\", # or input_data=s3_path\n", - " # wait_until_complete=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Great! We created our first quantum machine learning job! \n", - "\n", - "Since we set `wait_until_complete=False`, the hybrid job will run asynchronously and silently. We can check the status of the hybrid job with " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'QUEUED'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "job.state()" - ] - }, - { - "attachments": { - "running_job.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also monitor the status of the hybrid job with the AWS Console.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once it’s complete, we can grab the result with `job.result()` which will wait for the hybrid job to finish. In `qcbm_job.py`, we set the results to be the final parameters of the QCBM that minimized the loss function. Results are returned as a dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 666 ms, sys: 133 ms, total: 799 ms\n", - "Wall time: 17min 12s\n" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quantum machine learning in Amazon Braket Hybrid Jobs\n", + "\n", + "This notebook demonstrates a typical quantum machine learning workflow, including uploading data, monitoring training, and tuning hyperparameters. We focus on training a parameterized quantum circuit for an unsupervised generative modelling task.\n", + "\n", + "\n", + "## Learning outcomes\n", + "\n", + "* Set input data \n", + "* Set hyperparameters \n", + "* Submit multiple hybrid jobs asynchronously \n", + "* Monitor hybrid job progress via the AWS Console \n", + "* Download and plot results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Background: Generative modelling \n", + "\n", + "Generative modelling is an unsupervised learning task where the goal is to generate new synthetic samples from an unknown target probability distribution. We denote the target probability distribution as $p(x)$, and the estimated distribution as $p_{\\theta}(x)$. The goal is to learn $p_{\\theta}(x)$ that closely resembles the target $p(x)$. One metric to quantify the difference between probability distributions is the maximum mean discrepancy (MMD) loss . \n", + "\n", + "$$MMD(x, y) = \\sum_{j=1}^N \\sum_{j'=1}^N k(y_j, y_{j'}) + \\sum_{i=1}^N \\sum_{i'=1}^N k(x_i, x_{i'}) - 2 \\sum_{j=1}^N \\sum_{i=1}^N k(y_j, x_i)$$\n", + "where $x$ is a sample from the target data $p(x)$, $y$ is a sample from the generative model $p_{\\theta}(x)$, and $k$ is a Gaussian kernel\n", + "\n", + "$$ k(x,y)= \\sum_{\\sigma} e^{-(x-y)^2/(2 \\sigma^2))}$$\n", + "\n", + "The MMD loss is zero if and only if $p(x)=p_{\\theta}(x)$ for all $x$. \n", + "\n", + "Learning a good approximation $p_{\\theta}$ depends on the expressibility of the model, the effectiveness of the training algorithm, and the ability to sample the circuit efficiently. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quantum Circuit Born Machine \n", + "\n", + "Quantum circuits are a natural fit for generative modelling because they are inherently probabilistic; the wavefunction encodes a probability according to the Born rule:\n", + "\n", + "$$p(x)=|\\langle x|\\psi\\rangle|^2$$\n", + "\n", + "In quantum mechanics, we do not have access to $p(x)$ directly, but we can efficiently sample using projective measurements [1]. This is an implicit generative model similar to generative adversarial networks (GANs). Quantum circuits allow fast sampling from a high-dimension distribution, and have large expressive power. \n", + "\n", + "The QCBM in this tutorial consists of alternating layers of single qubit rotations ($RX, RZ, RX$), followed by an entangling layer of CNot gates on each neighboring qubits. The final measurement layer computes the bit string samples of each outcome. Run the cell below to print a circuit diagram of a QCBM with randomly initialized parameters.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T : | 0 | 1 | 2 |3|4| 5 | 6 | 7 | 8 |9|10|11|Result Types|\n", + " \n", + "q0 : -Rx(0.53)-Rz(0.86)-Rx(0.21)-C---X----------Rx(0.24)-Rz(0.41)-Rx(0.88)-C----X--Probability--\n", + " | | | | | \n", + "q1 : -Rx(0.27)-Rz(0.55)-Rx(0.22)-X-C-|-Rx(0.65)-Rz(0.26)-Rx(0.95)----------X-C--|--Probability--\n", + " | | | | | \n", + "q2 : -Rx(0.92)-Rz(0.97)-Rx(0.43)---X-C----------Rx(0.25)-Rz(0.42)-Rx(0.98)---X--C--Probability--\n", + "\n", + "T : | 0 | 1 | 2 |3|4| 5 | 6 | 7 | 8 |9|10|11|Result Types|\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "from braket.devices import LocalSimulator\n", + "from qcbm.qcbm import QCBM\n", + "\n", + "\n", + "n_qubits = 3\n", + "n_layers = 2\n", + "\n", + "init_params = np.random.rand(3 * n_layers * n_qubits)\n", + "device = LocalSimulator()\n", + "qcbm = QCBM(device, n_qubits, n_layers, np.random.rand(1))\n", + "print(qcbm.create_circuit(init_params))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem setup\n", + "\n", + "This notebook demonstrates training a QCBM on a toy data set using Amazon Braket Hybrid Jobs. The code for the QCBM is in `qcbm` directory. The `qcbm_job.py` contains the code that will run when we create a Braket Hybrid Job. The other file (`qcbm.py`) contain the source code for the QCBM. \n", + "\n", + "In this tutorial, we use a small number of qubits to make it quick to test the algorithm. We use the on-demand simulator SV1 to run our circuits and gradient calculations in parallel (up to 35 concurrent tasks). \n", + "\n", + "We first set the number of qubits we want to use in our problem:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "n_qubits = 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate data\n", + "\n", + "As an example, we consider the toy example of learning a mixture of Gaussian distributions. We set a numpy random seed to produce the same data each time, but try experimenting with the number of peaks and number of qubits to produce harder or easier data sets. For this example, the target distribution $p(x)$ is a Gaussian on 5 bits (so $2^5$ possible values), with peaks at $\\mu_1=7$ and $\\mu_2=20$, with standard deviations $\\sigma_1=1$, $\\sigma_2 = 2$. We generate and plot the data as a probability density function in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline \n", + "\n", + "def gaussian(n_qubits, mu, sigma=1):\n", + " x = np.arange(2 ** n_qubits)\n", + " gaussian = (\n", + " 1.0\n", + " / np.sqrt(2 * np.pi * sigma ** 2)\n", + " * np.exp(-((x - mu) ** 2) / (2 * sigma ** 2))\n", + " )\n", + " return gaussian / sum(gaussian)\n", + "\n", + "\n", + "data = gaussian(n_qubits, mu=4, sigma=5) + gaussian(n_qubits, mu=20, sigma=2)\n", + "data = data / sum(data)\n", + "\n", + "\n", + "labels = [\"{0:{fill}6b}\".format(i, fill='0') for i in range(len(data))]\n", + "\n", + "plt.bar(range(2 ** n_qubits), data)\n", + "plt.xticks([i for i in range(len(data))], labels, rotation='vertical', size=12)\n", + "plt.yticks(size=12)\n", + "\n", + "plt.xlabel(\"Sample\", size=20)\n", + "plt.ylabel(\"Probability\", size=20)\n", + "plt.title(\"Two-peak Gaussian distribution\")\n", + "\n", + "fig = plt.gcf()\n", + "fig.set_size_inches(16, 8)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "np.save(\"data.npy\", data) # save data to file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training \n", + "\n", + "\n", + "Next, we train the circuit using the [limited-memory BFGS](https://en.wikipedia.org/wiki/Limited-memory_BFGS) optimizer from scipy. \n", + "Instead of using finite-difference gradients, we use the exact MMD loss function gradient. \n", + "\n", + "The training function has three arguments that act as hyperparameters: number of qubits `n_qubits`, number of layers in the QCBM `n_layers`, and the number of iterations in the optimization algorithm. \n", + "The number of layers determines how many rotation angles are in the quantum circuit. \n", + "For the QCBM, we need `n_params = 3 * n_layers * n_qubits` parameters. \n", + "\n", + "\n", + "Since we will eventually run this training function as an hybrid job, we add three convenience functions. \n", + "Firstly, we use `log_metric` to print the loss function for each iteration. \n", + "Once we run this as a hybrid job, the metrics will be displayed in near-real time on the Braket console, or via [Amazon CloudWatch](https://aws.amazon.com/cloudwatch/). \n", + "\n", + "Secondly, we write our results to a file prefixed with the path `get_results_dir()`. \n", + "This is necessary because hybrid jobs use a temporary filesystem.\n", + "Once the instance is terminated, all files on the instance are deleted.\n", + "\n", + "Lastly, the return statement of the function will be our hybrid job results returned by `job.result()`. \n", + "These can be a single object or a dictionary with string keys. \n", + "Note that while most native Python objects are supported, custom classes that do have have defined serialization methods may not work. \n", + "For example, we can return the numpy array of final parameters, but we cannot return the QCBM object itself. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from braket.tracking import Tracker\n", + "from scipy.optimize import minimize\n", + "\n", + "from braket.jobs import get_results_dir\n", + "from braket.jobs.metrics import log_metric\n", + "\n", + "from qcbm.qcbm import mmd_loss\n", + "\n", + "def train_circuit(n_qubits, n_layers, n_iterations=10):\n", + " global iteration_number\n", + " iteration_number = 0\n", + " \n", + " braket_task_costs = Tracker().start()\n", + "\n", + " device = LocalSimulator()\n", + "\n", + " data = np.load(\"data.npy\") # load the input data\n", + " \n", + " qcbm = QCBM(device, n_qubits, n_layers, data)\n", + " \n", + " init_params = np.random.rand(3 * n_layers * n_qubits)\n", + "\n", + " def callback(x):\n", + " global iteration_number\n", + " iteration_number += 1\n", + " loss = mmd_loss(qcbm.probabilities(x), data)\n", + " \n", + " log_metric( # log the metrics to Braket console\n", + " metric_name=\"loss\",\n", + " value=loss,\n", + " iteration_number=iteration_number,\n", + " )\n", + "\n", + " res = minimize(\n", + " lambda x: mmd_loss(qcbm.probabilities(x), data),\n", + " x0=init_params,\n", + " method=\"L-BFGS-B\",\n", + " jac=lambda x: qcbm.gradient(x),\n", + " options={\"maxiter\": n_iterations},\n", + " callback=callback,\n", + " )\n", + " final_params = res.x\n", + "\n", + " # save final parameters\n", + " np.save(get_results_dir() + \"/final_params.npy\", final_params)\n", + " \n", + " return {\n", + " \"params\": final_params,\n", + " \"task summary\": braket_task_costs.quantum_tasks_statistics(),\n", + " \"estimated cost\": float(braket_task_costs.qpu_tasks_cost() + braket_task_costs.simulator_tasks_cost()),\n", + " }\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's run the function to verify that it works as expected." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Metrics - timestamp=1697028322.742037; loss=0.0738105584324629; iteration_number=1;\n", + "Metrics - timestamp=1697028324.6127226; loss=0.05514942674966222; iteration_number=2;\n", + "Metrics - timestamp=1697028328.4326718; loss=0.029347326432547782; iteration_number=3;\n", + "Metrics - timestamp=1697028330.5567336; loss=0.02751726243188396; iteration_number=4;\n", + "Metrics - timestamp=1697028332.750267; loss=0.019362259334110732; iteration_number=5;\n", + "CPU times: user 3.87 s, sys: 515 ms, total: 4.39 s\n", + "Wall time: 13.9 s\n" + ] + }, + { + "data": { + "text/plain": [ + "{'params': array([ 0.55196856, 0.38672791, 1.35300743, 0.87330452, -0.54221993,\n", + " 0.74465571, 0.61411241, 0.06143268, 0.81830411, 0.28084566,\n", + " 0.98187881, 0.17193621, 0.04322529, 0.02989581, -0.08170342,\n", + " 0.58622265, 0.50212177, 0.32964731, 1.19606685, -0.28772351,\n", + " 0.60482847, -0.04202664, 0.33609895, 0.12554622, 0.46006129,\n", + " 0.44578169, 1.22449628, 1.1830697 , 0.19600585, 0.35640496]),\n", + " 'task summary': {},\n", + " 'estimated cost': 0.0}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "\n", + "train_circuit(n_qubits, n_layers=n_layers, n_iterations=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! Now for longer algorithms, or those that require priority queueing to a QPU, we can run this function on AWS by adding the `@hybrid_job` annotation and calling the function.\n", + "\n", + "## Training with hybrid jobs\n", + "\n", + "Amazon Braket Hybrid Jobs provides fully managed execution of hybrid quantum-classical algorithms, combining AWS classical compute resources based on Amazon EC2 (the \"job instance\") with Amazon Braket quantum processing units (QPUs) or quantum circuit simulators. \n", + "\n", + "There are three arguments to the `@hybrid_job` decorator that we will use in this example. Firstly, \n", + "since we do not require priority QPU access, we set `device=None` in the decorator arguments. \n", + "This argument is responsible for scheduling the hybrid job to run on a QPU. \n", + "In this example, we use the Braket local simulator running on the job instance. \n", + "If we find this simulator too slow, we could either increase the the classical compute by selecting a larger instance, or switch to using on-demand Braket simulators like [SV1](https://docs.aws.amazon.com/braket/latest/developerguide/braket-devices.html#braket-simulator-sv1). \n", + "\n", + "Next, we include the source code for the QCBM with `include_modules`. This can be a Python module, directory, or file. We may also specify multiple modules with a list.\n", + "\n", + "Lastly, as a quantum machine learning algorithm, the QCBM requires training data. \n", + "When you create a hybrid job, you may provide an input training datasets by specifying an Amazon Simple Storage Service (Amazon S3) bucket. \n", + "You may also specify a local path, in which case Braket will automatically upload the data to Amazon S3 at `s3:///jobs//data/`. \n", + "In this example, we use the local data file `input_data=\"data.npy\"`. You may specify multiple input datasets with a dictionary where the values are the paths to either S3 or local files. \n", + "\n", + "Now we run the training algorithm remotely as a hybrid job. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from braket.jobs import hybrid_job\n", + "\n", + "# For now, lets set local=False. This uses a local Docker container\n", + "@hybrid_job(device=None, local=False, include_modules=\"qcbm\", input_data=\"data.npy\")\n", + "def train_circuit_hybrid_job(n_qubits, n_layers, n_iterations):\n", + " return train_circuit(n_qubits, n_layers, n_iterations)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 509 ms, sys: 30.4 ms, total: 540 ms\n", + "Wall time: 3min 52s\n" + ] + } + ], + "source": [ + "%%time \n", + "\n", + "job = train_circuit_hybrid_job(n_qubits, n_layers=n_layers, n_iterations=10)\n", + "res = job.result()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! We created our first quantum machine learning job! \n", + "\n", + "We can check the status of the hybrid job with " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'COMPLETED'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "job.state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also monitor the status of the hybrid job with the AWS Console.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once it’s complete, we can grab the result with `job.result()` which will wait for the hybrid job to finish. In `qcbm_job.py`, we set the results to be the final parameters of the QCBM that minimized the loss function. Results are returned as a dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 10.4 ms, sys: 475 µs, total: 10.9 ms\n", + "Wall time: 341 ms\n" + ] + }, + { + "data": { + "text/plain": [ + "{'params': array([ 0.35736532, 0.31073256, 0.91610879, 0.98872712, -0.08082859,\n", + " 0.47862197, 0.68460835, -0.09855128, 0.90463028, 0.25120807,\n", + " 0.83142284, 0.42736354, -0.04195098, 0.69129029, 0.63572789,\n", + " 0.75008587, 0.63212422, 0.95159988, 0.78795069, 0.17262345,\n", + " 0.92826497, 0.75725417, 0.18248898, 0.80719954, 1.31568383,\n", + " 0.85844901, 0.5214184 , 0.04987039, 0.03008104, -0.03636072]),\n", + " 'task summary': {},\n", + " 'estimated cost': 0.0}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time \n", + "job.result()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Awesome! Our first quantum machine learning job finished! Now let’s look at the training metrics.\n", + "\n", + "Note that due to the inherent randomness in the training process, running this example repeatedly may yield different results each time." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Metrics and plotting\n", + "\n", + "In the `qcbm_job.py` script, we monitored the loss function during training with \n", + "```\n", + "log_metric(\n", + " metric_name=\"loss\",\n", + " value=loss,\n", + " iteration_number=iteration_number,\n", + ")\n", + "```\n", + "Metrics recorded in this way are visible from the \"Monitor\" tab in the AWS Console. It will look similar to the below image:\n", + "\n", + "
\n", + "\n", + "Metrics are also available by calling `job.metrics()`. Using pandas, and matplotlib, we plot the convergence of the loss below. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting the convergence of the loss function metric\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "plt.style.use(\"seaborn-v0_8-whitegrid\")\n", + "\n", + "\n", + "plt.style.use(\"seaborn-v0_8-colorblind\")\n", + "df.sort_values(by=[\"iteration_number\"]).plot(x=\"iteration_number\", y=\"loss\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Importantly, we can plot the predicted probability distribution vs the target probability from the data. \n", + "To do so, we first import the QCBM locally again, but now we initialize it with the parameters returned from our hybrid job." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting the original probability distribution, and the QCBM prediction probability\n", + "\n", + "device = LocalSimulator()\n", + "qcbm = QCBM(device, n_qubits, n_layers=n_layers, data=data)\n", + "\n", + "qcbm_probs = qcbm.probabilities(job.result()[\"params\"])\n", + "\n", + "labels = [\"{0:{fill}6b}\".format(i, fill='0') for i in range(len(data))]\n", + "\n", + "plt.bar(range(2 ** n_qubits), data, label=\"target probability\", alpha=0.6)\n", + "plt.bar(range(2 ** n_qubits), qcbm_probs, label=\"QCBM probability\", alpha=0.6)\n", + "plt.xticks([i for i in range(len(data))], labels, rotation='vertical', size=12)\n", + "plt.yticks(size=12)\n", + "\n", + "plt.xlabel(\"Sample\", size=20)\n", + "plt.ylabel(\"Probability\", size=20)\n", + "\n", + "fig = plt.gcf()\n", + "fig.set_size_inches(16, 8)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! As expected, the QCBM probability distribution closes matches the target distribution. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantum Task Summary\n", + "{}\n", + "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", + "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n" + ] + } + ], + "source": [ + "print(\"Quantum Task Summary\")\n", + "print(job.result()['task summary'])\n", + "print('Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).')\n", + "print(f\"Estimated cost to run quantum tasks in this hybrid job: {job.result()['estimated cost']} USD\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running different hyperparameters\n", + "\n", + "One of the strengths of Braket Hybrid Jobs is the ability to submit and monitor many hybrid jobs simultaneously. We can use this to perform a grid search to find good hyperparameters. Below we initialize 4 unique hybrid jobs with different `n_layers`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating job with 1 layers\n", + "Creating job with 2 layers\n", + "Creating job with 3 layers\n", + "Creating job with 4 layers\n" + ] + } + ], + "source": [ + "jobs = []\n", + "\n", + "for n_layers in range(1, 5):\n", + " print(f\"Creating job with {n_layers} layers\")\n", + " tmp_job = train_circuit_hybrid_job(n_qubits, n_layers, n_iterations=10)\n", + "\n", + " jobs.append(tmp_job)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To check the results, we could load the results as we did before, or we could check the \"Monitor\" tab in the Braket Jobs dashboard in the AWS Console.\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 238 ms, sys: 10.8 ms, total: 249 ms\n", + "Wall time: 5min 42s\n" + ] + } + ], + "source": [ + "%%time \n", + "jobs[-1].result(); # wait for the last job to finish" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now plot the results from all the hyperparameters experiments once they finish. If the cell below does not work, wait a few minutes for metrics to load and try again." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dfs = []\n", + "for i, j in enumerate(jobs):\n", + " df = pd.DataFrame(j.metrics())\n", + " df.sort_values(by=[\"iteration_number\"])\n", + " dfs.append(df)\n", + " plt.plot(df[\"iteration_number\"], df[\"loss\"], label=f\"n_layers={i+1}\")\n", + "\n", + "plt.xlabel(\"iteration_number\")\n", + "plt.ylabel(\"loss\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the plots above, we see that the loss is much lower for `n_layers=3` and `n_layers=4`. We can conclude for 5 qubits, we need at least 3 layers in the QCBM to accurately learn the two-peak Gaussian data. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantum Task Summary\n", + "{}\n", + "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", + "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n", + "Quantum Task Summary\n", + "{}\n", + "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", + "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n", + "Quantum Task Summary\n", + "{}\n", + "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", + "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n", + "Quantum Task Summary\n", + "{}\n", + "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", + "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n" + ] + } + ], + "source": [ + "for job in jobs:\n", + " print(\"Quantum Task Summary\")\n", + " print(job.result()['task summary'])\n", + " print('Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).')\n", + " print(f\"Estimated cost to run quantum tasks in this hybrid job: {job.result()['estimated cost']} USD\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion \n", + "\n", + "In this notebook, we submitted a single training hybrid job in Amazon Braket Hybrid Jobs. We then simultaneously submitted 4 new hybrid jobs with different hyperparameters to learn about the number of layers required in our circuit." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "\n", + "[1] Benedetti, Marcello, Delfina Garcia-Pintos, Oscar Perdomo, Vicente Leyton-Ortega, Yunseong Nam, and Alejandro Perdomo-Ortiz. “A Generative Modeling Approach for Benchmarking and Training Shallow Quantum Circuits.” Npj Quantum Information 5, no. 1 (May 27, 2019): 1–9. https://doi.org/10.1038/s41534-019-0157-8.\n", + "\n", + "[2] Liu, Jin-Guo, and Lei Wang. “Differentiable Learning of Quantum Circuit Born Machine.” Physical Review A 98, no. 6 (December 19, 2018): 062324. https://doi.org/10.1103/PhysRevA.98.062324.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.10 ('venv': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "vscode": { + "interpreter": { + "hash": "590fab68195cf107911461461f81d5c472d3d6127f579badfcfad30f03e5cab2" + } + } }, - { - "data": { - "text/plain": [ - "{'params': [0.318286378384603,\n", - " 0.8066883422428826,\n", - " 0.9965059377813476,\n", - " 0.3896290997335934,\n", - " 0.5206564140072295,\n", - " -0.2764708825453678,\n", - " -0.31186280728130517,\n", - " 1.0301286608615072,\n", - " 0.9152848732773509,\n", - " 0.5028883778151009,\n", - " -0.08787270522674204,\n", - " 0.7817302047193129,\n", - " 0.9277263943430585,\n", - " 0.013617062714961346,\n", - " 0.33473964459716515,\n", - " 0.5093557157601054,\n", - " -0.10513831465073813,\n", - " 0.7394344226370994,\n", - " 0.7317135947685797,\n", - " 0.22689351139220243,\n", - " 0.6120112028390869,\n", - " 0.12146385313274964,\n", - " 1.0822225752513248,\n", - " -0.03458224708388502,\n", - " -0.1581787215562068,\n", - " 0.6144033545499605,\n", - " -0.03156199169778191,\n", - " 0.9605311859582252,\n", - " 0.29158765472875725,\n", - " 0.4800667308210175,\n", - " 0.5278158981111709,\n", - " 0.2207241644995396,\n", - " -0.018214728388751607,\n", - " 1.0549851014368496,\n", - " 0.454472915580011,\n", - " 0.6728275933353882,\n", - " 0.5217854000399934,\n", - " 0.39243334991421674,\n", - " 0.9821097084351791,\n", - " 0.5170651734719544,\n", - " 0.7873812585011286,\n", - " 0.8900537481277946,\n", - " 0.21275468530086444,\n", - " 0.9275566358136947,\n", - " 0.39455123195247166]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time \n", - "job.result() # should take 17 min with SV1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Awesome! Our first quantum machine learning job finished! Now let’s look at the training metrics." - ] - }, - { - "attachments": { - "metrics.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Metrics and plotting\n", - "\n", - "In the `qcbm_job.py` script, we monitored the loss function during training with \n", - "```\n", - "log_metric(\n", - " metric_name=\"loss\",\n", - " value=loss,\n", - " iteration_number=iteration_number,\n", - ")\n", - "```\n", - "Metrics recorded in this way are visible from the \"Monitor\" tab in the AWS Console. It will look similar to the below image:\n", - "\n", - "
\n", - "\n", - "Metrics are also available by calling `job.metrics()`. Using pandas, and matplotlib, we plot the convergence of the loss below. " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plotting the convergence of the loss function metric\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "metrics = job.metrics()\n", - "\n", - "df = pd.DataFrame(metrics)\n", - "\n", - "plt.style.use(\"seaborn-v0_8-colorblind\")\n", - "df.sort_values(by=[\"iteration_number\"]).plot(x=\"iteration_number\", y=\"loss\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In `qcbm_job.py`, we called `save_job_result({\"params\": params.tolist()})\n", - "` to save the optimal parameters that minimized the loss function.\n", - "Importantly, we can plot the predicted probability distribution vs the target probability from the data. To do so, we first import the QCBM locally again, but now we initialize it with the parameters returned from our hybrid job." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plotting the original probability distribution, and the QCBM prediction probability\n", - "from braket.devices import LocalSimulator\n", - "from qcbm.qcbm import QCBM\n", - "\n", - "device = LocalSimulator()\n", - "qcbm = QCBM(device, n_qubits, 3, data)\n", - "\n", - "qcbm_probs = qcbm.probabilities(np.array(job.result()[\"params\"]))\n", - "\n", - "labels = [\"{0:{fill}6b}\".format(i, fill='0') for i in range(len(data))]\n", - "\n", - "plt.bar(range(2 ** n_qubits), data, label=\"target probability\", alpha=0.6)\n", - "plt.bar(range(2 ** n_qubits), qcbm_probs, label=\"QCBM probability\", alpha=0.6)\n", - "plt.xticks([i for i in range(len(data))], labels, rotation='vertical', size=12)\n", - "plt.yticks(size=12)\n", - "\n", - "plt.xlabel(\"Sample\", size=20)\n", - "plt.ylabel(\"Probability\", size=20)\n", - "\n", - "fig = plt.gcf()\n", - "fig.set_size_inches(16, 8)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great! As expected, the QCBM probability distribution closes matches the target distribution. " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Task Summary\n", - "{'arn:aws:braket:::device/quantum-simulator/amazon/sv1': {'shots': 11140000, 'tasks': {'COMPLETED': 1114}, 'execution_duration': 36.416, 'billed_execution_duration': 3342.0}}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run tasks in this job: 4.1775 USD\n" - ] - } - ], - "source": [ - "print(\"Quantum Task Summary\")\n", - "print(job.result()['task summary'])\n", - "print('Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).')\n", - "print(f\"Estimated cost to run quantum tasks in this hybrid job: {job.result()['estimated cost']} USD\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running different hyperparameters\n", - "\n", - "One of the strengths of Braket Hybrid Jobs is the ability to submit and monitor many hybrid jobs simultaneously. We can use this to perform a grid search to find good hyperparameters. Below we initialize 4 unique hybrid jobs with different `n_layers`." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating job with 1 layers\n", - "Creating job with 2 layers\n", - "Creating job with 3 layers\n", - "Creating job with 4 layers\n" - ] - } - ], - "source": [ - "jobs = []\n", - "names = []\n", - "\n", - "for n_layers in range(1, 5):\n", - " print(f\"Creating job with {n_layers} layers\")\n", - " name = f\"hyper-param-job-{n_qubits}-{n_layers}-\" + str(int(time.time()))\n", - " hyperparams = {\n", - " \"n_qubits\": n_qubits, \n", - " \"n_layers\": n_layers,\n", - " \"n_iterations\": 10, \n", - " \"local\": \"False\"\n", - " }\n", - "\n", - " tmp_job = AwsQuantumJob.create(\n", - " device=\"arn:aws:braket:::device/quantum-simulator/amazon/sv1\",\n", - " source_module=\"qcbm\",\n", - " entry_point=\"qcbm.qcbm_job:main\",\n", - " job_name=name,\n", - " hyperparameters=hyperparams,\n", - " input_data=\"data.npy\",\n", - " wait_until_complete=False,\n", - " )\n", - " jobs.append(tmp_job)\n", - " names.append(name)" - ] - }, - { - "attachments": { - "hp_job_console.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To check the results, we could load the results as we did before, or we could check the \"Monitor\" tab in the Braket Jobs dashboard in the AWS Console.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 655 ms, sys: 55.4 ms, total: 711 ms\n", - "Wall time: 20min 13s\n" - ] - }, - { - "data": { - "text/plain": [ - "{'params': [0.10582521466656217,\n", - " 0.6586736197367999,\n", - " 1.200696637224605,\n", - " 0.4795713744652507,\n", - " 0.0328225561013912,\n", - " 0.1416309729268562,\n", - " -0.5345646198096557,\n", - " 1.0538623482363394,\n", - " 0.7051532697159473,\n", - " 1.1289212962111743,\n", - " 0.3252133525383767,\n", - " 1.35930000770726,\n", - " 0.6822907707251727,\n", - " 0.12822412303073896,\n", - " 0.05970880743338971,\n", - " -0.12872775118257376,\n", - " -0.01491068861101457,\n", - " 0.32288291810762443,\n", - " 0.718775541054627,\n", - " 0.5784806341360708,\n", - " 0.7367894357120217,\n", - " 0.11594024932187227,\n", - " 0.733472297708508,\n", - " -0.026719234290999323,\n", - " 0.7264773526906863,\n", - " 0.7325621363172037,\n", - " -0.046684249654940044,\n", - " 0.39342685130747507,\n", - " 0.5008300653892479,\n", - " -0.029208732236774993,\n", - " 0.3555730806594967,\n", - " 0.3701310167636944,\n", - " -0.11995921589574741,\n", - " 1.5015116657129093,\n", - " 1.4200581102942063,\n", - " 1.3390578795535855,\n", - " -0.05630286546508683,\n", - " 0.5891673890248028,\n", - " 0.23324096170351397,\n", - " -0.09559073124226201,\n", - " 0.3409907544669093,\n", - " 0.0033502886823119516,\n", - " 0.06831464166264858,\n", - " 0.8591375203801005,\n", - " -0.2697950332966393,\n", - " 0.8423356850471669,\n", - " 0.1858015855270195,\n", - " 0.6236186948061898,\n", - " 0.9116432741463357,\n", - " 0.1584495053262102,\n", - " 1.3232439169325707,\n", - " 0.9815652537719447,\n", - " 1.238690541521269,\n", - " 1.6990444551463215,\n", - " 0.06688018372366623,\n", - " 0.8995581694378754,\n", - " -0.13698545689704805,\n", - " 0.16493418873276403,\n", - " -0.018569958408711576,\n", - " 0.29974234715189707]}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time \n", - "# this cell should take 20 min\n", - "jobs[-1].result(); # wait for the last job to finish" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also load the hybrid jobs directly from their hybrid job arns. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now plot the results from all the hyperparameters experiments once they finish. If the cell below does not work, wait a few minutes for metrics to load and try again." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "dfs = []\n", - "for i, j in enumerate(jobs):\n", - " df = pd.DataFrame(j.metrics())\n", - " df.sort_values(by=[\"iteration_number\"])\n", - " dfs.append(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for i, df in enumerate(dfs):\n", - " plt.plot(df[\"iteration_number\"], df[\"loss\"], label=f\"n_layers={i+1}\")\n", - "plt.legend()\n", - "plt.xlabel(\"iteration_number\")\n", - "plt.ylabel(\"loss\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the plots above, we see that the loss is much lower for `n_layers=3` and `n_layers=4`. We can conclude for 5 qubits, we need at least 3 layers in the QCBM to accurately learn the two-peak Gaussian data. " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Task Summary\n", - "{'arn:aws:braket:::device/quantum-simulator/amazon/sv1': {'shots': 4550000, 'tasks': {'COMPLETED': 455}, 'execution_duration': 16.96, 'billed_execution_duration': 1365.0}}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run tasks in this job: 1.70625 USD\n", - "Task Summary\n", - "{'arn:aws:braket:::device/quantum-simulator/amazon/sv1': {'shots': 8780000, 'tasks': {'COMPLETED': 878}, 'execution_duration': 33.115, 'billed_execution_duration': 2634.0}}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run tasks in this job: 3.2925 USD\n", - "Task Summary\n", - "{'arn:aws:braket:::device/quantum-simulator/amazon/sv1': {'shots': 13890000, 'tasks': {'COMPLETED': 1389}, 'execution_duration': 53.856, 'billed_execution_duration': 4167.0}}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run tasks in this job: 5.20875 USD\n", - "Task Summary\n", - "{'arn:aws:braket:::device/quantum-simulator/amazon/sv1': {'shots': 14740000, 'tasks': {'COMPLETED': 1474}, 'execution_duration': 57.8, 'billed_execution_duration': 4422.0}}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run tasks in this job: 5.5275 USD\n" - ] - } - ], - "source": [ - "for job in jobs:\n", - " print(\"Quantum Task Summary\")\n", - " print(job.result()['task summary'])\n", - " print('Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).')\n", - " print(f\"Estimated cost to run quantum tasks in this hybrid job: {job.result()['estimated cost']} USD\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion \n", - "\n", - "In this notebook, we submitted a single training hybrid job in Amazon Braket Hybrid Jobs. We then simultaneously submitted 4 new hybrid jobs with different hyperparameters to learn about the number of layers required in our circuit." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "\n", - "[1] Benedetti, Marcello, Delfina Garcia-Pintos, Oscar Perdomo, Vicente Leyton-Ortega, Yunseong Nam, and Alejandro Perdomo-Ortiz. “A Generative Modeling Approach for Benchmarking and Training Shallow Quantum Circuits.” Npj Quantum Information 5, no. 1 (May 27, 2019): 1–9. https://doi.org/10.1038/s41534-019-0157-8.\n", - "\n", - "[2] Liu, Jin-Guo, and Lei Wang. “Differentiable Learning of Quantum Circuit Born Machine.” Physical Review A 98, no. 6 (December 19, 2018): 062324. https://doi.org/10.1103/PhysRevA.98.062324.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.10 ('venv': venv)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - 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zS%Sp(6=fPsU6#A{%Xmo-%nxoSF5=e5QuRVPONz(8@Ze&`X817%zSuxF6Ci{9)71??`40Jm_d7S?{tZt34mD7kok%`Jz4JObeKSVF{7&Bkb zKz?}0c|}AK8kw#ydr8-YzAYS#1V1uL*lZqKh_gNW@$zUt>V&lGfb>7{{sX+2oIzLL zHUbt*!}7t`@aBcZ!rW<1lDqgnW&{!dboeyiH0hq!^qcq8mj|sUcxYo*ElRZ0X{7aq2j#Q*xeiD*Y? z0ik;?Nl=o%Nhv7;C}gE;{}qIPL-hxqe?tX;@c)5U5zO8!hr_LvJb4rUvQmLZiFdIf zZ1Kha+$R3ZWc;@uVq@3^2pjEa3(&t_^#Ajrup#M_M!)3#Y8y;|W#4Da`rqE-|NM>- z9zY0soO1Ga+n@#52Cmfmf42?tfRKdzzf+thN>Q za*O`w6tJ$e|0Q4v<{R8i2g_ITYz3>Y)B3O%q<_H<&Phg5^X!?*7Q0-Eo?&mi?7~5r zlauUUo^sERc-n2hb_sjiQ->D&b6Na#*A~>x=lR(icQCEDcbK{_V#kaH z_Ak|EO!isZZU4xbrMKx Date: Sun, 15 Oct 2023 08:31:25 -0400 Subject: [PATCH 06/24] decorator jobs example 2 QAOA (#403) --- ...ng_PennyLane_with_Braket_Hybrid_Jobs.ipynb | 852 +++++++++--------- .../console_figures/training.png | Bin 0 -> 45525 bytes .../qaoa/qaoa_algorithm_script.py | 188 ---- .../qaoa/qaoa_utils.py | 174 ---- 4 files changed, 446 insertions(+), 768 deletions(-) create mode 100644 examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/console_figures/training.png delete mode 100644 examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/qaoa/qaoa_algorithm_script.py delete mode 100644 examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/qaoa/qaoa_utils.py diff --git a/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb b/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb index b8a9333e3..c363dbf00 100644 --- a/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb +++ b/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb @@ -25,7 +25,7 @@ "\n", "QAOA is a variational algorithm that uses parameterized quantum circuits to evaluate a classical cost function given by a binary optimization problem; the circuit parameters are iteratively adjusted to minimize the cost function. The QAOA algorithm itself has different settings, such as circuit depth ($p$). In analogy to machine learning, these input settings are commonly referred to as _hyperparameters_. In the following, we show how to setup the problem, prepare input data and run QAOA using Braket Jobs.\n", "\n", - "For more information about QAOA and PennyLane, see [this example notebook](../../pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb) or [this PennyLane tutorial](https://pennylane.ai/qml/demos/tutorial_qaoa_intro.html)." + "For more information about QAOA and PennyLane, see [this example notebook](../pennylane/2_Graph_optimization_with_QAOA.ipynb) or [this PennyLane tutorial](https://pennylane.ai/qml/demos/tutorial_qaoa_intro.html)." ] }, { @@ -45,14 +45,9 @@ "metadata": {}, "outputs": [], "source": [ - "import time\n", - "\n", - "import networkx as nx\n", - "from braket.aws import AwsQuantumJob, AwsSession\n", - "from braket.jobs.image_uris import Framework, retrieve_image\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline" + "import pennylane as qml\n", + "from pennylane import numpy as np\n", + "import networkx as nx" ] }, { @@ -63,9 +58,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -74,7 +69,7 @@ ], "source": [ "# We generate a random graph with num_nodes nodes and num_edges edges to run Max Cut on.\n", - "# num_nodes is the number of nodes in your graph, each represented by one qubit. \n", + "# num_nodes is the number of nodes in your graph, each represented by one qubit.\n", "# Caution: Circuit runtimes will scale exponentially with num_nodes\n", "num_nodes = 6\n", "num_edges = 8\n", @@ -104,18 +99,8 @@ "outputs": [], "source": [ "input_file_path = \"input-data.adjlist\"\n", - "nx.write_adjlist(graph, input_file_path)" - ] - }, - { - "cell_type": "markdown", - "id": "fd8fc4a6", - "metadata": {}, - "source": [ - "## Parametric compilation with PennyLane\n", - "When solving the Max-Cut problem with QAOA, an optimizer updates the parameters in a parametrized circuit to minimize the cost function. While the parameters are updated in every iteration, the parametrized circuit is the same throughout the optimization process. In such a use case, parametric compilation can increase the performance of the hybrid algorithm by skipping recompilation compilation of the same circuit. \n", "\n", - "When using PennyLane with Braket devices, the `parametrize_differentiable` keyword argument of `qml.device` defines how the parameters are handled. If `parametrize_differentiable` is True, the circuits are parametrized for the differentiable parameters. When running hybrid job on a supported QPU, Braket will compile the circuit once, without the need to recompile for subsequent parameter updates to the same circuit, resulting in faster runtimes. The speedup depends on the complexity of the circuit. Generally, the speedup is higher for more complex circuits because the saving is higher in avoiding expensive recompilation. To learn more about parametric circuits, you can read the [Amazon Braket developer guide](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html)." + "nx.write_adjlist(graph, input_file_path)" ] }, { @@ -123,652 +108,709 @@ "id": "d49e9b83", "metadata": {}, "source": [ - "## Specify hyperparameters and configurations\n", + "## Define the circuit\n", + "\n", + "The ansatz represents a parameterized quantum circuit composed of alternating layers of a cost layer and a mixing layer. \n", + "The number of qubits is equal to the number of nodes in the graph. \n", + "We initialize the state to an even superposition over all basis states. \n", + "For this example, we use a variational circuit consisting of `p=2` QAOA layers. \n", "\n", - "\"The hyperparameters can be passed in when you create your job, through the keyword argument hyperparameters. It usually includes all the algorithm settings you might want to adjust between runs to tailor your algorithm to the problem. This includes, for instance, the optimizer to use and its learning rate, the number of iterations, or the number of shots per device execution. It also includes the parameter handling option, `parametrize_differentiable`, which parametrizes the differentiable parameters of the circuit when set to True. " + "Below, we use the QAOA utilities from PennyLane to construct the circuit ansatz. " ] }, { "cell_type": "code", "execution_count": 4, - "id": "b4360413", + "id": "da1f5b45", "metadata": {}, "outputs": [], "source": [ - "# Pick 'autograd', 'tf', or 'torch'. Autograd is base PennyLane\n", - "def define_hyperparameters(interface):\n", - " hyperparameters = {\n", - " # Number of quantum tasks per iteration = 2 * (num_nodes + num_edges) * p + 1\n", - " \"p\": 2,\n", - " \"seed\": seed,\n", - " # Maximum number of simultaneous quantum tasks allowed\n", - " \"max_parallel\": 10,\n", - " # Number of total optimization iterations, including those from previous checkpoint (if any)\n", - " \"num_iterations\": 5,\n", - " # Step size / learning rate for gradient descent\n", - " \"stepsize\": 0.1,\n", - " # Shots for each circuit execution\n", - " \"shots\": 1000,\n", - " \"interface\": interface,\n", - " \"parametrize_differentiable\": True,\n", - " }\n", - " return hyperparameters" + "from pennylane import qaoa\n", + "\n", + "p = 2 # number of QAOA layers\n", + "wires = range(num_nodes) # number of qubits\n", + "\n", + "params = np.random.rand(2, p) # random initial parameters\n", + "\n", + "cost_h, mixer_h = qaoa.maxcut(graph)\n", + "\n", + "dev = qml.device(\"default.qubit\", wires=len(wires))\n", + "\n", + "\n", + "# Defines a layer of the QAOA ansatz from the cost and mixer Hamiltonians\n", + "def qaoa_layer(gamma, alpha):\n", + " qaoa.cost_layer(gamma, cost_h)\n", + " qaoa.mixer_layer(alpha, mixer_h)\n", + "\n", + "\n", + "# Repeatedly applies layers of the QAOA ansatz\n", + "def circuit(params, **kwargs):\n", + " p = params.shape[1]\n", + " for w in wires:\n", + " qml.Hadamard(wires=w)\n", + " qml.layer(qaoa_layer, p, params[0], params[1])" ] }, { "cell_type": "markdown", - "id": "129ba3a9", + "id": "337a7f12", "metadata": {}, "source": [ - "Braket Jobs come with three pre-configured container environments: TensorFlow, PyTorch and Base. " + "We can visualize the circuit with the built-in PennyLane drawing function" ] }, { "cell_type": "code", "execution_count": 5, - "id": "6dec9253", + "id": "a17a5dd3", "metadata": {}, "outputs": [], "source": [ - "from braket.aws import AwsSession\n", - "\n", - "region = AwsSession().region\n", - "\n", - "# Choose the container based on which one we need.\n", - "def select_container(interface):\n", - " if interface == \"autograd\":\n", - " image_uri = retrieve_image(Framework.BASE, region)\n", - " elif interface == \"tf\":\n", - " image_uri = retrieve_image(Framework.PL_TENSORFLOW, region)\n", - " elif interface == \"torch\":\n", - " image_uri = retrieve_image(Framework.PL_PYTORCH, region)\n", - " return image_uri" + "circuit(params)" ] }, { - "cell_type": "markdown", - "id": "cc6ccf7b", + "cell_type": "code", + "execution_count": 6, + "id": "6c2ce4ce", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "## Algorithm script\n", - "\n", - "The algorithm script we are going to use for solving the Max-Cut problem with QAOA can be found [here](qaoa/qaoa_algorithm_script.py)." + "qml.draw_mpl(circuit)(params)" ] }, { "cell_type": "markdown", - "id": "02256371", + "id": "3133e8cf", "metadata": {}, "source": [ - "## Submitting a Braket Hybrid Job: Base Container\n", - "\n", - "We have now finished preparing the input data, algorithm script, hyperparameters and other configurations. It's time to submit our Braket Hybrid Job!\n", + "## Optimization with gradient descent\n", "\n", - "We specify the following arguments to create our hybrid job: \n", + "When solving the Max-Cut problem with QAOA, an optimizer updates the parameters in a parametrized circuit to minimize the cost function. While the parameters are updated in every iteration, the parametrized circuit is the same throughout the optimization process. \n", "\n", - "- device: The arn of the Braket simulator or QPU we want to use. It will be stored as an environment variable for the algorithm script.\n", - "- source_module: The path to a file or a python module that contains your algorithm script. It will be uploaded to the container for Braket Hybrid Job execution.\n", - "- job_name: A unique string to identify the hybrid job. It appears in the Braket Hybrid Job console and in the hybrid job arn.\n", - "- image_uri: The path to a Docker container image.\n", - "- entry point: The path relative to the source_module. It points to the piece of code to be executed when the Braket Job starts.\n", - "- copy_checkpoints_from_job: A string that specifies the hybrid job arn whose checkpoint you want to use in the current job. If `None` (default value), no checkpoints will be copied to the current hybrid job.\n", - "- hyperparameters: The Python dictionary containing the hyperparameter names and values.\n", - "- input_data: A dictionary that maps channel names to either a file location in the local environment or a path to S3. We can also specify only a file location, in which case the channel name is treated as \"input\".\n", - "- wait_until_complete: If True, the function call will wait until the Braket Hybrid Job is completed, and will additionally print logs to the local console. Otherwise, it will run asynchronously. Defaults to False." + "We use the gradient descent optimizer from PennyLane to optimize the variational parameters in the circuit ansatz. \n", + "Additionally, we print the value of the loss function at each training step using the `log_metric` function from `braket.jobs.metrics`.\n", + "This will be beneficial later so we can monitor training progress for long-running algorithms from the [Amazon Braket Console](https://console.aws.amazon.com/braket/home). " ] }, { "cell_type": "code", - "execution_count": 6, - "id": "eaaf03ea", + "execution_count": 7, + "id": "194c2e9c", "metadata": {}, "outputs": [], "source": [ - "# Using autograd interface with Base container\n", - "interface = \"autograd\"\n", - "hyperparameters = define_hyperparameters(interface)\n", + "from braket.jobs.metrics import log_metric\n", + "from braket.jobs import get_results_dir\n", + "\n", + "\n", + "def run_qaoa(p=1, steps=10):\n", + " graph = nx.read_adjlist(input_file_path, nodetype=int)\n", + " wires = list(graph.nodes)\n", + "\n", + " cost_h, mixer_h = qaoa.maxcut(graph)\n", + " params = np.random.rand(2, p)\n", "\n", - "# Specify device that the hybrid job will primarily be targeting\n", - "device = \"arn:aws:braket:::device/quantum-simulator/amazon/sv1\"\n", + " @qml.qnode(dev)\n", + " def cost_function(params):\n", + " circuit(params)\n", + " return qml.expval(cost_h)\n", "\n", - "# setting up the image_uri \n", - "image_uri = select_container(interface) " + " # training loop\n", + " optimizer = qml.GradientDescentOptimizer()\n", + " for i in range(steps):\n", + " params = optimizer.step(cost_function, params)\n", + "\n", + " log_metric(metric_name=\"loss\", value=cost_function(params), iteration_number=i)\n", + "\n", + " np.save(\"optimal_params.npy\", params)\n", + "\n", + " return cost_function(params)" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "0145c13c", + "execution_count": 8, + "id": "e1ff68a9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Metrics - timestamp=1697312862.3755994; loss=-3.9727919616465606; iteration_number=0;\n", + "Metrics - timestamp=1697312862.4235377; loss=-3.9881173902868836; iteration_number=1;\n", + "Metrics - timestamp=1697312862.4707115; loss=-4.001559566325473; iteration_number=2;\n", + "Metrics - timestamp=1697312862.518381; loss=-4.0144723081244145; iteration_number=3;\n", + "Metrics - timestamp=1697312862.5833714; loss=-4.0280958528140465; iteration_number=4;\n", + "Metrics - timestamp=1697312862.6350281; loss=-4.043645115652158; iteration_number=5;\n", + "Metrics - timestamp=1697312862.6824489; loss=-4.062383705037866; iteration_number=6;\n", + "Metrics - timestamp=1697312862.7291694; loss=-4.085676136136748; iteration_number=7;\n", + "Metrics - timestamp=1697312862.776633; loss=-4.115006017832663; iteration_number=8;\n", + "Metrics - timestamp=1697312862.8233068; loss=-4.151941049987223; iteration_number=9;\n" + ] + }, + { + "data": { + "text/plain": [ + "tensor(-4.15194105, requires_grad=True)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "job = AwsQuantumJob.create(\n", - " device=device,\n", - " source_module=\"qaoa\",\n", - " # Any unique name works. Note 50-character limit in hybrid job name\n", - " # (comment out to use default naming)\n", - " job_name=\"qaoa-job-\" + interface + \"-\" + str(int(time.time())),\n", - " image_uri=image_uri,\n", - " # Relative to the source_module\n", - " entry_point=\"qaoa.qaoa_algorithm_script\",\n", - " copy_checkpoints_from_job=None,\n", - " # general parameters\n", - " hyperparameters=hyperparameters,\n", - " input_data={\"input-graph\": input_file_path},\n", - " wait_until_complete=False,\n", - ")" + "run_qaoa(p=1, steps=10)" ] }, { "cell_type": "markdown", - "id": "5a125258", + "id": "626fce6c", "metadata": {}, "source": [ - "## View results" + "Great! The training seems to be working since the cost function is decreasing in every iteration." ] }, { "cell_type": "markdown", - "id": "0f8b84c4", + "id": "85e40486", "metadata": {}, "source": [ - "After the hybrid job is completed, we can view the result and the metric we defined in the algorithm script." + "## Run as a hybrid job\n", + "\n", + "Now let's run the algorithm asynchronously on Amazon Braket. \n", + "We simply annotate the function with `@hybrid_job` and call it as usual. \n", + "We also supply the input data set, in this case the graph information. \n", + "For more information of hybrid jobs see the [documentation](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html). \n", + "\n", + "Note that creating hybrid jobs with the `@hybrid_job` decorator is only supported on Python 3.10. \n", + "For other versions, you may submit Python [scripts](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html) or specify a [custom container image](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-byoc.html).\n", + "\n", + "\n", + "" ] }, { - "cell_type": "code", - "execution_count": 8, - "id": "7ece4c49", + "cell_type": "markdown", + "id": "6ce8f455", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'params': [[-0.2213488042362593, -0.34576879319477966], [-0.07285138802088253, -0.17771989905871677]], 'cost': -2.612}\n", - "CPU times: user 254 ms, sys: 14.4 ms, total: 268 ms\n", - "Wall time: 6min\n" - ] - } - ], "source": [ - "%%time\n", - "# This cell should take 7-8 minutes\n", - "print(job.result())" + "## Saving progress with checkpoints\n", + "\n", + "It is best practice to regularly save interim progress of your hybrid job as checkpoints. If your hybrid job terminates unexpectedly, for instance, if a QPU becomes unavailable, you can create a new hybrid job and load its training progress from a checkpoint. \n", + "\n", + "The following is a minimal working example for saving and loading checkpoints with a hybrid job decorator:\n", + "```python\n", + "from braket.jobs import save_job_checkpoint, load_job_checkpoint, hybrid_job\n", + "\n", + "@hybrid_job(device=None, wait_until_complete=True)\n", + "def function():\n", + " save_job_checkpoint({\"a\": 1})\n", + "\n", + "job = function()\n", + "job_name = job.name\n", + "job_arn = job.arn\n", + "\n", + "@hybrid_job(device=None, wait_until_complete=True, copy_checkpoints_from_job=job_arn)\n", + "def continued_function():\n", + " load_job_checkpoint(job_name)\n", + "\n", + "continued_job = continued_function()\n", + "```\n", + "\n", + "In the first hybrid job, we call `save_job_checkpoint` with a dictionary containing the data we want to save. \n", + "By default, every value must be serializable as text. \n", + "For checkpointing more complex Python objects, such as numpy arrays, you may set \n", + "`data_format = PersistedJobDataFormat.PICKLED_V4`. \n", + "This code creates and overwrites a checkpoint file with default name `.json` in your hybrid job artifacts under a subfolder called \"checkpoints\". \n", + "\n", + "To create a new hybrid job to continue from the checkpoint, we need to pass `copy_checkpoints_from_job=job_arn` where `job_arn` is the hybrid job ARN of the previous job. \n", + "Then we use `load_job_checkpoint(job_name)` to load from the checkpoint. \n", + "\n", + "Now we are ready to turn the entire QAOA training algorithm into a hybrid job. In the following code, we define the hybrid job algorithm, input data, and save a checkpoint each iteration." ] }, { "cell_type": "code", "execution_count": 9, - "id": "b9d29fe9", + "id": "5c5052d1", + "metadata": {}, + "outputs": [], + "source": [ + "from braket.jobs import hybrid_job, save_job_checkpoint\n", + "from braket.jobs_data import PersistedJobDataFormat\n", + "from braket.tracking import Tracker\n", + "\n", + "\n", + "@hybrid_job(device=None, input_data=input_file_path)\n", + "def run_qaoa_hybrid_job(p=1, steps=10):\n", + " params = np.random.rand(2, p)\n", + " \n", + " braket_task_tracker = Tracker()\n", + "\n", + " graph = nx.read_adjlist(input_file_path, nodetype=int)\n", + " wires = list(graph.nodes)\n", + " cost_h, mixer_h = qaoa.maxcut(graph)\n", + " \n", + " dev = qml.device(\"default.qubit\", wires=len(wires))\n", + "\n", + " @qml.qnode(dev)\n", + " def cost_function(params):\n", + " circuit(params)\n", + " return qml.expval(cost_h)\n", + "\n", + " # training loop\n", + " optimizer = qml.GradientDescentOptimizer()\n", + " for i in range(steps):\n", + " params = optimizer.step(cost_function, params)\n", + " cost = float(cost_function(params))\n", + " \n", + " log_metric(metric_name=\"loss\", value=cost, iteration_number=i)\n", + "\n", + " # save checkpoint data\n", + " save_job_checkpoint(checkpoint_data={\n", + " \"iteration\": i,\n", + " \"params\": params.numpy(),\n", + " \"cost\": cost,\n", + " }, data_format = PersistedJobDataFormat.PICKLED_V4)\n", + "\n", + " # save final results\n", + " np.save(\"optimal_params.npy\", params)\n", + "\n", + " return {\n", + " \"params\": params.numpy(),\n", + " \"cost\": cost,\n", + " \"task summary\": braket_task_tracker.quantum_tasks_statistics(),\n", + " \"estimated cost\": braket_task_tracker.qpu_tasks_cost()\n", + " + braket_task_tracker.simulator_tasks_cost(),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "1d5adbf5", + "metadata": {}, + "source": [ + "To run the hybrid job, we invoke the function as usual. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f63740d0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'timestamp': [1652488190.9026933, 1652488142.3026094], 'Cost': [-4.045, -4.038], 'iteration_number': [2.0, 0.0]}\n" + "AwsQuantumJob('arn':'arn:aws:braket:us-west-1:961591465522:job/run-qaoa-hybrid-job-1697312864578')\n" ] } ], "source": [ - "# May need to wait a bit before metrics show up\n", - "# If metrics aren't there, run a bit later\n", - "time.sleep(10)\n", - "print(job.metrics())" + "job = run_qaoa_hybrid_job(p=1, steps=5)\n", + "print(job)" + ] + }, + { + "cell_type": "markdown", + "id": "7be3a2b9", + "metadata": {}, + "source": [ + "We see the default name of the hybrid job is the function name." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "fd44a240", - "metadata": { - "scrolled": true - }, + "execution_count": 11, + "id": "ff272af4", + "metadata": {}, "outputs": [ { "data": { - "image/png": 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PEck6oYMn+dU7B/ja0gsyuqpVIvqlrYgIsKo2hBl8KsOrWiWiwBeRnNcRdlavr2fJjCJKxmR2VatEFPgikvNefruZxiMt3JGlJ2s7KfBFJOdVvR5i/PDBXD17UtBDSSkFvojktP3HT/P8tr3ccuEUBg/K7kjM7r0TEenBUxvqaQ971l57H0uBLyI5y92pqgmxaNoYZkwaGfRwUk6BLyI5a/2uQ7zbfII7Fk8LeigDQoEvIjmrqibE8MH5WVXVKhEFvojkpGMtbaypa+TGBSUMH5I9Va0SUeCLSE5aU9fIqbYObsuBk7WdFPgikpMerwlxwaQRXDh1TNBDGTAKfBHJOW81HePN0OGsrGqViAJfRHJOVU2IgnzjE4uy90Zp8SjwRSSnnG7v4Kk36rl2zmTGZWFVq0QU+CKSU57bupfDJ9ty6mRtJwW+iOSUqpoQU8YM5bLzs7OqVSIKfBHJGaGDJ3n1nf188qJS8rO0qlUiCnwRyRlPrK8HsruqVSJJBb6ZfdvM6qKFydeZWUmCtvlm9oaZrUmmTxGRs9ERdlbXhrjs/AmUjh0W9HACkewR/kPuXu7uC4E1wDcTtP0KsC3J/kREzsorbzfTcKQlZ26UFk9Sge/uR2MWhwMer52ZlQLLgEeS6U9E5Gytqg0xdlgBS+dMDHoogUn6jkFm9h3gTuAIcGU3zb4HfAPo8YbTZrYSWAkwbVrufhKLSP85cPw0z23dy+cuKWPIoPyghxOYHo/wzex5M9sc57ECwN3vd/epwKPA3XHevxzY5+7rezMgd3/Y3SvcvaKoqKiPuyMicqan39hDW0duVLVKpMcjfHdf2sttPQZUAw90WX8pcJOZVQKFwCgz+7G7f7ZPIxUROQudVa0WTh3DzMnZX9UqkWSv0pkRs3gTsL1rG3e/z91L3b0MuAP4pcJeRAbKht2HeXvfce7I8aN7SP4qnQej0zt1wLVErsTBzErMbG3SoxMRSdKqmhDDBuezfEG3V43njKRO2rr7rd2sbwAq46x/CXgpmT5FRHrr+Ol2fl7XwPLyYkbkSFWrRPRLWxHJWtV1DZxs7cj5k7WdFPgikrUerwlx/sQRLJo2NuihpAUFvohkpR17j/HG7sPcnmNVrRJR4ItIVqqqCTEoz7hl0ZSgh5I2FPgiknVa28M8/cYerpkziQkjhgQ9nLShwBeRrPP8tr0cPNGak1WtElHgi0jWebwmRPHoQpbM0O1ZYinwRSSr7Dl8ilfebuZTOVrVKhEFvohklSdqQwB8qkLTOV0p8EUka3SEnSdq67n0vAlMHZebVa0SUeCLSNb41Tv72XP4lH5Z2w0FvohkjaraEGOGFXDt3ElBDyUtKfBFJCscPNHKui1N3LxwSk5XtUpEgS8iWUFVrXqmwBeRjOfurKoJsaB0NLOLRwU9nLSlwBeRjLcxdJi39h7j9sXTgh5KWlPgi0jGW1UbYmhBPjcuKA56KGlNgS8iGe3E6XZ+trGBZeXFjCwsCHo4aS3ZIubfNrM6M9toZuvMLG7RSDMbY2arzWy7mW0zs48m06+ISKfqTY2cUFWrXkn2CP8hdy9394XAGuCb3bT7PvCMu88CFgDbkuxXRASI3Pf+3KLhVJyjqlY9SSrw3f1ozOJwwLu2MbNRwBLgh9H3tLr74WT6FREBeGffMdbvOqSqVr2UdBl3M/sOcCdwBLgyTpNzgWbg38xsAbAe+Iq7n0i2bxHJbZ1VrT6xqDTooWSEHo/wzex5M9sc57ECwN3vd/epwKPA3XE2MQhYBPyTu18InADuTdDfSjOrNbPa5ubms9opEcl+re1hntqwh6tnT6RopKpa9UaPR/juvrSX23oMqAYe6LK+Hqh399eiy6tJEPju/jDwMEBFRcUZU0QiIgAvbNvLgROtOlnbB8lepTMjZvEmYHvXNu7eBITMbGZ01dXA1mT6FRGpqg0xeZSqWvVFsnP4D0aDPAzsAu4CiF6e+Yi7V0bbfRl41MwGA+8Bn0+yXxHJYQ2HT/Hyjmb+5IrzGZSvnxP1VlKB7+63drO+AaiMWd4IVCTTl4hIp9Xr6wk73KaqVn2ij0YRySjhsLOqNsTHzhvPtPGqatUXCnwRySi/fvcA9YdU1epsKPBFJKNU1YYYPbSA6+ZODnooGUeBLyIZ49CJVp7d3MQtF06hsEBVrfpKgS8iGeMnG/fQ2hHWydqzpMAXkYzg7lTVhJg/ZTRzSlTV6mwo8EUkI9TVH2F70zGdrE2CAl9EMkJVbYjCgjxuWhi37Ib0ggJfRNLeydZIVavK+cWMUlWrs6bAF5G0t3ZTE8dPt3O7TtYmRYEvImmvqmY30ycM5+Lp44IeSkZT4ItIWnu3+Tg1Ow9xm6paJU2BLyJpbVVNiPw849aLpgQ9lIynwBeRtNXWEebJDfVcNWsiE0cWBj2cjJd0TVsRkVR4t/k4//mbXew/3qqTtf1EgS8iaeP9/SeormtgTV0j25uOYQbXzJnEFTNV1ao/KPBFJFC7DpxgTV0j1XWNbG08CkDFOWN54MY53DCvmMmjNZXTXxT4IjLgQgdPUr0pEvKb9hwBYNG0MfzV8jlUzp9M8eihAY8wOynwRWRA1B86ydpoyL9ZHwn5BVPHcH/lbCrLi5kyRiGfakkFvpl9G1hBpIj5PuAPovVsu7b7GvCHgAObgM+7e0syfYtI+ms4fCoS8psaeWP3YQDKS0dz3w2zqJxfzNRxKlE4kMzdz/7NZqPc/Wj0+Z8Bc9z9ri5tpgCvRl87ZWargLXu/u89bb+iosJra2vPenwiMvCajrR8EPLrdx0CYG7JKJaVF7NsfjHnjB8e8Aizm5mtd/eKeK8ldYTfGfZRw4kcwXfXz1AzawOGAWd8CxCRzLXv6O9CvmZnJORnF4/inutmUjm/mOkTFPLpIOk5fDP7DnAncAS4suvr7r7HzP4O2A2cAta5+7oE21sJrASYNm1assMTkRRpPnaaZzY3sqaukdd3HsQdZk4ayZ9fcwHLyos5r2hE0EOULnqc0jGz54F41YLvd/efxrS7Dyh09we6vH8s8CRwO3AYeAJY7e4/7mlwmtIRSS8Hjp/mmS1NrHmzkdfeP0DY4fyJI1gena6ZMWlk0EPMeUlN6bj70l728xhQDTzQZf1S4H13b44O5ingY0CPgS8iwTt4opVntzRRXdfIr9/dT9jh3KLh3H3VDJaXF3OBQj5jJHuVzgx3fzu6eBOwPU6z3cAlZjaMyJTO1YAO20XS2OGTkZBfU9fIr989QEfYKRs/jD+54nyWlRcza/JI3bkyAyU7h/+gmc0kclnmLuAuADMrAR5x90p3f83MVgMbgHbgDeDhJPsVkX525GQb67ZGQv5X7+ynPexMGzeMP1pyLsvKi5lTPEohn+GSuiwz1TSHL5JaR1vaeG7LXqo3NfLK2820dTilY4eyrLyY5fNLmDdFIZ9pUnZZpohknmMtbbywbR9r6hp4ecd+WjvCTBkzlM9fOp1l84spLx2tkM9SCnyRHHD8dDsvbNtLdV0jL+1oprU9TPHoQj730XNYVl7MhVPHKORzgAJfJEudbG3nhW37qK5r5MW39nG6PcykUUP4zEemsby8mAunjiUvTyGfSxT4IlnkVGsHL74VCfkXtu+lpS1M0cgh3LF4KssXlHDRNIV8LlPgi2S4lrYOXnprH2vqGnlh2z5OtXUwYcRgPnXRVJaVF7O4bBz5CnlBgS+SkVraOnh5RzPVmxp5futeTrR2MG74YG5ZNIXl5cV8ZPp4hbycQYEvkiFOt3fwyo79VG9q5Lmtezl+up2xwwq4aWEJy+aXcMm54xiUnxf0MCWNKfBF0lhre5hfvbOfNXWNrNvaxLGWdkYPLaBy/mSWl5fw0fPGU6CQl15S4IukmbaOSMhX1zXy7JYmjra0M7JwENfNncyy8mIuPW8Cgwcp5KXvFPgiaaC9I8yv3z0QCfmtTRw+2cbIIYO4Zu4klpcXc+n5ExgyKD/oYUqGU+CLBKS9I8xr7x9kTV0jz2xu5NDJNoYPzueaOZNYVl7Cx2dMoLBAIS/9R4EvMoA6ws7r7x9kTV0Dz2xu4sCJVoYNzmfp7EksKy/m8guKFPKSMgp8kRTrCDu1Ow9SvamRtZua2H/8NEML8rlq9kSWzy/mipkTGTpYIS+pp8AXSYFw2Fm/+xDVdY2s3dTIvmOnKSzI46pZE1k2v4QrZxUxbLD++cnA0v9xIv0kHHbeCB3+IOSbjrYweFAeV84sYll5CVfPmsjwIfonJ8HR/30iSXB3NsaEfMORFgbn53H5zCLuK5/F1bMnMUIhL2lC/yeK9JG7s2nPEarrGllT18iew6coyDeWzCji69fNZOmcSYwqLAh6mCJnUOCL9IK7s6XhKGvqGqne1EDo4CkG5RkfnzGBr11zAdfMmcTooQp5SW8KfJFuuDvbGo+xpq6B6k2N7Dpwkvw849LzJ/Dlq2Zw7ZxJjBk2OOhhivRavwS+mX0deAgocvf9cV6/Hvg+kE+kuPmD/dGvSH9zd97ae4zqukaq6xp5b/8J8vOMj503nj++/DyumzuZscMV8pKZkg58M5sKXAPs7ub1fOAfom3qgRoz+5m7b022b5H+smPvsch0TV0D7zafIM/gknPH84cfP5fr5k5i/IghQQ9RJGn9cYT/f4FvAD/t5vWLgXfc/T0AM3scWAEo8CVQ7+w7HjmS39TAjr3HMYOPTB/HH1w6nevnTqZopEJesktSgW9mNwF73P3NBAWQpwChmOV64CMJtrkSWAkwbdq0ZIYncob3mjtDvpHtTccwg8XnjONvVszl+nmTmTiyMOghiqRMj4FvZs8Dk+O8dD/wl8C1PW0izjrvrrG7Pww8DFBRUdFtO5He2rn/BNWbInPyWxuPAlBxzlgeuHEON8wrZvJohbzkhh4D392XxltvZvOB6UDn0X0psMHMLnb3ppim9cDUmOVSoOGsRyzSC7sPnIyE/KYGNu+JhPyiaWP4q+VzqJw/meLRQwMeocjAO+spHXffBEzsXDaznUBFnKt0aoAZZjYd2APcAfze2fYr0p36Qyc/mK6pqz8CwIKpY7i/cjY3zJ9M6dhhAY9QJFgpuQ7fzEqIXH5Z6e7tZnY38CyRyzL/1d23pKLfTnf953paO8LkGZgZ+Wbk5UWe55mRZ5BnhkX//N3y7553vveD1/P62L7r9vPObJ/X2T6vr9uLfT2y7T61j66zmPd2Lud/0N6wvK77e+b2gtZw+BRrN0V+8boxdBiA8tLR3HfDLCrnFzN1nEJepFO/Bb67l8U8bwAqY5bXAmv7q6+eHDhxmpa2MB1hJ+yOO4S963POeC3skeuwO18Lh3tun+vO+ADp+gGR18MHTjcfKB88z/vwh0zs68dPt7OlITJdM7dkFN+4fibL5hdzzvjhAf+tiKSnrPyl7RN3fWzA+vrQB0T0A6Hzg+aMD5CuHzjhHj5w4n5A/e698dpHPqjo+/Zi24fjfcAlaO9OR/h3zxN+mJ4xtl5++Ma2j25j9NAC7rluJpXzi5k+QSEv0pOsDPyBFJkygvy4FyOJiKSPvKAHICIiA0OBLyKSIxT4IiI5QoEvIpIjFPgiIjlCgS8ikiMU+CIiOUKBLyKSI8w9fe8PYGbNwK6zfPsE4Ixyi1lO+5z9cm1/QfvcV+e4e1G8F9I68JNhZrXuXhH0OAaS9jn75dr+gva5P2lKR0QkRyjwRURyRDYH/sNBDyAA2ufsl2v7C9rnfpO1c/giIvJh2XyELyIiMRT4IiI5IusC38yuN7O3zOwdM7s36PEMBDP7VzPbZ2abgx7LQDCzqWb2opltM7MtZvaVoMeUamZWaGavm9mb0X3+VtBjGihmlm9mb5jZmqDHMhDMbKeZbTKzjWZW26/bzqY5fDPLB3YA1wD1QA3waXffGujAUszMlgDHgR+5+7ygx5NqZlYMFLv7BjMbCawHbs7m/84WqRg/3N2Pm1kB8CrwFXf/bcBDSzkz+3OgAhjl7suDHk+qmdlOoMLd+/3HZtl2hH8x8I67v+furcDjwIqAx5Ry7v4ycDDocQwUd2909w3R58eAbcCUYEeVWh5xPLpYEH1kz9FaN8ysFFgGPBL0WLJBtgX+FCAUs1xPlgdBrjOzMuBC4LVgR3bl9eQAAATCSURBVJJ60amNjcA+4Dl3z/p9Br4HfAMIBz2QAeTAOjNbb2Yr+3PD2Rb48SqJZ/1RUK4ysxHAk8BX3f1o0ONJNXfvcPeFQClwsZll9fSdmS0H9rn7+qDHMsAudfdFwA3An0anbPtFtgV+PTA1ZrkUaAhoLJJC0XnsJ4FH3f2poMczkNz9MPAScH3AQ0m1S4GbonPajwNXmdmPgx1S6rl7Q/TPfcDTRKaq+0W2BX4NMMPMppvZYOAO4GcBj0n6WfQE5g+Bbe7+90GPZyCYWZGZjYk+HwosBbYHO6rUcvf73L3U3cuI/Fv+pbt/NuBhpZSZDY9eiICZDQeuBfrt6rusCnx3bwfuBp4lciJvlbtvCXZUqWdm/wX8BphpZvVm9sWgx5RilwKfI3LEtzH6qAx6UClWDLxoZnVEDmyec/ecuEwxx0wCXjWzN4HXgWp3f6a/Np5Vl2WKiEj3suoIX0REuqfAFxHJEQp8EZEcocAXEckRCnwRkRyhwBcRyREKfEkrZvbr6J9lZvZ7/bztv4zXV7ozs782s68HPQ7JfAp8SSvu/rHo0zKgT4EfvT12Ih8K/Ji+spZF6N+5AAp8STNm1nkL4AeBj0d/Rfu16J0iHzKzGjOrM7M/ira/IloM5TFgU3TdT6J3GtzSebdBM3sQGBrd3qOxfUVD8SEz2xwtPHF7zLZfMrPVZrbdzB6N3tahu7HvNLNvmdmG6HZmRdd/6Ag92k9Z9LHdzB6JrnvUzJaa2a/M7G0zi72HygIz+2V0/ZditnVPzN/Jt6LryixSHOYfgQ18+P5SksvcXQ890uYBHI/+eQWwJmb9SuB/Rp8PAWqB6dF2J4DpMW3HRf8cSuQ+JONjtx2nr1uB54B8Ij9t303kVgZXAEeI3IQvj8jtKy5LMPadwJejz/8EeCT6/K+Br8e020zkG0wZ0A7Mj25/PfCvRO76ugL4Scz734zuzwQitwAvIXKflYej7fOANcCS6HbDwCVB//fUI70eg/r28SASmGuBcjP7ZHR5NDADaAVed/f3Y9r+mZndEn0+NdruQIJtXwb8l7t3AHvN7L+BxcDR6LbrAaL3oi8jUm2qO5137lwPfKIX+/W+u3d+M9kCvODubmabon11+qm7nwJOmdmLRO6geBmRv5c3om1GRPd1N7DLc6AalvSNAl8yhRE5en72QyvNriByhB+7vBT4qLufNLOXgMJebLs7p2Oed9Dzv5nO9rFt2/nw9GlhnPYQOSo/HfM8tq+uN71yIuP+3+7+/2JfiBaFOYFIF5rDl3R1DBgZs/ws8MfR++BjZhdEbx/b1WjgUDTsZwGXxLzW1vn+Ll4Gbo+eJygiMi3yer/sRcROYFF03IuITEX11QqLFDIfT2SqqYbI38kXooVgMLMpZjaxX0YsWUlH+JKu6oD26G1i/x34PpEpjg3RE6fNwM1x3vcMcFf0NsJvAbHTGg8DdWa2wd0/E7P+aeCjRObJHfiGuzd1nnTtB08Cd0anhGqAHWexjdeBamAa8G2PFMloMLPZwG+i55KPA58l8u1C5Ay6PbKISI7QlI6ISI7QlI5IH5nZ05w5D/8XXU8oi6QbTemIiOQITemIiOQIBb6ISI5Q4IuI5AgFvohIjvj/z3Wb/wQJJKwAAAAASUVORK5CYII=", "text/plain": [ - "
" + "'run-qaoa-hybrid-job-1697312864578'" ] }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# A demonstration of plotting the metrics\n", - "import pandas as pd\n", - "from matplotlib.ticker import MaxNLocator\n", - "\n", - "metrics_data = job.metrics()\n", - "\n", - "if metrics_data: \n", - " df = pd.DataFrame(job.metrics())\n", - "\n", - " ax = plt.figure().gca()\n", - " ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n", - " figure = df.plot(x=\"iteration_number\", y=\"Cost\", ax=ax)\n", - "else:\n", - " print(\"Wait for metrics to populate and re-run the cell.\")" + "job.name" + ] + }, + { + "cell_type": "markdown", + "id": "8213820c", + "metadata": {}, + "source": [ + "We also record the hybrid job ARN for resuming with checkpoints in the next section. " ] }, { "cell_type": "code", - "execution_count": 11, - "id": "520d8833", + "execution_count": 12, + "id": "d6e8d720", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Task Summary\n", - "{'arn:aws:braket:::device/quantum-simulator/amazon/sv1': {'shots': 286000, 'tasks': {'COMPLETED': 286}, 'execution_duration': 7.078, 'billed_execution_duration': 858.0}}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run tasks in this job: 1.0725 USD\n" + "arn:aws:braket:us-west-1:961591465522:job/run-qaoa-hybrid-job-1697312864578\n", + "run-qaoa-hybrid-job-1697312864578\n" ] } ], "source": [ - "print(\"Quantum Task Summary\")\n", - "print(job.result()['task summary'])\n", - "print('Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).')\n", - "print(f\"Estimated cost to run quantum tasks in this hybrid job: {job.result()['estimated cost']} USD\")" + "previous_job_name = job.name\n", + "previous_job_arn = job.arn\n", + "print(previous_job_arn)\n", + "print(previous_job_name)" ] }, { "cell_type": "markdown", - "id": "85c277eb", - "metadata": {}, - "source": [ - "## Submitting a hybrid job: Tensorflow Container" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "72366ca9", + "id": "f190654e", "metadata": {}, - "outputs": [], "source": [ - "# Using tensorflow interface with Tensorflow container\n", - "interface = \"tf\"\n", - "hyperparameters = define_hyperparameters(interface)\n", - "\n", - "# Specify device that the hybrid job will primarily be targeting\n", - "device = \"arn:aws:braket:::device/quantum-simulator/amazon/sv1\"\n", - "\n", - "# setting up the image_uri \n", - "image_uri = select_container(interface) " + "We can check if the job has started running with:" ] }, { "cell_type": "code", "execution_count": 13, - "id": "856d66ea", + "id": "d72041fb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'QUEUED'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "job = AwsQuantumJob.create(\n", - " device=device,\n", - " source_module=\"qaoa\",\n", - " # Any unique name works. Note 50-character limit in hybrid job name\n", - " # (comment out to use default naming)\n", - " job_name=\"qaoa-job-\" + interface + \"-\" + str(int(time.time())),\n", - " image_uri=image_uri,\n", - " # Relative to the source_module\n", - " entry_point=\"qaoa.qaoa_algorithm_script\",\n", - " copy_checkpoints_from_job=None,\n", - " # general parameters\n", - " hyperparameters=hyperparameters,\n", - " input_data={\"input-graph\": input_file_path},\n", - " wait_until_complete=False,\n", - ")" + "job.state()" ] }, { "cell_type": "markdown", - "id": "caf00538", + "id": "552c429b", "metadata": {}, "source": [ - "## View results" + "We can monitor the training progress in near-real time on the [Amazon Braket Console](https://console.aws.amazon.com/braket/home) as shown below. \n", + "\n", + "![Monitor training progress in the Amazon Braket Console](console_figures/training.png)" ] }, { "cell_type": "markdown", - "id": "a66d9a70", + "id": "0f8b84c4", "metadata": {}, "source": [ - "After the hybrid job is completed, we can view the result and the metric we defined in the algorithm script." + "After the job is completed, we can view the result and the metrics." ] }, { "cell_type": "code", "execution_count": 14, - "id": "f868b367", + "id": "7ece4c49", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'params': [[0.09565119708100346, -0.0701687943257777], [-0.2417513917193508, -0.18191990185268442]], 'cost': -4.142}\n", - "CPU times: user 405 ms, sys: 26.7 ms, total: 432 ms\n", - "Wall time: 9min 46s\n" + "{'params': array([[-0.07810759],\n", + " [ 0.37406524]]), 'cost': -4.301169550370251, 'task summary': {}, 'estimated cost': Decimal('0')}\n", + "CPU times: user 117 ms, sys: 5.63 ms, total: 123 ms\n", + "Wall time: 2min 20s\n" ] } ], "source": [ "%%time\n", - "# This cell should take 8-9 minutes\n", + "\n", "print(job.result())" ] }, { "cell_type": "code", "execution_count": 15, - "id": "2cdf0a3e", + "id": "b9d29fe9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'timestamp': [1652488812.506036, 1652488788.0409968, 1652488761.6457057], 'Cost': [-3.955, -4.015, -4.054], 'iteration_number': [2.0, 1.0, 0.0]}\n" + "{'timestamp': [1697312993.3920696, 1697312993.45155, 1697312993.5168812, 1697312993.2486959, 1697312993.3235312], 'loss': [-3.998461954246763, -4.1551643757263115, -4.301169550370251, -3.663479949497142, -3.833555708468481], 'iteration_number': [2.0, 3.0, 4.0, 0.0, 1.0]}\n" ] } ], "source": [ "# May need to wait a bit before metrics show up\n", - "# If metrics aren't there, run a bit later\n", + "# If metrics aren't there, try again after 5 seconds\n", + "import time \n", + "\n", "time.sleep(10)\n", "print(job.metrics())" ] }, + { + "cell_type": "markdown", + "id": "29278499", + "metadata": {}, + "source": [ + "Now we plot the metrics recorded during training." + ] + }, { "cell_type": "code", "execution_count": 16, - "id": "e70110bb", - "metadata": {}, + "id": "fd44a240", + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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I2O2vquGyJxYye2UJt58V5KrTB/tdkgiZndMZe0RPBUEjFAQSVrsPVHPRo/P5cO12/nDuKKafkON3SSJfyguGWlhuLNvvdylRRUEgYVO2r5ILHv6Y5cW7uO+CMfzHcf39LknkK+pbWOqo4KsUBBIWW3dX8P0H57G2dB8PXTyWM4/u43dJIl8zoGdHRvTpyuyVCoKGFATSZhvL9nPugx9RsqeSJy8Zz+nDsvwuSaRJ+cEACzeohWVDCgJpkzUlezn3wY/YW1HDM5dPYMKRvfwuSeSQ8oPZOAdvFWnuoXoKAjlsBZt2M+2hj6lz8PyMiRzTv7vfJYk0a3h2Fwb07KDzBA0oCOSwLFy/g/Mf+pgOqcn8/YqJDMvu4ndJIi1iZuTnZvPR2jK1sPQoCKTV3l+zjYsenU/vLun8/cqJ5GR28rskkVbJH5lNVW0d76qFJaAgkFb6V8FWLn18ITmZnXjhyon07d7B75JEWi3UwjJNw0MeBYG02MtLirnqmcUE+3XlucuPJ7Nzut8liRyW5PoWlp9uo7JGLSzDEgRmdoOZOTPLbOL5b5jZp2a21sxuarC+p5m9aWZrvN89wlGPhN9TH2/gZy8sY8Kgnjx16QS6dVRDGYltebnZ7Kus4aO1ZX6X4rs2B4GZDQCmABubeD4Z+DPwTSAXON/Mcr2nbwLmOOeGAHO8ZYkyD7z3Gbe+UsCk4Vk89sNxdEpP8bskkTY7YbDXwlI3l4XliOAe4OdAU5N8jwfWOufWOeeqgOeAqd5zU4EnvMdPAGeHoR4JE+ccf5j1KXf+cxXfGdWX+y88joxUNZSR+JCeksxpw3qrhSVtDAIzOwvY5JxbdojN+gFfNFgu9tYBBJxzWwC837olNUrU1Tlu/9+V3PfOWs4bN4B7p40mVQ1lJM7kB7PZvq+KxRsTu4Vls8f4ZvYWkN3IU7cANwN5ze2ikXWtjl8zmwHMABg4cGBrXy6tUFvnuOnF5fx9UTGXnTSIW741Qr0EJC6dNqx3qIVlwVbG5fT0uxzfNPsnnnNusnNu5ME/wDpgELDMzNYD/YHFZnZwaBQDAxos9wc2e49LzKwPgPe79BB1POScG+ucG9u7d++Wfj5ppaqaOq55dgl/X1TMdZOHKAQkrnXJSOWEwb2YtTKxW1ge9rG+c26Fcy7LOZfjnMsh9IU/xjl38JmXBcAQMxtkZmnAecBr3nOvAdO9x9OBVw+3Hmm7iupaZvxtITNXbOHWb43guslDFQIS9/KD2Xyx4wCrtiZuC8uIDPqaWV8zewPAOVcDXA3MAoqAF5xzhd6mdwJTzGwNoSuP7oxEPdK8vRXVTH9sPu+t3sbvv3s0l518pN8libSLySMCWIK3sAzbdYDeUUH9483AmQ2W3wDeaOQ1ZcCkcNUgh2fX/iqmPzafws17uHfaaKaO7tf8i0TiRO8u6Yw9ogezCku4bvJQv8vxhS4DSXCleyuY9uDHFG3dywMXHqcQkISUH8ymaMsevtiRmC0sFQQJrHjnfr7/wDy+2Lmfx384jsm5Ab9LEvFFXm5it7BUECSoddv28f0H5rGjvIqnLpvACYMbnR1EJCEM7NWR4dldmF2YmM1qFAQJqGjLHr7/4Dwqa+p4dsbxjBmoKZ5E8oPZLNiwg+37Eq+FpYIgwSzZuJNpD84jNTmJF66cSLBvN79LEokKX7awXJl4RwUKggTy0Wfb+cEjn9CjUxovXDGRo3p39rskkagxok/itrBUECSIt1eV8KO/LqB/jw78/YqJDOjZ0e+SRKKKmZGXm82HCdjCUkGQAF5fvpkZTy5iWHYXnp8xkayuGX6XJBKV8oOhFpbvrU6sFpYKgjhWV+d47IPPuebZJYwZ2IOnL5tAj05pfpclErWOO6IHvTqlMSvBrh5Sh5E4VbBpN7e8UsCyL3YxaXgW910whg5p6iUgcij1LSxfX76Fyppa0lMS49+MjgjizN6Kam7/30LOuu8DNu3cz3+dN5pHpo9VCIi0UH7Qa2H5WeK0sNQRQZxwzvHGiq385vVCSvdWcuGEI7ghfxjdOqi3sEhrTDyqF53SkplduJXThyVGrywFQRzYUFbOL18tZO7qbYzs15UHLxrL6AHd/S5LJCZlpCZz2vAs3lxZwv8925GcFP9TsSsIYlhlTS0PvreO+95ZS1pyEr/+Ti4XTcxJiP9wRSIpP5jNzOVbWLJxJ2MToHOZgiBGfbh2O798pYB128v59jF9+OW3cwnoslCRsDi9voVl4daECAKdLI4xpXsruPa5JfzgkU+odY4nLxnPfReMUQiIhNGXLSwLSxKihaWOCGJEbZ3j6U82cPesT6msruPaSUP48WlHkZGqq4FEIiEvN5ubX17Bqq17GdGnq9/lRJSOCGLAiuLdnPOXD7nt1UJG9e/Ov647meunDFUIiETQlNxQC8tEmJpaRwRRbE9FNX+avZon562nV+d0/vv8Y/nOMX3UUF6kHfTuks5xA3swq3Ar104e4nc5EaUgiELOOV5fvoU7Xl/Jtn2VXHz8Efxn/jC6ZuieAJH2lB/M5rdvFPHFjv1xPVGjhoaizOfby7n4sfn89NklBLpm8OpVJ3L71JEKAREf5AVD7VvjfWpqHRFEiYrqWh547zP+8u5npCcn8ZupQX4w4QjdEyDioyN6dQq1sFxZwmUnH+l3ORGjIIgC76/Zxi9fKWB92X7OGtWXW781QlNFi0SJvGA29729hrJ9lfTqnO53ORGhoSEfle6p4KfPLuGiR+djZjx16QT++/xjFQIiUSQ/GKDOwVtF8Xv1kI4IfFBb53jq4w38YdanVNbWcf3koVxx6pG6HFQkCuX26Ur/Hh2YVVjCtHED/S4nIhQE7Wx58S5uebmAFZt2c/KQTO6YOpKczE5+lyUiTTAz8oPZ/G3eBvZV1tA5Pf6+NjU01E52H6jmtlcLmPrnDynZU8F9FxzLk5eMVwiIxIC83ECoheWn8dnCMv6iLco453ht2WbueL2IHeWVTJ+Yw3/mDaWLLgcViRljc3p6LSy38q1j+vhdTtgpCCJo3bZ9/PLVAj5cW8ao/t14/EfjGNmvm99liUgrJScZk0cEeGPFFqpq6khLia/BFAVBBFRU1/KXdz/jgXc/Iz01iTumBrlA9wSIxLT8kQGeX/gFH322ndPirHOZgiDM3lu9jdteLWBD2X7OHt2Xm781gqwuuhxUJNadcFQmndKSmVVYEndBEF/HNz4q2VPBVc8sZvpj80k24+nLJnDveccqBETiREZqMqcNC7WwrKuLrx4FOiJoo5raOp6ct4E/vbmaqto6/nPKUGaceiTpKbonQCTe5AUDzFyxhSVf7OS4I+Knc5mCoA2WfrGLW15eQeHmPZw6tDe/mRrkiF66HFQkXp0+PIvUZGNWYYmCINHtPlDN3bNW8fQnG8nqks6fLxjDmUdnq0+ASJzrmpHKCUdlMqtwK7/45vC4+TevIGgF5xyvLN3Eb2cWsaO8ih+dMIjrpwzRPQEiCSQvGOCWlwv4tGQvw7Pjo4WlTha30NrSfVzw8Cdc//wy+vfoyGtXn8Rt38lVCIgkmHhsYakjgmZUVNdy39treXDuZ3RITea354zk/HEDSdI9ASIJKatLBmO8FpbXTIqPFpYKgkN459NSfvVqIRt37Oe7x/bjF2eOoHeX+JyPXERaLj8Y4HdvrIqbFpZhGRoysxvMzJlZZhPPf8PMPjWztWZ2U4P1vzazTWa21Ps5Mxz1tNWW3Qf48VOL+NFfF5CabDxz+QT+NG20QkBEAMjLzQZg9sr4GB5q8xGBmQ0ApgAbm3g+Gfizt00xsMDMXnPOrfQ2ucc594e21hEONbV1PP7Reu55czU1dY4b84dx+clHxt28IiLSNjmZnRgW6MKswq1cetIgv8tps3AMDd0D/Bx4tYnnxwNrnXPrAMzsOWAqsLKJ7X2xeONObnm5gKItezh9WG9uP2skA3vF/iGfiERGfjDAfe+sjYsWlm36U9fMzgI2OeeWHWKzfsAXDZaLvXX1rjaz5Wb2mJn1aEs9h2PX/ip+8dIKvnf/R+wsr+KBC8fw2A/HKQRE5JDygtnUOZhTVOp3KW3W7BGBmb0FZDfy1C3AzUBec7toZF39RB33A3d4y3cAfwQuaaKOGcAMgIED294uzjnHS4s38bs3ith1oJpLTxzEdVOGxmX3IREJv2DfrvTr3oFZhVv5/rgBfpfTJs1+6znnJje23syOBgYBy7y76/oDi81svHNua4NNi4GG/yv1BzZ7+/7yTIuZPQy8fog6HgIeAhg7dmybZnxaW7qXW18p4ON1Ozh2YHf+dvbR5PaNjxtDRKR91LewfOqT2G9hedhDQ865Fc65LOdcjnMuh9AX/piDQgBgATDEzAaZWRpwHvAagJk1bPVzDlBwuPW0xIGqWu761yq++V/vU7RlL7//7tG8eOUJCgEROSx5wQBVNbHfwjIiEWZmfYFHnHNnOudqzOxqYBaQDDzmnCv0Nr3LzEYTGhpaD1wRiXrq/eKl5byydDPfG9OfX5w5nMwYP8EjIv4al9OTnp3SmL0ytltYhi0IvKOC+sebgTMbLL8BvNHIay4K1/u3xNVnDOG88QM5/she7fm2IhKnQi0ss/hnwdaYbmEZm1UfpsFZnRUCIhJW+cFs9lbUMG9dmd+lHLaECgIRkXA7cXAmHdOSmVV48OnR2KEgEBFpg1ALy94x3cJSQSAi0kb5wWy27a1kyRe7/C7lsCgIRETaqL6F5ewYHR5SEIiItFHXjFQmei0snYu94SEFgYhIGOQHA6wv28/qkn1+l9JqCgIRkTCYMiLUwjIWrx5SEIiIhEFW1wyOHdCd2SsVBCIiCSs/mE3Bpj0U79zvdymtoiAQEQmT/KDXwrIwtlpYKghERMKkYQvLWKIgEBEJo7xggAXrd7CjvMrvUlpMQSAiEkb5XgvLt4piZ3hIQSAiEkb1LSxj6S5jBYGISBiZGXnBAHPXbKe8ssbvclpEQSAiEmb5wexQC8vVsdHCUkEgIhJmY4/oQY+OqTEzPKQgEBEJs5TkJCaPCDBnVSlVNXV+l9MsBYGISATUt7D8OAZaWCoIREQi4KQhsdPCUkEgIhIBsdTCUkEgIhIhebnZlO6tZGlxdLewVBCIiETI6cOzSEmyqB8eUhCIiERItw6pTDyqF7MLS6K6haWCQEQkgvKD2Xy+vZw1pdHbwlJBICISQVNyAwDMKoje4SEFgYhIBAW6ZnDswO7MXhm9s5EqCEREIiw/mM2KTbvZtOuA36U0SkEgIhJh/25hGZ3DQwoCEZEIG5TZiaGBzlF7GamCQESkHeTlZjP/8+hsYakgEBFpB/UtLOdEYQtLBYGISDsY2S/UwnJWoYJARCQhmRlTcgO8v2Yb+6uiq4WlgkBEpJ3kB7OprKnjvU+jq4WlgkBEpJ2Mywm1sIy2q4cUBCIi7SQlOYlJXgvL6troaWGpIBARaUfR2MIyLEFgZjeYmTOzzCaef8zMSs2s4KD1Pc3sTTNb4/3uEY56RESi1clR2MKyzUFgZgOAKcDGQ2z2OPCNRtbfBMxxzg0B5njLIiJxKyM1mVOH9mZ2YfS0sAzHEcE9wM+BJj+Rc24usKORp6YCT3iPnwDODkM9IiJRLS8YiKoWlm0KAjM7C9jknFt2mLsIOOe2AHi/s9pSj4hILDhjWICUJGN2lNxc1mwQmNlbZlbQyM9U4BbgtsiXCWY2w8wWmtnCbdui6xpcEZHW6NaxvoXl1qhoYdlsEDjnJjvnRh78A6wDBgHLzGw90B9YbGbZrXj/EjPrA+D9Lj1EHQ8558Y658b27t27FW8hIhJ98oLZrNteztooaGF52ENDzrkVzrks51yOcy4HKAbGOOdacyr8NWC693g68Orh1iMiEkvy6ltYRsHVQxG5j8DM+prZGw2WnwXmAcPMrNjMLvWeuhOYYmZrCF15dGck6hERiTaBrhmMHhAdLSxTwrUj76ig/vFm4MwGy+c38ZoyYFK4ahARiSX5wWz+379WsXnXAfp27+BbHbqzWETEJ/nB0PCQ3y0sFQQiIj45sndnhmR19r1HgYJARMRH+cFs5q/fwU4fW1gqCEREfJQXDFBb55izqsmr5yNOQSAi4qOj+3Wjb7cMXy8jVTE0aA8AAAl/SURBVBCIiPjIzMgLZjN3tX8tLBUEIiI+ywsGqKypY+5qf6bPURCIiPhsfE5PundM9e3qIQWBiIjPUpKTmDQ8wJyiEl9aWCoIRESiQH4wwJ6KGj5Z11jrlshSEIiIRIFThvamQ6o/LSwVBCIiUeDLFpYrt7Z7C0sFgYhIlMgfGaBkTyXL2rmFpYJARCRKfNnCsp2nplYQiIhEiW4dUzn+yF7tfp5AQSAiEkXygwHWbStnbenedntPBYGISBSZkhtq+96eN5cpCEREokh2twxGDejersNDCgIRkSiTHwywvHg3m3cdaJf3UxCIiESZ/GBoeOjNdrp6SEEgIhJljurdmcFZndtteEhBICIShfKDAT75vH1aWCoIRESiUF5udru1sFQQiIhEoWP6d6NPtwxmt8PwkIJARCQKmRl5uQHmrtnGgaraiL6XgkBEJErlB7OpqK7jvQi3sFQQiIhEqfGDetKtQ2rEh4cUBCIiUSolOYlJI7J4K8ItLBUEIiJRLD+YzZ6KGuZ/HrkWlgoCEZEodsqQ3mSkJkX05jIFgYhIFOuQ5rWwLCyJWAtLBYGISJTLD2azdU8Fyzftjsj+FQQiIlHujOFZJCdZxIaHFAQiIlGue8c0jj+yZ8QuI1UQiIjEgPxgNp9tK2dt6b6w71tBICISA/Jyswn27crO/eGfjTQl7HsUEZGwy+6WwcxrTo7IvnVEICKS4BQEIiIJLixBYGY3mJkzs8wmnn/MzErNrOCg9b82s01mttT7OTMc9YiISMu1OQjMbAAwBdh4iM0eB77RxHP3OOdGez9vtLUeERFpnXAcEdwD/Bxo8t5n59xcIHIzJomIyGFrUxCY2VnAJufcsjbs5mozW+4NH/VoSz0iItJ6zQaBmb1lZgWN/EwFbgFua8P73w8cBYwGtgB/PEQdM8xsoZkt3LYtst16REQSSbP3ETjnJje23syOBgYBy8wMoD+w2MzGO+dadB+0c66kwf4eBl4/xLYPAQ8BjB07NjJT8ImIJKDDvqHMObcCyKpfNrP1wFjn3PaW7sPM+jjntniL5wAFh9q+3qJFi7ab2YZWlNtQJtDiGuOEPnNi0GdODG35zEc0tjIidxabWV/gEefcmd7ys8BpQKaZFQO/cs49CtxlZqMJnWheD1zRkv0753q3obaFzrmxh/v6WKTPnBj0mRNDJD5z2ILAOZfT4PFm4MwGy+c38ZqLwvX+IiJyeHRnsYhIgkvEIHjI7wJ8oM+cGPSZE0PYP7M5pwtwREQSWSIeEYiISAMKAhGRBJdQQWBm3zCzT81srZnd5Hc9kdbUrK/xyswGmNk7ZlZkZoVmdq3fNUWamWWY2XwzW+Z95tv9rqm9mFmymS0xsyZvRI0nZrbezFZ4MzUvDOu+E+UcgZklA6sJzZRaDCwAznfOrfS1sAgys1OAfcCTzrmRftcTaWbWB+jjnFtsZl2ARcDZcf7/sQGdnHP7zCwV+AC41jn3sc+lRZyZ/QwYC3R1zn3b73oi7XBu2m2pRDoiGA+sdc6tc85VAc8BU32uKaISbdZX59wW59xi7/FeoAjo529VkeVC6ruZp3o/cf/XnZn1B74FPOJ3LfEgkYKgH/BFg+Vi4vxLIpGZWQ5wLPCJv5VEnjdEshQoBd50zsX9ZwbuJTT9fZ3fhbQjB8w2s0VmNiOcO06kILBG1sX9X06JyMw6Ay8C1znn9vhdT6Q552qdc6MJTfw43sziehjQzL4NlDrnFvldSzs70Tk3BvgmcJU39BsWiRQExcCABsv9gc0+1SIR4o2Tvwg87Zx7ye962pNzbhfwLk13A4wXJwJneWPmzwFnmNlT/pYUed7UPTjnSoGXCQ13h0UiBcECYIiZDTKzNOA84DWfa5Iw8k6cPgoUOef+5Hc97cHMeptZd+9xB2AysMrfqiLLOfcL51x/b36z84C3nXMX+lxWRJlZJ+8CCMysE5BHC2drbomECQLnXA1wNTCL0EnEF5xzhf5WFVnerK/zgGFmVmxml/pdU4SdCFxE6C/Epd7Pmc29KMb1Ad4xs+WE/th50zmXEJdTJpgA8IGZLQPmAzOdc/8K184T5vJRERFpXMIcEYiISOMUBCIiCU5BICKS4BQEIiIJTkEgIpLgFAQiIglOQSAxwcw+8n7nmNkFYd73zY29V7Qzs1+b2Q1+1yGxT0EgMcE5d4L3MAdoVRB4U5AfyleCoMF7xS0L0b9/ARQEEiPMrH6q5TuBk727hq/3Zt6828wWmNlyM7vC2/40r0nNM8AKb90r3syNhfWzN5rZnUAHb39PN3wv78vybjMr8BqCTGuw73fN7B9mtsrMnvamt2iq9vVmdruZLfb2M9xb/5W/6L33yfF+VpnZI966p81sspl9aGZrzKzhHDOjzOxtb/3lDfZ1Y4P/TW731uVYqGnPX4DFfHXuLUlgKX4XINJKNwE31Dci8b7QdzvnxplZOvChmc32th0PjHTOfe4tX+Kc2+HNybPAzF50zt1kZld7s3ce7LvAaGAUkOm9Zq733LFAkNDEhR8Smt7ig0PUvd05N8bMfgLcAFzWzOccDJwLzCA0dcQFwEnAWYSOYM72tjsGOB7oBCwxs5nASGCI9/kNeM2bqXIjMAz4kXPuJ828vyQQBYHEujzgGDP7D2+5G6EvwSpgfoMQALjGzM7xHg/wtis7xL5PAp51ztUCJWb2HjAO2OPtuxjA6wWQw6GDoH4m1EWEAqY5nzvn6o9kCoE5zjlnZiu896r3qnPuAHDAzN4h9OV/EqH/XZZ423T2PutGYEMidC+T1lEQSKwz4KfOuVlfWWl2GlB+0PJkYKJzbr+ZvQtktGDfTals8LiW5v8t1W/fcNsavjo8m9HI9hBqvlLZ4HHD9zp4sjBHqO7fO+cebPiE16ynHJGD6ByBxJq9QJcGy7OAH3t9CDCzod40vQfrBuz0QmA4oeGUetX1rz/IXGCadx6iN3AKoZkfw2U9MMarewww6DD2MdVCDex7AacRGkaaBVxioQY9mFk/M8sKS8USl3REILFmOVDjTcf7OPBfhIZKFnsnbLfx7/Hzhv4FXOlN1/wp0HB45CFguZktds79oMH6l4GJwDJCf2n/3Dm3tf5kbxi8CFzsDS0tAFYfxj7mAzOBgcAdXvOSzWY2ApjnncPeB1xI6GhE5Gs0DbWISILT0JCISILT0JBImJjZy3x9nP//HHwiWyTaaGhIRCTBaWhIRCTBKQhERBKcgkBEJMEpCEREEtz/BwWLNntYmCMrAAAAAElFTkSuQmCC", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "# A demonstration of plotting the metrics\n", "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", "from matplotlib.ticker import MaxNLocator\n", "\n", - "metrics_data = job.metrics()\n", "\n", - "if metrics_data: \n", - " df = pd.DataFrame(job.metrics())\n", + "df = pd.DataFrame(job.metrics()).sort_values(\"iteration_number\")\n", "\n", - " ax = plt.figure().gca()\n", - " ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n", - " figure = df.plot(x=\"iteration_number\", y=\"Cost\", ax=ax)\n", - "else:\n", - " print(\"Wait for metrics to populate and re-run the cell.\")" + "ax = plt.figure().gca()\n", + "figure = df.plot(x=\"iteration_number\", y=\"loss\", ax=ax, marker=\"o\")" ] }, { - "cell_type": "code", - "execution_count": 25, - "id": "5d134a94", + "cell_type": "markdown", + "id": "72f009ca", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Task Summary\n", - "{'arn:aws:braket:::device/quantum-simulator/amazon/sv1': {'shots': 286000, 'tasks': {'COMPLETED': 286}, 'execution_duration': 9.721, 'billed_execution_duration': 858.0}}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run tasks in this job: 1.0725 USD\n" - ] - } - ], "source": [ - "print(\"Quantum Task Summary\")\n", - "print(job.result()['task summary'])\n", - "print('Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).')\n", - "print(f\"Estimated cost to run quantum tasks in this hybrid job: {job.result()['estimated cost']} USD\")" + "## Continuing from a previous hybrid job with checkpoints" ] }, { "cell_type": "markdown", - "id": "72f009ca", + "id": "a3a1cb80", "metadata": {}, "source": [ - "## Checkpoints" + "Since we saved a checkpoint in the hybrid job, we can create a new hybrid job to resume by loading the data from that checkpoint.\n", + "This next hybrid job adds an additional 10 steps to the QAOA training." ] }, { - "cell_type": "markdown", - "id": "a3a1cb80", + "cell_type": "code", + "execution_count": 17, + "id": "45535a4f", "metadata": {}, + "outputs": [], "source": [ - "It is best practice to regularly save interim progress of your hybrid job as checkpoints. If your hybrid job terminates unexpectedly, for instance, if a QPU becomes unavailable, you can create a new hybrid job and load its training progress from a *checkpoint*. To save checkpoints, the algorithm script should contain code like the following:" + "from braket.jobs import load_job_checkpoint\n", + "\n", + "@hybrid_job(device=None, input_data=input_file_path, copy_checkpoints_from_job=previous_job_arn)\n", + "def continued_run_qaoa_hybrid_job(p=1, steps=10):\n", + "\n", + " # Resume from last checkpoint\n", + " checkpoint = load_job_checkpoint(previous_job_name)\n", + "\n", + " start_iteration = checkpoint[\"iteration\"]\n", + " params = checkpoint[\"params\"]\n", + "\n", + " # code below is similar for both hybrid jobs \n", + " braket_task_tracker = Tracker()\n", + "\n", + " graph = nx.read_adjlist(input_file_path, nodetype=int)\n", + " wires = list(graph.nodes)\n", + " cost_h, mixer_h = qaoa.maxcut(graph)\n", + "\n", + " dev = qml.device(\"default.qubit\", wires=len(wires))\n", + "\n", + " @qml.qnode(dev)\n", + " def cost_function(params):\n", + " circuit(params)\n", + " return qml.expval(cost_h)\n", + "\n", + " # training loop\n", + " optimizer = qml.GradientDescentOptimizer()\n", + " for i in range(start_iteration, steps):\n", + " params = optimizer.step(cost_function, params)\n", + " cost = float(cost_function(params))\n", + " \n", + " log_metric(metric_name=\"loss\", value=cost, iteration_number=i)\n", + "\n", + " # save final results\n", + " np.save(\"continued_optimal_params.npy\", params)\n", + "\n", + " return {\n", + " \"params\": params,\n", + " \"cost\": cost,\n", + " \"task summary\": braket_task_tracker.quantum_tasks_statistics(),\n", + " \"estimated cost\": braket_task_tracker.qpu_tasks_cost()\n", + " + braket_task_tracker.simulator_tasks_cost(),\n", + " }" ] }, { "cell_type": "code", "execution_count": 18, - "id": "7f000ebb", + "id": "999e550a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Code in the algorithm script for saving a checkpoint\n" + "AwsQuantumJob('arn':'arn:aws:braket:us-west-1:961591465522:job/continued-run-qaoa-hybrid-job-1697313031869')\n" ] } ], "source": [ - "%%script echo \"Code in the algorithm script for saving a checkpoint\"\n", - "\n", - "for iteration in range(start_iteration, num_iterations):\n", - " # Training code omitted ...\n", - " # ...\n", - " save_job_checkpoint(\n", - " checkpoint_data={\n", - " \"iteration\": iteration + 1,\n", - " \"params\": np_params.tolist(),\n", - " \"cost_before\": cost_before,\n", - " },\n", - " checkpoint_file_suffix=\"checkpoint-1\",\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "40268f20", - "metadata": {}, - "source": [ - "This code creates and overwrites a checkpoint file `-checkpoint-1.json` in your Hybrid Job artifacts under a subfolder checkpoints. To load from a checkpoint at the start of a new hybrid , the algorithm script should contain code like this:" + "continued_job = continued_run_qaoa_hybrid_job(p=1, steps=10)\n", + "print(continued_job)" ] }, { "cell_type": "code", "execution_count": 19, - "id": "789beb4b", + "id": "b8f895ec", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Code in the algorithm script for loading a checkpoint\n" + "CPU times: user 140 ms, sys: 9.1 ms, total: 149 ms\n", + "Wall time: 3min 7s\n" ] } ], "source": [ - "%%script echo \"Code in the algorithm script for loading a checkpoint\"\n", - "\n", - "if \"copy_checkpoints_from_job\" in hyperparams:\n", - " copy_checkpoints_from_job = hyperparams[\"copy_checkpoints_from_job\"].split(\"/\", 2)[-1]\n", - "\n", - "if copy_checkpoints_from_job:\n", - " checkpoint_1 = load_job_checkpoint(\n", - " copy_checkpoints_from_job,\n", - " checkpoint_file_suffix=\"checkpoint-1\",\n", - " )\n", - " start_iteration = checkpoint_1[\"iteration\"]\n", - " params = interface.initialize_params(np.array(checkpoint_1[\"params\"]))\n", - " print(\"Checkpoint loaded\")" - ] - }, - { - "cell_type": "markdown", - "id": "235c3325", - "metadata": {}, - "source": [ - "This starts off the algorithm from the iteration and the parameters specified in the checkpoint file." - ] - }, - { - "cell_type": "markdown", - "id": "72dbcd48", - "metadata": {}, - "source": [ - "Let us see loading a checkpoint in action. We create a new hybrid job using the checkpoint from our existing hybrid job, this time training for another 2 iterations:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "deffb4d7", - "metadata": {}, - "outputs": [], - "source": [ - "new_hyperparameters = dict(hyperparameters)\n", - "# Increase number of iterations by 2\n", - "new_hyperparameters[\"num_iterations\"] = hyperparameters[\"num_iterations\"] + 2\n", - "# Also add previous hybrid job arn in new hyperparameters\n", - "new_hyperparameters[\"copy_checkpoints_from_job\"] = job.arn\n", - "\n", - "start_time = time.time()\n", - "\n", - "continued_job = AwsQuantumJob.create(\n", - " device=device,\n", - " source_module=\"qaoa\",\n", - " job_name=\"qaoa-job-continued-\" + interface + \"-\" + str(int(time.time())),\n", - " image_uri=image_uri,\n", - " entry_point=\"qaoa.qaoa_algorithm_script\",\n", - " # We specify the previous hybrid job arn to copy checkpoints from\n", - " copy_checkpoints_from_job=job.arn,\n", - " hyperparameters=new_hyperparameters,\n", - " input_data={\"input-graph\": input_file_path},\n", - " wait_until_complete=False,\n", - ")\n", + "%%time\n", "\n", - "end_time = time.time()" + "continued_result = continued_job.result()" ] }, { "cell_type": "code", - "execution_count": 21, - "id": "6fcbbf9f", + "execution_count": 26, + "id": "beb01635", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 7 µs, sys: 0 ns, total: 7 µs\n", - "Wall time: 12.4 µs\n" - ] - }, { "data": { "text/plain": [ - "{'params': [[0.5304512035600284, 0.20428120976384603],\n", - " [-0.31945139287717106, -0.22541990250088492]],\n", - " 'cost': -4.9639999999999995}" + "{'timestamp': [1697313155.4130461,\n", + " 1697313155.4511778,\n", + " 1697313155.4890456,\n", + " 1697313155.5269554,\n", + " 1697313155.5648792,\n", + " 1697313155.6031256],\n", + " 'loss': [-4.301169550370251,\n", + " -4.301169550370251,\n", + " -4.301169550370251,\n", + " -4.301169550370251,\n", + " -4.301169550370251,\n", + " -4.301169550370251],\n", + " 'iteration_number': [4.0, 5.0, 6.0, 7.0, 8.0, 9.0]}" ] }, - "execution_count": 19, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "%time \n", - "# This cell should take 8-9 min\n", - "continued_job.result()" + "time.sleep(10) # wait for new metrics to load\n", + "continued_job.metrics()" ] }, { "cell_type": "code", - "execution_count": 22, - "id": "7f1bca94", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'timestamp': [1652489391.446638, 1652489388.6802447, 1652489363.5430026], 'Cost': [-4.9639999999999995, -5.303, -4.203], 'iteration_number': [7.0, 6.0, 5.0]}\n" - ] - } - ], - "source": [ - "# May need to wait a bit before metrics show up\n", - "# If metrics aren't there, run a bit later\n", - "time.sleep(10)\n", - "print(continued_job.metrics())" - ] - }, - { - "cell_type": "code", - "execution_count": 23, + "execution_count": 27, "id": "e3b35b73", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "metrics_data = continued_job.metrics()\n", - "\n", - "if metrics_data: \n", - " df = pd.DataFrame(continued_job.metrics())\n", + "df = pd.DataFrame(job.metrics())\n", + "continued_df = pd.DataFrame(continued_job.metrics())\n", + "df = pd.concat([df, continued_df], ignore_index=True).sort_values(\"iteration_number\")\n", "\n", - " ax = plt.figure().gca()\n", - " ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n", - " figure = df.plot(x=\"iteration_number\", y=\"Cost\", ax=ax)\n", - "else: \n", - " print(\"Wait for metrics to populate and re-run the cell.\")" + "ax = plt.figure().gca()\n", + "figure = df.plot(x=\"iteration_number\", y=\"loss\", ax=ax, marker=\"o\")" + ] + }, + { + "cell_type": "markdown", + "id": "a6a95cc1", + "metadata": {}, + "source": [ + "We see how adding 10 more iterations has improved the convergence significantly. " ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 28, "id": "7ccb702b", "metadata": {}, "outputs": [ @@ -776,34 +818,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "Task Summary\n", - "{'arn:aws:braket:::device/quantum-simulator/amazon/sv1': {'shots': 115000, 'tasks': {'COMPLETED': 115}, 'execution_duration': 4.814, 'billed_execution_duration': 345.0}}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run tasks in this job: 0.43125 USD\n" + "Estimated cost to run quantum tasks in this notebook: 0 USD\n" ] } ], "source": [ - "print(\"Quantum Task Summary\")\n", - "print(continued_job.result()['task summary'])\n", - "print('Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).')\n", - "print(f\"Estimated cost to run quantum tasks in this hybrid job: {continued_job.result()['estimated cost']} USD\")" + "print(f\"Estimated cost to run quantum tasks in this notebook: {job.result()['estimated cost'] + continued_job.result()['estimated cost']} USD\")" ] }, { "cell_type": "markdown", - "id": "ce4140c0", + "id": "977d5631", "metadata": {}, "source": [ "## Summary\n", "\n", - "In this tutorial, we set up a Max-Cut problem with a random graph using PennyLane. We saved the graph to a local file and provided it as input data to our Braket Hybrid Job, and used the pre-built PennyLane container image to run it. Variables that are required for the training process and the optimizer are passed as hyperparameters. The result is retrieved after the QAOA algorithm is completed. Lastly, we demonstrated how to use checkpoints to save and load training progress of a hybrid job." + "In this tutorial, we set up a Max-Cut problem with a random graph using PennyLane. \n", + "We saved the graph to a local file and provided it as input data to our hybrid job.\n", + "Hyperparameters required for the algorithm are passed in as function arguments. \n", + "The result is retrieved after the QAOA algorithm is completed. \n", + "Lastly, we demonstrated how to use checkpoints to save and load training progress of a hybrid job." ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.8.10 ('venv': venv)", "language": "python", "name": "python3" }, @@ -817,7 +857,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.10.13" }, "vscode": { "interpreter": { diff --git a/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/console_figures/training.png 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All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"). You -# may not use this file except in compliance with the License. A copy of -# the License is located at -# -# http://aws.amazon.com/apache2.0/ -# -# or in the "license" file accompanying this file. This file is -# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF -# ANY KIND, either express or implied. See the License for the specific -# language governing permissions and limitations under the License. - -import json -import os -import time - -import networkx as nx -import numpy as np -import pennylane as qml -from braket.jobs import load_job_checkpoint, save_job_checkpoint, save_job_result -from braket.jobs.metrics import log_metric -from braket.tracking import Tracker -from matplotlib import pyplot as plt - -import qaoa.qaoa_utils as qaoa_utils # isort:skip - - -def init_pl_device(device_arn, num_nodes, shots, max_parallel, parametrize_differentiable): - return qml.device( - "braket.aws.qubit", - device_arn=device_arn, - wires=num_nodes, - shots=shots, - # Set s3_destination_folder=None to output task results to a default folder - s3_destination_folder=None, - parallel=True, - max_parallel=max_parallel, - # poll_timeout_seconds=30, - parametrize_differentiable=parametrize_differentiable, - ) - - -def main(): - cost_tracker = Tracker().start() - - # lets see the env variables - # print statements can be viewed in cloudwatch - print(os.environ) - - input_dir = os.environ["AMZN_BRAKET_INPUT_DIR"] - output_dir = os.environ["AMZN_BRAKET_JOB_RESULTS_DIR"] - job_name = os.environ["AMZN_BRAKET_JOB_NAME"] # noqa - checkpoint_dir = os.environ["AMZN_BRAKET_CHECKPOINT_DIR"] # noqa - hp_file = os.environ["AMZN_BRAKET_HP_FILE"] - device_arn = os.environ["AMZN_BRAKET_DEVICE_ARN"] - - # Read the hyperparameters - with open(hp_file) as f: - hyperparams = json.load(f) - print(hyperparams) - - p = int(hyperparams["p"]) - seed = int(hyperparams["seed"]) - max_parallel = int(hyperparams["max_parallel"]) - num_iterations = int(hyperparams["num_iterations"]) - stepsize = float(hyperparams["stepsize"]) - shots = int(hyperparams["shots"]) - pl_interface = hyperparams["interface"] - parametrize_differentiable = json.loads(hyperparams["parametrize_differentiable"].lower()) - if "copy_checkpoints_from_job" in hyperparams: - copy_checkpoints_from_job = hyperparams["copy_checkpoints_from_job"].split("/", 2)[-1] - else: - copy_checkpoints_from_job = None - - interface = qaoa_utils.QAOAInterface.get_interface(pl_interface) - - # Read graph from input file - g = nx.read_adjlist(f"{input_dir}/input-graph/input-data.adjlist", nodetype=int) - num_nodes = len(g.nodes) - - # Draw graph to an output file - positions = nx.spring_layout(g, seed=seed) - nx.draw(g, with_labels=True, pos=positions, node_size=600) - plt.savefig(f"{output_dir}/graph.png") - - # Set up the QAOA problem - cost_h, mixer_h = qml.qaoa.maxcut(g) - - def qaoa_layer(gamma, alpha): - qml.qaoa.cost_layer(gamma, cost_h) - qml.qaoa.mixer_layer(alpha, mixer_h) - - def circuit(params, **kwargs): - for i in range(num_nodes): - qml.Hadamard(wires=i) - qml.layer(qaoa_layer, p, params[0], params[1]) - - dev = init_pl_device(device_arn, num_nodes, shots, max_parallel, parametrize_differentiable) - - np.random.seed(seed) - - @qml.qnode(dev, interface=pl_interface) - def cost_function(params, **kwargs): - circuit(params, **kwargs) - return qml.expval(cost_h) - - # Load checkpoint if it exists - if copy_checkpoints_from_job: - checkpoint_1 = load_job_checkpoint( - copy_checkpoints_from_job, - checkpoint_file_suffix="checkpoint-1", - ) - start_iteration = checkpoint_1["iteration"] - params = interface.initialize_params(np.array(checkpoint_1["params"])) - print("Checkpoint loaded") - else: - start_iteration = 0 - params = interface.initialize_params(0.01 * np.random.uniform(size=[2, p])) - - optimizer = interface.get_sgd_optimizer(stepsize, params) - print("Optimization start") - - for iteration in range(start_iteration, num_iterations): - t0 = time.time() - - # Evaluates the cost, then does a gradient step to new params - params, cost_before = interface.get_cost_and_step(cost_function, params, optimizer) - # Convert params to a Numpy array so they're easier to handle for us - np_params = interface.convert_params_to_numpy(params) - - t1 = time.time() - - if iteration == 0: - print("Initial cost:", cost_before) - else: - print(f"Cost at step {iteration}:", cost_before) - - # Track the cost as a metric - timestamp = time.time() - braket_tasks_cost = float( - cost_tracker.simulator_tasks_cost() + cost_tracker.qpu_tasks_cost() - ) - log_metric( - metric_name="braket_tasks_cost", - value=braket_tasks_cost, - iteration_number=iteration, - timestamp=timestamp, - ) - - # Log the loss before the update step as a metric - log_metric( - metric_name="Cost", - value=cost_before, - iteration_number=iteration, - timestamp=timestamp, - ) - - # Save the current params and previous cost to a checkpoint - save_job_checkpoint( - checkpoint_data={ - "iteration": iteration + 1, - "params": np_params.tolist(), - "cost_before": cost_before, - }, - checkpoint_file_suffix="checkpoint-1", - ) - - print(f"Completed iteration {iteration + 1}") - print(f"Time to complete iteration: {t1 - t0} seconds") - - final_cost = float(cost_function(params)) - print(f"Cost at step {num_iterations}:", final_cost) - - # We're done with the job, so save the result. - # This will be returned in job.result() - save_job_result( - { - "params": np_params.tolist(), - "cost": final_cost, - "task summary": cost_tracker.quantum_tasks_statistics(), - "estimated cost": cost_tracker.qpu_tasks_cost() + cost_tracker.simulator_tasks_cost(), - } - ) - - -if __name__ == "__main__": - main() diff --git a/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/qaoa/qaoa_utils.py b/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/qaoa/qaoa_utils.py deleted file mode 100644 index 4f5c4b27b..000000000 --- a/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/qaoa/qaoa_utils.py +++ /dev/null @@ -1,174 +0,0 @@ -# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"). You -# may not use this file except in compliance with the License. A copy of -# the License is located at -# -# http://aws.amazon.com/apache2.0/ -# -# or in the "license" file accompanying this file. This file is -# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF -# ANY KIND, either express or implied. See the License for the specific -# language governing permissions and limitations under the License. - -from abc import ABC, abstractmethod - - -class QAOAInterface(ABC): - @classmethod - @abstractmethod - def initialize_params(cls, np_array): - """Initialize the parameters to the appropriate format for the autodiff library. - Args: - np_array (np.ndarray): Input parameters in Numpy array format. - Returns: - array_like: Parameter format needed for the autodiff library. - """ - pass - - @classmethod - @abstractmethod - def get_sgd_optimizer(cls, stepsize, params): - """Returns the gradient descent optimizer to use, based on the ML framework. - Args: - stepsize (float): Step size for the gradient descent optimizer. - params (array_like): Input parameters, required for the PyTorch optimizer. - Returns: - callable: Gradient descent optimizer for the ML framework. - """ - pass - - @classmethod - @abstractmethod - def convert_params_to_numpy(cls, params): - """Convert params to base Numpy arrays. - Args: - params (array_like): A set of parameters. - Returns: - np.ndarray: The parameters converted to a Numpy array. - """ - pass - - @classmethod - @abstractmethod - def get_cost_and_step(cls, cost_function, params, optimizer): - """Evaluate the cost function, then take a step. - Args: - cost_function (callable): The cost function. - params (array_like): The current set of parameters. - optimizer (callable): The optimizer. - Returns: - tuple(array_like, float): The updated set of parameters, and the cost function evaluated - before the update. - """ - pass - - @staticmethod - def get_interface(interface): - """Get the appropriate interface to use based on the string input. - Args: - interface (string): A string specifying the interface to use. - Must be "autograd", "tf", or "torch". - Returns: - QAOAInterface: An interface matching the specified string. - """ - if interface == "autograd": - return AutogradInterface() - elif interface == "tf": - return TensorFlowInterface() - elif interface == "torch": - return PyTorchInterface() - else: - raise ValueError(f"Interface {interface} is invalid.") - - -class AutogradInterface(QAOAInterface): - # Import only in __init__ so we don't import a library that doesn't exist in the container. - def __init__(self): - global qml - qml = __import__("pennylane", globals(), locals()) - # Equiv to: import pennylane as qml - - @classmethod - def initialize_params(cls, np_array): - return qml.numpy.array(np_array, requires_grad=True) - - @classmethod - def get_sgd_optimizer(cls, stepsize, params): - return qml.GradientDescentOptimizer(stepsize=stepsize) - - @classmethod - def convert_params_to_numpy(cls, params): - return params.numpy() - - @classmethod - def get_cost_and_step(cls, cost_function, params, optimizer): - params, cost_before = optimizer.step_and_cost(cost_function, params) - return params, float(cost_before) - - -class TensorFlowInterface(QAOAInterface): - # Import only in __init__ so we don't import a library that doesn't exist in the container. - def __init__(self): - global tf - tf = __import__("tensorflow", globals(), locals()) - # Equiv to: import tensorflow as tf - - @classmethod - def initialize_params(cls, np_array): - return tf.Variable(np_array, dtype=tf.float64) - - @classmethod - def get_sgd_optimizer(cls, stepsize, params): - return tf.keras.optimizers.legacy.SGD(learning_rate=stepsize) - - @classmethod - def convert_params_to_numpy(cls, params): - return params.numpy() - - @classmethod - def get_cost_and_step(cls, cost_function, params, optimizer): - def tf_cost(): - global _cached_cost_before - _cached_cost_before = cost_function(params) - return _cached_cost_before - - optimizer.minimize(tf_cost, params) - cost_before = _cached_cost_before - - # Alternative: - # with tf.GradientTape() as tape: - # cost_before = cost_function(params) - # - # gradients = tape.gradient(cost_before, params) - # optimizer.apply_gradients(((gradients, params),)) - - return params, float(cost_before) - - -class PyTorchInterface(QAOAInterface): - # Import only in __init__ so we don't import a library that doesn't exist in the container. - def __init__(self): - global torch - torch = __import__("torch", globals(), locals()) - # Equiv to: import torch - - @classmethod - def initialize_params(cls, np_array): - return torch.tensor(np_array, requires_grad=True) - - @classmethod - def get_sgd_optimizer(cls, stepsize, params): - return torch.optim.SGD([params], lr=stepsize) - - @classmethod - def convert_params_to_numpy(cls, params): - return params.detach().numpy() - - @classmethod - def get_cost_and_step(cls, cost_function, params, optimizer): - optimizer.zero_grad() - cost_before = cost_function(params) - cost_before.backward() - optimizer.step() - return params, float(cost_before) From e052c6a55481de4fbcb986c93de8f3436e409daf Mon Sep 17 00:00:00 2001 From: Matt Beach Date: Sun, 15 Oct 2023 11:20:13 -0400 Subject: [PATCH 07/24] black formatting --- .../0_Creating_your_first_Hybrid_Job.ipynb | 14 +- ...earning_in_Amazon_Braket_Hybrid_Jobs.ipynb | 1599 +++++++++-------- .../qcbm/qcbm.py | 4 +- ...ng_PennyLane_with_Braket_Hybrid_Jobs.ipynb | 48 +- 4 files changed, 839 insertions(+), 826 deletions(-) diff --git a/examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/0_Creating_your_first_Hybrid_Job.ipynb b/examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/0_Creating_your_first_Hybrid_Job.ipynb index eb567753a..3e9c4bbf8 100644 --- a/examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/0_Creating_your_first_Hybrid_Job.ipynb +++ b/examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/0_Creating_your_first_Hybrid_Job.ipynb @@ -60,7 +60,6 @@ "import pennylane as qml\n", "from pennylane import numpy as np\n", "\n", - "\n", "device = qml.device(\"braket.local.qubit\", wires=1)" ] }, @@ -207,8 +206,8 @@ "metadata": {}, "outputs": [], "source": [ - "from braket.jobs import hybrid_job\n", - "from braket.jobs import save_job_result\n", + "from braket.jobs import hybrid_job, save_job_result\n", + "\n", "\n", "@hybrid_job(device=\"local:pennylane/lightning.qubit\", dependencies=\"requirements.txt\")\n", "def qubit_rotation_hybrid_job(num_steps=1, stepsize=0.5):\n", @@ -349,8 +348,8 @@ } ], "source": [ - "import pandas as pd\n", "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", "\n", "df = pd.DataFrame(job.metrics())\n", "df.sort_values(by=[\"iteration_number\"], inplace=True)\n", @@ -410,14 +409,15 @@ "# device_arn = Devices.Amazon.SV1\n", "device_arn = Devices.Rigetti.AspenM3\n", "\n", - "@hybrid_job(device=device_arn, dependencies=\"requirements.txt\") # set priority QPU\n", + "\n", + "@hybrid_job(device=device_arn, dependencies=\"requirements.txt\") # set priority QPU\n", "def qpu_qubit_rotation_hybrid_job(num_steps=10, stepsize=0.5):\n", " # AWS devices must be declared within the decorated function.\n", " device = qml.device(\n", " \"braket.aws.qubit\",\n", " device_arn=device_arn.value, # Make sure the device ARN matches the hybrid job device ARN\n", " wires=2,\n", - " shots=1_000\n", + " shots=1_000,\n", " )\n", "\n", " @qml.qnode(device)\n", @@ -425,7 +425,7 @@ " qml.RX(params[0], wires=0)\n", " qml.RY(params[1], wires=0)\n", " return qml.expval(qml.PauliZ(0))\n", - " \n", + "\n", " opt = qml.GradientDescentOptimizer(stepsize=stepsize)\n", " params = np.array([0.5, 0.75])\n", "\n", diff --git a/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb b/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb index 6b05099a1..a13af4088 100644 --- a/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb +++ b/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb @@ -1,799 +1,806 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Quantum machine learning in Amazon Braket Hybrid Jobs\n", - "\n", - "This notebook demonstrates a typical quantum machine learning workflow, including uploading data, monitoring training, and tuning hyperparameters. We focus on training a parameterized quantum circuit for an unsupervised generative modelling task.\n", - "\n", - "\n", - "## Learning outcomes\n", - "\n", - "* Set input data \n", - "* Set hyperparameters \n", - "* Submit multiple hybrid jobs asynchronously \n", - "* Monitor hybrid job progress via the AWS Console \n", - "* Download and plot results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Background: Generative modelling \n", - "\n", - "Generative modelling is an unsupervised learning task where the goal is to generate new synthetic samples from an unknown target probability distribution. We denote the target probability distribution as $p(x)$, and the estimated distribution as $p_{\\theta}(x)$. The goal is to learn $p_{\\theta}(x)$ that closely resembles the target $p(x)$. One metric to quantify the difference between probability distributions is the maximum mean discrepancy (MMD) loss . \n", - "\n", - "$$MMD(x, y) = \\sum_{j=1}^N \\sum_{j'=1}^N k(y_j, y_{j'}) + \\sum_{i=1}^N \\sum_{i'=1}^N k(x_i, x_{i'}) - 2 \\sum_{j=1}^N \\sum_{i=1}^N k(y_j, x_i)$$\n", - "where $x$ is a sample from the target data $p(x)$, $y$ is a sample from the generative model $p_{\\theta}(x)$, and $k$ is a Gaussian kernel\n", - "\n", - "$$ k(x,y)= \\sum_{\\sigma} e^{-(x-y)^2/(2 \\sigma^2))}$$\n", - "\n", - "The MMD loss is zero if and only if $p(x)=p_{\\theta}(x)$ for all $x$. \n", - "\n", - "Learning a good approximation $p_{\\theta}$ depends on the expressibility of the model, the effectiveness of the training algorithm, and the ability to sample the circuit efficiently. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quantum Circuit Born Machine \n", - "\n", - "Quantum circuits are a natural fit for generative modelling because they are inherently probabilistic; the wavefunction encodes a probability according to the Born rule:\n", - "\n", - "$$p(x)=|\\langle x|\\psi\\rangle|^2$$\n", - "\n", - "In quantum mechanics, we do not have access to $p(x)$ directly, but we can efficiently sample using projective measurements [1]. This is an implicit generative model similar to generative adversarial networks (GANs). Quantum circuits allow fast sampling from a high-dimension distribution, and have large expressive power. \n", - "\n", - "The QCBM in this tutorial consists of alternating layers of single qubit rotations ($RX, RZ, RX$), followed by an entangling layer of CNot gates on each neighboring qubits. The final measurement layer computes the bit string samples of each outcome. Run the cell below to print a circuit diagram of a QCBM with randomly initialized parameters.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "T : | 0 | 1 | 2 |3|4| 5 | 6 | 7 | 8 |9|10|11|Result Types|\n", - " \n", - "q0 : -Rx(0.53)-Rz(0.86)-Rx(0.21)-C---X----------Rx(0.24)-Rz(0.41)-Rx(0.88)-C----X--Probability--\n", - " | | | | | \n", - "q1 : -Rx(0.27)-Rz(0.55)-Rx(0.22)-X-C-|-Rx(0.65)-Rz(0.26)-Rx(0.95)----------X-C--|--Probability--\n", - " | | | | | \n", - "q2 : -Rx(0.92)-Rz(0.97)-Rx(0.43)---X-C----------Rx(0.25)-Rz(0.42)-Rx(0.98)---X--C--Probability--\n", - "\n", - "T : | 0 | 1 | 2 |3|4| 5 | 6 | 7 | 8 |9|10|11|Result Types|\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "from braket.devices import LocalSimulator\n", - "from qcbm.qcbm import QCBM\n", - "\n", - "\n", - "n_qubits = 3\n", - "n_layers = 2\n", - "\n", - "init_params = np.random.rand(3 * n_layers * n_qubits)\n", - "device = LocalSimulator()\n", - "qcbm = QCBM(device, n_qubits, n_layers, np.random.rand(1))\n", - "print(qcbm.create_circuit(init_params))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Problem setup\n", - "\n", - "This notebook demonstrates training a QCBM on a toy data set using Amazon Braket Hybrid Jobs. The code for the QCBM is in `qcbm` directory. The `qcbm_job.py` contains the code that will run when we create a Braket Hybrid Job. The other file (`qcbm.py`) contain the source code for the QCBM. \n", - "\n", - "In this tutorial, we use a small number of qubits to make it quick to test the algorithm. We use the on-demand simulator SV1 to run our circuits and gradient calculations in parallel (up to 35 concurrent tasks). \n", - "\n", - "We first set the number of qubits we want to use in our problem:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "n_qubits = 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate data\n", - "\n", - "As an example, we consider the toy example of learning a mixture of Gaussian distributions. We set a numpy random seed to produce the same data each time, but try experimenting with the number of peaks and number of qubits to produce harder or easier data sets. For this example, the target distribution $p(x)$ is a Gaussian on 5 bits (so $2^5$ possible values), with peaks at $\\mu_1=7$ and $\\mu_2=20$, with standard deviations $\\sigma_1=1$, $\\sigma_2 = 2$. We generate and plot the data as a probability density function in the cell below." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "

" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline \n", - "\n", - "def gaussian(n_qubits, mu, sigma=1):\n", - " x = np.arange(2 ** n_qubits)\n", - " gaussian = (\n", - " 1.0\n", - " / np.sqrt(2 * np.pi * sigma ** 2)\n", - " * np.exp(-((x - mu) ** 2) / (2 * sigma ** 2))\n", - " )\n", - " return gaussian / sum(gaussian)\n", - "\n", - "\n", - "data = gaussian(n_qubits, mu=4, sigma=5) + gaussian(n_qubits, mu=20, sigma=2)\n", - "data = data / sum(data)\n", - "\n", - "\n", - "labels = [\"{0:{fill}6b}\".format(i, fill='0') for i in range(len(data))]\n", - "\n", - "plt.bar(range(2 ** n_qubits), data)\n", - "plt.xticks([i for i in range(len(data))], labels, rotation='vertical', size=12)\n", - "plt.yticks(size=12)\n", - "\n", - "plt.xlabel(\"Sample\", size=20)\n", - "plt.ylabel(\"Probability\", size=20)\n", - "plt.title(\"Two-peak Gaussian distribution\")\n", - "\n", - "fig = plt.gcf()\n", - "fig.set_size_inches(16, 8)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "np.save(\"data.npy\", data) # save data to file" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training \n", - "\n", - "\n", - "Next, we train the circuit using the [limited-memory BFGS](https://en.wikipedia.org/wiki/Limited-memory_BFGS) optimizer from scipy. \n", - "Instead of using finite-difference gradients, we use the exact MMD loss function gradient. \n", - "\n", - "The training function has three arguments that act as hyperparameters: number of qubits `n_qubits`, number of layers in the QCBM `n_layers`, and the number of iterations in the optimization algorithm. \n", - "The number of layers determines how many rotation angles are in the quantum circuit. \n", - "For the QCBM, we need `n_params = 3 * n_layers * n_qubits` parameters. \n", - "\n", - "\n", - "Since we will eventually run this training function as an hybrid job, we add three convenience functions. \n", - "Firstly, we use `log_metric` to print the loss function for each iteration. \n", - "Once we run this as a hybrid job, the metrics will be displayed in near-real time on the Braket console, or via [Amazon CloudWatch](https://aws.amazon.com/cloudwatch/). \n", - "\n", - "Secondly, we write our results to a file prefixed with the path `get_results_dir()`. \n", - "This is necessary because hybrid jobs use a temporary filesystem.\n", - "Once the instance is terminated, all files on the instance are deleted.\n", - "\n", - "Lastly, the return statement of the function will be our hybrid job results returned by `job.result()`. \n", - "These can be a single object or a dictionary with string keys. \n", - "Note that while most native Python objects are supported, custom classes that do have have defined serialization methods may not work. \n", - "For example, we can return the numpy array of final parameters, but we cannot return the QCBM object itself. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from braket.tracking import Tracker\n", - "from scipy.optimize import minimize\n", - "\n", - "from braket.jobs import get_results_dir\n", - "from braket.jobs.metrics import log_metric\n", - "\n", - "from qcbm.qcbm import mmd_loss\n", - "\n", - "def train_circuit(n_qubits, n_layers, n_iterations=10):\n", - " global iteration_number\n", - " iteration_number = 0\n", - " \n", - " braket_task_costs = Tracker().start()\n", - "\n", - " device = LocalSimulator()\n", - "\n", - " data = np.load(\"data.npy\") # load the input data\n", - " \n", - " qcbm = QCBM(device, n_qubits, n_layers, data)\n", - " \n", - " init_params = np.random.rand(3 * n_layers * n_qubits)\n", - "\n", - " def callback(x):\n", - " global iteration_number\n", - " iteration_number += 1\n", - " loss = mmd_loss(qcbm.probabilities(x), data)\n", - " \n", - " log_metric( # log the metrics to Braket console\n", - " metric_name=\"loss\",\n", - " value=loss,\n", - " iteration_number=iteration_number,\n", - " )\n", - "\n", - " res = minimize(\n", - " lambda x: mmd_loss(qcbm.probabilities(x), data),\n", - " x0=init_params,\n", - " method=\"L-BFGS-B\",\n", - " jac=lambda x: qcbm.gradient(x),\n", - " options={\"maxiter\": n_iterations},\n", - " callback=callback,\n", - " )\n", - " final_params = res.x\n", - "\n", - " # save final parameters\n", - " np.save(get_results_dir() + \"/final_params.npy\", final_params)\n", - " \n", - " return {\n", - " \"params\": final_params,\n", - " \"task summary\": braket_task_costs.quantum_tasks_statistics(),\n", - " \"estimated cost\": float(braket_task_costs.qpu_tasks_cost() + braket_task_costs.simulator_tasks_cost()),\n", - " }\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's run the function to verify that it works as expected." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Metrics - timestamp=1697028322.742037; loss=0.0738105584324629; iteration_number=1;\n", - "Metrics - timestamp=1697028324.6127226; loss=0.05514942674966222; iteration_number=2;\n", - "Metrics - timestamp=1697028328.4326718; loss=0.029347326432547782; iteration_number=3;\n", - "Metrics - timestamp=1697028330.5567336; loss=0.02751726243188396; iteration_number=4;\n", - "Metrics - timestamp=1697028332.750267; loss=0.019362259334110732; iteration_number=5;\n", - "CPU times: user 3.87 s, sys: 515 ms, total: 4.39 s\n", - "Wall time: 13.9 s\n" - ] - }, - { - "data": { - "text/plain": [ - "{'params': array([ 0.55196856, 0.38672791, 1.35300743, 0.87330452, -0.54221993,\n", - " 0.74465571, 0.61411241, 0.06143268, 0.81830411, 0.28084566,\n", - " 0.98187881, 0.17193621, 0.04322529, 0.02989581, -0.08170342,\n", - " 0.58622265, 0.50212177, 0.32964731, 1.19606685, -0.28772351,\n", - " 0.60482847, -0.04202664, 0.33609895, 0.12554622, 0.46006129,\n", - " 0.44578169, 1.22449628, 1.1830697 , 0.19600585, 0.35640496]),\n", - " 'task summary': {},\n", - " 'estimated cost': 0.0}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "\n", - "train_circuit(n_qubits, n_layers=n_layers, n_iterations=5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great! Now for longer algorithms, or those that require priority queueing to a QPU, we can run this function on AWS by adding the `@hybrid_job` annotation and calling the function.\n", - "\n", - "## Training with hybrid jobs\n", - "\n", - "Amazon Braket Hybrid Jobs provides fully managed execution of hybrid quantum-classical algorithms, combining AWS classical compute resources based on Amazon EC2 (the \"job instance\") with Amazon Braket quantum processing units (QPUs) or quantum circuit simulators. \n", - "\n", - "There are three arguments to the `@hybrid_job` decorator that we will use in this example. Firstly, \n", - "since we do not require priority QPU access, we set `device=None` in the decorator arguments. \n", - "This argument is responsible for scheduling the hybrid job to run on a QPU. \n", - "In this example, we use the Braket local simulator running on the job instance. \n", - "If we find this simulator too slow, we could either increase the the classical compute by selecting a larger instance, or switch to using on-demand Braket simulators like [SV1](https://docs.aws.amazon.com/braket/latest/developerguide/braket-devices.html#braket-simulator-sv1). \n", - "\n", - "Next, we include the source code for the QCBM with `include_modules`. This can be a Python module, directory, or file. We may also specify multiple modules with a list.\n", - "\n", - "Lastly, as a quantum machine learning algorithm, the QCBM requires training data. \n", - "When you create a hybrid job, you may provide an input training datasets by specifying an Amazon Simple Storage Service (Amazon S3) bucket. \n", - "You may also specify a local path, in which case Braket will automatically upload the data to Amazon S3 at `s3:///jobs//data/`. \n", - "In this example, we use the local data file `input_data=\"data.npy\"`. You may specify multiple input datasets with a dictionary where the values are the paths to either S3 or local files. \n", - "\n", - "Now we run the training algorithm remotely as a hybrid job. " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from braket.jobs import hybrid_job\n", - "\n", - "# For now, lets set local=False. This uses a local Docker container\n", - "@hybrid_job(device=None, local=False, include_modules=\"qcbm\", input_data=\"data.npy\")\n", - "def train_circuit_hybrid_job(n_qubits, n_layers, n_iterations):\n", - " return train_circuit(n_qubits, n_layers, n_iterations)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 509 ms, sys: 30.4 ms, total: 540 ms\n", - "Wall time: 3min 52s\n" - ] - } - ], - "source": [ - "%%time \n", - "\n", - "job = train_circuit_hybrid_job(n_qubits, n_layers=n_layers, n_iterations=10)\n", - "res = job.result()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great! We created our first quantum machine learning job! \n", - "\n", - "We can check the status of the hybrid job with " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'COMPLETED'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "job.state()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also monitor the status of the hybrid job with the AWS Console.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once it’s complete, we can grab the result with `job.result()` which will wait for the hybrid job to finish. In `qcbm_job.py`, we set the results to be the final parameters of the QCBM that minimized the loss function. Results are returned as a dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 10.4 ms, sys: 475 µs, total: 10.9 ms\n", - "Wall time: 341 ms\n" - ] - }, - { - "data": { - "text/plain": [ - "{'params': array([ 0.35736532, 0.31073256, 0.91610879, 0.98872712, -0.08082859,\n", - " 0.47862197, 0.68460835, -0.09855128, 0.90463028, 0.25120807,\n", - " 0.83142284, 0.42736354, -0.04195098, 0.69129029, 0.63572789,\n", - " 0.75008587, 0.63212422, 0.95159988, 0.78795069, 0.17262345,\n", - " 0.92826497, 0.75725417, 0.18248898, 0.80719954, 1.31568383,\n", - " 0.85844901, 0.5214184 , 0.04987039, 0.03008104, -0.03636072]),\n", - " 'task summary': {},\n", - " 'estimated cost': 0.0}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time \n", - "job.result()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Awesome! Our first quantum machine learning job finished! Now let’s look at the training metrics.\n", - "\n", - "Note that due to the inherent randomness in the training process, running this example repeatedly may yield different results each time." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Metrics and plotting\n", - "\n", - "In the `qcbm_job.py` script, we monitored the loss function during training with \n", - "```\n", - "log_metric(\n", - " metric_name=\"loss\",\n", - " value=loss,\n", - " iteration_number=iteration_number,\n", - ")\n", - "```\n", - "Metrics recorded in this way are visible from the \"Monitor\" tab in the AWS Console. It will look similar to the below image:\n", - "\n", - "
\n", - "\n", - "Metrics are also available by calling `job.metrics()`. Using pandas, and matplotlib, we plot the convergence of the loss below. " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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iul86r0HMnDkTu3fvRnR0NNLS0rB06VJUVlZiwoQJAIDFixfj448/1u4/ffp0HD16FJs3b0ZaWhrWrVuHlJQUTJ06FQBQUVGBDz74AImJicjOzkZsbCxeeukl9OjRA1FRUe10mh3r9iXFvN8JERHR/dK552T06NEoKirC2rVrUVBQgICAAGzatEm7TJOXlwep9HbmCQsLw6pVq7BmzRqsXr0aXl5eWL9+PXx9fQHUT/GkpqZi7969KCsrQ5cuXRAZGYlXX30Vpqam7XSaHSvSp/7cT2cWobpOBTM5pyWJiIjaqk0NsVOnTtXOfPzR9u3bm20bNWoURo0a1eL+5ubm+Oqrr9pShmj4dLGGs7UZCsurkZB1S7vMQ0RERLoT/tISAyCRSLRLOyeusu+EiIjofjCctJNIn4Zwwr4TIiKi+8Jw0k4aHwKYeP0WKqrrBK6GiIhIfzGctBNPR0t4OlqgTq3BqcwiocshIiLSWwwn7SiiV/3sCS8pJiIiajuGk3YU0dB3cpxNsURERG3GcNKOwhuu2LmQV4riihqBqyEiItJPDCftqIuNOXxdraHRAHHpXNohIiJqC4aTdtZ41Q4vKSYiImobhpN21ri0w4cAEhERtQ3DSTsb1MsJUgmQXlCBGyVVQpdDRESkdxhO2pmdhQmC3O0AALHpnD0hIiLSFcNJB2jsOzl+lX0nREREumI46QCNDwGMTVNAo9EIXA0REZF+YTjpAAO8HGEikyDnViWuKZRCl0NERKRXGE46gIWpDP26OwDgJcVERES6YjjpIJGNfSe8pJiIiEgnDCcdpPE5O3FpCqjV7DshIiJqLYaTDtLXwx4WJjIoKmpwOb9M6HKIiIj0BsNJBzGVS/FgT0cA7DshIiLSBcNJB7p9STH7ToiIiFqL4aQDRfrUN8WeTC9CnUotcDVERET6geGkAwV0s4WdhQnKquuQnFMidDlERER6geGkA8mkEoT3ql/aYd8JERFR6zCcdLDGS4pPsO+EiIioVRhOOljjQwDPZBajqlYlcDVERETix3DSwbxdrNDFxgzVdWrEZxULXQ4REZHoMZx0MIlEor2k+MRV9p0QERH9GYaTThDRcEkx+06IiIj+HMNJJ2icOTmXXYLy6jqBqyEiIhI3hpNO4OFgiR5OllCpNTiVwaUdIiKie2E46STsOyEiImodhpNO0nhJ8XHejI2IiOieGE46SXjDzMnFvFIUVdQIXA0REZF4MZx0EmdrM/h3tQEAxHL2hIiI6K4YTjpR4+wJLykmIiK6O4aTTtTYd8KHABIREd0dw0knGtjLEVIJkFFYgdxblUKXQ0REJEoMJ53I1twEwR72ANh3QkREdDcMJ50ssqHv5Dj7ToiIiFrEcNLJGvtOYtMU0Gg0AldDREQkPgwnnay/lwNMZVLklVQho7BC6HKIiIhEh+Gkk5mbyBDWwx4Ar9ohIiJqCcOJACK1lxSz74SIiOiPGE4EEOFT3xQbm6aAWs2+EyIiojsxnAggxMMeVqYyFCtrcelGmdDlEBERiQrDiQBMZFI82NMRAJd2iIiI/ojhRCC8lT0REVHLGE4E0th3cjJdgVqVWuBqiIiIxIPhRCABXW3hYGmCihoVkrJLhC6HiIhINBhOBCKVShDecCv7E1fZd0JERNSoTeFk586dGDZsGIKDg/HEE08gKSnpnvsfPHgQI0eORHBwMMaOHYsjR47cdd8lS5bAz88PX3/9dVtK0yvh7DshIiJqRudwcuDAAaxcuRLz5s1DdHQ0/P39MWvWLCgULf+AjY+Px8KFCzFp0iTs3bsXw4cPx7x585Camtps319//RXnzp1Dly5ddD8TPdT4EMCzWcWoqlUJXA0REZE46BxOtmzZgsmTJ2PixInw8fHBsmXLYG5ujh9++KHF/bdt24aoqCjMnj0b3t7eWLBgAQIDA7Fjx44m++Xn5+Pdd9/FqlWrYGJi0raz0TM9na3Q1dYcNXVqnL1WLHQ5REREoqBTOKmpqcH58+cRERFx+wBSKSIiIpCQkNDiexITExEeHt5k2+DBg5GYmKj9vVqtxuuvv45Zs2ahd+/eupSk1yQSCSIa+054vxMiIiIAgFyXnYuLi6FSqeDk5NRku5OTE9LT01t8T2FhIZydnZvtX1h4+4fxxo0bIZfLMX36dF3KgUqlgkql38shg3o5Yk9CDo5fLcRrI9rnXBq/J/r+vTEUHA/x4ZiIC8dDXDpyPFp7TJ3CSUdISUnBtm3bsGfPHkgkEp3e21Lfir6xq6ofqKTrJTh+Oh5WJu13AVVycnK7HYvuH8dDfDgm4sLxEBchx0OncOLg4ACZTNas+VWhUDSbHWnk7OzcZJbkj/ufOXMGCoUCDz/8sPZ1lUqFDz74ANu2bcNvv/1213p8fX1haWmpyymIklfc78hUKFFl7YHIgPtvBlapVEhOTkZwcDBkMlk7VEj3g+MhPhwTceF4iEtHjodSqWzVxIJO4cTU1BR9+vRBbGwsRowYAaC+XyQ2NhZTp05t8T2hoaGIi4vDjBkztNtOnDiB0NBQAMD48eOb9LAAwKxZszB+/HhMmDDhnvXIZDKD+IMc4eOMTEUW4jKK8WhQt3Y7rqF8fwwFx0N8OCbiwvEQl44Yj9YeT+dlnZkzZ+KNN95AUFAQQkJCsHXrVlRWVmqDxOLFi+Hq6oqFCxcCAKZPn45p06Zh8+bNGDp0KA4cOICUlBQsX74cQP1sjIODQ5PPMDExgbOzM3r16qVreXop0tsZ35zMYlMsERER2hBORo8ejaKiIqxduxYFBQUICAjApk2btMs0eXl5kEpv902EhYVh1apVWLNmDVavXg0vLy+sX78evr6+7XcWem5Qr/onFF+6UYbC8mo4W5sJXBEREZFw2tQQO3Xq1Lsu42zfvr3ZtlGjRmHUqFGtPv69+kwMkZO1GQK62eJiXili0xQY29dN6JKIiIgEw2friMTt+53wVvZERGTcGE5EojGcxLLvhIiIjBzDiUg82NMRMqkEmQolcm5VCl0OERGRYBhORMLG3AQhHnYAgBNXOXtCRETGi+FERCK96694Yt8JEREZM4YTEbnzIYAajUbgaoiIiITBcCIiYT0cYCqXIr+0GmkFFUKXQ0REJAiGExExN5Ghf4/6u+Xyqh0iIjJWDCciE+lT33dy/Cr7ToiIyDgxnIhMeOP9TtIVUKvZd0JERMaH4URkQtztYG0mR0llLS7klQpdDhERUadjOBEZuUyKgT3rHwTIpxQTEZExYjgRoXA+Z4eIiIwYw4kINTbFnsooQk2dWuBqiIiIOhfDiQj5udrA0coUyhoVkrJvCV0OERFRp2I4ESGpVKJd2uElxUREZGwYTkTqzlvZExERGROGE5FqfAhgQtYtVNaoBK6GiIio8zCciFQPJ0u42ZmjRqXGmWtFQpdDRETUaRhOREoikSC8YfaElxQTEZExYTgRsUifhr6Tq+w7ISIi48FwImIRDTMnyTklKKmsFbgaIiKizsFwImJd7czRy8UKag1wMp1LO0REZBwYTkQugreyJyIiI8NwInKR2qZY9p0QEZFxYDgRuUG9nCCRAKn55Sgoqxa6HCIiog7HcCJyDlamCOxmC4CzJ0REZBwYTvRAY99JLPtOiIjICDCc6IEI3oyNiIiMCMOJHhjQ0xFyqQRZRUpcL1IKXQ4REVGHYjjRA9ZmcvT1tAfApR0iIjJ8DCd6IrKh7+Q4m2KJiMjAMZzoiTsfAqjRaASuhoiIqOMwnOiJsB72MJNLUVBWjas3y4Uuh4iIqMMwnOgJM7kMA7wcAfCqHSIiMmwMJ3okwqfxOTvsOyEiIsPFcKJHGu93EpumgErNvhMiIjJMDCd6JMjNFjZmcpRW1eFCbqnQ5RAREXUIhhM9IpdJMbAXLykmIiLDxnCiZxqfs8OmWCIiMlQMJ3om0qe+7+R0RhFq6tQCV0NERNT+GE70jK+rNZytTVFZq0Li9VtCl0NERNTuGE70jEQi0d4t9vhV9p0QEZHhYTjRQ419J3wIIBERGSKGEz0U2TBzknC9GMqaOoGrISIial8MJ3rI09EC7vYWqFVpcDqzWOhyiIiI2hXDiR6SSCR3XFLMvhMiIjIsDCd6qvGS4hNX2XdCRESGheFET4U3zJyk5JagRFkrcDVERETth+FET7namsOnizU0GiA2nbMnRERkOBhO9NjtS4rZd0JERIaD4USPRTTejI33OyEiIgPSpnCyc+dODBs2DMHBwXjiiSeQlJR0z/0PHjyIkSNHIjg4GGPHjsWRI0eavL5u3TqMHDkSoaGhGDBgAGbMmIFz5861pTSjMqiXIyQS4OrNctwsrRK6HCIionahczg5cOAAVq5ciXnz5iE6Ohr+/v6YNWsWFIqW/+89Pj4eCxcuxKRJk7B3714MHz4c8+bNQ2pqqnYfLy8vLFmyBPv27cM333wDd3d3PPfccygqKmr7mRkBe0tTBLnZAWDfCRERGQ6dw8mWLVswefJkTJw4ET4+Pli2bBnMzc3xww8/tLj/tm3bEBUVhdmzZ8Pb2xsLFixAYGAgduzYod1n7NixiIiIgKenJ3r37o233noL5eXluHz5ctvPzEg09p3wOTtERGQo5LrsXFNTg/Pnz2Pu3LnabVKpFBEREUhISGjxPYmJiZgxY0aTbYMHD0ZMTMxdP2PXrl2wsbGBn5/fPetRqVRQqVS6nILBGdjTAV/+DpxIU2i/F3/8LwmL4yE+HBNx4XiIS0eOR2uPqVM4KS4uhkqlgpOTU5PtTk5OSE9Pb/E9hYWFcHZ2brZ/YWHT/9M/dOgQXnvtNVRWVsLFxQWbN2+Go6PjPeu5c2nIWJnVqSGTANnFlfj52Bl0tb49pMnJyQJWRn/E8RAfjom4cDzERcjx0CmcdKSBAwdi7969KC4uxu7du7FgwQJ89913zYLQnXx9fWFpadmJVYpTv/iTOHOtGCVmrhgZ6gmVSoXk5GQEBwdDJpMJXZ7R43iID8dEXDge4tKR46FUKls1saBTOHFwcIBMJmvW/KpQKJrNjjRydnZuNkvS0v6Wlpbo0aMHevTogdDQUDz66KP4/vvvmywh/ZFMJuMfZNTfyv7MtWLEZhTj6UFe2u38/ogLx0N8OCbiwvEQl44Yj9YeT6eGWFNTU/Tp0wexsbHabWq1GrGxsejXr1+L7wkNDUVcXFyTbSdOnEBoaOg9P0utVqOmpkaX8ozWnTdj02g0AldDRER0f3S+WmfmzJnYvXs3oqOjkZaWhqVLl6KyshITJkwAACxevBgff/yxdv/p06fj6NGj2Lx5M9LS0rBu3TqkpKRg6tSpAOqneFavXo3ExETk5OQgJSUFb731FvLz8zFy5Mh2Ok3D1q+7A8xNpCgsr0FqfrnQ5RAREd0XnXtORo8ejaKiIqxduxYFBQUICAjApk2btMs0eXl5kEpvZ56wsDCsWrUKa9aswerVq+Hl5YX169fD19cXQP0UT3p6OqKjo1FcXAx7e3sEBwdj586d6N27dzudpmEzlUsxwMsRR68U4kRaIXxcugtdEhERUZu1qSF26tSp2pmPP9q+fXuzbaNGjcKoUaNa3N/MzAyfffZZW8qgO0T6ODeEEwWmD2I4ISIi/cVn6xiIxr6TuHQF6lRqgashIiJqO4YTA9HHzQ625nKUVdXhQl6Z0OUQERG1GcOJgZBJJRjUq3725ASfUkxERHqM4cSAaC8p5kMAiYhIjzGcGJBIn/orps5cK0ativc7ISIi/cRwYkB8uljDxcYMVbVqpBbVCl0OERFRmzCcGBCJRKJd2knKrxa4GiIiorZhODEwjeEk5SZv/U9ERPqJ4cTARHjX951cKapFRXWdwNUQERHpjuHEwHg6WsLTwQIqDbDq11Q+CJCIiPQOw4kBenW4DwBgW2wW3vlPCtRqBhQiItIfDCcG6PF+7pjX3xYSCbAjLgtv7UlmQCEiIr3BcGKghvW0xKpJIZBKgF1nrmPR9+egYkAhIiI9wHBiwP4a6oZPn+oHmVSCPfE5+NuuRD4UkIiIRI/hxMCN7euG9U+HwUQmwY/ncvHKvxNQy4BCREQixnBiBEYGdcXnzzwAU5kUB1Nu4MUd8aiuUwldFhERUYsYTozEiEBXbJj+AMzkUsRczMfc7WdRVcuAQkRE4sNwYkQe8uuCzTMGwNxEisOXCzB76xlU1jCgEBGRuDCcGJlIH2d8PfNBWJrKcOxqIWZ+fYp3kiUiIlFhODFCg3o5YfusB2FtJkdcehGe3XwKZVV8ijEREYkDw4mReqCHI3bMHghbcznOXCvGtK9OoaSSAYWIiITHcGLEQj3t8c2cQbC3NEHi9VuYuukkbin5NGMiIhIWw4mRC3K3w7/nDIKTlSmSc0owZeNJKMqrhS6LiIiMGMMJIaCbLb59fhCcrc1wMa8UUzbG4WZZldBlERGRkWI4IQBAb1cb7Jo7CK62ZkjNL8dTG+KQX8qAQkREnY/hhLS8Xayx6/lwuNmZI72gAk9+GYvcW5VCl0VEREaG4YSa8HK2wq654fBwsECmQoknN8TiepFS6LKIiMiIMJxQM56Oltg9NxxeTpa4XlSJJ7+MxTVFhdBlERGRkWA4oRa52Vtg19xw9HKxQm5JFSZ/GYu0gnKhyyIiIiPAcEJ35Wprjl3Ph8PX1Rr5pdV48ss4XMkvE7osIiIycAwndE8uNmb495xBCOhmi8Lyajy1IQ4X80qFLouIiAwYwwn9KSdrM/x7zkAEu9tBUVGDKRvjkJJTInRZRERkoBhOqFXsLU2xY/ZAhHra45ayFk9vjEPi9VtCl0VERAaI4YRazc7CBNtnPYj+PRxQWlWHqZtO4kxmkdBlERGRgWE4IZ3YmJtg63MPYlAvR5RX12H65lOIS1cIXRYRERkQhhPSmZWZHFtmPIjBPs5Q1qgwY8spHL9aKHRZRERkIBhOqE0sTGXY9Gx/POTngqpaNZ77+jQOX74pdFlERGQAGE6ozcxNZPhy2gMYEeCK6jo1nt92FjEX8oUui4iI9BzDCd0XM7kM/3omDKOCuqJGpcYLO87i55Q8ocsiIiI9xnBC981ULsW6Kf0wrq8b6tQazPsmAfvO5QpdFhER6SmGE2oXcpkUnzwZiglh7lCpNXj12wREJ2QLXRYREekhhhNqNzKpBKsm9cVTAzyh1gCv7T6H3aevC10WERHpGYYTaldSqQT/fDwYUwd1h0YDLP4hCTtPXhO6LCIi0iMMJ9TupFIJ3h0fhJmRXgCAt6NT8PXxDGGLIiIivcFwQh1CIpFgyV8CMXdILwDA0n0XsPH3dIGrIiIifcBwQh1GIpHgzVH+eGWYDwBgxYGLWH/oqsBVERGR2DGcUIeSSCRY+KgfXnvEFwDw0S+X8cmvqdBoNAJXRkREYsVwQp1i/vDeeGOkPwDg0/9dwUe/XGZAISKiFjGcUKd58SFv/H1MAADgX4fT8M8DFxlQiIioGYYT6lSzo3ph+fg+AICNRzOwbN8FBhQiImqC4YQ63fRwL6ycEAyJBPj6RCbe3psCtZoBhYiI6rUpnOzcuRPDhg1DcHAwnnjiCSQlJd1z/4MHD2LkyJEIDg7G2LFjceTIEe1rtbW1+OijjzB27FiEhoZi8ODBWLx4MfLz+XRbQzblwe74cGIIJBLgm5NZeOOHJKgYUIiICG0IJwcOHMDKlSsxb948REdHw9/fH7NmzYJCoWhx//j4eCxcuBCTJk3C3r17MXz4cMybNw+pqakAgKqqKly4cAEvvvgi9uzZg88++wwZGRl48cUX7+/MSPSe6O+JTyaHQioBvjubjUXfnUOdSi10WUREJDCdw8mWLVswefJkTJw4ET4+Pli2bBnMzc3xww8/tLj/tm3bEBUVhdmzZ8Pb2xsLFixAYGAgduzYAQCwsbHBli1bMHr0aPTq1QuhoaF45513cP78eeTm8sm2hu6v/dyxbkoYZFIJohNysGBXImoZUIiIjJpO4aSmpgbnz59HRETE7QNIpYiIiEBCQkKL70lMTER4eHiTbYMHD0ZiYuJdP6e8vBwSiQS2tra6lEd6akxIN/zrmTCYyCT4KSkPL38Tj5o6BhQiImMl12Xn4uJiqFQqODk5Ndnu5OSE9PSWb01eWFgIZ2fnZvsXFha2uH91dTVWrVqFMWPGwNra+p71qFQqqFQqHc7AODR+T/TpezPC3wWfP90PL32TgF/O5+OF7Wfw2dP9YCbX/55tfRwPQ8cxEReOh7h05Hi09pg6hZOOVltbi1dffRUajQbLli370/0b+1aoZcnJyUKXoBMHAG9E2OOD48X47XIBnv7XYSyOdICZTCJ0ae1C38bDGHBMxIXjIS5CjodO4cTBwQEymaxZ86tCoWg2O9LI2dm52SxJS/vX1tZiwYIFyM3NxdatW/901gQAfH19YWlpqcspGAWVSoXk5GQEBwdDJpMJXY5OQgH49lbg+e3xSMyvwdrEWsyO9EKgmy262JhBItG/oKLP42GoOCbiwvEQl44cD6VS2aqJBZ3CiampKfr06YPY2FiMGDECAKBWqxEbG4upU6e2+J7Q0FDExcVhxowZ2m0nTpxAaGio9veNweTatWvYtm0bHBwcWlWPTCbjH+R70NfvT5RvF2x97kHM3HIKcelFiEsvAgA4W5uhj5stgtxt0cfNDkFudvB0tNCbwKKv42HIOCbiwvEQl44Yj9YeT+dlnZkzZ+KNN95AUFAQQkJCsHXrVlRWVmLChAkAgMWLF8PV1RULFy4EAEyfPh3Tpk3D5s2bMXToUBw4cAApKSlYvnw5gPpgMn/+fFy4cAFffvklVCoVCgoKAAB2dnYwNTXVtUQyAA/2dMSuueH46lgGUnJKkFZQjsLyahxJLcCR1ALtfjbmcvRxqw8r9cHFDr2crSCX6X+vChGRsdI5nIwePRpFRUVYu3YtCgoKEBAQgE2bNmmXafLy8iCV3v7BEBYWhlWrVmHNmjVYvXo1vLy8sH79evj61j+lNj8/H7/99hsAYPz48U0+a9u2bRg4cGCbT470W5C7HT55MhQAUFmjwsUbpTifW4rzOSU4n1uKyzfKUFZV12R2BQDM5FIEdLPVhpU+brbwdbWBuQn/j4yISB+0qSF26tSpd13G2b59e7Nto0aNwqhRo1rc38PDA5cvX25LGWRELExlCOvugLDut5f8aurUuHqzHCm5JbiQW4qUnBJcyCuFskaFxOu3kHj9lnZfuVQCny7W2rAS5G6HgG62sDYTVU84ERFBZFfrEOnCVC5FoJstAt1u3w9HrdYgQ1HRZIblfG4JipW1uHSjDJdulOH7s/X7SiSAl5OVdlmosZfF0YpLiUREQmI4IYMilUrg7WINbxdrjOvrBgDQaDTILanC+ZwSpOSW4kJuCVJySnGjtAoZhRXIKKzAT0l52mO42Zkj8I6wEuRui6625nrTeEtEpO8YTsjgSSQSuNtbwN3eAo/26ardriivxvncUqTklmhnWjIVSuSWVCG3pAoxF28/fNLRyrTZDEsPR0tIpQwsRETtjeGEjJaTtRmG+LpgiK+LdltZVS0u5JZqQ8uF3FJcuVmOoooaHL1SiKNXbt+zx9pMjsBu9ctKjb0sPl2sYcIrhYiI7gvDCdEdbMxNMLCXEwb2uv2IhqpaFS7fKLs9w5Jbiot5pSivrsOpzCKcyrx9pZCpXAr/rjZNLm3u7cIbBRIR6YLhhOhPmJvI0NfTHn097bXb6lRqpBVUIKWh6TYltwQXc0tRVl2HpOwSJGWXaPeVSSXwtJFhsUk+Roe4CXAGRET6heGEqA3kMin8utrAr6sNJj5Qv02t1iCrSKm9QiiloY9FUVGDzJI6vPRNAiZdLsA/xgbCxtxE2BMgIhIxhhOidiKVSuDlbAUvZyuMCekGoOFKoWIlVv14GnsvV+D7s9mITVNg9eS+TZaOiIjoNnbuEXUgiUSCrnbmmBpsg29nD4SHgwVyblXiqY1xWHnwIqrr+Ih4IqI/Yjgh6iT9vRxw8NUoTO7vAY0G+PJIOsZ/dhyXbpQKXRoRkagwnBB1IhtzE3w4qS++nPYAHK1McelGGcatO46Nv6dDrdYIXR4RkSgwnBAJ4LE+XfHLgiEY7t8FNSo1Vhy4iKc3xSG7WCl0aUREgmM4IRKIi40ZNj3bH+9PCIalqQxx6UUYteYo9sRnQ6PhLAoRGS+GEyIBSSQSPPVgdxyYH4Ww7vYoq67Da7vPYd438SiuqBG6PCIiQTCcEImAl7MVds8Nx6JHfSGXSnAg+QYeW/M7Dl++KXRpRESdjuGESCTkMileHtYb0S9FwtvFCjfLqjFjy2m8szcFlTW85JiIjAfDCZHIBHvYYf/8KMyI8AIAbI+7hjFrjyLx+i1B6yIi6iwMJ0QiZG4iw9JxfbB91oPoamuO9MIKTPz8BNbEpKJWpRa6PCKiDsVwQiRiUb1d8MuCIRjb1w0qtQZrYq5g0hexSC8oF7o0IqIOw3BCJHJ2liZYN6UfPn0qFLbmcpy7fguj1x7F9rhrvOSYiAwSwwmRnhgf6o6fFwxBpI8TqmrVeGdvCmZ+fRo3S6uELo2IqF0xnBDpETd7C2x/biCW/CUQpnIpDl8uwGNrfsfB5DyhSyMiajcMJ0R6RiqV4LnBPbH/lcHo42aLYmUtXtwZj9d2J6K0qlbo8oiI7hvDCZGe6u1qg+iXIjHvYW9IJcCe+ByMWnMUcekKoUsjIrovDCdEesxULsXrj/lj99xwdHe0RM6tSkzZGId/HriI6jreuI2I9BPDCZEB6O/liAOvRuGpAZ7QaIANv6dj/GfHcTGvVOjSiIh0xnBCZCCszeR4f2IINk7vDycrU1y6UYbxnx3Hl0fSoFLzkmMi0h8MJ0QG5pFAV/zytyEYEeCKGpUaKw9ewpSNcbhepBS6NCKiVmE4ITJAztZm2Dj9AXwwMRiWpjKcyijCqE+P4vuz2bxxGxGJHsMJkYGSSCR4ckB3HHw1Cg/0cEB5dR0WfXcOL+6IR1FFjdDlERHdFcMJkYHr4WSF3XPD8fpjfpBLJfj5/A08+snvOHTpptClERG1iOGEyAjIpBLMe9gHe+dFoncXaxSWV2Pm16fxdnQylDV1QpdHRNQEwwmREQlyt8O+VwbjucieAICdJ7MwZu0xJGQVC1wZEdFtDCdERsbcRIYlYwOxc/ZAdLMzR0ZhBSZ9EYvVv6aiVqUWujwiIoYTImMV6eOMn18dgvGhblCpNVj7vyuY+PkJpBWUC10aERk5hhMiI2ZnaYJPn+qHtVP6wdZcjqTsEoxZexTbYjN5yTERCYbhhIgwrq8bfvnbEAz2cUZVrRpL/nMez245jfzSKqFLIyIjxHBCRACAbnYW2Pbcg1g6NhBmcil+Ty3AY2t+x/6kPKFLIyIjw3BCRFpSqQQzInti//zBCHK3xS1lLeZ9E4+/7UpESWWt0OURkZFgOCGiZny62GDPi5F4ZZgPpBIgOiEHo9b8jhNphUKXRkRGgOGEiFpkKpdi4aN++O6FCPRwskRuSRWe3ngSs7eeQcyFfNTxsmMi6iAMJ0R0Tw/0cMCB+VGY8qAnACDmYj5mbzuDiPd/wwc/X0JGYYXAFRKRoWE4IaI/ZWUmx8oJIYh5bQjmRPWEk5UpbpZV4/PDaXh41WFM/jIWe+KzUVmjErpUIjIAcqELICL94dPFBm+PCcTrj/njt0v52HX6Oo6kFuBURhFOZRThH/85j3GhbnhygCeC3e0gkUiELpmI9BDDCRHpzFQuxcigbhgZ1A15JZX4/kw2dp+9jutFldh5Mgs7T2bBv6sNnhrgib/2c4e9panQJRORHuGyDhHdl252FnhleG8cWfQwvpk9EOND3WAql+LSjTIs3XcBD/7zf3jl3wk4dqUQajXvOktEf44zJ0TULqRSCSJ8nBHh44zlylrsTczBrtPXcSGvFPvO5WLfuVx4OFjgiQc88UR/D7jZWwhdMhGJFMMJEbU7O0sTPBvhhWcjvJCSU4Jdp69jb2IOsosr8UlMKtb8LxVDervgyQGeGBHgClM5J3GJ6DaGEyLqUEHudghyt8PbYwJwMCUPu05fR1x6EY6kFuBIagEcrUzxeD93PDnAE76uNkKXS0QiwHBCRJ3C3ESGx/t54PF+HsgsrMDuM9fx/dls3CyrxlfHMvDVsQz0626PJ/t74i993WBtxn+eiIwV//YTUafzcrbC4pH+eO0RXxxJLcCu09fx26WbSMi6hYSsW1j+0wWMCe6Gpx70RFh3B16STGRkGE6ISDBymRTDA1wxPMAVBWXV2BOfjV2nryO9sALfnc3Gd2ez4e1ihScHeGJCmAecrc2ELpmIOkGbutB27tyJYcOGITg4GE888QSSkpLuuf/BgwcxcuRIBAcHY+zYsThy5EiT1//73//iueeew8CBA+Hn54eLFy+2pSwi0mMuNmaYO9Qb/1s4FN+9EI5JD3jAwkSGtIIK/PPAJQz65/8wd/sZ/HaJz/UhMnQ6h5MDBw5g5cqVmDdvHqKjo+Hv749Zs2ZBoVC0uH98fDwWLlyISZMmYe/evRg+fDjmzZuH1NRU7T5KpRJhYWFYtGhR28+EiAyCRCLBAC9HrHqiL069PRwrJwSjr6c96tQa/HI+H899fQaDPziEVb9cRpZCKXS5RNQBdA4nW7ZsweTJkzFx4kT4+Phg2bJlMDc3xw8//NDi/tu2bUNUVBRmz54Nb29vLFiwAIGBgdixY4d2n7/+9a94+eWXER4e3vYzISKDY2NugikPdsd/5kXilwVD8FxkTzhYmuBGaRU+O3QVQz46hCkb4vCfxBxU1fK5PkSGQqeek5qaGpw/fx5z587VbpNKpYiIiEBCQkKL70lMTMSMGTOabBs8eDBiYmJ0r/YPVCoVVCr+g/RHjd8Tfm/EgePRPnxcLPH2aD8serQ3Yi7m47sz2TiWpkBsev0vW3M5xvd1wxP9PdDHzfaex+KYiAvHQ1w6cjxae0ydwklxcTFUKhWcnJyabHdyckJ6enqL7yksLISzs3Oz/QsLC3X56BbduTREzSUnJwtdAt2B49F+3AEs6GeCp31dcDizEv/LVKJQWYftJ7Ow/WQWetrLMbynBaK6W8Da9O4TxBwTceF4iIuQ46HXV+v4+vrC0tJS6DJER6VSITk5GcHBwZDJZEKXY/Q4Hh3r0UhApdbgRJoCu89mI+ZCPjJu1WFTQhm2J1dgZB9XPNHfAwO9HCGV1l+SzDERF46HuHTkeCiVylZNLOgUThwcHCCTyZo1vyoUimazI42cnZ2bzZLca39dyGQy/kG+B35/xIXj0XFkMuAhf1c85O+K4ooaRCfUP9fncn4Z/nMuD/85l4fujpaY3N8Dkx7whIu1ScP7OCZiwvEQl44Yj9YeT6eGWFNTU/Tp0wexsbHabWq1GrGxsejXr1+L7wkNDUVcXFyTbSdOnEBoaKguH01E1CoOVqZ4bnBP/LwgCnvnRWLKg91hbSZHVpESq/6bioj3/4fZ287i+PVKVNawx4FIjHRe1pk5cybeeOMNBAUFISQkBFu3bkVlZSUmTJgAAFi8eDFcXV2xcOFCAMD06dMxbdo0bN68GUOHDsWBAweQkpKC5cuXa49569Yt5OXl4ebNmwCAjIwMAPWzLi4uLvd9kkRkfCQSCUI97RHqaY93/hKAA8k3sPv0dZzKLMKhywU4BODL+N/wSKArxoW6YbCPCx9ASCQSOoeT0aNHo6ioCGvXrkVBQQECAgKwadMm7TJNXl4epNLbf8HDwsKwatUqrFmzBqtXr4aXlxfWr18PX19f7T6//fYb3nrrLe3v//a3vwEAXn75ZbzyyittPjkiIgCwNJVj0gMemPSAB9IKyvHd6SzsOZOFm0oV9ibmYm9iLuwtTTAqqBvG9u2GgT2dIJPylvlEQpFoNBqN0EXoSqlU4uLFiwgICGBDbAtUKhUSExMRGhrK9VsR4HiIj0qlQkJCAjROXtifnI+fkvJQWF6tfd3V1gxjgt0wLtQNfT3s+GyfDsa/I+LSkePR2p/fen21DhFRW0kkEvTr7oABPZ3xzl8CEZeuwI+JuTiYkof80mpsPp6Bzccz0MPJEmND6oOKr6uN0GUTGQWGEyIyejKpBJE+zoj0ccbyv/bB76mF+PFcLmIu5OOaQonPDl3FZ4euwr+rDcb2dcO4vm7wdOSsLVFHYTghIrqDmVyGRwJd8UigK5Q1dfj1Qj72ncvDkdSbuHSjDJduXMZHv1xGqKc9xvV1w19CuqGLrbnQZRMZFIYTIqK7sDSVY3yoO8aHuqNEWYufz+fhx3O5iE1TIPH6LSRev4X39l/AoF5OGNfXDaOCusHO0kToson0HsMJEVEr2Fma4MkB3fHkgO64WVaF/Un1QSUh6xZOpClwIk2Bd/6TgqG+Lhjb1w2PBLrC0pT/xBK1Bf/mEBHpqIuNOWZG9sTMyJ64XqTEvqRc/JiYi0s3yhBz8SZiLt6EhYkMIwJdMa6vG4b4OsNMzqtQiFqL4YSI6D54OlripYd88NJDPriSX4Yfz+Xix3O5uKZQYt+5XOw7lwtbczlGBnXFuL7uCPfmPVSI/gzDCRFRO+ntaoOFj/rhtUd8kZRdgh/P5eKnpFzkl1Zj95ls7D6TDWdrM/wlpBvG9nVDWHd73kOFqAUMJ0RE7UwikaCvpz36etrj/0YH4FRGEfYl5eJAcv3N3r4+kYmvT2TCw8FCe2myf1cbBhWiBgwnREQdSCaVINzbCeHeTlg2rg+OXam/h8p/z99AdnElPj+chs8Pp8GnizXGNQQVL2crocsmEhTDCRFRJzGRSfGwfxc87N8FlTUq/HbpJn48l4NDlwtw9WY5Vv+aitW/piLEw67hHipu6GrHe6iQ8WE4ISISgIWpDGNCumFMSDeUVtXiv+fz8eO5XBy/Woik7BIkZZdgxYGLeNDLEeNC3TA6qBscrEyFLpuoUzCcEBEJzNbcRPvU5MLyahxMrr+HyunMYpzMKMLJjCL84z/nEdXbGeNC3fBIYFdYm/GfbzJc/NNNRCQiztZmmBbuhWnhXsi5VYmfGi5NPp9bikOXC3DocgHM5MkYHtAF4/q6IdLHGTbmvCstGRaGEyIikXK3t8Dcod6YO9QbaQXl2NcQVNILKnAg+QYOJN8AAHg4WMC/qy38u9rAv5sN/LvawsvJEnKZVOAzIGobhhMiIj3g7WKNBSN88erw3jifW4p953JxICUP14sqkV1c/yvmYr52fzO5FL6uNvDvagO/rjYI6FYfXpyszQQ8C6LWYTghItIjEokEQe52CHK3w1ujA3BLWVP/tOS8UlzOL8PFvDJcvlGGyloVknNKkJxT0uT9LjZm9TMsXetnWPy72cCnizVvr0+iwnBCRKTH7C1NMaiXEwb1ctJuU6s1yCpS4tKN0obgUoZLN0pxrUiJgrJqFJRV4+iVQu3+MqkE3i5W8GtYGgpoWBrqZmfOG8ORIBhOiIgMjFQqgZezFbycrTAyqJt2e0V1HVLz62dWLt0ow8W8+vBSUlmL1PxypOaXY9+528exNZdrZ1ca/+vnagMrXilEHYx/woiIjISVmRz9ujugX3cH7TaNRoMbpVVNZlgu5ZUhraAcpVV1OJVZhFOZRU2O093RsqH51hYBDf/t7mjJBxpSu2E4ISIyYhKJBN3sLNDNzgIP+3XRbq+uUyG9oEIbVi429LXcLKtGVpESWUVK/PfC7QZccxMp/Fxtms60dLXhjeOoTRhOiIioGTO5DAHdbBHQzRbod3t7UUWNNrA09rRcvlGGqlo1zmWX4Fx20wZcV1szbWAJ6GoLv6428HaxhqlcmMucNRoNalUaVNepUFOnRnXDr/qv79zW9PXqOjWqa1WoUalRXavW/vfO/Wrq1LAwlcHV1hxdbc3Q1c68/ms7c7hYm/HSbh0wnBARUas5WpkiwtsZEd7O2m0qtQbXFBXaq4Yu3qgPLteLKpFfWo380gIcSS3Q7i+XSuDTxbrhMmdb+HaxQr6iBpXpCtSpcUdYaP7Dv7pO1SQc1Kiab6tWNQSJJuFDpf1aCFJJ/Q32tIGlIbTc/toMrrbmvKFeA4YTIiK6LzKpBL1crNHLxRqjg2834JZX1zU03zadaSmrqqsPMjfKAOTecaSiZsfuaKYyKUzlUpg1/Kr/WnZ7m4kUprKm2xr30b6m3UeKihoVbpRUIb+0CjdKq5BfUoWbZdWoU2tws6waN8uqAZTctR4rUxlc7RoCi6259uvGGZiutuZwtjY1+FkYhhMiIuoQ1mZyPNDDAQ/0aNqAm1tShUsNVwpdzCvF5RtlKK2ohI2lOcxMZC2HhCZfNwYHWdNwYNKwX5Ntdx7vjmAhr39d2glNvGq1BoUV1cgvqcaNO0LLjdKGENPwdVlVHSpq6nt90gsq7no8qaT+fjV3hpZmszF25nr9/CX9rZyIiPSORCKBu70F3O0tMDzAFQCgUqmQmJiI0NBQyGSGdzM4qVSCLjbm6GJjjmDY3XU/ZU2dNqjUh5bqJuElv7R+Fkal1jQsl917FsbaTA5X23stJZnD2dpMlFdZMZwQERGJgKWpXLs8djcqtQaK8oYZmDuWj7RBpmFWpqy6DuXVdSgvqEPaPWZhZFIJXKzNGpaP6mdjutiYwbGuBqEdcI6txXBCRESkJ2RSCbrYmqOLrTlCPO6+X0V1XZPloyZfN/y3oGEWpvH1O+6/B7kEGD9UDUuBZrIYToiIiAyMlZkc3i7W8L7HLEydSo3C8ppmszB5typhoyqFmUCXewMMJ0REREZJLpPWXwFkZw543t7e2AMkJMO+FomIiIj0DsMJERERiQrDCREREYkKwwkRERGJCsMJERERiQrDCREREYkKwwkRERGJCsMJERERiQrDCREREYkKwwkRERGJCsMJERERiQrDCREREYkKwwkRERGJil4+lVitVgMAKisrBa5EnFQqFQBAqVRCJpMJXA1xPMSHYyIuHA9x6cjxaPy53fhz/G4kGo1G066f3AkUCgUyMzOFLoOIiIjawMvLC05OTnd9XS/DSV1dHUpKSmBmZgaplCtTRERE+kCtVqO6uhp2dnaQy+++eKOX4YSIiIgMF6cdiIiISFQYToiIiEhUGE6IiIhIVBhODMiXX36JiRMnol+/fggPD8dLL72E9PR0ocuiBhs2bICfnx9WrFghdClGKz8/H4sWLcLAgQMREhKCsWPHIjk5WeiyjJJKpcKaNWswbNgwhISEYMSIEVi/fj3YBtl5Tp8+jRdeeAGDBw+Gn58fYmJimryu0Wjw6aefYvDgwQgJCcGMGTM67UpZhhMDcurUKTzzzDPYvXs3tmzZgrq6OsyaNQtKpVLo0oxeUlISvv32W/j5+QlditEqKSnBlClTYGJigo0bN2L//v144403YGdnJ3RpRmnjxo3497//jSVLluDAgQNYtGgRNm3ahO3btwtdmtFQKpXw8/PDP/7xjxZf37hxI7Zv346lS5di9+7dsLCwwKxZs1BdXd3htenlTdioZV999VWT37///vsIDw/H+fPnMWDAAIGqooqKCrz++ut477338PnnnwtdjtHauHEjunbtipUrV2q3eXp6CliRcUtISMDw4cPx0EMPAQA8PDywf/9+JCUlCVuYERk6dCiGDh3a4msajQbbtm3Diy++iBEjRgAAPvzwQ0RERCAmJgZjxozp0No4c2LAysrKAID/Zyiw5cuXY+jQoYiIiBC6FKP222+/ISgoCPPnz0d4eDj++te/Yvfu3UKXZbT69euHuLg4ZGRkAAAuXbqEs2fPYsiQIQJXRgCQnZ2NgoKCJv9u2djYoG/fvkhISOjwz+fMiYFSq9X45z//ibCwMPj6+gpdjtHav38/Lly4gO+//17oUoze9evX8e9//xszZ87ECy+8gOTkZLz33nswMTHB448/LnR5Ruf5559HeXk5Ro0aBZlMBpVKhb/97W8YN26c0KURgIKCAgBodhdXJycnFBYWdvjnM5wYqGXLluHKlSv45ptvhC7FaOXl5WHFihXYvHkzzMzMhC7H6Gk0GgQFBeG1114DAAQGBuLKlSv49ttvGU4EcPDgQezbtw8ff/wxfHx8cPHiRaxcuRJdunTheBDDiSFavnw5Dh8+jB07dqBr165Cl2O0zp8/D4VCgQkTJmi3qVQqnD59Gjt37kRycjIfctaJXFxc4O3t3WRbr1698MsvvwhUkXH78MMP8fzzz2t7F/z8/JCbm4svv/yS4UQEXFxcANQ/y65Lly7a7QqFAv7+/h3++QwnBkSj0eDdd9/Fr7/+iu3bt7PZT2CDBg3Cvn37mmx766230KtXL8yZM4fBpJOFhYVp+xsaZWZmwt3dXaCKjFtVVRUkEkmTbTKZjJcSi4SHhwdcXFwQGxuLgIAAAEB5eTnOnTuHKVOmdPjnM5wYkGXLluGnn37Cv/71L1hZWWnXDG1sbGBubi5wdcbH2tq6Wb+PpaUl7O3t2QckgGeffRZTpkzBF198gVGjRiEpKQm7d+/G8uXLhS7NKD388MP44osv4Obmpl3W2bJlCyZOnCh0aUajoqICWVlZ2t9nZ2fj4sWLsLOzg5ubG6ZPn47PP/8cPXr0gIeHBz799FN06dJFe/VOR+KD/wzI3e6hsXLlyiZLCyScadOmwd/fH2+//bbQpRilQ4cOYfXq1cjMzISHhwdmzpyJyZMnC12WUSovL8enn36KmJgY7dLBmDFjMG/ePJiamgpdnlE4efIkpk+f3mz7448/jvfffx8ajQZr167F7t27UVpaigceeAD/+Mc/0LNnzw6vjeGEiIiIRIX3OSEiIiJRYTghIiIiUWE4ISIiIlFhOCEiIiJRYTghIiIiUWE4ISIiIlFhOCEiIiJRYTghIiIiUWE4IdIT06ZNw4oVK4Quowk/Pz/ExMQIXUanGDZsGL7++muhyyAyCgwnRHpi3bp1ePXVVwF0/g/KdevWYfz48c22Hzt2DEOGDOm0OojIOPDBf0R6wt7evt2PWVNTc1/PMWl8rDq1zf1+/4kMFWdOiPRE47LOtGnTkJOTg5UrV8LPz6/JAx/PnDmDp59+GiEhIRg6dCjee+89KJVK7evDhg3D+vXrsXjxYoSFhWHJkiUAgI8++giPPfYY+vbti+HDh2PNmjWora0FAOzZswefffYZLl26pP28PXv2AGi+rHP58mVMnz4dISEhGDhwIN555x1UVFRoX3/zzTfx0ksv4auvvsLgwYMxcOBALFu2TPtZf2bYsGH44osv8NZbb6Ffv3546KGHsGvXLu3rJ0+ehJ+fH0pLS7XbLl68CD8/P2RnZ2vPp3///jh06JD2nOfPn4/KykpER0dj2LBhGDBgAN577z2oVKomn19RUYHXXnsNoaGhiIqKws6dO5u8XlpairfffhuDBg1CWFgYpk+fjkuXLmlfb5yB+u677zBs2DCEhIS06ryJjA3DCZGeWbduHbp27Yr58+fj2LFjOHbsGAAgKysLc+bMwaOPPooff/wRn3zyCc6ePYt33323yfs3b94Mf39/7N27Fy+99BIAwMrKCitXrsT+/fvx9ttv47vvvtMuG40ePRrPPfccevfurf280aNHN6tLqVRi1qxZsLOzw/fff481a9bgxIkTzT7/5MmTyMrKwtatW/H+++8jOjoa0dHRrT7/LVu2ICgoCHv37sXTTz+NpUuXIj09XZdvIaqqqrB9+3Z88skn2LRpE06ePImXX34ZR44cwYYNG/Dhhx/i22+/xS+//NLkfV999RX8/f0RHR2N559/HitWrMDx48e1r7/66qtQKBTYuHEj9uzZgz59+uDZZ5/FrVu3tPtkZWXhl19+wWeffYa9e/fqVDeRseCyDpGesbe3h0wmg5WVVZNllS+//BJjx47FjBkzAABeXl54++23MW3aNCxduhRmZmYAgEGDBuG5555rcszGkAIAHh4eyMjIwP79+zFnzhyYm5vD0tISMpnsnss4P/30E2pqavDBBx/A0tISALBkyRK88MILWLRoEZydnQEAdnZ2WLJkCWQyGby9vTF06FDExsZi8uTJrTr/IUOG4JlnngEAzJkzB19//TVOnjyJXr16ter9AFBbW4ulS5eie/fuAIDHHnsMP/74I44fPw4rKyv4+Phg4MCBiIuLaxLEwsLC8PzzzwMAevbsifj4eHz99deIjIzEmTNnkJSUhNjYWO1SzRtvvIGYmBj88ssvePLJJ7Wf/eGHH8LR0bHV9RIZG4YTIgNx6dIlXL58Gfv27dNu02g0UKvVyM7Ohre3NwAgKCio2XsPHDiAbdu24fr161Aqlairq4O1tbVOn5+WlgY/Pz9tMAHqf5ir1WpkZGRow4mPjw9kMpl2HxcXF6Smprb6c+5cxpJIJHB2doZCodCpVgsLC20wAQBnZ2e4u7vDysqqybaioqIm7wsNDW32+61btwKoX9JSKpUYOHBgk32qqqqQlZWl/b2bmxuDCdGfYDghMhBKpRJPPfUUpk2b1uy1bt26ab+2sLBo8lpCQgIWLVqEV155BYMHD4aNjQ3279+PLVu2dEidcnnTf3YkEgk0Gk27vF8qrV+pvvN4LfWztHSMlrap1epW11VRUQEXFxds37692Ws2Njbar//4/Sei5hhOiPSQiYlJsx+cgYGBuHr1Knr06KHTsRISEuDm5oYXX3xRuy03N/dPP++PvL29ER0dDaVSqZ09iY+Ph1QqRc+ePXWqqa0aZyQKCgpgZ2cHAE0aUu/XuXPnmv2+cUaqT58+KCwshEwmg4eHR7t9JpExYkMskR5yd3fH6dOnkZ+fr116mDNnDhISErB8+XJcvHgRmZmZiImJwfLly+95rB49eiAvLw/79+9HVlYWtm3b1uzGau7u7sjOzsbFixdRVFSEmpqaZscZO3YsTE1N8eabbyI1NRVxcXF49913MX78eO2STkfr3r07unXrhnXr1iEzMxOHDx/G5s2b2+348fHx2LhxIzIyMrBz5078/PPPmD59OgAgIiICoaGhmDdvHo4dO4bs7GzEx8fjk08+QXJycrvVQGQMGE6I9ND8+fORk5ODESNGIDw8HADg7++P7du3IzMzE08//TQef/xxrF27Fl26dLnnsYYPH45nn30Wy5cvx/jx45GQkNBkFgWobxiNiorC9OnTER4ejp9++qnZcSwsLPDVV1/h1q1bmDRpEl599VWEh4fjnXfeab8T/xMmJib4+OOPkZ6ejnHjxmHjxo1YsGBBux1/5syZSElJweOPP47PP/8cb775JqKiogDULwNt2LABAwYMwFtvvYWRI0fitddeQ05OTqeFMyJDIdHosthLRERE1ME4c0JERESiwoZYIhKFM2fOYM6cOXd9PSEhoROrISIhcVmHiEShqqoK+fn5d31d16uQiEh/MZwQERGRqLDnhIiIiESF4YSIiIhEheGEiIiIRIXhhIiIiESF4YSIiIhEheGEiIiIRIXhhIiIiESF4YSIiIhE5f8B06WtxMaAh+oAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plotting the convergence of the loss function metric\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline\n", - "\n", - "plt.style.use(\"seaborn-v0_8-whitegrid\")\n", - "\n", - "\n", - "plt.style.use(\"seaborn-v0_8-colorblind\")\n", - "df.sort_values(by=[\"iteration_number\"]).plot(x=\"iteration_number\", y=\"loss\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Importantly, we can plot the predicted probability distribution vs the target probability from the data. \n", - "To do so, we first import the QCBM locally again, but now we initialize it with the parameters returned from our hybrid job." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plotting the original probability distribution, and the QCBM prediction probability\n", - "\n", - "device = LocalSimulator()\n", - "qcbm = QCBM(device, n_qubits, n_layers=n_layers, data=data)\n", - "\n", - "qcbm_probs = qcbm.probabilities(job.result()[\"params\"])\n", - "\n", - "labels = [\"{0:{fill}6b}\".format(i, fill='0') for i in range(len(data))]\n", - "\n", - "plt.bar(range(2 ** n_qubits), data, label=\"target probability\", alpha=0.6)\n", - "plt.bar(range(2 ** n_qubits), qcbm_probs, label=\"QCBM probability\", alpha=0.6)\n", - "plt.xticks([i for i in range(len(data))], labels, rotation='vertical', size=12)\n", - "plt.yticks(size=12)\n", - "\n", - "plt.xlabel(\"Sample\", size=20)\n", - "plt.ylabel(\"Probability\", size=20)\n", - "\n", - "fig = plt.gcf()\n", - "fig.set_size_inches(16, 8)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great! As expected, the QCBM probability distribution closes matches the target distribution. " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Quantum Task Summary\n", - "{}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n" - ] - } - ], - "source": [ - "print(\"Quantum Task Summary\")\n", - "print(job.result()['task summary'])\n", - "print('Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).')\n", - "print(f\"Estimated cost to run quantum tasks in this hybrid job: {job.result()['estimated cost']} USD\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running different hyperparameters\n", - "\n", - "One of the strengths of Braket Hybrid Jobs is the ability to submit and monitor many hybrid jobs simultaneously. We can use this to perform a grid search to find good hyperparameters. Below we initialize 4 unique hybrid jobs with different `n_layers`." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating job with 1 layers\n", - "Creating job with 2 layers\n", - "Creating job with 3 layers\n", - "Creating job with 4 layers\n" - ] - } - ], - "source": [ - "jobs = []\n", - "\n", - "for n_layers in range(1, 5):\n", - " print(f\"Creating job with {n_layers} layers\")\n", - " tmp_job = train_circuit_hybrid_job(n_qubits, n_layers, n_iterations=10)\n", - "\n", - " jobs.append(tmp_job)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To check the results, we could load the results as we did before, or we could check the \"Monitor\" tab in the Braket Jobs dashboard in the AWS Console.\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 238 ms, sys: 10.8 ms, total: 249 ms\n", - "Wall time: 5min 42s\n" - ] - } - ], - "source": [ - "%%time \n", - "jobs[-1].result(); # wait for the last job to finish" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now plot the results from all the hyperparameters experiments once they finish. If the cell below does not work, wait a few minutes for metrics to load and try again." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dfs = []\n", - "for i, j in enumerate(jobs):\n", - " df = pd.DataFrame(j.metrics())\n", - " df.sort_values(by=[\"iteration_number\"])\n", - " dfs.append(df)\n", - " plt.plot(df[\"iteration_number\"], df[\"loss\"], label=f\"n_layers={i+1}\")\n", - "\n", - "plt.xlabel(\"iteration_number\")\n", - "plt.ylabel(\"loss\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the plots above, we see that the loss is much lower for `n_layers=3` and `n_layers=4`. We can conclude for 5 qubits, we need at least 3 layers in the QCBM to accurately learn the two-peak Gaussian data. " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Quantum Task Summary\n", - "{}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n", - "Quantum Task Summary\n", - "{}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n", - "Quantum Task Summary\n", - "{}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n", - "Quantum Task Summary\n", - "{}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n" - ] - } - ], - "source": [ - "for job in jobs:\n", - " print(\"Quantum Task Summary\")\n", - " print(job.result()['task summary'])\n", - " print('Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).')\n", - " print(f\"Estimated cost to run quantum tasks in this hybrid job: {job.result()['estimated cost']} USD\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion \n", - "\n", - "In this notebook, we submitted a single training hybrid job in Amazon Braket Hybrid Jobs. We then simultaneously submitted 4 new hybrid jobs with different hyperparameters to learn about the number of layers required in our circuit." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "\n", - "[1] Benedetti, Marcello, Delfina Garcia-Pintos, Oscar Perdomo, Vicente Leyton-Ortega, Yunseong Nam, and Alejandro Perdomo-Ortiz. “A Generative Modeling Approach for Benchmarking and Training Shallow Quantum Circuits.” Npj Quantum Information 5, no. 1 (May 27, 2019): 1–9. https://doi.org/10.1038/s41534-019-0157-8.\n", - "\n", - "[2] Liu, Jin-Guo, and Lei Wang. “Differentiable Learning of Quantum Circuit Born Machine.” Physical Review A 98, no. 6 (December 19, 2018): 062324. https://doi.org/10.1103/PhysRevA.98.062324.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.10 ('venv': venv)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - }, - "vscode": { - "interpreter": { - "hash": "590fab68195cf107911461461f81d5c472d3d6127f579badfcfad30f03e5cab2" - } - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quantum machine learning in Amazon Braket Hybrid Jobs\n", + "\n", + "This notebook demonstrates a typical quantum machine learning workflow, including uploading data, monitoring training, and tuning hyperparameters. We focus on training a parameterized quantum circuit for an unsupervised generative modelling task.\n", + "\n", + "\n", + "## Learning outcomes\n", + "\n", + "* Set input data \n", + "* Set hyperparameters \n", + "* Submit multiple hybrid jobs asynchronously \n", + "* Monitor hybrid job progress via the AWS Console \n", + "* Download and plot results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Background: Generative modelling \n", + "\n", + "Generative modelling is an unsupervised learning task where the goal is to generate new synthetic samples from an unknown target probability distribution. We denote the target probability distribution as $p(x)$, and the estimated distribution as $p_{\\theta}(x)$. The goal is to learn $p_{\\theta}(x)$ that closely resembles the target $p(x)$. One metric to quantify the difference between probability distributions is the maximum mean discrepancy (MMD) loss . \n", + "\n", + "$$MMD(x, y) = \\sum_{j=1}^N \\sum_{j'=1}^N k(y_j, y_{j'}) + \\sum_{i=1}^N \\sum_{i'=1}^N k(x_i, x_{i'}) - 2 \\sum_{j=1}^N \\sum_{i=1}^N k(y_j, x_i)$$\n", + "where $x$ is a sample from the target data $p(x)$, $y$ is a sample from the generative model $p_{\\theta}(x)$, and $k$ is a Gaussian kernel\n", + "\n", + "$$ k(x,y)= \\sum_{\\sigma} e^{-(x-y)^2/(2 \\sigma^2))}$$\n", + "\n", + "The MMD loss is zero if and only if $p(x)=p_{\\theta}(x)$ for all $x$. \n", + "\n", + "Learning a good approximation $p_{\\theta}$ depends on the expressibility of the model, the effectiveness of the training algorithm, and the ability to sample the circuit efficiently. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quantum Circuit Born Machine \n", + "\n", + "Quantum circuits are a natural fit for generative modelling because they are inherently probabilistic; the wavefunction encodes a probability according to the Born rule:\n", + "\n", + "$$p(x)=|\\langle x|\\psi\\rangle|^2$$\n", + "\n", + "In quantum mechanics, we do not have access to $p(x)$ directly, but we can efficiently sample using projective measurements [1]. This is an implicit generative model similar to generative adversarial networks (GANs). Quantum circuits allow fast sampling from a high-dimension distribution, and have large expressive power. \n", + "\n", + "The QCBM in this tutorial consists of alternating layers of single qubit rotations ($RX, RZ, RX$), followed by an entangling layer of CNot gates on each neighboring qubits. The final measurement layer computes the bit string samples of each outcome. Run the cell below to print a circuit diagram of a QCBM with randomly initialized parameters.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T : | 0 | 1 | 2 |3|4| 5 | 6 | 7 | 8 |9|10|11|Result Types|\n", + " \n", + "q0 : -Rx(0.53)-Rz(0.86)-Rx(0.21)-C---X----------Rx(0.24)-Rz(0.41)-Rx(0.88)-C----X--Probability--\n", + " | | | | | \n", + "q1 : -Rx(0.27)-Rz(0.55)-Rx(0.22)-X-C-|-Rx(0.65)-Rz(0.26)-Rx(0.95)----------X-C--|--Probability--\n", + " | | | | | \n", + "q2 : -Rx(0.92)-Rz(0.97)-Rx(0.43)---X-C----------Rx(0.25)-Rz(0.42)-Rx(0.98)---X--C--Probability--\n", + "\n", + "T : | 0 | 1 | 2 |3|4| 5 | 6 | 7 | 8 |9|10|11|Result Types|\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from braket.devices import LocalSimulator\n", + "from qcbm.qcbm import QCBM\n", + "\n", + "n_qubits = 3\n", + "n_layers = 2\n", + "\n", + "init_params = np.random.rand(3 * n_layers * n_qubits)\n", + "device = LocalSimulator()\n", + "qcbm = QCBM(device, n_qubits, n_layers, np.random.rand(1))\n", + "print(qcbm.create_circuit(init_params))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem setup\n", + "\n", + "This notebook demonstrates training a QCBM on a toy data set using Amazon Braket Hybrid Jobs. The code for the QCBM is in `qcbm` directory. The `qcbm_job.py` contains the code that will run when we create a Braket Hybrid Job. The other file (`qcbm.py`) contain the source code for the QCBM. \n", + "\n", + "In this tutorial, we use a small number of qubits to make it quick to test the algorithm. We use the on-demand simulator SV1 to run our circuits and gradient calculations in parallel (up to 35 concurrent tasks). \n", + "\n", + "We first set the number of qubits we want to use in our problem:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "n_qubits = 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate data\n", + "\n", + "As an example, we consider the toy example of learning a mixture of Gaussian distributions. We set a numpy random seed to produce the same data each time, but try experimenting with the number of peaks and number of qubits to produce harder or easier data sets. For this example, the target distribution $p(x)$ is a Gaussian on 5 bits (so $2^5$ possible values), with peaks at $\\mu_1=7$ and $\\mu_2=20$, with standard deviations $\\sigma_1=1$, $\\sigma_2 = 2$. We generate and plot the data as a probability density function in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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6/fTfX7t/v5DQ4l67hmZbsJ1efvnlhbbTgmUL7gcAVi2O8AQAaIDevXunZcuWueyyy9K+ffu6r3fvtttuefLJJ/Pwww8vcnTn/vvvn7Fjx+aJJ56oWzZr1qwMHz48Xbt2zdZbb50k2XHHHeuOfOzXr1/d8gXuuuuuhY6WHDt2bJ566qnst99+SZJ11103ffv2zfXXX5+pU6cukv29996r+3NVVdUiRxbeeeedixyN+e8GDhyYn/3sZ/nFL36RH//4x0vcTsm/joDceuutc8EFF+TJJ59c7Jj/zNDQXJtuummS1J1PNfnXEYLDhw9faNyMGTMyd+7chZZtu+22qaysTG1tbZJ/fTX9Py04R+mCMf9pwRGC/5n1pz/96WLHL6+ddtopHTp0yC9+8YvMmTOnbvnIkSMXKXv/0yeffJLZs2cvtGzTTTdNmzZtFnpea6yxxlIfq6GmTJmS3/3ud3U/z5gxI7fcckt69OiR9dZbry5DsvBrN2vWrNx8882LPF5Ds+20005Zd91184tf/GKh53bvvffmH//4R/r377+8TwkAWIk5whMAoAFWX3317LjjjnnyySfz3//933VHoPXp0yezZs3KrFmzFik8TzvttPzmN7/JfvvtlxNOOCHt27fPzTffnAkTJuS3v/1t3UV6lmazzTbLrrvumu9+97upra3NT3/606y99toLHT157bXXZtddd822226bb33rW9lkk03yzjvv5Iknnshbb72V5557Lsm/jr48//zzc9RRR2XnnXfO3/72t/z6179e6nkhv//972fGjBn5yU9+kjXXXDNnnHFGvWNbtmyZ3/3ud9l3332z66675stf/nJ22223rLHGGnn77bfzhz/8IZMmTVqojGporu7du+eLX/xiTj/99EybNi3t27fPbbfdtki5OWbMmHz/+9/PgAED0q1bt8ydOze33nprqqqqcuihhyZJzj///DzyyCPp379/Ntpoo7z77rsZNmxYNthgg4UuNPXv2rZtW3dOzM8++yzrr79+7rvvvkyYMGGJ229ZtWzZMhdeeGG+/e1vZ88998wRRxyRCRMmZMSIEUt9rV555ZXstddedcVzixYt8rvf/S7vvPNOjjzyyLpxO+64Y6677rpceOGF2WyzzbLuuusucpRkQ3Xr1i3HHHNMxo0bl44dO+aXv/xl3nnnnYwYMaJuzD777JMNN9wwxxxzTH70ox+lqqoqv/zlL9OhQ4dMmjRpocdraLYFv4Q46qijsvvuu+crX/lK3nnnnVx99dXp2rVrTjrppOV6PgDASm6FXR8eAKBEfe973ysW98+kH/3oR0WS4rLLLlto+WabbVYkKV5//fVF5rz++uvFYYcdVrRr166oqakpevXqVdx9990NyjFhwoQiSTFkyJDiyiuvLLp06VJUV1cXu+22W/Hcc88tdl0DBw4s1ltvvaJly5bF+uuvXxxwwAHFqFGj6sbMnj27OOWUU4pOnToVrVq1KnbZZZfiiSeeKHbfffdi9913rxv34IMPFkmKO++8c6F1nHrqqUWS4pprrllq/o8++qg4//zzi+23375o3bp1sdpqqxVdunQpDjvssOKPf/zjQmMbmmvB8+zXr19RXV1ddOzYsTjjjDOK0aNHF0mKBx98sCiKonjjjTeKo48+uth0002Lmpqaon379sUee+xR3H///XWP88ADDxQHHXRQ0blz52K11VYrOnfuXHzlK18pXnnllUVegxEjRtQte+utt4pDDjmkaNeuXbHmmmsWAwYMKKZMmVIkKc4555y6ceecc06RpHjvvfcWyj9ixIgiSTFhwoSlbsNhw4YVG2+8cVFdXV3stNNOxSOPPLLINvnPjO+//37xve99r9hyyy2LNdZYo1hzzTWL3r17F3fcccdCj/3Pf/6z6N+/f9GmTZsiSd1jLsg3bty4RfIsLvtGG21U9O/fv/jLX/5SbLfddkV1dXWx5ZZbLrLvFEVR/N///V/Ru3fvYrXVVis23HDD4qqrrlrsY9aXbcF+ueB1XuD2228vtt9++6K6urpo37598bWvfa146623FhrzzW9+s1hjjTUWybTgdQIAykdFUTTh2dIBAFhuEydOzMYbb5whQ4Zk8ODBKzoOAACsFJzDEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLLhHJ4AAAAAQNlwhCcAAAAAUDYUngAAAABA2WixogOsCubPn58pU6akTZs2qaioWNFxAAAAAGClUhRFZs6cmc6dO6eycsnHcCo8m8GUKVPSpUuXFR0DAAAAAFZqkydPzgYbbLDEMQrPZtCmTZsk/3pB2rZtu4LTAAAAAMDKZcaMGenSpUtdz7YkCs9msOBr7G3btlV4AgAAAMByasjpIl20CAAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGytt4VlbW5sf//jH6dy5c1q1apXevXtn9OjRS5338ssv56STTsrOO++cmpqaVFRUZOLEifWO/8Mf/pAddtghNTU12XDDDXPOOedk7ty5jfhMAAAAAIDGstIWnoMGDcpVV12Vr33ta7n66qtTVVWV/fffP4899tgS5z3xxBP52c9+lpkzZ2arrbZa4th77703Bx98cNq1a5ef//znOfjgg3PhhRfmBz/4QWM+FQAAAACgkVQURVGs6BDLauzYsendu3eGDBmSwYMHJ0lmz56dbbbZJuuuu24ef/zxeudOmzYtLVu2TJs2bXLFFVfkRz/6USZMmJCuXbsuMrZ79+5p2bJlnn766bRo0SJJcuaZZ+biiy/Oiy++mC233LJBeWfMmJE111wz06dPT9u2bZf9CQMAAADAKmxZ+rWV8gjPUaNGpaqqKscdd1zdspqamhxzzDF54oknMnny5Hrntm/fPm3atFnqOl588cW8+OKLOe644+rKziQ5/vjjUxRFRo0a9fmeBAAAAADQ6FbKwnP8+PHp1q3bIm1ur169kiTPPvtso6wjSXbaaaeFlnfu3DkbbLBB3f0AAAAAQOlosfQhpWfq1Knp1KnTIssXLJsyZUqjrOPfH/M/17OkddTW1qa2trbu5xkzZnzuPAAAQGnreto9zb7OiZf2b/Z1AkCpWymP8Pz0009TXV29yPKampq6+xtjHUnqXc+S1nHJJZdkzTXXrLt16dLlc+cBAAAAAJZupSw8W7VqtdARlAvMnj277v7GWEeSetezpHWcfvrpmT59et1tSecUBQAAAAAaz0pZeHbq1KnuK+f/bsGyzp07N8o6/v0x/3M9S1pHdXV12rZtu9ANAAAAAGh6K2Xh2aNHj7zyyiuLnBvzqaeeqru/MdaRJE8//fRCy6dMmZK33nqrUdYBAAAAADSulbLwPOywwzJv3rwMHz68blltbW1GjBiR3r17150zc9KkSXnppZeWax3du3fPlltumeHDh2fevHl1y6+77rpUVFTksMMO+3xPAgAAAABodCvlVdp79+6dAQMG5PTTT8+7776bzTbbLDfffHMmTpyYm266qW7cwIED8/DDD6coirpl06dPz89//vMkyV//+tckyTXXXJN27dqlXbt2+f73v183dsiQITnwwAOzzz775Mgjj8wLL7yQa665Jscee2y22mqrZnq2AAAAAEBDVRT/3gauRGbPnp2zzjorv/rVr/Lhhx9mu+22ywUXXJB99923bkzfvn0XKTwnTpyYjTfeeLGPudFGG2XixIkLLbvrrrty3nnn5R//+Ec6dOiQQYMG5eyzz07Lli0bnHXGjBlZc801M336dOfzBACAMtX1tHuafZ0TL+3f7OsEgBVhWfq1lbbwXJkoPAEAoPwpPAGg6SxLv7ZSnsMTAAAAAGBxFJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZaPFig4AAACwvLqedk+zrm/ipf2bdX0AwLJzhCcAAAAAUDYc4QkAAFCGHP0KwKrKEZ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlYaQvP2tra/PjHP07nzp3TqlWr9O7dO6NHj27Q3LfffjuHH3542rVrl7Zt2+aggw7KG2+8sci46dOn59RTT83mm2+eVq1aZaONNsoxxxyTSZMmNfbTAQAAAAAaQYsVHWB5DRo0KKNGjcqJJ56YzTffPCNHjsz++++fBx98MLvuumu98z7++OPssccemT59es4444y0bNkyQ4cOze67755nn302a6+9dpJk/vz52XvvvfPiiy/m+OOPT7du3fLaa69l2LBh+ctf/pJ//OMfadOmTXM9XQAAAACgAVbKwnPs2LG57bbbMmTIkAwePDhJMnDgwGyzzTY59dRT8/jjj9c7d9iwYXn11VczduzY9OzZM0my3377ZZtttsmVV16Ziy++OEny5JNPZty4cbnmmmvyve99r27+FltskaOPPjr3339/DjnkkCZ8lgAAAADAslopv9I+atSoVFVV5bjjjqtbVlNTk2OOOSZPPPFEJk+evMS5PXv2rCs7k2TLLbfMXnvtlTvuuKNu2YwZM5IkHTt2XGh+p06dkiStWrVqlOcCAAAAADSelbLwHD9+fLp165a2bdsutLxXr15JkmeffXax8+bPn5/nn38+O+200yL39erVK6+//npmzpyZJNlpp52yxhpr5KyzzsqYMWPy9ttv5+GHH86pp56anj17pl+/fo37pAAAAACAz22lLDynTp1ad6Tlv1uwbMqUKYudN23atNTW1jZo7jrrrJPbb78906dPz1577ZUNNtggffv2TefOnTNmzJi0aFH/2QBqa2szY8aMhW4AAAAAQNNbKQvPTz/9NNXV1Yssr6mpqbu/vnlJGjy3Q4cO2X777XPRRRflrrvuyrnnnptHH300Rx111BLzXXLJJVlzzTXrbl26dGnYEwMAAAAAPpeV8qJFrVq1Sm1t7SLLZ8+eXXd/ffOSNGjuG2+8kT322CO33HJLDj300CTJQQcdlK5du2bQoEG59957s99++y12PaeffnpOPvnkup9nzJih9AQAAACAZrBSHuHZqVOnTJ06dZHlC5Z17tx5sfPat2+f6urqBs0dOXJkZs+enQMOOGChcQceeGCS5K9//Wu9+aqrq9O2bduFbgAAAABA01spC88ePXrklVdeWeTcmE899VTd/YtTWVmZbbfdNk8//fQi9z311FPZZJNN0qZNmyTJO++8k6IoMm/evIXGffbZZ0mSuXPnft6nAQAAAAA0spWy8DzssMMyb968DB8+vG5ZbW1tRowYkd69e9d9fXzSpEl56aWXFpk7bty4hUrPl19+OWPGjMmAAQPqlnXr1i1FUeSOO+5YaP5vfvObJMn222/f6M8LAAAAAPh8VspzePbu3TsDBgzI6aefnnfffTebbbZZbr755kycODE33XRT3biBAwfm4YcfTlEUdcuOP/743HDDDenfv38GDx6cli1b5qqrrkrHjh1zyimn1I0bNGhQrrjiinz729/O+PHj07179zzzzDO58cYb07179xxyyCHN+pwBAAAAgKVbKQvPJLnlllty1lln5dZbb82HH36Y7bbbLnfffXf69OmzxHlt2rTJQw89lJNOOikXXnhh5s+fn759+2bo0KHp0KFD3bi11147Tz/9dM4+++z88Y9/zC9+8YusvfbaOfroo3PxxRdntdVWa+qnCAAAAAAso5W28KypqcmQIUMyZMiQesc89NBDi12+wQYb5M4771zqOtZff/2FjhgFAAAAAErbSnkOTwAAAACAxVF4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2Wi0wnObbbbJ0KFD89577zXWQwIAAAAALJNGKzxffPHFDB48OBtssEEOPfTQ3H333Zk/f35jPTwAAAAAwFI1WuG5/fbbpyiKfPbZZ7nrrrty0EEHpUuXLjn99NPzyiuvNNZqAAAAAADq1WiF5//93//lueeeyw9/+MOsvfbaKYoiU6dOzeWXX56tttoqu+66a0aMGJFZs2Y11ioBAAAAABbSqBct2nbbbTN06NBMmTIlv/3tb3PAAQekqqoqRVHkiSeeyLHHHptOnTrlmGOOyWOPPdaYqwYAAAAAaJqrtLdo0SKHHHJI/vCHP2Ty5Mm59NJLs8UWW6Qoinz88ccZOXJkdt9992yxxRa57LLLMnXq1KaIAQAAAACsYpqk8Px3HTt2zKmnnpoXX3yx7ijPNm3apCiKvPrqqznjjDOy0UYb5b//+79z1113udARAAAAALDcmrzw/He9e/fO8OHD8+tf/zrrrbdeKioqkiRz587Nn/70pxx66KHZcMMN87Of/Szz5s1rzmgAAAAAQBlotsJz0qRJOf/887PpppvmwAMPzDvvvJOiKFJZWZl99tkn66+/foqiyJQpU3LSSSfli1/8Yj788MPmigcAAAAAlIEmLTxnz56dX//61+nXr1822WSTnHfeeZkwYUKKosgmm2ySiy66KJMmTcqf//znvPnmm7n33nvTt2/fFEWRZ555Juedd15TxgMAAAAAykyTFJ5PPvlkvv3tb6dTp04ZOHBgHnzwwcyfPz+rrbZavvKVr+SBBx7Iq6++mtNPPz2dOnVKklRUVGTffffNmDFjcvzxx6coivzhD39oingAAAAAQJlq0VgPNHXq1Nx6660ZOXJkXn755SRJURRJkm233TbHHntsvv71r2ettdZa6mMdc8wxGTZsWCZPntxY8QAAAACAVUCjFZ4bbrhh5s+fX1dytmnTJkceeWSOPfbY9OzZc5keq23btkniiu0AAAAAwDJptMJzwVXVv/SlL+XYY4/NEUcckdVXX325Hqtjx44ZMWJEY0UDAAAAAFYRjVZ4nnTSSTn22GOz1VZbfe7Hat26db75zW82QioAAAAAYFXSaIXnlVde2VgPBQAAAACwXBqt8Nxzzz1TUVGRX/7yl9loo40aNGfKlCn5+te/noqKijzwwAONFQUAAGhCXU+7p1nXN/HS/s26PgBg5dZohedDDz2UioqKzJo1q8FzPv3007p5AAAAAACfV+WKDrC8amtr8+Mf/zidO3dOq1at0rt374wePbpBc99+++0cfvjhadeuXdq2bZuDDjoob7zxxmLHvvPOO/n2t7+d9ddfPzU1NenatWuOOeaYxnwqAAAAAEAjabQjPJfHgqNBa2pqlnnuoEGDMmrUqJx44onZfPPNM3LkyOy///558MEHs+uuu9Y77+OPP84ee+yR6dOn54wzzkjLli0zdOjQ7L777nn22Wez9tpr142dPHlydtlllyTJd77znay//vqZMmVKxo4du8x5AQAAAICmt0ILz3vvvTdJssEGGyzTvLFjx+a2227LkCFDMnjw4CTJwIEDs8022+TUU0/N448/Xu/cYcOG5dVXX83YsWPTs2fPJMl+++2XbbbZJldeeWUuvvjiurHf/va306JFi4wbN26hIhQAAAAAKE3LXXgeffTRi11+5plnpl27dkucW1tbm9dffz3jxo1LRUVFdt9992Va96hRo1JVVZXjjjuubllNTU2OOeaYnHHGGZk8eXK6dOlS79yePXvWlZ1JsuWWW2avvfbKHXfcUVd4vvTSS7n33nszbNiwrL322pk9e3aqqqrSsmXLZcoKAACwqmvuC10lLnYFsCpb7sJz5MiRi1xsqCiK/P73v2/Q/KIokiTt27fP6aefvkzrHj9+fLp165a2bdsutLxXr15JkmeffXaxhef8+fPz/PPPL7as7dWrV+67777MnDkzbdq0yf33358k6dixY/baa6+MGTMmVVVV2XvvvXPdddela9euy5QZAAAAAGh6y114brjhhgsVnm+++WYqKirSqVOnJR4FWVFRkZqamnTq1Ck777xzvvvd76Zz587LtO6pU6emU6dOiyxfsGzKlCmLnTdt2rTU1tYude4WW2yRV199NUly3HHHpWfPnrn99tszadKknHfeeenXr1+ef/75rL766otdT21tbWpra+t+njFjxjI9PwAAAABg+Sx34Tlx4sSFfq6s/NcF3++7775svfXWnyvU0nz66aeprq5eZPmCix99+umn9c5L0qC5H3/8cZJkvfXWyz333FP3/DbYYIN85Stfyf/8z//k2GOPXex6Lrnkkpx33nnL8pQAAAAAgEZQ2VgP1KdPn/Tp0ydrrLFGYz1kvVq1arXQEZQLzJ49u+7++uYladDcBf89/PDD68rOJBkwYEBatGixxAsjnX766Zk+fXrdbfLkyQ15WgAAAADA59RoV2l/6KGHGuuhlqpTp055++23F1k+derUJKn3K/Lt27dPdXV13bglzV3w344dOy40rqqqKmuvvXY+/PDDevNVV1cv9ihSAAAAAKBpNdoRns2pR48eeeWVVxY5N+ZTTz1Vd//iVFZWZtttt83TTz+9yH1PPfVUNtlkk7Rp0yZJsuOOOybJIsXqnDlz8v7776dDhw6f92kAAAAAAI1spSw8DzvssMybNy/Dhw+vW1ZbW5sRI0akd+/edVdonzRpUl566aVF5o4bN26h0vPll1/OmDFjMmDAgLplffv2zbrrrptf//rXdV93T/51dfp58+Zl7733bqqnBwAAAAAsp2X+Svsmm2yS5F9XW3/99dcXWb48/vOxlqZ3794ZMGBATj/99Lz77rvZbLPNcvPNN2fixIm56aab6sYNHDgwDz/8cIqiqFt2/PHH54Ybbkj//v0zePDgtGzZMldddVU6duyYU045pW5cdXV1hgwZkm9+85vp06dPvvGNb2TSpEm5+uqrs9tuu+XLX/7ycj9fAAAAAKBpLHPhueDq7BUVFYtdvjz+87Ea4pZbbslZZ52VW2+9NR9++GG222673H333enTp88S57Vp0yYPPfRQTjrppFx44YWZP39++vbtm6FDhy7yNfWBAwdmtdVWy6WXXpof/ehHadeuXb797W/n4osvTlVV1TJnBgAAAACa1jIXnt/85jeXaXlTqampyZAhQzJkyJB6x9R3IaUNNtggd955Z4PWc+SRR+bII49cnogAAAAAQDNb5sJzxIgRy7QcAAAAAKC5rJQXLQIAAAAAWByFJwAAAABQNhSeAAAAAEDZWOZzeE6aNKkpcmTDDTdskscFAAAAAFYdy1x4brzxxo0eoqKiInPnzm30xwUAAAAAVi3LXHgWRdEUOQAAAAAAPrdlLjxHjBjRFDkAAAAAAD63ZS48v/nNbzZFDgAAAACAz81V2gEAAACAsqHwBAAAAADKhsITAAAAACgby3wOz1tuuaXuzwMHDlzs8uXx748FAAAAALA8lrnwHDRoUCoqKlJRUbFQSblg+fL4z8cCAAAAAFgey1x4JklRFMu0HAAAAACgOSxz4TlhwoRlWg4AAAAA0FyWufDcaKONlmk5AAAAAEBzcZV2AAAAAKBsKDwBAAAAgLKxXBctaohnnnkm999/f/72t79l2rRpSZL27dtnm222Sb9+/bLjjjs21aoBAAAAgFVUoxeezzzzTI4//viMGzeu3jFnnHFGdtppp1x77bXZaaedGjsCAAAAALCKatSvtI8aNSo777xzxo0bl6IoUhRFWrZsmY4dO6Zjx45p2bJl3fJx48Zll112yZ133tmYEQAAAACAVVijFZ4vv/xyvvGNb2TOnDmpqqrKd7/73YwbNy6zZs3KlClTMmXKlMyaNStPP/10vvvd76ZFixb57LPPMnDgwLz00kuNFQMAAAAAWIU1WuF52WWXpba2NjU1Nbnvvvty7bXXZscdd0xVVVXdmKqqquywww659tprM3r06NTU1GTOnDm5/PLLGysGAAAAALAKa7TC8/77709FRUVOPPHE9O3bd6njd99995x44okpiiL3339/Y8UAAAAAAFZhjVZ4vvfee0mS/fffv8Fz+vfvv9BcAAAAAIDPo9EKzw4dOiRJampqGjynuro6SbLOOus0VgwAAAAAYBXWaIXnLrvskiQZN25cg+eMHTs2SbLrrrs2VgwAAAAAYBXWaIXnySefnKqqqlx88cUN+or6u+++m0suuSQtW7bMSSed1FgxAAAAAIBVWKMVnj179sz111+fd999N717985dd92V+fPnLzJu/vz5+f3vf58vfelLee+993LdddelV69ejRUDAAAAAFiFtVjWCUcfffQS7996663z3HPP5dBDD81aa62V7bffPuuuu24qKiryzjvv5Nlnn820adOSJF/4whfy2GOP5a9//Wtuuumm5XsGAAAAAAD/zzIXniNHjkxFRcUSx1RUVKQoikybNi1jxoxZ6L6iKOrGPPfcc3nuueeSROEJAAAAAHxuy1x4brjhhkstPAEAAAAAVoRlLjwnTpzYBDEAAAAAAD6/RrtoEQAAAADAiqbwBAAAAADKhsITAAAAACgby3wOz2Uxb968fPjhh/n000/rrs5enw033LApowAAAAAAq4BGLzzff//9/PznP89dd92VF198MfPnz1/qnIqKisydO7exowAAAAAAq5hGLTwff/zxfPnLX85777231CM6AQAAAAAaW6MVnh988EEOOuigfPDBB2ndunWOPfbYtGvXLueee24qKipy4403Ztq0aXn66afzhz/8IbNnz84uu+ySY445prEiAAAAAACruEYrPK+55pp88MEHqa6uzhNPPJHu3bvn73//e84999wkyVFHHVU3durUqfnqV7+aRx55JF/60pdy2WWXNVYMAAAAAGAV1mhXab/33ntTUVGRo48+Ot27d1/i2E6dOuVPf/pTNt1001xxxRUZM2ZMY8UAAAAAAFZhjVZ4vvbaa0mSfv361S2rqKio+/O8efMWGt+qVaucdNJJKYoiv/jFLxorBgAAAACwCmu0wnPGjBlJko022qhuWU1NTd2fZ86cucicnXbaKUny1FNPNVYMAAAAAGAV1miFZ+vWrZMkc+fOrVvWvn37uj9PnDhxkTmzZ89Okrz77ruNFQMAAAAAWIU1WuG52WabJUkmTZpUt6xdu3ZZb731kiQPPvjgInMee+yxJMkaa6zRWDEAAAAAgFVYoxWevXv3TpKMGzduoeX/9V//laIocvnll+fVV1+tW/7kk09myJAhqaioSM+ePRsrBgAAAACwCmu0wnPfffdNURT53//934WWn3zyyWnRokXefffddO/ePT179szWW2+d3XbbLR999FGS5Ic//GFjxQAAAAAAVmGNWngOHDgwX/ziFzNhwoS65dtss02uu+66VFVVZe7cufm///u/vPTSS3VXbT/33HPzX//1X40VAwAAAABYhbVorAdq2bJlRo4cudj7jjnmmOy6664ZOXJk/v73v2fu3LnZfPPN841vfKPuSu0AAAAAAJ9XoxWeS7PFFlvkkksuaa7VAQAAAACroEb7SjsAAAAAwIrW5Ed4zp07Nx9++GGSZK211kqLFs12UCkAAAAAsIppkiM8X3zxxZxwwgnZeuutU1NTk/XWWy/rrbdeampqstVWW+UHP/hBXnjhhaZYNQAAAACwCmvUwnP+/Pk55ZRT8oUvfCHXXnttXnrppcyfPz9FUaQoisyfPz8vv/xyhg0blu233z4nnXRS5s+f35gRAAAAAIBVWKN+v/yrX/1q7rzzzhRFkSTp3r17evXqlY4dOyZJ3nnnnYwbNy4vvPBC5s2bl5/97GeZMmVKbr/99saMAQAAAACsohqt8Lzttttyxx13pKKiIl/4whcyfPjw9OzZc7Fjx40bl+985zsZP358Ro0aldtuuy1HHnlkY0UBVhJdT7un2dc58dL+zb5OAAAAoPk02lfahw8fniTp1q1bHnvssXrLziTp2bNnHnnkkWyxxRYpiiLXX399Y8UAAAAAAFZhjVZ4Pvfcc6moqMiPf/zjrLHGGksdv8Yaa+THP/5x3VwAAAAAgM+r0QrPOXPmJEm22267Bs9ZMPazzz5rrBgAAAAAwCqs0QrPjTbaKEkyffr0Bs+ZMWPGQnMBAAAAAD6PRis8Dz300BRFkd/+9rcNnjNq1KhUVFTkkEMOaawYAAAAAMAqrNGu0n7yySfn17/+da6//vr06dMnhx9++BLHjxo1Ktdff3023njjDB48uLFiAEvR3FdGX5muim7bAAAAwMqv0Y7wXHPNNXP//fdnhx12yFe+8pUcfPDBueuuu/L222/ns88+y9y5c/P222/nrrvuyiGHHJIjjjgiO+ywQx544IGsueaajRUDAAAAAFiFLfMRnlVVVUsdUxRF/vjHP+aPf/zjEsc8/fTT2WSTTVJRUZG5c+cuaxQAAAAAgIUsc+FZFEWjjWvoYwEAAAAANMQyF57nnHNOU+QAAAAAAPjcFJ4AAAAAQNlotIsWAQAAAACsaApPAAAAAKBsLPNX2hvqs88+yzPPPJMXXngh06ZNS5K0b98+22yzTXbYYYe0bNmyqVYNAAAAAKyiGr3w/OSTT3LBBRfkhhtuyIcffrjYMWuttVaOO+64nHnmmVl99dUbOwIAAAAAsIpq1K+0T5o0KT169Mjll1+eadOmpSiKxd6mTZuWyy67LNtvv33eeuutxowAAAAAAKzCGu0Iz88++yz77bdfXnvttSTJlltumaOOOiq9e/fOeuutlyT55z//mbFjx2bkyJF58cUX8+qrr2a//fbL+PHj06JFk327HgAAAABYRTTaEZ433nhj/vGPf6SioiI/+clP8re//S0/+tGP0qdPn3Tr1i3dunVLnz59Mnjw4Dz//PM588wzkyQvvvhibrzxxsaKAQAAAACswhqt8LzzzjtTUVGRgw8+OBdccEGqqqrqX2llZc4///wccsghKYoid955Z2PFAAAAAABWYY1WeL7wwgtJkqOPPrrBc4455pgkyd/+9rfGigEAAAAArMIarfCcPn16kqRz584NntOpU6ckyYwZMxorBgAAAACwCmu0wrN9+/ZJkgkTJjR4zoKxC+YCAAAAAHwejXZp9B122CH33ntvrr322nz5y19u0Jxhw4aloqIi22+/fWPFgJLT9bR7mn2dEy/t3+zrpHHZbwAAAGD5NNoRnl/5yleSJA899FCOPvrozJo1q96xn3zySY499tiMGTMmSfLVr361sWIAAAAAAKuwRjvC82tf+1p+8Ytf5PHHH8/NN9+cP/3pTzn88MPTu3fvrLvuuqmoqMg777yTp556KnfccUfee++9JMkuu+ySr33ta40VAwAAAABYhTVa4VlRUZE//vGP6d+/f5588sm8++67ufbaa3PttdcuMrYoiiTJl770pfz+979vrAgAAAAAwCqu0b7SniRrrbVWHnvssfz85z/PVlttlaIoFnvbaqutcs011+TRRx/NWmut1ZgRAAAAAIBVWKMd4blAZWVlvve97+V73/tepk6dmhdeeCHTpk1L8q+rsW+zzTbp1KlTY68WAAAAAKDxCs+jjz46SbLffvtlwIABSZJOnTopNwEAAACAZtNohefNN9+cJDniiCMa6yEBAAAAAJZJo53Ds0OHDkmSjh07NtZDAgAAAAAsk0YrPLfeeuskyZtvvtlYDwkAAAAAsEwarfD8+te/nqIo6r7aDgAAAADQ3Bqt8DzqqKOy11575fe//33OPffcFEXRWA8NAAAAANAgjXbRokcffTSDBw/Oe++9lwsuuCC33357jjjiiGy33XZZa621UlVVtcT5ffr0aawoAAAAAMAqqtEKz759+6aioqLu51deeSUXXHBBg+ZWVFRk7ty5jRUFAAAAAFhFNVrhmcTX2AEAAACAFarRCs8HH3ywsR4KAAAAAGC5NFrhufvuuzfWQwEAAAAALJfPXXjec889+fOf/5w333wz8+bNS+fOndO3b98cfvjhadmyZWNkBAAAAABokOUuPN95550cfPDBGTt27CL3/fKXv8zZZ5+du+66K9tuu+3nCggAAAAA0FCVyzNp3rx5OfDAA/PUU0+lKIrF3iZMmJB9990377//fmNnBgAAAABYrOUqPO+4446MGzcuFRUV2WyzzXLTTTflb3/7W1566aXceeed+eIXv5jkX0eBXnnllY0aGAAAAACgPstdeCZJ165dM3bs2Bx11FHp3r17unXrlkMPPTSPPvpodt999xRFkTvvvLNRAwMAAAAA1Ge5Cs/x48enoqIip5xyStq1a7fI/VVVVTnvvPOSJBMmTMjMmTM/V0gAAAAAgIZYrsLzvffeS5LstNNO9Y759/ucxxMAAAAAaA7LVXh++umnSZLWrVvXO2b11Vev+/Ps2bOXZzVLVFtbmx//+Mfp3LlzWrVqld69e2f06NENmvv222/n8MMPT7t27dK2bdscdNBBeeONN5Y457HHHktFRUUqKioUuAAAAABQolo0x0qKomj0xxw0aFBGjRqVE088MZtvvnlGjhyZ/fffPw8++GB23XXXeud9/PHH2WOPPTJ9+vScccYZadmyZYYOHZrdd989zz77bNZee+1F5syfPz8/+MEPssYaa2TWrFmN/lxofF1Pu6dZ1zfx0v7Nuj5obt5TAAAArCyW6wjPFW3s2LG57bbbcskll2TIkCE57rjjMmbMmGy00UY59dRTlzh32LBhefXVV3P33Xfn1FNPzUknnZT77rsvU6dOrfeK8sOHD8/kyZNz7LHHNsXTAQAAAAAayec6wnPYsGFZd911G2Xc2Wef3eD1jho1KlVVVTnuuOPqltXU1OSYY47JGWeckcmTJ6dLly71zu3Zs2d69uxZt2zLLbfMXnvtlTvuuCMXX3zxQuOnTZuWM888M+eff37efffdBmcEAAAAAJrf5yo8r7vuuiXeX1FR0aBxybIVnuPHj0+3bt3Stm3bhZb36tUrSfLss88utvCcP39+nn/++Rx99NGL3NerV6/cd999mTlzZtq0aVO3/Kyzzsp6662Xb3/727ngggsalK+2tja1tbV1P8+YMaNB8wAAAACAz2e5v9JeFEWj3ZbV1KlT06lTp0WWL1g2ZcqUxc6bNm1aamtrGzz3+eefz/XXX5+rrroqVVVVDc53ySWXZM0116y71Xe0KQAAAADQuJbrCM8HH3ywsXMsk08//TTV1dWLLK+pqam7v755SRo894QTTsh+++2XffbZZ5nynX766Tn55JPrfp4xY4bSEwAAAACawXIVnrvvvntj51gmrVq1Wugr4wvMnj277v765iVp0Nzbb789jz/+eF544YVlzlddXb3YUhUAAAAAaFqf6xyeK0qnTp3y9ttvL7J86tSpSZLOnTsvdl779u1TXV1dN25Jc3/0ox9lwIABWW211TJx4sQkyUcffZQkmTx5cubMmVPvegAAAACAFWOlLDx79OiRBx98MDNmzFjowkVPPfVU3f2LU1lZmW233TZPP/30Ivc99dRT2WSTTeouWDR58uT8z//8T/7nf/5nkbE77LBDvvCFL+TZZ5/9/E8GAAAAAGg0y33RohXpsMMOy7x58zJ8+PC6ZbW1tRkxYkR69+5dd77MSZMm5aWXXlpk7rhx4xYqPV9++eWMGTMmAwYMqFv2u9/9bpHbEUcckSS55ZZbMnTo0KZ8igAAAADAclgpj/Ds3bt3BgwYkNNPPz3vvvtuNttss9x8882ZOHFibrrpprpxAwcOzMMPP7zQleCPP/743HDDDenfv38GDx6cli1b5qqrrkrHjh1zyimn1I07+OCDF1nvgiM699tvv6yzzjpN9vwAAAAAgOWzUhaeyb+OsjzrrLNy66235sMPP8x2222Xu+++O3369FnivDZt2uShhx7KSSedlAsvvDDz589P3759M3To0HTo0KGZ0gMAAAAATWGlLTxramoyZMiQDBkypN4xDz300GKXb7DBBrnzzjuXeZ3nnntuzj333GWeBwAAAAA0j5XyHJ4AAAAAAIuj8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAykaLFR2A8tH1tHuadX0TL+3frOsDSoPPGgAAAJbEEZ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUjRYrOgAAALBkXU+7p9nXOfHS/s2+TgCAxuAITwAAAACgbCg8AQAAAICyofAEAAAAAMqGwhMAAAAAKBsKTwAAAACgbCg8AQAAAICyofAEAAAAAMqGwhMAAAAAKBsKTwAAAACgbLRY0QEAAACgOXU97Z5mXd/ES/s36/oAVnWO8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBstFjRAQBgZdb1tHuadX0TL+3frOsDAABY2TjCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBstFjRAQCAxtH1tHuadX0TL+3frOsDAABoCEd4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDZW2sKztrY2P/7xj9O5c+e0atUqvXv3zujRoxs09+23387hhx+edu3apW3btjnooIPyxhtvLDRm8uTJOe+889KrV6+stdZaWWedddK3b9/cf//9TfF0AAAAAIBGsNIWnoMGDcpVV12Vr33ta7n66qtTVVWV/fffP4899tgS53388cfZY4898vDDD+eMM87Ieeedl/Hjx2f33XfPBx98UDfu97//fS677LJsttlmufDCC3PWWWdl5syZ2XvvvTNixIimfnoAAAAAwHJosaIDLI+xY8fmtttuy5AhQzJ48OAkycCBA7PNNtvk1FNPzeOPP17v3GHDhuXVV1/N2LFj07NnzyTJfvvtl2222SZXXnllLr744iTJHnvskUmTJmWdddapm/ud73wnPXr0yNlnn52jjjqqCZ8hAAAAALA8VsojPEeNGpWqqqocd9xxdctqampyzDHH5IknnsjkyZOXOLdnz551ZWeSbLnlltlrr71yxx131C3r3r37QmVnklRXV2f//ffPW2+9lZkzZzbiMwIAAAAAGsNKWXiOHz8+3bp1S9u2bRda3qtXryTJs88+u9h58+fPz/PPP5+ddtppkft69eqV119/falF5j//+c+svvrqWX311ZcvPAAAAADQZFbKwnPq1Knp1KnTIssXLJsyZcpi502bNi21tbXLNTdJXnvttfzv//5vDj300FRVVdU7rra2NjNmzFjoBgAAAAA0vZWy8Pz0009TXV29yPKampq6++ubl2S55n7yyScZMGBAWrVqlUsvvXSJ+S655JKsueaadbcuXboscTwAAAAA0DhWysKzVatWqa2tXWT57Nmz6+6vb16SZZ47b968HHnkkXnxxRczatSodO7ceYn5Tj/99EyfPr3utqRzigIAAAAAjWelvEp7p06d8vbbby+yfOrUqUlSbyHZvn37VFdX141r6Nxvfetbufvuu/PrX/86e+6551LzVVdXL/YoUgAAAACgaa2UR3j26NEjr7zyyiLnxnzqqafq7l+cysrKbLvttnn66acXue+pp57KJptskjZt2iy0/Ec/+lFGjBiRoUOH5itf+UrjPAEAAAAAoEmslIXnYYcdlnnz5mX48OF1y2prazNixIj07t277pyZkyZNyksvvbTI3HHjxi1Uer788ssZM2ZMBgwYsNDYIUOG5IorrsgZZ5yRH/7wh034jAAAAACAxrBSfqW9d+/eGTBgQE4//fS8++672WyzzXLzzTdn4sSJuemmm+rGDRw4MA8//HCKoqhbdvzxx+eGG25I//79M3jw4LRs2TJXXXVVOnbsmFNOOaVu3O9+97uceuqp2XzzzbPVVlvlV7/61UIZ9t5773Ts2LHpnywAAAAA0GArZeGZJLfcckvOOuus3Hrrrfnwww+z3Xbb5e67706fPn2WOK9NmzZ56KGHctJJJ+XCCy/M/Pnz07dv3wwdOjQdOnSoG/fcc88lSV599dV84xvfWORxHnzwQYUnAAAAAJSYlbbwrKmpyZAhQzJkyJB6xzz00EOLXb7BBhvkzjvvXOLjn3vuuTn33HM/R0IAAAAAoLmtlOfwBAAAAABYHIUnAAAAAFA2VtqvtAMApavrafc0+zonXtq/2dcJAACUHkd4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDZarOgAAABQirqedk+zrm/ipf2bdX0AAOXKEZ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZUPhCQAAAACUDYUnAAAAAFA2FJ4AAAAAQNlQeAIAAAAAZaPFig4AANDUup52T7Oub+Kl/Zt1fQAAwP9P4QkAAAArSHP/Ui7xizmg/PlKOwAAAABQNhSeAAAAAEDZUHgCAAAAAGVD4QkAAAAAlA2FJwAAAABQNhSeAAAAAEDZUHgCAAAAAGVD4QkAAAAAlA2FJwAAAABQNhSeAAAAAEDZUHgCAAAAAGVD4QkAAAAAlA2FJwAAAABQNhSeAAAAAEDZUHgCAAAAAGVD4QkAAAAAlA2FJwAAAABQNhSeAAAAAEDZUHgCAAAAAGVD4QkAAAAAlI0WKzoAAMCqpOtp9zT7Oide2r/Z1wkAACuKIzwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAykaLFR0AAACSpOtp9zT7Oide2r/Z1wkAQNNyhCcAAAAAUDYUngAAAABA2VB4AgAAAABlQ+EJAAAAAJQNhScAAAAAUDZcpR0AYBXW3FdGd1V0AACamiM8AQAAAICyofAEAAAAAMqGwhMAAAAAKBsKTwAAAACgbCg8AQAAAICyofAEAAAAAMqGwhMAAAAAKBsKTwAAAACgbCg8AQAAAICyofAEAAAAAMqGwhMAAAAAKBsKTwAAAACgbCg8AQAAAICyofAEAAAAAMqGwhMAAAAAKBsKTwAAAACgbCg8AQAAAICyofAEAAAAAMpGixUdAAAAACgNXU+7p1nXN/HS/s26PmDV4AhPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAyobCEwAAAAAoGwpPAAAAAKBsKDwBAAAAgLKh8AQAAAAAykaLFR1gedXW1ubss8/Orbfemg8//DDbbbddLrzwwuy9995Lnfv222/npJNOyn333Zf58+dnjz32yNChQ7PJJpssMvamm27KFVdckQkTJqRLly454YQT8oMf/KApnhIAAADw/3Q97Z5mX+fES/s3+zqBxrfSHuE5aNCgXHXVVfna176Wq6++OlVVVdl///3z2GOPLXHexx9/nD322CMPP/xwzjjjjJx33nkZP358dt9993zwwQcLjb3++utz7LHHpnv37vn5z3+eL33pSznhhBNy2WWXNeVTAwAAAACW00p5hOfYsWNz2223ZciQIRk8eHCSZODAgdlmm21y6qmn5vHHH6937rBhw/Lqq69m7Nix6dmzZ5Jkv/32yzbbbJMrr7wyF198cZLk008/zU9+8pP0798/o0aNSpJ861vfyvz583PBBRfkuOOOy1prrdXEzxQAAAAAWBYr5RGeo0aNSlVVVY477ri6ZTU1NTnmmGPyxBNPZPLkyUuc27Nnz7qyM0m23HLL7LXXXrnjjjvqlj344IP54IMPcvzxxy80/3vf+15mzZqVe+5p/kPrAQAAAIAlWykLz/Hjx6dbt25p27btQst79eqVJHn22WcXO2/+/Pl5/vnns9NOOy1yX69evfL6669n5syZdetIssjYHXfcMZWVlXX3AwAAAAClY6X8SvvUqVPTqVOnRZYvWDZlypTFzps2bVpqa2uXOneLLbbI1KlTU1VVlXXXXXehcauttlrWXnvteteR/OuCSrW1tXU/T58+PUkyY8aMpTyzldv82k+adX1L2p6rcpaktPKUUpaktPKUUpaktPKsLFmS0sqzKmdJSitPKWVJSitPKWVJSitPKWVJSiuPLPUrpTyllCUprTyllCUprTyllCVZcp5tzvlLMyZJXjhv32ZdH5S6Be/PoiiWPrhYCW2yySbFfvvtt8jy119/vUhSDB06dLHzJk2aVCQpLrvsskXuu+mmm4okxfjx44uiKIqjjz66aNWq1WIfp0uXLsVBBx1Ub75zzjmnSOLm5ubm5ubm5ubm5ubm5ubm5ubWiLfJkycvtTtcKY/wbNWq1UJHUC4we/bsuvvrm5ekQXNbtWqVOXPmLPZxZs+eXe86kuT000/PySefXPfz/PnzM23atKy99tqpqKiod96qaMaMGenSpUsmT568yCkKVuUspZZHlpUjTyllKbU8pZSl1PKUUpZSy1NKWUotjywrR55SylJqeUopS6nlKaUspZZHlpUjTyllKbU8pZSl1PKUUpZSzFMqiqLIzJkz07lz56WOXSkLz06dOuXtt99eZPnUqVOTpN4n3r59+1RXV9eNW9LcTp06Zd68eXn33XcX+lr7nDlz8sEHHyxx41ZXV6e6unqhZe3atVvyk1rFtW3btmTexKWUJSmtPLLUr5TylFKWpLTylFKWpLTylFKWpLTylFKWpLTyyFK/UspTSlmS0spTSlmS0spTSlmS0sojS/1KKU8pZUlKK08pZUlKK08pZUlKL08pWHPNNRs0bqW8aFGPHj3yyiuvLHJujaeeeqru/sWprKzMtttum6effnqR+5566qlssskmadOmzUKP8Z9jn3766cyfP7/edQAAAAAAK85KWXgedthhmTdvXoYPH163rLa2NiNGjEjv3r3TpUuXJMmkSZPy0ksvLTJ33LhxCxWZL7/8csaMGZMBAwbULdtzzz3Tvn37XHfddQvNv+6667L66qunf//+TfHUAAAAAIDPYaX8Snvv3r0zYMCAnH766Xn33Xez2Wab5eabb87EiRNz00031Y0bOHBgHn744YWu3nT88cfnhhtuSP/+/TN48OC0bNkyV111VTp27JhTTjmlblyrVq1ywQUX5Hvf+14GDBiQfffdN48++mh+9atf5aKLLkr79u2b9TmXq+rq6pxzzjmLnAJgVc+SlFYeWepXSnlKKUtSWnlKKUtSWnlKKUtSWnlKKUtSWnlkqV8p5SmlLElp5SmlLElp5SmlLElp5ZGlfqWUp5SyJKWVp5SyJKWVp5SyJKWXZ2VUURQNuZZ76Zk9e3bOOuus/OpXv8qHH36Y7bbbLhdccEH23XffujF9+/ZdpPBMkrfeeisnnXRS7rvvvsyfPz99+/bN0KFDs9lmmy2ynhtuuCFXXnllJkyYkC5duuT73/9+fvjDH7r4EAAAAACUoJW28AQAAAAA+E8r5Tk8AQAAAAAWR+EJAAAAAJQNhScAAAAAUDYUngAAAABA2VB4AgAAAABlo8WKDgAs7LPPPsunn36aVq1apWXLlis6TkmxbRZv1qxZmTJlSt226dy5c9ZYY40VHask2GcWzz5TP9tm8T777LO88sori2ybbt26eW/9Pz5vFs97qn62zeL5vFky+82i7DP1s21YpRXQTEaPHl1861vfKnbaaaeic+fOxVprrVV07ty52GmnnYpjjz22uO+++1Z0xIX88Y9/LI466qgmX89nn31W3HDDDcXee+9drLPOOkVlZWXdbZ111in69etXDB8+vJgzZ06TZ2moW2+9tdhjjz2afD22Tf2mTZtW/OQnPyk233zzhbbLgttmm21WnHHGGcX777/f5Fka6pprrik23njjJl3HyrjPFEXz7Dcr4z5TFM2z39g29XvttdeKr33ta0Xr1q3rtkdFRUXdn1u3bl189atfLV555ZUmzVGKVsbPG39H1a853k9FYdssic+b+tlvFs8+Uz/bZuneeuut4u677y6GDx9eXH311cXw4cOLu+++u3jrrbdWdLTFmjFjRvHmm2+u6BgrlYqiKIoVXbpS3mbNmpXDDz88f/7zn7PGGmukR48e6dSpU2pqajJ79uxMnTo1zz77bGbNmpV99903d955Z0n8lvKiiy7K2WefnXnz5jXZOt5///3ss88+efbZZ9OtW7f06tVrkW0zduzYvPLKK/nCF76Q++67Lx06dGiyPA1l29SvObbNhAkT0rdv30yZMiV77bXXYrfNU089lTFjxqRTp0556KGHsskmmzRZnoZq6m2zsu4zSdNvm5V1n0lsmyVp6m0zfvz49O3bN1VVVTn88MMXu22efPLJjBo1KvPnz8+DDz6Y7bffvkmyLKuLLroo55xzTubOndskj7+yft74O6p+tk39mmPbrKyfN039WZPYb+qzsu4zSdPvN7bNkj3++OM59dRT88QTTyRJ/rMSq6ioyBe/+MVcfvnl2WWXXZosx7Jqjs/icuMr7TS5M844I2PGjMnw4cMzcODAxR46/9lnn+WWW27JD37wg5xxxhm5+uqrV0DS5nfyySfnzTffzOjRo7PXXnvVO+6BBx7I4YcfnsGDB+fmm29uxoQrjm1Tv5NOOilJ8sILL2SLLbaod9zLL7+cffbZJyeffHLuuuuuJskyadKkBo/96KOPmiTDAvaZ+pXSPpOU1n5j29TvlFNOyYYbbpgHH3ww66yzzmLHHH300bn44ouzxx57ZPDgwXnggQeaNNOyaMrf6fu8qV8pvadK6f2U2DZLsjJ/3jT18UP2m8VbmfeZpGn3G9umfvfff3/233//bLTRRrnooovqLYNHjhyZPffcM/fcc0/69evXZHloWo7wpMl16tQp3/rWt3L++ecvdeyZZ56ZG2+8Mf/85z+bJMuy/LZz+vTp+eijj5r0Nyhrr712fvSjH+W0005b6thLLrkkV1xxRT744IMmyVJVVbXMc2yb+jXltllzzTVz/vnn54c//OFSx/70pz/NOeeck+nTpzdJlsrKylRUVDRobFEUqaioaLJtU0r7TFJa+00p7TNJae03tk39WrdunSFDhuS73/3uUsded911OfXUUzNz5swmyZIkjzzySIPH3nLLLRkxYsQq8XlTSp81SWm9p0rp/ZTYNktSSp83pfRZk9hv6lNK+0xSWvuNbVO/L37xi2nRokUeeOCBVFdX1ztuzpw52WOPPTJv3rw8+eSTTZIl+dfzbag//OEP+d3vfucIz2XgCE+a3IwZM7LBBhs0aGyXLl2a9MN20qRJWX/99bPddtstdexrr73W5L+ZnDNnTtq0adOgsW3atMmcOXOaLEtVVVU23XTTBv0G6+mnn87YsWObLEti2yxJZWVlg7/mMXfu3FRWVjZZlurq6my99db56le/utSxDzzwQP7yl780WZZS2meS0tpvSmmfSUprv7Ft6rf66qs3uKR7//3306pVqybLkiR9+/Zd5v/Rbiql9HlTSp81SWm9p0rp/ZTYNktSSp83pfRZk9hv6lNK+0xSWvuNbVO/559/Pj/72c+WWHYmyWqrrZZBgwY16BcNn8egQYNSUVHR4KNam/rzptwoPGly22+/fYYPH56vfe1rSzw356xZszJ8+PDssMMOTZZlq622Srt27fLHP/5xqWMXnCOjKe2yyy65+uqrc/DBB2f99devd9zbb7+dq6++OrvuumuTZdluu+1SWVmZn//850sde9FFFzX5/zDZNvXbe++9c8UVV2SPPfZY4vvlmWeeyRVXXJF99tmnybLssMMO+fjjj3PKKacsdezs2bOb9B++pbTPJKW135TSPpOU1n5j29Tv4IMPzuWXX57u3bvnkEMOqXfc//7v/+aKK67IEUcc0WRZkn8dsbLddtvVfb1zSX7729/m9ttvb7IspfR5U0qfNUlpvadK6f2U2DZLUkqfN6X0WZPYb+pTSvtMUlr7jW1Tv7XWWiuvvfZag8a+9tprWWuttZosy4I8PXr0yOWXX77UsTfddFOuv/76Js1TbhSeNLnLL788/fr1yxZbbJFvfOMb2XHHHdOpU6dUV1entrY2U6dOzdNPP51f/epXmTZtWu6///4my9KrV6/cdtttmTdv3nJ9Bayx/fSnP81uu+2WLbbYIgcccEB22mmnxW6bu+++O6uvvnquuuqqJsvSq1ev/PKXv0xtbe1Sf+OVNP35imyb+v30pz9N375907Nnz/Ts2bPebTNu3LhssskmGTp0aJNl6dWrV6655prMmjVrqRcbK4qiSbdNKe0zSWntN6W0zySlt9/YNos3ZMiQ/P3vf8+hhx6a9dZbLzvssMMi2+aZZ57JO++8k969e2fIkCFNliVJdtppp0yZMiWHHnroUse+9NJLTZqllD5vSumzJimt91QpvZ8S22ZJSunzppQ+axL7TX1KaZ9JSmu/sW3q9/Wvfz1Dhw5Nx44d861vfSutW7deZMzHH3+c4cOH56c//WlOPPHEJs3Tq1evvPTSS9lxxx2XOvbPf/5zk2YpS017EXj4l/Hjxxf77bdf0bJly6KioqKorKysu1VUVBQtW7Ys9ttvv+KZZ55p0hz33XdfMWjQoGLq1KlLHfv8888XI0eObNI8RVEUb731VvHd73636NixY1FRUbHIbd111y2++93vFpMnT27SHGPHji3OPffc4t13313q2DfffLN46KGHmjRPUdg2S/Lxxx8Xl112WdGrV6+ipqZmoe1SU1NT9OzZs7jsssuKmTNnNmmOl19+uRg5cmTx4YcfLnXs9OnTi4kTJzZpnlLZZ4qi9PabUtlniqL09hvbpn7z588vbr/99uLwww8vNt9886J169ZFVVVV0bp162LzzTcvBgwYUNx+++3FvHnzmjRHURTFj3/846KysrJB2+aCCy4oKioqmjRPqXzelNpnTVGUznuq1N5PRWHbLEmpfN6U2mdNUdhv6lMq+0xRlN5+Y9ssXm1tbXHkkUfWdRDdu3cv+vXrV/Tv37/o169f0b1797rO4vDDDy9qa2ubLEtRFMXZZ59dVFRUFO+8885Sx15zzTVF165dmzRPuXHRIprVzJkz87e//S1Tp07Np59+mlatWqVTp07ZZptt0rZt2xUdb4WbMmXKItumc+fOKzpWSbBt6lcURaZNm1a3bdq3b+/8LrHPLIl9pn62Ten65z//mZdffjk77bTTUo8sam4+b+rnPVU/26Y0lfJnTWK/KVWlvt+sSKW4bcaOHZtRo0bl2WefXeTv7x49euSwww5Lr169mjzHrFmz8v7776dz585p2bJlk69vVaPwBAAAAADKhnN40qzefvvtPPvss5kyZUrdb1E6d+6cHj16LPGk/+WepSFmzpyZDz/8MBtuuOGKjtKsWYqiyBNPPJHx48cv9rXaeeedm+232qWUpaEmT56cCRMmpE+fPis6SrNlKbXXqdTyLE0p7TNJ8+WZPn167rnnnnpfp/79+6ddu3ZNmqGU8yzNCy+8kGeeeSYDBw5c0VGaVSm9v0spS0OV0udNc2Yppfd3KWVpqFX186aUXqtSytIQq+o+88orr2TUqFH1vk6HHXZYtthiixUdE5pO83+LnlXRX//612KXXXapO2fnf94qKyuLnXfeuXjssceaNcvi8jRnlmVx4YUXFpWVlSs6RlEUzZfl9ttvLzbccMMl7jddunQpbrvttlUqy7JY1fabUnudSi1PQ5TSPlMUzZPn8ssvL9q0aVNUVFQUVVVVxbrrrltsuOGGxbrrrltUVVUVFRUVRevWrYtLL720SXOUap6GKLX95m9/+1tx8803N+k6Sun9XUpZlkUp7TfNlaWU3t+llGVZlNJ+0xyfNUVRWq9VKWVpqFLaZ4qi6febuXPnFscff3zd69GlS5eiV69eRZ8+fYpevXoVXbp0qXv9vvOd7xRz585tsiz/7uWXXy4uuuii4rDDDit23nnnYvvtty923nnn4rDDDisuuuii4qWXXmqWHA31yCOPFOedd96KjlGn1PKsDBSeNLnRo0cXLVu2LDbbbLPikksuKR544IHixRdfLN54443ixRdfLB544IHioosuKjbffPNitdVWK0aPHr1KZFlWpfQXdXNk+c1vflNUVFQUffr0KX7zm98Ur7/+evHJJ58U8+fPLz755JPi9ddfL379618Xu+22W1FZWVn85je/WSWyLKtVab8ptdep1PI0VCntM0XR9Hl+/vOfFxUVFcXXv/714oknnijmzJmz0P1z5swp/vrXvxZf//rXi8rKyuJnP/tZk2UpxTwNtartN6X0/i6lLMuqlPab5shSSu/vUsqyrOw39ptlVUr7TFE0fZ5zzjmnqKqqKs4888xiypQpix0zZcqU4swzzyyqqqqKc845p8myFEXpFrBLs6rtN+XIOTxpcl/84hfTokWLPPDAA6murq533Jw5c7LHHntk3rx5efLJJ8s+S5LccsstDR77hz/8Ib/73e8yb968ss+SJF/4whey0UYb5Q9/+MNSxx5wwAGZNGlSnn/++bLPkiTnn39+g8c+/PDDeeihh5rstSqlLKX2OpVSnlJ6nUotT7du3bLzzjtn5MiRSx37zW9+M0888UReeeWVJslSanmOPvroBo997rnn8uyzzzbpfrMsLrroopx99tmrxOdNKWVJSuv9XUpZktJ6f5dSlmTl/bxp6s+apLReq1LKsrLuM0nT7zcbbbRRDjnkkPz0pz9d6tgf/vCHueuuu/Lmm282SZYkOffcc3PhhRfm9NNPz/HHH59OnTotMmbq1KkZNmxYLrnkkpx55pk599xzmyxPQzXH+3tZlFqelYFzeNLknn/++fzsZz9bYsGYJKuttloGDRqUH/7wh6tEliQZNGhQKioq0tDfOzTlubdKKUvyr3POnHDCCQ0ae8ghh+T73//+KpEl+dc/GkrltSqlLKX2OpVSnlJ6nUotz+TJk7Pbbrs1aGyfPn1yxx13NFmWUsszcuTItGzZMqutttpSx3722WdNlmOBZf2f26ZUSu/vUsqSlNb7u5SyJKX1/i6lLElpfd6U0mdNUlqvVSllKaV9Jimt/ebdd9/Ntttu26Cx2267bW644YYmzTNixIh8//vfzwUXXFDvmE6dOuWCCy7IjBkzMmLEiCYrPPfcc88Gj23KEniBUstTbhSeNLm11lorr732WoPGvvbaa1lrrbVWiSwL8vTo0SOXX375UsfedNNNuf7661eJLMm//tJ7+umnc8wxxyx17Lhx4xb7m8JyzJIk6667bnbYYYfceuutSx175ZVX5rLLLlslspTa61RKeUrpdSq1PBtvvHHuu+++Br1Of/nLX7Lxxhs3WZZSy9OlS5dss802ueeee5Y69sILL8w555zTZFmS0vqf21J6f5dSlqS03t+llCUprfd3KWVJSuvzppQ+a5LSeq1KKUsp7TNJae03W2+9dW677bYcddRRqaysrHdcURS57bbbstVWWzVpnlIqYB966KG0b9++QX8Xzpo1q8lylGqesrOCvkrPKuTUU08tVlttteKqq64qZs6cudgxM2fOLK688spitdVWK0499dRVIktRFMV//dd/FV27dm3Q2KY+Z0cpZSmKorjsssuKioqK4oQTTij+8Y9/LHbMP/7xj+IHP/hBUVlZ2aQnRi+lLEVRFAceeGDRuXPnBo1t6teqlLKU2utUSnlK6XUqtTw33nhjUVFRURx44IHFn//85+K9995b6P733nuvuPfee4sDDzywqKysLG688cYmy1JqeQYMGFCss846DRrbHPvNhhtuWOy///4NGnvBBResMp83pZSlKErr/V1KWYqitN7fpZSlKErr86aUPmuKorReq1LKUkr7TFGU1n7z+9//vqisrCx22GGH4he/+EUxbty44q233iree++94q233irGjRtXXHfddcX2229fVFVVFb///e+bLEtRFMUOO+xQ9OvXr5g3b94Sx82fP7/Ya6+9ih122KHJsmy++ebFXnvt1aCxzfH+LrU85UbhSZOrra0tjjzyyKKioqJo2bJl0b1796Jfv35F//79i379+hXdu3cvWrZsWVRUVBSHH354UVtbu0pkKYqiOPvss4uKiorinXfeWerYa665psGF5MqepSj+9RfeaaedVqy22mpFZWVl0aZNm2KzzTYrunfvXmy22WZF69ati8rKymYppkspS1H86x9tFRUVxZtvvrnUsbfeemvRt2/fVSJLqb1OpZSnlF6nUszzi1/8olhnnXWKysrKorKysmjZsmWxxhprFC1btqy76vXaa69dDBs2rElzlFqeYcOGFV27di0mTZq01LF//OMfi0GDBjVpnlL6n9tSen+XUpaiKK33dyllWaBU3t+llqWUPm9K6bNmgVJ6rUolSyntM0VRevvNn/70p2LrrbcuKioq6l6rf79VVFQUW221VXH33Xc3aY6iKK0C9utf/3rRtm3bBo1tjtep1PKUGxctotmMHTs2o0aNyrPPPpupU6fm008/TatWrdKpU6f06NEjhx12WHr16rVKZZk1a1bef//9dO7cOS1btmzy9a0sWf7dlClTctddd9X7Wh100EFZf/31V7ks1K/UXqdSy8PizZ49Ow8++GDGjx+/2Ndpzz33TE1NzSqbpxRcd911ufzyy/PII4+kS5cuSxx7991357e//W1GjBjRpJlK6f1dSllYslJ6f5dSllJRip81SWm9VqWUpVSU6n7zj3/8o97Xaeutt27y9S9w7733ZvDgwfnHP/6x2PMlF0WRLbfcMkOGDEn//v2bLMcdd9yR6667Lr/61a+W+nfio48+mjFjxjTp6RBKLU+5UXgCAAAA0KRKpYBl1aDwBKBsffbZZ3X/mCqFI5dLKU8pZSnFPLCsSmkfLqUspZanlLLA8pg1a1amTJlStx937tw5a6yxhiwllAUoDfVfsgsa2f3335/jjjsuPXv2zPrrr5/27dtn/fXXT8+ePfOtb30ro0ePXiWzlFqeUsrSUHfffXeOPvroFR0jSWllSUorT3NkmTt3bm688cbss88+6dChQ2pqarLWWmulpqYmHTp0yN57750bbrihWa6qWmp5SilLKeZpiF/96lfZc889V3SMOqWUp5SyNJdS2odLKUup5SmlLMuilN5TpZQlKb08zeHDDz/MmWeemW7duqVt27bZcssts/3222fLLbdM27Zts/nmm+cnP/lJPvjgA1lWUJb/NGvWrLz66qt5/vnn8+qrr5bsFbavvfbabLLJJis6BjQJR3jS5GbNmpXDDz88f/7zn7PGGmukR48e6dSpU2pqajJ79uxMnTo1zz77bGbNmpV99903d955Z5P9Nq6UspRanlLKsqwuuuiinH322Zk3b96KjlJSWZLSytPUWd5///3ss88+efbZZ9OtW7f06tVrkX147NixeeWVV/KFL3wh9913Xzp06NAkWUotTyllKcU8DVVK76ektPKUUpbkX4XIL3/5y4wZM6ZJHr+U9uFSylJqeUopy7IqpfdUKWVJSitPU3/WJMmECRPSt2/fTJkyJXvttddi9+OnnnoqY8aMSadOnfLQQw81WYEly5J9+OGHufLKK3PHHXfk9ddfX+T+TTbZJIcffnhOPvnkrL322k2apaFK6f2U/KuAvfLKK/PGG2+s6CgllSUpvTwrhRV2uSRWGSeccEJRU1NT3HjjjcWcOXMWO2bOnDnFjTfeWLRq1ao44YQTVokspZanlLIsq1K6Yl0pZSmK0srT1Fm+8Y1vFO3bty/uv//+JY67//77i/bt2xcDBw5ssiyllqeUspRinoYqpfdTUZRWnlLKUhSr1udNKWUptTyllGVZldJ7qpSyFEVp5WmOLAcddFCx4YYbFi+99NISx7300kvFhhtuWBx00EGyNHOWoiiKN954o9hwww2LFi1aFPvuu29x1llnFcOGDSt++ctfFsOGDSvOOuusYp999ilatGhRdOnSpXj99debLMubb77Z4NvgwYNL5v1UFKve+3tZlFqelYHCkya33nrrFWeddVaDxv7kJz8pOnbsuEpkKbU8pZSlKIpi4403bvCtffv2TfrhX0pZSi1PKWVp3759cckllzRo7MUXX1y0b9++ybKUWp5SylJqeSorK5f51pRKKU8pZVlWTf0/BaW0D5dSllLLU0pZiqK03lOllKUU8zRUcxQQbdu2LX760582aOzQoUOLtm3bytLMWYqitArYioqKBr+PFoxtSqVUwJZSllLMU25arOgjTCl/M2bMyAYbbNCgsV26dMnMmTNXiSyllqeUsiTJpEmTsv7662e77bZb6tjXXnstH3300SqRpdTylFKWOXPmpE2bNg0a26ZNm8yZM6fJspRanlLKUmp5qqqqsummm6Zfv35LHfv0009n7NixTZal1PKUUpYFeUpFKe3DpZSl1PKUUpaktN5TpZSl1PKU0mdNklRWVmbu3LkNGjt37txUVjbdZTpkqd+DDz6Y888/P1tsscUSx22xxRY56aSTcs455zRZlurq6my99db56le/utSxDzzwQP7yl780WZYk6dq1ayoqKho0tiiKBo9d2bOUYp5y4xyeNLldd901s2fPzsMPP7zEcz7OmjUrffr0yeqrr55HH3207LOUWp5SypIk2267bdq1a9egdTT1uWdKKUup5SmlLP/1/7V371FR1/kfx18zwMB4S3C9DKQgkeaiRSpg4AVdKFHXtk1Zsi0pL2mlXVQsM7OQTNdbXs7ZSMm0WjHXLdPqKKWUiEdIELQ2w1ALB/OCAqNAwOf3hz/ZDIaLOsx7vvN6nMM51Xxm5snnfBnj7XfmO3w4fvzxR+zevRs+Pj5W1xUWFiIiIgIBAQH47LPPbNIirUdSi7Se/v37Q6/XN+mX55b4+ZbUI6kFAAwGQ7MHIs7weiOpRVqPpBZA1s+UpBZpPZJeawAgJiYGX3/9NXbs2IG+fftaXXfw4EGMGDECQ4YMQUpKCltasAUAPD09MXfuXMyYMaPRtUuWLEFiYiKKi4tt0hIeHo6ysjIcOnSo0bUt8fNtNBqbPYC1VY+kFok9WsMzPMnmFi9ejMjISPTs2ROPPPII+vXrB5PJBHd3d1RUVMBsNiMrKwvvvfcezp8/j9TUVKdokdYjqQUAQkJCsGnTJlRXV9v9b9oltUjrkdSyYsUKDBo0CD179sSoUaPQv3//eo/h7du3o1WrVli2bJnT9EhqkdYTEhKC5ORkVFRUwN3dvdH1tv57Ykk9kloA4M4774Rer8eqVasaXZuYmGjTM8AkHcOSWqT1SGoBZP1MSWqR1iPptQa4chxHREQgODgYwcHBVo/jzMxM+Pv7Y/ny5Wxp4RYAiIqKwpIlSzB06NBGB7BLlizBvffea7OWkJAQrF69GhaLpdGLzKorH3NosxYA6Nu3L8rKypo0DC4vL7fpGaeSWiT2aI5d3khPTic7O1tFR0crNze3Op8potPplJubm4qOjlYHDx50qhZpPZJadu7cqeLi4pTZbG50bW5urlq/fr1TtEjrkdSilFI///yzmjp1qurcubPS6XR1vjp16qSmTp2qfvrpJ5t2SOyR1CKp58CBA2r+/Pnql19+aXTtiRMn1J49e5ymR1KLUkpNnTpVubu7q/Ly8kbXLliwQOl0Opv2SDmGpbVI65HUIulnSlKLtB5przVKKVVWVqYWLVqkQkJClIeHxzXHsIeHhwoODlaLFi1SpaWlbLFTS2Fhobr99tuVXq9XoaGh6qmnnlILFixQ//jHP9SCBQvUU089pUJDQ5Ver1cBAQGqsLDQZi3ff/+9Wr9+vSouLm507cWLF9Xx48dt1qKUUs8++6xydXVVZWVlja5NSEiw6c+UpBaJPVrDt7RTiyotLUVeXh7MZjMuX74Mo9EIk8mE3r17o127dk7bIq1HUgvR9Tp16lSdY9jb25s9wlok9pBMmZmZ+PTTT/Hkk0+iY8eODa49efIkCgoKMGTIkBZpk3QMS2qR1iOpheSS/FoDXDkj7/z587XHsZeXl90+148t17JYLFizZg3+/e9/Izc3FxUVFbW3ubu7o0+fPhgzZgyefPJJtGnTpkXb7Ono0aPIyMjA/fffj/bt2ze4tqSkBMXFxfD19dV8i8QereHAk4iIiIiIiIjoJpEwgCVydhx4UosqLCxETk4OTp06Vfvi7+3tjaCgoAY/TF7rLdJ6JLVI65HUIq1HUktjSktLUVxcjG7dutk7BYCsHkktgKweSS2ArB5JLS1JKYWMjAxkZ2fX+9oXFhbWYr/gSmqR1iOpRVqPpBaJPY7mp59+QkFBAQYPHmzvFLY4CO4NaVqLv4menFJ6eroKDw+v/SzI33/p9XoVFham9u7d61Qt0noktfy+p74me+2NvVuk9UhqaaoFCxYovV5v74xaknoktSglq0dSi1KyeiS1KKVUSUmJOnHihE2fIyUlRXXr1q3BPzO7du2qNm3aZNMOaS3SeiS1SOuR1CKxp6amRqWnp6vVq1erOXPmqOeee07NmTNHrV69Wu3du1fV1NS0SEdzSHotZot1J0+eVGlpafbOUEpxbxoiqUUpeT2OQG/vgStpX2pqKiIiInD69GkkJiYiNTUVR44cwbFjx3DkyBGkpqYiISEBZ86cwbBhw2x69W9JLdJ6JLXU17Nr1y4xe2PPFmk9klqIiK5auXIlunfvbrPH37RpE2JjY+Hn54f3338f+fn5sFgsqK6uhsViQX5+PjZu3Ag/Pz+MGzcOmzZtcooWaT2SWqT1SGqR2LN582b4+flh0KBBmDZtGhYuXIgVK1Zg4cKFmDZtGgYPHgxfX1+kpKTYtIO0acOGDRg6dKi9M0SStDeSWgB5PY6Ab2knmxswYABcXV3xxRdfwN3d3eq6yspKDB06FNXV1di/f7/mW6T1SGqR1iOpRVqPpJYNGzY0ee22bdvwn//8B9XV1TZpkdYjqUVaj6QWaT2SWporMTER8+bNs1nPXXfdBV9fX2zbtq3RtaNGjcLJkyeRm5ur+RZpPZJapPVIapHWs2nTJowbNw6DBg3C1KlTERISApPJBA8PD5SXl8NsNmP//v345z//ifT0dLz//vuIjY21SQsAvPbaa01em5aWhj179tjstY8tN4et/4zi3mivBZDX4whc7R1A2pebm4uVK1c2OAwBAIPBgLi4ODzzzDNO0SKtR1KLtB5JLdJ6JLXExcVBp9OhqX+PZ+vPAJPUI6lFWo+kFmk9klqA5g1gs7OzbVhy5aqq06dPb9LaBx54AE8//bRTtEjrkdQirUdSi7SehQsXYtSoUfUOX41GI/z9/eHv749x48Zh1KhReP3112068Jw/f76Y12K2WNfcIaMtcW+sk9QCyOvRGg48yeY8PT2Rn5/fpLX5+fnw9PR0ihZpPZJapPVIapHWI60lKCgIixcvbnTtunXr8NZbb9msRVqPpBZpPZJapPVIagFkDWBNJhOysrIwYcKERtdmZmbCZDI5RYu0Hkkt0noktUjrkTR8BYBOnTqhb9++2LhxY6Nrly5dikWLFrGlhVsAWUNG7o1jtEjs0ZyW/tBQcj7x8fHKYDCoZcuWqdLS0nrXlJaWqqVLlyqDwaDi4+OdokVaj6QWaT2SWqT1SGoZPny48vPza9LalviAdkk9klqk9UhqkdYjqUUppby8vNSwYcNUVlZWo19Tp061ac+iRYuUTqdT06dPV9999129a7777js1bdo0pdfr1RtvvOEULdJ6JLVI65HUIq2ne/fuasqUKU1a+8QTT6ju3bvbrEUppUaPHq28vb2btNbWr8Vssa5z584qOjpanT17ttGvF198kXtjp72R1CKxR2s48CSbq6ioULGxsUqn0yk3NzcVGBioIiMj1ciRI1VkZKQKDAxUbm5uSqfTqZiYGFVRUeEULdJ6JLVI65HUIq1HUsu8efOUTqdTp0+fbnTt6tWrmzzI0UKPpBZpPZJapPVIalFK1gC2pqZGvfDCC8pgMCi9Xq/atm2rAgICVGBgoAoICFBt2rRRer2+Rf4STFKLtB5JLdJ6JLVI65E0fFXqyuuZTqdTJ06caHTtxo0bVUREBFtauEUpWUNG7o1jtEjs0RpetIhazIEDB7Blyxbk5OTAbDbj8uXLMBqNMJlMCAoKwpgxYxASEuJ0LdJ6JLVI65HUIq1HQovFYsHZs2fh7e0NNzc3mz6Xo/VIapHWI6lFWo+kFgB45ZVXkJCQgKKiInTq1KnBtWvWrMGSJUtQUFBg06ZTp07ho48+svrad//998PHx8emDRJbpPVIapHWI6lFSo9SCnPmzMGyZctQVVWF1q1bo3PnznB3d0dFRQWKiopw6dIluLq64tlnn7X524HJMSQmJuLll1/G8ePH0a1btwbXvvfee1i3bh12797dQnX2JWlvJLVI7NEaDjyJiIiISDxpA1gi0jYJw1ciIrp+HHgSERERERERERGRZujtHUDOIzU1FZMnT0ZwcDB8fHzg5eUFHx8fBAcHY9KkSdi1a5dTtkjrkdQirUdSi7QetjhGj6QWaT2SWqT1SGpxRNu3b8fjjz9u7wwAsloAWT2SWgBZPZJaAHk9kkjaG7Y4Bu4NaRnP8CSbs1gsiImJweeff47WrVsjKCgIJpMJHh4eKC8vh9lsRk5ODiwWC+677z58+OGHaN26teZbpPVIapHWI6lFWg9bHKNHUou0Hkkt0noktfxWamoqNm/ejOzsbJw6dar2babe3t4ICgpCTEwMoqKibN7RVImJiZg3bx6qq6vtnSKqBZDVI6kFkNUjqQWQ1bN9+3Zs3boVycnJ9k4BIGtv2GKdpOOGe+MYLYC8Hodgt8slkdOYPn268vDwUGvXrlWVlZX1rqmsrFRr165VRqNRTZ8+3SlapPVIapHWI6lFWg9bHKNHUou0Hkkt0noktSilVFlZmRoxYkTtFZwHDRqkYmJi1KOPPqpiYmLUoEGDVNu2bZVer1fR0dGqrKzMpj1NJemqqpJalJLVI6lFKVk9klqUktUjqUUpWT1ssU5Sj6QWpWT1SGpRSl6PI+AZnmRzJpMJkyZNwmuvvdbo2rlz52Lt2rUoKirSfIu0Hkkt0noktUjrYYtj9EhqkdYjqUVaj6QWAHjmmWeQlJSE1atX49FHH633wkW//vorNmzYgGnTpmHSpEl48803bdLi7+/f5LUXL17EhQsXbHb2jKQWaT2SWqT1SGqR2NNULXF2nKS9YcvNYevjhnujvRZAXo8jcLV3AGlfSUkJbr311iat7dq1K0pLS52iRVqPpBZpPZJapPWwxTF6JLVI65HUIq1HUgsAbN68GbNmzcKECROsrnFzc8OECRNQUFCAtWvX2mzgefLkSfj4+ODOO+9sdG1+fj4uXLhgkw5pLdJ6JLVI65HUIq2nucMiW5O0N2yxTtJxw72xTlILIK9Ha3iGJ9ncwIEDUV5ejrS0tAY/28tisWDw4MFo1aoVvv76a823SOuR1CKtR1KLtB62OEaPpBZpPZJapPVIagGA1q1bY/ny5Zg8eXKja9966y08//zzsFgsNmnp06cP2rdv36Tv19ZnZEhqkdYjqUVaj6QWaT2urq7NGhYdPXrUafaGLdZJOm64N47RIrFHa3iGJ9nc4sWLERkZiZ49e+KRRx5Bv379YDKZ4O7ujoqKCpjNZmRlZeG9997D+fPnkZqa6hQt0noktUjrkdQirYctjtEjqUVaj6QWaT2SWgDg7rvvRlJSEh5++OFGB7BJSUno27evzVpCQkKwadMmVFdXw8XFxWbP42gt0noktUjrkdQiradXr15o3749Pvnkk0bXXh0W2ZKkvWGLdZKOG+6NY7RI7NEc+36EKDmL7OxsFR0drdzc3JROp1N6vb72S6fTKTc3NxUdHa0OHjzoVC3SeiS1SOuR1CKthy2O0SOpRVqPpBZpPZJa0tPTldFoVD4+PuqFF15QH374odq7d6/KzMxUe/fuVR9++KGaPXu28vHxUUajUaWnp9usZefOnSouLk6ZzeZG1+bm5qr169c7RYu0Hkkt0noktUjrefzxx1WrVq1UVVVVo2tb4iIikvaGLdZJOm64N47RIrFHa/iWdmpRpaWlyMvLg9lsxuXLl2E0GmEymdC7d2+0a9fOaVuk9UhqkdYjqUVaD1sco0dSi7QeSS3SeqS05OTkYM6cOUhNTUVVVRV0Ol3tbUopuLq6IjIyEomJibj77rtbrIuItGPXrl344IMPsHDhQnTp0qXBtXl5eTh48CDGjx/fQnUkFY8b6yTtjaQWiT1aw7e0U4sqKSlBcXExzp8/X/sLk4eHB0pLS1v8lzdJLdJ6JLVI65HUIq2HLY7RI6lFWo+kFmk9UlqCgoLw6aefihnAAkBhYSFycnJw6tSp2hZvb28EBQXBx8fHaVuk9UhqkdYjqUVCT1RUFKKiopq0tk+fPujTp4+Ni/7H3nvDFuukHjfcG7ktEns0x85nmJKTSE9PV+Hh4de8De63X3q9XoWFham9e/c6VYu0Hkkt0noktUjrYYtj9EhqkdYjqUVaj6SW3/r555/V9u3bVVJSknrzzTdVUlKS2r59u/r5559brEHS3khqkdYjqUVaj6QWiT1KyXitUUrW3rClcRKOG+6NY7VI7NECvqWdbC41NRUjRoyAr68vJkyYgJCQEJhMJnh4eKC8vBxmsxn79+/H+vXrceLECezYsQORkZGab5HWI6lFWo+kFmk9bHGMHkkt0noktUjrkdRy1b59+xAfH4+MjAwAV97G/ls6nQ4DBgzA4sWLER4ebrMOSXsjqUVaj6QWaT2SWiT2SHmtAWTtDVsaJuW44d44TovEHi3hwJNsbsCAAXB1dcUXX3wBd3d3q+sqKysxdOhQVFdXY//+/ZpvkdYjqUVaj6QWaT1scYweSS3SeiS1SOuR1ALI+gVO0t5IapHWI6lFWo+kFmk9kl5rAFl7wxbrJB033BvHaJHYozn2OrWUnIfRaFRvv/12k9YmJSUpo9HoFC3SeiS1SOuR1CKthy2O0SOpRVqPpBZpPZJalFIqNDRUhYeHq/Ly8gbXVVRUqLCwMBUaGmqzFkl7I6lFWo+kFmk9klqk9Uh6rVFK1t6wxTpJxw33xjFaJPZojd7eA1fSPk9PT+Tn5zdpbX5+Pjw9PZ2iRVqPpBZpPZJapPWwxTF6JLVI65HUIq1HUgsA5ObmIi4ursGzVQDAYDAgLi4Oubm5NmuRtDeSWqT1SGqR1iOpRVqPpNcaQNbesMU6SccN98YxWiT2aI69J66kffHx8cpgMKhly5ap0tLSeteUlpaqpUuXKoPBoOLj452iRVqPpBZpPZJapPWwxTF6JLVI65HUIq1HUotSSnl7e6vZs2c3aW18fLzy9va2WYukvZHUIq1HUou0Hkkt0nokvdZcfQ4pe8MW6yQdN9wbx2iR2KM1HHiSzVVUVKjY2Fil0+mUm5ubCgwMVJGRkWrkyJEqMjJSBQYGKjc3N6XT6VRMTIyqqKhwihZpPZJapPVIapHWwxbH6JHUIq1HUou0HkktSsn6BU7S3khqkdYjqUVaj6QWaT2SXmuUkrU3bLFO0nHDvXGMFok9WsOLFlGLOXDgALZs2YKcnByYzWZcvnwZRqMRJpMJQUFBGDNmDEJCQpyuRVqPpBZpPZJapPWwxTF6JLVI65HUIq1HSktlZSXGjx+PlJQUuLq6okePHjCZTHB3d0dFRQXMZjOOHj2KqqoqjB07Fhs3boTBYLBpk5S9kdYirUdSi7QeSS1SeiS+1gAy9oYt1kk8brg3slsk9mgNB55ERERE5DCk/AJHRNrG1xq6HjxurJO0N5JaJPZoBQeeREREREREREREpBm8SjsRERERERERERFpBgeeREREREREREREpBkceBIREREREREREZFmcOBJRERERGRHOp0OOp0O8+fPt3cKERERkSa42juAiIiIiLTDYrFg48aN2LZtGw4dOoRz585BKYV27drBz88Pffr0wT333IPhw4eja9eu9s4lIiIiIg3iwJOIiIiIboqMjAzExsbi5MmTdW47e/Yszp49i6ysLLzzzjvo3LkzioqK7FBJRERERFrHgScRERER3bCjR4/ivvvuQ2lpKQBg9OjRGDNmDHr06AGDwYCzZ8/i0KFD2LVrF3bv3m3nWiIiIiLSMg48iYiIiOiGvfTSS7XDznfeeQdxcXF11kRFRWHmzJk4c+YMNm/e3MKFREREROQseNEiIiIiIroh1dXV2LFjBwCgf//+9Q47f6tjx4546qmnWqCMiIiIiJwRB55EREREdEPOnDmDy5cvAwACAgKu+3EqKyvxySef4Omnn0ZwcDA8PT3h5uaGDh06IDQ0FPPnz8fZs2cbfAw/Pz/odLraoevBgwfx8MMPo2vXrjAajQgICMDzzz9f53H27duHsWPHolu3bvDw8MBtt92G2bNn1561Wp+IiAjodDpEREQAAL7//ntMnjwZ3bt3h4eHB0wmE2JiYrB///7r3pPfOnjwIKZMmYKePXuiTZs2aN26NXr27ImpU6fi6NGjN+U5iIiIiLRAp5RS9o4gIiIiIsd1/vx5dOjQAQBw1113IScn57oeJy4uDu+++26Dazp06ICPP/4Y4eHh9d7u5+eHEydOYPz48fjTn/6EiRMnorKyss66Hj16IC0tDV26dMGSJUsQHx+P+v63uG/fvkhLS0ObNm3q3BYREYG0tDQMGTIEs2fPxtixY2GxWOqs0+v1WLp0KZ599tl6m3U6HQDglVdewfz58+vcXlNTg5kzZ2LFihX1NgKAq6sr1qxZg8mTJ9d7OxEREZEz4RmeRERERHRDvLy84OvrCwA4dOgQFi1ahJqammY/TlVVFfz9/TFjxgykpKQgIyMDmZmZ2LJlC6ZMmQKDwYBz587hgQcewC+//NLgYx06dAgTJ05EQEAAkpOTkZmZiS+//BJ///vfAVy5yNLMmTOxdetWzJo1C6GhoXj//feRlZWFzz//HCNGjABw5azKBQsWNPhcp06dwrhx4+Dq6orXX38d+/btw759+5CYmIh27dqhpqYGzz33HD766KNm7wkATJs2DcuXL4dSCoMHD0ZycjL27NmDAwcO4O2330ZgYCCqqqrwxBNPYNu2bdf1HERERERawjM8iYiIiOiGLV26FDNnzqz9dz8/P4wePRphYWEICQlB9+7dG32MY8eOwd/fv/aMx9/Ly8tDWFgYysrKMHfuXCQkJNRZc/UMTwAICwvDrl270KpVq2vWjB07Flu2bIGLiwtuueUWDB06FCkpKXBxcaldU11djYEDB2L//v3o0KEDioqK4Op67fU+r57hCQC33HILMjIy0KtXr2vWHDlyBGFhYSgpKYGPjw8KCgrg5uZ2zZqGzvDctWsX7r33XgDA2rVrMWHChDrfc3l5OUaOHIkvv/wSvr6+yM/Pr9NKRERE5Ex4hicRERER3bDnnnsOjz/+eO2/Hz9+HCtXrkRsbCz8/f3RpUsXxMbG4pNPPrH6tuzbbrvN6rATAPr06YOJEycCQKNnS+p0Oqxdu7bOsBMAnnzySQBXhprl5eVISkq6ZtgJAC4uLrVvDz937hy+/fbbBp/v5ZdfrjPsBIDAwEC89NJLAIDCwkJ8/PHHDT7O773xxhsAgAcffLDeYScAeHh4YPXq1QCAEydOYPfu3c16DiIiIiKt4cCTiIiIiG6YXq/HunXrsHPnTgwfPrzOGYanT59GSkoKRo8ejZCQEBw7dqzRxywuLsaxY8dw5MgRHD58GIcPH0b79u0BAN9++y1+/fVXq/e988476x1AAlc+Z/SqqKgoeHl5Nbruxx9/tPpcOp0O48ePt3r7Y489VjvITU1Ntbru90pKSrBnzx4AwJgxYxpc26tXL/zhD38AAGRkZDT5OYiIiIi0iO91ISIiIqKbJioqClFRUSgpKUF6ejoyMzORlZWFr776ChcvXgQAZGVlYdCgQfjmm29gMpmuuX9eXh6WL1+Ozz77DEVFRVafp6amBsXFxejUqVO9t/fo0cPqfa8OTZuzrqGrtXfv3r122Fifjh07ws/PDwUFBcjLy7O67veys7NrPwv1oYcewkMPPdSk+zW0b0RERETOgGd4EhEREdFN165dO0RHR2PevHnYtm0bTp8+jeTkZHh6egIAzGYzXn755Wvus27dOvTt2xfvvPNOk4Z2ly9ftnpbfW9lv0qv1zd7XXV1tdV11oauv9W5c2cAV65o31SNXZjJmkuXLl3X/YiIiIi0gmd4EhEREZHNubu747HHHoO3tzeGDx8OANi6dSuSkpKg1+vx3//+F1OmTEFVVRU6deqEWbNmYdiwYfDz80Pbtm1rL/STnJxc+1mWUq692dDnjt6I3w5Z33rrLYSFhTXpfleHykRERETOigNPIiIiImox9913H7p27YqffvoJxcXFOHfuHDp27Ij169ejqqoKLi4uSEtLwx133FHv/ZtzhmRLOX36dJPXWPu80Pp06NCh9p9btWqF3r17Nz+OiIiIyAnxLe1ERERE1KK8vb1r//nq2ZFHjhwBcOVCQdaGncCVz/+UpqCgAOfOnbN6+5kzZ3D8+HEAaNbQMigoqHZ/0tPTb6iRiIiIyJlw4ElERERELebSpUv49ttvAVz5nM+rZzFWVVUBACwWi9X7ms1mbNu2zfaRzaSUwoYNG6zevn79+tq330dGRjb5cTt27IgBAwYAAD744AOcOXPmxkKJiIiInAQHnkRERER0Q8rKyhAaGort27fXXlW8PjU1NZg2bVrtFc9Hjx5dewbj7bffDgD44YcfsG/fvjr3vXTpEsaNG9fghYrsKSEhAd9//32d//7dd98hMTERAGAymXD//fc363Hnzp0LACgpKcGYMWNw4cIFq2srKiqwZs0alJeXN+s5iIiIiLSGn+FJRERERDfswIED+POf/wwfHx/85S9/wT333ANfX1+0bdsWFy5cQHZ2NpKTk5GXlwcAuOWWW5CQkFB7/0ceeQSrVq1CTU0NRo4ciVmzZmHgwIHw8PDAN998g+XLl+OHH35AeHi4uLd3BwQE4MyZMxgwYABmz56NiIgIAMCePXvwxhtv4OLFiwCAVatWwWAwNOuxR4wYgWeeeQZvvvkmvvrqK/Tq1QtTpkzBwIED0aFDB1gsFuTn5+Prr7/G1q1bUVxcjPHjx9/sb5GIiIjIoXDgSUREREQ3xNXVFV26dEFRUREKCwuxZs0arFmzxur622+/Hf/617/g5+dX+9+Cg4Px6quv4pVXXsGFCxfw0ksv1bnfjBkz0Lt3b3EDTx8fH6xYsQIxMTF48cUX69yu1+uxePFiPPjgg9f1+MuXL4eXlxcSEhJQVFSE+fPnW13bunVruLi4XNfzEBEREWkF39JORERERDfEw8MDhYWFSE9Px6uvvoro6Gj4+/vXDt/atWuHO+64A3/729/wwQcf4PDhw+jXr1+dx5k3bx527NiBe++9F56enjAYDLj11lvx17/+FTt37sSSJUvs8N01zciRI5GVlYXHHnsMvr6+MBgM6NSpEx588EHs3bsXM2bMuO7H1ul0mDdvHo4ePYr4+Hj0798fXl5ecHFxQdu2bfHHP/4RDz/8MN59912YzWYYjcab+J0REREROR6duvoJ6kRERERE1GQRERFIS0vDkCFDsGfPHnvnEBEREdH/4xmeREREREREREREpBkceBIREREREREREZFmcOBJREREREREREREmsGBJxEREREREREREWkGB55ERERERERERESkGbxKOxEREREREREREWkGz/AkIiIiIiIiIiIizeDAk4iIiIiIiIiIiDSDA08iIiIiIiIiIiLSDA48iYiIiIiIiIiISDM48CQiIiIiIiIiIiLN4MCTiIiIiIiIiIiINIMDTyIiIiIiIiIiItIMDjyJiIiIiIiIiIhIMzjwJCIiIiIiIiIiIs34P3dEOCNf3DaaAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "%matplotlib inline\n", + "\n", + "\n", + "def gaussian(n_qubits, mu, sigma=1):\n", + " x = np.arange(2**n_qubits)\n", + " gaussian = 1.0 / np.sqrt(2 * np.pi * sigma**2) * np.exp(-((x - mu) ** 2) / (2 * sigma**2))\n", + " return gaussian / sum(gaussian)\n", + "\n", + "\n", + "data = gaussian(n_qubits, mu=4, sigma=5) + gaussian(n_qubits, mu=20, sigma=2)\n", + "data = data / sum(data)\n", + "\n", + "\n", + "labels = [\"{0:{fill}6b}\".format(i, fill=\"0\") for i in range(len(data))]\n", + "\n", + "plt.bar(range(2**n_qubits), data)\n", + "plt.xticks([i for i in range(len(data))], labels, rotation=\"vertical\", size=12)\n", + "plt.yticks(size=12)\n", + "\n", + "plt.xlabel(\"Sample\", size=20)\n", + "plt.ylabel(\"Probability\", size=20)\n", + "plt.title(\"Two-peak Gaussian distribution\")\n", + "\n", + "fig = plt.gcf()\n", + "fig.set_size_inches(16, 8)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "np.save(\"data.npy\", data) # save data to file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training \n", + "\n", + "\n", + "Next, we train the circuit using the [limited-memory BFGS](https://en.wikipedia.org/wiki/Limited-memory_BFGS) optimizer from scipy. \n", + "Instead of using finite-difference gradients, we use the exact MMD loss function gradient. \n", + "\n", + "The training function has three arguments that act as hyperparameters: number of qubits `n_qubits`, number of layers in the QCBM `n_layers`, and the number of iterations in the optimization algorithm. \n", + "The number of layers determines how many rotation angles are in the quantum circuit. \n", + "For the QCBM, we need `n_params = 3 * n_layers * n_qubits` parameters. \n", + "\n", + "\n", + "Since we will eventually run this training function as an hybrid job, we add three convenience functions. \n", + "Firstly, we use `log_metric` to print the loss function for each iteration. \n", + "Once we run this as a hybrid job, the metrics will be displayed in near-real time on the Braket console, or via [Amazon CloudWatch](https://aws.amazon.com/cloudwatch/). \n", + "\n", + "Secondly, we write our results to a file prefixed with the path `get_results_dir()`. \n", + "This is necessary because hybrid jobs use a temporary filesystem.\n", + "Once the instance is terminated, all files on the instance are deleted.\n", + "\n", + "Lastly, the return statement of the function will be our hybrid job results returned by `job.result()`. \n", + "These can be a single object or a dictionary with string keys. \n", + "Note that while most native Python objects are supported, custom classes that do have have defined serialization methods may not work. \n", + "For example, we can return the numpy array of final parameters, but we cannot return the QCBM object itself. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from braket.jobs import get_results_dir\n", + "from braket.jobs.metrics import log_metric\n", + "from braket.tracking import Tracker\n", + "from qcbm.qcbm import mmd_loss\n", + "from scipy.optimize import minimize\n", + "\n", + "\n", + "def train_circuit(n_qubits, n_layers, n_iterations=10):\n", + " global iteration_number\n", + " iteration_number = 0\n", + "\n", + " braket_task_costs = Tracker().start()\n", + "\n", + " device = LocalSimulator()\n", + "\n", + " data = np.load(\"data.npy\") # load the input data\n", + "\n", + " qcbm = QCBM(device, n_qubits, n_layers, data)\n", + "\n", + " init_params = np.random.rand(3 * n_layers * n_qubits)\n", + "\n", + " def callback(x):\n", + " global iteration_number\n", + " iteration_number += 1\n", + " loss = mmd_loss(qcbm.probabilities(x), data)\n", + "\n", + " log_metric( # log the metrics to Braket console\n", + " metric_name=\"loss\",\n", + " value=loss,\n", + " iteration_number=iteration_number,\n", + " )\n", + "\n", + " res = minimize(\n", + " lambda x: mmd_loss(qcbm.probabilities(x), data),\n", + " x0=init_params,\n", + " method=\"L-BFGS-B\",\n", + " jac=lambda x: qcbm.gradient(x),\n", + " options={\"maxiter\": n_iterations},\n", + " callback=callback,\n", + " )\n", + " final_params = res.x\n", + "\n", + " # save final parameters\n", + " np.save(get_results_dir() + \"/final_params.npy\", final_params)\n", + "\n", + " return {\n", + " \"params\": final_params,\n", + " \"task summary\": braket_task_costs.quantum_tasks_statistics(),\n", + " \"estimated cost\": float(\n", + " braket_task_costs.qpu_tasks_cost() + braket_task_costs.simulator_tasks_cost()\n", + " ),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's run the function to verify that it works as expected." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Metrics - timestamp=1697028322.742037; loss=0.0738105584324629; iteration_number=1;\n", + "Metrics - timestamp=1697028324.6127226; loss=0.05514942674966222; iteration_number=2;\n", + "Metrics - timestamp=1697028328.4326718; loss=0.029347326432547782; iteration_number=3;\n", + "Metrics - timestamp=1697028330.5567336; loss=0.02751726243188396; iteration_number=4;\n", + "Metrics - timestamp=1697028332.750267; loss=0.019362259334110732; iteration_number=5;\n", + "CPU times: user 3.87 s, sys: 515 ms, total: 4.39 s\n", + "Wall time: 13.9 s\n" + ] }, - "nbformat": 4, - "nbformat_minor": 4 + { + "data": { + "text/plain": [ + "{'params': array([ 0.55196856, 0.38672791, 1.35300743, 0.87330452, -0.54221993,\n", + " 0.74465571, 0.61411241, 0.06143268, 0.81830411, 0.28084566,\n", + " 0.98187881, 0.17193621, 0.04322529, 0.02989581, -0.08170342,\n", + " 0.58622265, 0.50212177, 0.32964731, 1.19606685, -0.28772351,\n", + " 0.60482847, -0.04202664, 0.33609895, 0.12554622, 0.46006129,\n", + " 0.44578169, 1.22449628, 1.1830697 , 0.19600585, 0.35640496]),\n", + " 'task summary': {},\n", + " 'estimated cost': 0.0}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "\n", + "train_circuit(n_qubits, n_layers=n_layers, n_iterations=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! Now for longer algorithms, or those that require priority queueing to a QPU, we can run this function on AWS by adding the `@hybrid_job` annotation and calling the function.\n", + "\n", + "## Training with hybrid jobs\n", + "\n", + "Amazon Braket Hybrid Jobs provides fully managed execution of hybrid quantum-classical algorithms, combining AWS classical compute resources based on Amazon EC2 (the \"job instance\") with Amazon Braket quantum processing units (QPUs) or quantum circuit simulators. \n", + "\n", + "There are three arguments to the `@hybrid_job` decorator that we will use in this example. Firstly, \n", + "since we do not require priority QPU access, we set `device=None` in the decorator arguments. \n", + "This argument is responsible for scheduling the hybrid job to run on a QPU. \n", + "In this example, we use the Braket local simulator running on the job instance. \n", + "If we find this simulator too slow, we could either increase the the classical compute by selecting a larger instance, or switch to using on-demand Braket simulators like [SV1](https://docs.aws.amazon.com/braket/latest/developerguide/braket-devices.html#braket-simulator-sv1). \n", + "\n", + "Next, we include the source code for the QCBM with `include_modules`. This can be a Python module, directory, or file. We may also specify multiple modules with a list.\n", + "\n", + "Lastly, as a quantum machine learning algorithm, the QCBM requires training data. \n", + "When you create a hybrid job, you may provide an input training datasets by specifying an Amazon Simple Storage Service (Amazon S3) bucket. \n", + "You may also specify a local path, in which case Braket will automatically upload the data to Amazon S3 at `s3:///jobs//data/`. \n", + "In this example, we use the local data file `input_data=\"data.npy\"`. You may specify multiple input datasets with a dictionary where the values are the paths to either S3 or local files. \n", + "\n", + "Now we run the training algorithm remotely as a hybrid job. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from braket.jobs import hybrid_job\n", + "\n", + "\n", + "# For now, lets set local=False. This uses a local Docker container\n", + "@hybrid_job(device=None, local=False, include_modules=\"qcbm\", input_data=\"data.npy\")\n", + "def train_circuit_hybrid_job(n_qubits, n_layers, n_iterations):\n", + " return train_circuit(n_qubits, n_layers, n_iterations)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 509 ms, sys: 30.4 ms, total: 540 ms\n", + "Wall time: 3min 52s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "job = train_circuit_hybrid_job(n_qubits, n_layers=n_layers, n_iterations=10)\n", + "res = job.result()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! We created our first quantum machine learning job! \n", + "\n", + "We can check the status of the hybrid job with " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'COMPLETED'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "job.state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also monitor the status of the hybrid job with the AWS Console.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once it’s complete, we can grab the result with `job.result()` which will wait for the hybrid job to finish. In `qcbm_job.py`, we set the results to be the final parameters of the QCBM that minimized the loss function. Results are returned as a dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 10.4 ms, sys: 475 µs, total: 10.9 ms\n", + "Wall time: 341 ms\n" + ] + }, + { + "data": { + "text/plain": [ + "{'params': array([ 0.35736532, 0.31073256, 0.91610879, 0.98872712, -0.08082859,\n", + " 0.47862197, 0.68460835, -0.09855128, 0.90463028, 0.25120807,\n", + " 0.83142284, 0.42736354, -0.04195098, 0.69129029, 0.63572789,\n", + " 0.75008587, 0.63212422, 0.95159988, 0.78795069, 0.17262345,\n", + " 0.92826497, 0.75725417, 0.18248898, 0.80719954, 1.31568383,\n", + " 0.85844901, 0.5214184 , 0.04987039, 0.03008104, -0.03636072]),\n", + " 'task summary': {},\n", + " 'estimated cost': 0.0}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "job.result()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Awesome! Our first quantum machine learning job finished! Now let’s look at the training metrics.\n", + "\n", + "Note that due to the inherent randomness in the training process, running this example repeatedly may yield different results each time." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Metrics and plotting\n", + "\n", + "In the `qcbm_job.py` script, we monitored the loss function during training with \n", + "```\n", + "log_metric(\n", + " metric_name=\"loss\",\n", + " value=loss,\n", + " iteration_number=iteration_number,\n", + ")\n", + "```\n", + "Metrics recorded in this way are visible from the \"Monitor\" tab in the AWS Console. It will look similar to the below image:\n", + "\n", + "
\n", + "\n", + "Metrics are also available by calling `job.metrics()`. Using pandas, and matplotlib, we plot the convergence of the loss below. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Plotting the convergence of the loss function metric\n", + "import pandas as pd\n", + "\n", + "%matplotlib inline\n", + "\n", + "plt.style.use(\"seaborn-v0_8-whitegrid\")\n", + "\n", + "\n", + "plt.style.use(\"seaborn-v0_8-colorblind\")\n", + "df.sort_values(by=[\"iteration_number\"]).plot(x=\"iteration_number\", y=\"loss\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Importantly, we can plot the predicted probability distribution vs the target probability from the data. \n", + "To do so, we first import the QCBM locally again, but now we initialize it with the parameters returned from our hybrid job." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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kWHmKlCUpVh5ZmlekPEXKkhQrT5GyJMXKU6QsSbHyyNK8IuUpUpakWHmKlCUpVp4iZUmKladIWZJi5ZGleUXKU6QsSfHyFEUt+6NSrVar7ZilXQwbNixbbrllfvazny2x/KWXXsqwYcMyZsyYJZ4K1Zyf//znOeecc5b5tPSf/vSnueiii/KnP/0p3bt3X+K1gw8+OPPnz8/NN9+81DoXLFjgrE4AAFgDXPb/5q3U7X3pP9ZfqdsDgFVt4MCBWXvttVscU8ozN7t27Zr6+vqlli9atKjp9bbYRpJmt7O8bfTr12+5O39N09DQkClTpmTQoEGpq6uTpaB5ipSlaHmKlKVoeWQpR54iZSlaniJlKVqeImUpWh5ZypGnPbN0n/5kzXOq1Wrmzp2bbt26pVKp1DR36NDtat5eS4r0ORUtT5GyFC1PkbIULY8s5chTpCxFzFMUCxYsyDPPPLNCY0tZbvbo0SMzZ85cavniS8l79uzZJttYvM5/fzjR7Nmzm+7v2Zy6ujoHZTOKtG+KlCUpVp4iZUmKladIWZJi5ZGleUXKU6QsSbHyFClLUqw8RcqSFCuPLM0rUp72yFKptOYRBo3/N7dS8/z22pdF+pySYuUpUpakWHmKlCUpVh5ZmlekPEXKkhQvz6pWy74o5QOFBgwYkOnTp2f+/PlLLH/iiSeSvHPK6nu1eB1TpkxZYvnMmTPz97//PQMGDHjP2wAAAAAAWq+U5ebw4cPT0NCQ6667rmlZfX19JkyYkCFDhjSdaTljxow8++yzrdrGNttsk6222iq/+tWvlriJ6S9/+ctUKpUMHz78vb0JAAAAAGjGww8/nP79++eNN954T+s55JBDcsYZZ7Q4ZrfddsvYsWObft+/f//cc889SZKXX345/fv3L+wzZkp5WfqQIUMyfPjwnH/++ZkzZ0569+6diRMn5pVXXlniwzrllFPyyCOPZNq0aU3L/vnPf2bcuHFJksceeyxJcs0112TdddfNeuutly984QtNY08++eQce+yxOeKII7LnnnvmmWeeyTXXXJNRo0alb9++K+ndAgAAAKz+Xn311Vx00UW577778vrrr6dHjx755Cc/ma985SvZcMMNlxj7wgsv5LLLLssDDzyQuXPnpmfPnhk6dGgOP/zwDBo0KMk7Bd1idXV16dmzZ3bffffstttuTcsnTJiQMWPGZKuttsodd9yxxDbuuOOOnHDCCdl0003z29/+th3f+ap3ww03ZK211lrma7169cr999/f9Bk8/PDDGT16dP785z9nvfXWW5kxl6mU5WaSnHPOObngggtyyy23ZN68eenfv38uu+yyfOhDH2px3rx583LhhRcusezKK69Mkmy66aZLlJuf+MQncvHFF+fiiy/O97///XTr1i3HHHNMvvKVr7T9GwIAAABoL7cev3K3N+LC5Y95l5deeikHHHBA+vTpk/PPPz+bbbZZ/vrXv+bcc8/Nfffdl+uuuy4bbLBBknduIXjYYYdlm222yfe+971stdVWefPNN3Pvvffmhz/8Yf73f/+3ab1nnXVWdt1117z99tt5+umnM2bMmMybN2+J/mjttdfO3Llz8/jjj2f77bdvWn7DDTdkk002eW/7YRmq1WoaGhrSsWNxarlu3bo1+1pdXV3Ts2mKqDh7sUZdunTJKaecklNOOaXZMYvP0Hy3zTbbbIkzOZdn2LBhGTZsWKsyAgAAALB8//3f/51OnTrlyiuvTNeuXZMkm2yySbbddtt86lOfyo9//OP893//d6rVasaMGZPevXtn/Pjx6dDh/7/j4sCBAzN69Ogl1rveeus1FXO9evXKbrvtttQtDOvq6rLXXnvlxhtvbCo3//73v+eRRx7JYYcdlttuu63Z3C+//HI++clP5vzzz8+4cePyl7/8Jb179853vvOdfPjDH07y/5/pePnll+fCCy/MM888k5///OfZfvvtc/bZZ+fWW2/NwoULs91222XMmDFLPcT6sccey49+9KNMnz49AwcOzA9+8IP069cvSfKPf/wj3//+9/PnP/85b7zxRrbYYoscc8wx2WuvvZZYR0NDQ773ve/l5ptvTseOHXPggQfm+OOPT6VSSfLOZemjR4/OYYcd1ux7vOmmm7Luuus27ePFBfE+++yT//zP/8xZZ52V++67L507d26a++Uvfznve9/7cu655za7D9+rUt5zEwAAAIDVw+uvv577778/Bx10UFOxuViPHj0yYsSI3HHHHalWq5k6dWr++te/5ogjjlii2Fyspcukn3/++Tz88MPZeuutl3ptv/32yx133JF//etfSd65XH3XXXdN9+7dV+g9nHPOOTn88MNz0003ZejQofnSl76Uf/zjH0uM+dGPfpSvf/3ruf3229O/f/+cc845ufvuu/OlL30pN9xwQ3r37p0vfvGLef3115da96mnnpobbrgh3bp1y5e+9KW89dZbSd55Bs0HPvCBXH755fn1r3+dz33uczn55JMzefLkJdYxceLE1NXV5frrr8/pp5+esWPH5vrrr1+h9/ZuvXr1yk9+8pMkyZ133pn7778/p59+etPzce69996msXPmzMkf/vCH7LfffjVvpxbKTQAAAABWmRdeeCHVarXZ55v07ds38+bNy9y5czN9+vQkyVZbbbVC6z7ppJOy/fbbZ9CgQRk+fHi23nrr7L333kuN23bbbbP55pvnrrvuSrVazcSJE2sq5Q4++ODsscce6du3b7773e9m3XXXzQ033LDEmK997Wv5yEc+ki222CKdO3fOtddem2984xsZOnRott5663z/+99Ply5dlpr31a9+NR/5yEfSv3//nH322ZkzZ07uvvvuJMnGG2+cI488MgMHDszmm2+eQw45JLvuuutS9w/t1atXTjvttGy11VbZe++984UvfGGJBwitqLq6uqy//vpJku7du6dHjx5Zd91107Vr1+y1116ZMGFC09hbbrklvXr1yo477ljzdmqh3AQAAABglatWqy2+3qlTp5rXOWbMmNx00025+eab8z//8z+ZPn16LrnkkmWO3W+//XLjjTfmkUceyb/+9a987GMfW+HtvPtenR07dsx2222X5557bokxix90lCQvvvhi3nrrrSXmderUKYMHD17qsvmhQ4c2/XqDDTbIlltu2bTuhoaG/PSnP82IESPy4Q9/ONtvv33uv//+zJgxY4l1DBkypOkS9MXrfOGFF9LQ0LDC73F5Pve5z+WBBx7IzJkzk7xz9us+++yzxHbbg3ITAAAAgFVmiy22SKVSWarUW+zZZ59Nt27dst5666VPnz5JslRx2JwePXqkd+/e2WqrrfLxj388X/3qV/PQQw/lhRdeWGrsiBEj8sQTT+Tiiy/O3nvv3eYP/GnuaeTvxc9//vNcffXV+eIXv5irr746N910U3bZZZemy9ZXpm233TYDBgzITTfdlCeffDJ/+9vfsu+++7b7dpWbAAAAAKwyG264YT7ykY9k/PjxWbhw4RKvzZ49O7feemv22WefJO88NGjrrbfOlVdemcbGxqXW9cYbb7S4rcX36Vy0aNFSr22wwQbZbbfd8sgjj9R8n8hJkyY1/frtt9/OX/7ylxYvnd9iiy3SqVOnPP74403L3nrrrUyZMmWpe4K+e93z5s3L9OnTm9b92GOP5ZOf/GQ++9nPZsCAAdl8882bLt1/t3+/B+cTTzyR3r17p66uroZ3+Y7FZ9Au66zP/fffPxMmTMiECROy8847p1evXjWvv1bKTQAAAABWqW9/+9upr6/PkUcemT//+c959dVX88c//jFHHHFE+vTpk6985StJkkqlkrPOOivTp0/PQQcdlD/84Q956aWX8vTTT+fSSy/Nl7/85SXW+8Ybb2T27NmZOXNmHnnkkVx66aXp1atXs8Xj2WefnYceeqjZ+382Z/z48bn77rvz7LPP5nvf+17mzZvXYkG69tpr58ADD8x5552XJ554In/729/y7W9/OwsXLsz++++/xNhLLrkkDz74YJ555pmceuqp2XDDDTNs2LAkSe/evfOnP/0pjz32WJ599tl85zvfyWuvvbbU9mbMmJGzzjorzz33XH7961/nf//3f5d6svyK2nTTTVOpVPL73/8+c+fOzZtvvtn02ogRIzJz5sz86le/avcHCS3WtufXAgAAAECN+vTpkxtuuCEXX3xxTjjhhMyZMyfVajW77757zjnnnCUu6R48eHBuvPHGXHbZZfnWt76Vf/zjH+nZs2e23377nHbaaUusd8yYMUneKUU32mijfPCDH8zuu+/e7CXnXbt2XeqJ7Svi61//ei6//PJMnTo1vXv3zqWXXppu3bq1OOcb3/hGGhoacskll+THP/5xtttuu1xxxRVND+x597rPOOOMTJ8+PQMHDsyll16azp07J0mOPfbYvPTSSznyyCOz1lpr5XOf+1yGDRuWf/7zn0usY+TIkVm4cGFGjRqVurq6jB49OgcccEDN7zN55yFGxx13XH70ox9lzJgxGTlyZM4+++wkybrrrpvdd989f/jDH5oK2Pam3AQAAABY3Y24cFUnWK7NNtusqSRLkosuuihXXXVVpk2btsRDdZJkyy23zA9/+MMW1zdt2rSlljU0NCxxmfe+++7b4n0hDzvssBx22GHLzd63b99cf/31y3xtxx13XGaWLl265PTTT8+ee+6ZoUOHLnWJ+LvnfeITn1jmujfYYINmH5C02Lhx45p+/d///d/LHPPb3/52id+/O+9mm222VP6vfOUrTWfT/ruZM2dmxIgRTQVse1NuAgAAAFA4X/va17Lppptm0qRJGTx4cNP9MimmefPm5ZFHHskjjzyS//qv/1pp21VuAgAAAFBIK+u+jbx3++yzT+bNm5dvfOMbLT5Mqa0pNwEAAACgFZZ1yfaa6t8vbV9ZnM8LAAAAAJSSchMAAAAAKCXlJgAAAABQSspNAAAAAKCUlJsAAAAAQCkpNwEAAACAUlJuAgAAAAClpNwEAAAAAEpJuQkAAAAAlJJyEwAAAAAoJeUmAAAAAFBKyk0AAAAAoJSUmwAAAABAKSk3AQAAAIBSUm4CAAAAAKWk3AQAAAAASkm5CQAAAACUknITAAAAACgl5SYAAAAAUErKTQAAAACglJSbAAAAAEApKTcBAAAAgFJSbgIAAAAApaTcBAAAAABKSbkJAAAAAJSSchMAAAAAKCXlJgAAAABQSspNAAAAAKCUlJsAAAAAQCkpNwEAAACAUlJuAgAAAAClpNwEAAAAAEpJuQkAAAAAlJJyEwAAAAAoJeUmAAAAAFBKyk0AAAAAoJSUmwAAAABAKSk3AQAAAIBSUm4CAAAAAKWk3AQAAAAASkm5CQAAAACUknITAAAAACiljqs6AAAAwAq59fiap1Sq1Wzx2pxUXumeVCq1b3PEhbXPAQBWGmduAgAAAAClpNwEAAAAAEpJuQkAAAAAlJJyEwAAAAAoJeUmAAAAAFBKyk0AAAAAoJQ6ruoAAAAAK+Lh5+e2YlY1CxfWp+v8uUkqNc/esRVbBABWHmduAgAAAACl5MxNAACAkhszYUrNc6rVxsyZMy/dpz+ZSqW2817O2ndQzdsDgPbgzE0AAAAAoJSUmwAAAABAKSk3AQAAAIBSUm4CAAAAAKWk3AQAAAAASkm5CQAAAACUknITAAAAACgl5SYAAAAAUErKTQAAAACglJSbAAAAAEApKTcBAAAAgFJSbgIAAAAApaTcBAAAAABKSbkJAAAAAJSSchMAAAAAKCXlJgAAAABQSspNAAAAAKCUlJsAAAAAQCkpNwEAAACAUlJuAgAAAAClpNwEAAAAAEpJuQkAAAAAlJJyEwAAAAAoJeUmAAAAAFBKyk0AAAAAoJSUmwAAAABAKSk3AQAAAIBSUm4CAAAAAKWk3AQAAAAASkm5CQAAAACUknITAAAAACgl5SYAAAAAUErKTQAAAACglJSbAAAAAEApKTcBAAAAgFJSbgIAAAAApaTcBAAAAABKSbkJAAAAAJSSchMAAAAAKCXlJgAAAABQSspNAAAAAKCUSltu1tfX59xzz80uu+ySwYMHZ9SoUXnggQdWaO7MmTNz/PHH54Mf/GB22GGHHHvssXnppZeWGvfPf/4z55xzTnbfffcMHjw4n/jEJ3LaaadlxowZbf12AAAAAIAadVzVAVrr1FNPzV133ZXRo0enT58+mThxYo4++uj84he/yAc/+MFm57355psZPXp0/vnPf+aYY45Jp06dMnbs2HzhC1/ITTfdlA033DBJ0tjYmMMPPzzPPvtsDjzwwGy55ZZ54YUXMn78+Nx///25/fbbs84666ystwsAAAAA/JtSlpuTJ0/ObbfdlpNPPjlHHnlkkmTkyJHZa6+9ct555+Xaa69tdu748eMzffr0XH/99Rk8eHCSZNddd82IESNy1VVX5aSTTkqSTJo0KVOmTMl3vvOdHHzwwU3zt9xyy5x22ml58MEH86lPfaod3yUAAAAA0JJSXpZ+5513pq6uLgcccEDTsi5dumT//ffP448/nldffbXZuXfddVcGDRrUVGwmSd++fbPTTjvljjvuaFo2f/78JEn37t2XmN+jR4+m7QEAAAAAq04py82pU6emT58+S10WvriwnDp16jLnNTY2Ztq0adluu+2Wem3QoEF58cUXm0rN7bbbLmuvvXYuvPDCPPjgg5k5c2YeeeSRnHvuuRk0aFB23nnnNn5XAAAAAEAtSnlZ+uzZs5vOoHy3xctmzZq1zHmvv/566uvrlzt3nXXWSbdu3fLjH/843/rWt3LYYYc1jdtll11y0UUXpWPHlnddQ0NDGhoaVvQtrREW748i7JciZUmKladIWZJi5SlSlqRYeWRpXpHyFClLUqw8RcqSFCtPkbIkxcojS/PaL0+19VOqSSq1z2/pPVSrjbXHqVbf9d/a5pclS2sV6TguUpakWHmKlCUpVh5ZmlekPEXKkhQvT1HUsj9KWW4uXLgwnTt3Xmr54kvFFy5cuMx5ixYtSpIW5y4ekyTdunXLtttumx122CFbb711nn766VxxxRUZM2ZMLrroohYzPvPMMyv2ZtZAU6ZMWdURmhQpS1KsPEXKkhQrT5GyJMXKI0vzipSnSFmSYuUpUpakWHmKlCUpVh5ZmtfWeRYuXLT8Qc3NXdS6uZMmTWr2tTlz5rUyTTJ37tzVNst7VaTjuEhZkmLlKVKWpFh5ZGlekfIUKUtSvDxlUspys2vXrqmvr19q+eJismvXrsuct7jAbGnu4jEvvfRSRo8enR/+8IfZY489kiTDhg3LpptumlNPPTV/+MMf8rGPfazZjP369cvaa69dw7ta/TU0NGTKlCkZNGhQ6urqZCloniJlKVqeImUpWh5ZypGnSFmKlqdIWYqWp0hZipZHlpWf59EHWnHf++o7xWbXLl2SSu3Thw4d2uxr3ac/WXucajVz585Nt27dUqnUFmjo0KVvr1XELK1VpOO4SFmKlqdIWYqWR5Zy5ClSliLmKYoFCxas8ImDpSw3e/TokZkzZy61fPbs2UmSnj17LnPeBhtskM6dOzeNa2nuhAkTsmjRonziE59YYtxuu+2WJHnsscdaLDfr6uoclM0o0r4pUpakWHmKlCUpVp4iZUmKlUeW5hUpT5GyJMXKU6QsSbHyFClLUqw8sjSv7fO0op1cfCl6pXXzW8pfqbTmEQaN/ze3UvP8smR5r4p0HBcpS1KsPEXKkhQrjyzNK1KeImVJipdnVatlX5TygUIDBgzI9OnTmx7+s9gTTzyRJBk4cOAy53Xo0CH9+vXLk08u/a+akydPzuabb970kKI5c+akWq0udY3/22+/ncS9EAAAAABgVStluTl8+PA0NDTkuuuua1pWX1+fCRMmZMiQIenVq1eSZMaMGXn22WeXmLvHHntkypQpS9zL4LnnnstDDz2U4cOHNy3r06dPqtVq7rjjjiXm//rXv06SbLvttm3+vgAAAACAFVfKy9KHDBmS4cOH5/zzz8+cOXPSu3fvTJw4Ma+88krOOOOMpnGnnHJKHnnkkUybNq1p2UEHHZTrr78+xxxzTI444oh07NgxY8eOTffu3XPEEUc0jdtnn31y5ZVX5jvf+U6eeuqpbLPNNvnLX/6SG264Idtss02GDRu2Ut8zAAAAALCkUpabSXLOOefkggsuyC233JJ58+alf//+ueyyy/KhD32oxXnrrLNOxo0blzPPPDOXXnppGhsbs+OOO2bMmDHp1q1b07gNN9wwN954Yy688ML87ne/y7XXXpsNNtgg++23X0488cRlPnEdAAAAAFh5SltudunSJaecckpOOeWUZseMGzdumcvf//7356KLLlruNjbeeOOceeaZrc4IAAAAALSfUt5zEwAAAABAuQkAAAAAlJJyEwAAAAAoJeUmAAAAAFBKyk0AAAAAoJSUmwAAAABAKSk3AQAAAIBSUm4CAAAAAKWk3AQAAAAASkm5CQAAAACUknITAAAAACgl5SYAAAAAUErKTQAAAACglDqu6gAAAABlNPLlc1oxq5qFCxel68IuSSo1zh3Xiu0BwOrNmZsAAAAAQCkpNwEAAACAUlJuAgAAAAClpNwEAAAAAEpJuQkAAAAAlJJyEwAAAAAoJeUmAAAAAFBKyk0AAAAAoJSUmwAAAABAKSk3AQAAAIBSUm4CAAAAAKWk3AQAAAAASkm5CQAAAACUknITAAAAACgl5SYAAAAAUErKTQAAAACglJSbAAAAAEApKTcBAAAAgFJSbgIAAAAApaTcBAAAAABKSbkJAAAAAJSSchMAAAAAKCXlJgAAAABQSspNAAAAAKCUlJsAAAAAQCkpNwEAAACAUlJuAgAAAAClpNwEAAAAAEpJuQkAAAAAlJJyEwAAAAAoJeUmAAAAAFBKyk0AAAAAoJSUmwAAAABAKSk3AQAAAIBSUm4CAAAAAKWk3AQAAAAASkm5CQAAAACUknITAAAAACgl5SYAAAAAUErKTQAAAACglJSbAAAAAEApKTcBAAAAgFJSbgIAAAAApaTcBAAAAABKSbkJAAAAAJSSchMAAAAAKCXlJgAAAABQSspNAAAAAKCUlJsAAAAAQCkpNwEAAACAUlJuAgAAAAClpNwEAAAAAEpJuQkAAAAAlJJyEwAAAAAoJeUmAAAAAFBKyk0AAAAAoJSUmwAAAABAKSk3AQAAAIBSUm4CAAAAAKWk3AQAAAAASkm5CQAAAACUknITAAAAACgl5SYAAAAAUErKTQAAAACglJSbAAAAAEApKTcBAAAAgFJSbgIAAAAApaTcBAAAAABKSbkJAAAAAJSSchMAAAAAKKU2Kzf32muvjB07NnPnzm2rVQIAAAAANKvNys2//e1v+eEPf5iPfvSjOe644/K73/0ujY2NbbV6AAAAAIAltFm5ue2226Zarebtt9/OPffcky9/+cv52Mc+lh/96Ed5/vnn22ozAAAAAABJ2rDcnDBhQm6++eaMHj06G2ywQarVambPnp0rrrgin/nMZ3LggQfmxhtvzIIFC9pqkwAAAADAGqxNHyjUv3//nHbaabnvvvvyk5/8JB//+MdTV1eXarWaSZMm5Vvf+lZ22WWXnHbaaXn00UfbctMAAAAAwBqmY7ustGPHfOpTn8qnPvWpvPbaa5k4cWImTpyY5557LgsWLGj6/RZbbJH99tsvI0eOTM+ePdsjCgAAAACwmmrTMzeXZaONNspRRx2V22+/Pdddd11GjRqV973vfalWq3nhhRfy4x//OLvttlu+9KUv5Z577vEQIgAAAABghbR7ufluQ4YMyfe///2cd9552WijjVKpVJIkb7/9dv7whz/kuOOOy8c//vFcffXVaWhoWJnRAAAAAICSWWnl5owZM3LxxRdn2LBhOfbYYzNnzpxUq9V06NAhH/nIR7LxxhunWq1m1qxZOeuss/K5z30u8+bNW1nxAAAAAICSaZd7bi62aNGi3HXXXZkwYUIeeeSRVKvVVKvVJGm63+Y+++yTnj17plqt5v7778/PfvazPPLII3nqqady8cUX5/TTT2/PiAAAAABASbVLuTlp0qRMmDAhd9xxR+bPn58kqVar6dy5cz71qU9l1KhR+c///M8l5lQqley6667Zdddd873vfS/jx4/Pb3/7W+UmAAAAALBMbVZuzpo1KzfffHMmTpyY559/PkmaztLs169fRo0alb333jvrr7/+cte1//77Z/z48fn73//eVvEAAAAAgNVMm5Wbn/jEJ9LY2NhUaL7vfe/Lnnvumf333z+DBw+uaV3rrLNOknhyOgAAAADQrDYrNxc/3Xzo0KEZNWpUPvOZz2SttdZq1bq6d++es846q62iAQAAAACroTYrNw877LCMGjUqffv2fc/ret/73pd99tmnDVIBAAAAAKurNis3Tz311LZaFQAAAADAcrVZuTl69OhUKpWceeaZ2XTTTVdozsyZM/PNb34zlUolv/jFL9oqCgAA0EbGTJhS85xqtTFz5sxL9+lPplLpUNPcs/YdVPP2AIA1V5uVm4888kgqlUr+9a9/rfCcRYsWNc0DAAAAAKhFbf+MCgAAAABQEKu03FywYEGSpEuXLjXPra+vz7nnnptddtklgwcPzqhRo/LAAw+s0NyZM2fm+OOPzwc/+MHssMMOOfbYY/PSSy8tc+xrr72W73znO9l1110zaNCg7LbbbjnttNNqzgsAAAAAtK02uyy9Nf74xz8mSTbeeOOa55566qm56667Mnr06PTp0ycTJ07M0UcfnV/84hf54Ac/2Oy8N998M6NHj84///nPHHPMMenUqVPGjh2bL3zhC7npppuy4YYbNo199dVXc+CBByZJPv/5z2fjjTfOrFmzMnny5JrzAgAAAABtq9Xl5pgxY5a5/IILLsi6667b4tz6+vq89NJLmTJlSiqVSj784Q/XtO3Jkyfntttuy8knn5wjjzwySTJy5MjstddeOe+883Lttdc2O3f8+PGZPn16rr/++gwePDhJsuuuu2bEiBG56qqrctJJJzWN/c53vpO6urrccMMNS5SeAAAAAMCq1+pyc+LEiUs9CKharebee+9dofnVajVJsv766+foo4+uadt33nln6urqcsABBzQt69KlS/bff/+cf/75efXVV9OrV69lzr3rrrsyaNCgpmIzSfr27Zuddtopd9xxR1O5+eyzz+aPf/xj/uu//isbbrhhFi1alA4dOqRTp041ZQUAAFjTPHzRIa2YVc3ChYvy6ANdktT20NkdvzauFdsDYHXQ6nJzk002WeL3M2bMSKVSSY8ePdKxY/OrrVQq6dKlS3r06JHtt98+Bx54YM2XpU+dOjV9+vTJOuuss8TyxYXl1KlTl1luNjY2Ztq0adlvv/2Wem3QoEG5//77M3/+/Kyzzjp58MEHkyQbbbRRDj300Dz00EOpq6vLzjvvnO9+97vZbLPNasoMAAAAALStVpebv/3tb5f4/YABA5IkV155Zbbeeuv3lmo5Zs+enR49eiy1fPGyWbNmLXPe66+/nvr6+uXOXWeddTJ9+vQkybe//e0MGjQoP/7xj/Pqq6/m4osvzuGHH55bbrkla621VrMZGxoa0tDQUOtbW60t3h9F2C9FypIUK0+RsiTFylOkLEmx8sjSvCLlKVKWpFh5ipQlKVaeImVJipVnTclSrTa2Yk71Xf+tbX7L76Fac5amKdUkldrnFylPS1mK9TklRdo3rVGkn++kWHmKlCUpVh5ZmlekPEXKkhQvT1HUsj/a7IFCH/rQh5KkxcKvrSxcuDCdO3deavnip64vXLhwmfMWLVqUJC3OXTxm8ZPce/TokcsvvzwdOrzzYPn3v//9Oemkk/LrX/86o0aNajbjM888s6JvZ40zZcqUVR2hSZGyJMXKU6QsSbHyFClLUqw8sjSvSHmKlCUpVp4iZUmKladIWZJi5Vnds8yZM6/Vc+fOnVvznEmTJjX72sKFi1qdZeGi1s0tUp6WshTpc0qKtW/eiyL9fCfFylOkLEmx8sjSvCLlKVKWpHh5yqTNys1x41bePU66du2a+vr6pZYvLia7du26zHmLC8yW5i4es3gdw4cPbyo2F//+5JNPzmOPPdZiudmvX7+svfbaK/J21hgNDQ2ZMmVKBg0alLq6OlkKmqdIWYqWp0hZipZHlnLkKVKWouUpUpai5SlSlqLlWVOydJ/+ZM1zqtVq5s6dm27dui11n/7lGTp0u2Zfe+dejLWGeacs69qlS623cfy/PEMLk6elLEX6nJJi7ZvWKNLPd9HyFClL0fLIUo48RcpSxDxFsWDBghU+cbDNys2VqUePHpk5c+ZSy2fPnp0k6dmz5zLnbbDBBuncuXPTuJbmLv7vRhtttMS4urq6bLDBBnnjjTdazFhXV+egbEaR9k2RsiTFylOkLEmx8hQpS1KsPLI0r0h5ipQlKVaeImVJipWnSFmSYuVZ3bNUKh2WP2gpjf83t1Lz/Jbzt6KdXHx5c6V184uUp6UsxfqckiLtm/eiSD/fSbHyFClLUqw8sjSvSHmKlCUpXp5VrZZ90Zo/AVe5AQMGZPr06Zk/f/4Sy5944okkycCBA5c5r0OHDunXr1+efHLpf9WcPHlyNt9886aHFH3gAx9IkqVK1Pr6+vzjH/9It27d3vP7AAAAAABar+YzNz/5yU8meedf9+65556llrfGv69reYYPH54rr7wy1113XY488sgk75SOEyZMyJAhQ5qelD5jxoz861//St++fZvm7rHHHvnRj37UdMpvkjz33HN56KGHcsQRRzSN23HHHdO9e/fceuut+dKXvtR0ufrEiRPT0NCQnXfeudXvFwAAAAB472ouN1955ZUkWeqeLIuXt0at93cZMmRIhg8fnvPPPz9z5sxJ7969M3HixLzyyis544wzmsadcsopeeSRRzJt2rSmZQcddFCuv/76HHPMMTniiCPSsWPHjB07Nt27d1+i3OzcuXNOPvnknHLKKTn44IPz2c9+NjNmzMi4cePywQ9+MLvvvnur3y8AAAAA8N7VXG7us88+NS1vL+ecc04uuOCC3HLLLZk3b1769++fyy67rOmp7c1ZZ511Mm7cuJx55pm59NJL09jYmB133DFjxoxZ6lLzkSNHplOnTrn88stzzjnnZL311ssBBxyQE0880X0QAAAAAGAVq7ncPOuss2pa3l66dOmSU045JaecckqzY5p7gvv73//+XHTRRSu0nT333DN77rlnqzICAAAAAO2nlA8UAgAAAABQbgIAAAAApaTcBAAAAABKqeZ7bs6YMaM9cmSTTTZpl/UCAACtN/Llc1oxq5qFCxel68IuSSo1zl32ffMBAJal5nLzk5/8ZJuHqFQqeeqpp9p8vQAAAADA6qvmcrNarbZHDgAAAACAmtRcbp511lntkQMAAAAAoCY1l5v77LNPe+QAAAAAAKiJp6UDAAAAAKWk3AQAAAAASkm5CQAAAACUUs333Lzpppuafj1y5MhlLm+Nd68LAAAAAGB5ai43Tz311FQqlVQqlSUKycXLW+Pf1wUAAAAAsDw1l5tJUq1Wa1oOAAAAANDWai4377333pqWAwAAAAC0h5rLzU033bSm5QAAAAAA7cHT0gEAAACAUlJuAgAAAACl1KoHCq2Iv/zlL/nTn/6UZ555JvPmzUuSrL/++tlmm22y8847Z7vttmuvTbOmufX4mqdUqtVs8dqcVF7pnlQqtU0ecWHN2wMAAACg7bV5ufmXv/wl//3f/50pU6Y0O+bHP/5xtttuu3znO9/JoEGD2joCAAAAALAGaNPL0u+88858/vOfz5QpU1KtVlOtVtOxY8d079493bt3T8eOHZuWT5kyJQceeGDuuOOOtowAAAAAAKwh2uzMzeeeey4nn3xy3nrrrXTs2DGjRo3Kfvvtl4EDB6auri5J0tDQkKeffjo33HBDrr/++rz99ts55ZRT0q9fv/Tt27etogAAAAAAa4A2Kzd/9rOfpb6+Pl26dMnll1+eHXfccakxdXV1+cAHPpAPfOAD+fSnP52jjjoq9fX1ueKKK3LWWWe1VRQAAAAAYA3QZpelP/jgg6lUKjn00EOXWWz+uw9/+MM59NBDU61W8+CDD7ZVDAAAAABgDdFm5ebcuXOTJB/96EdXeM7HPvaxJeYCAAAAAKyoNis3u3XrliTp0qXLCs/p3LlzkmTDDTdsqxgAAAAAwBqizcrNHXbYIUkyZcqUFZ4zefLkJMl//Md/tFUMAAAAAGAN0Wbl5mGHHZa6urr8z//8zwpdZj5nzpxcfvnl6dixYw477LC2igEAAAAArCHarNwcPHhw/vu//ztz5szJqFGjcs8996SxsXGpcY2NjbnnnntywAEHZO7cufnud7+bwYMHt1UMAAAAAGAN0bHWCWPGjGnx9a233jpPP/10jjvuuKy33nrZdttt061bt1QqlcyZMydTp07NvHnzkiQDBgzI//t//y+PPfZYzjzzzNa9AwAAAABgjVRzuTlx4sRUKpUWx1QqlVSr1cybNy8PPfTQEq9Vq9WmMU8//XSefvrpJFFuAgAAAAA1qbnc3GSTTdojBwAAAABATWouN3/729+2Rw4AAAAAgJq02QOFAAAAAABWJuUmAAAAAFBKNV+WDgAAtKNbj695SqVazRavzUnlle7Jch7+uUwjLqx9DgBAAbRrudnQ0JB58+Zl0aJFTU9Jb44HFQEAAAAAtWjzcnPu3Ln53//939xzzz159tln09jYuNw5lUolTz31VFtHAQAAAABWY21abj722GM57rjjMnfu3OWeqQkAAAAA8F60Wbn5j3/8I1/+8pfz+uuvZ+21186oUaOy7rrr5uKLL06lUskPfvCDzJs3L08++WR++9vfZtGiRdlhhx2y//77t1UEgHJqxb3Vkvd4fzX3VgMAAGA10Gbl5jXXXJPXX389nTt3znXXXZdtttkmf/3rX3PxxRcnSfbbb7+msbNmzcrXv/71PProoxk6dGi++c1vtlUMAAAAAGAN0aGtVvTHP/4xlUol++23X7bZZpsWx/bs2TM/+9nPssUWW+TKK6/Mgw8+2FYxAAAAAIA1RJuVmy+88EKSZOedd25aVnnXZZINDQ1LjO/atWsOPfTQVKvVXHvttW0VAwAAAABYQ7RZufnmm28mSTbZZJOmZV26dFnq9XfbbrvtkiSTJ09uqxgAAAAAwBqizcrNtddeO8mSZ2iuv/76Tb9+5ZVXlppTX1+fJJkzZ05bxQAAAAAA1hBtVm5uscUWSZIZM2Y0LVtvvfWy0UYbJUkefvjhpeb8v//3/5Ika621VlvFAAAAAADWEG1Wbg4ZMiRJMmXKlCWW77rrrqlWq7niiisyffr0puWTJk3Kz3/+81QqlQwaNKitYgAAAAAAa4g2Kzd32WWXVKvV3H333UssP/zww9OxY8fMmTMne+21V/bbb7985jOfycEHH5w33ngjSTJ69Oi2igEAAAAArCHatNwcOXJkhgwZkpdeeqlpeb9+/fJf//Vfqaury9tvv52//OUvee6555ruzfnVr341H/3oR9sqBgAAAACwhujYVivq1KlTzj777GW+NmrUqPzHf/xHJk6cmL/+9a9paGhI796989nPftYl6QAAAABAq7RZubk8W221Vb7+9a+vrM0BAAAAAKu5NrssHQAAAABgZWr3MzfffvvtpgcHrbfeeunYcaWdLAoAAAAArMbapWn829/+lmuvvTZ/+tOfMn369FSr1SRJpVJJ7969s/POO+eAAw5Iv3792mPzAAAAAMAaoE3LzcbGxpxzzjkZN25cGhsbm0rNxarVap5//vlMnz491157bQ4++OCceuqp6dDB1fEAAAAAQG3atNz8+te/njvvvLOp1Nxmm20yaNCgbLTRRkmS1157LVOmTGl6Yvq4ceMya9asXHDBBW0ZAwAAAABYA7RZuXnbbbfljjvuSKVSyYABA/K9730vgwcPXubYyZMn57vf/W6eeuqp3HXXXbntttuy5557tlUUAAAAAGAN0Gbl5nXXXZck6dOnT8aPH5+111672bGDBw/O//7v/2a//fbL888/n2uvvVa5Cau5MROmtGpetdqYOXPmpfv0J1Op1HYLi7P2HdSqbQIAAADl0GY3u5w2bVoqlUqOOuqoFovNxdZee+0cddRRTXMBAAAAAGrRZuVmfX19kqR///4rPGfx2LfeequtYgAAAAAAa4g2Kzc33XTTJMk///nPFZ4zf/78JeYCAAAAAKyoNis3d99991Sr1fzmN79Z4Tl33XVXKpVKhg0b1lYxAAAAAIA1RJuVm4cffng233zzXHfddbn99tuXO/7OO+/Mddddl8022yxHHnlkW8UAAAAAANYQbfa09HXXXTdXXXVVTjzxxHz961/Pr3/96+y7774ZNGhQunXrlkqlkjlz5mTKlCmZOHFifvvb32a77bbLBRdckHXXXbetYgDv0ponlK8pTye3bwAAAKD8ai43Bw4cuNwx1Wo1v/vd7/K73/2uxTFPPvlkhg0blkqlkqeeeqrWKAAAAADAGqzmcrNarbbZuBVdFwAAAADAv6u53PzqV7/aHjkAAAAAAGqi3AQAACi5kS+f04pZ1SxcuChdF3ZJUqlx7rhWbA8A2l6bPS0dAAAAAGBlUm4CAAAAAKVU82XpK+qtt97KU089lWeeeSbz5s1Lkqy//vrp169ftt1223Tq1Km9Ng0AAAAArAHavNz817/+lUsuuSS/+tWv8sYbbyxzzHrrrZcDDjggxx57bNZaa622jgAAAAAArAHa9LL0GTNmZOTIkbniiisyb968VKvVZf5v3rx5+dnPfpaRI0fm73//e1tGAAAAAADWEG125uZbb72Vo446Ki+88EKSZKuttsq+++6bIUOGZKONNkqSvPbaa5k8eXImTpyYv/3tb3nhhRdy1FFHZeLEienYsd2ukAcAAAAAVkNt1ihef/31efbZZ1OpVHLMMcfkuOOOS11d3RJjttxyy3zoQx/K4Ycfnp/85Ce59NJL87e//S3XX399DjzwwLaKAgAAAACsAdrssvQ777wzlUolw4YNywknnLBUsbnERjt0yPHHH59PfepTqVarufPOO9sqBgAAAACwhmizcvOvf/1rkmS//fZb4Tn7779/kuSZZ55pqxgAAAAAwBqizcrNf/7zn0mSnj17rvCcHj16JEnmz5/fVjEAAAAAgDVEm5WbG2ywQZLk5ZdfXuE5i8euv/76bRUDAAAAAFhDtNkDhbbddtv88Y9/zDXXXJPdd999heaMHz8+lUol2267bVvFgFVuzIQpNc+pVhszZ868dJ/+ZCqV2v7N4ax9B9W8PYrl4efntnJmNQsX1qfr/LlJKjXN3LGVWwQAAIAiabMzN/fcc88kySOPPJIxY8ZkwYIFzY7917/+ldNPPz0PPfRQkmSvvfZqqxgAAAAAwBqizc7c3HvvvXPttdfm8ccfz0033ZQ//vGP+fSnP53Bgwene/fuqVQqee211zJ58uTccccdmTv3nTOVdthhh+y9995tFQMAAAAAWEO0WblZqVRy2WWX5ZhjjsmkSZMyZ86cXHPNNbnmmmuWGlutVpMkQ4cOzSWXXNJWEQAAAACANUiblZvJOw8GGj9+fH75y19m/PjxefbZZ5c5rm/fvjnooINy4IEHpkOHNrsyHgAAAABYg7RpuZkkHTp0yMEHH5yDDz44s2bNyl//+tfMmzcvyTvl5zbbbJOePXu29WYBAAAAgDVMm5WbY8aMSZJ89KMfzac//ekkSc+ePRWZAAAAAEC7aLNy86abbkqSfOYzn2mrVQIAAAAANKvNbnjZrVu3JMlGG23UVqsEAAAAAGhWm5252bdv38ydOzevvPJKBg4c2FarpahuPb7mKZVqNVu8NieVV7onlUptk0dcWPP2KJaRL5/TypnVLFy4KF0XdklS43GTca3cJgBrHH+3AQAopTY7c3PvvfdOtVptujwdAAAAAKA9tVm5ud9++2WnnXbKvffem5/85CepVqtttWoAAAAAgKW02WXpjz76aI444ojMnTs3l1xySW6//fZ85jOfSf/+/bPeeuulrq6uxfkf+tCH2ioKAAAAALAGaLNy85BDDknlXfcamj59ei655JIVmlupVPLUU0+1VRQAAAAAYA3QZuVmEpeiAwAAAAArTZuVm1dffXVbrQoAAAAAYLnarNz88Ic/3FarAgAAAABYrvdcbv7+97/Pfffdl1deeSWNjY3p2bNnPvzhD+fTn/50OnXq1BYZAQAAAACW0upy87XXXstXvvKVTJ48eanXbrzxxlx00UX56U9/mv79+7+ngADtYeTL57RiVjULFy5K14VdklSWO3pJ41qxPQAAAKAlHVozqaGhIccee2yeeOKJVKvVZf7v5ZdfzpFHHpm5c+e2dWYAAAAAgNaVm3fccUemTJmSSqWS3r1754wzzsitt96aO+64IxdeeGGGDh2aJJkzZ06uuuqqtswLAAAAAJDkPZSbSbLpppvm+uuvz3777ZdtttkmW265ZfbYY49cc801+dCHPpRqtZo777yzTQMDAAAAACStLDenTp2aSqWSww8/POutt95Sr9fV1eVrX/takuTll1/O/Pnz31tKAAAAAIB/06pyc/F9NAcNGtTsmO22267p1//4xz9asxkAAAAAgGa1qtxcuHBhkmTttddudsxaa63V9Ov6+vrWbKZF9fX1Offcc7PLLrtk8ODBGTVqVB544IEVmjtz5swcf/zx+eAHP5gddtghxx57bF566aUW5zz66KPp379/+vfv7yFJAAAAAFAArSo3a1WtVtt8naeeemrGjh2bESNG5PTTT09dXV2OPvroPProoy3Oe/PNNzN69Oj8+c9/zjHHHJOvfe1rmTp1ar7whS80e4ZpY2NjfvCDH7RY5gIAAAAAK1fHVR2gNSZPnpzbbrstJ598co488sgkyciRI7PXXnvlvPPOy7XXXtvs3PHjx2f69Om5/vrrM3jw4CTJrrvumhEjRuSqq67KSSedtNSc6667Lq+++mr233//XH311e3zpmi1h59vzZm01SxcWJ+u8+cmqdQ0c8dWbA3KZMyEKTXPqVYbM2fOvHSf/mQqldr+3eysfZu/xQkAAAC05D2Vm+PHj0+3bt3aZNxXv/rVFd7unXfembq6uhxwwAFNy7p06ZL9998/559/fl599dX06tVrmXPvuuuuDBo0qKnYTJK+fftmp512yh133LFUufn666/nggsuyNe+9jWXowMAAABAgbyncvOXv/xli69XKpUVGpfUVm5OnTo1ffr0yTrrrLPE8sWF5dSpU5dZbjY2NmbatGnZb7/9lnpt0KBBuf/++zN//vwl1nvhhRemR48e+fznP59LLrlkhTMCAAAAAO2r1eVmW95Hc3EJuqJmz56dHj16LLV88bJZs2Ytc97rr7+e+vr65c5dXG4+/fTTue6663L55Zenrq6upowNDQ1paGioaU6ZVFrx+Vcbq03/bazxbq/VFvdlK47F6rv+W6lt/vI+12q1sfY4/7c/3/lvbfNbylOkLK36nN49rc0/qyIdN8XaN8U6bmq3eH1F+Q4uUp4iZUmKladIWZJi5SlSlqT98hTp7zYrO8vy8pT+z8z3kKVoecqTpYh5arOmfPe1RpGyJMXKI0vzipSnSFmS4uUpilr2R6vKzVV938mFCxemc+fOSy3v0qVL0+vLsmjRoiRpce7iMUlyxhln5KMf/Wh22WWXmjM+88wzNc8pky1em9Pqua25vP/FSZOafW3hwkXNvrY8CxfVPndSC1mSZM6cea1M07p901KeImV5L59T0vafVZGOm6LtmyIdN+/FlCm13zu0PRUpT5GyJMXKU6QsSbHyFClL0vZ5ivR3m5WdJSnP37VWdpakWHnKkiUpXp7WWt2/+96LImVJipVHluYVKU+RsiTFy1MmrSo3P/zhD7d1jpp07do19fX1Sy1fXEx27dp1mfMWF5gtzV085vbbb8/jjz+eW2+9tVUZ+/Xrt1o/Xb3ySvea51Qbq5k7d266deuWSofaztbtNnRos689+kCXmrOk+s5fmrp26VLr84QytIUsSdJ9+pO1x6m+a9/UeCbz0KHblSJLqz6npN0+qyIdN0XbN0U6blqjoaEhU6ZMyaBBg2o+6749FClPkbIULU+RshQtT5GytGeeIv3dZmVnWV6e0v+Z+R6yFC1PWbIUMU+t1pTvvrJnKVoeWcqRp0hZipinKBYsWLDCJw6W8mnpPXr0yMyZM5daPnv27CRJz549lzlvgw02SOfOnZvGtTT3nHPOyR577JFOnTrl5ZdfTpK88cYbSZK///3veeutt7Lxxhs3m7Gurm71PihrLC+SNF0iVelQSYda57e4L1vxt9TFl7lUap+/vM+11idFv6Px/+ZWap7fUp4iZWnV55S042dVpOOmWPumWMdN6xXte7hIeYqUJSlWniJlSYqVp0hZknbIU6S/26zsLMvLU/Y/M99DlqLlKU+WIuZpndX+u+89KFKWpFh5ZGlekfIUKUtSvDyrWi37opTl5oABA/Lwww8v9fCfJ554IkkycODAZc7r0KFD+vXrlyefXPqspMmTJ2fzzTdvWt+rr76aX//61/n1r3+91Nh99tknAwYMyM0339wWbwcAAAAAaIVSlpvDhw/PlVdemeuuuy5HHnlkkncuNZ8wYUKGDBnS9KT0GTNm5F//+lf69u3bNHePPfbIj370o6ZTfpPkueeey0MPPZQjjjiiadxPf/rTpbZ722235fbbb88Pf/jDvP/972/PtwgAAAAALEcpy80hQ4Zk+PDhOf/88zNnzpz07t07EydOzCuvvJIzzjijadwpp5ySRx55JNOmTWtadtBBB+X666/PMccckyOOOCIdO3bM2LFj07179yXKzWHDhi213alTpyZJPvrRj6Zbt27t+A4BAAAAgOUpZbmZvHNPzAsuuCC33HJL5s2bl/79++eyyy7Lhz70oRbnrbPOOhk3blzOPPPMXHrppWlsbMyOO+6YMWPGKCwBAAAAoERKW2526dIlp5xySk455ZRmx4wbN26Zy9///vfnoosuqnmbxx13XI477ria5wEAAAAAba81j8QFAAAAAFjllJsAAAAAQCkpNwEAAACAUlJuAgAAAAClpNwEAAAAAEqptE9Lh6Ia+fI5rZhVzcKFi9J1YZcklRrnjmvF9gAAYA1x6/E1T6lUq9nitTmpvNI9qdT49/MRF9a8PQBaT7kJqzFFKwAAALA6c1k6AAAAAFBKyk0AAAAAoJSUmwAAAABAKSk3AQAAAIBSUm4CAAAAAKWk3AQAAAAASkm5CQAAAACUknITAAAAACgl5SYAAAAAUErKTQAAAACglJSbAAAAAEApKTcBAAAAgFLquKoDUE4PPz+3FbOqWbiwPl3nz01SqWnmjq3YGlB+YyZMqXlOtdqYOXPmpfv0J1Op1P5veGftO6jmOQAAAKwaztwEAAAAAEpJuQkAAAAAlJJyEwAAAAAoJeUmAAAAAFBKyk0AAAAAoJSUmwAAAABAKSk3AQAAAIBS6riqAwAAAP+/h5+f24pZ1SxcWJ+u8+cmqdQ8e8dWbBEAoAicuQkAAAAAlJJyEwAAAAAoJeUmAAAAAFBKyk0AAAAAoJSUmwAAAABAKSk3AQAAAIBSUm4CAAAAAKWk3AQAAAAASkm5CQAAAACUUsdVHQAAAADay8PPz23FrGoWLqxP1/lzk1RqmrljK7YGQOs5cxMAAAAAKCXlJgAAAABQSspNAAAAAKCU3HMTAFbUrcfXPKVSrWaL1+ak8kr3pFLbPbsy4sKatwcAALAmceYmAAAAAFBKyk0AAAAAoJSUmwAAAABAKSk3AQAAAIBSUm4CAAAAAKXkaekALGHky+e0YlY1CxcuSteFXZLU+ETwjGvF9gAAAMCZmwAAAABASSk3AQAAAIBSUm4CAAAAAKWk3AQAAAAASkm5CQAAAACUkqelA8AKevj5ua2YVc3ChfXpOn9uan2S/I6t2BoAAMCaxJmbAAAAAEApKTcBAAAAgFJSbgIAAAAApaTcBAAAAABKSbkJAAAAAJSSchMAAAAAKCXlJgAAAABQSspNAAAAAKCUlJsAAAAAQCkpNwEAAACAUlJuAgAAAAClpNwEAAAAAEpJuQkAAAAAlJJyEwAAAAAoJeUmAAAAAFBKyk0AAAAAoJSUmwAAAABAKSk3AQAAAIBSUm4CAAAAAKWk3AQAAAAASkm5CQAAAACUknITAAAAACgl5SYAAAAAUErKTQAAAACglJSbAAAAAEApKTcBAAAAgFJSbgIAAAAApaTcBAAAAABKqeOqDgAAwEpy6/E1T6lUq9nitTmpvNI9qVRqmzziwpq3BwAAtXDmJgAAAABQSspNAAAAAKCUlJsAAAAAQCkpNwEAAACAUvJAIQAooTETptQ8p1ptzJw589J9+pOpVGr/982z9h1U8xwAAID25MxNAAAAAKCUlJsAAAAAQCkpNwEAAACAUlJuAgAAAAClpNwEAAAAAErJ09IBKKyRL5/TilnVLFy4KF0XdklSacX8ca2YAwAAwKrgzE0AAAAAoJSUmwAAAABAKSk3AQAAAIBSUm4CAAAAAKWk3AQAAAAASkm5CQAAAACUknITAAAAACgl5SYAAAAAUErKTQAAAACglJSbAAAAAEApdVzVAVqrvr4+F154YW6++ea88cYb6d+/f0444YR85CMfWe7cmTNn5swzz8wDDzyQxsbG7LjjjjnttNOy+eabN4159dVXc+ONN+b3v/99XnjhhXTo0CH9+vXLsccem5133rk93xoAlM+tx9c8pVKtZovX5qTySvekUqlt8ogLa94eAACw+intmZunnnpqxo4dmxEjRuT0009PXV1djj766Dz66KMtznvzzTczevTo/PnPf84xxxyTr33ta5k6dWq+8IUv5B//+EfTuHvvvTc/+9nP0rt375xwwgn58pe/nDfffDOHH354brzxxvZ+ewAAAADAcpTyzM3Jkyfntttuy8knn5wjjzwySTJy5MjstddeOe+883Lttdc2O3f8+PGZPn16rr/++gwePDhJsuuuu2bEiBG56qqrctJJJyVJdtxxx/zud79Lt27dmuYeeOCB+exnP5uLLroo++23Xzu+QwAAAABgeUp55uadd96Zurq6HHDAAU3LunTpkv333z+PP/54Xn311Wbn3nXXXRk0aFBTsZkkffv2zU477ZQ77rijadk222yzRLGZJJ07d87HPvax/P3vf8/8+fPb8B0BAAAAALUqZbk5derU9OnTJ+uss84SyxcXllOnTl3mvMbGxkybNi3bbbfdUq8NGjQoL7744nJLy9mzZ2ettdbKWmut1cr0AAAAAEBbKOVl6bNnz06PHj2WWr542axZs5Y57/XXX099ff1y5/57abrYCy+8kLvvvjvDhw9PXV1dixkbGhrS0NDQ4phyq7Z+SjVJpbb5Le/LImUpWp6SZylaniJlKVqe1SRL0fK0lKVabaw9SrX6rv/WPr+lPJVq7fum2lht+m9jjf/cWm3jP2MXv7ei/NndXnnK/jkla8q+8d1XlCxFy1OeLEXLU6QsrVOkP6eKlCUpVh5ZmlekPEXKkhQvT1HUsj9KWW4uXLgwnTt3Xmp5ly5dml5flkWLFiVJi3MXj/l3//rXv3L88cena9eu+frXv77cjM8888xyx5TZwoXL3k8rNLeZfdySSZMmlSJLUqw8q0uWpFh5ipQlKVaesmdJipWnpSxz5sxrdZa5c+e2al5LebZ4bU4r07Quz4vL+S5urSlTprTLelurrfOsLp9TsnrvG999xcmSFCtPWbIkxcpTpCzvVZH+nCpSlqRYeWRpXpHyFClLUrw8ZVLKcrNr166pr69favniYrJr167LnLe4wGxp7uIx79bQ0JATTzwxf/vb3/Kzn/0sG2+88XIz9uvXL2uvvfZyx5XVow8svZ+Wq/rOXw66dumSVGqbOnTo0FJkKVqe0mcpWp4iZSlantUkS9HytJSl+/Qna49SrWbu3Lnp1q1bKpXad87QoUvf1mWxyivda8/T+K48HWrL020538W1amhoyJQpUzJo0KDlXp2xMrRXnrJ/TsmasW989xUnS9HylCVL0fIUKUtrFenPqSJlKVoeWcqRp0hZipinKBYsWLDCJw6Wstzs0aNHZs6cudTy2bNnJ0l69uy5zHkbbLBBOnfu3DRuRed+61vfyu9///ucd9552WmnnVYoY11d3Wp+ULbib4aLL+eo1D6/5X1ZpCxFy1PyLEXLU6QsRcuzmmQpWp6WslQqrbltduP/za20an6L+6YVZeniy3grHSrpUOv8dvoztmh/frd5ntXkc3pn1avzvvHdV5QsRctTnixFy1OkLO9Nkf6cKlKWpFh5ZGlekfIUKUtSvDyrWi37opQPFBowYECmT5++1MN/nnjiiSTJwIEDlzmvQ4cO6devX558cumzXSZPnpzNN998qftt/vCHP8yECRMyZsyY7LXXXm30DgAAAACA96qU5ebw4cPT0NCQ6667rmlZfX19JkyYkCFDhqRXr15JkhkzZuTZZ59dYu4ee+yRKVOmLHEvg+eeey4PPfRQhg8fvsTYK664IldeeWW+9KUv5dBDD23HdwQAAAAA1KqUl6UPGTIkw4cPz/nnn585c+akd+/emThxYl555ZWcccYZTeNOOeWUPPLII5k2bVrTsoMOOijXX399jjnmmBxxxBHp2LFjxo4dm+7du+eII45oGnf33Xfn3HPPTZ8+fbLVVlvl5ptvXiLDRz7ykWy00Ubt/2YBgNrcenzNUyrVarZ4bc47912s9fLiERfWvD0AAKBtlLLcTJJzzjknF1xwQW655ZbMmzcv/fv3z2WXXZYPfehDLc5bZ511Mm7cuJx55pm59NJL09jYmB133DFjxoxJt27dmsY9/fTTSZLp06fn5JNPXmo9V199tXITAAAAAFah0pabXbp0ySmnnJJTTjml2THjxo1b5vL3v//9ueiii1pc/3HHHZfjjjvuPWUEAAAAANpPactNAABKrBW3D0jcQgAAgCUpNwGA9+zh5+e2YlY1CxfWp+v8uUlqK6l2bMXWAACA1U8pn5YOAAAAAKDcBAAAAABKSbkJAAAAAJSSchMAAAAAKCXlJgAAAABQSspNAAAAAKCUlJsAAAAAQCkpNwEAAACAUlJuAgAAAAClpNwEAAAAAEpJuQkAAAAAlJJyEwAAAAAopY6rOgAAAKxqDz8/txWzqlm4sD5d589NUqlp5o6t2BoAAEtz5iYAAAAAUErKTQAAAACglJSbAAAAAEApKTcBAAAAgFJSbgIAAAAApaTcBAAAAABKSbkJAAAAAJSSchMAAAAAKCXlJgAAAABQSspNAAAAAKCUlJsAAAAAQCkpNwEAAACAUlJuAgAAAAClpNwEAAAAAEqp46oOAADQlh5+fm4rZlWzcGF9us6fm6RS08wdW7E1AACgbSg3AQAAYCUYM2FKq+ZVq42ZM2deuk9/MpVKbRdgnrXvoFZtE6AsXJYOAAAAAJSSchMAAAAAKCXlJgAAAABQSspNAAAAAKCUlJsAAAAAQCkpNwEAAACAUlJuAgAAAAClpNwEAAAAAEpJuQkAAAAAlJJyEwAAAAAoJeUmAAAAAFBKyk0AAAAAoJSUmwAAAABAKSk3AQAAAIBSUm4CAAAAAKWk3AQAAAAASkm5CQAAAACUknITAAAAACgl5SYAAAAAUErKTQAAAACglDqu6gAAAKurMROmtGpetdqYOXPmpfv0J1Op1PZv0WftO6hV2wQAgDJy5iYAAAAAUErKTQAAAACglJSbAAAAAEApuecmAAAAwLvdenzNUyrVarZ4bU4qr3RPKpXaJo+4sObtAe9w5iYAAAAAUErKTQAAAACglFyWDgDASvfw83NbObOahQvr03X+3CS1XfK3Yyu3CABAcTlzEwAAAAAoJeUmAAAAAFBKLksHAACANZEnggOrAWduAgAAAAClpNwEAAAAAErJZekAAGuI1j2h3NPJAQAoLmduAgAAAAClpNwEAAAAAEpJuQkAAAAAlJJyEwAAAAAoJQ8UAgBoJyNfPqeVM6tZuHBRui7sklof4pOMa+U2AQCgfJy5CQAAAACUknITAAAAACgl5SYAAAAAUErKTQAAAACglDxQCAAAAFi1bj2+VdMq1Wq2eG1OKq90Tyo1PoRvxIWt2iZQLM7cBAAAAABKSbkJAAAAAJSSy9IBAAAAKJ9W3M7ArQxWP87cBAAAAABKSbkJAAAAAJSSy9IBoIRGvnxOK2ZVs3DhonRd2CVJjZfgJEnGtWIOAABA+3HmJgAAAABQSspNAAAAAKCUXJYOAAAAa6CHn5/bilnVLFxYn67z56bW29zs2IqtkZX/RPDEU8EpFWduAgAAAAClpNwEAAAAAEpJuQkAAAAAlJJyEwAAAAAoJQ8UAgAAAGDFrOwHHHm4Ecuh3AQAAICVYOTL57RyZjULFy5K14VdUusTypNxrdwmQDm4LB0AAAAAKCXlJgAAAABQSspNAAAAAKCUlJsAAAAAQCkpNwEAAACAUlJuAgAAAAClpNwEAAAAAEpJuQkAAAAAlJJyEwAAAAAoJeUmAAAAAFBKyk0AAAAAoJQ6ruoAAAAAAFB6tx5f85RKtZotXpuTyivdk0qltskjLqx5e6sj5SYAAACwSj38/NxWzqxm4cL6dJ0/N0ltxdCOrdwiUCylvSy9vr4+5557bnbZZZcMHjw4o0aNygMPPLBCc2fOnJnjjz8+H/zgB7PDDjvk2GOPzUsvvbTMsddff30+/elPZ9CgQdl9990zbty4tnwbAAAAAEArlbbcPPXUUzN27NiMGDEip59+eurq6nL00Ufn0UcfbXHem2++mdGjR+fPf/5zjjnmmHzta1/L1KlT84UvfCH/+Mc/lhh77bXX5lvf+la22WabfPvb387QoUPzgx/8IJdffnl7vjUAAAAAYAWU8rL0yZMn57bbbsvJJ5+cI488MkkycuTI7LXXXjnvvPNy7bXXNjt3/PjxmT59eq6//voMHjw4SbLrrrtmxIgRueqqq3LSSSclSRYuXJgf//jH+fjHP56LLrooSfK5z30ujY2NufTSS3PAAQdk/fXXb+d3CgAAAAA0p5Rnbt55552pq6vLAQcc0LSsS5cu2X///fP444/n1VdfbXbuXXfdlUGDBjUVm0nSt2/f7LTTTrnjjjualj388MN5/fXXc9BBBy0x/+CDD86CBQvy+9//vu3eEAAAAABQs1KWm1OnTk2fPn2yzjrrLLF8cWE5derUZc5rbGzMtGnTst122y312qBBg/Liiy9m/vz5SZKnnnoqSZYa+4EPfCAdOnRodhsAAAAAwMpRysvSZ8+enR49eiy1fPGyWbNmLXPe66+/nvr6+uXOXWeddTJ79uzU1dWle/fuS4zr3LlzNthgg2a30djYmOSde3s2NDSs+Jsqm/ctvQ+Xq5p06FSfdOpc60Ps8s9//rMcWYqWp+xZipanSFmKlmc1yVK0PEXKUrQ8q3WWouUpUpai5VlNshQtT5GyFC1PabIULU/ZsxQtT5GytGOep2bVtSpL/VsbpvOsjjVn2baFLJWOG9YepbGa+rXq8mbdeql0qP3Lr1qgPGXJUsQ8ZbZw4cIk/3/P1pJKtVqttnegtjZs2LBsueWW+dnPfrbE8pdeeinDhg3LmDFjcthhhy0179VXX83HP/7xfOMb38hRRx21xGs33HBDTj/99Nx0000ZOHBgTjvttNx222154oknllrPxz/+8Wy77ba55JJLlnptzpw5mT59+nt6fwAAAACwpuvTp89SJx7+u1Keudm1a9fU19cvtXzRokVNry9Lly5dkqTFuYvHdO3aNW+99dYy17No0aJmt7H++uunT58+6dKlSzp0KOVV/wAAAACwyjQ2NmbRokUr9DDvUpabPXr0yMyZM5daPnv27CRJz549lzlvgw02SOfOnZvGtTS3R48eaWhoyJw5c5ZoiOvr6/P66683u42OHTsut1EGAAAAAJr378/aaU4pTy0cMGBApk+f3vTwn8UWX0I+cODAZc7r0KFD+vXrlyeffHKp1yZPnpzNN9+8acctXse/j33yySfT2NiYAQMGvOf3AQAAAAC0XinLzeHDh6ehoSHXXXdd07L6+vpMmDAhQ4YMSa9evZIkM2bMyLPPPrvE3D322CNTpkzJlClTmpY999xzeeihhzJ8+PCmZf/5n/+ZDTbYIL/85S+XmP/LX/4ya621Vj7+8Y+3wzsDAAAAAFZUKR8olCTHH3987rnnnhx66KHp3bt3Jk6cmClTpmTs2LH50Ic+lCQ55JBD8sgjj2TatGlN8+bPn5999tknb775Zo444oh07NgxY8eOTUNDQ26++eZ069ataew111yT733ve9ljjz2y66675tFHH81NN92UE088MV/60pdW+nsGAAAAAP5/pS03Fy1alAsuuCC33npr5s2bl/79++f444/Prrvu2jRmWeVmkvz973/PmWeemQceeCCNjY3ZcccdM2bMmPTu3Xup7fzqV7/KlVdemZdffjm9evXKwQcfnEMPPTSVSqXd3yMAAAAA0LzSlpsAAAAAwJqtlE9Lh9XFW2+9lUWLFqVLly7p1KnTqo5TKPbNsi1YsCCzZs3KwoUL07Vr1/Ts2TNrr732qo5VGI6bpTlmmmffLNtbb72V6dOnL7Vv+vTp4+fq//iuWTY/U82zb5bN903LHDfL5rhpnn2zbDNnzszUqVOX2i8DBw7MxhtvvKrjLWX+/Pl54403sskmm6zqKKWh3KRd/OlPf8odd9yRp556KrNmzWr6PwCLv0A+/elP5yMf+ciqjtnkd7/7XX7zm9/krLPOatftvP3225k4cWLTvpk3b17Ta+uvv37Tvtlnn30K84fPzTffnBtvvDFXX311u27HvmnevHnzcuWVV+bOO+/Miy++uNTrm2++eT796U/nsMMOy4YbbtiuWVbUNddckyuvvDL33ntvu27HcbNsZTxmkpVz3Ng3zXvxxRdz0UUX5d57783ChQuTJNVqtelWPF27ds1uu+2W4447Ln369Gm3HLW69NJL85Of/CRPPfVUu22jjN81ie+b5qysP6Psm+aV8ftmZXzXJI6bljhummffLNtjjz2Wc889N5MmTUryzj55t0qlkiFDhuSb3/xm/uM//qPdctRq3LhxueiiizJ16tRVHaU0lJu0qQULFuSEE07Ifffdl7XWWisDBw7Mf/zHf6RLly5ZtGhRZs+endtvvz033nhjdtlll1x44YWF+NfHp59+OjfddFO7lptz587NkUcemalTp6ZPnz756Ec/mh49eiyxbyZPnpzvfOc7GT9+fK688solHnC1qsyYMSN//vOf23Ub9k3zXnrppYwePTqzZs3Kf/7nf+Yzn/nMMvfNFVdckZtvvjnjxo3L5ptv3q6ZVsQbb7yRGTNmtOs2HDfLVtZjJmn/48a+ad5TTz2VQw45JHV1dRkxYkQGDx681L554oknctddd+UPf/hDrr766my77bbtlqdW7XmXpbJ+1yS+b5qzMv6Msm+aV+bvm/a+o5vjpnmOm+bZN8v2pz/9KUcffXQ22WSTnHjiiRk0aFB69uyZzp07p76+PrNmzcoTTzyRiRMn5tBDD83ll1+enXfeud3y0L6Um7Sp888/Pw899FC+973vZeTIkcs8e+Gtt97KTTfdlB/84Ac5//zz861vfWsVJF35zj777MyYMSNXXXVVdtppp2bHPfjggznhhBPywx/+MD/84Q9XYsJVx75p3uLC/dZbb81WW23V7LjnnnsuRx55ZM4666xccskl7ZKllr+0vvHGG+2S4d0cN8tWpGMmKdZxY9807+yzz84mm2ySX/ziF80Wc/vvv39OPPHEHHroofnhD3+YX/ziF+2Wp5ZC7uWXX263HInvmpYU6WeqSD9PiX3TkiJ93xTpuyZx3LTEcdM8+2bZLrjgggwaNCi/+MUv0rlz56Ve79u3b3baaaccccQRGT16dC644IJ2LTdvuummFR7rjM3aKTdpU3feeWeOPPLIjBo1qtkxnTp1yqhRo/Lyyy/nhhtuaLdy85Of/OQKj50/f367ZHi3P/zhDznyyCNb/D9GSZq+YK+88sp2yzJw4MB2W3dr2DfNe/jhh/O1r32txb/gJslWW22VQw89ND/5yU/aLctuu+3WdGnL8rz7Mpj24rhZtiIdM0mxjhv7pnlTpkzJN7/5zeWecditW7cceOCBOffcc9stS5Iccsghhdk3RfquSXzfNKdIP0+JfdOSIn3fFOm7JnHctMRx0zz7ZtmmTZuWb33rW8ssNt+tc+fO2XfffXPGGWe0W5YkOfXUU1OpVFb4bNX2Pm5WN8pN2tT8+fPz/ve/f4XG9urVK2+++Wa7ZXn11Vez8cYbp3///ssd+8ILL7T7vzjW19fnfe973wqNfd/73pf6+vp2y1JXV5fNN998hf5l6sknn8zkyZPbLUti37SkQ4cOaWhoWKGxDQ0N6dChQ7tl6dy5c7beeuvstddeyx374IMP5v7772+3LInjpjlFOmaSYh039k3zunbtmtdff32Fxv7jH/9I165d2y1Lkqy99trp379/DjvssOWO/c1vfpPbb7+93bIU6bsm8X3TnCL9PCX2TUuK9H1TpO+axHHTEsdN8+ybZVtvvfXywgsvrNDYF154Ieutt167ZUneuUf3gAED8s1vfnO5Y2+44YZcd9117ZpndaPcpE0NHDgw1113XUaMGNHivTQXLFiQX/3qV+16r4++fftm3XXXzWWXXbbcsZdeemkuuuiidsuSJDvssEOuvvrqDBs2rMUnss2cOTNXX311u97QuF+/funQoUO+/e1vL3fspZde2u4Fnn3TvJ133jlXXnlldtxxx3zgAx9odtxf/vKXXHnlle36oK5tt902CxYsyBFHHLHcsYsWLWr3v+Q6bpatSMdMUqzjxr5p3rBhw3LFFVdkm222yac+9almx/3mN7/JlVdemc985jPtliVJtttuu8yaNSt77LHHcsc+99xz7ZqlSN81ie+b5hTp5ymxb1pSpO+bIn3XJI6bljhummffLNvee++dsWPHZqONNsqoUaOW+Q+Vb775Zn71q1/lF7/4RQ499NB2zTNo0KA899xz2W677ZY79r777mvXLKsj5SZt6pvf/GYOP/zwDB8+PJ/97GfzgQ98ID169Gi6ae/s2bPz5JNP5pZbbsm8efNy1VVXtVuWQYMG5fbbb09DQ0Pq6urabTsr6rTTTsvBBx+c4cOH5+Mf/3i22267Ze6b3//+9+natWvGjBnTblkGDx6cG2+8MfX19cs9TT9p/5tg2zfNO+200zJ69Ojsv//+GTRoULP7ZsqUKdl8883bfd9cc801WbBgwXIfBFatVh03LWjPfVOkYyYp1nFj3zTv5JNPzl//+tccd9xx2WijjZb55/df/vKXzJkzJ0OGDMnJJ5/cblmSd/bNz3/+87zxxhvLPZNiZRw3RfmuSXzfNKdIP0+JfdOSIn3fFOm7JnHctMRx0zz7ZtmOP/74vPrqqzn77LNz3nnnpU+fPkvtl+nTp+ftt9/O8OHDc/zxx7dbluSdfXP//fdnzpw56d69e4tj11tvvfTq1atd86xuKtX2/kljjTN16tScf/75efDBB/P2228vca+IarWajh07ZqeddsqJJ57YrmduPvDAA/n1r3+dk046KT169Ghx7LRp0/LUU09ln332abc8yTtndVx66aW5++67M2fOnKVe79atW3bfffd86UtfWuHL+1tj8uTJ+eMf/5iDDjpoufdmmTFjRl5++eV8+MMfbrc8iX3TkgULFuSaa67Jb37zm0ybNm2JSx47d+6cfv36ZY899shBBx20wpdOtsbzzz+fSZMm5ZOf/ORy/7Iyf/78zJs3L5tuumm75UkcN80pyjGTFO+4sW+aV61Wc8cdd+Q3v/lNpk6dmlmzZmXRokXp0qVLevbsmQEDBmSPPfbI8OHD2/2S/dmzZ+f555/Pdtttt9z/U70yFOW7JvF905yi/Twl9k1LivJ9U7TvmsRx0xLHTfPsm+ZNnjw5d955Z55++unMnj07CxcuTNeuXdOjR48MGDAgw4cPz+DBg9s9x4IFC/KPf/wjPXv2XOaDl3lvlJu0m/nz5+eZZ55Z6gukX79+WWeddVZ1vFVu5syZS+2bli53W5PYN82rVqt5/fXXm/6yssEGG7jZ9P9x3CybY6Z59g2t4bumeX6mmmff0BqOG4AVo9wEAAAAAErJPTdpNzNnzmw6JX7x2Q09e/bMwIEDV/oZDkXKsiLmz5+fN954I5tsssmqjrJSs1Sr1Tz++OPL/KwGDBiQHXbYYaX9a3WRsqyoV199NS+//HI+9KEPreooKzVLkT6rImVZEUU6ZpKVl+ef//xnfv/73+epp55a6rKtgQMH5uMf/3i7PzGzyHmW55lnnslTTz2VkSNHruooSVZenqL9fBctz/IU6ftmZWYp0s93kbKsqCJ936zMLEX6rIqUZUWtqcfN888/n7vuuqvZz2qPPfbIVltt1e45lpdl2223ze67777SsqyIRx99NA899FC++tWvruooSYqXpwycuUmbe+yxx3Luuedm0qRJy7xBcKVSyZAhQ/LNb36z3Z8o+u4sydI31V+ZWWqx+OntU6dOXdVRVlqW22+/Peeee27+/ve/N3vcbLzxxjn55JPb/Sm9RcpSC8eN46ZWRTpmkpWT54orrsgll1ySBQsWpEOHDtlggw3SpUuXLFq0KK+//noaGxuz1lpr5dhjj83RRx/dbjmKmmdFrInHTdF+vouWZ0UU6bhZWVmK9PNdpCy1cNw4blpjTTtuGhoa8oMf/CDXXXddGhsb8/73vz89evRo+qxmz56dv//97+nQoUNGjRqV73znO+32wN0iZalFkY6ZpHh5ysCZm7SpP/3pTzn66KOzySab5MQTT8ygQYPSs2fPpieSzZo1K0888UQmTpyYQw89NJdffnl23nnn1T4LLbvtttvy9a9/PR/84AfzjW98I4MHD17qD8FJkybl2muvzde//vVUq9Xsueeeq30WWlakz6pIWWje//7v/+a8887LiBEjcvDBB+cDH/jAEjd0f+uttzJlypT88pe/zI9//OOstdZaOeSQQ9aYPCxb0X6+i5aHZSvSz3eRstCyIn1WRcpCyy655JJcd911Ofroo3PQQQelZ8+eS42ZNWtWxo8fn8svvzwbbbRRjjvuuNU+C2sWZ27Spj73uc+lrq4uv/jFL9K5c+dmx9XX12f06NFpbGzMr371q9U+S5LcdNNNKzz2t7/9be6+++52+5eaImVJkr333jubbLJJLrvssuWOPeaYYzJjxozceuutq32WJLn44otXeOyf//znPPLII+32WRUpS1Ksz6pIWYr2ORUpzx577JHtt98+Z5999nLHnnLKKZk0aVLuuuuudslStDxjxoxZ4bHTpk3L1KlT2/W4KVKeIv18Fy1PkX6+i5QlKdbPd5GyJMX6+S5SlqRYn1WRsiTF+qyKlCVJPvGJT2TYsGE5/fTTlzv2Bz/4Qe6999787ne/W+2zjB49eoXHzpgxI6+88kq7fk5Fy7O6ceYmbWratGn51re+1WKZmCSdO3fOvvvumzPOOGONyJIkp556aiqVyjIvH1uW9rxPVpGyJMn06dNX+F96hw0blu9///trRJbknf+zVpTPqkhZkmJ9VkXKUrTPqUh5Xn311Xzxi19cobEf/OAHc8cdd7RblqLlmThxYjp27LjEWTnNefvtt9stRxHzFOnnu2h5ivTzXaQsSbF+vouUJSnWz3eRsiTF+qyKlCUp1mdVpCxJMmfOnPTr12+Fxvbv3z/XX3/9GpHlkUceyfrrr7/Ms0f/3b/+9a92y1HUPKsb5SZtar311ssLL7ywQmNfeOGFdr35dJGyJMn666+fAQMG5Jvf/OZyx95www257rrr1ogsSdKjR488+eSTGTVq1HLHTpkyJT169FgjsiRJ9+7ds+222+acc85Z7tirrroqP/vZz9aILEmxPqsiZSna51SkPJtttlkeeOCBFfqc7r///my22WbtlqVoeXr16pVtttkml19++XLHXnLJJfnJT37SblmKlqdIP99Fy1Okn+8iZUmK9fNdpCxJsX6+i5QlKdZnVaQsSbE+qyJlSZKtt946t99+e/bbb7906NCh2XHVajW33XZb+vbtu0Zk6d27d3r16pWxY8cud+zK+JyKlmd1o9ykTe29994ZO3ZsNtpoo4waNSrve9/7lhrz5ptv5le/+lV+8Ytf5NBDD10jsiTJoEGD8txzz2W77bZb7tj77rtvjcmSJJ///Ofzox/9KJ06dcqBBx64zD/knn322fzyl7/M9ddfnxNPPHGNyJIkgwcPzpNPPpkNN9xwuWPXWmutNSZLUqzPqkhZivY5FSnP4Ycfnm9/+9s59thjc9BBB+UDH/hAunXr1vT63Llz8+STT+aXv/xlfv/73+d73/veGpNn8ODBefjhh1do7Mp4AneR8hTp57toeYr0812kLEmxfr6LlCUp1s93kbIkxfqsipQlKdZnVaQsSfKVr3wlX/3qV7P//vvnc5/7XD7wgQ+kZ8+e6dSpU956663MmjUrTz75ZH71q19l2rRp7VqaFSnL4MGD89vf/naFxq6sv9sUKc/qRrlJmzr++OPz6quv5uyzz855552XPn36pEePHk0P8Zk9e3amT5+et99+O8OHD8/xxx+/RmRJ3vkyu//++zNnzpx07969xbHrrbdeevXqtUZkSZIvfvGLeeONN3LVVVflmmuuyVprrZWNNtpoic9q4cKF+f/au/eoqOv8j+MvGAVMRFMHEqIsC8GCvGUqtrXAJtVmalGubabQWu5aXsrdti1PJ2tN3XTLPeqxFkRiN8lLF9dcLc1UvNSaIgpYGhBiMAlCMAIB8/vDH5wMBgZj4PWdeT3+KubDfJ8y30F8872YTCbEx8c79W6MTC3Ahddq586dKCwsRGBgYItrAwMDMXz4cLdoAbheK6YWtteJqScuLg51dXX4+9//jk8++QQAYDKZGn/grqurg81mQ8+ePfH88887dLSKq/TccsstOHr0KM6cOdPq9/zQ0FCMHz/eaS1sPUzvb7Yepvc3UwvA9f5magG43t9MLQDXa8XUAnC9VkwtABAdHY1Vq1Zh8eLFeOGFF5odjNlsNlx77bVYsWIFbr/9drdo+eUvf4lvv/0WRUVFCAgIaHHtzTffjD/84Q9Oa2HscTW6oZA4RUZGBrZu3Yrs7OzGH7J9fHxgNpsRGhqK2NhYREREuFWL1WpFaWlp42+uOhNTy48VFRXho48+svtaxcTEtPoXgSu2SMuYXiumFrGvuroa+/fvR1ZWVpPXKSwsDCNHjoS3t7fb9kjz2N7fbD3SPKb3N1OLtIzptWJqkdadPHkSx48fb/a1uu6669y2RVyfhpsiIiIiIiIiIiJiSDotXUREXMYPP/yA6upqeHt7d/pRyWoxTo9IW7Htw0w9ahFpP1arFcXFxY1Hvfn7++Oyyy5z+xa2HqYWEXel4aY4RXp6Oj788EMcP34cxcXFjT9Y+vv7IywsDHfeeSciIyPdroWth6nFUTt37sS2bduwcOHCzk6hagG4ejqqpba2Fps2bWrcj8vKyhof69mzZ+N+PGHCBKf/w1YtxulxxHvvvYcNGzZg7dq1nZ0CgKuHqQXomB62fZipRy0/H9N7iqkF4OrpqJaysjIkJiZi69atyM/Pb/J4cHAw7rzzTkydOtWhG2i5SgtbD1NLW6SmpiIxMREff/xxZ6eopQVsPUag09KlXVmtVsyePRu7d+9Gt27dEBYWBrPZDG9vb1RXV8NisSArKwvnz5/HmDFj8Nprrzntt1pMLWw9TC1ttXLlSrz++uvIysrq7BSqFoCrpyNaSkpKkJCQgKysLPTv3x8RERFN9uOMjAzk5uYiNDQUiYmJF93lUy3Ob2HscRTT+wng6mFqAZzfw7YPM/WopX0wvaeYWgCuno5o+eabbzBlyhQUFxdj5MiRdvfj/fv3w2w2IyUlBcHBwS7fwtbD1NJW7vaeMmILwNdjBDpyU9rV0qVLsX//frz44osYP358s78V/+GHH/Duu+/ipZdewtKlS/Hcc8+5fAtbD1OLyKV65ZVXUFhYiKSkJIwaNcruun379mH27NlYtGgRFi1apJYObGHsEWkrtn2YqUctIu2r4YyXDz74ANdee63ddadOnUJCQgIWLlyIFStWuHwLWw9TCwAUFhY6vLa8vNxpHYBaWsLW42o03JR2tXXrViQkJCAuLs7umq5duyIuLg4FBQVYv36904ZmTC1sPUwtABAdHe3w2oqKCqd1AFwtAFcPUwsA7Nq1CwkJCS3+IxYARo0ahfj4eCQmJqqlg1vYesLCwpz23JeCqYepBeDqYdqH2XrUYh/TPszUAnD1MLUAwIEDB/Dkk0+2ODADgGuvvRaPPPIIli9f7hYtbD1MLQAQFRUFDw8Ph9babDaH16rFtXtcjYab0q4qKipwxRVXOLS2X79+qKysdIsWth6mFgA4c+YMAgICMHDgwFbX5uXlOfU3WUwtbD1MLQBQU1OD7t27O7S2e/fuqKmpUUsHt7D1mEwmBAcHY/To0a2uzczMREZGhtNa2HqYWth6mPZhth612Me0DzO1sPUwtQCAp6cn6urqHFpbV1cHT09Pt2hh62FqAQAvLy9cd911+PWvf93q2n379mHPnj1q6eAWxh5Xo+GmtKuwsDCsW7cO99xzT4vXaLRarUhLS8OgQYPcooWth6kFAAYMGIAePXpg1apVra5tuP6IO7Sw9TC1AMDQoUOxdu1axMTEICAgwO66oqIirF27FsOGDVNLB7ew9YSEhMDT0xPPP/98q2tXrlzp9H/EMvUwtbD1MO3DbD1qsY9pH2ZqYethagGA0aNHIzExEbfccgtuuOEGu+uOHTuGxMREp974k6mFrYepBQAGDRoEq9WK+Pj4VtdWV1c7dWimFuP0uBoNN6VdzZs3D9OmTUNsbCzuvfde3HDDDTCbzfDy8kJNTQ0sFgsyMzPx/vvvo6ysDElJSW7RwtbD1AIA4eHh2LJlC+rq6mAymZy6LSO1sPUwtQDAs88+i4ceegixsbG4/fbbceONNza7H3/yySfw8fHBn//8Z7V0cAtbT0REBDZs2ICamhp4eXm1ut7Z91xk6mFqYeth2ofZetRiH9M+zNTC1sPUAlzYj6dMmYL7778f4eHhdvfjo0ePIjg42OnvKZYWth6mFuDCfpyamgqr1drqTWBtNpvT31NqMUaPq9Hd0qXdZWVlYenSpdi3bx9qa2svulaEzWZDly5dMGrUKMyZM8fpRwQytbD1MLXs3bsXmzdvxty5c2E2m1tcm5OTg+PHj2PChAku38LWw9TSoKioCCtXrsT27dtx9uzZJo/37t0bd9xxBx5//HGHL8WgFtftycjIwKefforJkye3epfkwsJCFBQUYMSIEW7Rw9TC2MOyDzP2qKV5TPswUwtbD1NLA6vVitTUVGzbtg05OTkXXULBy8sLISEhGDt2LCZPnuzwpRhcoYWth6nl66+/xuHDhxEdHQ0/P78W11ZUVKCsrAxBQUFq6cAWxh5Xo+GmOE1FRQVOnDgBi8WCqqoq+Pj4wGw2IyQkBL6+vm7bwtbD1CLycxQVFTXZj1s6NVEtnYOtR6St2PZhph61iLQvm82Gc+fOobq6Gt7e3ujVq1en3WSEqYWth6lFxF1puCkiIiIiIiIiIiKGpGtuitMUFRUhKysLxcXFjb819/f3R1hYWIf/5pypha2HqYWth6mFrYepxREVFRUoLy9HYGBgZ6eopQVMPUwtAFcPUwvQcT02mw1ffPFFs9/7QkNDMXTo0A49UoepRy3G6GFqYethanHUmTNnUFBQgJtvvrmzU6haAK4ephaAq0ct9rH1GIGGm9LuDh06hCVLluDw4cPNXgTXw8MDN910E+bNm+f0O1UytbD1MLX8tAdoesH2zvradHYLWw9TS1ukpKTg9ddfR1ZWVmenqKUFTD1MLQBXD1ML0DE9W7ZswZIlS/Dtt9/a/TszICAAf/zjH3HXXXc5rYOxRy3G6GFqYethammLd999l+Z7MVMLwNXD1AJw9ajFPrYeI9BwU9pVeno6pk+fjsDAQMyZMwfh4eHw9/dvvHNccXExjhw5gk2bNuGRRx7B6tWrMXr0aJdvYethamHrYWph62FqERHpKP/5z3/w1FNPYfjw4Xj66acREREBs9kMb29vVFdXw2Kx4PDhw3j77bfx1FNPwWaz4e6773aLHrUYo4epha2HqUVERC6drrkp7eqBBx6AyWRCcnIyvLy87K6rqanBlClTUF9fj7S0NJdvYethamHrYWph62FqAS78RtNRO3bswPbt253220+1GKOHqYWth6mFrWfcuHEIDAzEqlWrWl372GOPobCwEB988IFTWth61GKMHqYWth6mFgD4xz/+4fDazz77DAcPHnTa9z6mFrYepha2HrUYp8fV6MhNaVc5OTl47rnnWhx8AICXlxcmTpyIl19+2S1a2HqYWth6mFrYephaAOCZZ56Bh4dHs6eQNceZ18pSizF6mFrYepha2Hpyc3Px8MMPO7Q2JiYGCxYscFoLW49ajNHD1MLWw9QCXBh+sHzvY2ph62FqYetRi3F6XI2Gm9Ku/Pz8kJeX59DavLw8+Pn5uUULWw9TC1sPUwtbD1MLAPTs2ROhoaGYN29eq2vXr1+PdevWqaWDW9h6mFrYepha2HrMZjMyMzMRFxfX6tqjR4/CbDY7rYWtRy3G6GFqYethagGAPn36YNCgQVi8eHGra5OSkvDGG2+4RQtbD1MLW49ajNPjajTclHY1btw4rFmzBn379kVcXBy6d+/eZE1lZSXS0tKQnJyMRx55xC1a2HqYWth6mFrYephaACA8PBynTp3CjTfe2Ora3bt3q6UTWth6mFrYepha2HomTZqEV199FV27dsVvfvMbDBgwoMmakydP4t///jfeeecdzJkzx2161GKMHqYWth6mFgCIiIhAZmYmLr/88lbXduvWzW1a2HqYWth61GKcHlej4aa0q1mzZuHMmTN45ZVX8Le//Q39+/eH2WxuvOGIxWJBbm4uamtrERsbi1mzZrlFC1sPUwtbD1MLWw9TC3DhB4Q9e/bg7Nmz6NOnT4tr/fz80K9fP7V0cAtbD1MLWw9TC1vPo48+ivLyciQlJSE1NRXdunVD3759L/reV1VVBZPJhPj4eEyfPt1pLWw9ajFGD1MLWw9TC3Dhe9/OnTtRWFiIwMDAFtcGBgZi+PDhbtHC1sPUwtajFuP0uBrdUEicIiMjA1u3bkV2dnbjDwU+Pj4wm80IDQ1FbGwsIiIi3K6FrYepha2HqYWth6XFarWitLQU/v7+6Nq1q9O3pxbj9zC1sPUwtTD2AEBRURE++ugju9/7YmJiEBAQ4JY9ajFGD1MLWw9Ti4iItJ2GmyIiIiIiIiIiImJInp0dICIiIiIiIiIiInIpdM1NcYr09HR8+OGHOH78OIqLi1FdXQ1vb2/4+/sjLCwMd955JyIjI92uha2HqYWth6mFrYepha1HLcboYWph62FqYexpzc6dO7Ft2zYsXLiws1MAcPWoxT6mHqYWgKuHqQXg6mFqAbh6mFoArh612MfWYwQ6LV3aldVqxezZs7F7925069YNYWFhMJvN8Pb2RnV1NSwWC7KysnD+/HmMGTMGr732Gi677DKXb2HrYWph62FqYethamHrUYsxepha2HqYWhh7HLVy5Uq8/vrryMrK6uwUAFw9arGPqYepBeDqYWoBuHqYWgCuHqYWgKtHLfax9RiCTaQdLViwwBYeHm5LS0uz1dTUNLumpqbGlpaWZouIiLAtWLDALVrYepha2HqYWth6mFrYetRijB6mFrYephbGHketWLHCFhoa2tkZjZh61GIfUw9Ti83G1cPUYrNx9TC12GxcPUwtNhtXj1rsY+sxAh25Ke1qzJgxiIuLw6xZs1pdu2zZMqxfvx579+51+Ra2HqYWth6mFrYepha2HrUYo4epha2HqYWtJzo62uG1FRUVKC8vd+qRFkw9ajFGD1MLWw9TC1sPUwtbD1MLW49ajNPjanTNTWlXFRUVuOKKKxxa269fP1RWVrpFC1sPUwtbD1MLWw9TC1uPWozRw9TC1sPUwtZz5swZBAQEYODAga2uzcvLQ3l5udNa2HrUYowepha2HqYWth6mFrYepha2HrUYp8fVaLgp7SosLAzr1q3DPffc0+K1r6xWK9LS0jBo0CC3aGHrYWph62FqYethamHrUYsxepha2HqYWth6BgwYgB49emDVqlWtrm24RpYzMfWoxRg9TC1sPUwtbD1MLWw9TC1sPWoxTo+r0XBT2tW8efMwbdo0xMbG4t5778UNN9wAs9kMLy8v1NTUwGKxIDMzE++//z7KysqQlJTkFi1sPUwtbD1MLWw9TC1sPWoxRg9TC1sPUwtbT3h4OLZs2YK6ujqYTCanbceIPWoxRg9TC1sPUwtbD1MLWw9TC1uPWozT42o03JR2NXToULz99ttYunQpkpKSUFtbCw8Pj8bHbTYbunTpglGjRmHOnDlOPdKCqYWth6mFrYepha2HqYWtRy3G6GFqYethamHrufvuu2Gz2VBSUgKz2dzi2qioKIdPp3eFHrUYo4epha2HqYWth6mFrYepha1HLcbpcTW6oZA4TUVFBU6cOAGLxYKqqir4+PjAbDYjJCQEvr6+btvC1sPUwtbD1MLWw9TC1qMWY/QwtbD1MLUw9oiIiIgIFx25KU5TWVmJ8vJylJWVoaqqCtXV1fD29kZlZWWH/2OEqYWth6mFrYepha2HqYWtRy3G6GFqYethamHrKSoqQlZWFoqLixsHrf7+/ggLC0NAQECHtrD1qMUYPUwtbD1MLWw9TC1sPUwtbD1qMU6PK9BwU9rdoUOHsGTJEhw+fBjAhVPHfszDwwM33XQT5s2bh2HDhrlNC1sPUwtbD1MLWw9TC1uPWozRw9TC1sPUwtbD1MLWoxZj9DC1sPUwtbD1MLWw9TC1sPWoxTg9rkSnpUu7Sk9Px/Tp0xEYGIj7778f4eHh8Pf3b7wBQHFxMY4cOYJNmzbh9OnTWL16NUaPHu3yLWw9TC1sPUwtbD1MLWw9ajFGD1MLWw9TC1sPUwtbj1qM0cPUwtbD1MLWw9TC1sPUwtajFuP0uBoNN6VdPfDAAzCZTEhOToaXl5fddTU1NZgyZQrq6+uRlpbm8i1sPUwtbD1MLWw9TC1sPWoxRg9TC1sPUwtbD1MLW49ajNHD1MLWw9TC1sPUwtbD1MLWoxbj9Lgaz84OENeSk5ODiRMntvhmBQAvLy9MnDgROTk5btHC1sPUwtbD1MLWw9TC1qMWY/QwtbD1MLWw9TC1sPWoxRg9TC1sPUwtbD1MLWw9TC1sPWoxTo+r0XBT2pWfnx/y8vIcWpuXlwc/Pz+3aGHrYWph62FqYethamHrUYsxepha2HqYWth6mFrYetRijB6mFrYepha2HqYWth6mFrYetRinx9VouCntaty4cVizZg3WrFmDysrKZtdUVlYiKSkJycnJGDdunFu0sPUwtbD1MLWw9TC1sPWoxRg9TC1sPUwtbD1MLWw9ajFGD1MLWw9TC1sPUwtbD1MLW49ajNPjanTNTWlXNTU1eOaZZ7BlyxZ06dIF/fv3h9lsbrxIrsViQW5uLmpraxEbG4vFixe3eli2K7Sw9TC1sPUwtbD1MLWw9ajFGD1MLWw9TC1sPUwtbD1qMUYPUwtbD1MLWw9TC1sPUwtbj1qM0+NqNNwUp8jIyMDWrVuRnZ0Ni8WCqqoq+Pj4wGw2IzQ0FLGxsYiIiHC7FrYepha2HqYWth6mFrYetRijh6mFrYepha2HqYWtRy3G6GFqYethamHrYWph62FqYetRi3F6XIWGmyIiIiIiIiIiImJIuuamiIiIiIiIiIiIGJKGmyIiIiIiIiIiImJIGm6KiIiIiIiIiIiIIWm4KSIiIiIiIiIiIoak4aaIiIiISAcZOHAgBg4ciOXLl3d2ioiIiIhL6NLZASIiIiJiTFarFe+99x527NiB7OxsnDt3DjabDb6+vggKCkJISAiGDBmCW2+9Ff369evsXBERERFxQRpuioiIiEibffHFF5g7dy4KCwubPFZaWorS0lJkZmZi48aN6Nu3L/bu3dsJlSIiIiLi6jTcFBEREZE2+frrr5GQkIDKykoAQFRUFMaOHYtrrrkGXbt2RWlpKbKzs5Geno4DBw50cq2IiIiIuDINN0VERESkTZYtW9Y42Fy4cCEmTpzYZE1kZCQSEhJQUlKCDz/8sKMTRURERMRN6IZCIiIiIuKwuro67Nq1CwBw4403NjvY/LHevXvjoYce6og0EREREXFDOnJTRERERBxWUlKCqqoqAMDVV199yc9TU1ODPXv2YM+ePThy5Ajy8/NhtVrh6+uLq666Cr/4xS/w0EMPoXfv3nafIyoqCqdPn8aECRPwyiuv4NixY0hMTMTnn3+O0tJSBAQEICoqCo899thFz3Po0CGsWbMGGRkZOHv2LAICAjB27FjMmDEDvr6+zW7r4YcfxsGDBzFixAikpKTg1KlTSEpKwt69e2GxWNCzZ08MGzYM06ZNw+DBgy/569Lg2LFjWLduHQ4cOIDi4mLYbDYEBARg5MiRmDp1Kq655pqfvQ0RERERV6DhpoiIiIg4rGvXro3/ffLkyUt+nvnz52PTpk1NPn7u3DmcO3cOGRkZeOutt7BixQoMGzas1ed799138dxzz+GHH35o/Fh+fj7WrFmDTz75BG+99RbMZjP++c9/YsmSJbDZbI3rvvnmG7z55pvYt28fUlJS0L179xa3tWvXLsyePRtWq7XxYxaLBVu3bsW2bdvwpz/9CVOnTnXgq9BUfX09Fi1ahOTk5IsaASA3Nxe5ublYv3495s+fjwcffPCStiEiIiLiSjTcFBERERGH9erVC0FBQTh9+jSys7OxevVqPProo/D0bNvVjmpraxEcHIxf/epXCA8PR2BgIEwmEwoLC5Geno4NGzbg3LlzmDlzJjZv3ow+ffrYfa7s7Gxs3rwZV199NeLj4xESEoLKykps2LAB77//PnJzc7Fo0SLccccdWLx4MQYPHozf/va3uOaaa1BaWoqUlBTs2rULx44dw8qVK/H000/b3VZxcTGefvppmEwmzJ07FyNGjAAAHDhwAG+88QYqKiqwcOFCXHnllYiJiWnT1wQAFixYgH/9618AgJtvvhkTJkxAcHAwfHx8kJOTg+TkZHz55ZeYP38++vbti+jo6DZvQ0RERMSVeNh++ithEREREZEWJCYmYtGiRY3/HxQUhKioKAwdOhTh4eEIDg5u9Tny8/MRHBwMDw+PZh/PycnBpEmTYLVaMWPGDMyePbvJmobT0gFgyJAhSEpKQrdu3S5a8+STT+K///0vTCYTevTogVtuuQXLli2DyWRqXFNXV4fJkyfj8OHD6NWrF/bu3YsuXS4+BqDhtHQA6NGjB9atW4cBAwZctObLL7/EpEmTUFFRgYCAAHz88ccXHekKAAMHDgQAzJw5E0888cRFj+3duxfx8fEAgJdeeglxcXFN/szV1dWYPn069u/fj6CgIGzbtq1Jq4iIiIg70Q2FRERERKRNpk6divvuu6/x/0+fPo2UlBTMmTMHMTExiIyMxJw5c7Bjx44mp1Y3uOqqq+wONoELQ8CG4d7HH3/cYo+HhwdefvnlJoNNAJg8eTKACwPM6upqvPjiixcNNgHAZDLhgQceAHDhtPivvvqqxe39/ve/bzLYBIDrr78ejz/+OACgqKio1e6fWr16NQBg7NixzQ42AcDb2xvz588HcOHrfuDAgTZtQ0RERMTVaLgpIiIiIm3i6emJv/71r0hMTMStt97a5MjB7777Dlu2bMGMGTNw//33Iz8/v9XnLCsrQ35+Pr788kucOHECJ06cgJ+fHwDgq6++uuhamj81cODAZoeNABAaGtr436NHj0avXr1aXVdQUGB3Wx4eHhg/frzdx++7777GoW16errddT9VUVHReGTo2LFjW1w7YMAAXH755QCAL774wuFtiIiIiLgincMiIiIiIpckMjISkZGRqKiowP/+9z8cPXoUmZmZ+Pzzz/H9998DADIzMzF58mRs3LgR/v7+F31+Tk4O1qxZg927d8NisdjdTn19PcrLy+1ed7N///52P7dhQNqWdRUVFXbXXXnllS3ewb13794ICgpCQUEBTpw4YXfdTx0/fhz19fUAgLlz52Lu3LkOfd53333n8DZEREREXJGGmyIiIiLys/j6+uK2227DbbfdBgCoqanBBx98gEWLFqGsrAwWiwWvvfYaXn755cbPeeedd/DCCy+gtrbWoW1UVVXZfay509Eb/PhGRy2t+/Ep8g1Dxua0dGOjBn379kVBQQHKyspaXdvg7NmzDq/9sZa+LiIiIiLuQMNNEREREWlXXl5euO++++Dv749HH30UALB9+3YsWLAAnp6eOHnyZONgs0+fPkhISMDIkSMRFBSE7t27N96EZ/369fjLX/4CAHav3dnRWrpO6M/x44Hqiy++iCFDhjj0eT179nRKj4iIiIhRaLgpIiIiIk5x6623ol+/fjhz5gzKyspw7tw59O7dG5s2bUJtbS1MJhNSUlLsXi+zLUc+dhRHTgNvWNOWweOPrwXq4+ODkJCQNreJiIiIuCPdUEhEREREnOan19kE0Hg38tDQULuDTeDC9TrZFBQUoLS01O7jJSUlOH36NAC0aUAZFhbWeFTooUOHfl6kiIiIiBvRcFNEREREnOL8+fONg0xfX9/GO3w3XGfTarXa/dzi4mLs2LHD+ZFtZLPZ8N5779l9fOPGjY2n0I8aNcrh5+3duzcGDx4MANi8eTNKSkp+VqeIiIiIu9BwU0REREQcVllZibi4OOzcubPFG+/U19djwYIFqKysBABERUU1HpnYcNfyvLy8Zo9SPH/+PJ566inam+WsWLECp06davLxkydPYtWqVQAAs9mM6OjoNj3vjBkzAFy4W/uTTz6J8vJyu2tramqQmpqK6urqNm1DRERExNXompsiIiIi0iYZGRl4/PHHERAQgJiYGAwePBiBgYHw9fVFeXk5jh8/jg0bNuDEiRMAgB49emDWrFmNnz9u3DikpKSgvr4ejz32GBISEjBs2DB4e3sjMzMTycnJyM3NxdChQ+lO0b766qtRUlKCBx98EL/73e8wYsQIAMDBgwexevVqfP/99wCA559/Hl5eXm167ttuuw1TpkzB2rVr8dlnn+Guu+7CpEmTMGzYMPTq1QtWqxX5+fn4/PPPsX37dpSVlWH8+PHw9vZu9z+niIiIiFFouCkiIiIiDuvSpQvMZjMsFguKioqQmpqK1NRUu+v79++PV199FVdeeWXjxyIiIvDEE09g+fLlKC8vx7Jly5p8Xnx8PK6//nq64WZAQACeffZZzJ49G6+++mqTxz09PTFv3jyMHTv2kp7/2WefRc+ePbFy5UpYLBYsX77c7trLLrsMJpPpkrYjIiIi4io03BQRERERh3l7e+PTTz/F4cOHkZ6ejiNHjuDrr7/G2bNnUV1djW7dusHf3x+hoaGIjo7GHXfc0ewRjDNnzkR4eDjWrl2Lo0ePwmq1ok+fPoiIiMCkSZMQGRmJjRs3dsKfsHW33347NmzYgDfffBMHDhxAcXEx/Pz8MHz4cEybNg1Dhgy55Of28PDAzJkzce+99+Ltt9/G/v37UVBQgO+//x4+Pj7o168fwsLCMGbMGMTExMDHx6cd/2QiIiIixuNha7jiuYiIiIiINOvhhx/GwYMHMWLECKSkpHR2joiIiIj8P91QSERERERERERERAxJw00RERERERERERExJA03RURERERERERExJA03BQRERERERERERFD0nBTREREREREREREDEl3SxcRERERERERERFD0pGbIiIiIiIiIiIiYkgaboqIiIiIiIiIiIghabgpIiIiIiIiIiIihqThpoiIiIiIiIiIiBiShpsiIiIiIiIiIiJiSBpuioiIiIiIiIiIiCFpuCkiIiIiIiIiIiKGpOGmiIiIiIiIiIiIGJKGmyIiIiIiIiIiImJI/wcPLP11Jsyu9QAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting the original probability distribution, and the QCBM prediction probability\n", + "\n", + "device = LocalSimulator()\n", + "qcbm = QCBM(device, n_qubits, n_layers=n_layers, data=data)\n", + "\n", + "qcbm_probs = qcbm.probabilities(job.result()[\"params\"])\n", + "\n", + "labels = [\"{0:{fill}6b}\".format(i, fill=\"0\") for i in range(len(data))]\n", + "\n", + "plt.bar(range(2**n_qubits), data, label=\"target probability\", alpha=0.6)\n", + "plt.bar(range(2**n_qubits), qcbm_probs, label=\"QCBM probability\", alpha=0.6)\n", + "plt.xticks([i for i in range(len(data))], labels, rotation=\"vertical\", size=12)\n", + "plt.yticks(size=12)\n", + "\n", + "plt.xlabel(\"Sample\", size=20)\n", + "plt.ylabel(\"Probability\", size=20)\n", + "\n", + "fig = plt.gcf()\n", + "fig.set_size_inches(16, 8)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! As expected, the QCBM probability distribution closes matches the target distribution. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantum Task Summary\n", + "{}\n", + "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", + "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n" + ] + } + ], + "source": [ + "print(\"Quantum Task Summary\")\n", + "print(job.result()[\"task summary\"])\n", + "print(\n", + " \"Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\"\n", + ")\n", + "print(\n", + " f\"Estimated cost to run quantum tasks in this hybrid job: {job.result()['estimated cost']} USD\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running different hyperparameters\n", + "\n", + "One of the strengths of Braket Hybrid Jobs is the ability to submit and monitor many hybrid jobs simultaneously. We can use this to perform a grid search to find good hyperparameters. Below we initialize 4 unique hybrid jobs with different `n_layers`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating job with 1 layers\n", + "Creating job with 2 layers\n", + "Creating job with 3 layers\n", + "Creating job with 4 layers\n" + ] + } + ], + "source": [ + "jobs = []\n", + "\n", + "for n_layers in range(1, 5):\n", + " print(f\"Creating job with {n_layers} layers\")\n", + " tmp_job = train_circuit_hybrid_job(n_qubits, n_layers, n_iterations=10)\n", + "\n", + " jobs.append(tmp_job)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To check the results, we could load the results as we did before, or we could check the \"Monitor\" tab in the Braket Jobs dashboard in the AWS Console.\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 238 ms, sys: 10.8 ms, total: 249 ms\n", + "Wall time: 5min 42s\n" + ] + } + ], + "source": [ + "%%time\n", + "jobs[-1].result(); # wait for the last job to finish" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now plot the results from all the hyperparameters experiments once they finish. If the cell below does not work, wait a few minutes for metrics to load and try again." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dfs = []\n", + "for i, j in enumerate(jobs):\n", + " df = pd.DataFrame(j.metrics())\n", + " df.sort_values(by=[\"iteration_number\"])\n", + " dfs.append(df)\n", + " plt.plot(df[\"iteration_number\"], df[\"loss\"], label=f\"n_layers={i+1}\")\n", + "\n", + "plt.xlabel(\"iteration_number\")\n", + "plt.ylabel(\"loss\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the plots above, we see that the loss is much lower for `n_layers=3` and `n_layers=4`. We can conclude for 5 qubits, we need at least 3 layers in the QCBM to accurately learn the two-peak Gaussian data. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantum Task Summary\n", + "{}\n", + "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", + "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n", + "Quantum Task Summary\n", + "{}\n", + "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", + "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n", + "Quantum Task Summary\n", + "{}\n", + "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", + "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n", + "Quantum Task Summary\n", + "{}\n", + "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", + "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n" + ] + } + ], + "source": [ + "for job in jobs:\n", + " print(\"Quantum Task Summary\")\n", + " print(job.result()[\"task summary\"])\n", + " print(\n", + " \"Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\"\n", + " )\n", + " print(\n", + " f\"Estimated cost to run quantum tasks in this hybrid job: {job.result()['estimated cost']} USD\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion \n", + "\n", + "In this notebook, we submitted a single training hybrid job in Amazon Braket Hybrid Jobs. We then simultaneously submitted 4 new hybrid jobs with different hyperparameters to learn about the number of layers required in our circuit." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "\n", + "[1] Benedetti, Marcello, Delfina Garcia-Pintos, Oscar Perdomo, Vicente Leyton-Ortega, Yunseong Nam, and Alejandro Perdomo-Ortiz. “A Generative Modeling Approach for Benchmarking and Training Shallow Quantum Circuits.” Npj Quantum Information 5, no. 1 (May 27, 2019): 1–9. https://doi.org/10.1038/s41534-019-0157-8.\n", + "\n", + "[2] Liu, Jin-Guo, and Lei Wang. “Differentiable Learning of Quantum Circuit Born Machine.” Physical Review A 98, no. 6 (December 19, 2018): 062324. https://doi.org/10.1103/PhysRevA.98.062324.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.10 ('venv': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "vscode": { + "interpreter": { + "hash": "590fab68195cf107911461461f81d5c472d3d6127f579badfcfad30f03e5cab2" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 } \ No newline at end of file diff --git a/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/qcbm/qcbm.py b/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/qcbm/qcbm.py index 79d5d978c..b2f7b2d28 100644 --- a/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/qcbm/qcbm.py +++ b/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/qcbm/qcbm.py @@ -105,7 +105,7 @@ def gradient(self, params: np.ndarray): result = [self.device.run(c, shots=self.shots).result() for c in circuits] res = [result[i].values[0] for i in range(len(circuits))] - res = np.array(res).reshape(2, len(params), 2 ** self.n_qubits) + res = np.array(res).reshape(2, len(params), 2**self.n_qubits) grad = np.zeros(len(params)) for i in range(len(params)): @@ -130,7 +130,7 @@ def compute_kernel(px: np.ndarray, py: np.ndarray, sigma_list=[0.1, 1]): """ x = np.arange(len(px)) y = np.arange(len(py)) - K = sum(np.exp(-np.abs(x[:, None] - y[None, :]) ** 2 / (2 * s ** 2)) for s in sigma_list) + K = sum(np.exp(-np.abs(x[:, None] - y[None, :]) ** 2 / (2 * s**2)) for s in sigma_list) kernel = px @ K @ py return kernel diff --git a/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb b/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb index c363dbf00..e5fdaf4e6 100644 --- a/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb +++ b/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb @@ -45,9 +45,9 @@ "metadata": {}, "outputs": [], "source": [ + "import networkx as nx\n", "import pennylane as qml\n", - "from pennylane import numpy as np\n", - "import networkx as nx" + "from pennylane import numpy as np" ] }, { @@ -127,10 +127,10 @@ "source": [ "from pennylane import qaoa\n", "\n", - "p = 2 # number of QAOA layers\n", - "wires = range(num_nodes) # number of qubits\n", + "p = 2 # number of QAOA layers\n", + "wires = range(num_nodes) # number of qubits\n", "\n", - "params = np.random.rand(2, p) # random initial parameters\n", + "params = np.random.rand(2, p) # random initial parameters\n", "\n", "cost_h, mixer_h = qaoa.maxcut(graph)\n", "\n", @@ -221,8 +221,8 @@ "metadata": {}, "outputs": [], "source": [ - "from braket.jobs.metrics import log_metric\n", "from braket.jobs import get_results_dir\n", + "from braket.jobs.metrics import log_metric\n", "\n", "\n", "def run_qaoa(p=1, steps=10):\n", @@ -371,13 +371,13 @@ "@hybrid_job(device=None, input_data=input_file_path)\n", "def run_qaoa_hybrid_job(p=1, steps=10):\n", " params = np.random.rand(2, p)\n", - " \n", + "\n", " braket_task_tracker = Tracker()\n", "\n", " graph = nx.read_adjlist(input_file_path, nodetype=int)\n", " wires = list(graph.nodes)\n", " cost_h, mixer_h = qaoa.maxcut(graph)\n", - " \n", + "\n", " dev = qml.device(\"default.qubit\", wires=len(wires))\n", "\n", " @qml.qnode(dev)\n", @@ -390,15 +390,18 @@ " for i in range(steps):\n", " params = optimizer.step(cost_function, params)\n", " cost = float(cost_function(params))\n", - " \n", + "\n", " log_metric(metric_name=\"loss\", value=cost, iteration_number=i)\n", "\n", " # save checkpoint data\n", - " save_job_checkpoint(checkpoint_data={\n", - " \"iteration\": i,\n", - " \"params\": params.numpy(),\n", - " \"cost\": cost,\n", - " }, data_format = PersistedJobDataFormat.PICKLED_V4)\n", + " save_job_checkpoint(\n", + " checkpoint_data={\n", + " \"iteration\": i,\n", + " \"params\": params.numpy(),\n", + " \"cost\": cost,\n", + " },\n", + " data_format=PersistedJobDataFormat.PICKLED_V4,\n", + " )\n", "\n", " # save final results\n", " np.save(\"optimal_params.npy\", params)\n", @@ -585,7 +588,7 @@ "source": [ "# May need to wait a bit before metrics show up\n", "# If metrics aren't there, try again after 5 seconds\n", - "import time \n", + "import time\n", "\n", "time.sleep(10)\n", "print(job.metrics())" @@ -619,12 +622,12 @@ } ], "source": [ + "import matplotlib.pyplot as plt\n", + "\n", "# A demonstration of plotting the metrics\n", "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", "from matplotlib.ticker import MaxNLocator\n", "\n", - "\n", "df = pd.DataFrame(job.metrics()).sort_values(\"iteration_number\")\n", "\n", "ax = plt.figure().gca()\n", @@ -657,6 +660,7 @@ "source": [ "from braket.jobs import load_job_checkpoint\n", "\n", + "\n", "@hybrid_job(device=None, input_data=input_file_path, copy_checkpoints_from_job=previous_job_arn)\n", "def continued_run_qaoa_hybrid_job(p=1, steps=10):\n", "\n", @@ -666,7 +670,7 @@ " start_iteration = checkpoint[\"iteration\"]\n", " params = checkpoint[\"params\"]\n", "\n", - " # code below is similar for both hybrid jobs \n", + " # code below is similar for both hybrid jobs\n", " braket_task_tracker = Tracker()\n", "\n", " graph = nx.read_adjlist(input_file_path, nodetype=int)\n", @@ -685,7 +689,7 @@ " for i in range(start_iteration, steps):\n", " params = optimizer.step(cost_function, params)\n", " cost = float(cost_function(params))\n", - " \n", + "\n", " log_metric(metric_name=\"loss\", value=cost, iteration_number=i)\n", "\n", " # save final results\n", @@ -770,7 +774,7 @@ } ], "source": [ - "time.sleep(10) # wait for new metrics to load\n", + "time.sleep(10) # wait for new metrics to load\n", "continued_job.metrics()" ] }, @@ -823,7 +827,9 @@ } ], "source": [ - "print(f\"Estimated cost to run quantum tasks in this notebook: {job.result()['estimated cost'] + continued_job.result()['estimated cost']} USD\")" + "print(\n", + " f\"Estimated cost to run quantum tasks in this notebook: {job.result()['estimated cost'] + continued_job.result()['estimated cost']} USD\"\n", + ")" ] }, { From 7da7865670b78f8b92f0b3d8f2f0efe6650d8327 Mon Sep 17 00:00:00 2001 From: Matthew Beach Date: Mon, 16 Oct 2023 14:27:29 +0000 Subject: [PATCH 08/24] update QCBM example comments --- ...earning_in_Amazon_Braket_Hybrid_Jobs.ipynb | 128 +++++++----------- 1 file changed, 51 insertions(+), 77 deletions(-) diff --git a/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb b/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb index a13af4088..981f0a703 100644 --- a/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb +++ b/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb @@ -62,11 +62,11 @@ "text": [ "T : | 0 | 1 | 2 |3|4| 5 | 6 | 7 | 8 |9|10|11|Result Types|\n", " \n", - "q0 : -Rx(0.53)-Rz(0.86)-Rx(0.21)-C---X----------Rx(0.24)-Rz(0.41)-Rx(0.88)-C----X--Probability--\n", + "q0 : -Rx(0.44)-Rz(0.16)-Rx(0.32)-C---X----------Rx(0.04)-Rz(0.19)-Rx(0.55)-C----X--Probability--\n", " | | | | | \n", - "q1 : -Rx(0.27)-Rz(0.55)-Rx(0.22)-X-C-|-Rx(0.65)-Rz(0.26)-Rx(0.95)----------X-C--|--Probability--\n", + "q1 : -Rx(0.86)-Rz(0.37)-Rx(0.36)-X-C-|-Rx(0.36)-Rz(0.22)-Rx(0.61)----------X-C--|--Probability--\n", " | | | | | \n", - "q2 : -Rx(0.92)-Rz(0.97)-Rx(0.43)---X-C----------Rx(0.25)-Rz(0.42)-Rx(0.98)---X--C--Probability--\n", + "q2 : -Rx(0.71)-Rz(0.88)-Rx(0.82)---X-C----------Rx(0.35)-Rz(0.64)-Rx(0.66)---X--C--Probability--\n", "\n", "T : | 0 | 1 | 2 |3|4| 5 | 6 | 7 | 8 |9|10|11|Result Types|\n" ] @@ -124,7 +124,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -188,15 +188,10 @@ "The number of layers determines how many rotation angles are in the quantum circuit. \n", "For the QCBM, we need `n_params = 3 * n_layers * n_qubits` parameters. \n", "\n", - "\n", "Since we will eventually run this training function as an hybrid job, we add three convenience functions. \n", "Firstly, we use `log_metric` to print the loss function for each iteration. \n", "Once we run this as a hybrid job, the metrics will be displayed in near-real time on the Braket console, or via [Amazon CloudWatch](https://aws.amazon.com/cloudwatch/). \n", "\n", - "Secondly, we write our results to a file prefixed with the path `get_results_dir()`. \n", - "This is necessary because hybrid jobs use a temporary filesystem.\n", - "Once the instance is terminated, all files on the instance are deleted.\n", - "\n", "Lastly, the return statement of the function will be our hybrid job results returned by `job.result()`. \n", "These can be a single object or a dictionary with string keys. \n", "Note that while most native Python objects are supported, custom classes that do have have defined serialization methods may not work. \n", @@ -252,7 +247,7 @@ " final_params = res.x\n", "\n", " # save final parameters\n", - " np.save(get_results_dir() + \"/final_params.npy\", final_params)\n", + " np.save(\"final_params.npy\", final_params)\n", "\n", " return {\n", " \"params\": final_params,\n", @@ -279,24 +274,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "Metrics - timestamp=1697028322.742037; loss=0.0738105584324629; iteration_number=1;\n", - "Metrics - timestamp=1697028324.6127226; loss=0.05514942674966222; iteration_number=2;\n", - "Metrics - timestamp=1697028328.4326718; loss=0.029347326432547782; iteration_number=3;\n", - "Metrics - timestamp=1697028330.5567336; loss=0.02751726243188396; iteration_number=4;\n", - "Metrics - timestamp=1697028332.750267; loss=0.019362259334110732; iteration_number=5;\n", - "CPU times: user 3.87 s, sys: 515 ms, total: 4.39 s\n", - "Wall time: 13.9 s\n" + "Metrics - timestamp=1697465727.9436598; loss=0.07978780483640374; iteration_number=1;\n", + "Metrics - timestamp=1697465729.9204695; loss=0.0580245150698116; iteration_number=2;\n", + "Metrics - timestamp=1697465732.116283; loss=0.04655256459424145; iteration_number=3;\n", + "Metrics - timestamp=1697465733.980105; loss=0.041929509647970764; iteration_number=4;\n", + "Metrics - timestamp=1697465735.990192; loss=0.02641284207090755; iteration_number=5;\n", + "CPU times: user 3.3 s, sys: 471 ms, total: 3.77 s\n", + "Wall time: 11.9 s\n" ] }, { "data": { "text/plain": [ - "{'params': array([ 0.55196856, 0.38672791, 1.35300743, 0.87330452, -0.54221993,\n", - " 0.74465571, 0.61411241, 0.06143268, 0.81830411, 0.28084566,\n", - " 0.98187881, 0.17193621, 0.04322529, 0.02989581, -0.08170342,\n", - " 0.58622265, 0.50212177, 0.32964731, 1.19606685, -0.28772351,\n", - " 0.60482847, -0.04202664, 0.33609895, 0.12554622, 0.46006129,\n", - " 0.44578169, 1.22449628, 1.1830697 , 0.19600585, 0.35640496]),\n", + "{'params': array([ 0.68900666, 0.03283901, 0.81187867, 1.26268392, 0.02617915,\n", + " 0.64396775, -0.11640445, 0.09168079, 0.38625248, 0.51137178,\n", + " 0.6702684 , 0.7341986 , 0.67538873, 0.11565373, 0.05984109,\n", + " -0.46864439, 1.20187778, -1.76293762, -0.27392566, 0.24333289,\n", + " -0.34868898, 0.20794765, 0.35201935, 0.84986321, 1.67362384,\n", + " 0.71571067, 0.50030453, 1.14005808, -0.76029752, 1.31453205]),\n", " 'task summary': {},\n", " 'estimated cost': 0.0}" ] @@ -320,8 +315,6 @@ "\n", "## Training with hybrid jobs\n", "\n", - "Amazon Braket Hybrid Jobs provides fully managed execution of hybrid quantum-classical algorithms, combining AWS classical compute resources based on Amazon EC2 (the \"job instance\") with Amazon Braket quantum processing units (QPUs) or quantum circuit simulators. \n", - "\n", "There are three arguments to the `@hybrid_job` decorator that we will use in this example. Firstly, \n", "since we do not require priority QPU access, we set `device=None` in the decorator arguments. \n", "This argument is responsible for scheduling the hybrid job to run on a QPU. \n", @@ -347,8 +340,7 @@ "from braket.jobs import hybrid_job\n", "\n", "\n", - "# For now, lets set local=False. This uses a local Docker container\n", - "@hybrid_job(device=None, local=False, include_modules=\"qcbm\", input_data=\"data.npy\")\n", + "@hybrid_job(device=None, include_modules=\"qcbm\", input_data=\"data.npy\")\n", "def train_circuit_hybrid_job(n_qubits, n_layers, n_iterations):\n", " return train_circuit(n_qubits, n_layers, n_iterations)" ] @@ -362,8 +354,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 509 ms, sys: 30.4 ms, total: 540 ms\n", - "Wall time: 3min 52s\n" + "CPU times: user 385 ms, sys: 33.4 ms, total: 419 ms\n", + "Wall time: 4min 18s\n" ] } ], @@ -428,19 +420,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 10.4 ms, sys: 475 µs, total: 10.9 ms\n", - "Wall time: 341 ms\n" + "CPU times: user 12.8 ms, sys: 1.38 ms, total: 14.2 ms\n", + "Wall time: 354 ms\n" ] }, { "data": { "text/plain": [ - "{'params': array([ 0.35736532, 0.31073256, 0.91610879, 0.98872712, -0.08082859,\n", - " 0.47862197, 0.68460835, -0.09855128, 0.90463028, 0.25120807,\n", - " 0.83142284, 0.42736354, -0.04195098, 0.69129029, 0.63572789,\n", - " 0.75008587, 0.63212422, 0.95159988, 0.78795069, 0.17262345,\n", - " 0.92826497, 0.75725417, 0.18248898, 0.80719954, 1.31568383,\n", - " 0.85844901, 0.5214184 , 0.04987039, 0.03008104, -0.03636072]),\n", + "{'params': array([ 0.77996265, 0.52813787, 0.61299074, -0.09124156, 0.17680213,\n", + " -0.02222335, 0.91524364, -0.31786518, 0.64940861, 0.62663773,\n", + " 0.87611417, -0.0715285 , -0.00379581, 1.04400452, 0.20672916,\n", + " 0.94888017, 0.55607485, 1.03805133, 1.08456977, -0.75108754,\n", + " 1.13637642, 0.72634854, 0.93536659, 0.17868376, 0.79434158,\n", + " 0.05315669, 0.81228023, -0.62405866, 0.10342629, -0.8736394 ]),\n", " 'task summary': {},\n", " 'estimated cost': 0.0}" ] @@ -487,7 +479,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -496,13 +488,13 @@ "" ] }, - "execution_count": 19, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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08qvI1fTrN2k7fTnV6FgiIiK3ReWkjHJ1MjNvcAsaVC7H+SvpDFmwjaQU3aRNRERsn8pJGebl7sSnj7eiSnkXjly4xojFO0jL1E3aRETEtqmclHE1Krjx6dBWeLo4su34JZ5ZHo1FN2kTEREbpnJiBxpWK89Hg+/AyWxi/d5zTFmzj+xsFRQREbFNKid2op2/LzMGhACwKPIEH/2im7SJiIhtUjmxI/c3q87LPRsC8Mb6g4RHnTI4kYiISF4qJ3ZmeId6DGtfF4Dnv9zDxiMXDU4kIiKSm8qJHXqpR0Pua1qNLGs2o5fsZO/pJKMjiYiI5FA5sUMODiZmDGhGm3oVSc6wMPTT7Zy8lGJ0LBEREUDlxG65OJr5aFALgqp6cvFqOo8t3EZicobRsURERFRO7JmXmxOfDm1FdS9Xjl5MZtii7aRm6CZtIiJiLJUTO1fVy5VPH29FeVdHdsVdZtzy3bpJm4iIGErlRAio4sn8x1ri7OhAxMELrNh/zehIIiJix1ROBIBWdSvydr+mAPz3UDKnEnWCrIiIGEPlRHLc36w6betVJNMKb3132Og4IiJip1ROJIfJZOLfYUGYgLUx59h5ItHoSCIiYodUTiSXRtXL09nPDYDXvtmvDwgUEZESp3IieTzSpBzuzmaiT15mzZ6zRscRERE7o3IieXi7mRnVsR4Ab64/SFqm7n0iIiIlR+VE8jXsTj+qebly+nIqn2w6ZnQcERGxIyonki83ZzMTugcC8P5Pv3HxarrBiURExF6onMhN9W5Wg6Y1vUjOsDDze11aLCIiJUPlRG7KwcHEK/c1AmD59jgOnrticCIREbEHKidySy39KtIjuCrWbHj9mwO6tFhERIqdyon8rRe6B+FsdmDTb/H8fOii0XFERKSMUzmRv1XHx4Mhd/oB8Pra/WRarMYGEhGRMk3lRG7L2M71qejhTOzFZD7fFmd0HBERKcNUTuS2eLk58Uy3BgC88/1hklIzDU4kIiJllcqJ3LaHW9WmfuVyJKZkMven34yOIyIiZVShysmyZcvo0qULwcHB9O/fnz179txy/fXr19O9e3eCg4Pp1asXGzZsyLNObGwso0eP5o477iAkJIS+ffty5syZwsSTYuJoduClHg0B+PTX45xISDY4kYiIlEUFLifr1q1j2rRpjB07lvDwcIKCghg2bBgJCQn5rr9r1y7Gjx9Pv379WL16NV27dmXs2LEcPvzHTb3i4uJ45JFHqFevHkuWLOHrr79mzJgxuLi4FH5kUizuCqxEhwa+ZFisvPntQaPjiIhIGVTgcrJw4UIGDBhA3759qV+/PpMnT8bV1ZWVK1fmu/7ixYvp0KEDw4cPx9/fn3HjxtGoUSOWLl2as84777xDx44dmTBhAo0aNaJ27dp07doVHx+fwo9MioXJZOKlng1xMMG6mHNsO3bJ6EgiIlLGOBZk5YyMDPbt28eoUaNyljk4ONCuXTuioqLyfU50dDRDhgzJtax9+/ZEREQAYLVa+fnnnxk+fDjDhg1j//791KxZk1GjRtGtW7db5rFYLFgsRfeJuTe2VZTbLG1uZw4aVPJgQIuafLH9FK99s49Vo9vi4GAqqYjFyt7fA/Y+ftAc2Pv4QXNQnOO/3W0WqJwkJiZisVjy7NHw8fHh6NGj+T4nPj4eX1/fPOvHx8cDkJCQQEpKCvPmzWPcuHE899xzbNy4kSeffJLFixfTqlWrm+b586GhohQTE1Ms2y1N/m4O7q5q4b+OJmJOX2HOmi10quNWQslKhr2/B+x9/KA5sPfxg+bAyPEXqJwUB6v1+g29unbtmrOHpWHDhuzatYsvvvjiluUkICAAd3f3IstisViIiYkhODgYs9lcZNstTQoyB0+mH+Xt/x1mxaF0Roa1ws259M+Zvb8H7H38oDmw9/GD5qA4x5+SknJbOxYKVE68vb0xm815Tn5NSEjIs3fkBl9f35y9JPmt7+3tjaOjI/7+/rnW8ff3Z+fOnbfMYzabi+WNU1zbLU1uZw6GdajHZ9tOcvpyKgs2n+Cprg1KKF3xs/f3gL2PHzQH9j5+0BwUx/hvd3sFOiHW2dmZxo0bExkZmbPMarUSGRlJ8+bN831OSEgIW7ZsybVs8+bNhISE5GwzODiYY8eO5Vrn+PHj1KhRoyDxpIS5Opl5ISwIgA9+juX8lTSDE4mISFlQ4Kt1hg4dyooVKwgPDyc2NpZJkyaRmppKnz59AJgwYQIzZszIWX/w4MFs3LiRBQsWEBsby5w5c9i7dy8DBw7MWWfYsGGsX7+eFStWcOLECZYuXcpPP/3Eww8/XARDlOLUq2k1mteuQGqmhRn/O2R0HBERKQMKfM5Jjx49uHTpErNnz+bixYs0bNiQ+fPn5xymOXv2LA4Of3Se0NBQpk+fzqxZs5g5cyZ+fn7MnTuXgICAnHXuvvtuJk2axMcff8zrr79O3bp1mT17Ni1atCiCIUpxMplMvHJfI/q8v5kvd57isXZ+NK7uZXQsEREpxQp1QuzAgQNz7fn4syVLluRZFhYWRlhY2C232a9fP/r161eYOGKw0Nre9GpWnTW7z/D6Nwf4bERrTKaycWmxiIiUPH22jhSJF7oH4uzoQOTRBCIOXDA6joiIlGIqJ1Ikanq7M7x9XQD+s+4AGVlWgxOJiEhppXIiReaJu/zxLefMsfhklm09YXQcEREppVROpMh4ujrx7N2BAMyKOMLllAyDE4mISGmkciJFakCLmgRW8SQpNZPZP/xmdBwRESmFVE6kSDmaHXj5voYALI48ztGL1wxOJCIipY3KiRS5Dg0q0TmwElnWbN5Yf9DoOCIiUsqonEix+HePhpgdTPxv/3k2x8b//RNERER+p3IixaJBFU8eaVUbgNe/OYDFmm1wIhERKS1UTqTYjOvWAE9XR/afvcKqXaeMjiMiIqWEyokUG59yLvxfl/oAvP3dIVIysgxOJCIipYHKiRSrx9r5UauiGxeupvPhhqNGxxERkVJA5USKlYujmYlh1y8t/viXWM4mpRqcSEREbJ3KiRS7sCZVaennTVqmlbe/O2R0HBERsXEqJ1LsTCYTL/dsBMCqXafZc+qysYFERMSmqZxIiWhWqwIPNK8BXL+0ODtblxaLiEj+VE6kxDx/byCuTg5sO36J7/adMzqOiIjYKJUTKTHVK7gxskM9AKatP0h6lsXgRCIiYotUTqREjerkTyVPF04kpLAk8oTRcURExAapnEiJ8nBx5Pl7AgF494cjXErOMDiRiIjYGpUTKXF976hJo2rluZqWxbsRh42OIyIiNkblREqc2cHEyz2v35ht6dY4frtwzeBEIiJiS1ROxBDt6vvSrWEVLNZspq07YHQcERGxISonYpiJPYJwdDDxw8ELbDoSb3QcERGxESonYhj/SuUY2KYOAK+v3Y/FqhuziYiIyokYbFy3Bni5OXHw3FW+3HHS6DgiImIDVE7EUBXcnXmqawMApv/vMNfSswxOJCIiRlM5EcMNalOHur4exF9L54OffzM6joiIGEzlRAzn7OjAxLAgAOZtPMapxBSDE4mIiJFUTsQm3N2oCm3qVSQjy8rb3x0yOo6IiBhI5URsgslk4uWejTCZ4L/RZ4iKSzQ6koiIGETlRGxGkxpe9A2tCcDraw+Qna1Li0VE7JHKidiU5+8NxM3JzM4TiayNOWt0HBERMYDKidiUKuVdGd3JH4A31h8kLdNicCIRESlpKidic0Z0rEvV8q6cSkzl083HjY4jIiIlTOVEbI67syPP3xsIwHs//kb8tXSDE4mISElSORGb9EDzGgTX8OJaehbvfH/Y6DgiIlKCVE7EJjk4mHi5Z0MAPt8Wx+HzVw1OJCIiJUXlRGxW63o+dG9cFWs2TF17wOg4IiJSQlROxKa9GBaEk9nEhsMX+fnQBaPjiIhICVA5EZvm5+vBkHZ+wPW9J1kWq7GBRESk2KmciM17sksDvN2dOHLhGl9sP2l0HBERKWYqJ2LzvNycGNctAIB3vj/MlbRMgxOJiEhxUjmRUuGR1rXxr+RBQnIGc3/6zeg4IiJSjFROpFRwMjvw0u+XFi/cdJyTl1IMTiQiIsVF5URKjc6BlWlf35cMi5U3vj1odBwRESkmhSony5Yto0uXLgQHB9O/f3/27Nlzy/XXr19P9+7dCQ4OplevXmzYsCHX4y+++CKBgYG5voYNG1aYaFKGmUwmXurZEJMJ1u45y84Tl4yOJCIixaDA5WTdunVMmzaNsWPHEh4eTlBQEMOGDSMhISHf9Xft2sX48ePp168fq1evpmvXrowdO5bDh3PfkrxDhw5s2rQp52vmzJmFG5GUaQ2rlefBFrUAmPLNAazWbIMTiYhIUStwOVm4cCEDBgygb9++1K9fn8mTJ+Pq6srKlSvzXX/x4sV06NCB4cOH4+/vz7hx42jUqBFLly7NtZ6zszOVKlXK+fLy8irciKTMe/aeADyczew+eZk1e84YHUdERIqYY0FWzsjIYN++fYwaNSpnmYODA+3atSMqKirf50RHRzNkyJBcy9q3b09ERESuZdu2baNt27aUL1+eNm3aMG7cOLy9vW+Zx2KxYLFYCjKEv93en/9rj0rDHPi4OzG6Uz1mfH+EN9YfpFtQJVydzEWy7dIw/uJk7+MHzYG9jx80B8U5/tvdZoHKSWJiIhaLBR8fn1zLfXx8OHr0aL7PiY+Px9fXN8/68fHxOT936NCBu+++m5o1a3Ly5ElmzpzJiBEjWL58OWbzzX/p/PXQUFGJiYkplu2WJrY+B3d4ZuPr5sDZpDSmfhVJ34blinT7tj7+4mbv4wfNgb2PHzQHRo6/QOWkuPTs2TPn+xsnxHbr1i1nb8rNBAQE4O7uXmQ5LBYLMTExBAcH37IUlWWlaQ5ecjzDMyv28N/DqTx1X0sqebr8422WpvEXB3sfP2gO7H38oDkozvGnpKTc1o6FApUTb29vzGZznpNfExIS8uwducHX1zfXXpK/Wx+gVq1aeHt7c+LEiVuWE7PZXCxvnOLabmlSGubgX81rsigyjuiTl5n1w2+80bdpkW27NIy/ONn7+EFzYO/jB81BcYz/drdXoBNinZ2dady4MZGRkTnLrFYrkZGRNG/ePN/nhISEsGXLllzLNm/eTEhIyE1f59y5c1y+fJlKlSoVJJ7YGZPJxCv3Xb8x2/IdJ9l/5orBiUREpCgU+GqdoUOHsmLFCsLDw4mNjWXSpEmkpqbSp08fACZMmMCMGTNy1h88eDAbN25kwYIFxMbGMmfOHPbu3cvAgQMBSE5O5s033yQ6OppTp04RGRnJmDFjqFOnDh06dCiiYUpZdUedivRsWo3sbJi6bj/Z2bq0WESktCvwOSc9evTg0qVLzJ49m4sXL9KwYUPmz5+fc5jm7NmzODj80XlCQ0OZPn06s2bNYubMmfj5+TF37lwCAq5/kJvZbObw4cOsXr2aq1evUrlyZe68806efvppnJ2di2iYUpa92D2I7/ed59ffEvjx4AW6NqxidCQREfkHCnVC7MCBA3P2fPzVkiVL8iwLCwsjLCws3/VdXV355JNPChNDBIBaFd15vH1dPtwQy9R1B+gYUAknsz6ZQUSktNLf4FImjOnsj4+HM0cvJvPZ1jij44iIyD+gciJlQnlXJ565+/qhwlkRh0lKyTQ4kYiIFJbKiZQZD7WsRUCVciSmZDLnxyNGxxERkUJSOZEyw9HswEs9GwGwKPI4x+OTDU4kIiKFoXIiZUqngEp0CqhEpiWbN9YfNDqOiIgUgsqJlDkv9WyI2cHEt/vOsfVowt8/QUREbIrKiZQ5AVU8ebhVLQBeX3sAq1U3ZhMRKU1UTqRMGtctAE8XR2JOJxEeddroOCIiUgAqJ1Im+ZZzYWyX+gC8/d0hUjKyDE4kIiK3S+VEyqwh7fyo6e3GuStpzPvlmNFxRETkNqmcSJnl6mRmYtj1Ty3+cEMs55LSDE4kIiK3Q+VEyrQewVW5o443qZkWpv/vkNFxRETkNqicSJlmMpl4uef1vScrd51i7+kkgxOJiMjfUTmRMq95bW96h1QnOxteX7uf7GxdWiwiYstUTsQuTOgehIujA1uOXuJ/+88bHUdERG5B5UTsQo0KbozoUA+AaesOkJFlNTiRiIjcjMqJ2I3Rd/njW86F4wkpLNlywug4IiJyEyonYjfKuTjy3D0BAMz+4QiXUzIMTiQiIvlRORG70r9FLYKqepKUmsmsiCNGxxERkXyonIhdMTuYeLlnIwCWbjlB7MVrBicSEZG/UjkRu9O+gS9dgyqTZc1m2rqDRscREZG/UDkRuzSxR0McHUxEHDjP5t/ijY4jIiJ/onIidql+5XIMbFMHgNfWHsBi1Y3ZRERshcqJ2K2nuzagvKsjB85eYeXOU0bHERGR36mciN3y9nDmqa4NAHj7f4dITs8yOJGIiIDKidi5QW3rUMfHnYtX0/loQ6zRcUREBJUTsXMujmYmhl3/1OKPNx7lzOVUgxOJiIjKidi9extXoVXdiqRlWpnxvW7MJiJiNJUTsXsmk4lXfr8x2+roM/x2KdPgRCIi9k3lRAQIrulFn9AaAMyPukJqhsXgRCIi9kvlROR3E+4Nwt3ZzJFLmQxcsI2Ea+lGRxIRsUsqJyK/q+rlysLHWlDOyUT0yST6fLCZY/HJRscSEbE7Kicif9LCz5upXXyo6e3GiYQU+n6wmZ0nEo2OJSJiV1RORP6iZnlHVo5uQ9OaXlxKzuCReVv4du85o2OJiNgNlRORfPiWc+GLkW3oGlSZ9CwrTyzbycJfjxkdS0TELqiciNyEu7MjHw26g0db1yY7Gyav2c/r3+zHqg8JFBEpVionIrfgaHbg9X81YUL3QADmbzrGk5/vIi1TlxqLiBQXlRORv2EymRhzV33efSgEJ7OJdTHnGDh/K4nJGUZHExEpk1RORG5T75AaLH68NZ6ujuw4kUjfDzYTl5BidCwRkTJH5USkANr6+7DyiXZU93LlaHwyfT74ld0nLxsdS0SkTFE5ESmggCqehI+9k0bVyhN/LYOHPt5CxP7zRscSESkzVE5ECqFKeVdWjG5Lx4BKpGZaGLlkB0u2nDA6lohImaByIlJI5Vwc+eSxFjzYohbWbHhl9V7eWH9QlxqLiPxDKici/4CT2YE3+gbz7N0BAHy4IZZxy6NJz9KlxiIihaVyIvIPmUwmnuragOn9m+HoYOLr3WcY/Mk2klIyjY4mIlIqqZyIFJF+d9Tk06GtKOfiyNZjl+j74WZOJepSYxGRgipUOVm2bBldunQhODiY/v37s2fPnluuv379erp3705wcDC9evViw4YNN1331VdfJTAwkE8//bQw0UQM1b6BL1+ObkvV8q78duEaD7y/mb2nk4yOJSJSqhS4nKxbt45p06YxduxYwsPDCQoKYtiwYSQkJOS7/q5duxg/fjz9+vVj9erVdO3albFjx3L48OE8637//ffs3r2bypUrF3wkIjaiYbXyhI9tR1BVTy5eTWfAR5H8dOiC0bFEREqNApeThQsXMmDAAPr27Uv9+vWZPHkyrq6urFy5Mt/1Fy9eTIcOHRg+fDj+/v6MGzeORo0asXTp0lzrnT9/ntdee43p06fj5ORUuNGI2IhqXm6sGN2WO+v7kJJhYfiiHXyxLc7oWCIipYJjQVbOyMhg3759jBo1KmeZg4MD7dq1IyoqKt/nREdHM2TIkFzL2rdvT0RERM7PVquV559/nmHDhtGgQYPbzmOxWLBYiu6qiBvbKsptljb2PgdFOX4PJwfmD7qDf6/eS3jUGV5cFcPJSyk8060+JpPpH2+/ONj7nz9oDux9/KA5KM7x3+42C1ROEhMTsVgs+Pj45Fru4+PD0aNH831OfHw8vr6+edaPj4/P+XnevHk4OjoyePDggsTJ99BQUYiJiSmW7ZYm9j4HRTn+R/2zcUz34Mv9ycz9OZa9x07zRAsvnBxss6CA/vxBc2Dv4wfNgZHjL1A5KQ579+5l8eLFrFq1qsD/mgwICMDd3b3IslgsFmJiYggODsZsNhfZdksTe5+D4hp/8+YQuuMUL/93HxtOpJHp6MH7j4Tg6WpbhzDt/c8fNAf2Pn7QHBTn+FNSUm5rx0KByom3tzdmsznPya8JCQl59o7c4Ovrm2svyV/X37FjBwkJCXTu3DnncYvFwptvvsnixYv58ccfb5rHbDYXyxunuLZbmtj7HBTH+B9uXYdqFdwYu2wXm2MTePDjbSwc2pLqFdyK9HWKgr3/+YPmwN7HD5qD4hj/7W6vQCfEOjs707hxYyIjI3OWWa1WIiMjad68eb7PCQkJYcuWLbmWbd68mZCQEAB69+7N119/zerVq3O+KleuzLBhw5g/f35B4onYvLsCK7N8VFsqebpw6PxV+ry/mQNnrxgdS0TEphT4ap2hQ4eyYsUKwsPDiY2NZdKkSaSmptKnTx8AJkyYwIwZM3LWHzx4MBs3bmTBggXExsYyZ84c9u7dy8CBA4Hre2MCAgJyfTk5OeHr60u9evWKaJgitqNJDS/Cx7SjQeVynLuSRv8PI9l45KLRsUREbEaBy0mPHj144YUXmD17Nr179+bAgQPMnz8/5zDN2bNnuXjxj79oQ0NDmT59OsuXL6d379589913zJ07l4CAgKIbhUgpU9Pbna9Gt6N13YpcS89i6MLtfLnjpNGxRERsQqFOiB04cGDOno+/WrJkSZ5lYWFhhIWF3fb2b3WeiUhZ4eXuxOJhrXj+yz18vfsMz3+1hzOX03iqq+1eaiwiUhL02ToiBnJxNDPrwRCeuMsfgHciDvPCyj1kWqwGJxMRMY7KiYjBHBxMvNA9iNf/1QQHE6zYcYphi3ZwLT3L6GgiIoZQORGxEQPb1GHe4Ba4OZn55fBFBnwYyfkraUbHEhEpcSonIjaka8MqfDGyDb7lnNl/9goPzP2Vw+evGh1LRKREqZyI2JhmtSoQPuZO6lXy4ExSGn0/2Mzm2Pi/f6KISBmhciJig2pVdGfl6Ha09PPmaloWjy3Yxn+jTxsdS0SkRKiciNgobw9nlgxrTc+m1ci0ZPP0F9HM/ek3srOzjY4mIlKsVE5EbJirk5k5DzVnRIe6ALz93SFeWr2XLF1qLCJlmMqJiI1zcDDxUs9GTOrVCJMJPtsax8glO0nWpcYiUkapnIiUEkPurMuHA+/AxdGBHw9e4KGPt3Dhqi41FpGyR+VEpBS5t3FVPh/ZhooezsScTqLP+5v57cI1o2OJiBQplRORUia0tjernmiHn487pxJT6fvBZrYdu2R0LBGRIqNyIlIK+fl6sPKJdjSvXYGk1EwGzt/KN3vOGB1LRKRIqJyIlFI+5Vz4bHgb7m1chQyLlSc/i+LjX2J1qbGIlHoqJyKlmJuzmfcfvYMh7fwA+M+6g0z6eh8WqwqKiJReKicipZzZwcSk+xvzcs+GACyKPMHopTtJzbAYnExEpHBUTkTKiOEd6jH3kVCcHR34fv95Hp63hYRr6UbHEhEpMJUTkTKkZ9NqLBvemgruTkSfvEyfDzZzLD7Z6FgiIgWiciJSxrT0q8jKJ9pRq6IbJxJS6PP+r+w8kWh0LBGR26ZyIlIG+Vcqx6on7qRpTS8SUzJ5ZN4Wvt171uhYIiK3ReVEpIyq5OnCFyPb0DWoMulZVp5YtosFm44ZHUtE5G+pnIiUYe7Ojnw06A4GtqlNdjZM+WY/r32zH6suNRYRG6ZyIlLGOZodeK13E14MCwLgk03HGPvZLtIydamxiNgmlRMRO2AymRjdyZ93HwrB2ezA+r3neHT+Vi4lZxgdTUQkD5UTETvSO6QGi4e1oryrIztPJNL3g83EJaQYHUtEJBeVExE706aeDyufaEeNCm4ci0/mgfd/JfrkZaNjiYjkUDkRsUMNqngSPqYdjauXJyE5g4c+juT7/eeNjiUiAqiciNityuVdWTGqLZ0CKpGWaWXUkh0s3RJndCwREZUTEXvm4eLI/Mda8FDLWliz4f+t2c+SPVf1qcYiYiiVExE752R2YFqfYJ67JwCA1YeS6f7uJv4bfVolRUQMoXIiIphMJp7s0oCZ/ZtSztnE0fhknv4imrvf2aCSIiIlTuVERHL0DqnOBz0qMf7uBlRwd+LoRZUUESl5Kicikou7kwNj7vJn0wtdeP7ewDwlZXWUSoqIFC+VExHJVzkXR8Z2rp+npIxbrpIiIsVL5UREbumWJWWmSoqIFD2VExG5LfmWlPg/Skp41CmyLFajY4pIGaByIiIF8ueSMqF7IN6/l5Rnlu/mnnd+UUkRkX9M5URECqWciyNj7qrPRpUUESliKici8o/cqqTc/c4vrNqlkiIiBaNyIiJFIr+Sciw+mWdXqKSISMGonIhIkfpzSXmhe5BKiogUmMqJiBSLci6OPPH7zdz+WlK6zdzAyp0qKSKSP5UTESlWHvmUlOMJKYz/UiVFRPKnciIiJeLPJeXFsCAqejirpIhIvlRORKREebg4MrqTPxsndM63pHylkiJi91RORMQQNyspz325m64qKSJ2rVDlZNmyZXTp0oXg4GD69+/Pnj17brn++vXr6d69O8HBwfTq1YsNGzbkenzOnDl0796dkJAQWrZsyZAhQ9i9e3dhoolIKZNfSTmhkiJi1wpcTtatW8e0adMYO3Ys4eHhBAUFMWzYMBISEvJdf9euXYwfP55+/fqxevVqunbtytixYzl8+HDOOn5+frz66qusWbOGzz77jBo1avD4449z6dKlwo9MREqVP5eUiSopInatwOVk4cKFDBgwgL59+1K/fn0mT56Mq6srK1euzHf9xYsX06FDB4YPH46/vz/jxo2jUaNGLF26NGedXr160a5dO2rVqkWDBg2YOHEi165d49ChQ4UfmYiUSh4ujoy6RUn5csdJlRSRMs6xICtnZGSwb98+Ro0albPMwcGBdu3aERUVle9zoqOjGTJkSK5l7du3JyIi4qavsXz5cjw9PQkMDLxlHovFgsViKcgQ/nZ7f/6vPbL3OdD4bWf8ro4mhrf34+GWNVm27STzfjnKiYQUnv9qD3N+/I2xnevxr2bVcTQX7alztjQHRrD38YPmoDjHf7vbLFA5SUxMxGKx4OPjk2u5j48PR48ezfc58fHx+Pr65lk/Pj4+17KffvqJZ599ltTUVCpVqsSCBQuoWLHiLfP8+dBQUYqJiSmW7ZYm9j4HGr9tjb+VJzS915tvY1P578FrxF1K4YWVe3nn2wP0beRBp9pumB1MRfqatjYHJc3exw+aAyPHX6ByUpxat27N6tWrSUxMZMWKFYwbN44vv/wyTxH6s4CAANzd3Yssg8ViISYmhuDgYMxmc5FttzSx9znQ+G17/G1awISMLJZtPcnHG49xLjmDuduvsCY2k7F3+dM7pDpO/3BPiq3PQXGz9/GD5qA4x5+SknJbOxYKVE68vb0xm815Tn5NSEjIs3fkBl9f3zx7SfJb393dnTp16lCnTh1CQkK45557+Oqrr3IdQvors9lcLG+c4tpuaWLvc6Dx2+74Pd3MjL6rPoPb+bF0ywk+2nCUuEupvLBqL3N/PsqTXerzQPMa/7ik2PIclAR7Hz9oDopj/Le7vQL93+vs7Ezjxo2JjIzMWWa1WomMjKR58+b5PickJIQtW7bkWrZ582ZCQkJu+VpWq5WMjIyCxBMRO+Lu7MjIjv5sfKEz/+4RhI+HM3GXUpjw1R66ztjAih0nydSJsyKlUoH/aTF06FBWrFhBeHg4sbGxTJo0idTUVPr06QPAhAkTmDFjRs76gwcPZuPGjSxYsIDY2FjmzJnD3r17GThwIHB9F8/MmTOJjo7m9OnT7N27l4kTJ3L+/Hm6d+9eRMMUkbLqViWly4yfWbFdJUWktCnwOSc9evTg0qVLzJ49m4sXL9KwYUPmz5+fc5jm7NmzODj80XlCQ0OZPn06s2bNYubMmfj5+TF37lwCAgKA67t4jh49Snh4OImJiVSoUIHg4GCWLVtGgwYNimiYIlLW3SgpA9vUYdmWOD76JZaTl1KZsHIPc346wv91bsADof/8cI+IFL9CnRA7cODAnD0ff7VkyZI8y8LCwggLC8t3fRcXF957773CxBARycPd2ZERHevxaJvaKikipZT+7xSRMulGSfllQmde6tEQ33LOOSWly4yfWb49Tod7RGyUyomIlGk3KykvrIyh83SVFBFbpHIiInbhRknZOKELL/e8XlJOJaqkiNgilRMRsStuzmaGd7h5SVmx4xRZ1myjY4rYNZUTEbFLNyspE8P38sz/4tlyNP9PWheR4qdyIiJ27a8lpaKHM2euWnj0k+08uyKahGvpRkcUsTsqJyIi/FFSfnimA/f6u2Eywapdp+k6cwPLt8dh1aEekRKjciIi8ifl3ZwYGerFlyPbEFTVk8spmbywMoYHP47k8PmrRscTsQsqJyIi+WheuwLf/F97XurREDcnM9uPJ9Lj3Y28+e1BUjMsRscTKdNUTkREbsLR7MCIjvWIGN+Jbg2rkGXN5oOfY7ln1gZ+OnTB6HgiZZbKiYjI36hRwY35j7Xgo0F3UM3LlZOXUhm6cDtjl+3i/JU0o+OJlDkqJyIit+nexlX5/tlODGtfFwcTrI05S7cZG1i0+TgWnTArUmRUTkRECqCciyOv3NeIr59sT7NaFbiansX/+3ofD7z/K3tPJxkdT6RMUDkRESmEJjW8WPVEO17r3RhPF0f2nEri/vc2MWXNfq6lZxkdT6RUUzkRESkks4OJQW39+GF8J+5rWg1rNiz49RjdZmzg271nyc7WoR6RwlA5ERH5hyqXd+W9R0JZ9Hgrald059yVNEYv3cXwRTs4lZhidDyRUkflRESkiHQKqMT/nunIk53r42Q28cPBC9w98xc+2hCrTzwWKQCVExGRIuTqZOa5ewNZ91QHWvlVJDXTwrT1B+k1ZxM7TyQaHU+kVFA5EREpBg2qeLJ8VBve6tcUb3cnDp67St8PNjNxVQxJKZlGxxOxaSonIiLFxGQyMaBFLX4Yfxf97qgJwOfb4ug682dWR53WCbMiN6FyIiJSzCp6ODO9fzO+GNkG/0oexF/LYNzyaAZ9so1j8clGxxOxOSonIiIlpE09H9Y93YHxdwfg7OjApt/iuXfWL7wbcYT0LH2YoMgNKiciIiXIxdHM/3VtwP/GdaRDA18ysqy8E3GYsHc3sjk23uh4IjZB5URExAB+vh4sfrwVsx9ujm85F45eTOaReVt5dkU0CdfSjY4nYiiVExERg5hMJu5vVp0fxndiYJvamEywatdpuszYwBfb4rDqwwTFTqmciIgYzMvNidf/FcyqJ9rRsFp5klIzeXFVDA9+HMnh81eNjidS4lRORERsRPPa3qx58k5e6tEQNycz248n0uPdjbz57UFSM3TCrNgPlRMRERviaHZgRMd6RIzvRLeGVciyZvPBz7HcM2sDPx26YHQ8kRKhciIiYoNqVHBj/mMt+GjQHVTzcuXkpVSGLtzOmGU7OX8lzeh4IsVK5URExIbd27gqEc92Ynj7upgdTKyLOUfXGRv49NdjWHTCrJRRKiciIjbOw8WRl+9rxNdP3kmzWhW4lp7FpDX7eeD9X9l7OsnoeCJFTuVERKSUaFzdi1VPtOO13o3xdHFkz6kk7n9vE5PX7ONaepbR8USKjMqJiEgpYnYwMaitHz+M78R9TathzYaFvx6n24wNfLv3rD5MUMoElRMRkVKocnlX3nsklEWPt6J2RXfOXUlj9NJdDF+0g5OXUoyOJ/KPqJyIiJRinQIq8b9nOvJk5/o4mU38cPAC97zzCx9uiCXTYjU6nkihqJyIiJRyrk5mnrs3kHVPdaBV3YqkZlp4Y/1Bes3ZxM4Tl4yOJ1JgKiciImVEgyqeLB/Zhrf6NcXb3YmD567S94NIJq6KISkl0+h4IrdN5UREpAwxmUwMaFGLH8bfRb87agLw+bY4us78mdVRp3XCrJQKKiciImVQRQ9npvdvxhcj2+BfyYP4axmMWx7NwE+2cvTiNaPjidySyomISBnWpp4P657uwHP3BODi6MCvvyXQ/d2NzIo4THqWPkxQbJPKiYhIGefiaObJLg343zMd6dDAl4wsK7MijhA2ayObY+ONjieSh8qJiIidqOPjweLHWzH74eb4lnPhaHwyj8zbyrPLo4m/lm50PJEcKiciInbEZDJxf7Pq/DC+EwPb1MZkglVRp+k6YwNfbIvDqg8TFBugciIiYoe83Jx4/V/BrHqiHQ2rlScpNZMXV8Xw0PytHL+sy47FWI5GBxAREeM0r+3Nmifv5NPNx5n5/WF2nrjMzhMwY/vPNK/tTWhtb5rXrkDj6uVxcTQbHVfshMqJiIidczQ7MLxDPcKCqzFlzT7+t+88Zy6ncebyWdbuOQuAs9mBxjXK07zW9bLSvHYFalRww2QyGZxeyqJClZNly5bxySefcPHiRYKCgnjllVdo2rTpTddfv3497777LqdPn8bPz4/nnnuOTp06AZCZmcmsWbP45ZdfOHnyJOXKlaNdu3aMHz+eKlWqFG5UIiJSYDUquPH+I83ZvH0XVKzD7lNJRMVdJurkZS4lZ1z/Pu4y/Hp9/cqeLr8XFW+a16pA05oVcHPW3hX55wpcTtatW8e0adOYPHkyzZo1Y9GiRQwbNoxvv/0WHx+fPOvv2rWL8ePH8+yzz9K5c2fWrFnD2LFjWbVqFQEBAaSlpbF//36eeOIJgoKCuHLlClOnTuWJJ55g1apVRTJIERG5fe5ODoT4+9AhoDIA2dnZxF1KYVdcYk5BOXD2CheupvPdvvN8t+88AGYHEw2ref5p74o3fj7u2rsiBVbgcrJw4UIGDBhA3759AZg8eTI///wzK1euZOTIkXnWX7x4MR06dGD48OEAjBs3js2bN7N06VKmTJmCp6cnCxcuzPWcV155hf79+3PmzBmqV69emHGJiEgRMZlM1PHxoI6PBw80v35L/NQMC3vPJLHrxPXCsisukQtX09l7+gp7T19hyZYTAHi7O+XsWWle25tmtbzwdHUycjhSChSonGRkZLBv3z5GjRqVs8zBwYF27doRFRWV73Oio6MZMmRIrmXt27cnIiLipq9z7do1TCYT5cuXv2Uei8WCxVJ0dzi8sa2i3GZpY+9zoPHb9/hBc3C743c2Q2gtL0JreQHX966cTUoj6uRlok9eJupkEvtOJ5GYksmPBy/w48ELAJhM0KByOUJqVfi9sFTA39cDBwfb2bui90Dxjf92t1mgcpKYmIjFYslz+MbHx4ejR4/m+5z4+Hh8fX3zrB8fn/9dCdPT05k+fTo9e/akXLlyt8xz+PDhAqS/fTExMcWy3dLE3udA47fv8YPmoLDjrwHUqA49q7uSaXHh2OVMDl/K5HBCJkcSMrmQYuHw+WscPn+NFTtOAeDuaKKBjxMNKjoR6ONMAx8nPJ2Nv9OF3gPGjd+mrtbJzMzk6aefJjs7m8mTJ//t+gEBAbi7uxfZ61ssFmJiYggODsZsts+Tuux9DjR++x4/aA6Kevwt//LzhStpRJ9KIvr3PSx7Tl0hJdPC7vMZ7D6fASQDUNfXnZBaFQitVYGQWhUIqFIOR3PJFBa9B4pv/CkpKbe1Y6FA5cTb2xuz2UxCQkKu5QkJCXn2jtzg6+ubZy9JfutnZmYybtw4zpw5w6JFi/52rwmA2WwuljdOcW23NLH3OdD47Xv8oDkorvFX8/agmrcHYcHXzyfMslg5eO4qUScvExWXSHTcZY7GJ3MsPoVj8SmER50BwM3JTNOaXoTW+eP8lUqeLkWe78/0Hij68d/u9gpUTpydnWncuDGRkZF069YNAKvVSmRkJAMHDsz3OSEhIWzZsiXXeSebN28mJCQk5+cbxeTEiRMsXrwYb2/vgsQSEZFSytHsQJMaXjSp4cWgNnUASEzOuH7eSlzi9XNY4i5zNT2LrccusfXYpZzn1vR2+/1GcdfLSqNq5XF2NP5wkPxzBT6sM3ToUF544QWaNGlC06ZNWbRoEampqfTp0weACRMmUKVKFcaPHw/A4MGDGTRoEAsWLKBTp06sW7eOvXv3MmXKFOB6MXnqqafYv38/H330ERaLhYsXLwLg5eWFs7NzUY1VRERKAW8PZzoHVaZz0PVLma3WbH67eO16Wfn9UubDF65yKjGVU4mprNl9fe+Ks6MDTaqX//2uttcvZ65ewc3IoUghFbic9OjRg0uXLjF79mwuXrxIw4YNmT9/fs5hmrNnz+Lg8EdzDQ0NZfr06cyaNYuZM2fi5+fH3LlzCQgIAOD8+fP8+OOPAPTu3TvXay1evJjWrVsXenAiIlL6OTiYCKjiSUAVTx5sWRuAK2mZ7DmZlLN3JSoukcSUTHbFXWZX3GXgGABVy7vm3NG2eW1vgmt44epkv4dqSotCnRA7cODAmx7GWbJkSZ5lYWFhhIWF5bt+zZo1OXToUGFiiIiInSrv6kT7Br60b3D9H8bZ2dkcT0jJ2buyKy6Rg+eucu5KGuv3nmP93nMAODqYaFS9fM55K6G1valVUbfhtzU2dbWOiIhIYZhMJur6elDX14M+oddvFJeSkUXMqaScPSu74i5z8Wo6e04lsedUEosir98ozsfDOddt+BtX9zRyKILKiYiIlFHuzo60rudD63rX782VnZ3N6cupOeet7IpLZN+ZJBKSM4g4cIGIA3/cKK6CiwM1ft1MVS83qpR3oUp5V6qUd6FyeVeqeF7/vqKHs/a4FBOVExERsQsmk4ma3u7U9HanV7PrlzKnZVrYf/bK74Xl+iGh05dTSUyzknjmCnvPXLnp9pzMJir/XlSulxdXKpd3oYqnK1W9/igzni6OKjEFpHIiIiJ2y9XJTOjv555AXQDOJ6WwYfseKlStw8XkDM5fSefClTTOX0nj/JV0zl9JIyE5g0zL9T0xpy+n3vI13JzMf+x1Ke9KFU8Xqnq5/r4X5o9io090/oPKiYiIyJ/4lnPB39uJkIaVb3rTsIwsKxevXS8qF/5UWs5dSePC79+fv5LGlbQsUjMtHE9I4XhCyi1f19PVkSrlXal6Yw/Mn8pL5RuHlTxd7eJeLionIiIiBeTs6ECNCm7U+Jv7qKRmWLhw9Xp5OZdTZP4oMxeupnMuKY3UTAtX07K4mnaN3y5cu+U2fTycc8rKjfNfbuyVqfr7cp9yLpht6MMUC0rlREREpJi4OZup4+NBHR+Pm66TnZ3N1fSsXHtg/igvaZxLuv7zhatpZFqySUjOICE5gwNnb/66DiaodGOvy+8Fpuqfz4v5/XtvdyebPB9G5URERMRAJpOJ8q5OlHd1on7lm1/GnJ2dTWJKZs4howu/7425UWau76FJ4+LVdKzZ/F5w0oGkm27T2exApd/Pgblx2KiypzPemRmEFP1Qb5vKiYiISClgMpmo6OFMRQ9nGlYrf9P1LNZsEq7dKC5/OS/m6h/LLiVnkGGx5ntSr6MJeney4m7QBx+qnIiIiJQhZgcTlX8/ifZW0rMsXLyanvtqpKvpnL2ciqf1Ki4GnnirciIiImKHXBzNOfd9+TOLxUJ0dLQxoX5X9q9HEhERkVJF5URERERsisqJiIiI2BSVExEREbEpKiciIiJiU1RORERExKaonIiIiIhNUTkRERERm6JyIiIiIjZF5URERERsisqJiIiI2BSVExEREbEpKiciIiJiU0rlpxJbrVYAUlNTi3S7FosFgJSUFMxmc5Fuu7Sw9znQ+O17/KA5sPfxg+agOMd/4/f2jd/jN2PKzs7OLtJXLgEJCQkcP37c6BgiIiJSCH5+fvj4+Nz08VJZTrKyskhKSsLFxQUHBx2ZEhERKQ2sVivp6el4eXnh6HjzgzelspyIiIhI2aXdDiIiImJTVE5ERETEpqiciIiIiE1ROQG2b9/O6NGjad++PYGBgURERBgdqUR99NFH9O3bl+bNm9O2bVvGjBnD0aNHjY5Voj777DN69epFaGgooaGhPPjgg2zYsMHoWIb5+OOPCQwMZOrUqUZHKTFz5swhMDAw11f37t2NjlWizp8/z3PPPUfr1q1p2rQpvXr1IiYmxuhYJaJLly55/vwDAwOZPHmy0dFKjMViYdasWXTp0oWmTZvSrVs35s6dixGnppbK+5wUtZSUFAIDA+nbty9PPvmk0XFK3LZt23j00UcJDg7GYrEwc+ZMhg0bxtq1a3F3dzc6XomoWrUqzz33HHXq1CE7O5vVq1czduxYwsPDadCggdHxStSePXv44osvCAwMNDpKiWvQoAELFy7M+dme7nGRlJTEww8/TOvWrZk3bx7e3t6cOHECLy8vo6OViK+++irn/h4AR44cYejQoXZVUOfNm8fnn3/Om2++Sf369dm7dy8TJ07E09OTwYMHl2gWlROgU6dOdOrUyegYhvnkk09y/fzGG2/Qtm1b9u3bR8uWLQ1KVbK6dOmS6+dnnnmGzz//nOjoaLsqJ8nJyTz//PO8/vrrfPDBB0bHKXFms5lKlSoZHcMQ8+bNo2rVqkybNi1nWa1atQxMVLIqVqyY6+ePP/6Y2rVr06pVK4MSlbyoqCi6du3KXXfdBUDNmjVZu3Yte/bsKfEsOqwjeVy9ehXAbv7F9FcWi4W1a9eSkpJC8+bNjY5ToqZMmUKnTp1o166d0VEMceLECdq3b0/Xrl0ZP348Z86cMTpSifnxxx9p0qQJTz31FG3btuVf//oXK1asMDqWITIyMvj666/p27cvJpPJ6Dglpnnz5mzZsoVjx44BcPDgQXbu3EnHjh1LPIv2nEguVquV//znP4SGhhIQEGB0nBJ16NAhHnroIdLT03F3d2fu3LnUr1/f6FglZu3atezfv5+vvvrK6CiGaNq0KdOmTaNu3bpcvHiRuXPn8uijj7JmzRrKlStndLxid/LkST7//HOGDh3K6NGjiYmJ4fXXX8fJyYkHHnjA6HglKiIigqtXr9rduEeOHMm1a9cICwvDbDZjsVh45plnuP/++0s8i8qJ5DJ58mSOHDnCZ599ZnSUEle3bl1Wr17N1atX+e6773jhhRdYunSpXRSUs2fPMnXqVBYsWICLi4vRcQzx50O7QUFBNGvWjM6dO7N+/Xr69+9vYLKSkZ2dTZMmTXj22WcBaNSoEUeOHOGLL76wu1/SK1eupGPHjlSpUsXoKCVq/fr1rFmzhhkzZlC/fn0OHDjAtGnTqFy5com/B1ROJMeUKVP4+eefWbp0KVWrVjU6TolzdnamTp06ADRp0oSYmBgWL17MlClTDE5W/Pbt20dCQgJ9+vTJWWaxWNi+fTvLli0jJibGrk4OBShfvjx+fn7ExcUZHaVEVKpUCX9//1zL6tWrx3fffWdQImOcPn2azZs3M2fOHKOjlLi33nqLkSNH0rNnTwACAwM5c+YMH330kcqJlLzs7Gxee+01vv/+e5YsWWJXJ8HditVqJSMjw+gYJaJNmzasWbMm17KJEydSr149RowYYXfFBK6fHHzy5Em7OUE2NDQ051yDG44fP06NGjUMSmSMVatW4ePjk3NSqD1JS0vLc46N2WzWpcRGSU5OzvWvo1OnTnHgwAG8vLyoXr26gclKxuTJk/nmm294//338fDw4OLFiwB4enri6upqcLqSMWPGDDp27Ei1atVITk7mm2++Ydu2bXmuZCqrypUrl+ccI3d3dypUqGA35x69+eabdO7cmerVq3PhwgXmzJmDg4MD9913n9HRSsRjjz3Gww8/zIcffkhYWBh79uxhxYoVdrHn8Aar1cqqVav417/+dcsPpSurOnfuzIcffkj16tVzDussXLiQvn37lngWffAfsHXr1nyv4X7ggQd44403DEhUsm52P4tp06bl2s1flv373/9my5YtXLhwAU9PTwIDAxkxYgR33nmn0dEMM2jQIIKCgnjppZeMjlIinnnmGbZv387ly5epWLEid9xxB8888wy1a9c2OlqJ+emnn5g5cybHjx+nZs2aDB06lAEDBhgdq8Rs2rSJYcOG8e2331K3bl2j45S4a9eu8e677xIREUFCQgKVK1emZ8+ejB07Fmdn5xLNonIiIiIiNkX3ORERERGbonIiIiIiNkXlRERERGyKyomIiIjYFJUTERERsSkqJyIiImJTVE5ERETEpqiciIiIiE1ROREpJQYNGsTUqVONjpFLYGAgERERRscoEV26dOHTTz81OoaIXVA5ESkl5syZw9NPPw2U/C/KOXPm0Lt37zzLN23aRMeOHUssh4jYB/v7ZCORUqpChQpFvs2MjIx/9JkZ9vKJvcXln86/SFmlPScipcSNwzqDBg3i9OnTTJs2jcDAwFwf3Lhjxw4eeeQRmjZtSqdOnXj99ddJSUnJebxLly7MnTuXCRMmEBoayquvvgrA22+/zb333kuzZs3o2rUrs2bNIjMzE7j+EfLvvfceBw8ezHm9VatWAXkP6xw6dIjBgwfTtGlTWrduzSuvvEJycnLO4y+++CJjxozhk08+oX379rRu3ZrJkyfnvNbf6dKlCx9++CETJ06kefPm3HXXXSxfvjzn8a1btxIYGMiVK1dylh04cIDAwEBOnTqVM54WLVrw008/5Yz5qaeeIjU1lfDwcLp06ULLli15/fXXsVgsuV4/OTmZZ599lpCQEDp06MCyZctyPX7lyhVeeukl2rRpQ2hoKIMHD+bgwYM5j9/YA/Xll1/SpUsXmjZtelvjFrE3KicipcycOXOoWrUqTz31FJs2bWLTpk0AxMXFMWLECO655x6+/vpr3nnnHXbu3Mlrr72W6/kLFiwgKCiI1atXM2bMGAA8PDyYNm0aa9eu5aWXXuLLL7/MOWzUo0cPHn/8cRo0aJDzej169MiTKyUlhWHDhuHl5cVXX33FrFmz2Lx5c57X37p1K3FxcSxatIg33niD8PBwwsPDb3v8CxcupEmTJqxevZpHHnmESZMmcfTo0YJMIWlpaSxZsoR33nmH+fPns3XrVp588kk2bNjAxx9/zFtvvcUXX3zBd999l+t5n3zyCUFBQYSHhzNy5EimTp3Kr7/+mvP4008/TUJCAvPmzWPVqlU0btyYxx57jMuXL+esExcXx3fffcd7773H6tWrC5RbxF7osI5IKVOhQgXMZjMeHh65Dqt89NFH9OrViyFDhgDg5+fHSy+9xKBBg5g0aRIuLi4AtGnThscffzzXNm+UFICaNWty7Ngx1q5dy4gRI3B1dcXd3R2z2XzLwzjffPMNGRkZvPnmm7i7uwPw6quvMnr0aJ577jl8fX0B8PLy4tVXX8VsNuPv70+nTp2IjIxkwIABtzX+jh078uijjwIwYsQIPv30U7Zu3Uq9evVu6/kAmZmZTJo0idq1awNw77338vXXX/Prr7/i4eFB/fr1ad26NVu2bMlVxEJDQxk5ciQAdevWZdeuXXz66afceeed7Nixgz179hAZGZlzqOaFF14gIiKC7777jgcffDDntd966y0qVqx423lF7I3KiUgZcfDgQQ4dOsSaNWtylmVnZ2O1Wjl16hT+/v4ANGnSJM9z161bx+LFizl58iQpKSlkZWVRrly5Ar1+bGwsgYGBOcUErv8yt1qtHDt2LKec1K9fH7PZnLNOpUqVOHz48G2/zp8PY5lMJnx9fUlISChQVjc3t5xiAuDr60uNGjXw8PDItezSpUu5nhcSEpLn50WLFgHXD2mlpKTQunXrXOukpaURFxeX83P16tVVTET+hsqJSBmRkpLCQw89xKBBg/I8Vq1atZzv3dzccj0WFRXFc889x//93//Rvn17PD09Wbt2LQsXLiyWnI6Ouf/aMZlMZGdnF8nzHRyuH6n+8/byO58lv23kt8xqtd52ruTkZCpVqsSSJUvyPObp6Znz/V/nX0TyUjkRKYWcnJzy/OJs1KgRv/32G3Xq1CnQtqKioqhevTpPPPFEzrIzZ8787ev9lb+/P+Hh4aSkpOTsPdm1axcODg7UrVu3QJkK68YeiYsXL+Ll5QWQ64TUf2r37t15fr6xR6px48bEx8djNpupWbNmkb2miD3SCbEipVCNGjXYvn0758+fzzn0MGLECKKiopgyZQoHDhzg+PHjREREMGXKlFtuq06dOpw9e5a1a9cSFxfH4sWL89xYrUaNGpw6dYoDBw5w6dIlMjIy8mynV69eODs78+KLL3L48GG2bNnCa6+9Ru/evXMO6RS32rVrU61aNebMmcPx48f5+eefWbBgQZFtf9euXcybN49jx46xbNkyvv32WwYPHgxAu3btCAkJYezYsWzatIlTp06xa9cu3nnnHWJiYoosg4g9UDkRKYWeeuopTp8+Tbdu3Wjbti0AQUFBLFmyhOPHj/PII4/wwAMPMHv2bCpXrnzLbXXt2pXHHnuMKVOm0Lt3b6KionLtRYHrJ4x26NCBwYMH07ZtW7755ps823Fzc+OTTz7h8uXL9OvXj6effpq2bdvyyiuvFN3A/4aTkxMzZszg6NGj3H///cybN49x48YV2faHDh3K3r17eeCBB/jggw948cUX6dChA3D9MNDHH39My5YtmThxIt27d+fZZ5/l9OnTJVbORMoKU3ZBDvaKiIiIFDPtORERERGbohNiRcQm7NixgxEjRtz08aioqBJMIyJG0mEdEbEJaWlpnD9//qaPF/QqJBEpvVRORERExKbonBMRERGxKSonIiIiYlNUTkRERMSmqJyIiIiITVE5EREREZuiciIiIiI2ReVEREREbIrKiYiIiNiU/w/YG+cVmTmIWQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -513,8 +505,6 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "\n", - "# Plotting the convergence of the loss function metric\n", "import pandas as pd\n", "\n", "%matplotlib inline\n", @@ -522,7 +512,8 @@ "plt.style.use(\"seaborn-v0_8-whitegrid\")\n", "\n", "\n", - "plt.style.use(\"seaborn-v0_8-colorblind\")\n", + "df = pd.DataFrame(job.metrics())\n", + "\n", "df.sort_values(by=[\"iteration_number\"]).plot(x=\"iteration_number\", y=\"loss\")" ] }, @@ -541,7 +532,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -583,23 +574,19 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Quantum Task Summary\n", - "{}\n", "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n" ] } ], "source": [ - "print(\"Quantum Task Summary\")\n", - "print(job.result()[\"task summary\"])\n", "print(\n", " \"Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\"\n", ")\n", @@ -661,7 +648,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 238 ms, sys: 10.8 ms, total: 249 ms\n", + "CPU times: user 243 ms, sys: 11.8 ms, total: 254 ms\n", "Wall time: 5min 42s\n" ] } @@ -685,7 +672,7 @@ "outputs": [ { "data": { - "image/png": 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bAcbc9VxtA4ry72W4Zs0aRo0axYoVK4iMjOTNN9+kTZs2dOzYsVz12NraMmvWLNzd3YmOjubdd9/F1taWsWPH4unpSXBwMKtXry6S7KxevZrHH38cpVJJamoqI0aMYPDgwUydOpWcnBxmz57NhAkTCAsLKxLvkCFDWL58OQBbtmxh4cKFzJkzh2bNmpGQkMCZM2cKy0+bNq3UHdYPHz58x3NpaWk4OTmV67OoCEl2qstDkw3JzvFf4eE3walRsSIvhTZlTcQVLiVl8f3u87zarVkJFQkhxD1Mr4f5j8ClgyWeVgGtATYZ+b4NH4RnN5c74fHz8+Oll14CwNvbmyVLlhAeHl7uZGfcuHGFx56ensTExLBhwwbGjh0LwKBBg3j//feZOnUqGo2GkydPEh0dzTffGP5zvWTJElq0aMHEiRML6/noo4/o3LkzMTExNG7cuDDGKVOmFJbZvXs3rq6uBAcHo1ar8fDwoGXLloXnX331VUaPHl2u91Lg6NGjbNq0ie+//75C15eHJDvVxfN+aNwZYnbDvi+gT/H5OzYaC97u489LyyL4Ztc/DGjTgIZ1bEwQrBBCmLPy97CYip+fX5Hv3dzcSExMLHc9GzduJCwsjEuXLpGZmUl+fn6R/bW6devGjBkz2Lp1K3369GHNmjW0b98eT0/DZtNnzpzh4MGDtG7duljdsbGxhclOQEBAkXM9e/Zk0aJFdOvWjZCQEDp37kyXLl0KNyx1cXHBxcXlrrFrtdpir0VHRzNu3DjGjx9Pp06dyvdhVIAkO9XpocmGZOfoYnjodbCvV6xIn8D6LG0SS/j5RGZuOMX3w+43QaBCCGGmFApDD8sdhrG0Wi3Hjx+nZcuWReaHVFoFh7H+u4u5QqEo90MoERERTJ48mZdffplOnTphb2/Phg0bWLBgQWEZjUZD//79Wb16Nd27d2f9+vW8/fbbheczMzPp0qULkydPLla/m5tb4bG1tXWRc/Xr12fz5s3s37+f/fv3M336dH7++WcWL16MWq2u0DDWP//8w8iRI3nyySeL9FhVJUl2qpN3CHi2g8t/QfjX0GNmsSIKhYLp/QLo9cUetpy8zu7oeDr7upVQmRBC3KMUCtDYlnxOq0VnYW04b8xkx4QiIiLw8PDgxRdfLHzt6tWrxcoNHjyYRx99lGXLlqHVaunRo0fhuYCAALZs2UKDBg2KJWClsbKyIjQ0lNDQUIYOHUqvXr2Ijo4mICCg3MNYf//9NyNGjKB///689tpr5YqjMuTR8+qkUBh6dwAOzYfMpBKL+da1Z2SwNwDT150kN19XTQEKIYQwN15eXsTFxbFhwwZiY2MJCwsrccFBHx8fWrVqxezZs+nTpw9WVlaF54YOHUpKSgoTJ07k+PHjxMbGsmfPHqZOnVriMFOB1atXs3LlSqKjo7l06RLr1q3DysoKDw8PwDCM5eXlddevAn///TfDhw+nY8eOjBo1ivj4eOLj40lKKvl3oTFJslPdmvWAeoGQlwEH7zwp69VuzXC1s+R8Qgbz98VUY4BCCCHMSdeuXRkxYgQzZsygX79+REREFOnlud2gQYPIy8tj4MCBRV6vW7cuy5cvR6fTMXr0aPr27ctHH32Evb09SuWdUwEHBwdWrlzJkCFDeOyxxwgPD+e7777D2dm53O9jy5YtJCUlsW7dOjp16lT4NWjQoHLXVV4K/T2+gl1mZianT5/G398fG5tqmgx8cg2sHAlWTvDaCbC0L7HYqiOXmbTyGDYaFTsmPUw9R6sSy1U1rVZLZGQkQUFBxh0DFxUi7WFepD3My73eHvPmzWPz5s2lzqOpTlXVJuX5/S09O6bg/xi4NIPsm3Do5zsWe7x1A9p6OZOZq+WjjaerLz4hhBA1SkZGBtHR0SxdupRhw4aZOhyzI8mOKShVEHJrrYPweZCXVXIxpYLpjwWgUMC6Y1c5cL78jysKIYQwL+vWraN169YlfvXp06dCdX7wwQcMGDCAdu3aFRvCEvI0VpWKio+ikUMjHC0di58MHAw7Z0FKrOFR9PbPlVjHfQ0cGdquEUsPxvLe/06y4ZVOWKgkRxVCiJoqNDSUVq1alXiuvE9KFfj444/5+OOPKxNWrSa/NatIVHwUQzcO5c09b5ZcQKWGjq8Yjvd9Afl33nBtcg8/nGzUnL2exuIDF6sgWiGEENXFzs7ujk8uNWjQwNTh1UqS7FQRF2vDipL7ruzjesb1kgu1HgZ2dSH1smEbiTtwttXw+iOGVTjn/BFNfFqO0eMVQgghaiuzSHaWLl1KaGgogYGBDB48mOPHj9+1/KZNm+jZsyeBgYH07duX3bt3Fznv5+dX4tdPP/1UlW+jCA87D4LcgtCjZ8uFLSUXUltBB8OeKez9HHR3XuvgqQcacV8DB9Jy8vlk85k7lhNCCCFEUSZPdjZu3MisWbMYP348a9asoXnz5owePfqOe4ccPXqUSZMmMWjQINauXUvXrl0ZP3480dHRhWX27t1b5Oujjz5CoVDwyCOPVNfbAqBX414AbIq5y4509z8L1s6QdM7wSPodqJQKpj92HwArj1zmaGyyUWMVQgghaiuTJzsLFizgiSeeYODAgTRt2pTp06djZWXFqlWrSiwfFhZGSEgIY8aMwcfHhwkTJtCiRQuWLFlSWMbNza3I1/bt22nfvj0NGzasrrcFQA/vHigVSk4kniA2NbbkQpZ20P7W4lB75oDuzqslt/VyZlBbw6Zu7/3vJFrdPb1EkhBCCFEmJk12cnNzOXnyJMHBwYWvKZVKgoODiYiIKPGayMhIOnToUOS1Tp06ERkZWWL5hIQEdu/eXS0rNP6Xq7Ur7eu1B0rp3Wn/HGjs4cZJiN581zrf6Nkce0sLoq6k8OuhS8YMVwghhKiVTProeXJyMlqtttj28C4uLpw/f77EaxISEnB1dS1WPiEhocTya9aswdbWtsiGaCXRarV33R+koh7xfoTwuHA2xmxkdMBoFCXtmqtxQHH/syj3f4H+z0/RNe1xx91169hY8Gq3pszccIZPNp+hRws3nG00Ro/7dgWfS1V8PqL8pD3Mi7SHeakt7dGiRQu+/PJLunXrZupQKq2q2qQ89dX6dXZWrVpF3759sbS0vGu52+f8GJOb1g0LhQXnU87z+8HfaWhV8lCahV0IgcpvUV49yrlt80lza3vHOlta6WnkYEFsah5v/xLOc21KWMenCkRFRVXLfUTZSHuYF2kP81Ib2iMmJuaOoxY10f79+5k3bx6xsbGkp6fj4OBA27ZtefLJJ6t8uyaTJjvOzs6oVKpik5ETExOL9d4UcHV1LdaLc6fyhw8fJiYmhrlz55Yai6+vb5V92CHpIey8tJMYyxj6BvW9c8HkkXDoB5rF/Q9d99F3rXOWUyJP/3yIP85nMe6RIO5rUHUJj1arJSoqisDAwHtyrxlzI+1hXqQ9zEttao/GjRsTFBRksvvn5uai0VR+5KCgTQICAujfvz/33Xcfzs7OxMbGMnPmTNasWcOnn35a7nozMzPL3FFh0mRHo9EQEBBAeHh4YVedTqcjPDycZ555psRrgoKCOHDgACNHjix8bf/+/SX+hfjtt98ICAigefPmpcaiUqmq7Aejd5Pe7Ly0ky0XtzCh7YSSh7IAOr0KRxaguLgX1ZXD0Kj9Hevs2Mydvq08WH/sKtN/P81vLwSjVN6hXiOpys9IlJ+0h3mR9jAv5tAew4YNw8/PD41Gw2+//YZareapp57i5ZdfLtP1t7+HTz/9lG3btnHt2jVcXV3p27cv48ePR61Wc/nyZbp168bKlSsJDAwsvH7hwoUsWrSI7du3o1QqiY6O5pNPPuHIkSNYW1vTsWNHpk6dSp06dQrjbdasGSqVinXr1uHr60tYWBhff/01q1atIiEhAScnJ3r27Mk777xT7s+jTp06PP3004XfN2rUiKFDh/Lzzz9XqK3Kc43Jh7FGjRrFG2+8wX333UfLli1ZtGgRWVlZDBgwAIApU6ZQt25dJk2aBMDw4cMZNmwY8+fPp3PnzmzcuJETJ04wY8aMIvWmp6ezefNm3njjjWp/T//V2bMzNhY2XEm/wrH4YwS5B5Vc0NETWj0FEYthz2x4euVd632rd3O2n77O0dibrIm4wsBbT2oJIURtptfrycoveU9BnU5Hji6HrPwslDrjPYNjbWF95/+o3sWaNWsYNWoUK1asIDIykjfffJM2bdrQsWPHctVja2vLrFmzcHd3Jzo6mnfffRdbW1vGjh2Lp6cnwcHBrF69ukiys3r1ah5//HGUSiWpqamMGDGCwYMHM3XqVHJycpg9ezYTJkwgLCysSLxDhgxh+fLlAGzZsoWFCxcyZ84cmjVrRkJCAmfO/LvW27Rp00rdYf3w4cMlvn79+nW2bt3KAw88UK7PoiJMnuz07t2bpKQkvvzyS+Lj4/H39+enn34qHJaKi4tDqfz3L2ybNm2YPXs2c+fOZc6cOXh7ezNv3jx8fX2L1Lthwwb0ej2PPvpotb6fklhbWNOlURc2nN/ApphNd052ADq9BpFL4e8/IO4Y1C95/xSA+o7WvBzajP/bfIZZm87QPaAuDlZq478BIYQwE3q9nuGbhhMZH3n3gqeMe9/W7q1Z1HNRuRMePz8/XnrJsHist7c3S5YsITw8vNzJzrhx4wqPPT09iYmJYcOGDYwdOxaAQYMG8f777zN16lQ0Gg0nT54kOjqab775BoAlS5bQokULJk6cWFjPRx99ROfOnYmJiaFx48aFMU6ZMqWwzO7du3F1dSU4OBi1Wo2HhwctW7YsPP/qq68yevTdp13818SJE9m+fTvZ2dl06dKFDz/8sFzXV4TJkx2AZ5555o7DVosXLy72Wq9evejVq9dd63zyySd58sknjRKfMfRu3JsN5zew5cIWXn/gdSyUd/joXXwgYACc+A32fAZPhJVc7pZnO3mz8vAlzidk8MW2v3n30RZVEL0QQpiPivSwmIqfn1+R793c3O64aO7dbNy4kbCwMC5dukRmZib5+fnY2dkVnu/WrRszZsxg69at9OnThzVr1tC+fXs8PQ09/mfOnOHgwYO0bt26WN2xsbGFyU5AQECRcz179mTRokV069aNkJAQOnfuTJcuXQo3LHVxcSn2RPV//fepqalTpzJ+/HguXLjAnDlzmDVrFu+//365P5PyMItk517QoX4HHC0dScxO5NC1Q3Tw6HDnwiGTDMnOqXUQfxbc/O5Y1NJCxXuPBTBi/l8s3H+BJx9oiG9d+yp4B0IIYXoKhYJFPRfddRjr+PHjtGzZssioQGVVdBjrv7uYKxQK9PryLQgbERHB5MmTefnll+nUqRP29vZs2LCBBQsWFJbRaDT079+f1atX0717d9avX8/bb79deD4zM5MuXbowefLkYvW7ubkVHltbWxc5V79+fTZv3sz+/fvZv38/06dP5+eff2bx4sWo1eoKDWMVLPjr4+ODo6MjTz/9NOPGjcPd3b1cn0t5SLJTTdQqNd29uvNb9G9sitl092Snbgvw6wNnNxj2zHr8u7vW3dnXjR4t6vLHqeu897+TLBvbvkb9z0cIIcpDoVBgoy756VmtVoul0hJrC2uTT1A2loiICDw8PHjxxRcLX7t69WqxcoMHD+bRRx9l2bJlaLXaIuvLBQQEsGXLFho0aFAsASuNlZUVoaGhhIaGMnToUHr16kV0dDQBAQEVGsa6XUHil5ubW+E6ysLk20XcS3o37g3AtovbyNWW0rAPGSZkc3wFJF8ote53H22BpYWS8POJbIiKq2SkQgghzIWXlxdxcXFs2LCB2NhYwsLC2LZtW7FyPj4+tGrVitmzZ9OnTx+srKwKzw0dOpSUlBQmTpzI8ePHiY2NZc+ePUydOvWui/OtXr2alStXEh0dzaVLl1i3bh1WVlZ4eHgAhmEsLy+vu34V2L17N6tWrSI6OprLly+za9cu3nvvPdq0aVM43FZVJNmpRm3c2+Bu7U5aXhp7r+y9e+EGbaFJF9BrYd+XpdbdsI4NLz7sA8CHG06TkZNvjJCFEEKYWNeuXRkxYgQzZsygX79+REREFOnlud2gQYPIy8tj4MCBRV6vW7cuy5cvR6fTMXr0aPr27ctHH32Evb39XYf7HBwcWLlyJUOGDOGxxx4jPDyc7777Dmdn53K/DysrK1auXMnQoUPp3bs3s2bNIjQ0lO+//77cdZWXQl/ewcNaJjMzk9OnT+Pv71/lKzgCfHLoExafWkxP75582rmURZQu7IWFfUBlCROOg329uxbPztPSbc5uLidnMe5hH6b0LH19obLQarVERkYSFBRUa7qFazJpD/Mi7WFe7vX2mDdvHps3by51Hk11qqo2Kc/vb+nZqWYFQ1m7Lu0iMy/z7oW9OkLDB0GbA/u/KrVuK7WKabeexvpxz3nOx6dXNlwhhBA1QEZGBtHR0SxdupRhw4aZOhyzI8lONQtwCaCRfSOytdnsvLTz7oUVCnjo1sz5wwsgM6nU+ru3qEtnXzfytHqmrz9V7ln/Qgghqta6deto3bp1iV99+vSpUJ0ffPABAwYMoF27dsWGsIQ8jVXtFAoFvRr34vvj37MpZhN9mpTyF7tpN8PCgnHH4MC3EPr2XYsrFAre69uCR+b+ye7oeLadvkH3FnWN+A6EEEJURmhoKK1albxgbHmflCrw8ccf8/HHH1cmrFpNenZMoGAoa9/VfaTkpNy9sEJhWHcH4K/vITu11PqbuNkxJqQJADN+P0l23p1n2gshhKhednZ2d3xyqUGDBqYOr1aSZMcEmjg1wc/Zj3xdPlsvbi39guZ9wdUXslPg0E9lusdLXZpSz8GKS0lZfL/7fCUjFkIIIWouSXZMpFdjw3YXm2I2lV5YqYROt/YzCZ8HuaVMbAZsLS14u48/AN/s+odLSaVfI4QQQtRGkuyYSEGyc+jaIa5nXC/9gsBB4NQIMhPg6N33yyrwaMv6PNikDjn5OmZuMPKueEIIIUQNIcmOiXjYeRDkFoQePVsubCn9ApUaOk4wHO//EvJLX1pboVAw/bH7UCkVbDl5nd3R8ZULWgghhKiBJNkxoXINZQEEPQ129SD1ChxbXqZL/OrZM6KDNwDT150kN19XkVCFEEKIGkuSHRPq4d0DpULJicQTxKbGln6B2gqCXzYc7/0ctGXbEmJC92a42mk4n5DB/H0xlYhYCCFEdfDz8ytx/ytRMZLsmJCrtSvt67UHytG7c/8osK4DyTFwck2ZLnGwUvNmL8Nk5S+3/821lOwKxSuEEEIYQ3JyMg899BB+fn6kppa+pEplSbJjYrcPZZVptWONLTw4znC85zPQlW1YakDrBrRp5ERmrpaPNp6uaLhCCCHuEbm5pc8Nrai3334bPz+/Kqv/vyTZMbGuXl1RK9WcSzlHdHJ02S5qNxYsHSD+NJzdWKZLlEoFM/rdh0IB645d5cD5xEpELYQQojTDhg1j5syZfPLJJ7Rr146OHTvy1Vel73NYkk8//ZRHHnmEVq1a0bVrV+bOnUteXh4Aly9fpnnz5kRFRRW5ZuHChXTp0gXdrf8UR0dHM2bMGFq3bk1wcDCvv/46SUn/bkM0bNgwZsyYwYcffkj79u0ZPXo0er2er776iocffpj77ruPTp06MXPmzAp+IgbLli0jLS2NZ599tlL1lIdsF2FiDhoHQhqEsOPSDjbFbMKvThkyXWsneGAM7J0De2ZD8z6GlZZLcV8DR4a2a8TSg7G897+TbHilExYqyXeFEDWLXq9Hn5VV4jmdVgvZ2egyM1EYcYdthbU1ijL8O/tfa9asYdSoUaxYsYLIyEjefPNN2rRpQ8eOHctVj62tLbNmzcLd3Z3o6GjeffddbG1tGTt2LJ6engQHB7N69WoCAwMLr1m9ejWPP/44SqWS1NRURowYweDBg5k6dSo5OTnMnj2bCRMmEBb273Ima9asYciQISxfbngIZsuWLSxcuJA5c+bQrFkzEhISOHPmTGH5adOmlbrD+uHDhwuP//nnH7755htWrFjBpUuXyvUZVIYkO2agV5Ne7Li0g80XNvNqm1fL9gPVYbxhr6yrEXBuBzTtWqZ7Te7hx4aoOM5eT2PxgYuM6ti4ktELIUT10ev1XBz6NFkREXcsYwP8Y+T7Wrdpg9fSJeVOePz8/HjppZcA8Pb2ZsmSJYSHh5c72Rk3blzhsaenJzExMWzYsIGxY8cCMGjQIN5//32mTp2KRqPh5MmTREdH88033wCwZMkSWrRowcSJEwvr+eijj+jcuTMxMTE0bty4MMYpU6YUltm9ezeurq4EBwejVqvx8PCgZcuWhedfffVVRo8eXab3kJuby8SJE3n99dfx8PCQZOde09mzMzYWNlxJv8Kx+GMEuQeVfpGtK7QdCQe/NczdKWOy42yr4fVH/Hh7zQnm/BHNoy09cLO3rFT8QghRrSrQw2Iq/52X4ubmRmJi+acRbNy4kbCwMC5dukRmZib5+fnY2dkVnu/WrRszZsxg69at9OnThzVr1tC+fXs8PT0BOHPmDAcPHqR169bF6o6NjS1MdgICAoqc69mzJ4sWLaJbt26EhITQuXNnunTpUrhhqYuLCy4uLneNXas17M/4+eef4+PjQ79+/cr9/itLkh0zYG1hTZdGXdhwfgObYjaVLdkBw2Poh36Ci/vgYjh4dSjTZU890Ijlf8Vy4koqn2w+w6eDS959VwghzI1CocBr6ZI7DmNptVqOHz9Oy5YtUZnBMNZ/dzFXKBRlexjlNhEREUyePJmXX36ZTp06YW9vz4YNG1iwYEFhGY1GQ//+/Vm9ejXdu3dn/fr1vP3224XnMzMz6dKlC5MnTy5Wv5ubW+GxtbV1kXP169dn8+bN7N+/n/379zN9+nR+/vlnFi9ejFqtLtcw1oEDB/j777/ZssWwkG7B5/Dggw/ywgsv8Morr5TrcykPSXbMRO/GvdlwfgNbLmzh9Qdex0JZhqZxbABBQ+HoIsPcHa9VZbqXSmlYWXngt/tZeeQyQ9o3ok0j50q+AyGEqB4KhQKFjU2J5/RaLVhZobSxQWnEZMeUIiIi8PDw4MUXXyx87erVq8XKDR48mEcffZRly5ah1Wrp0aNH4bmAgAC2bNlCgwYNiiVgpbGysiI0NJTQ0FCGDh1Kr169iI6OJiAgoFzDWF988UXhpGqAqKgo3nrrLZYuXUqjRo3KFVN5SbJjJjrU74CjpSOJ2YkcunaIDh5l66Wh0wSIWAz/bIOrkeARVKbL2no5M6itJ78ducx7/zvJ2vEdUSlrTtewEELcK7y8vIiLi2PDhg0EBgaya9euEhcc9PHxoVWrVsyePZuBAwdiZWVVeG7o0KGsWLGCiRMnMmbMGJycnLh48SIbN25k5syZd+wFW716NVqtllatWmFtbc26deuwsrLCw8MDKN8wVqNGjYrcJzk5uTBuBweH8n0o5SSP4pgJtUpNd6/uQDkWGASo0wTuG2Q43vNZue75Rs/m2FtaEHUlhV8PVd9EMSGEEGXXtWtXRowYwYwZM+jXrx8RERFFenluN2jQIPLy8hg4cGCR1+vWrcvy5cvR6XSMHj2avn378tFHH2Fvb49SeedUwMHBgZUrVzJkyBAee+wxwsPD+e6773B2rlmjAQp9eQcPa5nMzExOnz6Nv78/NnfoFq0uh64d4tktz2KvtmfXk7vQqDRlu/DGafjmQUAB4w+CW9kXapq/N4YZv5/CyUbNzkkP42xb/J5arZbIyEiCgoKMOgYuKkbaw7xIe5iXe7095s2bx+bNm0udR1OdqqpNyvP7W3p2zEgb9za4W7uTlpfG3it7y36huz80fxTQw5455brn8A5e+NW152ZmHp9tPVu+gIUQQpiFjIwMoqOjWbp0KcOGDTN1OGZHkh0zolKqeKTxI0A5h7IAHro1wz5qJSSVfbNPC5WS9x8zPGq49GAsJ66klO++QgghymXdunW0bt26xK8+ffpUqM4PPviAAQMG0K5du2JDWEImKJudPo37sPjUYnZd2kVmXiY26jIOrXm0Bp+ucG477PsC+s4t8z07+LjQt5UH649dZdr/TvDbC8EoZbKyEEJUidDQUFq1KnnJj/I+KVXg448/5uOPP65MWLWa9OyYmRYuLWhk34hsbTY7L+0s38UFvTuRSyG1+GOJd/NW7+bYaFQcjb3Jmogr5buvEEKIMrOzs8PLy6vErwYNGpg6vFpJkh0zo1AoiuyEXi5ewdAoGLS5sP/rcl1a39Gal0ObATBr0xnSsvNKuUIIIYSoGSTZMUO9G/cGYN/VfaTklHMOzUOTDH8eWQAZ5VuS/NlO3jRxtSUhPYcvtv1dvvsKIYQQZkqSHTPUxKkJfs5+5Ovy2Xpxa/ku9ukK9YMgLxMOfFOuSy0tVLx3a7Lygv0XiL6eVr57CyGEEGZIkh0zVeGhLIXi37k7f/0I2eXrGers60aPFnXR6vS8v+5kufdwEUIIIcyNJDtmqiDZOXTtEDcyb5TvYr8+4NYcclIMCU85vftoCywtlOw/l8jGqGvlvl4IIYQwJyZPdpYuXUpoaCiBgYEMHjyY48eP37X8pk2b6NmzJ4GBgfTt25fdu3cXK3Pu3DleeOEF2rZtS1BQEAMHDixx0zRz5mHnQZBbEHr0bI7ZXL6LlUroNNFwfOAbyM0o1+UN69jw4sM+AMzccIrM3Pzy3V8IIYQwIyZNdjZu3MisWbMYP348a9asoXnz5owePZrExJIn1h49epRJkyYxaNAg1q5dS9euXRk/fjzR0dGFZWJjYxk6dChNmjRh8eLFrFu3jnHjxmFpaVldb8toKjyUBXDfQHD2hsxEOLKo3Je/0NkHT2dr4lKy+WbX+fLfXwghhDATJk12FixYwBNPPMHAgQNp2rQp06dPx8rKilWrVpVYPiwsjJCQEMaMGYOPjw8TJkygRYsWLFmypLDM559/zkMPPcSUKVNo0aIFjRo1omvXrqXuymqOenj3QKlQciLxBLGpseW7WGUBHScYjvd/Cfk55brcSq1i2qMtAPh5bwxX06R3RwghRM1kshWUc3NzOXnyJM8//3zha0qlkuDgYCIiIkq8JjIykpEjRxZ5rVOnToVb3et0Onbt2sWYMWMYPXo0p06dwtPTk+eff55u3brdNR6tVlu4Db25cNY4065eOw7EHWDj+Y2MDRxbvgoCn0S5+/9QpMWhi1iCvs3Icl0e6ufKQ81c+fPvBKbvTiIi9TTdWtSlbSMnLFQmHwG9ZxX8PTW3v6/3KmkP8yLtYX6qqk3KU5/Jkp3k5GS0Wm2xHhcXFxfOny952CQhIQFXV9di5RMSEgBITEwkMzOTH3/8kQkTJjB58mT27NnDSy+9RFhYGO3atbtjPLcPhZmTAGUABzjAmjNruD//fhSK8m3j4N7ocRqe/Ia8nZ9ygkBQlm/H2cE+EBmrJCFLx/z9F5m//yJ2agVt6ltyv4clretZYqOWxMcUoqKiTB2CuI20h3mR9jA/pmyTWrU3lk6nA6Br166FPUD+/v4cPXqUX3755a7Jjq+vb6lbxJtCk9wmhP0WxtWcq9h62+Lr7Fu+Clo0Qx/zK5aZcbS2OIe+5RPlujwI6Ng2h2U7Ivgny5rd0QkkZ+bxZ2w2f8Zmo1YpaN+4DqHN3ena3A1PZ/P7DGsbrVZLVFQUgYGBqFTlS16F8Ul7mBdpD/NTVW2SmZlZ5o4KkyU7zs7OqFSqYpORExMTi/XeFHB1dS3sxSmpvLOzMxYWFvj4+BQp4+Pjw5EjR+4aj0qlMssfDGdrZ0IahLDj0g62XNyCv6t/+SqwdoAHx8GOD1DunwutnjQ8rVUOjjaWdPC04sWgVqBQcjQ2mW2nrrP19HXOx2ew959E9v6TyIzfT9O8nj3d/OvSrUVdWjZwlA1Fq5C5/p29V0l7mBdpD/Nj7DYpT10mG3/QaDQEBAQQHh5e+JpOpyM8PJzWrVuXeE1QUBAHDhwo8tr+/fsJCgoqrDMwMJCYmJgiZS5cuFCjN1fr1cTwVNbmC5srtshfu7Fg6QjxZ+DM75WKRaVU8IB3Hab29mfHpIfZMakzb/f2p13jOigVcOZaGl/v/If+8/bRftZ23lx1nG2nrpOVK+PnQgghTMOkw1ijRo3ijTfe4L777qNly5YsWrSIrKwsBgwYAMCUKVOoW7cukyYZ9nsaPnw4w4YNY/78+XTu3JmNGzdy4sQJZsyYUVjn6NGjee2113jggQdo3749e/bsYefOnYSFhZnkPRpDZ8/O2FjYcCX9CsfijxHkHlS+CqwcDQnPntmGL/++hpWWjaCJmx1N3OwY+1ATkjNy2RV9g22nb7D7bDzxaTn8cugSvxy6hKWFkpBmrnTzr0uovzvu9lZGub8QQghRGpMmO7179yYpKYkvv/yS+Ph4/P39+emnnwqHpeLi4lDeNuTSpk0bZs+ezdy5c5kzZw7e3t7MmzcPX99/57F0796d999/nx9++IGZM2fSuHFjvvzyS+6///5qf3/GYm1hTZdGXdhwfgObYjaVP9kBw1DWgW8g7hic2w5N7/50WkU422p4vLUnj7f2JDdfx8GYRLafvsHWU9e5cjOLbacNiRBAq4ZOdGvuTrcWdWlez77cE6+FEEKIslLo7/HNjzIzMzl9+jT+/v5mOUG5wJ+X/2T89vG4WLmwbfA2LJQVyFM3vwUH5kGjYHi27AsVarVaIiMjCQoKqtB4q16v5+z1tFvzfG5w7NLNIucbOFnTzd+Q+LRv7ILGQp7uupvKtocwLmkP8yLtYX6qqk3K8/u7Vj2NZU7yk5O5Nm0aDr1749CrV6Xr61C/A46WjiRmJ3Lo2iE6eHQofyXBL8OhHyF2P1zcD17BlY6rLBQKBc3rOdC8ngMvhTbjRmo2O87cYNvp6+z9J4ErN7NYFH6RReEXsbO0oLOvG91auPOwrzvOtppqiVEIIUTtJclOFck9f560rdvIjIzEvkcPFJXMZtUqNd29uvNb9G9sitlUsWTHoT4EPQ1HFsCfs2HY6krFVFHuDlY81a4RT7VrRFauln3/JLDt9HW2n7lBfFoOG6Li2BAVh1IB93vXobt/Xbr6u9PEzc4k8QohhKjZZLygilgHBqJyckIbn0DGf54gq6jejXsDsO3iNnK1uRWrpNMEUKgM83auHDVKXJVhrVHRrUVdPh7YkoNTu7J2fEdeDm1K83r26PTwV0wSH248Tehnuwn9bBezNp7mr5gk8rU6U4cuhBCihpBkp4ooNBrse/UEIHXdeqPU2ca9De7W7qTlpbH3yt6KVeLsDYGDDcd7PjNKXMaiVCoIaujEpB5+bJ7wEHvf6ML0xwIIaeaKWqXgfHwG3/95nie+D+eBD7cxcUUkG6PiSM+RfbuEEELcmSQ7Vcix72MApG3dii4rq9L1qZQqejY2JFAV2gm9QMhEQGFYc+fG6UrHVVU8nW0YEezN4tHtOfpud+YNbcPjrRvgZKMmOTOP1UevMG7pUdrM2Mqwnw8SFn6BKzcr/zkLIYSoXWTOThWybh2E2tOTvMuXSduxA8c+fSpdZ+/GvQk7FcauS7vIzMvERl2BJ8jc/Axr7ZxeB3vmwMAfKx1XVbO3UtOnZX36tKxPvlbHkYvJbDt9nW2nbxCTkMGevxPY83cC0/53Ev/6DnT3d6erf10CzWQV53ytjux8HVm5WrLztGTlacnKvfVnnpbs246LltEZzt9ePjefrMwMvM8ew8XOElc7DS52lrjYanCx0+Bia4mLnQY7Swt5pF8IIZBkp0opFAoc+j5K4rffkbpuvVGSnRYuLWhk34jYtFh2XtpJnyYVrPOhyYZk58Rv0GUq1GlS6diqi4VKSfsmLrRv4sLbfVpwLj6d7aevs+3UDQ5fTOJ0XCqn41L5csc/uNtb0tW/Lt383enY1BUrddGJ4jqdnpx83R0SDW3xRKNYYqIjO++2a0qoIztPR24VzDGKuhF31/MaC2XRBKjguITEyMXWEmuNPKYrhKidJNmpYo59+5L47Xek791LflISFnXqVKo+hUJBr8a9+P7492yK2VTxZKd+K2jaHf7ZCnvnwmNfViouU/Jxs8PHzY7nHvIhKSOXXWcNj7XvPhvPjbQclv8Vy/K/YrFSK/Fwsi6SrGTnVe9EZ4UCrNUqrNUqrNQqrDWqf7/XqLBWKw3fa26dL/i67XuNSsE/52Owd61PUmYeiem5JGbkkJiRazhOzyEjV0tuvo64lGziUrLLFJuNRlXmxKiOrUbWQxJC1BiS7FQxyyZNsLrvPrJPnCB10ybqPP10pevs3bg33x//nn1X95GSk4KjpWPFKnposiHZiVwGnd8Ax5q7f1iBOrYaBrTxZEAbT3LytRw8n2R4rP30Da7czOJ8fMYdr9VYKEtILpSFCYnVbefunKwUlFGWWN7SQlnpoSWtVkuk9hpBQd53XKArO097K/nJuZUM3TrOyCUhPYek2xKjhIxccvN1ZOZqyUzK4lJS2eY9OVhZFE2GCo5tb0+SDAmSs40GlRkMJwoh7k2S7FQDx8f6GpKddeuNkuw0cWqCn7MfZ5PPsvXiVgb5DqpYRY0eBK9OcHEv7P8Ken1c6djMiaWFiod83XjI143pj+mJvp7OzczcEpMVK7WqVv0ytlKraOBkTQMn61LL6vV6MnK1hsQnPfdWIlQ8MSo8zshFq9OTmp1PanY+MQl3TiALKBTgbKMp1kvU1suZfkE1P8kWQpg3SXaqgUOvXlz/+P/IOnaM3IsX0Xh5VbrOXo17cTb5LJtiNlU82QF4aBIs3gtHFkLIJLBzq3Rs5kihUOBXz97UYZglhUKBnaUFdpYWeLnYllpep9OTmp1XJDFKuPVnSYlRcmYuej0kZRjK/33j37rCwi/ysJ87jtbqKnyHQoh7nSQ71cDCzQ3b4GAy9u4l5fffcRs/vtJ19mrci7lH53Lo2iFuZN7A3ca9YhU16QIebeDqUcNGod3eq3RsonZTKhU42WhwsinbVh75Wh3JmXlFEqOkWz1HjerYSKIjhKhyMsOwmjg+1hcwLDBojL1XPew8CHILQo+eLRe2VLwihcLQowNw6CfIulnp2IS4nYVKiZu9JX717Alu6spjrTwY2bExk3r4Mfj+hqYOTwhxD5Bkp5rYd+2Kwtqa3IsXyY6KMkqdvRobNhjdeH5j5Sry6w1u/pCTCn+Z/5o7QgghRHlIslNNlLa22HftCkCKkbaP6OHdA6VCyYnEE8SmxlYiOOW/vTsHvoGcdKPEJ4QQQpgDSXaqUeFQ1saN6PPyKl2fq7Ur7eu1Byq5fQRAwOPg3BiykgyTlYUQQohaQpKdamQbHIyqTh20SUlkhIcbpc6CoaxNMZsqNxdIZQGdXjMc7/8K8sq2EJ0QQghh7iTZqUYKCwscevcGjDeU1dWrK2qlmnMp54hOjq5cZa2GgEMDSL8GkUuNEp8QQghhapLsVLOCoay07dvRZZS+GFtpHDQOhDQIAYwwlGWhgeBXDMf75oK28kNtQgghhKlJslPNrAID0Xh5oc/KIm37dqPU2auJYShr84XNlX+svc1wsHGFm7FwYpURohNCCCFMS5KdambYCd3Qu2OsoazOnp2xsbDhSvoVjsUfq1xlGhvocGvRwz1zQFe9G2UKIYQQxibJjgk49n0UgIz9+8mPj690fdYW1nRp1AUwwlAWwANjwMoREs7CGeMkZEIIIYSpSLJjAhovL6xbtQKdjtRNRkhOMOyEDrDlwhbydfmVq8zKAdo9bzj+czYYYcVnIYQQwlQk2TERh8eMO5TVoX4HHC0dScxO5NC1Q5Wv8MEXQW0L147DuW2Vr08IIYQwEUl2TMShVy9Qqcg+cYKc8+crXZ9apaa7V3fASENZNnXg/lEAKPfOkd4dIYQQNZYkOyZiUacOdp06AZCy3ji9OwVDWdsubiNXm1v5CoNfBpUliksHsUs8Xvn6hBBCCBOQZMeECoayUtf/bpSd0Nu4t8Hdxp20vDT2Xtlb6fqwrwetnwGg/t9LKl+fEEIIYQKS7JiQfWgoShsb8i5fJisistL1qZQqenr3BIw0lAXQaQJ6pRqHhCNwwQgJlBBCCFHNJNkxIaW1NfbdDfNsUtavM0qdBUNZuy7tIjMvs/IVOjVC32YEAMqdH8jcHSGEEDWOJDsmVjCUlbZpM/rcys+zaeHSgkb2jcjWZrPz0s5K1weg7zQRndISxeVDEL3FKHUKIYQQ1UWSHROzffBBVG6uaG/eJH3vvkrXp1AoiuyEbhT29bjR5HHD8Y4PZFVlIYQQNYokOyamUKlw7N0HMP5Q1r6r+0jJSTFKndd8nkJv6QDXT8DJ1UapUwghhKgOkuyYgYKhrPQdO9Gmp1e6viZOTfBz9iNfl8/Wi1srXR+AVuOAvsPLhm92fig7ogshhKgxJNkxA1YtWqDx8UGfk0PaH8ZJTow+lAXo2z8Ptm6QdB4i5FF0IYQQNYNZJDtLly4lNDSUwMBABg8ezPHjd1/AbtOmTfTs2ZPAwED69u3L7t27i5x/88038fPzK/I1evToqnwLlaJQKAo3BzXWUFZBsnPo2iFuZN4wSp1o7CBksuF49yeQl2WceoUQQogqZPJkZ+PGjcyaNYvx48ezZs0amjdvzujRo0lMTCyx/NGjR5k0aRKDBg1i7dq1dO3alfHjxxMdHV2kXEhICHv37i38mjNnTnW8nQpzeNSQ7GQeOEje9euVrs/DzoMgtyD06NlywYhPUN0/Chw8Ie0qHPrZePUKIYQQVcTkyc6CBQt44oknGDhwIE2bNmX69OlYWVmxatWqEsuHhYUREhLCmDFj8PHxYcKECbRo0YIlS4oOq2g0Gtzc3Aq/HB0dq+PtVJjG0xPrtm1Bryd1w0aj1FkVQ1lYWMLDbxqO93wG2anGq1sIIYSoAhamvHlubi4nT57k+eefL3xNqVQSHBxMREREiddERkYycuTIIq916tSJbduK7sz9119/0aFDBxwcHHjwwQeZMGECzs7Od4xFq9Wi1Wor/maMwP7RPmQdOULKunU4jRhe6fq6NezG/x36P6ISooi5GUMj+0YVqqfgcyn8fAKfQLnvCxSJf6Pb/zX6zm9UOlZRdsXaQ5iUtId5kfYwP1XVJuWpz6TJTnJyMlqtFhcXlyKvu7i4cP4OO4EnJCTg6uparHxCQkLh9yEhIXTv3h1PT08uXbrEnDlzGDt2LL/++isqlarEev87DGYSHh5Yq1TknDnDsfXr0TdsWOkq/W38OZlxkoXhC3nM/bFK1RUVFVV47OQ9BJ/EGej3f0WU1YNoLc2756w2ur09hOlJe5gXaQ/zY8o2MWmyU1X69OlTeFwwQblbt26FvT0l8fX1xcbGprpCvKMrnTuTsWMHHv/8g1vfvpWub7D9YE6GnyQyJ5J3W72LQqEodx1arZaoqCgCAwP/TRZbtUR/ZS2qa8dpmbYdffsZlY5VlE2J7SFMRtrDvEh7mJ+qapPMzMwyd1SYNNlxdnZGpVIVm4ycmJhYrPemgKura5FenNLKAzRs2BBnZ2cuXrx4x2RHpVKZxQ+G02OPkbFjB2kbN1J34kQUyspNq+ru3Z0PD37I+ZTznEs9h18dvwrXVfQzUkHX92DpQJSHfoIO48HBo1KxivIxl7+zwkDaw7xIe5gfY7dJeeoy6QRljUZDQEAA4eHhha/pdDrCw8Np3bp1idcEBQVx4MCBIq/t37+foKCgO97n2rVr3Lx5Ezc3N6PEXZXsujyM0s6O/KtxZB05Uun6HDQOhDQIAYw8URmgaVdoFAz52YZH0YUQQggzZPKnsUaNGsWKFStYs2YN586d4/333ycrK4sBAwYAMGXKFD777LPC8sOHD2fPnj3Mnz+fc+fO8dVXX3HixAmeeeYZADIyMvi///s/IiMjuXz5MuHh4YwbNw4vLy9CQkJM8h7LQ2lpif0jPQBIWbfeKHX2amJ4Kmvzhc3ojblruUIBXd81HEcshsRzxqtbCCGEMBKTz9np3bs3SUlJfPnll8THx+Pv789PP/1UOCwVFxeH8rahnDZt2jB79mzmzp3LnDlz8Pb2Zt68efj6+gKGbq3o6GjWrl1LWloa7u7udOzYkVdffRWNRmOS91hejn0fI2XValK3bKHuu++grGTcnT07Y2Nhw5X0KxyLP0aQe5BxAgXwCoam3eGfrbBrFgz8yXh1CyGEEEZg8mQH4JlnninsmfmvxYsXF3utV69e9OrVq8TyVlZW/PxzzV7szqbdA1jUq0f+tWuk796NQ/fularP2sKaLo26sOH8BjbFbDJusgOG3p1/tkLUb9BxAtS7z7j1CyGEEJVg8mEsUZxCqcShj2Hn8lQjDWUV7IS+5cIW8nX5RqmzUP1WEPA4oDdsEiqEEEKYEUl2zJTjY4Y1cdJ37UKbklLp+jrU74CjpSOJ2Ykcunao0vUV0+VtUKjg7Ea49Jfx6xdCCCEqSJIdM2Xl54elry/6vDxS//ij0vWpVWq6exmGw4z+VBaAazMIGmo43j4DjDkRWgghhKgESXbMmONjhkUFjT2Ute3iNnK1uUaps4jOb4BKAxf2wPldxq9fCCGEqABJdsyYQ58+oFCQeegQeVevVrq+tnXb4m7jTlpeGnuv7DVChP/h1BDuH204lt4dIYQQZkKSHTOmrl8fmwceACDl9w2Vrk+pUNLTuydQRUNZACGTQG0LV4/Cmd+r5h5CCCFEOUiyY+YKh7LWrzPKgoAFQ1m7Lu0iMy+z0vUVY+cGHcYZjnfMBJ3sPCyEEMK0JNkxc/Y9eqBQq8n5+x9yzp6tdH0tXFrQyL4R2dpsdl7aaYQIS9DhJbBygvgzcHxF1dxDCCGEKCNJdsycysEBuy5dAONsH6FQKOjV2LAgY5UNZVk7QafXDMe7PoL8KpgMLYQQQpSRJDs1QOFQ1oYN6LWVHxYqGMrad3UfKTmVX8OnRO2eA7u6cDMWji6qmnsIIYQQZSDJTg1g+9BDKB0dyb9+ncxDlV8QsIlTE/yc/cjX5bP14lYjRFgCjQ089Lrh+M9PIbcK5gcJIYQQZSDJTg2g1GhweOQRwIg7oVf1UBZAmxHg5AXp1+Gv76vuPkIIIcRdSLJTQxQMZaVt2YIuO7vS9RUkO4euHeJG5o1K11ciCw10ectwvHcuZN2smvsIIYQQdyHJTg1h3aYNag8PdBkZpO/aVen6POw8CHILQo+eLRe2VD7AOwkcDG7NIfsmhH9ddfcRQggh7kCSnRpCoVTi8OijQA0bylKqIPQdw3H4N5BeRb1IQgghxB1IslODFAxlpf/5J/nJyZWur4d3D5QKJVEJUVxKvVTp+u6o+aPg0QbyMmDPnKq7jxBCCFECSXZqEMumTbFs4Q/5+aRt3lzp+lytXWlfrz0AG2M2Vrq+O1IooOs0w/Hhn+FmFSZWQgghxH9IslPDOPZ9DICU9cbZd+r2oSxjbEdxR00eBu8Q0ObC7o+r7j5CCCHEf0iyU8M49O4NCgVZR4+Se6nyPSRdvbqiVqo5l3KO6ORoI0R4BwoFdH3PcBy5DBL+rrp7CSGEELeRZKeGUdd1x7bDgwCk/l753h0HjQMhDUKAKp6oDNDwAfDrDXod7Pywau8lhBBC3CLJTg3kcNtQljGGnno1MQxlbb6wuWqHsuDWk1kKOLkGrkZW7b2EEEIIJNmpkey7d0NhaUnu+fNknzxV6fo6e3bGxsKGK+lXOBZ/zAgR3kXdAMPaOwA7ZlbtvYQQQggqmOysWbOGXbctbPfJJ59w//3389RTT3HlyhVjxSbuQGVnh33XUABS16+rdH3WFtZ0aWTYWb3Kh7IAHn4TlBbwz1a4uL/q7yeEEOKeVqFk57vvvsPS0hKAiIgIli1bxuuvv46TkxOzZs0yaoCiZA59DWvupGzYiD4/v9L1FeyEvuXCFvJ1la/vrlx8oPUww/H2GVDVQ2dCCCHuaRVKdq5du4aXlxcA27Zto0ePHjz55JNMmjSJw4cPGzVAUTK7Tp1QOTmhTUgg48DBStfXoX4HHC0dScxO5NC1yu+sXqrOU8DCCmLD4Z9tVX8/IYQQ96wKJTs2NjbcvHkTgH379hEcHAyApaUlOTk5RgtO3JlCrcaht2FisTGGstQqNT28egDVNJTl4AHtxhqOt88Ana7q7ymEEOKeVKFkJzg4mHfeeYe3336bCxcu0LlzZwD+/vtvGjRoYNQAxZ0VDGWlbt2GLjOz0vUVLDC47eI2crW5la6vVB1fA409XDsOp/9X9fcTQghxT6pQsvPee+8RFBREUlISX375Jc7OzgCcPHmSPn36GDVAcWfWQUGoGzZEn5lJ2o6dla6vbd22uNu4k5aXxt4re40QYSlsXSD4JcPxjg9BW8VzhYQQQtyTKpTsODg4MG3aNL799lseeuihwtdfeeUVXnzxRaMFJ+5OoVDg2PfWTuhGGMpSKpT09O4JVNNQFkCH8WDjAol/w7Hl1XNPIYQQ95QKJTt//vlnkYnIS5cupV+/fkyaNImUlBSjBSdK5/CoYSgrY+8+8hMTK11fwVNZuy7tIjOv8kNjpbK0h04TDce7PoZ8mfMlhBDCuCqU7Hz66adkZGQAcPbsWT7++GM6d+7M5cuX+fhj2eSxOlk2aYxVYCBotaRurHxvTAuXFjSyb0S2Npudlyo/NFYmD4wGew9IvQyH51fPPYUQQtwzKpTsXL58GR8fHwD++OMPunTpwsSJE5k2bRp//vmnUQMUpXMsWHPn9/WVrkuhUBTZCb1aqK3h4TcMx3/Ohpz06rmvEEKIe0KFkh21Wk12djYA+/fvp2PHjgA4OjqSni6/qKqbQ+9eoFKRfew4uRcuVLq+gqGsfVf3kZJTTcOSQU9DnSaQmQAHv62eewohhLgnVCjZadOmDbNmzWLevHlERUXx8MMPA3DhwgXq1atnzPhEGVi4umJ7a62jlPWV3wm9iVMT/Jz9yNfls/Xi1krXVyYqNXR523C87yvITKqe+wohhKj1KpTsTJs2DQsLC7Zs2cJ7771H3bp1AcPE5ZCQkHLXt3TpUkJDQwkMDGTw4MEcP378ruU3bdpEz549CQwMpG/fvuzevfuusfr5+bFw4cJyx1WTOD52ayhr/Xrj7IRe3UNZAAEDoO59kJMC+76ovvsKIYSo1SqU7Hh4ePD999+zbt06Bg8eXPj6W2+9xTvvvFOuujZu3MisWbMYP348a9asoXnz5owePZrEOzxZdPToUSZNmsSgQYNYu3YtXbt2Zfz48URHRxcru3XrVo4dO4a7u3v53mANZB8aisLamrzYWLJLSRbLoiDZOXTtEDcyb1S6vjJRKiH0XcPxwe8h7Vr13FcIIUStVqFkB0Cr1bJlyxa++eYbvvnmG7Zu3YpWqy13PQsWLOCJJ55g4MCBNG3alOnTp2NlZcWqVatKLB8WFkZISAhjxozBx8eHCRMm0KJFC5YsWVKk3PXr1/nggw+YPXs2arW6Qu+xJlHa2mLfrRsAKesqP1HZw86DILcg9Oj54+Ifla6vzHwfAc92kJ8Ff35affcVQghRa1Uo2bl48SK9e/fmjTfeYOvWrWzdupXXX3+dPn36EBsbW+Z6cnNzOXnyZOHeWgBKpZLg4GAiIiJKvCYyMpIOHToUea1Tp05ERkYWfq/T6Xj99dcZPXo0zZo1K9+bq8EKhrJSN25En5dX6foKenc2X9hc6brKTKGArtMMx0cWQlJM9d1bCCFErWRRkYtmzpxJw4YN+fXXX3FycgIgOTmZ119/nZkzZ/LDDz+UqZ7k5GS0Wi0uLi5FXndxceH8+fMlXpOQkICrq2ux8gkJCYXf//jjj1hYWDB8+PAyvyetVluhnilzYtWuHSqXOmgTk0jduxe721a3rohuDbvxf4f+jxOJJ7hR50b1fT6NglE26YLi/E50uz5G3++b6rlvDVHQDjX972ttIe1hXqQ9zE9VtUl56qtQsnPo0KEiiQ6As7MzkydPZsiQIRWp0mhOnDhBWFgYq1evRqFQlPm6kub81ETq++9HveUPYsMWk+vgUOn6/G38OZlxkgMpB3CPqr65TzYNnsD//E4Ux3/ldJ3uZNs3rrZ71xRRUVGmDkHcRtrDvEh7mB9TtkmFkh2NRlO4gvLtMjIyyjU/xtnZGZVKVWwycmJiYrHemwKurq5FenH+W/7w4cMkJibSpUuXwvNarZb/+7//IywsjB07dpRYr6+vLzY2NmWO3Vxlq1TEbvkDdUQEzZs1Q2lrW6n6BtsP5mT4SbYlbmPo/UNpWqepkSItTRD6+A0ozvxOi7g16ELCqum+5k+r1RIVFUVgYCAqlcrU4dzzpD3Mi7SH+amqNsnMzCxzR0WFkp2HH36YadOm8eGHH9KyZUsAjh07xvvvv09oaGiZ69FoNAQEBBAeHk63W5NrdTod4eHhPPPMMyVeExQUxIEDBxg5cmTha/v37ycoKAiAfv36FZkDBDB69Gj69evHgAED7hiLSqWqFT8YNq1aofH2JvfCBTJ27MCpf/9K1de7SW+WnV5G9M1oxm4fy0+P/ISvs69xgi1N6LtwdiOKs7+juhYJDdpWz31riNryd7a2kPYwL9Ie5sfYbVKeuio0Qfmdd96hYcOGPPnkkwQGBhIYGMhTTz1Fo0aNeOutt8pV16hRo1ixYgVr1qzh3LlzvP/++2RlZRUmJlOmTOGzzz4rLD98+HD27NnD/PnzOXfuHF999RUnTpwoTI6cnZ3x9fUt8qVWq3F1daVJkyYVebs1ikKhwOHWTuipRlhg0NrCmh+6/4CXlRfJOcmM3jKa04mnK11vmbg3h5ZPGY63f1A99xRCCFHrVKhnx8HBgW+//ZaLFy9y7tw5AHx8fPDy8ip3Xb179yYpKYkvv/yS+Ph4/P39+emnnwqHpeLi4lAq/83J2rRpw+zZs5k7dy5z5szB29ubefPm4etbTb0NNYBj374kfPU1GeHh5N24gbqS6ww5WToxpfEUvr3xLScSTzD6j9F83+17At0CjRTxXTz8JkSthPM7IeZPaFy5SddCCCHuPQp9GZfbnTVrVpkrnTp1aoUDqm6ZmZmcPn0af3//WjFnp8CFp4aQFRmJ+5tv4HLbkF9FaLVaIiMjadqiKS/vepmIGxHYqm35ttu3tHZvbZyA72bj6/DXD+D5AIzeang8/R5W0B5BQUHSTW8GpD3Mi7SH+amqNinP7+8y9+ycOnWqTOXK8wSUqDoOj/UlKzKS1PW/VzrZKWCnseO7bt/x0o6XOHTtEM9vfZ55XefxQL0HjFL/HYVMhqOL4fIhiN4Mfr2q9n5CCCFqlTInO4sXL67KOISROfTqxfWPZpF98iQ5585h6eNjlHpt1DbM6zqPV3e8SnhcOOO2jeOL0C8I9ggu/eKKsq8LD74Aez83zN1p9ohhawkhhBCiDOQ3Ri1l4eyMXadOgGFzUGOytrDmq65f8ZDnQ2Rrs3l5+8v8eflPo96jmI6vgqUj3DgJJ0reSkQIIYQoiSQ7tVjh9hHrfzfKTui3s1RZMvfhuXRt1JVcXS6v7nyV7bHbjXqPIqydoeMrhuOdH4K28tthCCGEuDdIslOL2XXpgtLGhrwrV8i6w15jlaFWqfm086f09O5Jvi6fSbsmVe0+Wu1fAFs3SI6BCBlWFUIIUTaS7NRiSmtr7Hv0ACBl3boquYdaqWZWyCz6NumLVq/ljT/fYP054w6bFbK0M0xWBtj9CeRlVc19hBBC1CqS7NRyhUNZmzajz82tkntYKC34oOMHDGg2AJ1ex9t732bN32uq5F7cPwocG0JaHBz6qWruIYQQolaRZKeWs2nfHgs3N3QpKaTv2VNl91EpVbzX4T2e9HsSPXqm7Z/Gr2d+Nf6NLCwNCw0C7JkD2anGv4cQQohaRZKdWk6hUuHQpw8AKUbYPuJulAolb7d/m2f8DVt3zDw4k8WnqmBuTcunwNUXspIgfJ7x6xdCCFGrSLJzDygYykrfsQNtWlqV3kuhUDDlgSmMvm80AJ8c+oSfo3427k1UFtDlbcNx+NeQkWjc+oUQQtQqkuzcAyz9/dE09UGfm0vaH39U+f0UCgWvtnmVF1u9CMDco3P57th3xr2J/2NQvxXkpsPeOcatWwghRK0iyc49QKFQ4PiooXenqoeybr/nuKBxvNLasDbOvMh5fHn0S+Ot96NUQug0w/FfP0LKFePUK4QQotaRZOce4fDoowBkHjxI3rVr1XbfsS3HMvl+w+PiP0b9yGeHPzNewtO0K3h1BG0O/PmJceoUQghR60iyc4/QeDbA+v62oNeTumFDtd57RMAI3mr/FgCLTi1i1l+z0Ol1la9YoYDQdw3HRxdD4rnK1ymEEKLWkWTnHuLY9zEAUtZV0aJ/dzGk+RDe6/AeChQsP7OcGeEzjJPweHWAZj1Ar4WdH1W+PiGEELWOJDv3EIdHeoBaTc7Zs2Sfja72+w/yHcQHHT9AqVCy6u9VvLvvXbQ6beUrLujdOfEbXIuqfH1CCCFqFUl27iEqJyfsOj8EQOrv1d+7A9CvaT9mdZqFSqFi3bl1TN07lXxdfuUqrd8SAgYYjnd8WPkghRBC1CqS7NxjCoeyft+AXmeEYaQK6N2kN592/hQLhQWbYjYx5c8p5FV2F/Mub4NCBdGbIPagcQIVQghRK0iyc4+xe7gzSnt78uPiyDx82GRxdPfqzuddPketVLP14lYm7ppIrrYSe3e5NoXWTxuOt88AYz3xJYQQosaTZOceo7S0xP4Rw07oqetNM5RV4OGGD/Nl6JdYqizZdXkXr+x8hez87IpX2PkNUGng4l44v9N4gQohhKjRJNm5BxUMZaVu3oIuJ8eksXRq0Il5XedhbWHNviv7eGn7S2TmZVasMkdPeGCM4Vh6d4QQQtwiyc49yOaB+7GoVw9dWhrpu3abOhza12/Pt92+xcbChoPXDvLithfJyMuoWGWdJoLaFq5GwGnT9lwJIYQwD5Ls3IMUSiWOjxp2QjfVU1n/1bZuW37o8QP2anuO3jjKc1ufIzU3tfwV2blBh/GG4x0zwRiPtgshhKjRJNm5RzncGspK37Ub7c2bpg3mllZurfjxkR9x0DhwPP44Y/8YS0pOSvkrCn4JrJwg4SwcX2H0OIUQQtQskuzco6z8fLH080Ofl0fqlqrfCb2sAlwCmP/IfJwtnTmVeIrRW0aTlJ1UvkqsHKHTa4bjXR9BfiWe8hJCCFHjSbJzD3N8zLATuqmfyvovvzp+zH9kPi5WLpxNPsuzm58lISuhfJW0ew7s6sHNWDi6qGoCFUIIUSNIsnMPc+jTBxQKMg8fJu/KFVOHU0RT56Ys6LkAd2t3zqWcY9TmUVzPuF72CjQ20Pl1w/HuTyC3ghOehRBC1HiS7NzD1PXqYdOuHWBYUdncNHZszMKeC6lvW58LqRcYuXkkV9Ovlr2C1sPByQsybsDB76suUCGEEGZNkp17XMFQVsr6dejNcF2ahg4NWdhzIZ52nlxOv8zIzSO5lHapbBdbaAzbSADsmwtZN6sqTCGEEGZMkp17nH2PHig0GnL/OUfOmTOmDqdEHnYeLOi5AG8Hb+Iy4hi5eSQXUi6U7eLAQeDmD9kpsP+rKo1TCCGEeZJk5x6nsrfHrksXAFLWmddE5dvVs63Hgp4L8HH04UbmDUZuHsm5m+dKv1CpgtB3DMcHvoX0G1UbqBBCCLMjyY7496ms339HrzXfRfhcrV2Z33M+vs6+JGYn8uyWZzmbdLb0C5v3gQZtIS8D/pxd9YEKIYQwK5LsCOxCQlA5OpIfH0/mwYOmDueu6ljV4eceP9PCpQVJ2UmM/mM0JxNP3v0ihQK6TjMcH/oRYv6s+kCFEEKYDUl2BAqNBvuePQFIWf+7iaMpnZOVEz/2+JGWbi1JyUlh7JaxHIs/dveLmjwMrYaCXgcrR0GKeT1qL4QQoupIsiOAf4ey0v74A11WlomjKZ2DxoEfuv9AG/c2pOWl8dwfz3Hk+pG7X9TnM6gbCJkJsHKErKwshBD3CLNIdpYuXUpoaCiBgYEMHjyY48eP37X8pk2b6NmzJ4GBgfTt25fdu4vu3P3VV1/Rs2dPgoKCeOCBBxg5ciTHjpXyP/97nHXr1qgbNECXkUH6zp2mDqdMbNW2fNvtW9rXa09mfiYvbnuRg3F3GYbT2MCTYYbtJC4fgj/err5ghRBCmIzJk52NGzcya9Ysxo8fz5o1a2jevDmjR48mMTGxxPJHjx5l0qRJDBo0iLVr19K1a1fGjx9PdHR0YRlvb2+mTZvG+vXrWbZsGQ0aNODZZ58lKamceyzdQxRKJQ6PPgrUjKGsAjZqG77u+jUdPTqSlZ/F+O3j2Xdl350vqNMEHv/BcPzXD7JRqBBC3ANMnuwsWLCAJ554goEDB9K0aVOmT5+OlZUVq1atKrF8WFgYISEhjBkzBh8fHyZMmECLFi1YsmRJYZm+ffsSHBxMw4YNadasGVOnTiU9PZ2zZ8vw5M49zLGvIdlJ37OH/ORkE0dTdlYWVnwR+gUPez5MjjaHl3e8zO5Lu+98gV9PeGiK4XjdK3DtRPUEKoQQwiQsTHnz3NxcTp48yfPPP1/4mlKpJDg4mIiIiBKviYyMZOTIkUVe69SpE9u2bbvjPX799Vfs7e3x8/O7YyxarRatGT92XR0sGjfGskULck6dImXDRpyGPAVQ+LmY8+djgQWfhnzK1H1T2Ra7jQk7J/B/If9H10ZdS74g5HWUlw+hOL8T/Yph6EZvNwxv1QA1oT3uJdIe5kXaw/xUVZuUpz6TJjvJyclotVpcXFyKvO7i4sL58+dLvCYhIQFXV9di5RMSiu6KvXPnTiZOnEhWVhZubm7Mnz+fOnXq3DGW24fB7mUWbVqjOXWKuF9/5YJ/8yLnoqKiTBRV2Q2xH0KaYxoHUw7y+p+v85znczzo9GCJZVXNXsE/7hSWSedJC3uGcw9MB4XJOzvLrCa0x71E2sO8SHuYH1O2iUmTnarUvn171q5dS3JyMitWrGDChAmsXLmyWGJVwNfXFxsbm2qO0vzkN2jA+WXLUf39Ny1cXNA0bIhWqyUqKorAwEBUKpWpQyzVN62+4f0D77P+/Hp+uPIDDRo1oG+TviUX9lyGfmEvnK7vo3XGbvSdXqveYCugprVHbSftYV6kPcxPVbVJZmZmmTsqTJrsODs7o1Kpik1GTkxMLNZ7U8DV1bVYL05J5W1sbPDy8sLLy4ugoCB69OjBb7/9VmTI7HYqlUp+MABVvXrYPvggGfv3k75xI27jxv17roZ8RiqVipmdZqJRaVj19yqm7Z+GVq9loO/A4oUb3g+9Z8P6V1Du+hA824JPl+oPugJqSnvcK6Q9zIu0h/kxdpuUpy6T9tlrNBoCAgIIDw8vfE2n0xEeHk7r1q1LvCYoKIgDBw4UeW3//v0EBQXd9V46nY7cXFlXpSwcCraPWLfeLHdCLwulQsm0DtN4yu8p9Oh5P/x9lp9ZXnLhtiOg9TDDgoOrRkPK5eoNVgghRJUy+QSFUaNGsWLFCtasWcO5c+d4//33ycrKYsCAAQBMmTKFzz77rLD88OHD2bNnD/Pnz+fcuXN89dVXnDhxgmeeeQYwdGvNmTOHyMhIrly5wokTJ5g6dSrXr1+n561VgsXd2XfrjsLKitwLF8g+UcpWDGZMqVDyVvu3GN5iOAAfHfyIBScWlJzA9Z4N9VtBZiKsGA75OdUcrRBCiKpi8jk7vXv3JikpiS+//JL4+Hj8/f356aefCoel4uLiUCr/zcnatGnD7NmzmTt3LnPmzMHb25t58+bh6+sLGLq1zp8/z5o1a0hOTsbJyYnAwECWLl1Ks2bNTPIeaxqVnS32oaGkbtxIyvp1uLXwN3VIFaZQKJh8/2QsVZb8GPUjc47M4Z+b/zCtwzQsVZb/FlRbwROL4YfOcOUIbH4THv3cdIELIYQwGoW+po5TGElmZianT5/G399fJijfJm3XLi6/8CIqFxea7NjOsRMnCAoKqrFj4Hq9nsWnFvPZkc/Q6XXc53Ifn3f5nHq29YoW/HsbLB0E6KH/txA01CTx3o1WqyUyMrJGt0dtIu1hXqQ9zE9VtUl5fn+bfBhLmCe7jh1ROTujTUwkM/xA6ReYOYVCwfCA4XzX7TscLR05kXiCJ39/svh+Ws26wcNTDce/vwZxd9+6RAghhPmTZEeUSKFW49CrFwCpG2rO9hGl6eDRgV/6/IKvsy9J2UmM2TKGX8/8WnQez0OvQ7MekJ8NK4ZBVs1ZTVoIIURxkuyIOyrYCT1923bIzjZxNMbjae/J4l6L6endk3x9PjMPzuT98PfJ1d56Wk+phMe/BycvSL4Aq58Hnc6kMQshhKg4SXbEHVm1aoW6USP0WVmojhwp/YIaxEZtwycPfcJrbV9DqVCy+u/VjNoyihuZN24VqANPLgYLK/h7C+z57O4VCiGEMFuS7Ig7UigUON7aCV29cSP58QmlXFGzKBQKnr3vWb7p+g32GnuOxx/nyd+fJPJGpKFA/VbQZ47heOeH8E/J+68JIYQwb5LsiLtyHDAAhbU1ygsXufj446Tt2GHqkIyuY4OO/NrnV5o6NSUhK4FRW0axMnql4WTrp6HtSEAPq8ZA8kVThiqEEKICJNkRd6XxbECjX5aja9QIbXIyl8eNJ27ae+gyM00dmlE1dGjI0t5L6e7VnXxdPjPCZzAjfAZ52jzo9Ql4tDFMVF4xHPJqz/wlIYS4F0iyI0pl2bQp2TOm4zxqJAA3V6wgZsBAsqJOmDYwI7NR2/BZ5894tc2rKFCwMnolz255lvjcVHgiDKzrQFwkbJpi6lCFEEKUgyQ7omzUatwmT6bRwgVY1K1L7oULXBgyhITvvkOv1Zo6OqNRKBSMCRzDvK7zsFfbExkfyVO/P8WxvCQY9DOggKOL4GiYqUMVQghRRpLsiHKxffBBmvxvLfY9e0J+PvFzv+Di8BHkXr5i6tCMKsQzhOWPLqeJYxNuZN1g1OZRrNGlQOjbhgIbJsPVCNMGKYQQokwk2RHlpnJyosHnc6g/axZKGxuyjhwhpn9/UtavN3VoRuXl4MWyPssIbRhKni6Pafun8aEmh7xmPUGbY5i/k5lk6jCFEEKUQpIdUSEKhQKnx/vT+H9rsQ4KQpeeztXXp3Bl0mS0qammDs9obNW2fN7lc8YHjQfgl7O/MsZJTUIdb7gZC6vHgq72DOMJIURtJMmOqBRNw4Z4LVmM68svgUpF6oYNnO/Xn4y//jJ1aEajVCh5odULfBX6FXZqO44mHOOpuk6csLE3rL2z+xNThyiEEOIuJNkRlaawsMBt/Hi8ly5B3agR+XFxxI4YyY3PPkOfm2vq8Izm4YYPs6zPMrwdvLmencSIeq78z84Wdn8M0X+YOjwhhBB3IMmOMBrroCCarFmN46CBoNeT+ONPXHhqCDnnz5s6NKNp7NiYZX2W8bDnw+Tqtbzj5sLHdZzJWz0GkmJMHZ4QQogSSLIjjEppa4vHzJk0+PILVI6OZJ86RcyAgSQtW1Z0Z/EazF5jzxehX/BiqxcBWOpoz3NOliSteBryskwcnRBCiP+SZEdUCYcePWi8bh22wcHos7O5PuMDLr/wIvkJtWN/LaVCybigccztMhcbC2sOW1vxlDqZU+ueh1qS1AkhRG0hyY6oMuq67jT86UfqTn0ThUZD+u7dnO/Xn7Rdu0wdmtF0bdSVZX2W42XtRpyFBcNTj7B+u6ywLIQQ5kSSHVGlFEoldUaMwHvlSiybNUObmMjlF14kbvp0dFm1Y8jHx8mHZf3X8pBNQ3KUSt66splPd04mX5dv6tCEEEIgyY6oJlZ+vnj/tpI6I4YDcHP5L8QMHETWyZMmjsw4HDQOfDVwPc+p3AEIi93CC1tGk5ydbOLIhBBCSLIjqo3S0pK6U6fS8OefsHBzI/f8eS48NYSEH3+sFftrKZUqXh60mjkZKqx1Og7eOMqQ35/ibNJZU4cmhBD3NEl2RLWz69iRxuv+h3337pCXR/xnc4gdOYq8q1dNHVrlWTnSfeAylt5IoWFeHlcyrvLMxmfYFLPJ1JEJIcQ9S5IdYRIWzs40+PIL6n84E4WNDZmHDnG+X39Sft9g6tAqr24LmvWey/Kr1+iYmUW2Npspf05hzuE5aGVrCSGEqHaS7AiTUSgUOA0cSJM1q7Fq1RJdWhpXJ0/myutT0KalmTq8ygkchOMDzzPvejyj07IBWHByAS9ue5GUnBQTByeEEPcWSXaEyWm8vPBesgTXceNAqSR1/Xpi+vUn8/BhU4dWOd0/QNWwPRMSbvBpjjXWKivC48J56veniE6ONnV0Qghxz5BkR5gFhVqN2ysv47VkCWpPT/KuXuXi8BHc+Hwu+rw8U4dXMRYaGLwIbN3pefUsiy39aGDXgMvpl3lm4zNsubDF1BEKIcQ9QZIdYVZs2rSm8do1OD7+OOh0JH7/PReGDCUnpobuO+VQHwYvAIUKv5Pr+cWjDx3qdyArP4vJuyfzxdEvZB6PEEJUMUl2hNlR2dnhMesjGsydi9LRkewTJ4gZMJDkX1fUzP21vDtB9+kAOG2dzjd+zzIyYCQAP0X9xEs7XpJ5PEIIUYUk2RFmy6HnIzT531psHnwQfVYW1957j8vjXyI/KcnUoZVfh5egRT/Q5WGx6lkmNR/O/4X8H1YqK/Ze2cvQDUP5J/kfU0cphBC1kiQ7wqyp69Wj0fyfcZ8yBYVaTfqOHZx/rB/pf/5p6tDKR6GAfvPA1RdSr8CqZ+nt1YPFvRfjYetBbFosT298mm0Xt5k6UiGEqHUk2RFmT6FU4vLsKLxXrkDT1AdtQgKXnnueax/MRJedberwys7SHp5cAho7iPkTds6keZ3m/PLoL7Sv157M/Exe2/UaX0V8hU6vM3W0QghRa0iyI2oMq+bNafzbbzg/8wwAyUuXEjNoENmnT5s4snJw84N+XxuO934Op3/H2cqZ77p/x7AWwwD44fgPvLLjFdJya/haQ0IIYSYk2RE1itLKinrvvE3DH39A5epK7j/nuPDEkyT+PB+9rob0hgQ8bpjDA7D2RUj4BwulBVMemMJHnT7CUmXJ7su7GbphKOdvnjdtrEIIUQtIsiNqJLuQEJqs+x92Xbuiz8vjxqefEvvsaPKuXTN1aGXT7X1oFAw5qfDrM5CbAUBfn74s6rWIerb1uJB6gaEbh7IzdqdpYxVCiBrOLJKdpUuXEhoaSmBgIIMHD+b48eN3Lb9p0yZ69uxJYGAgffv2Zffu3YXn8vLy+PTTT+nbty9BQUF06tSJKVOmcP369ap+G6KaWdSpg+fXX1FvxnQU1tZkHjjA+X79Sd282dShlU6lhsELwa4exJ+Gda/ArcfqA1wC+KXPL9xf934y8jJ4ZecrfBP5jczjEUKICjJ5srNx40ZmzZrF+PHjWbNmDc2bN2f06NEkJiaWWP7o0aNMmjSJQYMGsXbtWrp27cr48eOJjjYsv5+dnc2pU6d48cUXWb16NV9//TUxMTG8+OKL1fm2RDVRKBQ4P/EEjVevwuq++9ClpHBlwmtcfXMq2vR0U4d3d/Z1DQmP0gJO/AZ//VB4ysXahR96/MDQ5kMB+PbYt7y681XSc838PQkhhBkyebKzYMECnnjiCQYOHEjTpk2ZPn06VlZWrFq1qsTyYWFhhISEMGbMGHx8fJgwYQItWrRgyZIlANjb27NgwQJ69+5NkyZNCAoK4t133+XkyZNcvXq1Ot+aqEaWjRvjvXwZLi88D0olKWvXEtP/cTKPRpg6tLvz6gA9ZhqOt7wFsQcKT6mVaqa2n8oHHT9Ao9Sw69Iuhm4cysXUi6aJVQghaigLU948NzeXkydP8vzzzxe+plQqCQ4OJiKi5F9SkZGRjBw5sshrnTp1Ytu2O69Pkp6ejkKhwMHB4Y5ltFotWq0s21+Sgs/F7D8fpRKXl1/GOjiYa29OJe/yZS4+8wx1nnsOlxeeR6FWmzrCkt0/FkXsQZSn1qBfORLdmB1gV7fwdN/GfWls35iJf04kJiWGoZuG8nTdp/HO8sbJ2sl0cQugBv183COkPcxPVbVJeeozabKTnJyMVqvFxcWlyOsuLi6cP1/yUygJCQm4uroWK5+QkFBi+ZycHGbPnk2fPn2ws7O7YywFw2DizqKiokwdQtmoVDD9fTQLF2Gxbx9J331H/Nat5I57EX29eqaOrkRKrzE0jz2KddpFMsOeIvrB2aBUFSnzdqO3mRc7j+jMaH64/AM/Xv6RhlYN8bP1w9fGFz9bPxws7pzQi6pVY34+7hHSHubHlG1i0mSnquXl5fHqq6+i1+uZPn36Xcv6+vpiY2NTTZHVLFqtlqioKAIDA1GpVKVfYC6Cg0nduJEbMz6Ac+eweeddnJ95Gk3TpqgbNULTqBFKR0cUCoWpIzXwXoH+567YJx6jddL/0HebUaxIcOtgvjv+HRv+3sC13GvEZscSmx3L1sStADR2aEwb9za0qduGtu5tqWdrnsldbVJjfz5qKWkP81NVbZKZmVnmjgqTJjvOzs6oVKpik5ETExOL9d4UcHV1LdaLU1L5vLw8JkyYwNWrV1m0aNFde3UAVCqV/GCUoiZ+Rs59+2LXti1X35xK5l9/kfTjT0XOKx0c0DRqhKZRw1sJkBcaL0MipHJ1rd5EqG5z6P8trBiGMvxr8HwAAvoXKaJSqXi59cuEKELw9PMkMiGSI9ePcOT6EaKTo4lJjSEmNYZV/xjmvDWwa0Dbum25v+79tK3blob2Dc0nuatlauLPR20m7WF+jN0m5anLpMmORqMhICCA8PBwunXrBoBOpyM8PJxnbq2S+19BQUEcOHCgyLyd/fv3ExQUVPh9QaJz8eJFwsLCcHZ2rsq3Icyc2sODRgvmk7J2LZlHj5J3MZbcS5fIv34dXWoq2SdOkH3iRLHrFDY2aBo2NCRDXo2KJEMWdeuiUFbB/P4Wj0HwK7D/S/jfeHBvAW6+JRZ1tXblEe9HeMT7EQBSclI4ev1oYfJzOuk0V9KvcCX9CuvOrQPAzdqNtnXbFn75OPmgVJj8OQUhhKhSJh/GGjVqFG+88Qb33XcfLVu2ZNGiRWRlZTFgwAAApkyZQt26dZk0aRIAw4cPZ9iwYcyfP5/OnTuzceNGTpw4wYwZhi7/vLw8XnnlFU6dOsX333+PVqslPj4eAEdHRzQajWneqDAphUqF08CBOA0cWPiaLiuL3EuXyIuNJfdiLLmxseRdMhznxcWhz8wk5+xZcs6eLV6fRoO6IBFq1Ai117+JkLp+fRQWlfjR6voeXI2AC3sMCw6O3QGWd++ZBHC0dKRLoy50adQFgIy8DI7dOMbh64c5cv0IUQlRxGfFs/nCZjZf2Fx4TRv3NoW9P351/LBQmvyfBSGEMCqT/6vWu3dvkpKS+PLLL4mPj8ff35+ffvqpcFgqLi4O5W3/g27Tpg2zZ89m7ty5zJkzB29vb+bNm4evr+F/v9evX2fHjh0A9OvXr8i9wsLCaN++fTW9M2HulNbWWPn6YuVbvOdEl5tL3uUr5MZeNCRDsZcMxxdjyb1yBX1uLrnnzpF77lzxii0sUDfwMCQ//+kVUns2QFlawq2ygEHz4fuHIOEsrHsJBi0w7JxeDrZqW4IbBBPcIBiAHG0OUfFRhcnPsfhjpOSksPPSTnZe2ll4TZB7UOGwV4BLABqV/AdBCFGzKfT6W8u23qMyMzM5ffo0/v7+MkH5DrRaLZGRkQQFBckYOKDPzycvLs7QA3Tp314hQ2J0CX1u7p0vVihQ16//b09QQTLU0DBvSGlt/W/ZS3/Bgt6gy4NHPoIO4wHjtUeeLo/TiacLh72OXj9KWl7RzUctVZa0dGtZOOzV0rUlNmoj/Jxo8yHjBqg0YFvy/LyaQn4+zIu0h/mpqjYpz+9vk/fsCFHTKCwsDHN5GjYEOhY5p9fpyL9+/d+eoNjbk6FY9JmZ5F29St7Vq2SGHyhWt4W7e9FhMeeRqP9eiGbDNFQercEr2GjvQ61U09KtJS3dWjLqvlFodVr+uflPYc/PketHSMpO4tC1Qxy6dsgQn8KCFq4tCoe9Wru3xl5j/2+ledmQfg3Srt/9z4wE4Nb/s+q1hGY9DF+e9xd75F4IISpLkh0hjEihVBp6burXx7Z9uyLn9Ho92sREQ+JzMfbfYbFbiZAuNZX8GzfIv3EDDh++7Uo3AFS/P4um2X1YNG6ChYsruubNUdnaGi12lVKFXx0//Or48bT/0+j1emJSYzhy7QhH4g5w+PoRrmcncjz+OMfjj7PgxAIUQHM0tM3V0jYjlTapydQp6+7zChXodXDtuOFrz2ywdgafUEPi07Rbje/1EUKYB0l2hKgmCoUCC1dXLFxdsWnTpth57c2bRROhgrlCFy+iTUpCm60gK+okRJ1EA8T8/juuzz2H0xODUVpalj8gvR4yk271uFyD9OtF/lSkX6dJ2jWapF9ncF4meuCqhYojVlYcsbLkiJUlF9VqTpPLaQ0s0diCsy1N8vJpq7OgrYUTbW08qOfQyLAitH09w8an9nUNf9q4QGYinNsOf/8B/2yHrGQ4scrwhQIatLmV+HQHj9ZQFU/ACSFqPUl2hDATKicnrJ2csG7Zstg5bWwUeV8+Sm5SNtmOHYj/KwHi47n+4Yck/vQTLs+NxWnwYMPkZ20+ZMSXYTjpumE+UBkpNPY0sK9HA/t6PHYreYm3sucIWRzOiedI2kX+SY/lvNqC88BKbkLOTRpkpXC/gyVt7Xy4v64fnvae/671Y+cGrZ4yfGnz4coRQ+Lz9x+G3p4rRwxfu2aBjauht6dZd0Pvj00d43zwQohaTyYoywTlUsmEPzNxZgP8YtgF/XLTkdhcyifxfwfIv5kJgIWdEteW+Th63kCpLONQEoB1nVu9LnXv/qem9CGzm9k3OXqj6Fo/On3RWNyt3QsnPAd7BNPQoWHJlaXGwT/b4J+tcG4n5KT+e06hNCy62Ky7oeenXstyP61mLPLzYV6kPcyPOUxQlmRHkp1SyT8eZmTbdNg7p/BbnRZunrch8ZQ9+VmGtrGwycc1IAOn++xRONUtOnRU+OetJMauLlhU3aPl6bnpHIs/Vpj8RCVEkXdbb5ICBT29e/JC0As0cWxy54q0eXDp4K1en61w41TR83b1oFk3w3CXTxewcqyid1RCaPLzYVakPcyPOSQ7MowlRE0S+g66zEQyYw5h694EpUM96nSrh5OlCzf3nydx9Q7yE5K5dsiRxCseuLzwPE6PP26yHd/tNHZ0bNCRjg0MT61l52cTlWBY66fgKa9NFzax5eIWejfuzQutXsDLwat4RSo1eHcyfHWfASmXDUnP31vh/C7D0FzEEsOX0gIaPvhvr4+7v8l6fYQQ5kF6dqRnp1TyPyXzcrf20OXkcPPXFST8+APaeMMecuoGDXB98QUc+/UzWdJzJ2eTzvJN5DfsuGRYCFSlUNHXpy/PtXyOhvZ3GN76r/wciA2/lfz8AQn/2RjQocG/iU/jzmVajbo85OfDvEh7mB9z6NmRZEeSnVLJPx7mpSztocvO5uavv5Lw409ob22cq/b0NCQ9jz1mdknPycSTfBv5Lbsv7wYM6/n0a9qP51o+h4edR/kqS4oxzPX5eyvE/An5Wf+eU6oNaxUVrOvj2qzSvT7y82FepD3MjzkkO/IcpxC1kNLKijojRtB06x+4v/EGKhcX8i5fJu7tdzjX51Furl6DPj/f1GEWCnAJ4OuuX7Os9zI6NuhIvj6fVX+vos+aPsw8MJNrGdfKXlmdxtBuLDy9At6IgadXQbvnwbmx4emzmN3wx9sw7wH4ohVsmAzRWyA3s+reoBDCpCTZEaIWU1pb4zJqpCHpef11VHXqkBcbS9xbb3GuTx9url1rVklPoFsg33X7jsW9FvNg/QfJ1+Xz69lf6b26N7MOzuJG5o3yVai2Nkxc7v0JvBoJLx+Fnh8bHl1XaeDmRTj0Iyx7Av7PG5YMhIPfQ2IJe54JIWosSXaEuAcobWxwGf0sTbdtxf31yaicncm7GEvcm1M53+dRUtatQ6/VmjrMQkHuQfzY40cWPLKA++veT54uj2VnltF7dW8+OfQJCVkJFavYxQcefBGGrYE3LsCQX+D+0eDYELQ5huGvTVPgqzbwZRvY9KZhscO8bKO+PyFE9ZI5OzJnp1QyBm5ejNEeuowMkpYtI+nn+Whv3gRA07gxruPG4dC7Fwoza+e/4v7i68ivibgRAYCVyoohzYcw8r6R1LEywuKCej3En/13QcPYcNDd1uOltjFMbm7WzTDXx6lR4Sn5+TABnQ7yMiE3A3LTixxrs9O5EHMO7/aPonLzlSfxzIA5zNmRZEeSnVLJP+bmxZjtoU3PIHnpUpLmz0ebkgKAxscH13Ev4tCzp1klPXq9nvC4cOZFzON4wnEArC2sGdp8KCMDRuJk5WS8m2WnGub2FKzrkxZX9Lxb88InvLQNHiAy6pT8fJREr4e8rBKTEsOfmf8e5912/N+v/57LK+P8Kus6hsUnGz4Anu0M249Y2pd+nTAqSXbMgCQ7pZNkx7xURXto09NJXrKExAUL0RUkPU19cBs3DvuePVGY0Z5Uer2evVf2Mi9yHicTTwJgq7blaf+nGd5iOI6WRl5QUK+H6yf+Xdfn0kHQ/zvkp9fYkebgi72zKwqlhWHXdoXK8KfS4tax8rbj288XHP/3uv++rvxPGQtDnWW6thx1or8tubhDUpKbAXklJCR3OkdV/opRgMYONDaGFb41tujVtmSkpWCbdh6FNuc/xZXg3uJWAtTOkAC5+EjvTxWTZMcMSLJTOkl2zEtVtoc2LY2kxYtJWrgIXaphewbLZk1xHT8e+x49zC7p2X15N/Mi53Em6QwA9mp7hgUM4xn/Z7DXVNH/4LOSDdtXFDzenlHOSdP3KvW/CQkau+LfFyYsdoY/1Tb/Hv/3S13wp3WxRKXw5yOwBaobp+DyX3DpL7h8GFJii8dVrPenrdHXYrrXSbJjBiTZKZ0kO+alOtpDm5pKUthikhYtQpeWBoClr68h6enezaySHp1ex87Yncw7No+/k/8GwEHjwMiAkQz1H4qtuvQ9vSp+cx3aqxHEHv4Dr4YeKNEb5vrodIbeH12+YU8PvdbwZ+Fxweu6/5Qx4bUFLKyKJxXFvsqZrKhtqm3H+rv+fKTGweVDtxKgQ3A1wjAx/XYKJbgHgOf90vtjJJLsmAFJdkonyY55qc720KamkrRwEUlhYejS0wGw9PPD9aXx2Hfr9u/u5WZAp9ex9eJWvon8hvMp5wFwsnRi1H2jeMrvKWzUVfPzXWt+PnQ6QG8Y1qrBytUe+blwLUp6f4wp6yYkXzAs65B8EW5eRJeRwNk63fHtMkSSHVORZKd0teYf81rCFO2hTUkhceFCksMWo8vIAMDS3x+3l8ZjFxpqVkmPVqdly4UtfHvsWy6kXgCgjlUdnr3vWZ7wewJrC2vj3k9+PsxKpdujPL0/BcmP5wP3Tu9PXjbcjL2VzFwoltiQnVLiZdebDML16R8k2TEVSXZKJ/+YmxdTtof25s1/k55MwxMxVi1a4PrSS9h1ediskp58XT6bYjbx7bFvuZR2CQBXa1fGBI5hkO8gLFWWRrmP/HyYF6O3R7Hen0OQcql4udrS+6PTQuqVf5OX5ItFE5r0MqxmbusGTl7g7AVOXuicG3Msvwkt7+8gyY6pSLJTOvnH3LyYQ3vkJyeTtGAhSUuWoC9Ieu67D9eXxmPXubNZJT15ujx+P/c73x//nivpVwBwt3ZnbMuxDGg2AI1KU6n6zaE9xL+qpT0q0vvTsB3UaWL63h+9HjIS/u2Zub1XJvkCpFwuOoerJBr7wkTm3z+9bx03MszTuo3M2TEDkuyUTv4xNy/m1B75yckkzZ9P0tJl/yY9gYG4vTQe24ceMq+kR5vH/879j++Pf1+411Y923qMDRzL400fR62q2Oao5tQewkTtYW69Pznptw0zXfxPQnPRsDzA3SjV4NTQkMAUSWi8DHvMWTuXK2mTZMcMSLJTOvnH3LyYY3vkJyWR+PPPJC9bjj7LsMu4VauWuL30EradOplV0pOrzWXN32v4IeqHwr22PGw9eL7V8/T16YtaWb6kxxzb415mNu1Rlb0/+bmGZKqkYaabFyEzsZTgFGBfv4RemVsJjX19o05Ul2THDEiyUzqz+cdDAObdHvmJiST+9DPJy5ejzzbsJ2UdFITrSy9h2zHYrJKeHG0Ov0X/xk9RPxXutdXQviEvtHqB3o17Y6G0KFM95twe9yKzbY+y9v7YuBh6fzzvh3qtICup+PyZtKuG5QPuxtq5hF4Zb3DyNvTaWBhnzlpZSLJjBiTZKZ3Z/uNxj6oJ7ZEfH29Ien75BX2O4X+z1q1b4/byS9h06GBWSU92fjYrzq7g5xM/k5SdBIC3gzcvtHqBnt49UZXyP9ya0B73khrVHqlxRR97L6n3504srA3zY/7bK1Pwp5WRVxKvBHNIdsr2XxchhCgHCzc36k59kzqjnyXxp5+4+cuvZEVEEPvsaKzbtjUkPe3bm0XSY2VhxfCA4QzyHcSvZ39l/on5XEi9wJt73uSH4z/wYtCL9PDqgVJhPgspilrCoT606Gf4guK9PzdOg61r0V6ZgoTGzt30k51rEEl2hBBVRu3uTr233sJl9BhD0vPrr2QdOULsyFHY3H8/Ls8/j237dig0lXsiyhhs1DaMum8UT/g9wfIzy1lwYgHnU87z+u7X+d7pe8YHjSe0UagkPaLqWGjAs63h68EXTR1NrSI/tUKIKqeu6069t9/CZ+sfOD/9NAq1mszDh7k0dizRD3bg0viXSP7lF3IvXzZ1qNiqbRkTOIbNAzczPmg89mp7/rn5D6/teo0nf3+SnbE7ucdH/4WocSTZEUJUG3XdutR7953CpEdVpw66zEzSt2/n2vvTOdetO+d69uLahx+R/uef6G492WUK9hp7Xmj1ApsGbuL5ls9jq7blTNIZXtn5Ck9teIo/L/8pSY8QNYQMYwkhqp26Xj3qvfsOdd9+i+zTp8nYs5f0vXvIiogk98IFci9cIHnxYhQaDTYPPIBtSCfsOnVC4+NT7fN8HC0dean1Szzj/wyLTi1i6emlnEo8xfjt42np2pIXWr6Atd64W1AIIYxLkh0hhMkolEqsAwKwDgjA9YXn0aalkREefiv52Ut+XBwZ+/aRsW8fN/g/LOrXx65TJ2xDOmHboQMqe/tqi9XJyolX27zKsBbDWHhiIcvPLOd4wnHG7RhHQ6uGBGYE4mHvQX3b+tSzrYeHrQf17epX7a7rQogykWRHCGE2VPb2OPTogUOPHuj1enLPnyd9zx4y9uwl89Ah8uPiuLlyJTdXrgSVCuvWQdh1CsE2pBNW/v4olFU/Ml/Hqg4T75/I8IDhzD8xnxVnV3Ap+xKXLpSwZgqG4bD6tvX/TYLsPIp872btVurj7UKIypFkRwhhlhQKBZY+Plj6+OAyciS6rCwyDx0ifc9eMvbsIffCBbIOHyHr8BHi585F5eKCbcdg7EJCsO3YEYs6dao0PldrV6Y8MIUR/iNY9dcqLF0tuZ51nbiMOOLS44jLiCM1N5W03DTSctOITo4usR4LhQV1betSz7ZekSSovm39wsTIRi1rgAlRGZLsCCFqBKW1NXYPPYTdQw8BkHv5Mhl795K+Zy+Z4eFoExNJXbee1HXrQaHAKiDAMNcnJATrli1RWFTNP3eu1q60d2xPUEDxBdMy8jK4lnHNkADdlgTFZcRxLeMa1zOuk6/P50r6lcJNSkvioHEosXeoIClytXaV3iEh7sLkyc7SpUv5+eefiY+Pp3nz5rz77ru0bNnyjuU3bdrEF198wZUrV/D29mby5Ml07ty58Pwff/zBL7/8wsmTJ7l58yZr167F39+/Ot6KEKIaaTw90Tz1FM5PPYU+N5fMiEgy9u4hfe8+ck6fJvvECbJPnCDx2+9Q2ttj26FD4URndf361RKjrdoWHycffJx8Sjyv1WmJz4ovlhBdy7jG1YyrxGXEkZabRmpuKqm5qZxNPltiPRZKC+ra1C3aM2RnOPaw9aCebT3pHRL3NJMmOxs3bmTWrFlMnz6dVq1asWjRIkaPHs3mzZtxcXEpVv7o0aNMmjSJiRMn0qVLF9avX8/48eNZvXo1vr6+gGH56DZt2tCrVy/eeeed6n5LQggTUGg02LZvh237drhPmkTejRtk7NtPxp49ZOzbhzYlhbQ//iDtjz8AsGzWFNtOIdiFdML6/vtRmmhRQ5VSRT3betSzrUcQQSWWSc9NL5oM/Scpup55nXxd6b1DjpaORYfIbD2oZ/fv0JmrtassmChqLZMmOwsWLOCJJ55g4MCBAEyfPp1du3axatUqnnvuuWLlw8LCCAkJYcyYMQBMmDCB/fv3s2TJEmbMmAFA//79AbhsBouTCSFMQ+3ujtPj/XF6vD96rZbsEydI37uXjD17yTp+nJy//yHn739IWrAAhbU1tu3aYRtiSH40Xl6mDr8IO40dTTVNaerctMTzBb1DJQ2TXc24yrX0a6TlpZGSk0JKTgpnks6UWI+F0oL6tvXxcfQp7I1q6tSUxo6NsbKwqsq3KESVM1myk5uby8mTJ3n++ecLX1MqlQQHBxMREVHiNZGRkYwcObLIa506dWLbtm1VGaoQogZTqFRYt2qFdatWuI0fj/bmTTLCwwsnOufHx5O+ezfpu3dzHVA3bIhdSCdsO4Vg274dSlvzfnT89t6h1u6tSyyTlptW2Dt0LeMaV9OvFh7/f3t3Ht1Unfdx/J21O0ktZWnL0rK0LNZSllKGxQEeHfWgg3qcUYcicsqDqDg6jMjhiIgwMMyoKDouFVAZl0GPeMSiHPvgMii0LIVWKYLQ2lUopXvSNsm9zx8pgZiArUADyfd1Tk6Su36Te0o+/H6/e29VcxUnLCewK3bKGssoayzji/IvXOtqNVriwuMYaB7oCkADzAOIN8Vj1Pn+Nh9CdITPwk5tbS0Oh8OjuyoqKopjx455XefkyZN0797dY/mTJ09ecD0OhwOHw3HB2/FHp78X+X4uD3I8LlBEBGHXXUfY6dPbDx92Xstnx9dY9+7FVlZG7dvvUPv2O6DXEzJyJGHjf0PY+PEYBw3yuKjhlXA8QnWhJHRLIKFbgtf5dsXOSetJyhrLOFp/1Pmocz7q2+opbSyltLGU7WXbXevoNDr6RPRhoHkgCaYEZ2uQaQD9Ivph0Bm66qN5uBKOR6C5VMekM9vz+QDly8Xhw95PCxVnFBYW+roEcRY5HhdRaqrz0dKC7uBBtAcK0B04gLa6GmtuLtbcXE4+/QxKZCRK8tU4kpNxDB8O4eGuTfjD8TBgIIkkkoxJ0APUaJV6ez2VrZVUtFZQ0VJBRWsF5S3lWBUrJQ0llDSUuG1Dh46eQT2JDYolLjiOmKAYYoNj6WnsiU7TdWeM+cPx8De+PCY+CzuRkZHodDpqamrcptfU1Hi03pzWvXt3j1ac8y3fGYMHDyY0VM5W8MbhcFBYWMjVV1/tcWqt6HpyPC6xsWMBUFUVW2kpzTu+xvL1Diy5eWhra9F++RX6L78CrZbg5KsJGTeOqp49GTptGvqgIB8X3zVUVeWE9YSz9af+KMfqjrlahJptzVS2VlLZWsnuht2udQxaA/279T8zJqj9OS487qKeNi9/H5efS3VMLBZLhxsqfBZ2jEYjw4YNY+fOnUydOhUARVHYuXMnf/rTn7yuk5KSwq5du9zG7XzzzTekpKRccD06nU7+MH6BfEeXFzkel54+IYGQhATImIHS2op1717nWJ8d/6X1yA+07D9Ay/4DBAPFf19N6KiRhKWlETpmjPOKzpfo2j6Xg5iIGGIiYpjQZ4JrmqqqHLcc50jtEY7WHeWHuh9cgchqt3Kk7ghH6o7Aj2e2E6QLIt4Uf2Y8kMn5HBsRe0Fnh8nfx+XnYh+TzmzLp3+Js2bNYuHChQwfPpzk5GTeeOMNrFYrt956KwCPPvooPXv25C9/+QsAGRkZzJgxg/Xr1zNp0iS2bt3Kt99+6zoTC6Curo6qqipOnDgBQHFxMeBsFYqOju7iTyiE8BfaoCDCxo0jbNw4WPgotvb7djV++RWN33wDzc00f/Vfmr/6r3P58HBCR44kdMwYQtPSCB6ShMbPf3w1Go1rsPSEuDMhSFEVqpqrOFp31C0IFdcX0+Jo4dCpQx5niYXoQ4g3xXsMjO4d1ltOkRed5tOwc+ONN3Lq1Cmef/55qqurGTJkCK+99pqrW6qqqgrtWfe6SU1N5Z///Cdr1qzhmWeeoX///rz44ouua+wAbN++nUWLFrneP/zwwwA88MADPPjgg130yYQQ/s7Quzfm228nYvp09u/bR1JIKK1799CctxvL7t0oDQ2us7wAtBERhI4a5Qw/Y0YTnOT/4ec0rUZLbHgsseGxTIyb6JruUBxUNlU6W4DqzwSh4vpirHYrB2sOcrDmoNu2QvQhHgFooHkgPUN7egweF+I0jaqqqq+L8CWLxUJRURFDhgyRMTvn4HA42L9/PykpnpfDF11PjsflxdvxUB0OWr//nubcPCx5eVj27EFpbHRbT9utG6GjRhGWNobQMWMISkzskhuZXgnsip3yxnK3rrAf6p0tQXbF7nWdcEM4CeYEBnQbgKHJQHyfePQ6PVqNFq1Gi06jQ6PRoNPovL4/e7lzvfc2r7PbPPt1oISzS/VvVmd+v/23Q1kIIXxEo9MRPHQowUOHEjXrHueFDYsOYcnNPRN+Ghpo2r6dpu3O07m1JhOho0cR1t7tFTRoUMCGH71WT39Tf/qb+jOl3xTXdJtio6yh7EwAan/+seFHmmxNFFQXUFBd4Fz4uI+K7wQN5whGWi1atOi1eroZu2EONhMZFHnmOchMZHCk83HW9BB9SMAEqM6SsCOEEJeYRqcjZPgwQoYPI2r2vah2Oy1FRVjy8mjOzcW6Zy9KfT1NOf9HU87/AaAzmQgdM5rQMc4Bz0GDBgZs+DnNoDWQYE4gwex+vSCbw0ZJQ4lrTFBhaSER5ghUVBRVwaE6UFTF9fD2XlVV1/Sfv3dbT1FQOMd2FIdrnqIqv/h5VFTsqh3O079Sba2G+o59P0at8ZzB6FzPQbrAOINQwo4QQnQxjV5PyNVXE3L11UTNnu0MPwcP0pybiyVvN5a9e5338/osh8bPnFeI10VGEjp6NKFjnPcAMw4cKP+Lb2fQGRgUOYhBkYP4n77/w37V9928qqqiclaAUhxn3p8dmJT2wISCorgHKJtio6GtgdrWWupa6jye61qdr2tbaml1tNKmtHHCcoITlhMdrjNEH+IWjs4ZjNqXMQWZMGh9d9HIX0vCjhBC+JhGryckOZmQ5GTIzES12Wj57jvnYOfcXCz79uGorXW7manuqqtcg53DxozBOGCAhJ/LiEajQYPmzJljlzh3We1WaltqzxmMaltrneGoxflc11KHXbVjtVux2q1UNld2eF8RxoiOtx4FRRKm9/0tVyTsCCHEZUZjMBCSkkJISgrMcYYfa+G3zvE+eXnO8HPqFI2ffkrjp58CoIuKcgaf9uv8GOPjJfwEkBB9CCHhIcSEx3RoeVVVabI1eQ1CHs9ntSSpqDS2NdLY1khpY2mH9mXUGpnZeyYppFzAJ7wwEnaEEOIypzEYCE0dQWjqCJj7v6htbVi//RZLbi7NeXlY9+XjqKmh8ZNPafykPfxEdyds9Jj26/yMwdi/v4Qf4aLRaIgwRhBhjKAPfTq0jkNx0NjW6DUcuVqV2luNTgekRlsjbUobjY7GX97BJSRhRwghrjAao5HQ1FRCU1Ppft99KG1ttBQU0JyXhyU3D2t+Po7qkzRs3UrD1q0A6KOjXcEnbMwYDP36SfgRnaLT6jAHmzEHmzu8jk2x0dzazNGDRy9dYR0gYUcIIa5wWqPRecHCUaNg3jznrS0OHHAOds7Nxbp/P/bqahqys2nIzgZA37PnmTE/aWkY+vSR8CMuOoPWQIQxwtdlSNgRQgh/ow0KImyMswWHB+5HaWnBeqDAdZ0f64ED2I8fp2HLFhq2bAFA36sXoSNHYuzXD0NcHIa4WIxxceh79gyYKz0L/yVhRwgh/Jw2OJiwNOcp64Az/Ozf336dnzysBQXYf/rJ1erjxmDAENMbY2xcewiKw9jnzGud2SwtQuKyJ2FHCCECjDY4mLCxYwkbO5ZoQLFase7fj7WgEFt5ObaKctrKK7BVVoLNhu3HUmw/ej/zRhsa6h6CXKHI2TKkldvwiMuAhB0hhAhw2pAQwtLTCUtPd5uuOhzYjx+nraz8rBBUjq28Alt5OfYTJ1AsFloPH6b18GGv29ZFRTmDz9khqE8f5+tevdAYrrwL1Ikrj4QdIYQQXml0OgwxMRhiYqC9C+xsSksLtspKbOXuIaitvAxbeQVKQwOOmhocNTW0HCjw3IFWi6FXL2fw6ROHsb2FyNk6FIs+Olq6yMRFIWFHCCHEr6INDiYoIYGghASv8x0NDT8LQmVnXldUoLa2OsNSZSXk5XmsrwkKOtMa5KVlSBfh+7N8OktVVbDbUe12VJvtzLPNjmprc847Pd3hQNetGzqzGZ3JhEYvP9m/lnxzQgghLgldt27o2u/+/nOqomA/edIzBJW3d5n99BNqayttR4/SdvQozV62rzWZMMbGuo0Z0vWOQVNXS2toKBqHAnabe6iw21Hbzp7Whmq3g0fwOHsd5/J4TO/g/LNeY7P96u9TazKhM5vQmyOdASgy0vkwm9FFmtGZzegjz5pnMkk3YTsJO0IIIbqcRqvF0KMHhh49IHWEx3zVZsNWVeWli8wZhhynTqHU19NSX0/LwYNu64YAP3bR57godDo0BgMavd7tGa0WR2MjSr3ztudKfT1Kff05B4t7o42IcAtEbkHJ7flMUPLHgCRhRwghxGVHYzBg7NsXY9++eLuNpNLcTFtFhcc4obbyMlqrfkJvNKIxGj0ChCtIGE6/N5yZrtejMRpA72WewYCmfR2v841e9nF6vuFcNbS/1mrP+12odjuO+nocdXU4amtx1NVhb3921J6Z5vbc0ACqitLYiNLYiK20EwEpPPysliMvLUZmMzqze6uS1mjs5BHuWhJ2hBBCXHG0YWEEDx5M8ODBbtMdDgf79+8nJSUFnZ9cDFGj16OPikIfFdXhdVSHA0dDgzMM1dV6BCJnWKp3n15fD4qC0tSE0tSErby8w/vThob+rFvtrCB0VRTEduwGpZeKhB0hhBDCz2h0OvSRkegjI4H4Dq2jKgpKQ4N7q5ErIHlpUapzPnA4UCwWFIsFW0WF120bbvgdjBt38T5gJ0nYEUIIIQQarba9i8rc4XXU9pYgR62z9cjuEZLqcFgtnEhLu3SFd4CEHSGEEEL8Khqt1nnWXbdu0K+f12UcDgfH9+/v2sJ+5vyjooQQQgghrnASdoQQQgjh1yTsCCGEEMKvSdgRQgghhF+TsCOEEEIIvyZhRwghhBB+TcKOEEIIIfyahB0hhBBC+DUJO0IIIYTwaxJ2hBBCCOHXJOwIIYQQwq9J2BFCCCGEX5OwI4QQQgi/FvB3PVcUBQCr1erjSi5fDocDAIvFgk6n83E1Qo7H5UWOx+VFjsfl51Idk9O/26d/x89Ho6qqetH2fAWqqamhpKTE12UIIYQQ4lfo378/UVFR510m4MOO3W6nvr6eoKAgtFrp1RNCCCGuBIqi0NraislkQq8/f0dVwIcdIYQQQvg3acoQQgghhF+TsCOEEEIIvyZhRwghhBB+TcKO8OqVV17htttuY8SIEaSnpzNv3jyOHTvm67JEu1dffZXExERWrFjh61IC2vHjx1mwYAFpaWkkJyczbdo0CgsLfV1WQHI4HKxZs4bJkyeTnJzM1KlTefHFF5FhqV1j9+7dzJ07l/Hjx5OYmEhOTo7bfFVVee655xg/fjzJycncc889XXomtIQd4VVeXh533303mzZtYsOGDdjtdmbPno3FYvF1aQGvoKCAd999l8TERF+XEtDq6+u58847MRgMZGVlkZ2dzcKFCzGZTL4uLSBlZWXxzjvvsGTJErZu3cqCBQt47bXX2Lhxo69LCwgWi4XExESeeOIJr/OzsrLYuHEjS5cuZdOmTYSEhDB79mxaW1u7pL6Av6ig8G7dunVu71etWkV6ejrfffcdo0eP9lFVorm5mb/+9a8sX76cl156ydflBLSsrCx69erFypUrXdP69Onjw4oCW35+PlOmTOHaa68FIC4ujuzsbAoKCnxbWICYNGkSkyZN8jpPVVXefPNN7rvvPqZOnQrA6tWrGTduHDk5Odx0002XvD5p2REd0tjYCCD/a/WxZcuWMWnSJMaNG+frUgLe9u3bGT58OPPnzyc9PZ3f//73bNq0yddlBawRI0awa9cuiouLATh06BB79+5l4sSJPq5MlJeXU11d7fbvVkREBNdccw35+fldUoO07IhfpCgKf/vb30hNTWXw4MG+LidgZWdnc/DgQd5//31flyKAsrIy3nnnHWbNmsXcuXMpLCxk+fLlGAwGpk+f7uvyAs6cOXNoamrihhtuQKfT4XA4ePjhh7n55pt9XVrAq66uBvC4ynFUVBQnT57skhok7Ihf9OSTT3LkyBHefvttX5cSsKqqqlixYgXr168nKCjI1+UInE3zw4cP55FHHgFg6NChHDlyhHfffVfCjg988sknbNmyhaeffpqBAwdSVFTEypUr6dGjhxwPIWFHnN+yZcv44osv+Pe//02vXr18XU7A+u6776ipqeHWW291TXM4HOzevZu33nqLwsJCuelhF4uOjmbAgAFu0xISEti2bZuPKgpsq1evZs6cOa7xH4mJiVRWVvLKK69I2PGx6OhowHkvyh49erim19TUkJSU1CU1SNgRXqmqylNPPcVnn33Gxo0bZeClj40dO5YtW7a4TVu0aBEJCQlkZmZK0PGB1NRU1/iQ00pKSoiNjfVRRYGtpaUFjUbjNk2n08mp55eBuLg4oqOj2blzJ0OGDAGgqamJAwcOcOedd3ZJDRJ2hFdPPvkkH3/8Mf/6178ICwtz9blGREQQHBzs4+oCT3h4uMd4qdDQUMxms4yj8pGZM2dy55138vLLL3PDDTdQUFDApk2bWLZsma9LC0i//e1vefnll4mJiXF1Y23YsIHbbrvN16UFhObmZkpLS13vy8vLKSoqwmQyERMTQ0ZGBi+99BL9+vUjLi6O5557jh49erjOzrrU5EagwqtzXcNl5cqVbl0pwndmzJhBUlISixcv9nUpAevzzz/nmWeeoaSkhLi4OGbNmsUdd9zh67ICUlNTE8899xw5OTmu7pKbbrqJ+++/H6PR6Ovy/F5ubi4ZGRke06dPn86qVatQVZXnn3+eTZs20dDQwMiRI3niiSeIj4/vkvok7AghhBDCr8l1doQQQgjh1yTsCCGEEMKvSdgRQgghhF+TsCOEEEIIvyZhRwghhBB+TcKOEEIIIfyahB0hhBBC+DUJO0IIIYTwaxJ2hAhAM2bMYMWKFb4uw01iYiI5OTm+LqNLTJ48mddff93XZQgRMCTsCBGA1q5dy0MPPQR0/Q/v2rVrueWWWzym79ixg4kTJ3ZZHUKIwCE3AhUiAJnN5ou+zba2tgu6B1F0dPRFrCbwXOj3L4Q/k5YdIQLQ6W6sGTNmUFFRwcqVK0lMTHS7AeyePXu46667SE5OZtKkSSxfvhyLxeKaP3nyZF588UUeffRRUlNTWbJkCQD/+Mc/uP7667nmmmuYMmUKa9aswWazAfDBBx/wwgsvcOjQIdf+PvjgA8CzG+v7778nIyOD5ORk0tLSePzxx2lubnbNf+yxx5g3bx7r1q1j/PjxpKWl8eSTT7r29UsmT57Myy+/zKJFixgxYgTXXnst//nPf1zzc3NzSUxMpKGhwTWtqKiIxMREysvLXZ9n1KhRfP75567PPH/+fKxWK5s3b2by5MmMHj2a5cuX43A43Pbf3NzMI488QkpKChMmTOCtt95ym9/Q0MDixYsZO3YsqampZGRkcOjQIdf80y1k7733HpMnTyY5OblDn1uIQCRhR4gAtnbtWnr16sX8+fPZsWMHO3bsAKC0tJTMzEyuu+46PvroI5599ln27t3LU0895bb++vXrSUpK4sMPP2TevHkAhIWFsXLlSrKzs1m8eDHvvfeeq5vsxhtv5N5772XQoEGu/d14440edVksFmbPno3JZOL9999nzZo1fPPNNx77z83NpbS0lDfeeINVq1axefNmNm/e3OHPv2HDBoYPH86HH37IXXfdxdKlSzl27FhnvkJaWlrYuHEjzz77LK+99hq5ubk88MADfPnll7z66qusXr2ad999l23btrmtt27dOpKSkti8eTNz5sxhxYoVfP311675Dz30EDU1NWRlZfHBBx8wbNgwZs6cSV1dnWuZ0tJStm3bxgsvvMCHH37YqbqFCCTSjSVEADObzeh0OsLCwty6kV555RWmTZvGPffcA0D//v1ZvHgxM2bMYOnSpQQFBQEwduxY7r33Xrdtng49AHFxcRQXF5OdnU1mZibBwcGEhoai0+nO22318ccf09bWxt///ndCQ0MBWLJkCXPnzmXBggV0794dAJPJxJIlS9DpdAwYMIBJkyaxc+dO7rjjjg59/okTJ3L33XcDkJmZyeuvv05ubi4JCQkdWh/AZrOxdOlS+vbtC8D111/PRx99xNdff01YWBgDBw4kLS2NXbt2uQW71NRU5syZA0B8fDz79u3j9ddf5ze/+Q179uyhoKCAnTt3urqmFi5cSE5ODtu2beMPf/iDa9+rV6/mqquu6nC9QgQiCTtCCA+HDh3i+++/Z8uWLa5pqqqiKArl5eUMGDAAgOHDh3usu3XrVt58803KysqwWCzY7XbCw8M7tf+jR4+SmJjoCjrgDAeKolBcXOwKOwMHDkSn07mWiY6O5vDhwx3ez9nddhqNhu7du1NTU9OpWkNCQlxBB6B79+7ExsYSFhbmNu3UqVNu66WkpHi8f+ONNwBnF57FYiEtLc1tmZaWFkpLS13vY2JiJOgI0QESdoQQHiwWC3/84x+ZMWOGx7zevXu7XoeEhLjNy8/PZ8GCBTz44IOMHz+eiIgIsrOz2bBhwyWpU693/ydMo9GgqupFWV+rdfbyn709b+OBvG3D2zRFUTpcV3NzM9HR0WzcuNFjXkREhOv1z79/IYR3EnaECHAGg8Hjh3jo0KH88MMP9OvXr1Pbys/PJyYmhvvuu881rbKy8hf393MDBgxg8+bNWCwWV+vOvn370Gq1xMfHd6qmX+t0i0l1dTUmkwnAbYDwhTpw4IDH+9MtZsOGDePkyZPodDri4uIu2j6FCFQyQFmIABcbG8vu3bs5fvy4q6slMzOT/Px8li1bRlFRESUlJeTk5LBs2bLzbqtfv35UVVWRnZ1NaWkpb775pseFAmNjYykvL6eoqIhTp07R1tbmsZ1p06ZhNBp57LHHOHz4MLt27eKpp57illtucXVhXWp9+/ald+/erF27lpKSEr744gvWr19/0ba/b98+srKyKC4u5q233uLTTz8lIyMDgHHjxpGSksL999/Pjh07KC8vZ9++fTz77LMUFhZetBqECBQSdoQIcPPnz6eiooKpU6eSnp4OQFJSEhs3bqSkpIS77rqL6dOn8/zzz9OjR4/zbmvKlCnMnDmTZcuWccstt5Cfn+/WygPOAbwTJkwgIyOD9PR0Pv74Y4/thISEsG7dOurq6rj99tt56KGHSE9P5/HHH794H/wXGAwGnn76aY4dO8bNN99MVlYWf/7zny/a9mfNmsW3337L9OnTeemll3jssceYMGEC4Oz2evXVVxk9ejSLFi3id7/7HY888ggVFRVdFvaE8CcatTMd3EIIIYQQVxhp2RFCCCGEX5MBykIIv7Nnzx4yMzPPOT8/P78LqxFC+Jp0Ywkh/E5LSwvHjx8/5/zOnmUmhLiySdgRQgghhF+TMTtCCCGE8GsSdoQQQgjh1yTsCCGEEMKvSdgRQgghhF+TsCOEEEIIvyZhRwghhBB+TcKOEEIIIfyahB0hhBBC+LX/Bw9JACY0/6LNAAAAAElFTkSuQmCC", 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", "text/plain": [ "
" ] @@ -717,42 +704,29 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Quantum Task Summary\n", - "{}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n", - "Quantum Task Summary\n", - "{}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n", - "Quantum Task Summary\n", - "{}\n", "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n", - "Quantum Task Summary\n", - "{}\n", - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run quantum tasks in this hybrid job: 0.0 USD\n" + "Estimated cost to run quantum tasks in these hybrid jobs: 0.0 USD\n" ] } ], "source": [ + "cost = 0\n", "for job in jobs:\n", - " print(\"Quantum Task Summary\")\n", - " print(job.result()[\"task summary\"])\n", - " print(\n", - " \"Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\"\n", - " )\n", - " print(\n", - " f\"Estimated cost to run quantum tasks in this hybrid job: {job.result()['estimated cost']} USD\"\n", - " )" + " cost += job.result()['estimated cost']\n", + " \n", + "print(\n", + " \"Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\"\n", + ") \n", + "print(\n", + " f\"Estimated cost to run quantum tasks in these hybrid jobs: {cost} USD\"\n", + ")" ] }, { @@ -793,7 +767,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.13" }, "vscode": { "interpreter": { @@ -803,4 +777,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} From aed722ffde07586d663db34d5e5b77b9f8cce683 Mon Sep 17 00:00:00 2001 From: Matthew Beach Date: Mon, 16 Oct 2023 14:28:19 +0000 Subject: [PATCH 09/24] formatting --- ...machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb b/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb index 981f0a703..71ba120b1 100644 --- a/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb +++ b/examples/hybrid_jobs/1_Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs/Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb @@ -719,14 +719,12 @@ "source": [ "cost = 0\n", "for job in jobs:\n", - " cost += job.result()['estimated cost']\n", - " \n", + " cost += job.result()[\"estimated cost\"]\n", + "\n", "print(\n", " \"Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\"\n", - ") \n", - "print(\n", - " f\"Estimated cost to run quantum tasks in these hybrid jobs: {cost} USD\"\n", - ")" + ")\n", + "print(f\"Estimated cost to run quantum tasks in these hybrid jobs: {cost} USD\")" ] }, { From e1e47b820be65504121a9a2daa361520d325d122 Mon Sep 17 00:00:00 2001 From: Matthew Beach Date: Mon, 16 Oct 2023 14:31:47 +0000 Subject: [PATCH 10/24] adding parametric note in PL --- .../Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb b/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb index e5fdaf4e6..d7a3b12fa 100644 --- a/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb +++ b/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb @@ -310,8 +310,11 @@ "For other versions, you may submit Python [scripts](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html) or specify a [custom container image](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-byoc.html).\n", "\n", "\n", + "\n", "
\n", - " Note: When using PennyLane with Braket QPU devices many training algorithms will benefit from enabling parametric compilation. When running hybrid job on a supported QPU, Braket will compile the circuit once, without the need to recompile for subsequent parameter updates to the same circuit, resulting up to 10x faster runtime. To learn more about parametric circuits, you can read the \n", + " Note: When using PennyLane with Braket QPU devices many training algorithms will benefit from enabling parametric compilation. When running hybrid job on a supported QPU, Braket will compile the circuit once, without the need to recompile for subsequent parameter updates to the same circuit, resulting up to 10x faster runtime. To use parametric circuits with PennyLane, all you need to do is specify the flag: parametrize_differentiable=True\n", + " when instantiating the Braket device through PennyLane in your algorithm script, and Amazon Braket will handle the rest.\n", + " To learn more about parametric circuits, you can read the \n", " Amazon Braket developer guide\n", "
" ] From 88d44b2ef88e86a93cdd37d9355e67b7d7e7d092 Mon Sep 17 00:00:00 2001 From: Matthew Beach Date: Mon, 16 Oct 2023 14:32:24 +0000 Subject: [PATCH 11/24] formatting --- .../Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb b/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb index d7a3b12fa..7d4222042 100644 --- a/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb +++ b/examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Hybrid_Jobs/Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb @@ -666,7 +666,6 @@ "\n", "@hybrid_job(device=None, input_data=input_file_path, copy_checkpoints_from_job=previous_job_arn)\n", "def continued_run_qaoa_hybrid_job(p=1, steps=10):\n", - "\n", " # Resume from last checkpoint\n", " checkpoint = load_job_checkpoint(previous_job_name)\n", "\n", From 812490944dc345cd95ba35978ff1022d0c0af80d Mon Sep 17 00:00:00 2001 From: Matthew Beach Date: Mon, 16 Oct 2023 14:38:40 +0000 Subject: [PATCH 12/24] rm some duplicate text in PL VQE --- .../3_Hydrogen_Molecule_geometry_with_VQE.ipynb | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/3_Hydrogen_Molecule_geometry_with_VQE.ipynb b/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/3_Hydrogen_Molecule_geometry_with_VQE.ipynb index c5f0b7a0f..eaec41729 100644 --- a/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/3_Hydrogen_Molecule_geometry_with_VQE.ipynb +++ b/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/3_Hydrogen_Molecule_geometry_with_VQE.ipynb @@ -520,17 +520,15 @@ "\n", "For long-running VQE algorithms, you can run the entire algorithm on Amazon Braket by using [Hybrid Jobs](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html). As a fully managed solution, hybrid jobs give you access to monitor near-real time metrics like the energy during the training phase. \n", "\n", - "Amazon Braket Hybrid Jobs provides fully managed orchestration of hybrid quantum-classical algorithms, combining Amazon EC2 compute resources with Amazon Braket QPU access. Quantum tasks created in a hybrid job have priority queueing over individual quantum tasks so that your algorithms will not be interrupted by fluctuations in the quantum task queue. Each QPU maintains a separate hybrid jobs queue, ensuring that only one hybrid job can run at any given time.\n", + "You can run your local Python code as an Amazon Braket hybrid job. You can do this by annotating your code with an `@hybrid_job` decorator, as shown in the following code example. Only Python 3.10 is supported by default with hybrid job decorators. For custom environments, you can opt to use hybrid job scripts, or specify a custom container from Amazon Elastic Container Registry (ECR) (see [BYOC](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-byoc.html)). \n", "\n", - "\n", - "You can run your local Python code as an Amazon Braket hybrid job. You can do this by annotating your code with an @hybrid_job decorator, as shown in the following code example. Only Python 3.10 is supported by default with hybrid job decorators. For custom environments, you can opt to use hybrid job scripts, or specify a custom container from Amazon Elastic Container Registry (ECR) (see [BYOC](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-byoc.html)). \n", - "\n", - "The device argument in the @hybrid_job decorator specifies the device that the hybrid job will have priority access to. \n", + "The device argument in the `@hybrid_job` decorator specifies the device that the hybrid job will have priority access to. \n", "In this case, we run with a simulator, so we don't need to target a QPU. \n", "If you want to run a large number of circuits, consider using built-in MPI support to run local simulators on multiple instances within a single hybrid job. \n", "See [embedded simulators](https://docs.aws.amazon.com/braket/latest/developerguide/pennylane-embedded-simulators.html) for further information. \n", "\n", - "In this example set set `device=None` since we are using the lighning.qubit simulator. We also change the classical instance to a large instance called \"ml.c5.xlarge\"." + "In this example, we set `device=\"local:pennylane/lightning\"` since we are using the lightning.qubit simulator. \n", + "We also change the classical instance to a large instance called \"ml.c5.xlarge\"." ] }, { @@ -545,7 +543,11 @@ "large_instance = InstanceConfig(instanceType=\"ml.c5.xlarge\")\n", "\n", "\n", - "@hybrid_job(device=None, dependencies=\"requirements.txt\", instance_config=large_instance)\n", + "@hybrid_job(\n", + " device=\"local:pennylane/lightning\",\n", + " dependencies=\"requirements.txt\",\n", + " instance_config=large_instance,\n", + ")\n", "def run_large_vqe(iterations):\n", " task_tracker = Tracker().start() # track Braket quantum tasks costs\n", "\n", From 34ba0090800b4c60481d10ddd60bdf4f4f302bc9 Mon Sep 17 00:00:00 2001 From: Matthew Beach Date: Mon, 16 Oct 2023 14:41:01 +0000 Subject: [PATCH 13/24] formatting --- .../2_Graph_optimization_with_QAOA.ipynb | 2074 ++++++++--------- 1 file changed, 1037 insertions(+), 1037 deletions(-) diff --git a/examples/pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb b/examples/pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb index 06530b640..c1a6563d6 100644 --- a/examples/pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb +++ b/examples/pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb @@ -1,1040 +1,1040 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Graph optimization with QAOA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One application area where near-term quantum hardware is expected to shine is in graph optimization. Graph-based problems are interesting to explore because they have both strong links to practical use-cases (such as logistics and social networks) and are also often hard to solve." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Graphs are composed of a collection of interconnected nodes. For example, here is a six-node graph:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import networkx as nx\n", - "\n", - "n_nodes = 6\n", - "p = 0.5 # probability of an edge\n", - "seed = 1967\n", - "\n", - "g = nx.erdos_renyi_graph(n_nodes, p=p, seed=seed)\n", - "positions = nx.spring_layout(g, seed=seed)\n", - "\n", - "nx.draw(g, with_labels=True, pos=positions, node_size=600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Many practical use-cases can be mapped to a graph structure. In a social network, the nodes of a graph can represent users and the edges can represent connections between the users.\n", - "\n", - "We often need to solve optimization problems to identify important properties of the graph. These problems can include:\n", - "\n", - "- finding large clusters of fully connected nodes (known as [maximum clique](https://en.wikipedia.org/wiki/Clique_problem))\n", - "- finding a minimum number of nodes that connect to every edge in the graph (known as [minimum vertex cover](https://en.wikipedia.org/wiki/Vertex_cover))\n", - "- finding a partition of the nodes into two subsets so that the greatest number of edges are intersected (known as [maximum cut](https://en.wikipedia.org/wiki/Maximum_cut))\n", - "\n", - "This tutorial shows how a quantum algorithm called QAOA can be run using PennyLane and Braket to solve graph-based optimization problems. We begin with a small 6-node graph and then push the limits to run a 20-node graph using parallel executions on SV1." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Note This notebook requires PennyLane version 0.17 or above.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## QAOA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The quantum approximate optimization algorithm (QAOA) is an algorithm designed for near-term hardware. It can find approximate solutions to combinatorial optimization problems such as graph-based problems.\n", - "\n", - "QAOA is covered in more depth in the [QAOA_braket](../../hybrid_quantum_algorithms/QAOA/QAOA_braket.ipynb) notebook as well as in PennyLane [tutorials](https://pennylane.ai/qml/demos/tutorial_qaoa_intro.html). The following is a short summary to refresh the key concepts.\n", - "\n", - "\n", - "QAOA begins by associating the optimization problem with a cost Hamiltonian $H_C$ and choosing a mixer Hamiltonian $H_{M}$. It proceeds by repetitively applying multiple layers of the unitaries $\\exp{(-i \\gamma_i H_C)}$ and $\\exp{(-i \\alpha_i H_M)}$ with controllable parameters $\\gamma_i$ and $\\alpha_i$, as shown in the diagram below." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The algorithm then measures the cost Hamiltonian $H_C$. By varying the controllable parameters $\\gamma_i$ and $\\alpha_i$, the expectation value of the cost Hamiltonian is minimized. Applying the optimized unitaries prepares a quantum state that contains information about the optimal configuration for the problem. Sampling from the state will give a candidate solution.\n", - "\n", - "
\n", - "Summary If you are less familiar with QAOA and quantum algorithms, the key takeaway message is that the algorithm involves an optimization of the controllable parameters $\\gamma_i$ and $\\alpha_i$ that the quantum circuit depends on. This can be tackled naturally using the PennyLane/Braket pipeline.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing the problem" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's consider the graph above and aim to find the maximum clique, i.e., the largest set of nodes that are fully connected.\n", - "\n", - "To solve this using QAOA in PennyLane and Braket, we first calculate the cost Hamiltonian $H_C$ and corresponding mixer Hamiltonian $H_M$" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cost Hamiltonian:\n", - " (-0.5) [Z1]\n", - "+ (-0.5) [Z5]\n", - "+ (0.25) [Z0]\n", - "+ (0.25) [Z4]\n", - "+ (0.25) [Z2]\n", - "+ (0.25) [Z3]\n", - "+ (0.75) [Z0 Z1]\n", - "+ (0.75) [Z1 Z4]\n", - "+ (0.75) [Z2 Z5]\n", - "+ (0.75) [Z3 Z5]\n", - "Mixer Hamiltonian:\n", - " (1) [X0]\n", - "+ (1) [X1]\n", - "+ (1) [X2]\n", - "+ (1) [X3]\n", - "+ (1) [X4]\n", - "+ (1) [X5]\n" - ] - } - ], - "source": [ - "import pennylane as qml\n", - "from pennylane import numpy as np\n", - "\n", - "cost_h, mixer_h = qml.qaoa.max_clique(g, constrained=False)\n", - "# constrained=True results in greater circuit depth but potentially better solutions\n", - "\n", - "print(\"Cost Hamiltonian:\\n\", cost_h)\n", - "print(\"Mixer Hamiltonian:\\n\", mixer_h)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up the algorithm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We begin by setting up a single QAOA layer" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This layer contains the controllable parameters $\\gamma_i$ and $\\alpha_i$." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def qaoa_layer(gamma, alpha):\n", - " qml.qaoa.cost_layer(gamma, cost_h)\n", - " qml.qaoa.mixer_layer(alpha, mixer_h)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The full QAOA circuit is then given by:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "n_layers = 1\n", - "wires = n_nodes\n", - "\n", - "\n", - "def circuit(params, **kwargs):\n", - " for i in range(wires): # Prepare an equal superposition over all qubits\n", - " qml.Hadamard(wires=i)\n", - "\n", - " qml.layer(qaoa_layer, n_layers, params[0], params[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Note We have chosen to use a single QAOA layer. The choice of depth is a tradeoff between improved solutions (for greater depth) and increasing runtime.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are overall two controllable parameters: the first one is $\\gamma_i$ of the cost Hamiltonian and the one is $\\alpha_i$ of the mixer Hamiltonian:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[0.72511958],\n", - " [0.57312068]], requires_grad=True)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.random.seed(1967)\n", - "params = np.random.uniform(size=[2, n_layers])\n", - "params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this part of the tutorial, we will use the local Braket simulator (see the [introduction tutorial](./0_Getting_started.ipynb) for further details):" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "dev = qml.device(\"braket.local.qubit\", wires=wires)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The final step is to define the cost function. In QAOA, the output cost function is given by the expectation value of the cost Hamiltonian $H_C$, i.e.," - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "@qml.qnode(dev, diff_method=\"parameter-shift\")\n", - "def cost_function(params, **kwargs):\n", - " circuit(params)\n", - " return qml.expval(cost_h)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running the algorithm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have set up the cost function, we just need to pick an optimizer and run the standard optimization loop." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = qml.GradientDescentOptimizer()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial cost: 1.2873831170401528\n", - "Completed iteration 1, cost function: 1.0283347453164076\n", - "Completed iteration 2, cost function: 0.6850453114142451\n", - "Completed iteration 3, cost function: 0.26816570914692445\n", - "Completed iteration 4, cost function: -0.18397174104834577\n", - "Completed iteration 5, cost function: -0.6134512514217036\n", - "CPU times: user 9.66 s, sys: 3.35 ms, total: 9.66 s\n", - "Wall time: 9.66 s\n" - ] - } - ], - "source": [ - "%%time\n", - "print(\"Initial cost:\", cost_function(params))\n", - "\n", - "for i in range(5):\n", - " params = optimizer.step(cost_function, params)\n", - " cost_eval = cost_function(params)\n", - " print(f\"Completed iteration {i + 1}, cost function:\", cost_eval)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Investigating the result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How do we know how well the algorithm has performed? To do this, we can sample from the circuit using the optimized parameters. This will give us binary samples that allow us to select which nodes of the graph to use as part of our clique, e.g., either by simply selecting the most common sample or selecting the sample with the lowest corresponding energy.\n", - "\n", - "Let's take some samples and see which ones occur most frequently. To start, we'll create a QNode designed for sampling." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "shots = 10_000\n", - "dev = qml.device(\"braket.local.qubit\", wires=wires, shots=shots)\n", - "\n", - "\n", - "@qml.qnode(dev, diff_method=\"parameter-shift\")\n", - "def samples(params):\n", - " circuit(params)\n", - " return np.array([qml.sample(qml.PauliZ(i)) for i in range(wires)])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Samples can now be generated and converted into probabilities:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from collections import Counter\n", - "\n", - "s = samples(params).T\n", - "s = (1 - s.numpy()) / 2\n", - "s = map(tuple, s)\n", - "\n", - "counts = Counter(s)\n", - "indx = np.ndindex(*[2] * wires)\n", - "\n", - "probs = {p: counts.get(p, 0) / shots for p in indx}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now plot the probability distribution over all possible samples:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline\n", - "\n", - "plt.style.use(\"seaborn-v0_8-colorblind\")\n", - "labels = [\"{0:{fill}6b}\".format(i, fill='0') for i in range(len(probs))]\n", - "\n", - "plt.bar(range(2**wires), probs.values())\n", - "plt.xticks([i for i in range(len(probs))], labels, rotation=\"vertical\", size=12)\n", - "plt.yticks(size=12)\n", - "\n", - "plt.xlabel(\"Sample\", size=20)\n", - "plt.ylabel(\"Probability\", size=20)\n", - "\n", - "fig = plt.gcf()\n", - "fig.set_size_inches(16, 8)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the plot, it is clear that the sample ``101110`` has the greatest probability. Since each qubit corresponds to a node, this sample selects the nodes ``[0, 2, 3, 4]`` to form a subgraph. Let's check if this is a clique, i.e., if all of the nodes are connected:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sub = g.subgraph([0, 2, 3, 4])\n", - "nx.draw(g, pos=positions, with_labels=True)\n", - "nx.draw(sub, pos=positions, node_color=\"r\", edge_color=\"r\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great, this is a clique! Moreover, it is the *largest* clique in this six-node graph. QAOA, using PennyLane and Braket, has helped us to solve the maximum clique problem!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Scaling up QAOA for larger graphs with hybrid jobs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have seen how we can use PennyLane on Braket to solve graph optimization problems with QAOA. However, we have so far restricted to a simple six-node graph and used the local Braket device. Let's now be more ambitious and try to solve an optimization problem on a 18 node graph! We will use [Amazon Braket Hybrid Jobs](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html) to scale up the classical resources, and run the entire algorithm asynchronously. " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Use Braket SDK Cost Tracking to estimate the cost to run this example\n", - "from braket.tracking import Tracker\n", - "\n", - "task_tracker = Tracker().start() # track Braket tasks costs" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import networkx as nx\n", - "\n", - "nodes = wires = 18\n", - "edges = 60\n", - "seed = 1967\n", - "\n", - "g = nx.gnm_random_graph(nodes, edges, seed=seed)\n", - "positions = nx.spring_layout(g, seed=seed)\n", - "\n", - "nx.draw(g, with_labels=True, pos=positions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A 18 node graph (which maps to the same number of qubits) definitely puts us in a regime where the local simulator will be slow to execute. As we have discussed in the [parallelization tutorial](../1_Parallelized_optimization_of_quantum_circuits/1_Parallelized_optimization_of_quantum_circuits.ipynb), this slowness will be compounded when it comes to training the circuit, with each optimization step resulting in multiple device executions due to calculation of the gradient. Thankfully, the remote SV1 simulator is highly suited to speeding up gradient calculations through parallelization or adjoint differentiation. We now show that this makes training the circuit for QAOA solvable within a reasonable time.\n", - "\n", - "Let's first load a new device:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "from braket.devices import Devices\n", - "\n", - "device_arn = Devices.Amazon.SV1\n", - "# device_arn = \"arn:aws:braket:::device/quantum-simulator/amazon/sv1\" # alternatively use the device ARN" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "dev = qml.device(\"lightning.qubit\", wires=wires)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now just need to set up the QAOA circuit and optimization problem in the same way as before. However, we will switch to a new optimization problem to keep things interesting: aiming to solve maximum cut, with the objective of partitioning the graph's nodes into two groups so that the greatest number of edges are shared between the groups (see the image below). This problem is NP-hard, so we expect it to be tough as we increase the number of graph nodes." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "cost_h, mixer_h = qml.qaoa.maxcut(g)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "def qaoa_layer(gamma, alpha):\n", - " qml.qaoa.cost_layer(gamma, cost_h)\n", - " qml.qaoa.mixer_layer(alpha, mixer_h)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "n_layers = 2\n", - "\n", - "\n", - "@qml.qnode(dev)\n", - "def cost_function(params, **kwargs):\n", - " for i in range(wires): # Prepare an equal superposition over all qubits\n", - " qml.Hadamard(wires=i)\n", - "\n", - " qml.layer(qaoa_layer, n_layers, params[0], params[1])\n", - " return qml.expval(cost_h)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(1967)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A variety of [optimizers](https://pennylane.readthedocs.io/en/stable/introduction/optimizers.html) are available in PennyLane. Let's choose ``AdagradOptimizer``:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = qml.AdagradOptimizer(stepsize=0.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We're now set up to train the circuit! Note, if you are training this circuit yourself, you may want to increase the number of iterations in the optimization loop and also investigate changing the number of QAOA layers.\n", - "\n", - "\n", - "We create a hybrid job by annotating our main function with `@hybrid_job`. \n", - "This allows us to choose a target QPU for priority queueing, and additional arguments such as the type of classical instances to use. \n", - "In this example, we use an \"ml.c5.xlarge\" instance. \n", - "\n", - "Note that creating hybrid jobs is only supported on Python 3.10. For other versions, you may use [scripts](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html) or a [custom container image](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-byoc.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "from braket.jobs import hybrid_job, InstanceConfig\n", - "from braket.jobs.metrics import log_metric\n", - "\n", - "large_instance = InstanceConfig(instanceType=\"ml.c5.xlarge\")\n", - "\n", - "\n", - "@hybrid_job(device=\"local:pennylane/lightning.qubit\", instance_config=large_instance)\n", - "def qaoa_training(iterations, n_layers=2):\n", - " task_tracker = Tracker().start() # track Braket tasks costs\n", - "\n", - " dev = qml.device(\"lightning.qubit\", wires=wires)\n", - "\n", - " @qml.qnode(dev)\n", - " def cost_function(params, **kwargs):\n", - " for i in range(wires): # Prepare an equal superposition over all qubits\n", - " qml.Hadamard(wires=i)\n", - "\n", - " qml.layer(qaoa_layer, n_layers, params[0], params[1])\n", - " return qml.expval(cost_h)\n", - "\n", - " params = 0.01 * np.random.uniform(size=[2, n_layers])\n", - "\n", - " for i in range(iterations):\n", - " params, cost = optimizer.step_and_cost(cost_function, params)\n", - "\n", - " # Record the value of the cost function with each iteration\n", - " log_metric(metric_name=\"cost\", value=cost, iteration_number=i)\n", - "\n", - " # Additionally, keep track of cost in USD for Braket tasks\n", - " braket_task_cost = float(\n", - " task_tracker.qpu_tasks_cost() + task_tracker.simulator_tasks_cost()\n", - " )\n", - " log_metric(metric_name=\"braket_cost\", value=braket_task_cost, iteration_number=i)\n", - "\n", - " return {\n", - " \"parameters\": params,\n", - " \"final_cost\": cost_function(params),\n", - " \"braket_tasks_cost\": task_tracker.qpu_tasks_cost() + task_tracker.simulator_tasks_cost(),\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next cell, we create the hybrid job by calling the function as usual. The function arguments are logged as hyperparamters for the hybrid job. \n", - "\n", - "
\n", - "Caution: Running the following cell will take a long time and will result in usage fees charged to your AWS account. Only uncomment the cell if you are comfortable with the potential wait-time and costs. We recommend monitoring the Billing & Cost Management Dashboard on the AWS console and being aware that hybrid jobs involving a large number of qubits can be costly.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AwsQuantumJob('arn':'arn:aws:braket:us-west-1:961591465522:job/qaoa-training-1697141494101')\n" - ] - } - ], - "source": [ - "job = qaoa_training(iterations=5, n_layers=2)\n", - "print(job)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The hybrid job will be scheduled to run and will appear in the \"QUEUED\" state. \n", - "If the target is a QPU, the hybrid job will be queued with other hybrid jobs. \n", - "If the target device is not a QPU, the hybrid job should start immediately. \n", - "\n", - "Note that since the algorithm code is run in a containerized environment, it takes approximately 1 minute to start running your algorithm. " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'QUEUED'" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "job.state()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once the state is \"COMPLETED\", we retrieve the results with " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 337 ms, sys: 18 ms, total: 355 ms\n", - "Wall time: 8min 54s\n" - ] - }, - { - "data": { - "text/plain": [ - "{'parameters': tensor([[-0.01460419, 0.00094966],\n", - " [ 0.0187241 , 0.00412996]], requires_grad=True),\n", - " 'final_cost': array(-30.03947181),\n", - " 'braket_tasks_cost': Decimal('0')}" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "\n", - "job.result()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The results included the three values from the return statement of our function.\n", - "\n", - "Additionally, we can retrieve the metrics recorded during the training with:" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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iteration_numbertimestampcostbraket_cost
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" - ], - "text/plain": [ - " iteration_number timestamp cost braket_cost\n", - "0 0.0 3.394283e+09 -29.992536 0.0\n", - "1 1.0 3.394284e+09 -27.007548 0.0\n", - "2 2.0 3.394284e+09 -29.993231 0.0\n", - "3 3.0 3.394284e+09 -30.002011 0.0\n", - "4 4.0 3.394284e+09 -30.009532 0.0" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "metrics = job.metrics()\n", - "df = pd.DataFrame(metrics)\n", - "df = df.groupby(\"iteration_number\").sum().reset_index()\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The metrics are plotted below. " - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'cost function')" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plotting the convergence of the loss function metric\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline\n", - "\n", - "df.sort_values(by=[\"iteration_number\"]).plot(x=\"iteration_number\", y=\"cost\", marker=\"o\")\n", - "plt.xlim(1, 5)\n", - "plt.ylabel(\"cost function\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example shows us that a 18-qubit QAOA problem can be trained using the \"lightning.qubit\" with adjoint gradients by using hybrid jobs. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "What's next? See if you can analyze the trained QAOA circuit for the 18-node graph by adapting the earlier analysis. Also, check out the followup tutorial on quantum chemistry.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run this example: 0.000 USD\n" - ] - } - ], - "source": [ - "job_cost = job.result()[\"braket_tasks_cost\"]\n", - "\n", - "print(\n", - " \"Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\"\n", - ")\n", - "print(f\"Estimated cost to run this example: {job_cost :.3f} USD\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - }, - "vscode": { - "interpreter": { - "hash": "590fab68195cf107911461461f81d5c472d3d6127f579badfcfad30f03e5cab2" - } - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Graph optimization with QAOA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One application area where near-term quantum hardware is expected to shine is in graph optimization. Graph-based problems are interesting to explore because they have both strong links to practical use-cases (such as logistics and social networks) and are also often hard to solve." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Graphs are composed of a collection of interconnected nodes. For example, here is a six-node graph:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import networkx as nx\n", + "\n", + "n_nodes = 6\n", + "p = 0.5 # probability of an edge\n", + "seed = 1967\n", + "\n", + "g = nx.erdos_renyi_graph(n_nodes, p=p, seed=seed)\n", + "positions = nx.spring_layout(g, seed=seed)\n", + "\n", + "nx.draw(g, with_labels=True, pos=positions, node_size=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many practical use-cases can be mapped to a graph structure. In a social network, the nodes of a graph can represent users and the edges can represent connections between the users.\n", + "\n", + "We often need to solve optimization problems to identify important properties of the graph. These problems can include:\n", + "\n", + "- finding large clusters of fully connected nodes (known as [maximum clique](https://en.wikipedia.org/wiki/Clique_problem))\n", + "- finding a minimum number of nodes that connect to every edge in the graph (known as [minimum vertex cover](https://en.wikipedia.org/wiki/Vertex_cover))\n", + "- finding a partition of the nodes into two subsets so that the greatest number of edges are intersected (known as [maximum cut](https://en.wikipedia.org/wiki/Maximum_cut))\n", + "\n", + "This tutorial shows how a quantum algorithm called QAOA can be run using PennyLane and Braket to solve graph-based optimization problems. We begin with a small 6-node graph and then push the limits to run a 20-node graph using parallel executions on SV1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Note This notebook requires PennyLane version 0.17 or above.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## QAOA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The quantum approximate optimization algorithm (QAOA) is an algorithm designed for near-term hardware. It can find approximate solutions to combinatorial optimization problems such as graph-based problems.\n", + "\n", + "QAOA is covered in more depth in the [QAOA_braket](../../hybrid_quantum_algorithms/QAOA/QAOA_braket.ipynb) notebook as well as in PennyLane [tutorials](https://pennylane.ai/qml/demos/tutorial_qaoa_intro.html). The following is a short summary to refresh the key concepts.\n", + "\n", + "\n", + "QAOA begins by associating the optimization problem with a cost Hamiltonian $H_C$ and choosing a mixer Hamiltonian $H_{M}$. It proceeds by repetitively applying multiple layers of the unitaries $\\exp{(-i \\gamma_i H_C)}$ and $\\exp{(-i \\alpha_i H_M)}$ with controllable parameters $\\gamma_i$ and $\\alpha_i$, as shown in the diagram below." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The algorithm then measures the cost Hamiltonian $H_C$. By varying the controllable parameters $\\gamma_i$ and $\\alpha_i$, the expectation value of the cost Hamiltonian is minimized. Applying the optimized unitaries prepares a quantum state that contains information about the optimal configuration for the problem. Sampling from the state will give a candidate solution.\n", + "\n", + "
\n", + "Summary If you are less familiar with QAOA and quantum algorithms, the key takeaway message is that the algorithm involves an optimization of the controllable parameters $\\gamma_i$ and $\\alpha_i$ that the quantum circuit depends on. This can be tackled naturally using the PennyLane/Braket pipeline.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing the problem" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's consider the graph above and aim to find the maximum clique, i.e., the largest set of nodes that are fully connected.\n", + "\n", + "To solve this using QAOA in PennyLane and Braket, we first calculate the cost Hamiltonian $H_C$ and corresponding mixer Hamiltonian $H_M$" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost Hamiltonian:\n", + " (-0.5) [Z1]\n", + "+ (-0.5) [Z5]\n", + "+ (0.25) [Z0]\n", + "+ (0.25) [Z4]\n", + "+ (0.25) [Z2]\n", + "+ (0.25) [Z3]\n", + "+ (0.75) [Z0 Z1]\n", + "+ (0.75) [Z1 Z4]\n", + "+ (0.75) [Z2 Z5]\n", + "+ (0.75) [Z3 Z5]\n", + "Mixer Hamiltonian:\n", + " (1) [X0]\n", + "+ (1) [X1]\n", + "+ (1) [X2]\n", + "+ (1) [X3]\n", + "+ (1) [X4]\n", + "+ (1) [X5]\n" + ] + } + ], + "source": [ + "import pennylane as qml\n", + "from pennylane import numpy as np\n", + "\n", + "cost_h, mixer_h = qml.qaoa.max_clique(g, constrained=False)\n", + "# constrained=True results in greater circuit depth but potentially better solutions\n", + "\n", + "print(\"Cost Hamiltonian:\\n\", cost_h)\n", + "print(\"Mixer Hamiltonian:\\n\", mixer_h)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up the algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We begin by setting up a single QAOA layer" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This layer contains the controllable parameters $\\gamma_i$ and $\\alpha_i$." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def qaoa_layer(gamma, alpha):\n", + " qml.qaoa.cost_layer(gamma, cost_h)\n", + " qml.qaoa.mixer_layer(alpha, mixer_h)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The full QAOA circuit is then given by:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "n_layers = 1\n", + "wires = n_nodes\n", + "\n", + "\n", + "def circuit(params, **kwargs):\n", + " for i in range(wires): # Prepare an equal superposition over all qubits\n", + " qml.Hadamard(wires=i)\n", + "\n", + " qml.layer(qaoa_layer, n_layers, params[0], params[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Note We have chosen to use a single QAOA layer. The choice of depth is a tradeoff between improved solutions (for greater depth) and increasing runtime.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are overall two controllable parameters: the first one is $\\gamma_i$ of the cost Hamiltonian and the one is $\\alpha_i$ of the mixer Hamiltonian:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0.72511958],\n", + " [0.57312068]], requires_grad=True)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(1967)\n", + "params = np.random.uniform(size=[2, n_layers])\n", + "params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this part of the tutorial, we will use the local Braket simulator (see the [introduction tutorial](./0_Getting_started.ipynb) for further details):" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "dev = qml.device(\"braket.local.qubit\", wires=wires)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The final step is to define the cost function. In QAOA, the output cost function is given by the expectation value of the cost Hamiltonian $H_C$, i.e.," + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "@qml.qnode(dev, diff_method=\"parameter-shift\")\n", + "def cost_function(params, **kwargs):\n", + " circuit(params)\n", + " return qml.expval(cost_h)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have set up the cost function, we just need to pick an optimizer and run the standard optimization loop." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = qml.GradientDescentOptimizer()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 1.2873831170401528\n", + "Completed iteration 1, cost function: 1.0283347453164076\n", + "Completed iteration 2, cost function: 0.6850453114142451\n", + "Completed iteration 3, cost function: 0.26816570914692445\n", + "Completed iteration 4, cost function: -0.18397174104834577\n", + "Completed iteration 5, cost function: -0.6134512514217036\n", + "CPU times: user 9.66 s, sys: 3.35 ms, total: 9.66 s\n", + "Wall time: 9.66 s\n" + ] + } + ], + "source": [ + "%%time\n", + "print(\"Initial cost:\", cost_function(params))\n", + "\n", + "for i in range(5):\n", + " params = optimizer.step(cost_function, params)\n", + " cost_eval = cost_function(params)\n", + " print(f\"Completed iteration {i + 1}, cost function:\", cost_eval)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Investigating the result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How do we know how well the algorithm has performed? To do this, we can sample from the circuit using the optimized parameters. This will give us binary samples that allow us to select which nodes of the graph to use as part of our clique, e.g., either by simply selecting the most common sample or selecting the sample with the lowest corresponding energy.\n", + "\n", + "Let's take some samples and see which ones occur most frequently. To start, we'll create a QNode designed for sampling." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "shots = 10_000\n", + "dev = qml.device(\"braket.local.qubit\", wires=wires, shots=shots)\n", + "\n", + "\n", + "@qml.qnode(dev, diff_method=\"parameter-shift\")\n", + "def samples(params):\n", + " circuit(params)\n", + " return np.array([qml.sample(qml.PauliZ(i)) for i in range(wires)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Samples can now be generated and converted into probabilities:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import Counter\n", + "\n", + "s = samples(params).T\n", + "s = (1 - s.numpy()) / 2\n", + "s = map(tuple, s)\n", + "\n", + "counts = Counter(s)\n", + "indx = np.ndindex(*[2] * wires)\n", + "\n", + "probs = {p: counts.get(p, 0) / shots for p in indx}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now plot the probability distribution over all possible samples:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "plt.style.use(\"seaborn-v0_8-colorblind\")\n", + "labels = [\"{0:{fill}6b}\".format(i, fill=\"0\") for i in range(len(probs))]\n", + "\n", + "plt.bar(range(2**wires), probs.values())\n", + "plt.xticks([i for i in range(len(probs))], labels, rotation=\"vertical\", size=12)\n", + "plt.yticks(size=12)\n", + "\n", + "plt.xlabel(\"Sample\", size=20)\n", + "plt.ylabel(\"Probability\", size=20)\n", + "\n", + "fig = plt.gcf()\n", + "fig.set_size_inches(16, 8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the plot, it is clear that the sample ``101110`` has the greatest probability. Since each qubit corresponds to a node, this sample selects the nodes ``[0, 2, 3, 4]`` to form a subgraph. Let's check if this is a clique, i.e., if all of the nodes are connected:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sub = g.subgraph([0, 2, 3, 4])\n", + "nx.draw(g, pos=positions, with_labels=True)\n", + "nx.draw(sub, pos=positions, node_color=\"r\", edge_color=\"r\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great, this is a clique! Moreover, it is the *largest* clique in this six-node graph. QAOA, using PennyLane and Braket, has helped us to solve the maximum clique problem!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scaling up QAOA for larger graphs with hybrid jobs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have seen how we can use PennyLane on Braket to solve graph optimization problems with QAOA. However, we have so far restricted to a simple six-node graph and used the local Braket device. Let's now be more ambitious and try to solve an optimization problem on a 18 node graph! We will use [Amazon Braket Hybrid Jobs](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html) to scale up the classical resources, and run the entire algorithm asynchronously. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Use Braket SDK Cost Tracking to estimate the cost to run this example\n", + "from braket.tracking import Tracker\n", + "\n", + "task_tracker = Tracker().start() # track Braket tasks costs" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import networkx as nx\n", + "\n", + "nodes = wires = 18\n", + "edges = 60\n", + "seed = 1967\n", + "\n", + "g = nx.gnm_random_graph(nodes, edges, seed=seed)\n", + "positions = nx.spring_layout(g, seed=seed)\n", + "\n", + "nx.draw(g, with_labels=True, pos=positions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A 18 node graph (which maps to the same number of qubits) definitely puts us in a regime where the local simulator will be slow to execute. As we have discussed in the [parallelization tutorial](../1_Parallelized_optimization_of_quantum_circuits/1_Parallelized_optimization_of_quantum_circuits.ipynb), this slowness will be compounded when it comes to training the circuit, with each optimization step resulting in multiple device executions due to calculation of the gradient. Thankfully, the remote SV1 simulator is highly suited to speeding up gradient calculations through parallelization or adjoint differentiation. We now show that this makes training the circuit for QAOA solvable within a reasonable time.\n", + "\n", + "Let's first load a new device:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from braket.devices import Devices\n", + "\n", + "device_arn = Devices.Amazon.SV1\n", + "# device_arn = \"arn:aws:braket:::device/quantum-simulator/amazon/sv1\" # alternatively use the device ARN" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "dev = qml.device(\"lightning.qubit\", wires=wires)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now just need to set up the QAOA circuit and optimization problem in the same way as before. However, we will switch to a new optimization problem to keep things interesting: aiming to solve maximum cut, with the objective of partitioning the graph's nodes into two groups so that the greatest number of edges are shared between the groups (see the image below). This problem is NP-hard, so we expect it to be tough as we increase the number of graph nodes." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "cost_h, mixer_h = qml.qaoa.maxcut(g)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def qaoa_layer(gamma, alpha):\n", + " qml.qaoa.cost_layer(gamma, cost_h)\n", + " qml.qaoa.mixer_layer(alpha, mixer_h)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "n_layers = 2\n", + "\n", + "\n", + "@qml.qnode(dev)\n", + "def cost_function(params, **kwargs):\n", + " for i in range(wires): # Prepare an equal superposition over all qubits\n", + " qml.Hadamard(wires=i)\n", + "\n", + " qml.layer(qaoa_layer, n_layers, params[0], params[1])\n", + " return qml.expval(cost_h)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(1967)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A variety of [optimizers](https://pennylane.readthedocs.io/en/stable/introduction/optimizers.html) are available in PennyLane. Let's choose ``AdagradOptimizer``:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = qml.AdagradOptimizer(stepsize=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're now set up to train the circuit! Note, if you are training this circuit yourself, you may want to increase the number of iterations in the optimization loop and also investigate changing the number of QAOA layers.\n", + "\n", + "\n", + "We create a hybrid job by annotating our main function with `@hybrid_job`. \n", + "This allows us to choose a target QPU for priority queueing, and additional arguments such as the type of classical instances to use. \n", + "In this example, we use an \"ml.c5.xlarge\" instance. \n", + "\n", + "Note that creating hybrid jobs is only supported on Python 3.10. For other versions, you may use [scripts](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html) or a [custom container image](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-byoc.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "from braket.jobs import hybrid_job, InstanceConfig\n", + "from braket.jobs.metrics import log_metric\n", + "\n", + "large_instance = InstanceConfig(instanceType=\"ml.c5.xlarge\")\n", + "\n", + "\n", + "@hybrid_job(device=\"local:pennylane/lightning.qubit\", instance_config=large_instance)\n", + "def qaoa_training(iterations, n_layers=2):\n", + " task_tracker = Tracker().start() # track Braket tasks costs\n", + "\n", + " dev = qml.device(\"lightning.qubit\", wires=wires)\n", + "\n", + " @qml.qnode(dev)\n", + " def cost_function(params, **kwargs):\n", + " for i in range(wires): # Prepare an equal superposition over all qubits\n", + " qml.Hadamard(wires=i)\n", + "\n", + " qml.layer(qaoa_layer, n_layers, params[0], params[1])\n", + " return qml.expval(cost_h)\n", + "\n", + " params = 0.01 * np.random.uniform(size=[2, n_layers])\n", + "\n", + " for i in range(iterations):\n", + " params, cost = optimizer.step_and_cost(cost_function, params)\n", + "\n", + " # Record the value of the cost function with each iteration\n", + " log_metric(metric_name=\"cost\", value=cost, iteration_number=i)\n", + "\n", + " # Additionally, keep track of cost in USD for Braket tasks\n", + " braket_task_cost = float(\n", + " task_tracker.qpu_tasks_cost() + task_tracker.simulator_tasks_cost()\n", + " )\n", + " log_metric(metric_name=\"braket_cost\", value=braket_task_cost, iteration_number=i)\n", + "\n", + " return {\n", + " \"parameters\": params,\n", + " \"final_cost\": cost_function(params),\n", + " \"braket_tasks_cost\": task_tracker.qpu_tasks_cost() + task_tracker.simulator_tasks_cost(),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, we create the hybrid job by calling the function as usual. The function arguments are logged as hyperparamters for the hybrid job. \n", + "\n", + "
\n", + "Caution: Running the following cell will take a long time and will result in usage fees charged to your AWS account. Only uncomment the cell if you are comfortable with the potential wait-time and costs. We recommend monitoring the Billing & Cost Management Dashboard on the AWS console and being aware that hybrid jobs involving a large number of qubits can be costly.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AwsQuantumJob('arn':'arn:aws:braket:us-west-1:961591465522:job/qaoa-training-1697141494101')\n" + ] + } + ], + "source": [ + "job = qaoa_training(iterations=5, n_layers=2)\n", + "print(job)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The hybrid job will be scheduled to run and will appear in the \"QUEUED\" state. \n", + "If the target is a QPU, the hybrid job will be queued with other hybrid jobs. \n", + "If the target device is not a QPU, the hybrid job should start immediately. \n", + "\n", + "Note that since the algorithm code is run in a containerized environment, it takes approximately 1 minute to start running your algorithm. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'QUEUED'" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "job.state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the state is \"COMPLETED\", we retrieve the results with " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 337 ms, sys: 18 ms, total: 355 ms\n", + "Wall time: 8min 54s\n" + ] }, - "nbformat": 4, - "nbformat_minor": 4 + { + "data": { + "text/plain": [ + "{'parameters': tensor([[-0.01460419, 0.00094966],\n", + " [ 0.0187241 , 0.00412996]], requires_grad=True),\n", + " 'final_cost': array(-30.03947181),\n", + " 'braket_tasks_cost': Decimal('0')}" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "\n", + "job.result()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results included the three values from the return statement of our function.\n", + "\n", + "Additionally, we can retrieve the metrics recorded during the training with:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " iteration_number timestamp cost braket_cost\n", + "0 0.0 3.394283e+09 -29.992536 0.0\n", + "1 1.0 3.394284e+09 -27.007548 0.0\n", + "2 2.0 3.394284e+09 -29.993231 0.0\n", + "3 3.0 3.394284e+09 -30.002011 0.0\n", + "4 4.0 3.394284e+09 -30.009532 0.0" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics = job.metrics()\n", + "df = pd.DataFrame(metrics)\n", + "df = df.groupby(\"iteration_number\").sum().reset_index()\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The metrics are plotted below. " + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'cost function')" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting the convergence of the loss function metric\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "df.sort_values(by=[\"iteration_number\"]).plot(x=\"iteration_number\", y=\"cost\", marker=\"o\")\n", + "plt.xlim(1, 5)\n", + "plt.ylabel(\"cost function\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example shows us that a 18-qubit QAOA problem can be trained using the \"lightning.qubit\" with adjoint gradients by using hybrid jobs. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "What's next? See if you can analyze the trained QAOA circuit for the 18-node graph by adapting the earlier analysis. Also, check out the followup tutorial on quantum chemistry.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", + "Estimated cost to run this example: 0.000 USD\n" + ] + } + ], + "source": [ + "job_cost = job.result()[\"braket_tasks_cost\"]\n", + "\n", + "print(\n", + " \"Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\"\n", + ")\n", + "print(f\"Estimated cost to run this example: {job_cost :.3f} USD\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "vscode": { + "interpreter": { + "hash": "590fab68195cf107911461461f81d5c472d3d6127f579badfcfad30f03e5cab2" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 } \ No newline at end of file From a758078741d357256ce2cfb644c90da1d69e1424 Mon Sep 17 00:00:00 2001 From: Matthew Beach Date: Mon, 16 Oct 2023 17:32:33 +0000 Subject: [PATCH 14/24] rm dependancies files --- .../0_Creating_your_first_Hybrid_Job.ipynb | 4 ++-- .../requirements.txt | 1 - .../3_Hydrogen_Molecule_geometry_with_VQE.ipynb | 15 +++------------ .../requirements..txt | 1 - .../requirements.txt | 1 - 5 files changed, 5 insertions(+), 17 deletions(-) delete mode 100644 examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/requirements.txt delete mode 100644 examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/requirements..txt delete mode 100644 examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/requirements.txt diff --git a/examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/0_Creating_your_first_Hybrid_Job.ipynb b/examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/0_Creating_your_first_Hybrid_Job.ipynb index 3e9c4bbf8..096b7ae57 100644 --- a/examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/0_Creating_your_first_Hybrid_Job.ipynb +++ b/examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/0_Creating_your_first_Hybrid_Job.ipynb @@ -209,7 +209,7 @@ "from braket.jobs import hybrid_job, save_job_result\n", "\n", "\n", - "@hybrid_job(device=\"local:pennylane/lightning.qubit\", dependencies=\"requirements.txt\")\n", + "@hybrid_job(device=\"local:pennylane/lightning.qubit\")\n", "def qubit_rotation_hybrid_job(num_steps=1, stepsize=0.5):\n", " opt = qml.GradientDescentOptimizer(stepsize=stepsize)\n", " params = np.array([0.5, 0.75])\n", @@ -410,7 +410,7 @@ "device_arn = Devices.Rigetti.AspenM3\n", "\n", "\n", - "@hybrid_job(device=device_arn, dependencies=\"requirements.txt\") # set priority QPU\n", + "@hybrid_job(device=device_arn) # set priority QPU\n", "def qpu_qubit_rotation_hybrid_job(num_steps=10, stepsize=0.5):\n", " # AWS devices must be declared within the decorated function.\n", " device = qml.device(\n", diff --git a/examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/requirements.txt b/examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/requirements.txt deleted file mode 100644 index b02528770..000000000 --- a/examples/hybrid_jobs/0_Creating_your_first_Hybrid_Job/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -pennylane==0.32.0 \ No newline at end of file diff --git a/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/3_Hydrogen_Molecule_geometry_with_VQE.ipynb b/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/3_Hydrogen_Molecule_geometry_with_VQE.ipynb index eaec41729..cf96beb44 100644 --- a/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/3_Hydrogen_Molecule_geometry_with_VQE.ipynb +++ b/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/3_Hydrogen_Molecule_geometry_with_VQE.ipynb @@ -518,17 +518,9 @@ "source": [ "## Scaling up with hybrid jobs\n", "\n", - "For long-running VQE algorithms, you can run the entire algorithm on Amazon Braket by using [Hybrid Jobs](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html). As a fully managed solution, hybrid jobs give you access to monitor near-real time metrics like the energy during the training phase. \n", - "\n", - "You can run your local Python code as an Amazon Braket hybrid job. You can do this by annotating your code with an `@hybrid_job` decorator, as shown in the following code example. Only Python 3.10 is supported by default with hybrid job decorators. For custom environments, you can opt to use hybrid job scripts, or specify a custom container from Amazon Elastic Container Registry (ECR) (see [BYOC](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-byoc.html)). \n", - "\n", - "The device argument in the `@hybrid_job` decorator specifies the device that the hybrid job will have priority access to. \n", - "In this case, we run with a simulator, so we don't need to target a QPU. \n", - "If you want to run a large number of circuits, consider using built-in MPI support to run local simulators on multiple instances within a single hybrid job. \n", - "See [embedded simulators](https://docs.aws.amazon.com/braket/latest/developerguide/pennylane-embedded-simulators.html) for further information. \n", - "\n", - "In this example, we set `device=\"local:pennylane/lightning\"` since we are using the lightning.qubit simulator. \n", - "We also change the classical instance to a large instance called \"ml.c5.xlarge\"." + "Next, we scale up the experiment using Amazon Braket [Hybrid Jobs](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html). \n", + "You can do this by annotating your code with the `@hybrid_job` decorator. We set `device=\"local:pennylane/lightning\"` since we are using the lightning.qubit simulator. \n", + "We also change the classical instance to a larger instance called \"ml.c5.xlarge\"." ] }, { @@ -545,7 +537,6 @@ "\n", "@hybrid_job(\n", " device=\"local:pennylane/lightning\",\n", - " dependencies=\"requirements.txt\",\n", " instance_config=large_instance,\n", ")\n", "def run_large_vqe(iterations):\n", diff --git a/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/requirements..txt b/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/requirements..txt deleted file mode 100644 index 69c65e72a..000000000 --- a/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/requirements..txt +++ /dev/null @@ -1 +0,0 @@ -pennylane>=0.32 \ No newline at end of file diff --git a/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/requirements.txt b/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/requirements.txt deleted file mode 100644 index 69c65e72a..000000000 --- a/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -pennylane>=0.32 \ No newline at end of file From c12332730d49da94d70d31131b729c58463d091d Mon Sep 17 00:00:00 2001 From: Aaron Berdy Date: Mon, 16 Oct 2023 12:43:25 -0700 Subject: [PATCH 15/24] Update requirements.txt --- requirements.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index e7250d04f..cdb5e9061 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,10 +1,10 @@ botocore==1.31.62 awscli==1.29.62 boto3==1.28.62 -amazon-braket-default-simulator==1.20.0.post0 -amazon-braket-pennylane-plugin==1.20.3.post1 +amazon-braket-default-simulator==1.20.1 +amazon-braket-pennylane-plugin==1.21.0 amazon-braket-schemas==1.19.1.post0 -amazon-braket-sdk==1.57.1 +amazon-braket-sdk==1.58.0 amazon-braket-algorithm-library==1.4.1 cvxpy==1.3.2 ipykernel==6.25.2 From f40ad5b3e49f31955fdf73528301766853f60544 Mon Sep 17 00:00:00 2001 From: Matthew Beach Date: Mon, 16 Oct 2023 20:19:46 +0000 Subject: [PATCH 16/24] fix imports --- examples/pennylane/0_Getting_started/0_Getting_started.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/pennylane/0_Getting_started/0_Getting_started.ipynb b/examples/pennylane/0_Getting_started/0_Getting_started.ipynb index 597f221d7..2c198fc09 100644 --- a/examples/pennylane/0_Getting_started/0_Getting_started.ipynb +++ b/examples/pennylane/0_Getting_started/0_Getting_started.ipynb @@ -490,9 +490,9 @@ "metadata": {}, "outputs": [], "source": [ - "from braket.jobs.decorator import hybrid_job\n", + "from braket.jobs import hybrid_job\n", "from braket.devices import Devices\n", - "from braket.jobs import log_metric\n", + "from braket.jobs.metrics import log_metric\n", "\n", "device_arn = Devices.Amazon.SV1\n", "# device_arn = Devices.Rigetti.AspenM3\n", From b23ae477ce1d8baf3937c58387df6517ec09295e Mon Sep 17 00:00:00 2001 From: Matthew Beach Date: Mon, 16 Oct 2023 20:21:24 +0000 Subject: [PATCH 17/24] fix typoe --- examples/pennylane/0_Getting_started/0_Getting_started.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/pennylane/0_Getting_started/0_Getting_started.ipynb b/examples/pennylane/0_Getting_started/0_Getting_started.ipynb index 2c198fc09..60d91aafa 100644 --- a/examples/pennylane/0_Getting_started/0_Getting_started.ipynb +++ b/examples/pennylane/0_Getting_started/0_Getting_started.ipynb @@ -501,7 +501,6 @@ "@hybrid_job(device=device_arn) # set priority QPU\n", "def qubit_rotation(stepsize=0.1, iterations=5):\n", " task_tracker = Tracker().start() # track Braket quantum tasks costs\n", - "\n", " dev = qml.device(\"braket.aws.qubit\", device_arn=device_arn.value, wires=2, shots=1_000)\n", "\n", " params = np.array([0.1, 0.2])\n", From 532af4180a7863c780c3368661eba4e2fc787262 Mon Sep 17 00:00:00 2001 From: Matthew Beach Date: Mon, 16 Oct 2023 20:29:26 +0000 Subject: [PATCH 18/24] exclude new notebooks --- test/notebook_tests/test_notebooks.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/test/notebook_tests/test_notebooks.py b/test/notebook_tests/test_notebooks.py index aceb90888..431c10e43 100644 --- a/test/notebook_tests/test_notebooks.py +++ b/test/notebook_tests/test_notebooks.py @@ -34,7 +34,14 @@ # Temporarily exclude Aquila notebooks "01_Introduction_to_Aquila.ipynb", "02_Ordered_phases_in_Rydberg_systems.ipynb", - "03_Parallel_tasks_on_Aquila.ipynb" + "03_Parallel_tasks_on_Aquila.ipynb", + # Python 3.10 required for decorators notebooks + "0_Creating_your_first_Hybrid_Job.ipynb", + "Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb", + "Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb", + "0_Getting_started.ipynb", + "2_Graph_optimization_with_QAOA.ipynb", + "3_Hydrogen_Molecule_geometry_with_VQE.ipynb", ] From 212357006e13167a4f915baa1912c2cb2f7bf1e5 Mon Sep 17 00:00:00 2001 From: Matthew Beach Date: Mon, 16 Oct 2023 20:41:00 +0000 Subject: [PATCH 19/24] pytho nversion check --- test/notebook_tests/test_notebooks.py | 26 +++++++++++++++++++------- 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/test/notebook_tests/test_notebooks.py b/test/notebook_tests/test_notebooks.py index 431c10e43..b11afb097 100644 --- a/test/notebook_tests/test_notebooks.py +++ b/test/notebook_tests/test_notebooks.py @@ -25,6 +25,14 @@ test_path = "examples/" test_notebooks = [] +import platform + + +def _version_lt_3_10(): + version = platform.python_version() + return int(version.split(".")[1]) >= 10 + + # These notebooks would not be tested. EXCLUDED_NOTEBOOKS = [ "bring_your_own_container.ipynb", @@ -35,15 +43,19 @@ "01_Introduction_to_Aquila.ipynb", "02_Ordered_phases_in_Rydberg_systems.ipynb", "03_Parallel_tasks_on_Aquila.ipynb", - # Python 3.10 required for decorators notebooks - "0_Creating_your_first_Hybrid_Job.ipynb", - "Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb", - "Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb", - "0_Getting_started.ipynb", - "2_Graph_optimization_with_QAOA.ipynb", - "3_Hydrogen_Molecule_geometry_with_VQE.ipynb", ] +# Python 3.10 required for decorators notebooks +if _version_lt_3_10(): + EXCLUDED_NOTEBOOKS += [ + "0_Creating_your_first_Hybrid_Job.ipynb", + "Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb", + "Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb", + "0_Getting_started.ipynb", + "2_Graph_optimization_with_QAOA.ipynb", + "3_Hydrogen_Molecule_geometry_with_VQE.ipynb", + ] + for dir_, _, files in os.walk(test_path): for file_name in files: From 9750ba74b8b302b810fec859e74831cbcb86efb7 Mon Sep 17 00:00:00 2001 From: Matthew Beach Date: Mon, 16 Oct 2023 20:59:31 +0000 Subject: [PATCH 20/24] pytho nversion check --- test/notebook_tests/test_notebooks.py | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/test/notebook_tests/test_notebooks.py b/test/notebook_tests/test_notebooks.py index b11afb097..110d62609 100644 --- a/test/notebook_tests/test_notebooks.py +++ b/test/notebook_tests/test_notebooks.py @@ -11,6 +11,7 @@ # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. +import sys import logging import os import traceback @@ -25,14 +26,6 @@ test_path = "examples/" test_notebooks = [] -import platform - - -def _version_lt_3_10(): - version = platform.python_version() - return int(version.split(".")[1]) >= 10 - - # These notebooks would not be tested. EXCLUDED_NOTEBOOKS = [ "bring_your_own_container.ipynb", @@ -46,7 +39,7 @@ def _version_lt_3_10(): ] # Python 3.10 required for decorators notebooks -if _version_lt_3_10(): +if sys.version_info.minor != 10: EXCLUDED_NOTEBOOKS += [ "0_Creating_your_first_Hybrid_Job.ipynb", "Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb", From 6d9c11364757ab10e334dfeef2503d0c7c1989a8 Mon Sep 17 00:00:00 2001 From: Matthew Beach Date: Mon, 16 Oct 2023 21:11:38 +0000 Subject: [PATCH 21/24] exclude notebooks --- test/integ_tests/test_all_notebooks.py | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/test/integ_tests/test_all_notebooks.py b/test/integ_tests/test_all_notebooks.py index 1d60c265d..c9209a443 100644 --- a/test/integ_tests/test_all_notebooks.py +++ b/test/integ_tests/test_all_notebooks.py @@ -19,7 +19,15 @@ # Some AHS examples are running long especially on Mac. Removing while investigating "04_Maximum_Independent_Sets_with_Analog_Hamiltonian_Simulation.ipynb", "05_Running_Analog_Hamiltonian_Simulation_with_local_simulator.ipynb", - +] +# Python 3.10 required for decorators notebooks +EXCLUDED_NOTEBOOKS += [ + "0_Creating_your_first_Hybrid_Job.ipynb", + "Quantum_machine_learning_in_Amazon_Braket_Hybrid_Jobs.ipynb", + "Using_PennyLane_with_Braket_Hybrid_Jobs.ipynb", + "0_Getting_started.ipynb", + "2_Graph_optimization_with_QAOA.ipynb", + "3_Hydrogen_Molecule_geometry_with_VQE.ipynb", ] logging.basicConfig(level=logging.INFO) @@ -45,7 +53,9 @@ def get_mock_paths(notebook_dir, notebook_file): mock_dir = os.path.join(*split_notebook_dir[1:]) path_to_mocks = os.path.join(path_to_root, "test", "integ_tests", mock_dir, mock_file) if not os.path.exists(path_to_mocks): - path_to_mocks = os.path.join(path_to_root, "test", "integ_tests", "default_mocks", "default_mocks.py") + path_to_mocks = os.path.join( + path_to_root, "test", "integ_tests", "default_mocks", "default_mocks.py" + ) path_to_utils = os.path.join(path_to_root, "test", "integ_tests", "mock_utils.py") return path_to_utils, path_to_mocks @@ -73,7 +83,7 @@ def test_notebook_to_html_conversion(notebook_dir, notebook_file, mock_level): os.chdir(root_path) os.chdir(notebook_dir) - html_exporter = HTMLExporter(template_name='classic') + html_exporter = HTMLExporter(template_name="classic") html_exporter.from_file(notebook_file) @@ -102,7 +112,7 @@ def test_record(): mock_utils = SourceFileLoader("notebook_mock_utils","{path_to_utils}").load_module() """, run=False, - before=0 + before=0, ) tb.execute() @@ -128,7 +138,7 @@ def execute_with_mocks(tb, mock_level, path_to_utils, path_to_mocks): test_mocks.pre_run_inject(mock_utils) """, run=False, - before=0 + before=0, ) tb.execute() test_mocks = SourceFileLoader("notebook_mocks", path_to_mocks).load_module() From 1153aba6472a1a80da7a6b757f65644213511260 Mon Sep 17 00:00:00 2001 From: Aaron Berdy Date: Mon, 16 Oct 2023 15:48:26 -0700 Subject: [PATCH 22/24] exclude another nb --- test/integ_tests/test_all_notebooks.py | 1 + 1 file changed, 1 insertion(+) diff --git a/test/integ_tests/test_all_notebooks.py b/test/integ_tests/test_all_notebooks.py index c9209a443..a9e05440a 100644 --- a/test/integ_tests/test_all_notebooks.py +++ b/test/integ_tests/test_all_notebooks.py @@ -28,6 +28,7 @@ "0_Getting_started.ipynb", "2_Graph_optimization_with_QAOA.ipynb", "3_Hydrogen_Molecule_geometry_with_VQE.ipynb", + "8_Creating_Hybrid_Job_Scripts-Creating_your_first_Hybrid_Job.ipynb", ] logging.basicConfig(level=logging.INFO) From 213687a3d29ba9739d9ac84fc3ef3de832300f54 Mon Sep 17 00:00:00 2001 From: Aaron Berdy Date: Mon, 16 Oct 2023 15:56:43 -0700 Subject: [PATCH 23/24] rename excluded notebook --- test/integ_tests/test_all_notebooks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/integ_tests/test_all_notebooks.py b/test/integ_tests/test_all_notebooks.py index a9e05440a..3fc829e45 100644 --- a/test/integ_tests/test_all_notebooks.py +++ b/test/integ_tests/test_all_notebooks.py @@ -28,7 +28,7 @@ "0_Getting_started.ipynb", "2_Graph_optimization_with_QAOA.ipynb", "3_Hydrogen_Molecule_geometry_with_VQE.ipynb", - "8_Creating_Hybrid_Job_Scripts-Creating_your_first_Hybrid_Job.ipynb", + "Creating_your_first_Hybrid_Job.ipynb", ] logging.basicConfig(level=logging.INFO) From ae15c5d5887c4cdd1229001c3f21eb02c7394b36 Mon Sep 17 00:00:00 2001 From: Aaron Berdy Date: Mon, 16 Oct 2023 16:40:49 -0700 Subject: [PATCH 24/24] update bdk version --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index cdb5e9061..863804db1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,7 +4,7 @@ boto3==1.28.62 amazon-braket-default-simulator==1.20.1 amazon-braket-pennylane-plugin==1.21.0 amazon-braket-schemas==1.19.1.post0 -amazon-braket-sdk==1.58.0 +amazon-braket-sdk==1.58.1 amazon-braket-algorithm-library==1.4.1 cvxpy==1.3.2 ipykernel==6.25.2