From ef8707ca5591ec18fa89873dedf245fcb125c9c1 Mon Sep 17 00:00:00 2001
From: oxBinaryBrain <udayg6269@gmail.com>
Date: Mon, 25 Mar 2024 12:31:45 +0530
Subject: [PATCH 1/3]  created and added an BFS algorithm folder and added the
 algorithm too

---
 .../Breadth-First Search (BFS)/BFS.java       | 36 +++++++++++++++++++
 1 file changed, 36 insertions(+)
 create mode 100644 Java/Algorithm/Breadth-First Search (BFS)/BFS.java

diff --git a/Java/Algorithm/Breadth-First Search (BFS)/BFS.java b/Java/Algorithm/Breadth-First Search (BFS)/BFS.java
new file mode 100644
index 00000000..31e9447d
--- /dev/null
+++ b/Java/Algorithm/Breadth-First Search (BFS)/BFS.java	
@@ -0,0 +1,36 @@
+import java.util.*;
+
+public class BFS {
+    public static void bfs(Map<Integer, List<Integer>> graph, int start) {
+        Queue<Integer> queue = new LinkedList<>();
+        Set<Integer> visited = new HashSet<>();
+
+        queue.offer(start);
+        visited.add(start);
+
+        while (!queue.isEmpty()) {
+            int node = queue.poll();
+            System.out.print(node + " ");
+
+            List<Integer> neighbors = graph.getOrDefault(node, new ArrayList<>());
+            for (int neighbor : neighbors) {
+                if (!visited.contains(neighbor)) {
+                    queue.offer(neighbor);
+                    visited.add(neighbor);
+                }
+            }
+        }
+    }
+
+    public static void main(String[] args) {
+        Map<Integer, List<Integer>> graph = new HashMap<>();
+        graph.put(0, Arrays.asList(1, 2));
+        graph.put(1, Arrays.asList(3, 4));
+        graph.put(2, Arrays.asList(5));
+        graph.put(3, new ArrayList<>());
+        graph.put(4, new ArrayList<>());
+        graph.put(5, new ArrayList<>());
+
+        bfs(graph, 0);
+    }
+}

From b0789e3aade1728524c30b1d1c2385ea880b8d46 Mon Sep 17 00:00:00 2001
From: UDAY G <124630227+oxBinaryBrain@users.noreply.github.com>
Date: Wed, 25 Dec 2024 22:50:56 +0530
Subject: [PATCH 2/3] initial commit

---
 Shadowfox_Advanced.ipynb | 2339 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 2339 insertions(+)
 create mode 100644 Shadowfox_Advanced.ipynb

diff --git a/Shadowfox_Advanced.ipynb b/Shadowfox_Advanced.ipynb
new file mode 100644
index 00000000..5dfda967
--- /dev/null
+++ b/Shadowfox_Advanced.ipynb
@@ -0,0 +1,2339 @@
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+        "colab_type": "text"
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+      "source": [
+        "<a href=\"https://colab.research.google.com/github/oxBinaryBrain/AlgorithmsAndDataStructure/blob/master/Shadowfox_Advanced.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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+      ],
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+        "id": "MlNlTswYxmwB"
+      }
+    },
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+        "import torch\n",
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+      ],
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+        "id": "IRNj7DdkHJQk"
+      },
+      "execution_count": 9,
+      "outputs": []
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+        "# Load pre-trained model tokenizer (vocabulary)\n",
+        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
+        "\n",
+        "# Load pre-trained model\n",
+        "model = BertForMaskedLM.from_pretrained('bert-base-uncased')\n",
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+        "id": "6UoX-fOxHQrQ",
+        "outputId": "315a2ff7-87dc-40dc-a7e1-52859212c597",
+        "collapsed": true
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f635926eba744755b145e134b49cca6a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4ad17c6b4cc041a19979078d741fee1e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b2c6b996611c462287b17f6a1989f209"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c87a7c708d144ebbaad3908ff053442b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c52d5014a3db44e385170eb1fc08e54f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "BertForMaskedLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n",
+            "  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes\n",
+            "  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n",
+            "  - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n",
+            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
+            "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+            "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "BertForMaskedLM(\n",
+              "  (bert): BertModel(\n",
+              "    (embeddings): BertEmbeddings(\n",
+              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
+              "      (position_embeddings): Embedding(512, 768)\n",
+              "      (token_type_embeddings): Embedding(2, 768)\n",
+              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "      (dropout): Dropout(p=0.1, inplace=False)\n",
+              "    )\n",
+              "    (encoder): BertEncoder(\n",
+              "      (layer): ModuleList(\n",
+              "        (0-11): 12 x BertLayer(\n",
+              "          (attention): BertAttention(\n",
+              "            (self): BertSdpaSelfAttention(\n",
+              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
+              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
+              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
+              "              (dropout): Dropout(p=0.1, inplace=False)\n",
+              "            )\n",
+              "            (output): BertSelfOutput(\n",
+              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "              (dropout): Dropout(p=0.1, inplace=False)\n",
+              "            )\n",
+              "          )\n",
+              "          (intermediate): BertIntermediate(\n",
+              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+              "            (intermediate_act_fn): GELUActivation()\n",
+              "          )\n",
+              "          (output): BertOutput(\n",
+              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "            (dropout): Dropout(p=0.1, inplace=False)\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "    )\n",
+              "  )\n",
+              "  (cls): BertOnlyMLMHead(\n",
+              "    (predictions): BertLMPredictionHead(\n",
+              "      (transform): BertPredictionHeadTransform(\n",
+              "        (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+              "        (transform_act_fn): GELUActivation()\n",
+              "        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "      )\n",
+              "      (decoder): Linear(in_features=768, out_features=30522, bias=True)\n",
+              "    )\n",
+              "  )\n",
+              ")"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def predict_masked_words(text):\n",
+        "    # Encode the text and create a tensor\n",
+        "    input_ids = tokenizer.encode(text, return_tensors='pt')\n",
+        "\n",
+        "    # Find the position of the [MASK] token\n",
+        "    mask_token_index = torch.where(input_ids == tokenizer.mask_token_id)[1]\n",
+        "\n",
+        "    # Predict tokens\n",
+        "    with torch.no_grad():\n",
+        "        outputs = model(input_ids)\n",
+        "        predictions = outputs[0]\n",
+        "\n",
+        "    # Extract the predicted word\n",
+        "    predicted_index = torch.argmax(predictions[0, mask_token_index]).item()\n",
+        "    predicted_word = tokenizer.decode([predicted_index])\n",
+        "\n",
+        "    return predicted_word"
+      ],
+      "metadata": {
+        "id": "IazZikrjHRye"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example sentences with [MASK]\n",
+        "sentences = [\n",
+        "    \"The quick brown fox jumps over the lazy [MASK].\",\n",
+        "    \"I love to play [MASK] on weekends.\",\n",
+        "    \"The capital of France is [MASK].\"\n",
+        "]\n",
+        "\n",
+        "# Generate predictions\n",
+        "for sentence in sentences:\n",
+        "    predicted_word = predict_masked_words(sentence)\n",
+        "    completed_sentence = sentence.replace(\"[MASK]\", predicted_word)\n",
+        "    print(f\"Original: {sentence}\\nCompleted: {completed_sentence}\\n\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wYYH4rdKHS4o",
+        "outputId": "c7d75810-fa98-4972-d676-3ee8d9352b78",
+        "collapsed": true
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Original: The quick brown fox jumps over the lazy [MASK].\n",
+            "Completed: The quick brown fox jumps over the lazy water.\n",
+            "\n",
+            "Original: I love to play [MASK] on weekends.\n",
+            "Completed: I love to play football on weekends.\n",
+            "\n",
+            "Original: The capital of France is [MASK].\n",
+            "Completed: The capital of France is paris.\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Example data for visualization\n",
+        "sentences = [\"The quick brown fox jumps over the lazy [MASK].\",\n",
+        "             \"I love playing [MASK] on weekends.\",\n",
+        "             \"The Eiffel Tower is located in [MASK].\"] # Added [MASK] to the sentences\n",
+        "predicted_words = [predict_masked_words(sentence) for sentence in sentences]\n",
+        "original_words = [\"dog\", \"soccer\", \"Paris\"]  # Expected words for comparison\n",
+        "\n",
+        "# Create a bar chart comparing predicted vs expected words\n",
+        "x = range(len(sentences))\n",
+        "expected_heights = [1] * len(sentences)\n",
+        "predicted_heights = [1 if predicted == original else 0 for predicted, original in zip(predicted_words, original_words)]\n",
+        "\n",
+        "plt.bar(x, expected_heights, label='Expected', alpha=0.5, color='blue')\n",
+        "plt.bar(x, predicted_heights, label='Predicted', alpha=0.5, color='orange')\n",
+        "\n",
+        "plt.xticks(x, [f\"Sentence {i+1}\" for i in range(len(sentences))])\n",
+        "plt.ylabel('Correct Prediction (1) / Incorrect (0)')\n",
+        "plt.title('BERT Predictions vs Expected Words')\n",
+        "plt.legend()\n",
+        "\n",
+        "# Adding value annotations\n",
+        "for i in x:\n",
+        "    plt.text(i, predicted_heights[i], str(predicted_heights[i]), ha='center', va='bottom')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "oqVO5BEIHVQK",
+        "outputId": "fd6129ae-d0eb-44fd-f834-a6cf77875b72"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Version 2"
+      ],
+      "metadata": {
+        "id": "JebN2BpPzcLV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "BD6_ypfbzctq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from transformers import BertTokenizer, BertForMaskedLM"
+      ],
+      "metadata": {
+        "id": "hOSIqRHWzgfV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load pre-trained model tokenizer (vocabulary)\n",
+        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
+        "\n",
+        "# Load pre-trained model\n",
+        "model = BertForMaskedLM.from_pretrained('bert-base-uncased')\n",
+        "model.eval()  # Set the model to evaluation mode"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dMvdExq6zi_i",
+        "outputId": "5d95a71d-8afb-455f-e90e-c155b78bac8f"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
+            "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+            "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "BertForMaskedLM(\n",
+              "  (bert): BertModel(\n",
+              "    (embeddings): BertEmbeddings(\n",
+              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
+              "      (position_embeddings): Embedding(512, 768)\n",
+              "      (token_type_embeddings): Embedding(2, 768)\n",
+              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "      (dropout): Dropout(p=0.1, inplace=False)\n",
+              "    )\n",
+              "    (encoder): BertEncoder(\n",
+              "      (layer): ModuleList(\n",
+              "        (0-11): 12 x BertLayer(\n",
+              "          (attention): BertAttention(\n",
+              "            (self): BertSdpaSelfAttention(\n",
+              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
+              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
+              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
+              "              (dropout): Dropout(p=0.1, inplace=False)\n",
+              "            )\n",
+              "            (output): BertSelfOutput(\n",
+              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "              (dropout): Dropout(p=0.1, inplace=False)\n",
+              "            )\n",
+              "          )\n",
+              "          (intermediate): BertIntermediate(\n",
+              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+              "            (intermediate_act_fn): GELUActivation()\n",
+              "          )\n",
+              "          (output): BertOutput(\n",
+              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "            (dropout): Dropout(p=0.1, inplace=False)\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "    )\n",
+              "  )\n",
+              "  (cls): BertOnlyMLMHead(\n",
+              "    (predictions): BertLMPredictionHead(\n",
+              "      (transform): BertPredictionHeadTransform(\n",
+              "        (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+              "        (transform_act_fn): GELUActivation()\n",
+              "        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "      )\n",
+              "      (decoder): Linear(in_features=768, out_features=30522, bias=True)\n",
+              "    )\n",
+              "  )\n",
+              ")"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ipython-input-12-e304e0132941\n",
+        "import numpy as np # Import numpy at the beginning of the cell\n",
+        "def predict_masked_words_with_visualization(text):\n",
+        "    # Encode the text and create a tensor\n",
+        "    input_ids = tokenizer.encode(text, return_tensors='pt')\n",
+        "\n",
+        "    # Find the position of the [MASK] token\n",
+        "    mask_token_index = torch.where(input_ids == tokenizer.mask_token_id)[1]\n",
+        "\n",
+        "    # Predict tokens\n",
+        "    with torch.no_grad():\n",
+        "        outputs = model(input_ids)\n",
+        "        predictions = outputs[0]\n",
+        "\n",
+        "    # Extract the predicted probabilities for the masked token\n",
+        "    predicted_probs = torch.softmax(predictions[0, mask_token_index], dim=-1).squeeze().numpy()\n",
+        "    # .squeeze() is added to remove dimensions of size 1\n",
+        "    # allowing you to properly index into the probabilities\n",
+        "\n",
+        "    # Get the top 5 predicted tokens and their probabilities\n",
+        "    top_k = 5\n",
+        "    top_k_indices = np.argsort(predicted_probs)[-top_k:][::-1] # Now np is defined\n",
+        "    top_k_probs = predicted_probs[top_k_indices]\n",
+        "    top_k_tokens = tokenizer.convert_ids_to_tokens(top_k_indices)\n",
+        "\n",
+        "    return top_k_tokens, top_k_probs"
+      ],
+      "metadata": {
+        "id": "MRBM8qx-zje-"
+      },
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example sentence with [MASK]\n",
+        "sentence = \"The quick brown fox jumps over the lazy [MASK].\"\n",
+        "\n",
+        "# Generate predictions\n",
+        "top_k_tokens, top_k_probs = predict_masked_words_with_visualization(sentence)\n",
+        "\n",
+        "# Print the predictions\n",
+        "for token, prob in zip(top_k_tokens, top_k_probs):\n",
+        "    print(f\"Predicted Token: {token}, Probability: {prob:.4f}\")\n",
+        "\n",
+        "# Visualization\n",
+        "plt.figure(figsize=(10, 5))\n",
+        "plt.bar(top_k_tokens, top_k_probs)\n",
+        "plt.xlabel('Predicted Tokens')\n",
+        "plt.ylabel('Probability')\n",
+        "plt.title('Top 5 Predicted Tokens for Masked Word')\n",
+        "plt.ylim(0, 1)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 574
+        },
+        "id": "NEH7vymnzlhF",
+        "outputId": "ed89a722-5bf4-46b4-9930-df2b363644b2"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted Token: water, Probability: 0.1065\n",
+            "Predicted Token: river, Probability: 0.0641\n",
+            "Predicted Token: grass, Probability: 0.0578\n",
+            "Predicted Token: stream, Probability: 0.0385\n",
+            "Predicted Token: lake, Probability: 0.0205\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 5589843bb94b811334e6c7ef1109ef94bd3304e7 Mon Sep 17 00:00:00 2001
From: UDAY G <124630227+oxBinaryBrain@users.noreply.github.com>
Date: Wed, 25 Dec 2024 22:51:31 +0530
Subject: [PATCH 3/3] Delete Shadowfox_Advanced.ipynb

Signed-off-by: UDAY G <124630227+oxBinaryBrain@users.noreply.github.com>
---
 Shadowfox_Advanced.ipynb | 2339 --------------------------------------
 1 file changed, 2339 deletions(-)
 delete mode 100644 Shadowfox_Advanced.ipynb

diff --git a/Shadowfox_Advanced.ipynb b/Shadowfox_Advanced.ipynb
deleted file mode 100644
index 5dfda967..00000000
--- a/Shadowfox_Advanced.ipynb
+++ /dev/null
@@ -1,2339 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "authorship_tag": "ABX9TyN//2tOajCrkmPCwM6Wa/pL",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "widgets": {
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-            "acb896418e0c486ab30542140df17aff",
-            "bbdb8d032d7646a48fc78d97a52c3ee9",
-            "425bcb27f7c34c9aa3052a93afbeaf7b",
-            "0afffc206b6740f2895e06b7c2ed8536",
-            "0a6a855931304686a5a59f9b157b80a3"
-          ]
-        },
-        "id": "6UoX-fOxHQrQ",
-        "outputId": "315a2ff7-87dc-40dc-a7e1-52859212c597",
-        "collapsed": true
-      },
-      "execution_count": 2,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
-            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
-            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
-            "You will be able to reuse this secret in all of your notebooks.\n",
-            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
-            "  warnings.warn(\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "f635926eba744755b145e134b49cca6a"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "4ad17c6b4cc041a19979078d741fee1e"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "b2c6b996611c462287b17f6a1989f209"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "c87a7c708d144ebbaad3908ff053442b"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "c52d5014a3db44e385170eb1fc08e54f"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "BertForMaskedLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n",
-            "  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes\n",
-            "  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n",
-            "  - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n",
-            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
-            "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-            "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "BertForMaskedLM(\n",
-              "  (bert): BertModel(\n",
-              "    (embeddings): BertEmbeddings(\n",
-              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
-              "      (position_embeddings): Embedding(512, 768)\n",
-              "      (token_type_embeddings): Embedding(2, 768)\n",
-              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-              "      (dropout): Dropout(p=0.1, inplace=False)\n",
-              "    )\n",
-              "    (encoder): BertEncoder(\n",
-              "      (layer): ModuleList(\n",
-              "        (0-11): 12 x BertLayer(\n",
-              "          (attention): BertAttention(\n",
-              "            (self): BertSdpaSelfAttention(\n",
-              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
-              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
-              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
-              "              (dropout): Dropout(p=0.1, inplace=False)\n",
-              "            )\n",
-              "            (output): BertSelfOutput(\n",
-              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
-              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-              "              (dropout): Dropout(p=0.1, inplace=False)\n",
-              "            )\n",
-              "          )\n",
-              "          (intermediate): BertIntermediate(\n",
-              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
-              "            (intermediate_act_fn): GELUActivation()\n",
-              "          )\n",
-              "          (output): BertOutput(\n",
-              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
-              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-              "            (dropout): Dropout(p=0.1, inplace=False)\n",
-              "          )\n",
-              "        )\n",
-              "      )\n",
-              "    )\n",
-              "  )\n",
-              "  (cls): BertOnlyMLMHead(\n",
-              "    (predictions): BertLMPredictionHead(\n",
-              "      (transform): BertPredictionHeadTransform(\n",
-              "        (dense): Linear(in_features=768, out_features=768, bias=True)\n",
-              "        (transform_act_fn): GELUActivation()\n",
-              "        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-              "      )\n",
-              "      (decoder): Linear(in_features=768, out_features=30522, bias=True)\n",
-              "    )\n",
-              "  )\n",
-              ")"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 2
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def predict_masked_words(text):\n",
-        "    # Encode the text and create a tensor\n",
-        "    input_ids = tokenizer.encode(text, return_tensors='pt')\n",
-        "\n",
-        "    # Find the position of the [MASK] token\n",
-        "    mask_token_index = torch.where(input_ids == tokenizer.mask_token_id)[1]\n",
-        "\n",
-        "    # Predict tokens\n",
-        "    with torch.no_grad():\n",
-        "        outputs = model(input_ids)\n",
-        "        predictions = outputs[0]\n",
-        "\n",
-        "    # Extract the predicted word\n",
-        "    predicted_index = torch.argmax(predictions[0, mask_token_index]).item()\n",
-        "    predicted_word = tokenizer.decode([predicted_index])\n",
-        "\n",
-        "    return predicted_word"
-      ],
-      "metadata": {
-        "id": "IazZikrjHRye"
-      },
-      "execution_count": 3,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Example sentences with [MASK]\n",
-        "sentences = [\n",
-        "    \"The quick brown fox jumps over the lazy [MASK].\",\n",
-        "    \"I love to play [MASK] on weekends.\",\n",
-        "    \"The capital of France is [MASK].\"\n",
-        "]\n",
-        "\n",
-        "# Generate predictions\n",
-        "for sentence in sentences:\n",
-        "    predicted_word = predict_masked_words(sentence)\n",
-        "    completed_sentence = sentence.replace(\"[MASK]\", predicted_word)\n",
-        "    print(f\"Original: {sentence}\\nCompleted: {completed_sentence}\\n\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "wYYH4rdKHS4o",
-        "outputId": "c7d75810-fa98-4972-d676-3ee8d9352b78",
-        "collapsed": true
-      },
-      "execution_count": 4,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Original: The quick brown fox jumps over the lazy [MASK].\n",
-            "Completed: The quick brown fox jumps over the lazy water.\n",
-            "\n",
-            "Original: I love to play [MASK] on weekends.\n",
-            "Completed: I love to play football on weekends.\n",
-            "\n",
-            "Original: The capital of France is [MASK].\n",
-            "Completed: The capital of France is paris.\n",
-            "\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Example data for visualization\n",
-        "sentences = [\"The quick brown fox jumps over the lazy [MASK].\",\n",
-        "             \"I love playing [MASK] on weekends.\",\n",
-        "             \"The Eiffel Tower is located in [MASK].\"] # Added [MASK] to the sentences\n",
-        "predicted_words = [predict_masked_words(sentence) for sentence in sentences]\n",
-        "original_words = [\"dog\", \"soccer\", \"Paris\"]  # Expected words for comparison\n",
-        "\n",
-        "# Create a bar chart comparing predicted vs expected words\n",
-        "x = range(len(sentences))\n",
-        "expected_heights = [1] * len(sentences)\n",
-        "predicted_heights = [1 if predicted == original else 0 for predicted, original in zip(predicted_words, original_words)]\n",
-        "\n",
-        "plt.bar(x, expected_heights, label='Expected', alpha=0.5, color='blue')\n",
-        "plt.bar(x, predicted_heights, label='Predicted', alpha=0.5, color='orange')\n",
-        "\n",
-        "plt.xticks(x, [f\"Sentence {i+1}\" for i in range(len(sentences))])\n",
-        "plt.ylabel('Correct Prediction (1) / Incorrect (0)')\n",
-        "plt.title('BERT Predictions vs Expected Words')\n",
-        "plt.legend()\n",
-        "\n",
-        "# Adding value annotations\n",
-        "for i in x:\n",
-        "    plt.text(i, predicted_heights[i], str(predicted_heights[i]), ha='center', va='bottom')\n",
-        "\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 452
-        },
-        "id": "oqVO5BEIHVQK",
-        "outputId": "fd6129ae-d0eb-44fd-f834-a6cf77875b72"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Version 2"
-      ],
-      "metadata": {
-        "id": "JebN2BpPzcLV"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [],
-      "metadata": {
-        "id": "BD6_ypfbzctq"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import torch\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from transformers import BertTokenizer, BertForMaskedLM"
-      ],
-      "metadata": {
-        "id": "hOSIqRHWzgfV"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Load pre-trained model tokenizer (vocabulary)\n",
-        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
-        "\n",
-        "# Load pre-trained model\n",
-        "model = BertForMaskedLM.from_pretrained('bert-base-uncased')\n",
-        "model.eval()  # Set the model to evaluation mode"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "dMvdExq6zi_i",
-        "outputId": "5d95a71d-8afb-455f-e90e-c155b78bac8f"
-      },
-      "execution_count": 11,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
-            "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-            "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "BertForMaskedLM(\n",
-              "  (bert): BertModel(\n",
-              "    (embeddings): BertEmbeddings(\n",
-              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
-              "      (position_embeddings): Embedding(512, 768)\n",
-              "      (token_type_embeddings): Embedding(2, 768)\n",
-              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-              "      (dropout): Dropout(p=0.1, inplace=False)\n",
-              "    )\n",
-              "    (encoder): BertEncoder(\n",
-              "      (layer): ModuleList(\n",
-              "        (0-11): 12 x BertLayer(\n",
-              "          (attention): BertAttention(\n",
-              "            (self): BertSdpaSelfAttention(\n",
-              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
-              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
-              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
-              "              (dropout): Dropout(p=0.1, inplace=False)\n",
-              "            )\n",
-              "            (output): BertSelfOutput(\n",
-              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
-              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-              "              (dropout): Dropout(p=0.1, inplace=False)\n",
-              "            )\n",
-              "          )\n",
-              "          (intermediate): BertIntermediate(\n",
-              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
-              "            (intermediate_act_fn): GELUActivation()\n",
-              "          )\n",
-              "          (output): BertOutput(\n",
-              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
-              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-              "            (dropout): Dropout(p=0.1, inplace=False)\n",
-              "          )\n",
-              "        )\n",
-              "      )\n",
-              "    )\n",
-              "  )\n",
-              "  (cls): BertOnlyMLMHead(\n",
-              "    (predictions): BertLMPredictionHead(\n",
-              "      (transform): BertPredictionHeadTransform(\n",
-              "        (dense): Linear(in_features=768, out_features=768, bias=True)\n",
-              "        (transform_act_fn): GELUActivation()\n",
-              "        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
-              "      )\n",
-              "      (decoder): Linear(in_features=768, out_features=30522, bias=True)\n",
-              "    )\n",
-              "  )\n",
-              ")"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 11
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# ipython-input-12-e304e0132941\n",
-        "import numpy as np # Import numpy at the beginning of the cell\n",
-        "def predict_masked_words_with_visualization(text):\n",
-        "    # Encode the text and create a tensor\n",
-        "    input_ids = tokenizer.encode(text, return_tensors='pt')\n",
-        "\n",
-        "    # Find the position of the [MASK] token\n",
-        "    mask_token_index = torch.where(input_ids == tokenizer.mask_token_id)[1]\n",
-        "\n",
-        "    # Predict tokens\n",
-        "    with torch.no_grad():\n",
-        "        outputs = model(input_ids)\n",
-        "        predictions = outputs[0]\n",
-        "\n",
-        "    # Extract the predicted probabilities for the masked token\n",
-        "    predicted_probs = torch.softmax(predictions[0, mask_token_index], dim=-1).squeeze().numpy()\n",
-        "    # .squeeze() is added to remove dimensions of size 1\n",
-        "    # allowing you to properly index into the probabilities\n",
-        "\n",
-        "    # Get the top 5 predicted tokens and their probabilities\n",
-        "    top_k = 5\n",
-        "    top_k_indices = np.argsort(predicted_probs)[-top_k:][::-1] # Now np is defined\n",
-        "    top_k_probs = predicted_probs[top_k_indices]\n",
-        "    top_k_tokens = tokenizer.convert_ids_to_tokens(top_k_indices)\n",
-        "\n",
-        "    return top_k_tokens, top_k_probs"
-      ],
-      "metadata": {
-        "id": "MRBM8qx-zje-"
-      },
-      "execution_count": 16,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Example sentence with [MASK]\n",
-        "sentence = \"The quick brown fox jumps over the lazy [MASK].\"\n",
-        "\n",
-        "# Generate predictions\n",
-        "top_k_tokens, top_k_probs = predict_masked_words_with_visualization(sentence)\n",
-        "\n",
-        "# Print the predictions\n",
-        "for token, prob in zip(top_k_tokens, top_k_probs):\n",
-        "    print(f\"Predicted Token: {token}, Probability: {prob:.4f}\")\n",
-        "\n",
-        "# Visualization\n",
-        "plt.figure(figsize=(10, 5))\n",
-        "plt.bar(top_k_tokens, top_k_probs)\n",
-        "plt.xlabel('Predicted Tokens')\n",
-        "plt.ylabel('Probability')\n",
-        "plt.title('Top 5 Predicted Tokens for Masked Word')\n",
-        "plt.ylim(0, 1)\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 574
-        },
-        "id": "NEH7vymnzlhF",
-        "outputId": "ed89a722-5bf4-46b4-9930-df2b363644b2"
-      },
-      "execution_count": 17,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Predicted Token: water, Probability: 0.1065\n",
-            "Predicted Token: river, Probability: 0.0641\n",
-            "Predicted Token: grass, Probability: 0.0578\n",
-            "Predicted Token: stream, Probability: 0.0385\n",
-            "Predicted Token: lake, Probability: 0.0205\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1000x500 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file