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  "cells": [
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        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/aelkhodary/AI_ML_Coders/blob/main/Translate_Ar_To_En.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Install ENV"
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      "source": [
        "!pip install transformers\n",
        "!pip install torch\n",
        "!pip install pandas\n",
        "!pip install openpyxl\n",
        "!pip install sentencepiece  # Required for tokenization"
      ],
      "metadata": {
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      },
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        {
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          "name": "stdout",
          "text": [
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            "Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch)\n",
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            "\u001b[?25hDownloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)\n",
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            "\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n",
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            "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m85.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n",
            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-curand-cu12\n",
            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
            "  Attempting uninstall: nvidia-cufft-cu12\n",
            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cublas-cu12\n",
            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
            "  Attempting uninstall: nvidia-cusparse-cu12\n",
            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
            "  Attempting uninstall: nvidia-cudnn-cu12\n",
            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
            "  Attempting uninstall: nvidia-cusolver-cu12\n",
            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
            "Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.2)\n",
            "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2025.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
            "Requirement already satisfied: openpyxl in /usr/local/lib/python3.11/dist-packages (3.1.5)\n",
            "Requirement already satisfied: et-xmlfile in /usr/local/lib/python3.11/dist-packages (from openpyxl) (2.0.0)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.11/dist-packages (0.2.0)\n"
          ]
        }
      ],
      "execution_count": 1
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import necessary libraries:"
      ],
      "metadata": {
        "id": "9bb36af9-961a-455b-95db-2b138ba48263"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer  # Note the corrected class name\n",
        "import torch\n",
        "import pandas as pd\n",
        "from typing import List, Union"
      ],
      "metadata": {
        "id": "7860fbf4-5074-4de2-a3e7-afae008440dd"
      },
      "outputs": [],
      "execution_count": 10
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load the model and tokenizer:"
      ],
      "metadata": {
        "id": "7cb93f48-5466-4c36-913c-ccc034a661c3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def initialize_model():\n",
        "    # Load model and tokenizer\n",
        "    model_name = \"Helsinki-NLP/opus-mt-ar-en\"\n",
        "    # model_name = \"NAMAA-Space/masrawy-english-to-egyptian-arabic-translator-v2.9\"\n",
        "    # model_name = \"facebook/m2m100_418M\"\n",
        "    print(\"Loading tokenizer...\")\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "    print(\"Loading model...\")\n",
        "    model = AutoModelForSeq2SeqLM.from_pretrained(\n",
        "        model_name,\n",
        "        torch_dtype=torch.float16  # Use half precision to save memory\n",
        "    )\n",
        "\n",
        "    # Move model to GPU (Colab provides GPU if enabled)\n",
        "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "    model = model.to(device)\n",
        "    print(f\"Model loaded on: {device}\")\n",
        "\n",
        "    return model, tokenizer"
      ],
      "metadata": {
        "id": "6a64dc80-c08f-4758-a2c5-b73f35498fa3"
      },
      "outputs": [],
      "execution_count": 22
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM\n",
        "def initialize_model():\n",
        "    # Load model directly\n",
        "\n",
        "    #model = AutoModelForCausalLM.from_pretrained(\"deepseek-ai/DeepSeek-R1\", trust_remote_code=True)\n",
        "    # Load DeepSeek model and tokenizer\n",
        "    model_name = \"deepseek-ai/DeepSeek-R1\"  # Replace with the actual DeepSeek model name on Hugging Face\n",
        "\n",
        "    print(\"Loading tokenizer...\")\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "    print(\"Loading model...\")\n",
        "    model = AutoModelForSeq2SeqLM.from_pretrained(\n",
        "        model_name,\n",
        "        torch_dtype=torch.float16  # Use half precision to save memory\n",
        "    )\n",
        "\n",
        "    # Move model to GPU (Colab provides GPU if enabled)\n",
        "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "    model = model.to(device)\n",
        "    print(f\"Model loaded on: {device}\")\n",
        "\n",
        "    return model, tokenizer"
      ],
      "metadata": {
        "id": "RSbKQQgy275O"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# #!pip install --upgrade transformers\n",
        "\n",
        "# from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "# import torch\n",
        "\n",
        "# # Use a pipeline as a high-level helper\n",
        "# from transformers import pipeline\n",
        "\n",
        "# # Define model\n",
        "# model_name = \"deepseek-ai/DeepSeek-R1\"\n",
        "\n",
        "# # Initialize the text-generation pipeline\n",
        "# pipe = pipeline(\"text-generation\", model=model_name, trust_remote_code=True)\n",
        "\n",
        "# # Correct input format (plain string instead of a dictionary)\n",
        "# response = pipe(\"Who are you?\")\n",
        "\n",
        "# # Print response\n",
        "# print(response)\n",
        "\n",
        "\n",
        "# def initialize_model_deepseek():\n",
        "#     # Define model name\n",
        "#     model_name = \"deepseek-ai/DeepSeek-R1\"\n",
        "\n",
        "#     print(\"Loading tokenizer...\")\n",
        "#     tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "\n",
        "#     print(\"Checking quantization settings before initializing the model...\")\n",
        "\n",
        "#     # Attempt to retrieve model config before loading\n",
        "#     try:\n",
        "#         model_config = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True).config\n",
        "#         quantization_type = getattr(model_config, \"quantization_config\", None)\n",
        "\n",
        "#         # Print quantization type\n",
        "#         if quantization_type is None:\n",
        "#             print(\"No quantization detected.\")\n",
        "#         else:\n",
        "#             print(f\"Detected Quantization Type: {quantization_type}\")\n",
        "\n",
        "#     except Exception as e:\n",
        "#         print(f\"Error retrieving model configuration: {e}\")\n",
        "#         quantization_type = \"Unknown\"\n",
        "\n",
        "#     print(\"Loading model...\")\n",
        "#     model = AutoModelForCausalLM.from_pretrained(\n",
        "#         model_name,\n",
        "#         trust_remote_code=True,\n",
        "#         torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,  # Handle CPU/GPU dtype\n",
        "#         quantization_config=None  # Disable unwanted quantization\n",
        "#     )\n",
        "\n",
        "#     # Move model to GPU if available\n",
        "#     device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "#     model = model.to(device)\n",
        "#     print(f\"Model loaded on: {device}\")\n",
        "\n",
        "#     return model, tokenizer\n",
        "\n",
        "\n",
        "\n",
        "# # Initialize the model\n",
        "# model, tokenizer = initialize_model()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mWSJlu-Z5Fnx",
        "outputId": "38942bcc-7b2c-4bb9-c35a-2aaf4e22c441"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Could not load model deepseek-ai/DeepSeek-R1 with any of the following classes: (<class 'transformers.models.auto.modeling_auto.AutoModelForCausalLM'>, <class 'transformers.models.auto.modeling_tf_auto.TFAutoModelForCausalLM'>). See the original errors:\n\nwhile loading with AutoModelForCausalLM, an error is thrown:\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.11/dist-packages/transformers/pipelines/base.py\", line 289, in infer_framework_load_model\n    model = model_class.from_pretrained(model, **kwargs)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/transformers/models/auto/auto_factory.py\", line 559, in from_pretrained\n    return model_class.from_pretrained(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/transformers/modeling_utils.py\", line 3659, in from_pretrained\n    config.quantization_config = AutoHfQuantizer.merge_quantization_configs(\n                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/transformers/quantizers/auto.py\", line 173, in merge_quantization_configs\n    quantization_config = AutoQuantizationConfig.from_dict(quantization_config)\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/transformers/quantizers/auto.py\", line 97, in from_dict\n    raise ValueError(\nValueError: Unknown quantization type, got fp8 - supported types are: ['awq', 'bitsandbytes_4bit', 'bitsandbytes_8bit', 'gptq', 'aqlm', 'quanto', 'eetq', 'hqq', 'compressed-tensors', 'fbgemm_fp8', 'torchao', 'bitnet']\n\nwhile loading with TFAutoModelForCausalLM, an error is thrown:\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.11/dist-packages/transformers/pipelines/base.py\", line 289, in infer_framework_load_model\n    model = model_class.from_pretrained(model, **kwargs)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/transformers/models/auto/auto_factory.py\", line 567, in from_pretrained\n    raise ValueError(\nValueError: Unrecognized configuration class <class 'transformers_modules.deepseek-ai.DeepSeek-R1.f7361cd9ff99396dbf6bd644ad846015e59ed4fc.configuration_deepseek.DeepseekV3Config'> for this kind of AutoModel: TFAutoModelForCausalLM.\nModel type should be one of BertConfig, CamembertConfig, CTRLConfig, GPT2Config, GPT2Config, GPTJConfig, MistralConfig, OpenAIGPTConfig, OPTConfig, RemBertConfig, RobertaConfig, RobertaPreLayerNormConfig, RoFormerConfig, TransfoXLConfig, XGLMConfig, XLMConfig, XLMRobertaConfig, XLNetConfig.\n\n\n",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-ab4d7c63c67d>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Initialize the text-generation pipeline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mpipe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"text-generation\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrust_remote_code\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# Correct input format (plain string instead of a dictionary)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/pipelines/__init__.py\u001b[0m in \u001b[0;36mpipeline\u001b[0;34m(task, model, config, tokenizer, feature_extractor, image_processor, processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[0m\n\u001b[1;32m    938\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mframework\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    939\u001b[0m         \u001b[0mmodel_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"tf\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtargeted_task\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"tf\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtargeted_task\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 940\u001b[0;31m         framework, model = infer_framework_load_model(\n\u001b[0m\u001b[1;32m    941\u001b[0m             \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    942\u001b[0m             \u001b[0mmodel_classes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_classes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/pipelines/base.py\u001b[0m in \u001b[0;36minfer_framework_load_model\u001b[0;34m(model, config, model_classes, task, framework, **model_kwargs)\u001b[0m\n\u001b[1;32m    300\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mclass_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrace\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mall_traceback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m                 \u001b[0merror\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34mf\"while loading with {class_name}, an error is thrown:\\n{trace}\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    303\u001b[0m                 \u001b[0;34mf\"Could not load model {model} with any of the following classes: {class_tuple}. See the original errors:\\n\\n{error}\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m             )\n",
            "\u001b[0;31mValueError\u001b[0m: Could not load model deepseek-ai/DeepSeek-R1 with any of the following classes: (<class 'transformers.models.auto.modeling_auto.AutoModelForCausalLM'>, <class 'transformers.models.auto.modeling_tf_auto.TFAutoModelForCausalLM'>). See the original errors:\n\nwhile loading with AutoModelForCausalLM, an error is thrown:\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.11/dist-packages/transformers/pipelines/base.py\", line 289, in infer_framework_load_model\n    model = model_class.from_pretrained(model, **kwargs)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/transformers/models/auto/auto_factory.py\", line 559, in from_pretrained\n    return model_class.from_pretrained(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/transformers/modeling_utils.py\", line 3659, in from_pretrained\n    config.quantization_config = AutoHfQuantizer.merge_quantization_configs(\n                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/transformers/quantizers/auto.py\", line 173, in merge_quantization_configs\n    quantization_config = AutoQuantizationConfig.from_dict(quantization_config)\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/transformers/quantizers/auto.py\", line 97, in from_dict\n    raise ValueError(\nValueError: Unknown quantization type, got fp8 - supported types are: ['awq', 'bitsandbytes_4bit', 'bitsandbytes_8bit', 'gptq', 'aqlm', 'quanto', 'eetq', 'hqq', 'compressed-tensors', 'fbgemm_fp8', 'torchao', 'bitnet']\n\nwhile loading with TFAutoModelForCausalLM, an error is thrown:\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.11/dist-packages/transformers/pipelines/base.py\", line 289, in infer_framework_load_model\n    model = model_class.from_pretrained(model, **kwargs)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/transformers/models/auto/auto_factory.py\", line 567, in from_pretrained\n    raise ValueError(\nValueError: Unrecognized configuration class <class 'transformers_modules.deepseek-ai.DeepSeek-R1.f7361cd9ff99396dbf6bd644ad846015e59ed4fc.configuration_deepseek.DeepseekV3Config'> for this kind of AutoModel: TFAutoModelForCausalLM.\nModel type should be one of BertConfig, CamembertConfig, CTRLConfig, GPT2Config, GPT2Config, GPTJConfig, MistralConfig, OpenAIGPTConfig, OPTConfig, RemBertConfig, RobertaConfig, RobertaPreLayerNormConfig, RoFormerConfig, TransfoXLConfig, XGLMConfig, XLMConfig, XLMRobertaConfig, XLNetConfig.\n\n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create translation function:"
      ],
      "metadata": {
        "id": "abb4803c-9c2d-47b0-a687-ea05c2620d90"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def translate_text(text: str, model, tokenizer, max_length: int = 128) -> str:\n",
        "    if pd.isna(text):\n",
        "        return \"\"\n",
        "\n",
        "    # Prepare the prompt\n",
        "    prompt = f\"Translate this Arabic name to English: {text}\"\n",
        "\n",
        "    # Tokenize input\n",
        "    inputs = tokenizer(prompt, return_tensors=\"pt\", truncation=True, max_length=max_length)\n",
        "    inputs = inputs.to(model.device)\n",
        "\n",
        "    # Generate translation\n",
        "    with torch.no_grad():\n",
        "        outputs = model.generate(\n",
        "            **inputs,\n",
        "            max_length=max_length,\n",
        "            num_return_sequences=1,\n",
        "            temperature=0.7,\n",
        "            do_sample=False\n",
        "        )\n",
        "\n",
        "    # Decode and return translation\n",
        "    translation = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "    return translation.strip()"
      ],
      "metadata": {
        "id": "bad99fc9-5d0e-4440-993c-82dbe38411df"
      },
      "outputs": [],
      "execution_count": 23
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Main processing function:"
      ],
      "metadata": {
        "id": "d9216f93-6790-4464-b80a-196eca216297"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Simple translation function\n",
        "def test_model(input_text: str, model, tokenizer, max_length: int = 128) -> str:\n",
        "    # Prepare the prompt\n",
        "    prompt = f\"Translate this Arabic Name to English : {input_text}\"\n",
        "\n",
        "    print(f\"Input prompt: {prompt}\")\n",
        "\n",
        "    # Tokenize input\n",
        "    inputs = tokenizer(prompt, return_tensors=\"pt\", truncation=True, max_length=max_length)\n",
        "    inputs = inputs.to(model.device)\n",
        "\n",
        "    # Generate output\n",
        "    with torch.no_grad():\n",
        "        outputs = model.generate(\n",
        "            **inputs,\n",
        "            max_length=max_length,\n",
        "            num_return_sequences=1,\n",
        "            temperature=0,\n",
        "            do_sample=False\n",
        "        )\n",
        "\n",
        "    # Decode and return result\n",
        "    result = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "    return result.strip()\n",
        "\n",
        "\n",
        "# 5. Test with a simple input\n",
        "def run_test():\n",
        "    print(\"Initializing model...\")\n",
        "    model, tokenizer = initialize_model()\n",
        "\n",
        "    # Test input\n",
        "    test_input = \"منصور\"  # Replace with your test input\n",
        "\n",
        "    print(\"\\nProcessing test input...\")\n",
        "    result = test_model(test_input, model, tokenizer)\n",
        "\n",
        "    print(\"\\nResults:\")\n",
        "    print(f\"Input: {test_input}\")\n",
        "    print(f\"Output: {result}\")\n",
        "\n",
        "# 6. Run the test\n",
        "run_test()\n"
      ],
      "metadata": {
        "id": "fdf8f67e-e0fd-41a0-bfc9-2989ad8082ad",
        "colab": {
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        },
        "outputId": "925a80f4-7a50-44d0-ff0b-e0d35f70cda9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initializing model...\n",
            "Loading tokenizer...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/42.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3481068faf514254b86bbf236f1ca907"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.38k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5911cbc178384a2f91ba9170ff271231"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "source.spm:   0%|          | 0.00/917k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "42865eb31c034f20a4c4cb1725509e13"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "target.spm:   0%|          | 0.00/802k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3b99227483604ae2ae648a14ae762f50"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/2.13M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0173f04c6d34487aa784708b818dcbe5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n",
            "  warnings.warn(\"Recommended: pip install sacremoses.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading model...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/308M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b361a92698dd4ec9bd46062d0cf3d9e9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/293 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "322f9aff468f46d6ab257e6eecd61374"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model loaded on: cuda\n",
            "\n",
            "Processing test input...\n",
            "Input prompt: Translate this Arabic Name to English : منصور\n",
            "\n",
            "Results:\n",
            "Input: منصور\n",
            "Output: == sync, corrected by elderman == @elder_man\n"
          ]
        }
      ],
      "execution_count": 24
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Use Langchain"
      ],
      "metadata": {
        "id": "XYXapy9qHAmo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def translate_arabic_to_english(text, model, tokenizer):\n",
        "    try:\n",
        "        # Get the device the model is on\n",
        "        device = next(model.parameters()).device\n",
        "\n",
        "        # Tokenize the input text\n",
        "        inputs = tokenizer(text, return_tensors=\"pt\", padding=True).to(device)\n",
        "\n",
        "        # Generate translation\n",
        "        outputs = model.generate(**inputs, max_length=128)\n",
        "\n",
        "        # Decode the translation\n",
        "        translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "\n",
        "        return translated_text.strip()\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Translation error for '{text}': {e}\")\n",
        "        return text\n",
        "\n",
        "# Test the function\n",
        "def test_translation():\n",
        "    # Initialize model\n",
        "    model, tokenizer = initialize_model()\n",
        "\n",
        "    # Test cases\n",
        "    arabic_texts = [\"منصور\" ,\n",
        "        \"مرحبا\",\n",
        "        \"محمد\",\n",
        "        \"كيف حالك\"\n",
        "    ]\n",
        "\n",
        "    print(\"Testing translations:\")\n",
        "    print(\"-\" * 40)\n",
        "    for text in arabic_texts:\n",
        "        translation = translate_arabic_to_english(text, model, tokenizer)\n",
        "        print(f\"Arabic: {text}\")\n",
        "        print(f\"English: {translation}\")\n",
        "        print(\"-\" * 40)\n",
        "\n",
        "# Run the test\n",
        "if __name__ == \"__main__\":\n",
        "    test_translation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VOB5ml_LHFGP",
        "outputId": "e668c71a-add6-4f29-a325-830ea375f258"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading tokenizer...\n",
            "Loading model...\n",
            "Model loaded on: cuda\n",
            "Testing translations:\n",
            "----------------------------------------\n",
            "Arabic: منصور\n",
            "English: Mansour\n",
            "----------------------------------------\n",
            "Arabic: مرحبا\n",
            "English: Hey.\n",
            "----------------------------------------\n",
            "Arabic: محمد\n",
            "English: Muhammad.\n",
            "----------------------------------------\n",
            "Arabic: كيف حالك\n",
            "English: How are you?\n",
            "----------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def main():\n",
        "    try:\n",
        "        # Initialize model\n",
        "        model, tokenizer = initialize_model()\n",
        "\n",
        "        # Read the Excel file\n",
        "        df = pd.read_excel(\"/content/translated_names_with_id_name.xlsx\")\n",
        "        df = df.head(2)\n",
        "        # Columns to translate\n",
        "        name_columns = ['Q2_MEM_NAME_FIRST', 'Q3_MEM_NAME_FATHER', 'Q4_MEM_NAME_GRAND']\n",
        "\n",
        "        # Translate each column\n",
        "        for col in name_columns:\n",
        "            if col in df.columns:\n",
        "                print(f\"\\nTranslating {col}...\")\n",
        "                df[f'{col}_ENGLISH'] = df[col].apply(\n",
        "                    lambda x: translate_arabic_to_english(x, model, tokenizer)\n",
        "                )\n",
        "            else:\n",
        "                print(f\"Warning: Column {col} not found in Excel file\")\n",
        "\n",
        "        # Save to Excel\n",
        "        output_file = \"/content/translated_names_with_id_name_01.xlsx\"\n",
        "        df.to_excel(output_file)\n",
        "        print(f\"\\nTranslations completed and saved to {output_file}\")\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error processing Excel file: {e}\")\n",
        "        raise"
      ],
      "metadata": {
        "id": "EI44i8mYOywI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6VApIxnJPkHg",
        "outputId": "3722c9ea-faae-457d-a014-41703e72ddc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading tokenizer...\n",
            "Loading model...\n",
            "Model loaded on: cpu\n",
            "\n",
            "Translating Q2_MEM_NAME_FIRST...\n",
            "\n",
            "Translating Q3_MEM_NAME_FATHER...\n",
            "\n",
            "Translating Q4_MEM_NAME_GRAND...\n",
            "\n",
            "Translations completed and saved to /content/translated_names_with_id_name_01.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def main_patch():\n",
        "    try:\n",
        "         # Initialize model\n",
        "        model, tokenizer = initialize_model()\n",
        "\n",
        "        # Read the Excel file\n",
        "        df = pd.read_excel(\"/content/translated_names_with_id.xlsx\")\n",
        "\n",
        "        # Columns to translate\n",
        "        # name_columns = ['Q2_MEM_NAME_FIRST', 'Q3_MEM_NAME_FATHER', 'Q4_MEM_NAME_GRAND']\n",
        "        name_columns = ['full_name_ar']\n",
        "        # Calculate number of batches\n",
        "        batch_size = 5000\n",
        "        total_rows = len(df)\n",
        "        num_batches = (total_rows + batch_size - 1) // batch_size\n",
        "\n",
        "        for batch_num in range(num_batches):\n",
        "            start_idx = batch_num * batch_size\n",
        "            end_idx = min((batch_num + 1) * batch_size, total_rows)\n",
        "\n",
        "            # Get batch of data\n",
        "            batch_df = df.iloc[start_idx:end_idx].copy()\n",
        "\n",
        "            # Translate each column for this batch\n",
        "            for col in name_columns:\n",
        "                if col in batch_df.columns:\n",
        "                    print(f\"\\nTranslating {col} for batch {batch_num + 1}/{num_batches}...\")\n",
        "                    batch_df[f'{col}_ENGLISH'] = batch_df[col].apply(\n",
        "                         lambda x: translate_arabic_to_english(x, model, tokenizer)\n",
        "                    )\n",
        "                else:\n",
        "                    print(f\"Warning: Column {col} not found in Excel file\")\n",
        "\n",
        "            # Save batch to Excel with sequence number\n",
        "            output_file = f\"/content/translated_names_with_id_name_{batch_num + 1:03d}.xlsx\"\n",
        "            batch_df.to_excel(output_file, index=False)\n",
        "            print(f\"Batch {batch_num + 1} saved to {output_file}\")\n",
        "\n",
        "        print(\"\\nAll batches processed successfully\")\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error processing Excel file: {e}\")\n",
        "        raise"
      ],
      "metadata": {
        "id": "javVGUfcR4wn"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "main_patch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJsAKngxSYfo",
        "outputId": "c743af0a-a5b5-4eaa-d36e-2b8a1fe4e63f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading tokenizer...\n",
            "Loading model...\n",
            "Model loaded on: cuda\n",
            "\n",
            "Translating full_name_ar for batch 1/5...\n",
            "Batch 1 saved to /content/translated_names_with_id_name_001.xlsx\n",
            "\n",
            "Translating full_name_ar for batch 2/5...\n",
            "Batch 2 saved to /content/translated_names_with_id_name_002.xlsx\n",
            "\n",
            "Translating full_name_ar for batch 3/5...\n",
            "Batch 3 saved to /content/translated_names_with_id_name_003.xlsx\n",
            "\n",
            "Translating full_name_ar for batch 4/5...\n",
            "Batch 4 saved to /content/translated_names_with_id_name_004.xlsx\n",
            "\n",
            "Translating full_name_ar for batch 5/5...\n",
            "Batch 5 saved to /content/translated_names_with_id_name_005.xlsx\n",
            "\n",
            "All batches processed successfully\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test Single Name"
      ],
      "metadata": {
        "id": "D7p-iNI_tp-O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def translate_single_text(arabic_text):\n",
        "    try:\n",
        "        # Initialize model\n",
        "        print(\"Initializing model...\")\n",
        "        model, tokenizer = initialize_model()\n",
        "\n",
        "        # Sample Arabic text input\n",
        "        # arabic_text = \"محمد احمد علي\"  # Replace with your Arabic text\n",
        "\n",
        "        print(\"\\nTranslating text...\")\n",
        "        print(f\"Input text: {arabic_text}\")\n",
        "\n",
        "        # Translate the text\n",
        "        translated_text = translate_arabic_to_english(arabic_text, model, tokenizer)\n",
        "\n",
        "        print(f\"Translated text: {translated_text}\")\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error during translation: {e}\")\n",
        "        raise\n",
        "\n",
        "# Run the translation\n",
        "if __name__ == \"__main__\":\n",
        "    translate_single_text('خلفان  سالم  خلفان ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2q6IP9sKtnnr",
        "outputId": "5cb596a5-93c5-4bc0-afd6-2eae9ebe453a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initializing model...\n",
            "Loading tokenizer...\n",
            "Loading model...\n",
            "Model loaded on: cuda\n",
            "\n",
            "Translating text...\n",
            "Input text: خلفان  سالم  خلفان \n",
            "Translated text: Two left... two left... two left... two left... two left... two left... two left... two left... two left... two left... two left... two left... two left... two left... two left... two left... two left... two left... two left... two left... two left... two left... two left... one left... one left... one\n"
          ]
        }
      ]
    }
  ]
}