{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5bef53d9",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'transformers'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AutoModelForCausalLM, AutoTokenizer\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Load model and tokenizer from the repository\u001b[39;00m\n\u001b[1;32m      4\u001b[0m model_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/data/katha_lab/pranjul/BrainGPT-7B-v0.1/\u001b[39m\u001b[38;5;124m\"\u001b[39m  \u001b[38;5;66;03m# Replace with the actual path to the repository\u001b[39;00m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'transformers'"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "# Load model and tokenizer from the repository\n",
    "model_name = \"/data/katha_lab/pranjul/BrainGPT-7B-v0.1/\"  # Replace with the actual path to the repository\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
    "\n",
    "# Now you can use model to generate text, etc.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "15402023",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: pip: command not found\r\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9179f5ca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'torch.utils._pytree'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AutoModelForCausalLM, AutoTokenizer\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Assuming the model has been properly uploaded and shared on Hugging Face's model hub,\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# replace \"your-username/BrainGPT-7B-v0.1\" with the actual model repository path.\u001b[39;00m\n\u001b[1;32m      5\u001b[0m model_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/data/katha_lab/pranjul/BrainGPT-7B-v0.1/\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/transformers/__init__.py:26\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TYPE_CHECKING\n\u001b[1;32m     25\u001b[0m \u001b[38;5;66;03m# Check the dependencies satisfy the minimal versions required.\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dependency_versions_check\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     28\u001b[0m     OptionalDependencyNotAvailable,\n\u001b[1;32m     29\u001b[0m     _LazyModule,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     48\u001b[0m     logging,\n\u001b[1;32m     49\u001b[0m )\n\u001b[1;32m     52\u001b[0m logger \u001b[38;5;241m=\u001b[39m logging\u001b[38;5;241m.\u001b[39mget_logger(\u001b[38;5;18m__name__\u001b[39m)  \u001b[38;5;66;03m# pylint: disable=invalid-name\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/transformers/dependency_versions_check.py:16\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright 2020 The HuggingFace Team. All rights reserved.\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# See the License for the specific language governing permissions and\u001b[39;00m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;66;03m# limitations under the License.\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdependency_versions_table\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m deps\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mversions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m require_version, require_version_core\n\u001b[1;32m     19\u001b[0m \u001b[38;5;66;03m# define which module versions we always want to check at run time\u001b[39;00m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# (usually the ones defined in `install_requires` in setup.py)\u001b[39;00m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;66;03m# order specific notes:\u001b[39;00m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;66;03m# - tqdm must be checked before tokenizers\u001b[39;00m\n\u001b[1;32m     25\u001b[0m pkgs_to_check_at_runtime \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     26\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     27\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtqdm\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpyyaml\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     38\u001b[0m ]\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/transformers/utils/__init__.py:33\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstants\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdoc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     26\u001b[0m     add_code_sample_docstrings,\n\u001b[1;32m     27\u001b[0m     add_end_docstrings,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     31\u001b[0m     replace_return_docstrings,\n\u001b[1;32m     32\u001b[0m )\n\u001b[0;32m---> 33\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeneric\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     34\u001b[0m     ContextManagers,\n\u001b[1;32m     35\u001b[0m     ExplicitEnum,\n\u001b[1;32m     36\u001b[0m     ModelOutput,\n\u001b[1;32m     37\u001b[0m     PaddingStrategy,\n\u001b[1;32m     38\u001b[0m     TensorType,\n\u001b[1;32m     39\u001b[0m     add_model_info_to_auto_map,\n\u001b[1;32m     40\u001b[0m     cached_property,\n\u001b[1;32m     41\u001b[0m     can_return_loss,\n\u001b[1;32m     42\u001b[0m     expand_dims,\n\u001b[1;32m     43\u001b[0m     find_labels,\n\u001b[1;32m     44\u001b[0m     flatten_dict,\n\u001b[1;32m     45\u001b[0m     infer_framework,\n\u001b[1;32m     46\u001b[0m     is_jax_tensor,\n\u001b[1;32m     47\u001b[0m     is_numpy_array,\n\u001b[1;32m     48\u001b[0m     is_tensor,\n\u001b[1;32m     49\u001b[0m     is_tf_symbolic_tensor,\n\u001b[1;32m     50\u001b[0m     is_tf_tensor,\n\u001b[1;32m     51\u001b[0m     is_torch_device,\n\u001b[1;32m     52\u001b[0m     is_torch_dtype,\n\u001b[1;32m     53\u001b[0m     is_torch_tensor,\n\u001b[1;32m     54\u001b[0m     reshape,\n\u001b[1;32m     55\u001b[0m     squeeze,\n\u001b[1;32m     56\u001b[0m     strtobool,\n\u001b[1;32m     57\u001b[0m     tensor_size,\n\u001b[1;32m     58\u001b[0m     to_numpy,\n\u001b[1;32m     59\u001b[0m     to_py_obj,\n\u001b[1;32m     60\u001b[0m     transpose,\n\u001b[1;32m     61\u001b[0m     working_or_temp_dir,\n\u001b[1;32m     62\u001b[0m )\n\u001b[1;32m     63\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhub\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     64\u001b[0m     CLOUDFRONT_DISTRIB_PREFIX,\n\u001b[1;32m     65\u001b[0m     HF_MODULES_CACHE,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     91\u001b[0m     try_to_load_from_cache,\n\u001b[1;32m     92\u001b[0m )\n\u001b[1;32m     93\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimport_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     94\u001b[0m     ACCELERATE_MIN_VERSION,\n\u001b[1;32m     95\u001b[0m     ENV_VARS_TRUE_AND_AUTO_VALUES,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    204\u001b[0m     torch_only_method,\n\u001b[1;32m    205\u001b[0m )\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/transformers/utils/generic.py:465\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    461\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m(\u001b[38;5;28mself\u001b[39m[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[1;32m    464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_torch_available():\n\u001b[0;32m--> 465\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_pytree\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_torch_pytree\u001b[39;00m\n\u001b[1;32m    467\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_model_output_flatten\u001b[39m(output: ModelOutput) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[List[Any], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_torch_pytree.Context\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m    468\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(output\u001b[38;5;241m.\u001b[39mvalues()), \u001b[38;5;28mlist\u001b[39m(output\u001b[38;5;241m.\u001b[39mkeys())\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'torch.utils._pytree'"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "# Assuming the model has been properly uploaded and shared on Hugging Face's model hub,\n",
    "# replace \"your-username/BrainGPT-7B-v0.1\" with the actual model repository path.\n",
    "model_name = \"/data/katha_lab/pranjul/BrainGPT-7B-v0.1/\"\n",
    "\n",
    "# Load the tokenizer and the model\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
    "\n",
    "# Now you can use the model for text generation or inference\n",
    "# For example, to generate text:\n",
    "input_ids = tokenizer.encode(\"Your prompt here\", return_tensors=\"pt\")\n",
    "\n",
    "# Generate the output\n",
    "outputs = model.generate(input_ids, max_length=512)\n",
    "\n",
    "# Decode and print the output\n",
    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "161873ad",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b561c105",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'torch.utils._pytree'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/transformers/__init__.py:26\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TYPE_CHECKING\n\u001b[1;32m     25\u001b[0m \u001b[38;5;66;03m# Check the dependencies satisfy the minimal versions required.\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dependency_versions_check\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     28\u001b[0m     OptionalDependencyNotAvailable,\n\u001b[1;32m     29\u001b[0m     _LazyModule,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     48\u001b[0m     logging,\n\u001b[1;32m     49\u001b[0m )\n\u001b[1;32m     52\u001b[0m logger \u001b[38;5;241m=\u001b[39m logging\u001b[38;5;241m.\u001b[39mget_logger(\u001b[38;5;18m__name__\u001b[39m)  \u001b[38;5;66;03m# pylint: disable=invalid-name\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/transformers/dependency_versions_check.py:16\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright 2020 The HuggingFace Team. All rights reserved.\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# See the License for the specific language governing permissions and\u001b[39;00m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;66;03m# limitations under the License.\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdependency_versions_table\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m deps\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mversions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m require_version, require_version_core\n\u001b[1;32m     19\u001b[0m \u001b[38;5;66;03m# define which module versions we always want to check at run time\u001b[39;00m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# (usually the ones defined in `install_requires` in setup.py)\u001b[39;00m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;66;03m# order specific notes:\u001b[39;00m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;66;03m# - tqdm must be checked before tokenizers\u001b[39;00m\n\u001b[1;32m     25\u001b[0m pkgs_to_check_at_runtime \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     26\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     27\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtqdm\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpyyaml\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     38\u001b[0m ]\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/transformers/utils/__init__.py:33\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstants\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdoc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     26\u001b[0m     add_code_sample_docstrings,\n\u001b[1;32m     27\u001b[0m     add_end_docstrings,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     31\u001b[0m     replace_return_docstrings,\n\u001b[1;32m     32\u001b[0m )\n\u001b[0;32m---> 33\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeneric\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     34\u001b[0m     ContextManagers,\n\u001b[1;32m     35\u001b[0m     ExplicitEnum,\n\u001b[1;32m     36\u001b[0m     ModelOutput,\n\u001b[1;32m     37\u001b[0m     PaddingStrategy,\n\u001b[1;32m     38\u001b[0m     TensorType,\n\u001b[1;32m     39\u001b[0m     add_model_info_to_auto_map,\n\u001b[1;32m     40\u001b[0m     cached_property,\n\u001b[1;32m     41\u001b[0m     can_return_loss,\n\u001b[1;32m     42\u001b[0m     expand_dims,\n\u001b[1;32m     43\u001b[0m     find_labels,\n\u001b[1;32m     44\u001b[0m     flatten_dict,\n\u001b[1;32m     45\u001b[0m     infer_framework,\n\u001b[1;32m     46\u001b[0m     is_jax_tensor,\n\u001b[1;32m     47\u001b[0m     is_numpy_array,\n\u001b[1;32m     48\u001b[0m     is_tensor,\n\u001b[1;32m     49\u001b[0m     is_tf_symbolic_tensor,\n\u001b[1;32m     50\u001b[0m     is_tf_tensor,\n\u001b[1;32m     51\u001b[0m     is_torch_device,\n\u001b[1;32m     52\u001b[0m     is_torch_dtype,\n\u001b[1;32m     53\u001b[0m     is_torch_tensor,\n\u001b[1;32m     54\u001b[0m     reshape,\n\u001b[1;32m     55\u001b[0m     squeeze,\n\u001b[1;32m     56\u001b[0m     strtobool,\n\u001b[1;32m     57\u001b[0m     tensor_size,\n\u001b[1;32m     58\u001b[0m     to_numpy,\n\u001b[1;32m     59\u001b[0m     to_py_obj,\n\u001b[1;32m     60\u001b[0m     transpose,\n\u001b[1;32m     61\u001b[0m     working_or_temp_dir,\n\u001b[1;32m     62\u001b[0m )\n\u001b[1;32m     63\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhub\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     64\u001b[0m     CLOUDFRONT_DISTRIB_PREFIX,\n\u001b[1;32m     65\u001b[0m     HF_MODULES_CACHE,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     91\u001b[0m     try_to_load_from_cache,\n\u001b[1;32m     92\u001b[0m )\n\u001b[1;32m     93\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimport_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     94\u001b[0m     ACCELERATE_MIN_VERSION,\n\u001b[1;32m     95\u001b[0m     ENV_VARS_TRUE_AND_AUTO_VALUES,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    204\u001b[0m     torch_only_method,\n\u001b[1;32m    205\u001b[0m )\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/transformers/utils/generic.py:465\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    461\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m(\u001b[38;5;28mself\u001b[39m[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[1;32m    464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_torch_available():\n\u001b[0;32m--> 465\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_pytree\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_torch_pytree\u001b[39;00m\n\u001b[1;32m    467\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_model_output_flatten\u001b[39m(output: ModelOutput) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[List[Any], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_torch_pytree.Context\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m    468\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(output\u001b[38;5;241m.\u001b[39mvalues()), \u001b[38;5;28mlist\u001b[39m(output\u001b[38;5;241m.\u001b[39mkeys())\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'torch.utils._pytree'"
     ]
    }
   ],
   "source": [
    "import transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b4ee293",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ver' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mver\u001b[49m(torch)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ver' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "ver(torch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a00a4499",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.7.1+cu110\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "print(torch.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9245fda1",
   "metadata": {},
   "outputs": [],
   "source": [
    "!export GROQ_API_KEY=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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d6b32fdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting groq\n",
      "  Downloading groq-0.4.2-py3-none-any.whl (65 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.7/65.7 kB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hCollecting typing-extensions<5,>=4.7\n",
      "  Using cached typing_extensions-4.10.0-py3-none-any.whl (33 kB)\n",
      "Requirement already satisfied: sniffio in /home/pranjul/.local/lib/python3.8/site-packages (from groq) (1.2.0)\n",
      "Collecting pydantic<3,>=1.9.0\n",
      "  Using cached pydantic-2.6.4-py3-none-any.whl (394 kB)\n",
      "Collecting anyio<5,>=3.5.0\n",
      "  Using cached anyio-4.3.0-py3-none-any.whl (85 kB)\n",
      "Collecting distro<2,>=1.7.0\n",
      "  Using cached distro-1.9.0-py3-none-any.whl (20 kB)\n",
      "Collecting httpx<1,>=0.23.0\n",
      "  Using cached httpx-0.27.0-py3-none-any.whl (75 kB)\n",
      "Collecting exceptiongroup>=1.0.2\n",
      "  Using cached exceptiongroup-1.2.0-py3-none-any.whl (16 kB)\n",
      "Requirement already satisfied: idna>=2.8 in /home/pranjul/.local/lib/python3.8/site-packages (from anyio<5,>=3.5.0->groq) (2.10)\n",
      "Requirement already satisfied: certifi in /home/pranjul/.local/lib/python3.8/site-packages (from httpx<1,>=0.23.0->groq) (2020.12.5)\n",
      "Collecting httpcore==1.*\n",
      "  Using cached httpcore-1.0.4-py3-none-any.whl (77 kB)\n",
      "Collecting h11<0.15,>=0.13\n",
      "  Using cached h11-0.14.0-py3-none-any.whl (58 kB)\n",
      "Collecting pydantic-core==2.16.3\n",
      "  Using cached pydantic_core-2.16.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n",
      "Collecting annotated-types>=0.4.0\n",
      "  Using cached annotated_types-0.6.0-py3-none-any.whl (12 kB)\n",
      "\u001b[31mERROR: Will not install to the user site because it will lack sys.path precedence to typing-extensions in /shared/venvs/py3.8-torch1.7.1/lib/python3.8/site-packages\u001b[0m\u001b[31m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.2.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install groq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2f69c58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7ff788d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_swiss_roll\n",
    "from sklearn.manifold import SpectralEmbedding\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate sample data\n",
    "X, _ = make_swiss_roll(n_samples=1000, noise=0.1)\n",
    "\n",
    "# Use Spectral Embedding for initial low-dimensional embedding\n",
    "n_neighbors = 10\n",
    "spectral = SpectralEmbedding(n_components=2, affinity='nearest_neighbors', n_neighbors=n_neighbors)\n",
    "X_spectral = spectral.fit_transform(X)\n",
    "\n",
    "# PCA for comparison (optional step for visualizing the difference)\n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X)\n",
    "\n",
    "# Use a simple linear model to refine the embedding (as a substitute for the neural network)\n",
    "# Setup the SGDRegressor, which will be used to refine the initial spectral embedding\n",
    "regressor_x = SGDRegressor(max_iter=1000, tol=1e-3)\n",
    "regressor_y = SGDRegressor(max_iter=1000, tol=1e-3)\n",
    "\n",
    "# Train the regressors on the original high-dimensional data to predict the low-dimensional embedding\n",
    "regressor_x.fit(X, X_spectral[:, 0])\n",
    "regressor_y.fit(X, X_spectral[:, 1])\n",
    "\n",
    "# Use the trained regressors to predict the refined low-dimensional embedding\n",
    "X_embedded_sgd_x = regressor_x.predict(X)\n",
    "X_embedded_sgd_y = regressor_y.predict(X)\n",
    "\n",
    "# Visualization\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "# Original Spectral Embedding\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.scatter(X_spectral[:, 0], X_spectral[:, 1], c='orange', label='Spectral Embedding')\n",
    "plt.title('Spectral Embedding')\n",
    "plt.xlabel('Component 1')\n",
    "plt.ylabel('Component 2')\n",
    "plt.legend()\n",
    "\n",
    "# PCA for comparison\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c='blue', label='PCA')\n",
    "plt.title('PCA Embedding')\n",
    "plt.xlabel('Component 1')\n",
    "plt.ylabel('Component 2')\n",
    "plt.legend()\n",
    "\n",
    "# Refined Embedding\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.scatter(X_embedded_sgd_x, X_embedded_sgd_y, c='green', label='Refined Embedding')\n",
    "plt.title('Refined Embedding with SGD')\n",
    "plt.xlabel('Component 1')\n",
    "plt.ylabel('Component 2')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "92cf069c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c1e6b6af",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/shared/venvs/py3.8-torch1.7.1/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:795: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/shared/venvs/py3.8-torch1.7.1/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:805: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "# Initialize t-SNE with a fixed random state to get reproducible results\n",
    "tsne = TSNE(n_components=2, random_state=42)\n",
    "\n",
    "# Fit and transform the data using t-SNE\n",
    "X_tsne = tsne.fit_transform(X)\n",
    "\n",
    "# Visualization\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(X_tsne[:, 0], X_tsne[:, 1])\n",
    "plt.title('t-SNE Embedding')\n",
    "plt.xlabel('Component 1')\n",
    "plt.ylabel('Component 2')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "334f45f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/shared/venvs/py3.8-torch1.7.1/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:795: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/shared/venvs/py3.8-torch1.7.1/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:805: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Generate a sample dataset with clear clusters\n",
    "X, y = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)\n",
    "\n",
    "# Apply PCA for dimensionality reduction\n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X)\n",
    "\n",
    "# Apply t-SNE for dimensionality reduction\n",
    "tsne = TSNE(n_components=2, random_state=42)\n",
    "X_tsne = tsne.fit_transform(X)\n",
    "\n",
    "# Visualization\n",
    "plt.figure(figsize=(16, 8))\n",
    "\n",
    "# PCA result\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y)\n",
    "plt.title('PCA result')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "\n",
    "# t-SNE result\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=y)\n",
    "plt.title('t-SNE result')\n",
    "plt.xlabel('t-SNE Feature 1')\n",
    "plt.ylabel('t-SNE Feature 2')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f8ad1b7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 2)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "32b43e81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 0, 3, 1, 1, 2, 0, 3, 3, 2, 3, 0, 3, 1, 0, 0, 1, 2, 2, 1, 1,\n",
       "       0, 2, 2, 0, 1, 0, 2, 0, 3, 3, 0, 3, 3, 3, 3, 3, 2, 1, 0, 2, 0, 0,\n",
       "       2, 2, 3, 2, 3, 1, 2, 1, 3, 1, 1, 2, 3, 2, 3, 1, 3, 0, 3, 2, 2, 2,\n",
       "       3, 1, 3, 2, 0, 2, 3, 2, 2, 3, 2, 0, 1, 3, 1, 0, 1, 1, 3, 0, 1, 0,\n",
       "       3, 3, 0, 1, 3, 2, 2, 0, 1, 1, 0, 2, 3, 1, 3, 1, 0, 1, 1, 0, 3, 0,\n",
       "       2, 2, 1, 3, 1, 0, 3, 1, 1, 0, 2, 1, 2, 1, 1, 1, 1, 2, 1, 2, 3, 2,\n",
       "       2, 1, 3, 2, 2, 3, 0, 3, 3, 2, 0, 2, 0, 2, 3, 0, 3, 3, 3, 0, 3, 0,\n",
       "       1, 2, 3, 2, 1, 0, 3, 0, 0, 1, 0, 2, 2, 0, 1, 0, 0, 3, 1, 0, 2, 3,\n",
       "       1, 1, 0, 2, 1, 0, 2, 2, 0, 0, 0, 0, 1, 3, 0, 2, 0, 0, 2, 2, 2, 0,\n",
       "       2, 3, 0, 2, 1, 2, 0, 3, 2, 3, 0, 3, 0, 2, 0, 0, 3, 2, 2, 1, 1, 0,\n",
       "       3, 1, 1, 2, 1, 2, 0, 3, 3, 0, 0, 3, 0, 1, 2, 0, 1, 2, 3, 2, 1, 0,\n",
       "       1, 3, 3, 3, 3, 2, 2, 3, 0, 2, 1, 0, 2, 2, 2, 1, 1, 3, 0, 0, 2, 1,\n",
       "       3, 2, 0, 3, 0, 1, 1, 2, 2, 0, 1, 1, 1, 0, 3, 3, 1, 1, 0, 1, 1, 1,\n",
       "       3, 2, 3, 0, 1, 1, 3, 3, 3, 1, 1, 0, 3, 2])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1076bd65",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a912d73",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fef1fd7a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23253932",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1641e2a2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52f67145",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}