{
 "cells": [
  {
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   "execution_count": 1,
   "id": "426ead53-211f-4f07-b327-ce1f75f923e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting transformers\n",
      "  Downloading transformers-4.41.2-py3-none-any.whl (9.1 MB)\n",
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      "Collecting safetensors>=0.4.1\n",
      "  Downloading safetensors-0.4.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
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      "\u001b[?25hCollecting tokenizers<0.20,>=0.19\n",
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      "Collecting huggingface-hub<1.0,>=0.23.0\n",
      "  Downloading huggingface_hub-0.23.4-py3-none-any.whl (402 kB)\n",
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      "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /home/user/miniconda/lib/python3.9/site-packages (from transformers) (4.61.2)\n",
      "Requirement already satisfied: requests in /home/user/miniconda/lib/python3.9/site-packages (from transformers) (2.32.3)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/user/miniconda/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (4.12.2)\n",
      "Collecting fsspec>=2023.5.0\n",
      "  Downloading fsspec-2024.6.0-py3-none-any.whl (176 kB)\n",
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      "\u001b[?25hRequirement already satisfied: urllib3<3,>=1.21.1 in /home/user/miniconda/lib/python3.9/site-packages (from requests->transformers) (1.26.6)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/user/miniconda/lib/python3.9/site-packages (from requests->transformers) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/user/miniconda/lib/python3.9/site-packages (from requests->transformers) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/user/miniconda/lib/python3.9/site-packages (from requests->transformers) (2021.5.30)\n",
      "Installing collected packages: fsspec, filelock, huggingface-hub, tokenizers, safetensors, regex, numpy, transformers, sentencepiece\n",
      "Successfully installed filelock-3.15.4 fsspec-2024.6.0 huggingface-hub-0.23.4 numpy-2.0.0 regex-2024.5.15 safetensors-0.4.3 sentencepiece-0.2.0 tokenizers-0.19.1 transformers-4.41.2\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers sentencepiece"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5bee7472-9458-428b-b315-2a6579ecbcd7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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     "metadata": {},
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    {
     "data": {
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       "model_id": "524b814ec5164db5a86203078b303a9c",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import AutoModel\n",
    "\n",
    "xlm_base = AutoModel.from_pretrained(\"xlm-roberta-base\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3c5bfded-aa37-4c1b-8fe8-c6fe64e184e7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "unit_scale": true
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       "model_id": "67617bf347cf46bb95d1559e756b3e64",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "unit_scale": true
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7cd0c7418dc4483083054e541b6dd1ac",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlm_large = AutoModel.from_pretrained(\"xlm-roberta-large\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba1b9da7-eafe-4c81-b47d-63ae94585b37",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XLMRobertaModel(\n",
       "  (embeddings): XLMRobertaEmbeddings(\n",
       "    (word_embeddings): Embedding(250002, 768, padding_idx=1)\n",
       "    (position_embeddings): Embedding(514, 768, padding_idx=1)\n",
       "    (token_type_embeddings): Embedding(1, 768)\n",
       "    (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (encoder): XLMRobertaEncoder(\n",
       "    (layer): ModuleList(\n",
       "      (0-11): 12 x XLMRobertaLayer(\n",
       "        (attention): XLMRobertaAttention(\n",
       "          (self): XLMRobertaSelfAttention(\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): XLMRobertaSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): XLMRobertaIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): XLMRobertaOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (pooler): XLMRobertaPooler(\n",
       "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "    (activation): Tanh()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xlm_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "53fcc03e-d6f5-41c3-9dc2-2922e6747896",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.0578, -0.0071, -0.0068,  ...,  0.0061, -0.0260, -0.0291],\n",
       "        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
       "        [-0.1564, -0.0728, -0.2477,  ..., -0.0778, -0.3088, -0.0090],\n",
       "        ...,\n",
       "        [ 0.0118,  0.0458, -0.0054,  ..., -0.0865,  0.0374,  0.0040],\n",
       "        [ 0.0525, -0.0270, -0.0141,  ..., -0.0552,  0.0349,  0.0274],\n",
       "        [-0.0479, -0.0293,  0.1079,  ..., -0.0825,  0.2908,  0.0861]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_embeddings = xlm_base.embeddings.position_embeddings.weight.data\n",
    "old_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fc69704e-51a6-489f-8b6a-b776d1027a2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([514, 768])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_embeddings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "714faf1f-ec8c-4e39-986f-5624658c8a9d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "new_embeddings = torch.zeros((2050, 768))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4eb8dfff-57a7-4e2c-a060-54d7ec84e2d7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "new_embeddings[:514, :] = old_embeddings.clone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0a4dcbb8-20f1-476c-83b0-199d3e406888",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "514 1026\n",
      "1026 1538\n",
      "1538 2050\n"
     ]
    }
   ],
   "source": [
    "num_pos = 514\n",
    "\n",
    "for i in range(3):\n",
    "    start_idx = num_pos+512*i\n",
    "    end_idx = start_idx + 512\n",
    "    new_embeddings[start_idx:end_idx, :] = old_embeddings[2:, :].clone()\n",
    "    print(start_idx, end_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6530a6fa-fe8e-4d37-9e35-1272643d04c4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of XLMRobertaModel were not initialized from the model checkpoint at xlm-roberta-base and are newly initialized because the shapes did not match:\n",
      "- roberta.embeddings.position_embeddings.weight: found shape torch.Size([514, 768]) in the checkpoint and torch.Size([2050, 768]) in the model instantiated\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "xlm_base = AutoModel.from_pretrained(\"xlm-roberta-base\", max_position_embeddings=2050, ignore_mismatched_sizes=True)\n",
    "\n",
    "xlm_base.embeddings.position_embeddings.weight.data = new_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ae8250bb-2d81-4ad9-b281-84d9ad3d9114",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    xlm_base(input_ids=seq_2048)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1e77aea0-65ff-403c-87cc-14afd87d7646",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2050, 768])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.0578, -0.0071, -0.0068,  ...,  0.0061, -0.0260, -0.0291],\n",
       "        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
       "        [-0.1564, -0.0728, -0.2477,  ..., -0.0778, -0.3088, -0.0090],\n",
       "        ...,\n",
       "        [ 0.0118,  0.0458, -0.0054,  ..., -0.0865,  0.0374,  0.0040],\n",
       "        [ 0.0525, -0.0270, -0.0141,  ..., -0.0552,  0.0349,  0.0274],\n",
       "        [-0.0479, -0.0293,  0.1079,  ..., -0.0825,  0.2908,  0.0861]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(xlm_base.embeddings.position_embeddings.weight.data.shape)\n",
    "xlm_base.embeddings.position_embeddings.weight.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "74297602-9c8e-4341-96cc-b26435046082",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "514 1026\n",
      "1026 1538\n",
      "1538 2050\n"
     ]
    }
   ],
   "source": [
    "old_embeddings = xlm_large.embeddings.position_embeddings.weight.data\n",
    "\n",
    "new_embeddings = torch.zeros((2050, old_embeddings.shape[1]))\n",
    "\n",
    "new_embeddings[:514, :] = old_embeddings.clone()\n",
    "\n",
    "num_pos = 514\n",
    "\n",
    "for i in range(3):\n",
    "    start_idx = num_pos+512*i\n",
    "    end_idx = start_idx + 512\n",
    "    new_embeddings[start_idx:end_idx, :] = old_embeddings[2:, :].clone()\n",
    "    print(start_idx, end_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "57ccd5e8-667a-4fae-be04-55c14aa1a316",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of XLMRobertaModel were not initialized from the model checkpoint at xlm-roberta-large and are newly initialized because the shapes did not match:\n",
      "- roberta.embeddings.position_embeddings.weight: found shape torch.Size([514, 1024]) in the checkpoint and torch.Size([2050, 1024]) in the model instantiated\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "xlm_large = AutoModel.from_pretrained(\"xlm-roberta-large\", max_position_embeddings=2050, ignore_mismatched_sizes=True)\n",
    "\n",
    "xlm_large.embeddings.position_embeddings.weight.data = new_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1b584a7b-19b0-45ac-824c-57e37bf2bf75",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
      "Token is valid (permission: write).\n",
      "Your token has been saved to /home/user/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    }
   ],
   "source": [
    "!huggingface-cli login"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3b5f110a-c30d-4b83-997b-384cf159f1c4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/json": {
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       "prefix": "model.safetensors",
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       "total": 2245898632,
       "unit": "B",
       "unit_divisor": 1000,
       "unit_scale": true
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d1193e52abce44fdba074b5996560954",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/2.25G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/nbroad/xlm-roberta-large-2048/commit/8270d498aad31c695866c0fbcf5c7eb932b69590', commit_message='Upload model', commit_description='', oid='8270d498aad31c695866c0fbcf5c7eb932b69590', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xlm_large.push_to_hub(\"nbroad/xlm-roberta-large-2048\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b0448ee6-e471-44f3-ac8e-e18ed66adcbb",
   "metadata": {
    "tags": []
   },
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    "xlm_base.push_to_hub(\"nbroad/xlm-roberta-base-2048\")"
   ]
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   "metadata": {
    "tags": []
   },
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   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"xlm-roberta-base\")"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "tokenizer.push_to_hub(\"nbroad/xlm-roberta-base-2048\")"
   ]
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   "id": "2ab6599e-3621-469f-8bdd-389cf7f60088",
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    {
     "ename": "AttributeError",
     "evalue": "'CommitInfo' object has no attribute 'push_to_hub'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[45], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m AutoTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxlm-roberta-large\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mpush_to_hub(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnbroad/xlm-roberta-large-2048\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      2\u001b[0m tokenizer\u001b[38;5;241m.\u001b[39mmodel_max_length \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2048\u001b[39m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mtokenizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpush_to_hub\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnbroad/xlm-roberta-large-2048\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'CommitInfo' object has no attribute 'push_to_hub'"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"xlm-roberta-large\").push_to_hub(\"nbroad/xlm-roberta-large-2048\")\n",
    "tokenizer.model_max_length = 2048\n",
    "tokenizer.push_to_hub(\"nbroad/xlm-roberta-large-2048\")"
   ]
  },
  {
   "cell_type": "code",
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   "source": []
  }
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