AI创作工具箱.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 图像生成\n",
    "#### PGAN - 人脸\n",
    "#### DCGAN - 服装\n",
    "https://pytorch.org/hub/facebookresearch_pytorch-gan-zoo_pgan/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "use_gpu = True if torch.cuda.is_available() else False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /Users/shadow/.cache/torch/hub/facebookresearch_pytorch_GAN_zoo_hub\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average network found !\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "PROGRESSIVE GROWING OF GANS (PGAN)\n",
    "High-quality image generation of fashion, celebrity faces\n",
    "'''\n",
    "model = torch.hub.load('facebookresearch/pytorch_GAN_zoo:hub',\n",
    "                       'PGAN', model_name='celebAHQ-512',\n",
    "                       pretrained=True, useGPU=use_gpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc4f3e0e450>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_images = 4\n",
    "noise, _ = model.buildNoiseData(num_images)\n",
    "with torch.no_grad():\n",
    "    generated_images = model.test(noise)\n",
    "\n",
    "# let's plot these images using torchvision and matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import torchvision\n",
    "grid = torchvision.utils.make_grid(generated_images.clamp(min=-1, max=1), scale_each=True, normalize=True)\n",
    "plt.imshow(grid.permute(1, 2, 0).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 3, 512, 512])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generated_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /Users/shadow/.cache/torch/hub/facebookresearch_pytorch_GAN_zoo_hub\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average network found !\n"
     ]
    }
   ],
   "source": [
    "import torchvision.transforms as Transforms\n",
    "\n",
    "model = torch.hub.load('facebookresearch/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)\n",
    "\n",
    "num_images = 1\n",
    "\n",
    "def dcgan():\n",
    "    noise, _ = model.buildNoiseData(num_images)\n",
    "    with torch.no_grad():\n",
    "        generated_images = model.test(noise)\n",
    "\n",
    "    transform = Transforms.Compose([Transforms.Normalize((-1., -1., -1.), (2, 2, 2)),\n",
    "                                    Transforms.ToPILImage()])\n",
    "    generated_images = generated_images[0]\n",
    "    generated_images = transform(generated_images.clamp(min=-1, max=1))\n",
    "    return generated_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "im=dcgan()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=64x64 at 0x7FC52EDE9710>"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 文本生成\n",
    "#### CPM-Generate\n",
    "#### gpt2-base-chinese "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting opencc\n",
      "  Downloading OpenCC-1.1.2-cp37-cp37m-macosx_10_9_x86_64.whl (727 kB)\n",
      "\u001b[K     |████████████████████████████████| 727 kB 1.4 MB/s eta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: opencc\n",
      "Successfully installed opencc-1.1.2\n",
      "\u001b[33mWARNING: You are using pip version 20.3.3; however, version 21.2.4 is available.\n",
      "You should consider upgrading via the '/Users/shadow/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install opencc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from opencc import OpenCC\n",
    "s2t = OpenCC('s2t')\n",
    "t2s = OpenCC('t2s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = pipeline(\"text-generation\", model=\"ckiplab/gpt2-base-chinese\")\n",
    "#generator = pipeline(\"text-generation\", model=\"TsinghuaAI/CPM-Generate\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['设计师', '老师', '司机', '程序员', '产品经理', '厨师', '作家', '无业游民', '学生', '高中生', '女生']"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ns=[x.strip() for x in '''设计师\n",
    " 老师\n",
    " 司机\n",
    " 程序员\n",
    " 产品经理\n",
    " 厨师\n",
    " 作家\n",
    " 无业游民\n",
    " 学生\n",
    " 高中生\n",
    " 女生'''.split(\"\\n\")]\n",
    "ns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_text(n):\n",
    "    actions=['出生','来自','毕业于','曾经','父亲是','母亲是','喜欢','觉得','想要成为','的梦想是','认为','自豪地宣布','将要']\n",
    "    for a in actions:\n",
    "        t=s2t.convert('我是'+n+'，我'+a)\n",
    "        [text] = generator(t, max_length=100)\n",
    "        s=''.join(text['generated_text'].split())\n",
    "        s=t2s.convert(s)\n",
    "        print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我是设计师，我出生在台湾的家庭的生活起居、外籍女佣家庭的生活起居和婚姻关系，而家庭的生活起居和婚姻关系，我是在台的华人，她的年龄层来源于日本，目前是台湾第十三。她的年龄层为四十岁，也是在台湾的，也\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我是设计师，我来自大林寺，在台北小巨蛋。因此在日本全国，.p.p.也从来没有提及过。除了..p.p.外，.p.q.p.也在日本，比皆是..而其中..4是在日本，比皆是..而其中.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-213-2ea5365e6084>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mcreate_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\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      3\u001b[0m     \u001b[0mprint\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<ipython-input-212-af20b87266de>\u001b[0m in \u001b[0;36mcreate_text\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mactions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0mt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ms2t\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'我是'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'，我'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m         \u001b[0;34m[\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\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      6\u001b[0m         \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'generated_text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\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      7\u001b[0m         \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mt2s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/transformers/pipelines.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, text_inputs, return_tensors, return_text, clean_up_tokenization_spaces, prefix, **generate_kwargs)\u001b[0m\n\u001b[1;32m    882\u001b[0m                 ), \"Batch generation is currently not supported. See https://github.com/huggingface/transformers/issues/3021 for more information.\"\n\u001b[1;32m    883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 884\u001b[0;31m                 \u001b[0moutput_sequences\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mgenerate_kwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# BS x SL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    885\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    886\u001b[0m             \u001b[0mresult\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",
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      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/transformers/generation_utils.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, input_ids, max_length, min_length, do_sample, early_stopping, num_beams, temperature, top_k, top_p, repetition_penalty, bad_words_ids, bos_token_id, pad_token_id, eos_token_id, length_penalty, no_repeat_ngram_size, num_return_sequences, decoder_start_token_id, use_cache, num_beam_groups, diversity_penalty, prefix_allowed_tokens_fn, **model_kwargs)\u001b[0m\n\u001b[1;32m    578\u001b[0m                 \u001b[0mpad_token_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpad_token_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m                 \u001b[0meos_token_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0meos_token_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m                 \u001b[0;34m**\u001b[0m\u001b[0mmodel_kwargs\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    581\u001b[0m             )\n\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/transformers/generation_utils.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, input_ids, logits_processor, logits_warper, max_length, pad_token_id, eos_token_id, **model_kwargs)\u001b[0m\n\u001b[1;32m    896\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m             \u001b[0;31m# forward pass to get next token\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 898\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_dict\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    899\u001b[0m             \u001b[0mnext_token_logits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\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    900\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    890\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    891\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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[0;32m~/opt/anaconda3/lib/python3.7/site-packages/transformers/models/gpt2/modeling_gpt2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    893\u001b[0m             \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m             \u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m             \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_dict\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    896\u001b[0m         )\n\u001b[1;32m    897\u001b[0m         \u001b[0mhidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    890\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    891\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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[0;32m~/opt/anaconda3/lib/python3.7/site-packages/transformers/models/gpt2/modeling_gpt2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    738\u001b[0m                     \u001b[0mencoder_attention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoder_attention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    739\u001b[0m                     \u001b[0muse_cache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_cache\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 740\u001b[0;31m                     \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_attentions\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    741\u001b[0m                 )\n\u001b[1;32m    742\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    890\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    891\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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[0;32m~/opt/anaconda3/lib/python3.7/site-packages/transformers/models/gpt2/modeling_gpt2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, layer_past, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions)\u001b[0m\n\u001b[1;32m    318\u001b[0m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutputs\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcross_attn_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# add cross attentions if we output attention weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m         \u001b[0mfeed_forward_hidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmlp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mln_2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\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[0m\u001b[1;32m    321\u001b[0m         \u001b[0;31m# residual connection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m         \u001b[0mhidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhidden_states\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfeed_forward_hidden_states\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    890\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    891\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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[0;32m~/opt/anaconda3/lib/python3.7/site-packages/transformers/models/gpt2/modeling_gpt2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\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--> 260\u001b[0;31m         \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_fc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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[0m\u001b[1;32m    261\u001b[0m         \u001b[0mh2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_proj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    890\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    891\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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[0;32m~/opt/anaconda3/lib/python3.7/site-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1201\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\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   1202\u001b[0m         \u001b[0msize_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\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[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnf\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-> 1203\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddmm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\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   1204\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0msize_out\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1205\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for n in ns:\n",
    "    create_text(n)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我是小狮子，我出生后，就进入公学。前三名是小狮子，其中一名是小狮子，一名是小狮子，其二名是小狮子，一名是小狮子，二名是小狮子。三名是小狮子，其三名是小狮子，其三名为小狮子，其二名是小狮子，其二名是小狮\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我是小狮子，我来自澳大利亚，我以自由基金会为基金经理人。（，），是日本乐器、作曲家、动画技术家。于1960年代开始组成乐团，曾进入日本东京国立音乐学校（今）。之后在电台担任乐\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我是小狮子，我毕业于武汉中央文化出版社，是中国的一处出版单位。该书分为三个部分，分别为中国漫画，杂志，中国出版总公司）和中国漫画出版社（现北京市博新区中华文化出版社）。另外，《中国漫画人物》《》中\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我是小狮子，我曾经是一名高人，被认为是一名最为著名的人。2017年1月18日，西班牙国家羽毛球队官方宣布在2018年10月21日对土耳其羽毛球公开赛中，王楠成了小狮子的双打项目的四号种子，并且获得了本赛季的最\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我是小狮子，我父亲是一个人。可是，我也没有一个母亲；有一位小狮子是一个男孩。是指人，其子女可能有不同的父亲或同母异家，与亲兄分家（除小狮子和其他同姓）之后，其子女可以从小狮子一侧向外寻亲，或成\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我是小狮子，我母亲是小狮子，他是天狼星，他可以是小狮子，母亲是小狮子，他是小狮子。是一个非常重要的视频，用于发送数据。这用于支持信息、广播、广播和广播电台。它包含一个叫的广播器，用于提供广播和广\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我是小狮子，我喜欢你你，在中国的女学生，他们只有四岁，当她小时候是个小朋友，并说她喜欢你，当我小时候是个小宝贝，有时小宝贝就像小一样，当然可以。我喜欢你，但是觉得我喜欢你。当然，我喜欢小一点，\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我是小狮子，我觉得是日常生活的，看这一切正当的事。就这本书这样，李明通便成了一个明治时代的一个有效。一个说明是说明了当时的人民的生活方式、政治方式、情况、资源问题、社会问题以及宗教问题。在台湾，有\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我是小狮子，我想要成为这个名字，就是「狮子的生命」，当这个暱称被发现时，他把一句名字「当成是「你的兄弟」。其后他在小狮子的母亲的帮助下开始他的第二次旅行，小狮子和小狮子分别住从波士顿。他的母亲也成了那\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我是小狮子，我的梦想是由在西门答腊国王阿里斯的领导下，西门答腊方面对东门答腊和雅典是双赢的。这个时候他在王子的领导下成为阿里斯国王。因为他认为在这种领导下，他的儿子就是雅典人，是雅典人。因为他们的儿\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我是小狮子，我认为是金子。在上代时，金子都以此称呼金子，而小狮子也以此称呼。根据《金子纪》记载，其大小狮子在小狮子以外则称作小狮子。从小狮子称呼的时候，牠们称呼比喻「金」，这种现象在所取得，不如说其\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我是小狮子，我自豪地宣布了将来她成为一名义的歌手。在小学三年级时，小狮子的原声带就是同性恋。小狮母亲是个非同性恋的人。在1999年5月，的发布了一个的照片，并在同学们的声中被广泛使用。随后\n",
      "我是小狮子，我将要再继续这样一辈子。这些都不能算是一种不小的，你必须要用对这些力量来维持不变的权力。一个特别的意义就是把这样的权力所在的所在的所在的所在的所在的所在的所在的所在的所在的所在的所在的所在\n"
     ]
    }
   ],
   "source": [
    "create_text('小狮子')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:102 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "作为30岁左右的男性，我很生气，因为一个是好友」的意思，同时也是他的理想理想。在这段时间里，我相信这个男性要有好朋友，他们要求我们和我所说的人一样，为了我们的理想，我为了我们的理想，我们必须要做好他们对\n"
     ]
    }
   ],
   "source": [
    "t=s2t.convert('作为30岁左右的男性，我很生气，因为')\n",
    "[text] = generator(t, max_length=100)\n",
    "s=''.join(text['generated_text'].split())\n",
    "s=t2s.convert(s)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 人脸分析\n",
    "https://github.com/serengil/deepface\n",
    "/Users/shadow/.deepface/weights/race_model_single_batch.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting deepface\n",
      "  Downloading deepface-0.0.68-py3-none-any.whl (61 kB)\n",
      "\u001b[K     |████████████████████████████████| 61 kB 1.3 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: tqdm>=4.30.0 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from deepface) (4.48.2)\n",
      "Requirement already satisfied: pandas>=0.23.4 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from deepface) (1.1.4)\n",
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      "Requirement already satisfied: numpy>=1.14.0 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from deepface) (1.18.5)\n",
      "Requirement already satisfied: tensorflow>=1.9.0 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from deepface) (2.3.1)\n",
      "Requirement already satisfied: Pillow>=5.2.0 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from deepface) (7.2.0)\n",
      "Requirement already satisfied: Flask>=1.1.2 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from deepface) (1.1.2)\n",
      "Requirement already satisfied: opencv-python>=3.4.4 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from deepface) (4.4.0.44)\n",
      "Requirement already satisfied: Jinja2>=2.10.1 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from Flask>=1.1.2->deepface) (2.11.1)\n",
      "Requirement already satisfied: itsdangerous>=0.24 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from Flask>=1.1.2->deepface) (1.1.0)\n",
      "Requirement already satisfied: click>=5.1 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from Flask>=1.1.2->deepface) (7.0)\n",
      "Requirement already satisfied: Werkzeug>=0.15 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from Flask>=1.1.2->deepface) (1.0.1)\n",
      "Collecting gdown>=3.10.1\n",
      "  Downloading gdown-3.13.0.tar.gz (9.3 kB)\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h    Preparing wheel metadata ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: requests[socks]>=2.12.0 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from gdown>=3.10.1->deepface) (2.24.0)\n",
      "Requirement already satisfied: six in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from gdown>=3.10.1->deepface) (1.15.0)\n",
      "Requirement already satisfied: filelock in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from gdown>=3.10.1->deepface) (3.0.12)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from Jinja2>=2.10.1->Flask>=1.1.2->deepface) (1.1.1)\n",
      "Requirement already satisfied: scipy>=0.14 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from keras>=2.2.0->deepface) (1.5.2)\n",
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      "Requirement already satisfied: h5py in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from keras>=2.2.0->deepface) (2.10.0)\n",
      "Collecting mtcnn>=0.1.0\n",
      "  Downloading mtcnn-0.1.1-py3-none-any.whl (2.3 MB)\n",
      "\u001b[K     |████████████████████████████████| 2.3 MB 204 kB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: pytz>=2017.2 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.23.4->deepface) (2020.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.23.4->deepface) (2.8.1)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from requests[socks]>=2.12.0->gdown>=3.10.1->deepface) (2.10)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from requests[socks]>=2.12.0->gdown>=3.10.1->deepface) (3.0.4)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from requests[socks]>=2.12.0->gdown>=3.10.1->deepface) (1.25.11)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from requests[socks]>=2.12.0->gdown>=3.10.1->deepface) (2020.12.5)\n",
      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from requests[socks]>=2.12.0->gdown>=3.10.1->deepface) (1.7.1)\n",
      "Collecting retina-face>=0.0.1\n",
      "  Downloading retina_face-0.0.5-py3-none-any.whl (14 kB)\n",
      "Requirement already satisfied: wrapt>=1.11.1 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from tensorflow>=1.9.0->deepface) (1.12.1)\n",
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      "Requirement already satisfied: keras-preprocessing<1.2,>=1.1.1 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from tensorflow>=1.9.0->deepface) (1.1.2)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from tensorflow>=1.9.0->deepface) (3.3.0)\n",
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      "Requirement already satisfied: setuptools>=41.0.0 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from tensorboard<3,>=2.3.0->tensorflow>=1.9.0->deepface) (51.0.0)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from tensorboard<3,>=2.3.0->tensorflow>=1.9.0->deepface) (1.7.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from tensorboard<3,>=2.3.0->tensorflow>=1.9.0->deepface) (3.3.3)\n",
      "Requirement already satisfied: google-auth<2,>=1.6.3 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from tensorboard<3,>=2.3.0->tensorflow>=1.9.0->deepface) (1.23.0)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow>=1.9.0->deepface) (4.2.0)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow>=1.9.0->deepface) (4.6)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow>=1.9.0->deepface) (0.2.8)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow>=1.9.0->deepface) (1.3.0)\n",
      "Requirement already satisfied: importlib-metadata in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow>=1.9.0->deepface) (1.7.0)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow>=1.9.0->deepface) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow>=1.9.0->deepface) (3.1.0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: zipp>=0.5 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from importlib-metadata->markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow>=1.9.0->deepface) (3.4.0)\n",
      "Building wheels for collected packages: gdown\n",
      "  Building wheel for gdown (PEP 517) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for gdown: filename=gdown-3.13.0-py3-none-any.whl size=9033 sha256=0f65c1c205ade9cbaba5ed3e409a0c3ba742e378a0d12f305d4c04baaf7aaeee\n",
      "  Stored in directory: /Users/shadow/Library/Caches/pip/wheels/2f/2a/2f/86449b6bdbaa9aef873f68332b68be6bfbc386b9219f47157d\n",
      "Successfully built gdown\n",
      "Installing collected packages: gdown, retina-face, mtcnn, deepface\n",
      "Successfully installed deepface-0.0.68 gdown-3.13.0 mtcnn-0.1.1 retina-face-0.0.5\n",
      "\u001b[33mWARNING: You are using pip version 20.3.3; however, version 21.2.4 is available.\n",
      "You should consider upgrading via the '/Users/shadow/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install deepface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory  /Users/shadow /.deepface created\n",
      "Directory  /Users/shadow /.deepface/weights created\n"
     ]
    }
   ],
   "source": [
    "from deepface import DeepFace\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = {'angry':'生氣', 'disgust':'厭惡', 'fear':'恐懼',\n",
    "          'happy':'開心', 'neutral':'沒什麼特別表情', \n",
    "          'sad':'悲傷', 'surprise':'吃驚',\n",
    "          'Man':'男', 'Woman':'女',\n",
    "          'asian':'亞洲', 'black':'黑', 'indian':'印弟安',\n",
    "          'latino hispanic':'拉丁美洲 (西班牙裔)', \n",
    "          'middle eastern':'中東', 'white':'白'}\n",
    "def show_info(obj):\n",
    "    age = obj['age']\n",
    "    emotion = labels[obj['dominant_emotion']]\n",
    "    race = labels[obj['dominant_race']]\n",
    "    gender = labels[obj['gender']]\n",
    "    text = f\"這是一位 {age} 歲的{race}人{gender}子, 他感覺是{emotion}的。\"\n",
    "    print(text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "def face_read(im):\n",
    "    obj = DeepFace.analyze(img_path = im, actions = ['age', 'gender', 'race', 'emotion'])\n",
    "    show_info(obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Finding actions:   0%|          | 0/4 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Action: age:   0%|          | 0/4 [00:00<?, ?it/s]    \u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Action: age:  25%|██▌       | 1/4 [00:03<00:09,  3.24s/it]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Action: gender:  25%|██▌       | 1/4 [00:03<00:09,  3.24s/it]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Action: gender:  50%|█████     | 2/4 [00:03<00:04,  2.42s/it]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Action: race:  50%|█████     | 2/4 [00:03<00:04,  2.42s/it]  \u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Action: race:  75%|███████▌  | 3/4 [00:04<00:01,  1.81s/it]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Action: emotion:  75%|███████▌  | 3/4 [00:04<00:01,  1.81s/it]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Action: emotion: 100%|██████████| 4/4 [00:04<00:00,  1.13s/it]\u001b[A\u001b[A\u001b[A\n"
     ]
    }
   ],
   "source": [
    "obj = DeepFace.analyze(img_path = 'fcfa508ce11a3d4475f9c3c52746.jpeg', actions = ['age', 'gender', 'race', 'emotion'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "這是一位 26 歲的亞洲人女子, 他感覺是沒什麼特別表情的。\n"
     ]
    }
   ],
   "source": [
    "show_info(obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Finding actions:   0%|          | 0/4 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Action: age:   0%|          | 0/4 [00:00<?, ?it/s]    \u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Action: age:  25%|██▌       | 1/4 [00:00<00:00,  3.98it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Action: gender:  25%|██▌       | 1/4 [00:00<00:00,  3.98it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Action: gender:  50%|█████     | 2/4 [00:00<00:00,  4.22it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Action: race:  50%|█████     | 2/4 [00:00<00:00,  4.22it/s]  \u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Action: race:  75%|███████▌  | 3/4 [00:00<00:00,  4.42it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Action: emotion:  75%|███████▌  | 3/4 [00:00<00:00,  4.42it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Action: emotion: 100%|██████████| 4/4 [00:00<00:00,  5.27it/s]\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "這是一位 28 歲的白人女子, 他感覺是沒什麼特別表情的。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "face_read('77777-s.jpeg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 翻译\n",
    "#### huggingface  \n",
    "#### Helsinki-NLP/opus-mt-zh-en \n",
    "#### Helsinki-NLP/opus-mt-zh-en"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shadow/opt/anaconda3/lib/python3.7/site-packages/transformers/models/auto/modeling_auto.py:890: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n",
      "  FutureWarning,\n",
      "Some weights of MarianMTModel were not initialized from the model checkpoint at Helsinki-NLP/opus-mt-zh-en and are newly initialized: ['lm_head.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline,AutoTokenizer, AutoModelForSeq2SeqLM,AutoModelWithLMHead\n",
    "\n",
    "model_zh_en = AutoModelWithLMHead.from_pretrained(\"Helsinki-NLP/opus-mt-zh-en\")\n",
    "tokenizer_zh_en = AutoTokenizer.from_pretrained(\"Helsinki-NLP/opus-mt-zh-en\")\n",
    "translation_zh_en = pipeline(\"translation_zh_to_en\", model=model_zh_en, tokenizer=tokenizer_zh_en)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your input_length: 59 is bigger than 0.9 * max_length: 40. You might consider increasing your max_length manually, e.g. translator('...', max_length=400)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It has been more than nine months since 2021, and the most important thing I have learned during this period is that all of us should not worry about the unfinished business and, above all\n"
     ]
    }
   ],
   "source": [
    "text = \"转眼2021年已经过去有九个多月的时间了，这段时间里，我学会的最重要的事情是，我们所有人都不要为未到来的事情烦恼，还有最重要的是无论如何好好吃饭，好好睡觉，愿我们过好每一天，这样我们才能在一起更多天。\"\n",
    "translated_text = translation_zh_en(text, max_length=40)[0]['translation_text']\n",
    "print(translated_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of MarianMTModel were not initialized from the model checkpoint at Helsinki-NLP/opus-mt-en-zh and are newly initialized: ['lm_head.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "model_en_zh = AutoModelWithLMHead.from_pretrained(\"Helsinki-NLP/opus-mt-en-zh\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"Helsinki-NLP/opus-mt-en-zh\")\n",
    "translation = pipeline(\"translation_en_to_zh\", model=model, tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your input_length: 39 is bigger than 0.9 * max_length: 40. You might consider increasing your max_length manually, e.g. translator('...', max_length=400)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021年以来已经九个多月了,在此期间,我学到的最重要的经验教训是,我们大家不应担心未完成的事业,最重要的是,\n"
     ]
    }
   ],
   "source": [
    "text = \"It has been more than nine months since 2021, and the most important thing I have learned during this period is that all of us should not worry about the unfinished business and, above all\"\n",
    "translated_text = translation(text, max_length=40)[0]['translation_text']\n",
    "print(translated_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 景深"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting timm\n",
      "  Downloading timm-0.4.12-py3-none-any.whl (376 kB)\n",
      "\u001b[K     |████████████████████████████████| 376 kB 1.7 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: torch>=1.4 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from timm) (1.8.1)\n",
      "Requirement already satisfied: torchvision in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from timm) (0.9.1)\n",
      "Requirement already satisfied: typing-extensions in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from torch>=1.4->timm) (3.7.4.3)\n",
      "Requirement already satisfied: numpy in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from torch>=1.4->timm) (1.18.5)\n",
      "Requirement already satisfied: pillow>=4.1.1 in /Users/shadow/opt/anaconda3/lib/python3.7/site-packages (from torchvision->timm) (7.2.0)\n",
      "Installing collected packages: timm\n",
      "Successfully installed timm-0.4.12\n",
      "\u001b[33mWARNING: You are using pip version 20.3.3; however, version 21.2.4 is available.\n",
      "You should consider upgrading via the '/Users/shadow/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install timm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /Users/shadow/.cache/torch/hub/intel-isl_MiDaS_master\n",
      "Downloading: \"https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt\" to /Users/shadow/.cache/torch/hub/checkpoints/dpt_hybrid-midas-501f0c75.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8746f3002ea4ea4a9f8926268c57cb4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=492757791.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# model_type = \"DPT_Large\"     # MiDaS v3 - Large     (highest accuracy, slowest inference speed)\n",
    "model_type = \"DPT_Hybrid\"   # MiDaS v3 - Hybrid    (medium accuracy, medium inference speed)\n",
    "#model_type = \"MiDaS_small\"  # MiDaS v2.1 - Small   (lowest accuracy, highest inference speed)\n",
    "\n",
    "midas = torch.hub.load(\"intel-isl/MiDaS\", model_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DPTDepthModel(\n",
       "  (pretrained): Module(\n",
       "    (model): VisionTransformer(\n",
       "      (patch_embed): HybridEmbed(\n",
       "        (backbone): ResNetV2(\n",
       "          (stem): Sequential(\n",
       "            (conv): StdConv2dSame(3, 64, kernel_size=(7, 7), stride=(2, 2), bias=False)\n",
       "            (norm): GroupNormAct(\n",
       "              32, 64, eps=1e-05, affine=True\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (pool): MaxPool2dSame(kernel_size=(3, 3), stride=(2, 2), padding=(0, 0), dilation=(1, 1), ceil_mode=False)\n",
       "          )\n",
       "          (stages): Sequential(\n",
       "            (0): ResNetStage(\n",
       "              (blocks): Sequential(\n",
       "                (0): Bottleneck(\n",
       "                  (downsample): DownsampleConv(\n",
       "                    (conv): StdConv2dSame(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                    (norm): GroupNormAct(\n",
       "                      32, 256, eps=1e-05, affine=True\n",
       "                      (act): Identity()\n",
       "                    )\n",
       "                  )\n",
       "                  (conv1): StdConv2dSame(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm1): GroupNormAct(\n",
       "                    32, 64, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv2): StdConv2dSame(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "                  (norm2): GroupNormAct(\n",
       "                    32, 64, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv3): StdConv2dSame(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm3): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): Identity()\n",
       "                  )\n",
       "                  (drop_path): Identity()\n",
       "                  (act3): ReLU(inplace=True)\n",
       "                )\n",
       "                (1): Bottleneck(\n",
       "                  (conv1): StdConv2dSame(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm1): GroupNormAct(\n",
       "                    32, 64, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv2): StdConv2dSame(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "                  (norm2): GroupNormAct(\n",
       "                    32, 64, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv3): StdConv2dSame(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm3): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): Identity()\n",
       "                  )\n",
       "                  (drop_path): Identity()\n",
       "                  (act3): ReLU(inplace=True)\n",
       "                )\n",
       "                (2): Bottleneck(\n",
       "                  (conv1): StdConv2dSame(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm1): GroupNormAct(\n",
       "                    32, 64, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv2): StdConv2dSame(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "                  (norm2): GroupNormAct(\n",
       "                    32, 64, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv3): StdConv2dSame(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm3): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): Identity()\n",
       "                  )\n",
       "                  (drop_path): Identity()\n",
       "                  (act3): ReLU(inplace=True)\n",
       "                )\n",
       "              )\n",
       "            )\n",
       "            (1): ResNetStage(\n",
       "              (blocks): Sequential(\n",
       "                (0): Bottleneck(\n",
       "                  (downsample): DownsampleConv(\n",
       "                    (conv): StdConv2dSame(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "                    (norm): GroupNormAct(\n",
       "                      32, 512, eps=1e-05, affine=True\n",
       "                      (act): Identity()\n",
       "                    )\n",
       "                  )\n",
       "                  (conv1): StdConv2dSame(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm1): GroupNormAct(\n",
       "                    32, 128, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv2): StdConv2dSame(128, 128, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
       "                  (norm2): GroupNormAct(\n",
       "                    32, 128, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv3): StdConv2dSame(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm3): GroupNormAct(\n",
       "                    32, 512, eps=1e-05, affine=True\n",
       "                    (act): Identity()\n",
       "                  )\n",
       "                  (drop_path): Identity()\n",
       "                  (act3): ReLU(inplace=True)\n",
       "                )\n",
       "                (1): Bottleneck(\n",
       "                  (conv1): StdConv2dSame(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm1): GroupNormAct(\n",
       "                    32, 128, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv2): StdConv2dSame(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "                  (norm2): GroupNormAct(\n",
       "                    32, 128, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv3): StdConv2dSame(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm3): GroupNormAct(\n",
       "                    32, 512, eps=1e-05, affine=True\n",
       "                    (act): Identity()\n",
       "                  )\n",
       "                  (drop_path): Identity()\n",
       "                  (act3): ReLU(inplace=True)\n",
       "                )\n",
       "                (2): Bottleneck(\n",
       "                  (conv1): StdConv2dSame(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm1): GroupNormAct(\n",
       "                    32, 128, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv2): StdConv2dSame(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "                  (norm2): GroupNormAct(\n",
       "                    32, 128, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv3): StdConv2dSame(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm3): GroupNormAct(\n",
       "                    32, 512, eps=1e-05, affine=True\n",
       "                    (act): Identity()\n",
       "                  )\n",
       "                  (drop_path): Identity()\n",
       "                  (act3): ReLU(inplace=True)\n",
       "                )\n",
       "                (3): Bottleneck(\n",
       "                  (conv1): StdConv2dSame(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm1): GroupNormAct(\n",
       "                    32, 128, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv2): StdConv2dSame(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "                  (norm2): GroupNormAct(\n",
       "                    32, 128, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv3): StdConv2dSame(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm3): GroupNormAct(\n",
       "                    32, 512, eps=1e-05, affine=True\n",
       "                    (act): Identity()\n",
       "                  )\n",
       "                  (drop_path): Identity()\n",
       "                  (act3): ReLU(inplace=True)\n",
       "                )\n",
       "              )\n",
       "            )\n",
       "            (2): ResNetStage(\n",
       "              (blocks): Sequential(\n",
       "                (0): Bottleneck(\n",
       "                  (downsample): DownsampleConv(\n",
       "                    (conv): StdConv2dSame(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "                    (norm): GroupNormAct(\n",
       "                      32, 1024, eps=1e-05, affine=True\n",
       "                      (act): Identity()\n",
       "                    )\n",
       "                  )\n",
       "                  (conv1): StdConv2dSame(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm1): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv2): StdConv2dSame(256, 256, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
       "                  (norm2): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv3): StdConv2dSame(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm3): GroupNormAct(\n",
       "                    32, 1024, eps=1e-05, affine=True\n",
       "                    (act): Identity()\n",
       "                  )\n",
       "                  (drop_path): Identity()\n",
       "                  (act3): ReLU(inplace=True)\n",
       "                )\n",
       "                (1): Bottleneck(\n",
       "                  (conv1): StdConv2dSame(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm1): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv2): StdConv2dSame(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "                  (norm2): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv3): StdConv2dSame(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm3): GroupNormAct(\n",
       "                    32, 1024, eps=1e-05, affine=True\n",
       "                    (act): Identity()\n",
       "                  )\n",
       "                  (drop_path): Identity()\n",
       "                  (act3): ReLU(inplace=True)\n",
       "                )\n",
       "                (2): Bottleneck(\n",
       "                  (conv1): StdConv2dSame(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm1): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv2): StdConv2dSame(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "                  (norm2): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv3): StdConv2dSame(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm3): GroupNormAct(\n",
       "                    32, 1024, eps=1e-05, affine=True\n",
       "                    (act): Identity()\n",
       "                  )\n",
       "                  (drop_path): Identity()\n",
       "                  (act3): ReLU(inplace=True)\n",
       "                )\n",
       "                (3): Bottleneck(\n",
       "                  (conv1): StdConv2dSame(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm1): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv2): StdConv2dSame(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "                  (norm2): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv3): StdConv2dSame(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm3): GroupNormAct(\n",
       "                    32, 1024, eps=1e-05, affine=True\n",
       "                    (act): Identity()\n",
       "                  )\n",
       "                  (drop_path): Identity()\n",
       "                  (act3): ReLU(inplace=True)\n",
       "                )\n",
       "                (4): Bottleneck(\n",
       "                  (conv1): StdConv2dSame(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm1): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv2): StdConv2dSame(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "                  (norm2): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv3): StdConv2dSame(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm3): GroupNormAct(\n",
       "                    32, 1024, eps=1e-05, affine=True\n",
       "                    (act): Identity()\n",
       "                  )\n",
       "                  (drop_path): Identity()\n",
       "                  (act3): ReLU(inplace=True)\n",
       "                )\n",
       "                (5): Bottleneck(\n",
       "                  (conv1): StdConv2dSame(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm1): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv2): StdConv2dSame(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "                  (norm2): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv3): StdConv2dSame(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm3): GroupNormAct(\n",
       "                    32, 1024, eps=1e-05, affine=True\n",
       "                    (act): Identity()\n",
       "                  )\n",
       "                  (drop_path): Identity()\n",
       "                  (act3): ReLU(inplace=True)\n",
       "                )\n",
       "                (6): Bottleneck(\n",
       "                  (conv1): StdConv2dSame(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm1): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv2): StdConv2dSame(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "                  (norm2): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv3): StdConv2dSame(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm3): GroupNormAct(\n",
       "                    32, 1024, eps=1e-05, affine=True\n",
       "                    (act): Identity()\n",
       "                  )\n",
       "                  (drop_path): Identity()\n",
       "                  (act3): ReLU(inplace=True)\n",
       "                )\n",
       "                (7): Bottleneck(\n",
       "                  (conv1): StdConv2dSame(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm1): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv2): StdConv2dSame(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "                  (norm2): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv3): StdConv2dSame(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm3): GroupNormAct(\n",
       "                    32, 1024, eps=1e-05, affine=True\n",
       "                    (act): Identity()\n",
       "                  )\n",
       "                  (drop_path): Identity()\n",
       "                  (act3): ReLU(inplace=True)\n",
       "                )\n",
       "                (8): Bottleneck(\n",
       "                  (conv1): StdConv2dSame(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm1): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv2): StdConv2dSame(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "                  (norm2): GroupNormAct(\n",
       "                    32, 256, eps=1e-05, affine=True\n",
       "                    (act): ReLU(inplace=True)\n",
       "                  )\n",
       "                  (conv3): StdConv2dSame(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "                  (norm3): GroupNormAct(\n",
       "                    32, 1024, eps=1e-05, affine=True\n",
       "                    (act): Identity()\n",
       "                  )\n",
       "                  (drop_path): Identity()\n",
       "                  (act3): ReLU(inplace=True)\n",
       "                )\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "          (norm): Identity()\n",
       "          (head): ClassifierHead(\n",
       "            (global_pool): SelectAdaptivePool2d (pool_type=, flatten=Identity())\n",
       "            (fc): Identity()\n",
       "            (flatten): Identity()\n",
       "          )\n",
       "        )\n",
       "        (proj): Conv2d(1024, 768, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "      (pos_drop): Dropout(p=0.0, inplace=False)\n",
       "      (blocks): Sequential(\n",
       "        (0): Block(\n",
       "          (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (attn): Attention(\n",
       "            (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "            (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (drop_path): Identity()\n",
       "          (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (mlp): Mlp(\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (act): GELU()\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (1): Block(\n",
       "          (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (attn): Attention(\n",
       "            (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "            (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (drop_path): Identity()\n",
       "          (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (mlp): Mlp(\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (act): GELU()\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (2): Block(\n",
       "          (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (attn): Attention(\n",
       "            (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "            (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (drop_path): Identity()\n",
       "          (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (mlp): Mlp(\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (act): GELU()\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (3): Block(\n",
       "          (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (attn): Attention(\n",
       "            (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "            (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (drop_path): Identity()\n",
       "          (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (mlp): Mlp(\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (act): GELU()\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (4): Block(\n",
       "          (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (attn): Attention(\n",
       "            (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "            (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (drop_path): Identity()\n",
       "          (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (mlp): Mlp(\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (act): GELU()\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (5): Block(\n",
       "          (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (attn): Attention(\n",
       "            (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "            (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (drop_path): Identity()\n",
       "          (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (mlp): Mlp(\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (act): GELU()\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (6): Block(\n",
       "          (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (attn): Attention(\n",
       "            (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "            (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (drop_path): Identity()\n",
       "          (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (mlp): Mlp(\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (act): GELU()\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (7): Block(\n",
       "          (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (attn): Attention(\n",
       "            (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "            (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (drop_path): Identity()\n",
       "          (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (mlp): Mlp(\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (act): GELU()\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (8): Block(\n",
       "          (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (attn): Attention(\n",
       "            (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "            (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (drop_path): Identity()\n",
       "          (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (mlp): Mlp(\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (act): GELU()\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (9): Block(\n",
       "          (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (attn): Attention(\n",
       "            (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "            (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (drop_path): Identity()\n",
       "          (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (mlp): Mlp(\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (act): GELU()\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (10): Block(\n",
       "          (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (attn): Attention(\n",
       "            (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "            (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (drop_path): Identity()\n",
       "          (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (mlp): Mlp(\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (act): GELU()\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (11): Block(\n",
       "          (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (attn): Attention(\n",
       "            (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "            (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (drop_path): Identity()\n",
       "          (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "          (mlp): Mlp(\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (act): GELU()\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (drop): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (pre_logits): Identity()\n",
       "      (head): Linear(in_features=768, out_features=1000, bias=True)\n",
       "    )\n",
       "    (act_postprocess1): Sequential(\n",
       "      (0): Identity()\n",
       "      (1): Identity()\n",
       "      (2): Identity()\n",
       "    )\n",
       "    (act_postprocess2): Sequential(\n",
       "      (0): Identity()\n",
       "      (1): Identity()\n",
       "      (2): Identity()\n",
       "    )\n",
       "    (act_postprocess3): Sequential(\n",
       "      (0): ProjectReadout(\n",
       "        (project): Sequential(\n",
       "          (0): Linear(in_features=1536, out_features=768, bias=True)\n",
       "          (1): GELU()\n",
       "        )\n",
       "      )\n",
       "      (1): Transpose()\n",
       "      (2): Unflatten(dim=2, unflattened_size=torch.Size([24, 24]))\n",
       "      (3): Conv2d(768, 768, kernel_size=(1, 1), stride=(1, 1))\n",
       "    )\n",
       "    (act_postprocess4): Sequential(\n",
       "      (0): ProjectReadout(\n",
       "        (project): Sequential(\n",
       "          (0): Linear(in_features=1536, out_features=768, bias=True)\n",
       "          (1): GELU()\n",
       "        )\n",
       "      )\n",
       "      (1): Transpose()\n",
       "      (2): Unflatten(dim=2, unflattened_size=torch.Size([24, 24]))\n",
       "      (3): Conv2d(768, 768, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (4): Conv2d(768, 768, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "    )\n",
       "  )\n",
       "  (scratch): Module(\n",
       "    (layer1_rn): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (layer2_rn): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (layer3_rn): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (layer4_rn): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (refinenet1): FeatureFusionBlock_custom(\n",
       "      (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (resConfUnit1): ResidualConvUnit_custom(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (activation): ReLU()\n",
       "        (skip_add): FloatFunctional(\n",
       "          (activation_post_process): Identity()\n",
       "        )\n",
       "      )\n",
       "      (resConfUnit2): ResidualConvUnit_custom(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (activation): ReLU()\n",
       "        (skip_add): FloatFunctional(\n",
       "          (activation_post_process): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): Identity()\n",
       "      )\n",
       "    )\n",
       "    (refinenet2): FeatureFusionBlock_custom(\n",
       "      (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (resConfUnit1): ResidualConvUnit_custom(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (activation): ReLU()\n",
       "        (skip_add): FloatFunctional(\n",
       "          (activation_post_process): Identity()\n",
       "        )\n",
       "      )\n",
       "      (resConfUnit2): ResidualConvUnit_custom(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (activation): ReLU()\n",
       "        (skip_add): FloatFunctional(\n",
       "          (activation_post_process): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): Identity()\n",
       "      )\n",
       "    )\n",
       "    (refinenet3): FeatureFusionBlock_custom(\n",
       "      (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (resConfUnit1): ResidualConvUnit_custom(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (activation): ReLU()\n",
       "        (skip_add): FloatFunctional(\n",
       "          (activation_post_process): Identity()\n",
       "        )\n",
       "      )\n",
       "      (resConfUnit2): ResidualConvUnit_custom(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (activation): ReLU()\n",
       "        (skip_add): FloatFunctional(\n",
       "          (activation_post_process): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): Identity()\n",
       "      )\n",
       "    )\n",
       "    (refinenet4): FeatureFusionBlock_custom(\n",
       "      (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (resConfUnit1): ResidualConvUnit_custom(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (activation): ReLU()\n",
       "        (skip_add): FloatFunctional(\n",
       "          (activation_post_process): Identity()\n",
       "        )\n",
       "      )\n",
       "      (resConfUnit2): ResidualConvUnit_custom(\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (activation): ReLU()\n",
       "        (skip_add): FloatFunctional(\n",
       "          (activation_post_process): Identity()\n",
       "        )\n",
       "      )\n",
       "      (skip_add): FloatFunctional(\n",
       "        (activation_post_process): Identity()\n",
       "      )\n",
       "    )\n",
       "    (output_conv): Sequential(\n",
       "      (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): Interpolate()\n",
       "      (2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (3): ReLU(inplace=True)\n",
       "      (4): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (5): ReLU(inplace=True)\n",
       "      (6): Identity()\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "midas.to(device)\n",
    "midas.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /Users/shadow/.cache/torch/hub/intel-isl_MiDaS_master\n"
     ]
    }
   ],
   "source": [
    "midas_transforms = torch.hub.load(\"intel-isl/MiDaS\", \"transforms\")\n",
    "\n",
    "if model_type == \"DPT_Large\" or model_type == \"DPT_Hybrid\":\n",
    "    transform = midas_transforms.dpt_transform\n",
    "else:\n",
    "    transform = midas_transforms.small_transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('fcfa508ce11a3d4475f9c3c52746.jpeg')\n",
    "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "input_batch = transform(img).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    prediction = midas(input_batch)\n",
    "\n",
    "    prediction = torch.nn.functional.interpolate(\n",
    "        prediction.unsqueeze(1),\n",
    "        size=img.shape[:2],\n",
    "        mode=\"bicubic\",\n",
    "        align_corners=False,\n",
    "    ).squeeze()\n",
    "\n",
    "output = prediction.cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc4f3d9c8d0>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output=255*output/np.max(output)\n",
    "cv2.imwrite('fcfa508ce11a3d4475f9c3c52746-output.jpeg',img = cv2.cvtColor(output, cv2.COLOR_GRAY2RGB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 聊天机器人\n",
    "https://huggingface.co/liam168/chat-DialoGPT-small-zh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3dcded7e9e37458fb08e8afe6cc959ff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=510403817.0, style=ProgressStyle(descri…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "import torch\n",
    "\n",
    "mode_name = 'liam168/chat-DialoGPT-small-zh'\n",
    "tokenizer = AutoTokenizer.from_pretrained(mode_name)\n",
    "model = AutoModelForCausalLM.from_pretrained(mode_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Answer: 厉害了我的主编\n",
      "Answer: 哈哈我尽量克制一点\n",
      "Answer: 哈哈我尽量克制一点\n",
      "Answer: 挺好看的\n",
      "Answer: !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Let's chat for 5 lines\n",
    "for step in range(5):\n",
    "    # encode the new user input, add the eos_token and return a tensor in Pytorch\n",
    "    new_user_input_ids = tokenizer.encode('你好呀' + tokenizer.eos_token, return_tensors='pt')\n",
    "\n",
    "    # append the new user input tokens to the chat history\n",
    "    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids\n",
    "\n",
    "    # generated a response while limiting the total chat history to 1000 tokens, \n",
    "    chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)\n",
    "\n",
    "    # pretty print last ouput tokens from bot\n",
    "    print(\"Answer: {}\".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 问答\n",
    "https://huggingface.co/yechen/question-answering-chinese"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertModel were not initialized from the model checkpoint at uer/roberta-base-chinese-extractive-qa and are newly initialized: ['bert.pooler.dense.weight', 'bert.pooler.dense.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "#question_answerer = pipeline(\"question-answering\", model=\"yechen/question-answering-chinese\")\n",
    "question_answerer = pipeline(\"question-answering\", model=\"uer/roberta-base-chinese-extractive-qa\")\n",
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract(context):\n",
    "    q=\"公司成立于什么时间\"\n",
    "    create_time = question_answerer(question=q, context=context)\n",
    "    create_time['question']=q\n",
    "    print(f\"Answer: '{create_time['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}\")\n",
    "    \n",
    "    q=\"公司名称是什么\"\n",
    "    name = question_answerer(question=q, context=context)\n",
    "    name['question']=q\n",
    "    print(f\"Answer: '{name['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}\")\n",
    "\n",
    "    q=\"核心技术是什么\"\n",
    "    tech = question_answerer(question=q, context=context)\n",
    "    tech['question']=q\n",
    "    print(f\"Answer: '{tech['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}\")\n",
    "\n",
    "    q=\"融资阶段是什么，是第几轮融资\"\n",
    "    step = question_answerer(question=q, context=context)\n",
    "    step['question']=q\n",
    "    print(f\"Answer: '{step['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}\")\n",
    "\n",
    "    q=\"融资金额是多少\"\n",
    "    mount = question_answerer(question=q, context=context)\n",
    "    mount['question']=q\n",
    "    print(f\"Answer: '{mount['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}\")\n",
    "\n",
    "    q=\"应用场景是什么，应用在什么领域\"\n",
    "    scene = question_answerer(question=q, context=context)\n",
    "    scene['question']=q\n",
    "    print(f\"Answer: '{scene['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}\")\n",
    "\n",
    "    q=\"用户是哪些人，服务于什么用户\"\n",
    "    user = question_answerer(question=q, context=context)\n",
    "    user['question']=q\n",
    "    print(f\"Answer: '{user['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}\")\n",
    "\n",
    "    q=\"投资方包括哪些\"\n",
    "    fund = question_answerer(question=q, context=context)\n",
    "    fund['question']=q\n",
    "    print(f\"Answer: '{fund['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}\")\n",
    "    return {\n",
    "        \"create_time\":create_time,\n",
    "        \"name\":name,\n",
    "        \"tech\":tech,\n",
    "        \"step\":step,\n",
    "        \"mount\":mount,\n",
    "        \"scene\":scene,\n",
    "        \"user\":user,\n",
    "        \"fund\":fund\n",
    "    }\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "context = '''网易首页应用\n",
    "快速导航\n",
    "登录\n",
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    "ACE虚拟歌姬完成数百万美元融资，虚拟偶像发展规模及趋势解读\n",
    "2021-09-09 11:28:16　来源: 艾媒网举报\n",
    "0\n",
    "分享至\n",
    "据艾媒网(www.iimedia.cn)获悉，ACE虚拟歌姬于近日完成数百万美元Pre-A轮融资，本轮融资由知春资本领投，老股东五源资本继续跟投。\n",
    "\n",
    "相关资料显示，ACE虚拟歌姬为时域科技旗下产品，是一款结合了AI歌声合成技术的音乐创作App。ACE虚拟歌姬为用户提供多个AI虚拟歌手，用户可以在ACE的创作工具中，输入歌曲的旋律、歌词，并且选择AI虚拟歌手进行歌曲演唱，“填词玩法”可以让用户在其他用户创作的歌曲旋律上进行二次创作。目前，ACE虚拟歌姬达到了上百万的累计用户。\n",
    "\n",
    "虚拟歌手，是指在电脑上用歌声合成软件制作，并赋予虚拟二次元形象的歌手，属于虚拟偶像中的一大类。近年来，中国虚拟偶像产业处于持续高速增长的态势中，而近段时间众多明星/代言人接连爆出丑闻，各大品牌商的目光开始瞄向虚拟偶像，可见虚拟偶像未来发展前景较为广阔。\n",
    "\n",
    "2017-2023年中国虚拟偶像市场规模及预测分析\n",
    "\n",
    "iiMedia Research(艾媒咨询)的数据显示，2020年中国虚拟偶像核心市场规模为34.6亿元，预计2021年将达到62.2亿元；2020年虚拟偶像带动周边市场规模为645.6亿元，预计2021年为1074.9亿元。艾媒咨询分析师认为，虚拟偶像在内容和周边产品产出方面有着较强的可塑性，能根据时代潮流发展不断开发新的爆点，因此行业增长的延续性较强，未来一段时间内都将保持稳定增长态势。\n",
    "\n",
    "\n",
    "\n",
    "虚拟偶像产业商业应用案例分析\n",
    "\n",
    "随着年轻用户逐步开始主导消费市场，虚拟偶像作为深受年轻用户喜爱的元素，已经成为品牌营销发力的风口。各大品牌纷纷着手打造虚拟偶像或是与之进行合作，以期能够与用户进行深度交流，建立好感。\n",
    "\n",
    "\n",
    "\n",
    "2021中国虚拟偶像爱好者画像\n",
    "\n",
    "\n",
    "\n",
    "2021中国虚拟偶像爱好者调查：喜好角色\n",
    "\n",
    "iiMedia Research(艾媒咨询)数据显示，有超过六成的虚拟偶像爱好者喜欢游戏领域的虚拟偶像，其次是影视领域和综艺领域；在喜好角色的男女比例调查中，有61.8%的男性更喜欢男性虚拟偶像角色，而女性也同样更喜欢女性角色。\n",
    "\n",
    "\n",
    "\n",
    "中国虚拟偶像产业发展趋势与挑战(一)\n",
    "\n",
    "1、虚拟偶像生产及运营成本高，入局门槛较高\n",
    "\n",
    "现阶段，国内单支虚拟偶像单曲的制作，包括编曲、建模、形象设计、舞台方案定制等，成本便高达200万元，而流量传播等方面的花费也相当庞大。艾媒咨询分析师认为，虚拟偶像在养成阶段所需要投入的成本要比真人偶像更多，一旦在传播过程中收不到好的效果，很有可能导致投入资金后生产内容无法变现的情况出现，入局门槛较高。\n",
    "\n",
    "2、头部IP蓬勃发展，但腰部和尾部IP破圈难度较大\n",
    "\n",
    "由于在目前的市场上，粉丝经济依然是虚拟偶像最重要的变现手段，头部IP主要依靠演出、授权、周边等来获取收益，而腰部和尾部IP由于缺少稳定的粉丝群体，在传播属性上相对较弱，因此无法迅速获得庞大的流量积累，商业化的过程较为艰难。目前，腰部和尾部虚拟偶像IP主要活跃于直播电商、实景应用等场景，由于版权价格较低，品牌更愿意用这些IP替代真人工作，降低劳动力的成本，但也造成了这些IP进一步破圈的难度加大。\n",
    "\n",
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    "Notice: The content above (including the pictures and videos if any) is uploaded and posted by a user of NetEase Hao, which is a social media platform and only provides information storage services.\n",
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    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Answer: '网易', score: 0.0599, start: 79, end: 123\n",
      "Answer: '网易公司', score: 0.0599, start: 79, end: 123\n",
      "Answer: '歌声合成技术', score: 0.0599, start: 79, end: 123\n",
      "Answer: 'A轮融资', score: 0.0599, start: 79, end: 123\n",
      "Answer: '数百万美元', score: 0.0599, start: 79, end: 123\n",
      "Answer: '虚拟偶像', score: 0.0599, start: 79, end: 123\n",
      "Answer: '虚拟偶像', score: 0.0599, start: 79, end: 123\n",
      "Answer: '知春资本领投', score: 0.0599, start: 79, end: 123\n"
     ]
    }
   ],
   "source": [
    "res=extract(context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'score': 0.007233948912471533, 'start': 854, 'end': 860, 'answer': '虚拟人”风潮'}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question_answerer(question='本文核心的观点是', context=context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'score': 0.1296655833721161, 'start': 854, 'end': 861, 'answer': '虚拟人”风潮。'}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question_answerer(question='发生了什么事情？', context=context)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## python GUI\n",
    "https://github.com/beeware/toga"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 情绪分析\n",
    "https://huggingface.co/finiteautomata/beto-sentiment-analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "classifier = pipeline(\"sentiment-analysis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: NEGATIVE, with score: 0.9991\n",
      "label: POSITIVE, with score: 0.9999\n"
     ]
    }
   ],
   "source": [
    "result = classifier(\"I hate you\")[0]\n",
    "print(f\"label: {result['label']}, with score: {round(result['score'], 4)}\")\n",
    "\n",
    "result = classifier(\"I love you\")[0]\n",
    "print(f\"label: {result['label']}, with score: {round(result['score'], 4)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'translation_zh_en' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-2a585a133bb2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtranslated_text\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtranslation_zh_en\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'I hate you'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'translation_text'\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      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtranslated_text\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtranslated_text\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"label: {result['label']}, with score: {round(result['score'], 4)}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'translation_zh_en' is not defined"
     ]
    }
   ],
   "source": [
    "translated_text = translation_zh_en('I hate you')[0]['translation_text']\n",
    "print(translated_text)\n",
    "result = classifier(translated_text)[0]\n",
    "print(f\"label: {result['label']}, with score: {round(result['score'], 4)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征提取\n",
    "句子\n",
    "\n",
    ">>> # Download vocabulary from huggingface.co and cache.\n",
    ">>> tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\n",
    "\n",
    ">>> # Download vocabulary from huggingface.co (user-uploaded) and cache.\n",
    ">>> tokenizer = AutoTokenizer.from_pretrained('dbmdz/bert-base-german-cased')\n",
    "\n",
    ">>> # If vocabulary files are in a directory (e.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`)\n",
    ">>> tokenizer = AutoTokenizer.from_pretrained('./test/bert_saved_model/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "# your approach\n",
    "feature_extraction = pipeline('feature-extraction', model=\"ckiplab/gpt2-base-chinese\", tokenizer=\"ckiplab/gpt2-base-chinese\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "features = feature_extraction(\"你好呀\")\n",
    "# Note that the output has a weird list output that requires to index with 0.\n",
    "print(len(features[0])) # 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  -1.1755646467208862]]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#测试\n",
    "texts=[\n",
    "    '河北通勤班车坠河已致2死12失联499万新',\n",
    "    '山西为何遭遇罕见秋汛490万热',\n",
    "    '印方不合理要求为谈判增加困难479万热',\n",
    "    '绑架案牵出腐败窝案:76人被处分461万',\n",
    "    '山西284.96万亩农作物受灾458万新',\n",
    "    '台风“圆规”向我国靠近445万',\n",
    "    '山西洪灾建救命文档女孩发声434万新',\n",
    "    '新氧就侵权明星照片向当事人道歉428万',\n",
    "    '郑州一网红主播被追征662万税款411万'\n",
    "]\n",
    "features=[]\n",
    "for t in texts:\n",
    "    feature= feature_extraction(t)\n",
    "    features.append(feature[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   -0.9793893694877625]]]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_extraction('山西')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  0.06400231271982193,\n",
       "  0.37878087162971497,\n",
       "  0.32276806235313416,\n",
       "  0.19332227110862732,\n",
       "  0.1892203539609909,\n",
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       "  0.17637915909290314,\n",
       "  -0.16774523258209229,\n",
       "  0.6036462783813477,\n",
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       "  -0.1551750749349594,\n",
       "  -0.26098668575286865,\n",
       "  0.15091700851917267,\n",
       "  -0.011726021766662598,\n",
       "  -0.471219539642334,\n",
       "  -0.4217424988746643,\n",
       "  0.3625001907348633,\n",
       "  -0.23001965880393982,\n",
       "  0.48244744539260864,\n",
       "  0.05537424981594086,\n",
       "  -0.09122201800346375,\n",
       "  0.021634571254253387,\n",
       "  0.11313305795192719,\n",
       "  -0.1983320116996765,\n",
       "  -0.6609383225440979,\n",
       "  0.11381891369819641,\n",
       "  -0.40206873416900635]]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 向量检索\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 命名实体识别\n",
    "https://universaldependencies.org/u/pos/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9490cf8428d463087344b036a9a13b9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=406933136.0, style=ProgressStyle(descri…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertModel were not initialized from the model checkpoint at KoichiYasuoka/chinese-bert-wwm-ext-upos and are newly initialized: ['bert.pooler.dense.weight', 'bert.pooler.dense.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1202c3fe70994be583385041e81e5eef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=109540.0, style=ProgressStyle(descripti…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "46723447a28d43b782c3f2a6f7e42f5c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=112.0, style=ProgressStyle(description_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d4c7baef8034ae8b089011a5d92b791",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=227.0, style=ProgressStyle(description_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "ner=pipeline('ner',model=\"KoichiYasuoka/chinese-bert-wwm-ext-upos\",grouped_entities=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'entity_group': 'PRON',\n",
       "  'score': 0.9824743270874023,\n",
       "  'word': '你',\n",
       "  'start': 0,\n",
       "  'end': 1},\n",
       " {'entity_group': 'ADJ',\n",
       "  'score': 0.9068554639816284,\n",
       "  'word': '好',\n",
       "  'start': 1,\n",
       "  'end': 2},\n",
       " {'entity_group': 'PART',\n",
       "  'score': 0.781984269618988,\n",
       "  'word': '呀',\n",
       "  'start': 2,\n",
       "  'end': 3},\n",
       " {'entity_group': 'NOUN',\n",
       "  'score': 0.9990893304347992,\n",
       "  'word': '同 学',\n",
       "  'start': 4,\n",
       "  'end': 6}]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ner('你好呀 同学')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7fbb205be4ed44b6bc77a58c940e1151",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1350.0, style=ProgressStyle(description…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3366932901b74640b0fc6a5ccd5af279",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=109540.0, style=ProgressStyle(descripti…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45412de9e6bc4cd4b07ff1b26683cbc4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=2.0, style=ProgressStyle(description_wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "90192ea985f04ed2854ece1df5076155",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=112.0, style=ProgressStyle(description_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f640f13a4c66448a859c724bb31a121c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=417930559.0, style=ProgressStyle(descri…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(\"Connection broken: ConnectionResetError(54, 'Connection reset by peer')\", ConnectionResetError(54, 'Connection reset by peer'))\n"
     ]
    },
    {
     "ename": "OSError",
     "evalue": "Can't load weights for 'Langboat/mengzi-oscar-base-caption'. Make sure that:\n\n- 'Langboat/mengzi-oscar-base-caption' is a correct model identifier listed on 'https://huggingface.co/models'\n\n- or 'Langboat/mengzi-oscar-base-caption' is the correct path to a directory containing a file named one of pytorch_model.bin, tf_model.h5, model.ckpt.\n\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mConnectionResetError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/urllib3/response.py\u001b[0m in \u001b[0;36m_error_catcher\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    424\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 425\u001b[0;31m                 \u001b[0;32myield\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/urllib3/response.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, amt, decode_content, cache_content)\u001b[0m\n\u001b[1;32m    506\u001b[0m                 \u001b[0mcache_content\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfp_closed\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34mb\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m                 if (\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m    456\u001b[0m             \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytearray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\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    458\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mmemoryview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtobytes\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~/opt/anaconda3/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    500\u001b[0m         \u001b[0;31m# (for example, reading in 1k chunks)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 501\u001b[0;31m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\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    502\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    588\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 589\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\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    590\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1070\u001b[0m                   self.__class__)\n\u001b[0;32m-> 1071\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\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   1072\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m    928\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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[0;32m--> 929\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\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    930\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mConnectionResetError\u001b[0m: [Errno 54] Connection reset by peer",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mProtocolError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/requests/models.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m()\u001b[0m\n\u001b[1;32m    750\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 751\u001b[0;31m                     \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_content\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    752\u001b[0m                         \u001b[0;32myield\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/urllib3/response.py\u001b[0m in \u001b[0;36mstream\u001b[0;34m(self, amt, decode_content)\u001b[0m\n\u001b[1;32m    563\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_fp_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fp\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--> 564\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecode_content\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    565\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/urllib3/response.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, amt, decode_content, cache_content)\u001b[0m\n\u001b[1;32m    528\u001b[0m                         \u001b[0;31m# Content-Length are caught.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 529\u001b[0;31m                         \u001b[0;32mraise\u001b[0m \u001b[0mIncompleteRead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fp_bytes_read\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlength_remaining\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    530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m    129\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\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    131\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/urllib3/response.py\u001b[0m in \u001b[0;36m_error_catcher\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    442\u001b[0m                 \u001b[0;31m# This includes IncompleteRead.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 443\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mProtocolError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Connection broken: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\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    444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mProtocolError\u001b[0m: (\"Connection broken: ConnectionResetError(54, 'Connection reset by peer')\", ConnectionResetError(54, 'Connection reset by peer'))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mChunkedEncodingError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   1009\u001b[0m                     \u001b[0mlocal_files_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_files_only\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1010\u001b[0;31m                     \u001b[0muse_auth_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_auth_token\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   1011\u001b[0m                 )\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/transformers/file_utils.py\u001b[0m in \u001b[0;36mcached_path\u001b[0;34m(url_or_filename, cache_dir, force_download, proxies, resume_download, user_agent, extract_compressed_file, force_extract, use_auth_token, local_files_only)\u001b[0m\n\u001b[1;32m   1084\u001b[0m             \u001b[0muse_auth_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_auth_token\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1085\u001b[0;31m             \u001b[0mlocal_files_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_files_only\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   1086\u001b[0m         )\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/transformers/file_utils.py\u001b[0m in \u001b[0;36mget_from_cache\u001b[0;34m(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, use_auth_token, local_files_only)\u001b[0m\n\u001b[1;32m   1302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1303\u001b[0;31m             \u001b[0mhttp_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl_to_download\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresume_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\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   1304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/transformers/file_utils.py\u001b[0m in \u001b[0;36mhttp_get\u001b[0;34m(url, temp_file, proxies, resume_size, headers)\u001b[0m\n\u001b[1;32m   1165\u001b[0m     )\n\u001b[0;32m-> 1166\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_content\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1024\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[0m\u001b[1;32m   1167\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# filter out keep-alive new chunks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/requests/models.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m()\u001b[0m\n\u001b[1;32m    753\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mProtocolError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 754\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mChunkedEncodingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\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    755\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mDecodeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mChunkedEncodingError\u001b[0m: (\"Connection broken: ConnectionResetError(54, 'Connection reset by peer')\", ConnectionResetError(54, 'Connection reset by peer'))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-2037c0ab90d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtokenizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAutoTokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Langboat/mengzi-oscar-base-caption\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAutoModelForMaskedLM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Langboat/mengzi-oscar-base-caption\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/transformers/models/auto/modeling_auto.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   1086\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mMODEL_FOR_MASKED_LM_MAPPING\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\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   1087\u001b[0m             return MODEL_FOR_MASKED_LM_MAPPING[type(config)].from_pretrained(\n\u001b[0;32m-> 1088\u001b[0;31m                 \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmodel_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1089\u001b[0m             )\n\u001b[1;32m   1090\u001b[0m         raise ValueError(\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   1017\u001b[0m                     \u001b[0;34mf\"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a file named one of {WEIGHTS_NAME}, {TF2_WEIGHTS_NAME}, {TF_WEIGHTS_NAME}.\\n\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1018\u001b[0m                 )\n\u001b[0;32m-> 1019\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mEnvironmentError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\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   1020\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresolved_archive_file\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0marchive_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: Can't load weights for 'Langboat/mengzi-oscar-base-caption'. Make sure that:\n\n- 'Langboat/mengzi-oscar-base-caption' is a correct model identifier listed on 'https://huggingface.co/models'\n\n- or 'Langboat/mengzi-oscar-base-caption' is the correct path to a directory containing a file named one of pytorch_model.bin, tf_model.h5, model.ckpt.\n\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
    "  \n",
    "tokenizer = AutoTokenizer.from_pretrained(\"Langboat/mengzi-oscar-base-caption\")\n",
    "\n",
    "model = AutoModelForMaskedLM.from_pretrained(\"Langboat/mengzi-oscar-base-caption\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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