{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c69a901b-8c3f-4188-97bd-5594c4496ec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from huggingface_hub import HfApi\n",
    "api = HfApi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b249adc-ccd0-4145-86ce-64509ad276cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "faf7b41b81e54705bae8921f2a86e9fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_hub import login\n",
    "login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41815b66-b12b-45da-9a5e-92c527fe2b4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"POST /api/spaces/kevinwang676/ChatGLM2-SadTalker/preupload/main HTTP/1.1\" 200 19320\n",
      "DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"POST /api/spaces/kevinwang676/ChatGLM2-SadTalker/preupload/main HTTP/1.1\" 200 5340\n",
      "DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"POST /spaces/kevinwang676/ChatGLM2-SadTalker.git/info/lfs/objects/batch HTTP/1.1\" 200 166580\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1940ef6fb945ea840699eb03089c2c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "mapping_00109-model.pth.tar:   0%|          | 0.00/156M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c49564abb3eb4d339511032af673dfbf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload 33 LFS files:   0%|          | 0/33 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "21c257b08fbc48908977d7f1929500a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "SadTalker_V0.0.2_512.safetensors:   0%|          | 0.00/725M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5fc65c358ba24152b3d3fb3c1c30ec1d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "SadTalker_V0.0.2_256.safetensors:   0%|          | 0.00/725M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c7a7473c7fa4528accd6c2ae6da0b72",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "freevc-24.pth:   0%|          | 0.00/473M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c2bab9515c3d42baa05dd6b401af8b96",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "mapping_00229-model.pth.tar:   0%|          | 0.00/156M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): s3.us-east-1.amazonaws.com:443\n",
      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): s3.us-east-1.amazonaws.com:443\n",
      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): s3.us-east-1.amazonaws.com:443\n",
      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): s3.us-east-1.amazonaws.com:443\n",
      "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): s3.us-east-1.amazonaws.com:443\n",
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     "metadata": {},
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     "name": "stdout",
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     ]
    }
   ],
   "source": [
    "api.upload_folder(\n",
    "    folder_path=\"/workspace/SadTalker\",\n",
    "    repo_id=\"kevinwang676/ChatGLM2-SadTalker\",\n",
    "    repo_type=\"space\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c15c0fad-c4ac-4fd5-b886-89d2f97bc285",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7862\n",
      "Running on public URL: https://a27628aaedb3347604.gradio.live\n",
      "\n",
      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"https://a27628aaedb3347604.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using safetensor as default\n",
      "{'checkpoint': 'checkpoints/SadTalker_V0.0.2_256.safetensors', 'dir_of_BFM_fitting': 'src/config', 'audio2pose_yaml_path': 'src/config/auido2pose.yaml', 'audio2exp_yaml_path': 'src/config/auido2exp.yaml', 'use_safetensor': True, 'mappingnet_checkpoint': 'checkpoints/mapping_00229-model.pth.tar', 'facerender_yaml': 'src/config/facerender.yaml'}\n",
      "/tmp/gradio/ee3af0f3e1518c57a03610bc7c5b9f774dd29f5c/image.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "landmark Det:: 100%|██████████| 1/1 [00:00<00:00, 26.62it/s]\n",
      "3DMM Extraction In Video:: 100%|██████████| 1/1 [00:00<00:00, 119.22it/s]\n",
      "mel:: 100%|██████████| 92/92 [00:00<00:00, 45445.29it/s]\n",
      "audio2exp:: 100%|██████████| 10/10 [00:00<00:00, 102.60it/s]\n",
      "Face Renderer:: 100%|██████████| 46/46 [00:05<00:00,  8.18it/s]\n",
      "Numba: Attempted to fork from a non-main thread, the TBB library may be in an invalid state in the child process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The generated video is named ./results/c6e207ec-5e2b-4164-8cdd-8d13be7a51f3/image##nana_speech_cut_4sec 1-0-100.mp4\n",
      "face enhancer....\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Face Enhancer::   0%|          | 0/92 [00:00<?, ?it/s]Numba: Attempted to fork from a non-main thread, the TBB library may be in an invalid state in the child process.\n",
      "Face Enhancer:: 100%|██████████| 92/92 [00:17<00:00,  5.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The generated video is named ./results/c6e207ec-5e2b-4164-8cdd-8d13be7a51f3/image##nana_speech_cut_4sec 1-0-100_enhanced.mp4\n",
      "The generated video is named image##nana_speech_cut_4sec 1-0-100 in ./results/c6e207ec-5e2b-4164-8cdd-8d13be7a51f3\n"
     ]
    }
   ],
   "source": [
    "\n",
    "    with gr.Row().style(equal_height=False):\n",
    "        with gr.Column(variant='panel'):\n",
    "            with gr.Tabs(elem_id=\"sadtalker_source_image\"):\n",
    "                with gr.TabItem('Upload image'):\n",
    "                    with gr.Row():\n",
    "                        source_image = gr.Image(label=\"Source image\", source=\"upload\", type=\"filepath\", elem_id=\"img2img_image\").style(width=512)\n",
    "\n",
    "            with gr.Tabs(elem_id=\"sadtalker_driven_audio\"):\n",
    "                with gr.TabItem('Upload OR TTS'):\n",
    "                    with gr.Column(variant='panel'):\n",
    "                        driven_audio = gr.Audio(label=\"Input audio\", source=\"upload\", type=\"filepath\")\n",
    "\n",
    "        with gr.Column(variant='panel'): \n",
    "            with gr.Tabs(elem_id=\"sadtalker_checkbox\"):\n",
    "                with gr.TabItem('Settings'):\n",
    "                    gr.Markdown(\"need help? please visit our [best practice page](https://github.com/OpenTalker/SadTalker/blob/main/docs/best_practice.md) for more detials\")\n",
    "                    with gr.Column(variant='panel'):\n",
    "                        # width = gr.Slider(minimum=64, elem_id=\"img2img_width\", maximum=2048, step=8, label=\"Manually Crop Width\", value=512) # img2img_width\n",
    "                        # height = gr.Slider(minimum=64, elem_id=\"img2img_height\", maximum=2048, step=8, label=\"Manually Crop Height\", value=512) # img2img_width\n",
    "                        pose_style = gr.Slider(minimum=0, maximum=46, step=1, label=\"Pose style\", value=0) # \n",
    "                        size_of_image = gr.Radio([256, 512], value=256, label='face model resolution', info=\"use 256/512 model?\") # \n",
    "                        preprocess_type = gr.Radio(['crop', 'resize','full', 'extcrop', 'extfull'], value='crop', label='preprocess', info=\"How to handle input image?\")\n",
    "                        is_still_mode = gr.Checkbox(label=\"Still Mode (fewer hand motion, works with preprocess `full`)\")\n",
    "                        batch_size = gr.Slider(label=\"batch size in generation\", step=1, maximum=10, value=2)\n",
    "                        enhancer = gr.Checkbox(label=\"GFPGAN as Face enhancer\")\n",
    "                        submit = gr.Button('Generate', elem_id=\"sadtalker_generate\", variant='primary')\n",
    "                            \n",
    "            with gr.Tabs(elem_id=\"sadtalker_genearted\"):\n",
    "                    gen_video = gr.Video(label=\"Generated video\", format=\"mp4\").style(width=256)\n",
    "\n",
    "    submit.click(\n",
    "                fn=sad_talker.test, \n",
    "                inputs=[source_image,\n",
    "                        driven_audio,\n",
    "                        preprocess_type,\n",
    "                        is_still_mode,\n",
    "                        enhancer,\n",
    "                        batch_size,                            \n",
    "                        size_of_image,\n",
    "                        pose_style\n",
    "                        ], \n",
    "                outputs=[gen_video]\n",
    "                )\n",
    "\n",
    "\n",
    "\n",
    "demo.queue().launch(share=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4d898fab-53de-4acd-b65f-f31faf43f184",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded the voice encoder model on cuda in 0.03 seconds.\n",
      "INFO:root:Loaded checkpoint 'checkpoint/freevc-24.pth' (iteration 3461)\n",
      "DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"HEAD /microsoft/wavlm-large/resolve/main/config.json HTTP/1.1\" 200 0\n",
      "DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"HEAD /THUDM/chatglm2-6b/resolve/main/tokenizer_config.json HTTP/1.1\" 200 0\n",
      "DEBUG:filelock:Attempting to acquire lock 140053383728096 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/79b212c00ecdde03c51bb791f7fdfc25f4176ff3.lock\n",
      "DEBUG:filelock:Lock 140053383728096 acquired on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/79b212c00ecdde03c51bb791f7fdfc25f4176ff3.lock\n",
      "DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"GET /THUDM/chatglm2-6b/resolve/main/tokenizer_config.json HTTP/1.1\" 200 244\n"
     ]
    },
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/244 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG:filelock:Attempting to release lock 140053383728096 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/79b212c00ecdde03c51bb791f7fdfc25f4176ff3.lock\n",
      "DEBUG:filelock:Lock 140053383728096 released on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/79b212c00ecdde03c51bb791f7fdfc25f4176ff3.lock\n",
      "DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"HEAD /THUDM/chatglm2-6b/resolve/main/tokenization_chatglm.py HTTP/1.1\" 200 0\n",
      "DEBUG:filelock:Attempting to acquire lock 140053383729920 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/d4ce41652736b14c5c0a9263d32ba33566adf73b.lock\n",
      "DEBUG:filelock:Lock 140053383729920 acquired on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/d4ce41652736b14c5c0a9263d32ba33566adf73b.lock\n",
      "DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"GET /THUDM/chatglm2-6b/resolve/main/tokenization_chatglm.py HTTP/1.1\" 200 10061\n"
     ]
    },
    {
     "data": {
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       "model_id": "b7448f529c574a0687bc5b763594f07a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)enization_chatglm.py:   0%|          | 0.00/10.1k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG:filelock:Attempting to release lock 140053383729920 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/d4ce41652736b14c5c0a9263d32ba33566adf73b.lock\n",
      "DEBUG:filelock:Lock 140053383729920 released on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/d4ce41652736b14c5c0a9263d32ba33566adf73b.lock\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm2-6b:\n",
      "- tokenization_chatglm.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"HEAD /THUDM/chatglm2-6b/resolve/main/tokenizer.model HTTP/1.1\" 302 0\n",
      "DEBUG:filelock:Attempting to acquire lock 140053382719296 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/e7dc4c393423b76e4373e5157ddc34803a0189ba96b21ddbb40269d31468a6f2.lock\n",
      "DEBUG:filelock:Lock 140053382719296 acquired on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/e7dc4c393423b76e4373e5157ddc34803a0189ba96b21ddbb40269d31468a6f2.lock\n",
      "DEBUG:urllib3.connectionpool:Resetting dropped connection: cdn-lfs.huggingface.co\n",
      "DEBUG:urllib3.connectionpool:https://cdn-lfs.huggingface.co:443 \"GET /repos/a6/9c/a69cdfd86b704ca3e2aef541707690cf6c7500f9d7f5a0dd0f7f990f31a0b5a4/e7dc4c393423b76e4373e5157ddc34803a0189ba96b21ddbb40269d31468a6f2?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27tokenizer.model%3B+filename%3D%22tokenizer.model%22%3B&Expires=1690051432&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY5MDA1MTQzMn19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy9hNi85Yy9hNjljZGZkODZiNzA0Y2EzZTJhZWY1NDE3MDc2OTBjZjZjNzUwMGY5ZDdmNWEwZGQwZjdmOTkwZjMxYTBiNWE0L2U3ZGM0YzM5MzQyM2I3NmU0MzczZTUxNTdkZGMzNDgwM2EwMTg5YmE5NmIyMWRkYmI0MDI2OWQzMTQ2OGE2ZjI~cmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=v6PoUyqEYT1zJIWMbqgab6uMVCUTf4nGk57BpaZEh77EX3L6LPP44BLjNFMr29CyU07JeylFqtUcqVmNmGbeOHeQlfevnm0xuSoduPKRJiTvmhyg8FzLi~YOdftC-dMkn4Z7eYGMf7TVfaZS6zuPVcUgUGNZUqT4gR6VqWGZh6HNT4u~qJi~-GSVWH3~8u8~VNW1fkK34u1M1isvDXqQB7OO4l1iggMVykVHuQZT6ZUHbbXEgoGCpcIf7-uDWl1CB3HIsLVSStsvP2j043KzcgWo03jks7hXfy3lfj6yyaPgmqz4-fBEAUmyqP5rhXrPrE9lHMwo07as5BkCk8oLtA__&Key-Pair-Id=KVTP0A1DKRTAX HTTP/1.1\" 200 1018370\n"
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     "data": {
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       "model_id": "8464021703304b82ab3bf21818185bb4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading tokenizer.model:   0%|          | 0.00/1.02M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     "output_type": "stream",
     "text": [
      "DEBUG:filelock:Attempting to release lock 140053382719296 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/e7dc4c393423b76e4373e5157ddc34803a0189ba96b21ddbb40269d31468a6f2.lock\n",
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      "A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm2-6b:\n",
      "- configuration_chatglm.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
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      "DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"HEAD /THUDM/chatglm2-6b/resolve/main/modeling_chatglm.py HTTP/1.1\" 200 0\n",
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      "DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"GET /THUDM/chatglm2-6b/resolve/main/quantization.py HTTP/1.1\" 200 14692\n"
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      "DEBUG:filelock:Attempting to release lock 140053784823120 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/cb95bfe82b203ff6a2aa962326d2c7a438d6a52f.lock\n",
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      "A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm2-6b:\n",
      "- quantization.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
      "A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm2-6b:\n",
      "- modeling_chatglm.py\n",
      "- quantization.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
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      "DEBUG:filelock:Attempting to acquire lock 140053383728528 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/fb856bdb5fdf13d7227fa50e5e6b59b8f39d0bc7.lock\n",
      "DEBUG:filelock:Lock 140053383728528 acquired on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/fb856bdb5fdf13d7227fa50e5e6b59b8f39d0bc7.lock\n",
      "DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"GET /THUDM/chatglm2-6b/resolve/main/pytorch_model.bin.index.json HTTP/1.1\" 200 20438\n"
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      "DEBUG:filelock:Attempting to release lock 140053383728528 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/fb856bdb5fdf13d7227fa50e5e6b59b8f39d0bc7.lock\n",
      "DEBUG:filelock:Lock 140053383728528 released on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/fb856bdb5fdf13d7227fa50e5e6b59b8f39d0bc7.lock\n"
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      "DEBUG:filelock:Attempting to acquire lock 140053383729968 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/cdf1bf57d519abe11043e9121314e76bc0934993e649a9e438a4b0894f4e6ee8.lock\n",
      "DEBUG:filelock:Lock 140053383729968 acquired on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/cdf1bf57d519abe11043e9121314e76bc0934993e649a9e438a4b0894f4e6ee8.lock\n",
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      "DEBUG:filelock:Attempting to release lock 140053383729968 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/cdf1bf57d519abe11043e9121314e76bc0934993e649a9e438a4b0894f4e6ee8.lock\n",
      "DEBUG:filelock:Lock 140053383729968 released on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/cdf1bf57d519abe11043e9121314e76bc0934993e649a9e438a4b0894f4e6ee8.lock\n",
      "DEBUG:urllib3.connectionpool:https://huggingface.co:443 \"HEAD /THUDM/chatglm2-6b/resolve/main/pytorch_model-00002-of-00007.bin HTTP/1.1\" 302 0\n",
      "DEBUG:filelock:Attempting to acquire lock 140053383729968 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/1cd596bd15905248b20b755daf12a02a8fa963da09b59da7fdc896e17bfa518c.lock\n",
      "DEBUG:filelock:Lock 140053383729968 acquired on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/1cd596bd15905248b20b755daf12a02a8fa963da09b59da7fdc896e17bfa518c.lock\n",
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     "text": [
      "DEBUG:filelock:Attempting to release lock 140053383729968 on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/1cd596bd15905248b20b755daf12a02a8fa963da09b59da7fdc896e17bfa518c.lock\n",
      "DEBUG:filelock:Lock 140053383729968 released on /root/.cache/huggingface/hub/models--THUDM--chatglm2-6b/blobs/1cd596bd15905248b20b755daf12a02a8fa963da09b59da7fdc896e17bfa518c.lock\n"
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    {
     "ename": "OSError",
     "evalue": "[Errno 28] No space left on device",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[18], line 142\u001b[0m\n\u001b[1;32m    136\u001b[0m has_cuda \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mis_available()\n\u001b[1;32m    138\u001b[0m \u001b[38;5;66;03m# has_cuda = False  # force cpu\u001b[39;00m\n\u001b[1;32m    141\u001b[0m model_glm \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 142\u001b[0m     \u001b[43mAutoModel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrust_remote_code\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mcuda()\u001b[38;5;241m.\u001b[39mhalf()\n\u001b[1;32m    143\u001b[0m )  \u001b[38;5;66;03m# 3.92G\u001b[39;00m\n\u001b[1;32m    145\u001b[0m model_glm \u001b[38;5;241m=\u001b[39m model_glm\u001b[38;5;241m.\u001b[39meval()\n\u001b[1;32m    147\u001b[0m _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\"\"\u001b[39m\u001b[38;5;124mOverride Chatbot.postprocess\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py:488\u001b[0m, in \u001b[0;36m_BaseAutoModelClass.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m    486\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    487\u001b[0m         \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mregister(config\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m, model_class, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 488\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodel_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhub_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    490\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    491\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(config) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m    492\u001b[0m     model_class \u001b[38;5;241m=\u001b[39m _get_model_class(config, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/modeling_utils.py:2610\u001b[0m, in \u001b[0;36mPreTrainedModel.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   2607\u001b[0m \u001b[38;5;66;03m# We'll need to download and cache each checkpoint shard if the checkpoint is sharded.\u001b[39;00m\n\u001b[1;32m   2608\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_sharded:\n\u001b[1;32m   2609\u001b[0m     \u001b[38;5;66;03m# rsolved_archive_file becomes a list of files that point to the different checkpoint shards in this case.\u001b[39;00m\n\u001b[0;32m-> 2610\u001b[0m     resolved_archive_file, sharded_metadata \u001b[38;5;241m=\u001b[39m \u001b[43mget_checkpoint_shard_files\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2611\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2612\u001b[0m \u001b[43m        \u001b[49m\u001b[43mresolved_archive_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2613\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2614\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2615\u001b[0m \u001b[43m        \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2616\u001b[0m \u001b[43m        \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2617\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2618\u001b[0m \u001b[43m        \u001b[49m\u001b[43muse_auth_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2619\u001b[0m \u001b[43m        \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2620\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2621\u001b[0m \u001b[43m        \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2622\u001b[0m \u001b[43m        \u001b[49m\u001b[43m_commit_hash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommit_hash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2623\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2625\u001b[0m \u001b[38;5;66;03m# load pt weights early so that we know which dtype to init the model under\u001b[39;00m\n\u001b[1;32m   2626\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m from_pt:\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/utils/hub.py:958\u001b[0m, in \u001b[0;36mget_checkpoint_shard_files\u001b[0;34m(pretrained_model_name_or_path, index_filename, cache_dir, force_download, proxies, resume_download, local_files_only, use_auth_token, user_agent, revision, subfolder, _commit_hash)\u001b[0m\n\u001b[1;32m    955\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m shard_filename \u001b[38;5;129;01min\u001b[39;00m tqdm(shard_filenames, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownloading shards\u001b[39m\u001b[38;5;124m\"\u001b[39m, disable\u001b[38;5;241m=\u001b[39m\u001b[38;5;129;01mnot\u001b[39;00m show_progress_bar):\n\u001b[1;32m    956\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    957\u001b[0m         \u001b[38;5;66;03m# Load from URL\u001b[39;00m\n\u001b[0;32m--> 958\u001b[0m         cached_filename \u001b[38;5;241m=\u001b[39m \u001b[43mcached_file\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    959\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    960\u001b[0m \u001b[43m            \u001b[49m\u001b[43mshard_filename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    961\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    962\u001b[0m \u001b[43m            \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    963\u001b[0m \u001b[43m            \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    964\u001b[0m \u001b[43m            \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    965\u001b[0m \u001b[43m            \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    966\u001b[0m \u001b[43m            \u001b[49m\u001b[43muse_auth_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_auth_token\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    967\u001b[0m \u001b[43m            \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    968\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    969\u001b[0m \u001b[43m            \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    970\u001b[0m \u001b[43m            \u001b[49m\u001b[43m_commit_hash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_commit_hash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    971\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    972\u001b[0m     \u001b[38;5;66;03m# We have already dealt with RepositoryNotFoundError and RevisionNotFoundError when getting the index, so\u001b[39;00m\n\u001b[1;32m    973\u001b[0m     \u001b[38;5;66;03m# we don't have to catch them here.\u001b[39;00m\n\u001b[1;32m    974\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m EntryNotFoundError:\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/utils/hub.py:417\u001b[0m, in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, use_auth_token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash)\u001b[0m\n\u001b[1;32m    414\u001b[0m user_agent \u001b[38;5;241m=\u001b[39m http_user_agent(user_agent)\n\u001b[1;32m    415\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    416\u001b[0m     \u001b[38;5;66;03m# Load from URL or cache if already cached\u001b[39;00m\n\u001b[0;32m--> 417\u001b[0m     resolved_file \u001b[38;5;241m=\u001b[39m \u001b[43mhf_hub_download\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    418\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpath_or_repo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    419\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    420\u001b[0m \u001b[43m        \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    421\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    422\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    423\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    424\u001b[0m \u001b[43m        \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    425\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    426\u001b[0m \u001b[43m        \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    427\u001b[0m \u001b[43m        \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    428\u001b[0m \u001b[43m        \u001b[49m\u001b[43muse_auth_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_auth_token\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    429\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    430\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    432\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m RepositoryNotFoundError:\n\u001b[1;32m    433\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\n\u001b[1;32m    434\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath_or_repo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is not a local folder and is not a valid model identifier \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    435\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlisted on \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://huggingface.co/models\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mIf this is a private repository, make sure to \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    436\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpass a token having permission to this repo with `use_auth_token` or log in with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    437\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`huggingface-cli login` and pass `use_auth_token=True`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    438\u001b[0m     )\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m    116\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:1364\u001b[0m, in \u001b[0;36mhf_hub_download\u001b[0;34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, local_dir_use_symlinks, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, token, local_files_only, legacy_cache_layout)\u001b[0m\n\u001b[1;32m   1361\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m temp_file_manager() \u001b[38;5;28;01mas\u001b[39;00m temp_file:\n\u001b[1;32m   1362\u001b[0m     logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdownloading \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m to \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, url, temp_file\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m-> 1364\u001b[0m     \u001b[43mhttp_get\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1365\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl_to_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1366\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtemp_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1367\u001b[0m \u001b[43m        \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1368\u001b[0m \u001b[43m        \u001b[49m\u001b[43mresume_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1369\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1370\u001b[0m \u001b[43m        \u001b[49m\u001b[43mexpected_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexpected_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1371\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1373\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m local_dir \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1374\u001b[0m     logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStoring \u001b[39m\u001b[38;5;132;01m{\u001b[39;00murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m in cache at \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mblob_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:544\u001b[0m, in \u001b[0;36mhttp_get\u001b[0;34m(url, temp_file, proxies, resume_size, headers, timeout, max_retries, expected_size)\u001b[0m\n\u001b[1;32m    542\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m chunk:  \u001b[38;5;66;03m# filter out keep-alive new chunks\u001b[39;00m\n\u001b[1;32m    543\u001b[0m         progress\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mlen\u001b[39m(chunk))\n\u001b[0;32m--> 544\u001b[0m         \u001b[43mtemp_file\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    546\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m expected_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m expected_size \u001b[38;5;241m!=\u001b[39m temp_file\u001b[38;5;241m.\u001b[39mtell():\n\u001b[1;32m    547\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\n\u001b[1;32m    548\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConsistency check failed: file should be of size \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexpected_size\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m but has size\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    549\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtemp_file\u001b[38;5;241m.\u001b[39mtell()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdisplayed_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m).\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mWe are sorry for the inconvenience. Please retry download and\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    550\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m pass `force_download=True, resume_download=False` as argument.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mIf the issue persists, please let us\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    551\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m know by opening an issue on https://github.com/huggingface/huggingface_hub.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    552\u001b[0m     )\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/tempfile.py:483\u001b[0m, in \u001b[0;36m_TemporaryFileWrapper.__getattr__.<locals>.func_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    481\u001b[0m \u001b[38;5;129m@_functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    482\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfunc_wrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 483\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mOSError\u001b[0m: [Errno 28] No space left on device"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "import librosa\n",
    "import gradio as gr\n",
    "from scipy.io.wavfile import write\n",
    "from transformers import WavLMModel\n",
    "\n",
    "import utils\n",
    "from models import SynthesizerTrn\n",
    "from mel_processing import mel_spectrogram_torch\n",
    "from speaker_encoder.voice_encoder import SpeakerEncoder\n",
    "\n",
    "import time\n",
    "from textwrap import dedent\n",
    "\n",
    "import mdtex2html\n",
    "from loguru import logger\n",
    "from transformers import AutoModel, AutoTokenizer\n",
    "\n",
    "from tts_voice import tts_order_voice\n",
    "import edge_tts\n",
    "import tempfile\n",
    "import anyio\n",
    "\n",
    "import os, sys\n",
    "import gradio as gr\n",
    "from src.gradio_demo import SadTalker  \n",
    "\n",
    "\n",
    "try:\n",
    "    import webui  # in webui\n",
    "    in_webui = True\n",
    "except:\n",
    "    in_webui = False\n",
    "\n",
    "\n",
    "def toggle_audio_file(choice):\n",
    "    if choice == False:\n",
    "        return gr.update(visible=True), gr.update(visible=False)\n",
    "    else:\n",
    "        return gr.update(visible=False), gr.update(visible=True)\n",
    "    \n",
    "def ref_video_fn(path_of_ref_video):\n",
    "    if path_of_ref_video is not None:\n",
    "        return gr.update(value=True)\n",
    "    else:\n",
    "        return gr.update(value=False)\n",
    "\n",
    "sad_talker = SadTalker(\"checkpoints\", \"src/config\", lazy_load=True)\n",
    "\n",
    "'''\n",
    "def get_wavlm():\n",
    "    os.system('gdown https://drive.google.com/uc?id=12-cB34qCTvByWT-QtOcZaqwwO21FLSqU')\n",
    "    shutil.move('WavLM-Large.pt', 'wavlm')\n",
    "'''\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "smodel = SpeakerEncoder('speaker_encoder/ckpt/pretrained_bak_5805000.pt')\n",
    "\n",
    "print(\"Loading FreeVC(24k)...\")\n",
    "hps = utils.get_hparams_from_file(\"configs/freevc-24.json\")\n",
    "freevc_24 = SynthesizerTrn(\n",
    "    hps.data.filter_length // 2 + 1,\n",
    "    hps.train.segment_size // hps.data.hop_length,\n",
    "    **hps.model).to(device)\n",
    "_ = freevc_24.eval()\n",
    "_ = utils.load_checkpoint(\"checkpoint/freevc-24.pth\", freevc_24, None)\n",
    "\n",
    "print(\"Loading WavLM for content...\")\n",
    "cmodel = WavLMModel.from_pretrained(\"microsoft/wavlm-large\").to(device)\n",
    " \n",
    "def convert(model, src, tgt):\n",
    "    with torch.no_grad():\n",
    "        # tgt\n",
    "        wav_tgt, _ = librosa.load(tgt, sr=hps.data.sampling_rate)\n",
    "        wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)\n",
    "        if model == \"FreeVC\" or model == \"FreeVC (24kHz)\":\n",
    "            g_tgt = smodel.embed_utterance(wav_tgt)\n",
    "            g_tgt = torch.from_numpy(g_tgt).unsqueeze(0).to(device)\n",
    "        else:\n",
    "            wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(device)\n",
    "            mel_tgt = mel_spectrogram_torch(\n",
    "                wav_tgt, \n",
    "                hps.data.filter_length,\n",
    "                hps.data.n_mel_channels,\n",
    "                hps.data.sampling_rate,\n",
    "                hps.data.hop_length,\n",
    "                hps.data.win_length,\n",
    "                hps.data.mel_fmin,\n",
    "                hps.data.mel_fmax\n",
    "            )\n",
    "        # src\n",
    "        wav_src, _ = librosa.load(src, sr=hps.data.sampling_rate)\n",
    "        wav_src = torch.from_numpy(wav_src).unsqueeze(0).to(device)\n",
    "        c = cmodel(wav_src).last_hidden_state.transpose(1, 2).to(device)\n",
    "        # infer\n",
    "        if model == \"FreeVC\":\n",
    "            audio = freevc.infer(c, g=g_tgt)\n",
    "        elif model == \"FreeVC-s\":\n",
    "            audio = freevc_s.infer(c, mel=mel_tgt)\n",
    "        else:\n",
    "            audio = freevc_24.infer(c, g=g_tgt)\n",
    "        audio = audio[0][0].data.cpu().float().numpy()\n",
    "        if model == \"FreeVC\" or model == \"FreeVC-s\":\n",
    "            write(\"out.wav\", hps.data.sampling_rate, audio)\n",
    "        else:\n",
    "            write(\"out.wav\", 24000, audio)\n",
    "    out = \"out.wav\"\n",
    "    return out\n",
    "\n",
    "# GLM2\n",
    "\n",
    "language_dict = tts_order_voice\n",
    "\n",
    "# fix timezone in Linux\n",
    "os.environ[\"TZ\"] = \"Asia/Shanghai\"\n",
    "try:\n",
    "    time.tzset()  # type: ignore # pylint: disable=no-member\n",
    "except Exception:\n",
    "    # Windows\n",
    "    logger.warning(\"Windows, cant run time.tzset()\")\n",
    "\n",
    "# model_name = \"THUDM/chatglm2-6b\"\n",
    "model_name = \"THUDM/chatglm2-6b\"\n",
    "\n",
    "RETRY_FLAG = False\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
    "\n",
    "# model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda()\n",
    "\n",
    "# 4/8 bit\n",
    "# model = AutoModel.from_pretrained(\"THUDM/chatglm2-6b\", trust_remote_code=True).quantize(4).cuda()\n",
    "\n",
    "has_cuda = torch.cuda.is_available()\n",
    "\n",
    "# has_cuda = False  # force cpu\n",
    "\n",
    "\n",
    "model_glm = (\n",
    "    AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda().half()\n",
    ")  # 3.92G\n",
    "\n",
    "model_glm = model_glm.eval()\n",
    "\n",
    "_ = \"\"\"Override Chatbot.postprocess\"\"\"\n",
    "\n",
    "\n",
    "def postprocess(self, y):\n",
    "    if y is None:\n",
    "        return []\n",
    "    for i, (message, response) in enumerate(y):\n",
    "        y[i] = (\n",
    "            None if message is None else mdtex2html.convert((message)),\n",
    "            None if response is None else mdtex2html.convert(response),\n",
    "        )\n",
    "    return y\n",
    "\n",
    "\n",
    "gr.Chatbot.postprocess = postprocess\n",
    "\n",
    "\n",
    "def parse_text(text):\n",
    "    \"\"\"copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/\"\"\"\n",
    "    lines = text.split(\"\\n\")\n",
    "    lines = [line for line in lines if line != \"\"]\n",
    "    count = 0\n",
    "    for i, line in enumerate(lines):\n",
    "        if \"```\" in line:\n",
    "            count += 1\n",
    "            items = line.split(\"`\")\n",
    "            if count % 2 == 1:\n",
    "                lines[i] = f'<pre><code class=\"language-{items[-1]}\">'\n",
    "            else:\n",
    "                lines[i] = \"<br></code></pre>\"\n",
    "        else:\n",
    "            if i > 0:\n",
    "                if count % 2 == 1:\n",
    "                    line = line.replace(\"`\", r\"\\`\")\n",
    "                    line = line.replace(\"<\", \"&lt;\")\n",
    "                    line = line.replace(\">\", \"&gt;\")\n",
    "                    line = line.replace(\" \", \"&nbsp;\")\n",
    "                    line = line.replace(\"*\", \"&ast;\")\n",
    "                    line = line.replace(\"_\", \"&lowbar;\")\n",
    "                    line = line.replace(\"-\", \"&#45;\")\n",
    "                    line = line.replace(\".\", \"&#46;\")\n",
    "                    line = line.replace(\"!\", \"&#33;\")\n",
    "                    line = line.replace(\"(\", \"&#40;\")\n",
    "                    line = line.replace(\")\", \"&#41;\")\n",
    "                    line = line.replace(\"$\", \"&#36;\")\n",
    "                lines[i] = \"<br>\" + line\n",
    "    text = \"\".join(lines)\n",
    "    return text\n",
    "\n",
    "\n",
    "def predict(\n",
    "    RETRY_FLAG, input, chatbot, max_length, top_p, temperature, history, past_key_values\n",
    "):\n",
    "    try:\n",
    "        chatbot.append((parse_text(input), \"\"))\n",
    "    except Exception as exc:\n",
    "        logger.error(exc)\n",
    "        logger.debug(f\"{chatbot=}\")\n",
    "        _ = \"\"\"\n",
    "        if chatbot:\n",
    "            chatbot[-1] = (parse_text(input), str(exc))\n",
    "            yield chatbot, history, past_key_values\n",
    "        # \"\"\"\n",
    "        yield chatbot, history, past_key_values\n",
    "\n",
    "    for response, history, past_key_values in model_glm.stream_chat(\n",
    "        tokenizer,\n",
    "        input,\n",
    "        history,\n",
    "        past_key_values=past_key_values,\n",
    "        return_past_key_values=True,\n",
    "        max_length=max_length,\n",
    "        top_p=top_p,\n",
    "        temperature=temperature,\n",
    "    ):\n",
    "        chatbot[-1] = (parse_text(input), parse_text(response))\n",
    "        # chatbot[-1][-1] = parse_text(response)\n",
    "\n",
    "        yield chatbot, history, past_key_values, parse_text(response)\n",
    "\n",
    "\n",
    "def trans_api(input, max_length=4096, top_p=0.8, temperature=0.2):\n",
    "    if max_length < 10:\n",
    "        max_length = 4096\n",
    "    if top_p < 0.1 or top_p > 1:\n",
    "        top_p = 0.85\n",
    "    if temperature <= 0 or temperature > 1:\n",
    "        temperature = 0.01\n",
    "    try:\n",
    "        res, _ = model_glm.chat(\n",
    "            tokenizer,\n",
    "            input,\n",
    "            history=[],\n",
    "            past_key_values=None,\n",
    "            max_length=max_length,\n",
    "            top_p=top_p,\n",
    "            temperature=temperature,\n",
    "        )\n",
    "        # logger.debug(f\"{res=} \\n{_=}\")\n",
    "    except Exception as exc:\n",
    "        logger.error(f\"{exc=}\")\n",
    "        res = str(exc)\n",
    "\n",
    "    return res\n",
    "\n",
    "\n",
    "def reset_user_input():\n",
    "    return gr.update(value=\"\")\n",
    "\n",
    "\n",
    "def reset_state():\n",
    "    return [], [], None, \"\"\n",
    "\n",
    "\n",
    "# Delete last turn\n",
    "def delete_last_turn(chat, history):\n",
    "    if chat and history:\n",
    "        chat.pop(-1)\n",
    "        history.pop(-1)\n",
    "    return chat, history\n",
    "\n",
    "\n",
    "# Regenerate response\n",
    "def retry_last_answer(\n",
    "    user_input, chatbot, max_length, top_p, temperature, history, past_key_values\n",
    "):\n",
    "    if chatbot and history:\n",
    "        # Removing the previous conversation from chat\n",
    "        chatbot.pop(-1)\n",
    "        # Setting up a flag to capture a retry\n",
    "        RETRY_FLAG = True\n",
    "        # Getting last message from user\n",
    "        user_input = history[-1][0]\n",
    "        # Removing bot response from the history\n",
    "        history.pop(-1)\n",
    "\n",
    "    yield from predict(\n",
    "        RETRY_FLAG,  # type: ignore\n",
    "        user_input,\n",
    "        chatbot,\n",
    "        max_length,\n",
    "        top_p,\n",
    "        temperature,\n",
    "        history,\n",
    "        past_key_values,\n",
    "    )\n",
    "\n",
    "# print\n",
    "\n",
    "def print(text):\n",
    "    return text\n",
    "\n",
    "# TTS\n",
    "\n",
    "async def text_to_speech_edge(text, language_code):\n",
    "    voice = language_dict[language_code]\n",
    "    communicate = edge_tts.Communicate(text, voice)\n",
    "    with tempfile.NamedTemporaryFile(delete=False, suffix=\".mp3\") as tmp_file:\n",
    "        tmp_path = tmp_file.name\n",
    "\n",
    "    await communicate.save(tmp_path)\n",
    "\n",
    "    return tmp_path\n",
    "\n",
    "\n",
    "with gr.Blocks(title=\"ChatGLM2-6B-int4\", theme=gr.themes.Soft(text_size=\"sm\")) as demo:\n",
    "    gr.HTML(\"<center>\"\n",
    "            \"<h1>🥳💕🎶 - ChatGLM2 + 声音克隆：和你喜欢的角色畅所欲言吧！</h1>\"\n",
    "            \"</center>\")\n",
    "    gr.Markdown(\"## <center>💡 - 第二代ChatGLM大语言模型 + FreeVC变声，为您打造独一无二的沉浸式对话体验，支持中英双语</center>\")\n",
    "    gr.Markdown(\"## <center>🌊 - 更多精彩应用，尽在[滔滔AI](http://www.talktalkai.com)；滔滔AI，为爱滔滔！💕</center>\")\n",
    "    gr.Markdown(\"### <center>⭐ - 如果您喜欢这个程序，欢迎给我的[Github项目](https://github.com/KevinWang676/ChatGLM2-Voice-Cloning)点赞支持！</center>\")\n",
    "    with gr.Tab(\"Chat\"):\n",
    "        with gr.Accordion(\"📒 相关信息\", open=False):\n",
    "            _ = f\"\"\" ChatGLM2的可选参数信息：\n",
    "                * Low temperature: responses will be more deterministic and focused; High temperature: responses more creative.\n",
    "                * Suggested temperatures -- translation: up to 0.3; chatting: > 0.4\n",
    "                * Top P controls dynamic vocabulary selection based on context.\\n\n",
    "                如果您想让ChatGLM2进行角色扮演并与之对话，请先输入恰当的提示词，如“请你扮演成动漫角色蜡笔小新并和我进行对话”；您也可以为ChatGLM2提供自定义的角色设定\\n\n",
    "                当您使用声音克隆功能时，请先在此程序的对应位置上传一段您喜欢的音频\n",
    "                \"\"\"\n",
    "            gr.Markdown(dedent(_))\n",
    "        chatbot = gr.Chatbot(height=300)\n",
    "        with gr.Row():\n",
    "            with gr.Column(scale=4):\n",
    "                with gr.Column(scale=12):\n",
    "                    user_input = gr.Textbox(\n",
    "                        label=\"请在此处和GLM2聊天 (按回车键即可发送)\",\n",
    "                        placeholder=\"聊点什么吧\",\n",
    "                    )\n",
    "                    RETRY_FLAG = gr.Checkbox(value=False, visible=False)\n",
    "        with gr.Column(min_width=32, scale=1):\n",
    "            with gr.Row():\n",
    "                submitBtn = gr.Button(\"开始和GLM2交流吧\", variant=\"primary\")\n",
    "                deleteBtn = gr.Button(\"删除最新一轮对话\", variant=\"secondary\")\n",
    "                retryBtn = gr.Button(\"重新生成最新一轮对话\", variant=\"secondary\")\n",
    "                \n",
    "        with gr.Accordion(\"🔧 更多设置\", open=False):\n",
    "            with gr.Row():\n",
    "                emptyBtn = gr.Button(\"清空所有聊天记录\")\n",
    "                max_length = gr.Slider(\n",
    "                    0,\n",
    "                    32768,\n",
    "                    value=8192,\n",
    "                    step=1.0,\n",
    "                    label=\"Maximum length\",\n",
    "                    interactive=True,\n",
    "                )\n",
    "                top_p = gr.Slider(\n",
    "                    0, 1, value=0.85, step=0.01, label=\"Top P\", interactive=True\n",
    "                )\n",
    "                temperature = gr.Slider(\n",
    "                    0.01, 1, value=0.95, step=0.01, label=\"Temperature\", interactive=True\n",
    "                )\n",
    "\n",
    "\n",
    "        with gr.Row():\n",
    "            test1 = gr.Textbox(label=\"GLM2的最新回答 (可编辑)\", lines = 3)\n",
    "            with gr.Column():\n",
    "                language = gr.Dropdown(choices=list(language_dict.keys()), value=\"普通话 (中国大陆)-Xiaoxiao-女\", label=\"请选择文本对应的语言及您喜欢的说话人\")\n",
    "                tts_btn = gr.Button(\"生成对应的音频吧\", variant=\"primary\")\n",
    "            output_audio = gr.Audio(type=\"filepath\", label=\"为您生成的音频\", interactive=False)\n",
    "    \n",
    "        tts_btn.click(text_to_speech_edge, inputs=[test1, language], outputs=[output_audio])\n",
    "    \n",
    "        with gr.Row():\n",
    "            model_choice = gr.Dropdown(choices=[\"FreeVC\", \"FreeVC-s\", \"FreeVC (24kHz)\"], value=\"FreeVC (24kHz)\", label=\"Model\", visible=False) \n",
    "            audio1 = output_audio\n",
    "            audio2 = gr.Audio(label=\"请上传您喜欢的声音进行声音克隆\", type='filepath')\n",
    "            clone_btn = gr.Button(\"开始AI声音克隆吧\", variant=\"primary\")\n",
    "            audio_cloned =  gr.Audio(label=\"为您生成的专属声音克隆音频\", type='filepath')\n",
    "    \n",
    "        clone_btn.click(convert, inputs=[model_choice, audio1, audio2], outputs=[audio_cloned])\n",
    "        \n",
    "        history = gr.State([])\n",
    "        past_key_values = gr.State(None)\n",
    "    \n",
    "        user_input.submit(\n",
    "            predict,\n",
    "            [\n",
    "                RETRY_FLAG,\n",
    "                user_input,\n",
    "                chatbot,\n",
    "                max_length,\n",
    "                top_p,\n",
    "                temperature,\n",
    "                history,\n",
    "                past_key_values,\n",
    "            ],\n",
    "            [chatbot, history, past_key_values, test1],\n",
    "            show_progress=\"full\",\n",
    "        )\n",
    "        submitBtn.click(\n",
    "            predict,\n",
    "            [\n",
    "                RETRY_FLAG,\n",
    "                user_input,\n",
    "                chatbot,\n",
    "                max_length,\n",
    "                top_p,\n",
    "                temperature,\n",
    "                history,\n",
    "                past_key_values,\n",
    "            ],\n",
    "            [chatbot, history, past_key_values, test1],\n",
    "            show_progress=\"full\",\n",
    "            api_name=\"predict\",\n",
    "        )\n",
    "        submitBtn.click(reset_user_input, [], [user_input])\n",
    "    \n",
    "        emptyBtn.click(\n",
    "            reset_state, outputs=[chatbot, history, past_key_values, test1], show_progress=\"full\"\n",
    "        )\n",
    "\n",
    "        retryBtn.click(\n",
    "            retry_last_answer,\n",
    "            inputs=[\n",
    "                user_input,\n",
    "                chatbot,\n",
    "                max_length,\n",
    "                top_p,\n",
    "                temperature,\n",
    "                history,\n",
    "                past_key_values,\n",
    "            ],\n",
    "            # outputs = [chatbot, history, last_user_message, user_message]\n",
    "            outputs=[chatbot, history, past_key_values, test1],\n",
    "        )\n",
    "        deleteBtn.click(delete_last_turn, [chatbot, history], [chatbot, history])\n",
    "\n",
    "        with gr.Accordion(\"📔 提示词示例\", open=False):\n",
    "            etext = \"\"\"In America, where cars are an important part of the national psyche, a decade ago people had suddenly started to drive less, which had not happened since the oil shocks of the 1970s. \"\"\"\n",
    "            examples = gr.Examples(\n",
    "                examples=[\n",
    "                    [\"Explain the plot of Cinderella in a sentence.\"],\n",
    "                    [\n",
    "                        \"How long does it take to become proficient in French, and what are the best methods for retaining information?\"\n",
    "                    ],\n",
    "                    [\"What are some common mistakes to avoid when writing code?\"],\n",
    "                    [\"Build a prompt to generate a beautiful portrait of a horse\"],\n",
    "                    [\"Suggest four metaphors to describe the benefits of AI\"],\n",
    "                    [\"Write a pop song about leaving home for the sandy beaches.\"],\n",
    "                    [\"Write a summary demonstrating my ability to tame lions\"],\n",
    "                    [\"鲁迅和周树人什么关系\"],\n",
    "                    [\"从前有一头牛，这头牛后面有什么？\"],\n",
    "                    [\"正无穷大加一大于正无穷大吗？\"],\n",
    "                    [\"正无穷大加正无穷大大于正无穷大吗？\"],\n",
    "                    [\"-2的平方根等于什么\"],\n",
    "                    [\"树上有5只鸟，猎人开枪打死了一只。树上还有几只鸟？\"],\n",
    "                    [\"树上有11只鸟，猎人开枪打死了一只。树上还有几只鸟？提示：需考虑鸟可能受惊吓飞走。\"],\n",
    "                    [\"鲁迅和周树人什么关系 用英文回答\"],\n",
    "                    [\"以红楼梦的行文风格写一张委婉的请假条。不少于320字。\"],\n",
    "                    [f\"{etext} 翻成中文，列出3个版本\"],\n",
    "                    [f\"{etext} \\n 翻成中文，保留原意，但使用文学性的语言。不要写解释。列出3个版本\"],\n",
    "                    [\"js 判断一个数是不是质数\"],\n",
    "                    [\"js 实现python 的 range(10)\"],\n",
    "                    [\"js 实现python 的 [*(range(10)]\"],\n",
    "                    [\"假定 1 + 2 = 4, 试求 7 + 8\"],\n",
    "                    [\"Erkläre die Handlung von Cinderella in einem Satz.\"],\n",
    "                    [\"Erkläre die Handlung von Cinderella in einem Satz. Auf Deutsch\"],\n",
    "                ],\n",
    "                inputs=[user_input],\n",
    "                examples_per_page=30,\n",
    "            )\n",
    "    \n",
    "        with gr.Accordion(\"For Chat/Translation API\", open=False, visible=False):\n",
    "            input_text = gr.Text()\n",
    "            tr_btn = gr.Button(\"Go\", variant=\"primary\")\n",
    "            out_text = gr.Text()\n",
    "        tr_btn.click(\n",
    "            trans_api,\n",
    "            [input_text, max_length, top_p, temperature],\n",
    "            out_text,\n",
    "            # show_progress=\"full\",\n",
    "            api_name=\"tr\",\n",
    "        )\n",
    "        _ = \"\"\"\n",
    "        input_text.submit(\n",
    "            trans_api,\n",
    "            [input_text, max_length, top_p, temperature],\n",
    "            out_text,\n",
    "            show_progress=\"full\",\n",
    "            api_name=\"tr1\",\n",
    "        )\n",
    "        # \"\"\"\n",
    "\n",
    "    with gr.Tab(\"Video\"):\n",
    "        with gr.Row().style(equal_height=False):\n",
    "            with gr.Column(variant='panel'):\n",
    "                with gr.Tabs(elem_id=\"sadtalker_source_image\"):\n",
    "                    with gr.TabItem('Upload image'):\n",
    "                        with gr.Row():\n",
    "                            source_image = gr.Image(label=\"Source image\", source=\"upload\", type=\"filepath\", elem_id=\"img2img_image\").style(width=512)\n",
    "    \n",
    "                with gr.Tabs(elem_id=\"sadtalker_driven_audio\"):\n",
    "                    with gr.TabItem('Upload OR TTS'):\n",
    "                        with gr.Column(variant='panel'):\n",
    "                            driven_audio = gr.Audio(label=\"Input audio\", source=\"upload\", type=\"filepath\")\n",
    "    \n",
    "            with gr.Column(variant='panel'): \n",
    "                with gr.Tabs(elem_id=\"sadtalker_checkbox\"):\n",
    "                    with gr.TabItem('Settings'):\n",
    "                        gr.Markdown(\"need help? please visit our [best practice page](https://github.com/OpenTalker/SadTalker/blob/main/docs/best_practice.md) for more detials\")\n",
    "                        with gr.Column(variant='panel'):\n",
    "                            # width = gr.Slider(minimum=64, elem_id=\"img2img_width\", maximum=2048, step=8, label=\"Manually Crop Width\", value=512) # img2img_width\n",
    "                            # height = gr.Slider(minimum=64, elem_id=\"img2img_height\", maximum=2048, step=8, label=\"Manually Crop Height\", value=512) # img2img_width\n",
    "                            pose_style = gr.Slider(minimum=0, maximum=46, step=1, label=\"Pose style\", value=0) # \n",
    "                            size_of_image = gr.Radio([256, 512], value=256, label='face model resolution', info=\"use 256/512 model?\") # \n",
    "                            preprocess_type = gr.Radio(['crop', 'resize','full', 'extcrop', 'extfull'], value='crop', label='preprocess', info=\"How to handle input image?\")\n",
    "                            is_still_mode = gr.Checkbox(label=\"Still Mode (fewer hand motion, works with preprocess `full`)\")\n",
    "                            batch_size = gr.Slider(label=\"batch size in generation\", step=1, maximum=10, value=2)\n",
    "                            enhancer = gr.Checkbox(label=\"GFPGAN as Face enhancer\")\n",
    "                            submit = gr.Button('Generate', elem_id=\"sadtalker_generate\", variant='primary')\n",
    "                            \n",
    "                with gr.Tabs(elem_id=\"sadtalker_genearted\"):\n",
    "                        gen_video = gr.Video(label=\"Generated video\", format=\"mp4\").style(width=256)\n",
    "    \n",
    "        submit.click(\n",
    "                    fn=sad_talker.test, \n",
    "                    inputs=[source_image,\n",
    "                            driven_audio,\n",
    "                            preprocess_type,\n",
    "                            is_still_mode,\n",
    "                            enhancer,\n",
    "                            batch_size,                            \n",
    "                            size_of_image,\n",
    "                            pose_style\n",
    "                            ], \n",
    "                    outputs=[gen_video]\n",
    "                    )\n",
    "\n",
    "\n",
    "    gr.Markdown(\"### <center>注意❗：请不要生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及个人娱乐使用。</center>\")\n",
    "    gr.Markdown(\"<center>💡 - 如何使用此程序：输入您对ChatGLM的提问后，依次点击“开始和GLM2交流吧”、“生成对应的音频吧”、“开始AI声音克隆吧”三个按键即可；使用声音克隆功能时，请先上传一段您喜欢的音频</center>\")\n",
    "    gr.HTML('''\n",
    "        <div class=\"footer\">\n",
    "                    <p>🌊🏞️🎶 - 江水东流急，滔滔无尽声。 明·顾璘\n",
    "                    </p>\n",
    "        </div>\n",
    "    ''')\n",
    "\n",
    "\n",
    "demo.queue().launch(show_error=True, debug=True, share=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cba1dd99-c797-4459-9a06-c294f30275de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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