




"""simple docstring"""







import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Optional[Any]:






		"""simple docstring"""


		__lowercase				:		List[str]												=				XCLIPTextConfig()

		# derive patch size from model name
		__lowercase				:		Optional[int]												=				model_name.find("patch"				)
		__lowercase				:		str												=				int(model_name[start_idx + len("patch"				) : start_idx + len("patch"				) + 2]				)
		__lowercase				:		Optional[Any]												=				XCLIPVisionConfig(patch_size=_lowerCamelCase,  num_frames=_lowerCamelCase				)

		if "large" in model_name:
				__lowercase				:		str												=				7_68
				__lowercase				:		List[str]												=				30_72
				__lowercase				:		List[str]												=				12

				__lowercase				:		Tuple												=				10_24
				__lowercase				:		Optional[int]												=				40_96
				__lowercase				:		Tuple												=				16
				__lowercase				:		Optional[int]												=				24
				__lowercase				:		str												=				7_68
				__lowercase				:		List[str]												=				30_72

		if model_name == "xclip-large-patch14-16-frames":
				__lowercase				:		Optional[Any]												=				3_36

		__lowercase				:		Dict												=				XCLIPConfig.from_text_vision_configs(_lowerCamelCase,  _lowerCamelCase				)

		if "large" in model_name:
				__lowercase				:		str												=				7_68

		return config

def 						snake_case__       (					_lowerCamelCase				)			->Any:






		"""simple docstring"""


		if name == "token_embedding.weight":
				__lowercase				:		str												=				name.replace("token_embedding.weight",  "text_model.embeddings.token_embedding.weight"				)
		if name == "positional_embedding":
				__lowercase				:		Any												=				name.replace("positional_embedding",  "text_model.embeddings.position_embedding.weight"				)
		if "ln_1" in name:
				__lowercase				:		Optional[int]												=				name.replace("ln_1",  "layer_norm1"				)
		if "ln_2" in name:
				__lowercase				:		Dict												=				name.replace("ln_2",  "layer_norm2"				)
		if "c_fc" in name:
				__lowercase				:		List[Any]												=				name.replace("c_fc",  "fc1"				)
		if "c_proj" in name:
				__lowercase				:		Tuple												=				name.replace("c_proj",  "fc2"				)
		if name.startswith("transformer.resblocks"				):
				__lowercase				:		Optional[int]												=				name.replace("transformer.resblocks",  "text_model.encoder.layers"				)
		if "attn.out_proj" in name and "message" not in name:
				__lowercase				:		Optional[Any]												=				name.replace("attn.out_proj",  "self_attn.out_proj"				)
		if "ln_final" in name:
				__lowercase				:		Dict												=				name.replace("ln_final",  "text_model.final_layer_norm"				)
		# visual encoder
		if name == "visual.class_embedding":
				__lowercase				:		List[str]												=				name.replace("visual.class_embedding",  "vision_model.embeddings.class_embedding"				)
		if name == "visual.positional_embedding":
				__lowercase				:		Dict												=				name.replace("visual.positional_embedding",  "vision_model.embeddings.position_embedding.weight"				)
		if name.startswith("visual.transformer.resblocks"				):
				__lowercase				:		Optional[int]												=				name.replace("visual.transformer.resblocks",  "vision_model.encoder.layers"				)
		if "visual.conv1" in name:
				__lowercase				:		str												=				name.replace("visual.conv1",  "vision_model.embeddings.patch_embedding"				)
		if "visual.ln_pre" in name:
				__lowercase				:		List[str]												=				name.replace("visual.ln_pre",  "vision_model.pre_layernorm"				)
		if "visual.ln_post" in name:
				__lowercase				:		str												=				name.replace("visual.ln_post",  "vision_model.post_layernorm"				)
		if "visual.proj" in name:
				__lowercase				:		List[str]												=				name.replace("visual.proj",  "visual_projection.weight"				)
		if "text_projection" in name:
				__lowercase				:		Optional[Any]												=				name.replace("text_projection",  "text_projection.weight"				)
		# things on top
		if "prompts_visual_proj" in name:
				__lowercase				:		Optional[Any]												=				name.replace("prompts_visual_proj",  "prompts_visual_projection"				)
		if "prompts_visual_ln" in name:
				__lowercase				:		int												=				name.replace("prompts_visual_ln",  "prompts_visual_layernorm"				)
		# mit
		if name == "mit.positional_embedding":
				__lowercase				:		Union[str, Any]												=				name.replace("positional",  "position"				)
		if name.startswith("mit.resblocks"				):
				__lowercase				:		Optional[int]												=				name.replace("mit.resblocks",  "mit.encoder.layers"				)
		# prompts generator
		if name.startswith("prompts_generator.norm"				):
				__lowercase				:		int												=				name.replace("prompts_generator.norm",  "prompts_generator.layernorm"				)

		return name

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Dict:






		"""simple docstring"""


		for key in orig_state_dict.copy().keys():
				__lowercase				:		List[Any]												=				orig_state_dict.pop(_lowerCamelCase				)

				if "attn.in_proj" in key:
						__lowercase				:		Optional[Any]												=				key.split("."				)
						if key.startswith("visual"				):
								__lowercase				:		str												=				key_split[3]
								__lowercase				:		List[str]												=				config.vision_config.hidden_size
								if "message_attn" in key:
										if "weight" in key:
												__lowercase				:		List[str]												=				val[
												    :dim, :
												]
												__lowercase				:		Tuple												=				val[
												    dim : dim * 2, :
												]
												__lowercase				:		Union[str, Any]												=				val[
												    -dim:, :
												]
										else:
												__lowercase				:		Tuple												=				val[
												    :dim
												]
												__lowercase				:		List[Any]												=				val[
												    dim : dim * 2
												]
												__lowercase				:		Optional[int]												=				val[
												    -dim:
												]
								else:
										if "weight" in key:
												__lowercase				:		List[str]												=				val[
												    :dim, :
												]
												__lowercase				:		str												=				val[
												    dim : dim * 2, :
												]
												__lowercase				:		str												=				val[
												    -dim:, :
												]
										else:
												__lowercase				:		int												=				val[:dim]
												__lowercase				:		str												=				val[
												    dim : dim * 2
												]
												__lowercase				:		Union[str, Any]												=				val[-dim:]
						elif key.startswith("mit"				):
								__lowercase				:		int												=				key_split[2]
								__lowercase				:		Union[str, Any]												=				config.vision_config.mit_hidden_size
								if "weight" in key:
										__lowercase				:		Any												=				val[:dim, :]
										__lowercase				:		Union[str, Any]												=				val[dim : dim * 2, :]
										__lowercase				:		List[str]												=				val[-dim:, :]
								else:
										__lowercase				:		Optional[Any]												=				val[:dim]
										__lowercase				:		Union[str, Any]												=				val[dim : dim * 2]
										__lowercase				:		Optional[Any]												=				val[-dim:]
						else:
								__lowercase				:		Any												=				key_split[2]
								__lowercase				:		Union[str, Any]												=				config.text_config.hidden_size
								if "weight" in key:
										__lowercase				:		List[str]												=				val[:dim, :]
										__lowercase				:		Union[str, Any]												=				val[
										    dim : dim * 2, :
										]
										__lowercase				:		Any												=				val[-dim:, :]
								else:
										__lowercase				:		Union[str, Any]												=				val[:dim]
										__lowercase				:		Union[str, Any]												=				val[
										    dim : dim * 2
										]
										__lowercase				:		Union[str, Any]												=				val[-dim:]
				else:
						__lowercase				:		int												=				rename_key(_lowerCamelCase				)
						if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
								__lowercase				:		Union[str, Any]												=				val.T
						__lowercase				:		str												=				val

		return orig_state_dict

def 						snake_case__       (					_lowerCamelCase				)			->Dict:






		"""simple docstring"""


		if num_frames == 8:
				__lowercase				:		List[Any]												=				"eating_spaghetti_8_frames.npy"
		elif num_frames == 16:
				__lowercase				:		Tuple												=				"eating_spaghetti.npy"
		elif num_frames == 32:
				__lowercase				:		Any												=				"eating_spaghetti_32_frames.npy"
		__lowercase				:		Optional[int]												=				hf_hub_download(
		    repo_id="hf-internal-testing/spaghetti-video",  filename=_lowerCamelCase,  repo_type="dataset",  )
		__lowercase				:		Optional[Any]												=				np.load(_lowerCamelCase				)
		return list(_lowerCamelCase				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=None,  _lowerCamelCase=False				)			->List[Any]:






		"""simple docstring"""


		__lowercase				:		Optional[int]												=				{
		    # fully supervised kinetics-400 checkpoints
		    "xclip-base-patch32": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth",
		    "xclip-base-patch32-16-frames": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"
		    ),
		    "xclip-base-patch16": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth",
		    "xclip-base-patch16-16-frames": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"
		    ),
		    "xclip-large-patch14": "https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb",
		    "xclip-large-patch14-16-frames": "https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f",
		    # fully supervised kinetics-600 checkpoints
		    "xclip-base-patch16-kinetics-600": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"
		    ),
		    "xclip-base-patch16-kinetics-600-16-frames": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"
		    ),
		    "xclip-large-patch14-kinetics-600": "https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be",
		    # few shot
		    "xclip-base-patch16-hmdb-2-shot": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"
		    ),
		    "xclip-base-patch16-hmdb-4-shot": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"
		    ),
		    "xclip-base-patch16-hmdb-8-shot": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"
		    ),
		    "xclip-base-patch16-hmdb-16-shot": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"
		    ),
		    "xclip-base-patch16-ucf-2-shot": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"
		    ),
		    "xclip-base-patch16-ucf-4-shot": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"
		    ),
		    "xclip-base-patch16-ucf-8-shot": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"
		    ),
		    "xclip-base-patch16-ucf-16-shot": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"
		    ),
		    # zero shot
		    "xclip-base-patch16-zero-shot": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth",
		}

		__lowercase				:		int												=				model_to_url[model_name]
		__lowercase				:		Optional[int]												=				8
		if "16-frames" in model_name:
				__lowercase				:		Any												=				16
		elif "shot" in model_name:
				__lowercase				:		int												=				32

		__lowercase				:		Optional[Any]												=				get_xclip_config(_lowerCamelCase,  _lowerCamelCase				)
		__lowercase				:		Union[str, Any]												=				XCLIPModel(_lowerCamelCase				)
		model.eval()

		if "drive" in checkpoint_url:
				__lowercase				:		Dict												=				"pytorch_model.bin"
				gdown.cached_download(_lowerCamelCase,  _lowerCamelCase,  quiet=_lowerCamelCase				)
				__lowercase				:		Union[str, Any]												=				torch.load(_lowerCamelCase,  map_location="cpu"				)["model"]
		else:
				__lowercase				:		Optional[Any]												=				torch.hub.load_state_dict_from_url(_lowerCamelCase				)["model"]

		__lowercase				:		str												=				convert_state_dict(_lowerCamelCase,  _lowerCamelCase				)

		__lowercase				:		Optional[int]												=				XCLIPModel(_lowerCamelCase				)
		__lowercase						,__lowercase				:		int												=				model.load_state_dict(_lowerCamelCase,  strict=_lowerCamelCase				)
		assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
		model.eval()

		__lowercase				:		List[str]												=				3_36 if model_name == "xclip-large-patch14-16-frames" else 2_24
		__lowercase				:		Tuple												=				VideoMAEImageProcessor(size=_lowerCamelCase				)
		__lowercase				:		Union[str, Any]												=				CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"				)
		__lowercase				:		List[Any]												=				CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"				)
		__lowercase				:		Optional[int]												=				XCLIPProcessor(image_processor=_lowerCamelCase,  tokenizer=_lowerCamelCase				)

		__lowercase				:		Any												=				prepare_video(_lowerCamelCase				)
		__lowercase				:		str												=				processor(
		    text=["playing sports", "eating spaghetti", "go shopping"],  videos=_lowerCamelCase,  return_tensors="pt",  padding=_lowerCamelCase				)

		print("Shape of pixel values:",  inputs.pixel_values.shape				)

		with torch.no_grad():
				__lowercase				:		Optional[Any]												=				model(**_lowerCamelCase				)

		# Verify outputs
		__lowercase				:		Dict												=				outputs.logits_per_video
		__lowercase				:		str												=				logits_per_video.softmax(dim=1				)
		print("Probs:",  _lowerCamelCase				)
		# kinetics-400
		if model_name == "xclip-base-patch32":
				__lowercase				:		Union[str, Any]												=				torch.tensor([[0.0_0_1_9, 0.9_9_5_1, 0.0_0_3_0]]				)
		elif model_name == "xclip-base-patch32-16-frames":
				__lowercase				:		Optional[int]												=				torch.tensor([[7.0_9_9_9E-0_4, 9.9_8_8_3E-0_1, 4.5_5_8_0E-0_4]]				)
		elif model_name == "xclip-base-patch16":
				__lowercase				:		Tuple												=				torch.tensor([[0.0_0_8_3, 0.9_6_8_1, 0.0_2_3_6]]				)
		elif model_name == "xclip-base-patch16-16-frames":
				__lowercase				:		Dict												=				torch.tensor([[7.6_9_3_7E-0_4, 9.9_7_2_8E-0_1, 1.9_4_7_3E-0_3]]				)
		elif model_name == "xclip-large-patch14":
				__lowercase				:		Any												=				torch.tensor([[0.0_0_6_2, 0.9_8_6_4, 0.0_0_7_5]]				)
		elif model_name == "xclip-large-patch14-16-frames":
				__lowercase				:		List[Any]												=				torch.tensor([[3.3_8_7_7E-0_4, 9.9_9_3_7E-0_1, 2.8_8_8_8E-0_4]]				)
		# kinetics-600
		elif model_name == "xclip-base-patch16-kinetics-600":
				__lowercase				:		Dict												=				torch.tensor([[0.0_5_5_5, 0.8_9_1_4, 0.0_5_3_1]]				)
		elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
				__lowercase				:		Optional[int]												=				torch.tensor([[3.8_5_5_4E-0_4, 9.9_9_2_9E-0_1, 3.2_7_5_4E-0_4]]				)
		elif model_name == "xclip-large-patch14-kinetics-600":
				__lowercase				:		int												=				torch.tensor([[0.0_0_3_6, 0.9_9_2_0, 0.0_0_4_5]]				)
		# few shot
		elif model_name == "xclip-base-patch16-hmdb-2-shot":
				__lowercase				:		Union[str, Any]												=				torch.tensor([[7.1_8_9_0E-0_6, 9.9_9_9_4E-0_1, 5.6_5_5_9E-0_5]]				)
		elif model_name == "xclip-base-patch16-hmdb-4-shot":
				__lowercase				:		Union[str, Any]												=				torch.tensor([[1.0_3_2_0E-0_5, 9.9_9_9_3E-0_1, 6.2_4_3_5E-0_5]]				)
		elif model_name == "xclip-base-patch16-hmdb-8-shot":
				__lowercase				:		str												=				torch.tensor([[4.1_3_7_7E-0_6, 9.9_9_9_0E-0_1, 9.8_3_8_6E-0_5]]				)
		elif model_name == "xclip-base-patch16-hmdb-16-shot":
				__lowercase				:		Any												=				torch.tensor([[4.1_3_4_7E-0_5, 9.9_9_6_2E-0_1, 3.3_4_1_1E-0_4]]				)
		elif model_name == "xclip-base-patch16-ucf-2-shot":
				__lowercase				:		Optional[Any]												=				torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]]				)
		elif model_name == "xclip-base-patch16-ucf-4-shot":
				__lowercase				:		int												=				torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]]				)
		elif model_name == "xclip-base-patch16-ucf-8-shot":
				__lowercase				:		str												=				torch.tensor([[0.0_0_2_7, 0.9_9_0_4, 0.0_0_7_0]]				)
		elif model_name == "xclip-base-patch16-ucf-16-shot":
				__lowercase				:		Tuple												=				torch.tensor([[9.8_2_1_9E-0_4, 9.9_5_9_3E-0_1, 3.0_8_6_3E-0_3]]				)
		# zero shot
		elif model_name == "xclip-base-patch16-zero-shot":
				__lowercase				:		Dict												=				torch.tensor([[3.5_0_8_2E-0_4, 9.9_7_8_5E-0_1, 1.7_9_6_6E-0_3]]				)
		else:
				raise ValueError(F'Model name {model_name} not supported'				)
		assert torch.allclose(_lowerCamelCase,  _lowerCamelCase,  atol=1E-3				)
		print("Looks ok!"				)

		if pytorch_dump_folder_path is not None:
				print(F'Saving model {model_name} to {pytorch_dump_folder_path}'				)
				model.save_pretrained(_lowerCamelCase				)

		if push_to_hub:
				print("Pushing model, processor and slow tokenizer files to the hub..."				)
				model.push_to_hub(_lowerCamelCase,  organization="nielsr"				)
				processor.push_to_hub(_lowerCamelCase,  organization="nielsr"				)
				slow_tokenizer.push_to_hub(_lowerCamelCase,  organization="nielsr"				)


if __name__ == "__main__":
						__A   :  Dict					=      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='xclip-base-patch32',
						    type=str,
						    help='Name of the model.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)
						parser.add_argument(
						    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
						)

						__A   :  int					=      parser.parse_args()
						convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
__A   :  int					=      '\nHuman: <<task>>\n\nAssistant: '


__A   :  List[str]					=      'huggingface-tools/default-prompts'
__A   :  Tuple					=      {'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase="run"				)			->Optional[int]:






		"""simple docstring"""


		if prompt_or_repo_id is None:
				__lowercase				:		Any												=				DEFAULT_PROMPTS_REPO

		# prompt is considered a repo ID when it does not contain any kind of space
		if re.search("\\s",  _lowerCamelCase				) is not None:
				return prompt_or_repo_id

		__lowercase				:		Optional[Any]												=				cached_file(
		    _lowerCamelCase,  PROMPT_FILES[mode],  repo_type="dataset",  user_agent={"agent": agent_name}				)
		with open(_lowerCamelCase,  "r",  encoding="utf-8"				) as f:
				return f.read()
