





'''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 			_lowerCAmelCase     (    __snake_case    :					int					,				__snake_case    :					Tuple     )	->	List[str]:
     __A		:					Tuple						=			XCLIPTextConfig()

     # derive patch size from model name
     __A		:					Dict						=			model_name.find('patch'     )
     __A		:					Dict						=			int(model_name[start_idx + len('patch'     ) : start_idx + len('patch'     ) + 2]     )
     __A		:					List[str]						=			XCLIPVisionConfig(patch_size=__snake_case					,				num_frames=__snake_case     )

     if "large" in model_name:
          __A		:					Dict						=			7_68
          __A		:					List[Any]						=			30_72
          __A		:					int						=			12

          __A		:					Tuple						=			10_24
          __A		:					str						=			40_96
          __A		:					Any						=			16
          __A		:					str						=			24
          __A		:					Dict						=			7_68
          __A		:					Any						=			30_72

     if model_name == "xclip-large-patch14-16-frames":
          __A		:					List[str]						=			3_36

     __A		:					List[str]						=			XCLIPConfig.from_text_vision_configs(__snake_case					,				__snake_case     )

     if "large" in model_name:
          __A		:					List[str]						=			7_68

     return config






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

     return name






def 			_lowerCAmelCase     (    __snake_case    :					Optional[int]					,				__snake_case    :					Optional[int]     )	->	Tuple:
     for key in orig_state_dict.copy().keys():
          __A		:					Dict						=			orig_state_dict.pop(__snake_case     )

          if "attn.in_proj" in key:
               __A		:					Dict						=			key.split('.'     )
               if key.startswith('visual'     ):
                    __A		:					Dict						=			key_split[3]
                    __A		:					Tuple						=			config.vision_config.hidden_size
                    if "message_attn" in key:
                         if "weight" in key:
                              __A		:					Dict						=			val[
                                  :dim, :
                              ]
                              __A		:					Tuple						=			val[
                                  dim : dim * 2, :
                              ]
                              __A		:					Optional[Any]						=			val[
                                  -dim:, :
                              ]
                         else:
                              __A		:					Optional[Any]						=			val[
                                  :dim
                              ]
                              __A		:					Optional[int]						=			val[
                                  dim : dim * 2
                              ]
                              __A		:					Tuple						=			val[
                                  -dim:
                              ]
                    else:
                         if "weight" in key:
                              __A		:					Dict						=			val[
                                  :dim, :
                              ]
                              __A		:					Optional[Any]						=			val[
                                  dim : dim * 2, :
                              ]
                              __A		:					Any						=			val[
                                  -dim:, :
                              ]
                         else:
                              __A		:					Union[str, Any]						=			val[:dim]
                              __A		:					Union[str, Any]						=			val[
                                  dim : dim * 2
                              ]
                              __A		:					Optional[int]						=			val[-dim:]
               elif key.startswith('mit'     ):
                    __A		:					List[str]						=			key_split[2]
                    __A		:					Optional[Any]						=			config.vision_config.mit_hidden_size
                    if "weight" in key:
                         __A		:					Optional[Any]						=			val[:dim, :]
                         __A		:					Optional[Any]						=			val[dim : dim * 2, :]
                         __A		:					List[Any]						=			val[-dim:, :]
                    else:
                         __A		:					str						=			val[:dim]
                         __A		:					Dict						=			val[dim : dim * 2]
                         __A		:					Tuple						=			val[-dim:]
               else:
                    __A		:					Union[str, Any]						=			key_split[2]
                    __A		:					Optional[Any]						=			config.text_config.hidden_size
                    if "weight" in key:
                         __A		:					List[str]						=			val[:dim, :]
                         __A		:					int						=			val[
                             dim : dim * 2, :
                         ]
                         __A		:					Optional[Any]						=			val[-dim:, :]
                    else:
                         __A		:					Union[str, Any]						=			val[:dim]
                         __A		:					Tuple						=			val[
                             dim : dim * 2
                         ]
                         __A		:					str						=			val[-dim:]
          else:
               __A		:					Dict						=			rename_key(__snake_case     )
               if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                    __A		:					List[Any]						=			val.T
               __A		:					Any						=			val

     return orig_state_dict






def 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]     )	->	Optional[int]:
     if num_frames == 8:
          __A		:					Any						=			'eating_spaghetti_8_frames.npy'
     elif num_frames == 16:
          __A		:					List[str]						=			'eating_spaghetti.npy'
     elif num_frames == 32:
          __A		:					int						=			'eating_spaghetti_32_frames.npy'
     __A		:					Any						=			hf_hub_download(
         repo_id='hf-internal-testing/spaghetti-video'					,				filename=__snake_case					,				repo_type='dataset'					,				)
     __A		:					Any						=			np.load(__snake_case     )
     return list(__snake_case     )






def 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]					,				__snake_case    :					Tuple=None					,				__snake_case    :					List[Any]=False     )	->	Union[str, Any]:
     __A		:					List[Any]						=			{
         # 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',
     }

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

     __A		:					List[str]						=			get_xclip_config(__snake_case					,				__snake_case     )
     __A		:					str						=			XCLIPModel(__snake_case     )
     model.eval()

     if "drive" in checkpoint_url:
          __A		:					List[str]						=			'pytorch_model.bin'
          gdown.cached_download(__snake_case					,				__snake_case					,				quiet=__snake_case     )
          __A		:					int						=			torch.load(__snake_case					,				map_location='cpu'     )['model']
     else:
          __A		:					Optional[Any]						=			torch.hub.load_state_dict_from_url(__snake_case     )['model']

     __A		:					Dict						=			convert_state_dict(__snake_case					,				__snake_case     )

     __A		:					Union[str, Any]						=			XCLIPModel(__snake_case     )
     __A					,__A		:					List[str]						=			model.load_state_dict(__snake_case					,				strict=__snake_case     )
     assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
     model.eval()

     __A		:					List[str]						=			3_36 if model_name == 'xclip-large-patch14-16-frames' else 2_24
     __A		:					Optional[Any]						=			VideoMAEImageProcessor(size=__snake_case     )
     __A		:					Dict						=			CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'     )
     __A		:					Optional[Any]						=			CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'     )
     __A		:					str						=			XCLIPProcessor(image_processor=__snake_case					,				tokenizer=__snake_case     )

     __A		:					List[Any]						=			prepare_video(__snake_case     )
     __A		:					List[Any]						=			processor(
         text=['playing sports', 'eating spaghetti', 'go shopping']					,				videos=__snake_case					,				return_tensors='pt'					,				padding=__snake_case     )

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

     with torch.no_grad():
          __A		:					Optional[int]						=			model(**__snake_case     )

     # Verify outputs
     __A		:					List[str]						=			outputs.logits_per_video
     __A		:					Tuple						=			logits_per_video.softmax(dim=1     )
     print('Probs:'					,				__snake_case     )
     # kinetics-400
     if model_name == "xclip-base-patch32":
          __A		:					List[Any]						=			torch.tensor([[0.0_019, 0.9_951, 0.0_030]]     )
     elif model_name == "xclip-base-patch32-16-frames":
          __A		:					str						=			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":
          __A		:					List[Any]						=			torch.tensor([[0.0_083, 0.9_681, 0.0_236]]     )
     elif model_name == "xclip-base-patch16-16-frames":
          __A		:					int						=			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":
          __A		:					Any						=			torch.tensor([[0.0_062, 0.9_864, 0.0_075]]     )
     elif model_name == "xclip-large-patch14-16-frames":
          __A		:					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":
          __A		:					Optional[int]						=			torch.tensor([[0.0_555, 0.8_914, 0.0_531]]     )
     elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
          __A		:					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":
          __A		:					List[Any]						=			torch.tensor([[0.0_036, 0.9_920, 0.0_045]]     )
     # few shot
     elif model_name == "xclip-base-patch16-hmdb-2-shot":
          __A		:					Optional[int]						=			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":
          __A		:					int						=			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":
          __A		:					Dict						=			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":
          __A		:					Optional[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":
          __A		:					List[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":
          __A		:					str						=			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":
          __A		:					str						=			torch.tensor([[0.0_027, 0.9_904, 0.0_070]]     )
     elif model_name == "xclip-base-patch16-ucf-16-shot":
          __A		:					List[Any]						=			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":
          __A		:					Optional[Any]						=			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(__snake_case					,				__snake_case					,				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(__snake_case     )

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


if __name__ == "__main__":
  lowercase__       : str         =       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.'''
  )

  lowercase__       : Tuple         =       parser.parse_args()
  convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






_lowercase		:  Any			       ={"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
_lowercase		:  Union[str, Any]			       =["""a""", """b""", """c""", """d""", """e"""]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      start
		# add current to visited
		visited.append(lowerCAmelCase__						)
		lowerCamelCase_	:     Optional[int] 						=      edges[current]
		for neighbor in neighbors:
				# if neighbor not in visited, visit
				if neighbor not in visited:
						lowerCamelCase_	:     Dict 						=      topological_sort(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
    # if all neighbors visited add current to sort
		sort.append(lowerCAmelCase__						)
		# if all vertices haven't been visited select a new one to visit
		if len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
				for vertice in vertices:
						if vertice not in visited:
								lowerCamelCase_	:     Any 						=      topological_sort(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
    # return sort
		return sort


if __name__ == "__main__":
		_lowercase		:  str			       =topological_sort("""a""", [], [])
		print(sort)
