code: 
  
  
  
  
  import argparse
  import json
  
  import gdown
  import numpy as np
  import torch
  from huggingface_hub import hf_hub_download
  
  from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor
  def 					A_			(		snake_case_     :       List[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   int          =							TimesformerConfig()
  
      if "large" in model_name:
          UpperCamelCase				:   Optional[int]          =							9_6
  
      if "hr" in model_name:
          UpperCamelCase				:   Any          =							1_6
          UpperCamelCase				:   Optional[Any]          =							4_4_8
  
      UpperCamelCase				:   int          =							"huggingface/label-files"
      if "k400" in model_name:
          UpperCamelCase				:   List[Any]          =							4_0_0
          UpperCamelCase				:   int          =							"kinetics400-id2label.json"
      elif "k600" in model_name:
          UpperCamelCase				:   Dict          =							6_0_0
          UpperCamelCase				:   Optional[Any]          =							"kinetics600-id2label.json"
      elif "ssv2" in model_name:
          UpperCamelCase				:   Dict          =							1_7_4
          UpperCamelCase				:   Optional[Any]          =							"something-something-v2-id2label.json"
      else:
          raise ValueError("""Model name should either contain 'k400', 'k600' or 'ssv2'."""    )
      UpperCamelCase				:   str          =							json.load(open(hf_hub_download(snake_case_						,snake_case_						,repo_type="""dataset"""    )						,"""r"""    )    )
      UpperCamelCase				:   List[Any]          =							{int(snake_case_    ): v for k, v in idalabel.items()}
      UpperCamelCase				:   List[Any]          =							idalabel
      UpperCamelCase				:   Any          =							{v: k for k, v in idalabel.items()}
  
      return config
  def 					A_			(		snake_case_     :       Dict    ):
  
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
      if "encoder." in name:
          UpperCamelCase				:   List[Any]          =							name.replace("""encoder."""						,""""""    )
      if "cls_token" in name:
          UpperCamelCase				:   str          =							name.replace("""cls_token"""						,"""timesformer.embeddings.cls_token"""    )
      if "pos_embed" in name:
          UpperCamelCase				:   Optional[Any]          =							name.replace("""pos_embed"""						,"""timesformer.embeddings.position_embeddings"""    )
      if "time_embed" in name:
          UpperCamelCase				:   int          =							name.replace("""time_embed"""						,"""timesformer.embeddings.time_embeddings"""    )
      if "patch_embed.proj" in name:
          UpperCamelCase				:   int          =							name.replace("""patch_embed.proj"""						,"""timesformer.embeddings.patch_embeddings.projection"""    )
      if "patch_embed.norm" in name:
          UpperCamelCase				:   List[str]          =							name.replace("""patch_embed.norm"""						,"""timesformer.embeddings.norm"""    )
      if "blocks" in name:
          UpperCamelCase				:   str          =							name.replace("""blocks"""						,"""timesformer.encoder.layer"""    )
      if "attn.proj" in name:
          UpperCamelCase				:   Optional[Any]          =							name.replace("""attn.proj"""						,"""attention.output.dense"""    )
      if "attn" in name and "bias" not in name and "temporal" not in name:
          UpperCamelCase				:   str          =							name.replace("""attn"""						,"""attention.self"""    )
      if "attn" in name and "temporal" not in name:
          UpperCamelCase				:   Any          =							name.replace("""attn"""						,"""attention.attention"""    )
      if "temporal_norm1" in name:
          UpperCamelCase				:   Optional[Any]          =							name.replace("""temporal_norm1"""						,"""temporal_layernorm"""    )
      if "temporal_attn.proj" in name:
          UpperCamelCase				:   Dict          =							name.replace("""temporal_attn"""						,"""temporal_attention.output.dense"""    )
      if "temporal_fc" in name:
          UpperCamelCase				:   Union[str, Any]          =							name.replace("""temporal_fc"""						,"""temporal_dense"""    )
      if "norm1" in name and "temporal" not in name:
          UpperCamelCase				:   Any          =							name.replace("""norm1"""						,"""layernorm_before"""    )
      if "norm2" in name:
          UpperCamelCase				:   int          =							name.replace("""norm2"""						,"""layernorm_after"""    )
      if "mlp.fc1" in name:
          UpperCamelCase				:   int          =							name.replace("""mlp.fc1"""						,"""intermediate.dense"""    )
      if "mlp.fc2" in name:
          UpperCamelCase				:   Optional[int]          =							name.replace("""mlp.fc2"""						,"""output.dense"""    )
      if "norm.weight" in name and "fc" not in name and "temporal" not in name:
          UpperCamelCase				:   List[str]          =							name.replace("""norm.weight"""						,"""timesformer.layernorm.weight"""    )
      if "norm.bias" in name and "fc" not in name and "temporal" not in name:
          UpperCamelCase				:   List[Any]          =							name.replace("""norm.bias"""						,"""timesformer.layernorm.bias"""    )
      if "head" in name:
          UpperCamelCase				:   List[Any]          =							name.replace("""head"""						,"""classifier"""    )
  
      return name
  def 					A_			(		snake_case_     :       List[Any]						,snake_case_     :       Any    ):
  
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
      for key in orig_state_dict.copy().keys():
          UpperCamelCase				:   int          =							orig_state_dict.pop(snake_case_    )
  
          if key.startswith("""model."""    ):
              UpperCamelCase				:   Optional[int]          =							key.replace("""model."""						,""""""    )
  
          if "qkv" in key:
              UpperCamelCase				:   Tuple          =							key.split("""."""    )
              UpperCamelCase				:   Optional[Any]          =							int(key_split[1]    )
              UpperCamelCase				:   Tuple          =							"timesformer.encoder.layer."
              if "temporal" in key:
                  UpperCamelCase				:   str          =							".temporal_attention.attention.qkv."
              else:
                  UpperCamelCase				:   str          =							".attention.attention.qkv."
              if "weight" in key:
                  UpperCamelCase				:   str          =							val
              else:
                  UpperCamelCase				:   Dict          =							val
          else:
              UpperCamelCase				:   int          =							val
  
      return orig_state_dict
  def 					A_			(		):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Dict          =							hf_hub_download(
          repo_id="""hf-internal-testing/spaghetti-video"""						,filename="""eating_spaghetti.npy"""						,repo_type="""dataset"""    )
      UpperCamelCase				:   str          =							np.load(snake_case_    )
      return list(snake_case_    )
  def 					A_			(		snake_case_     :       Optional[Any]						,snake_case_     :       Dict						,snake_case_     :       Tuple						,snake_case_     :       Union[str, Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   int          =							get_timesformer_config(snake_case_    )
  
      UpperCamelCase				:   Dict          =							TimesformerForVideoClassification(snake_case_    )
  
      # download original checkpoint, hosted on Google Drive
      UpperCamelCase				:   Dict          =							"pytorch_model.bin"
      gdown.cached_download(snake_case_						,snake_case_						,quiet=snake_case_    )
      UpperCamelCase				:   Tuple          =							torch.load(snake_case_						,map_location="""cpu"""    )
      if "model" in files:
          UpperCamelCase				:   Optional[Any]          =							files["model"]
      elif "module" in files:
          UpperCamelCase				:   Dict          =							files["module"]
      else:
          UpperCamelCase				:   int          =							files["model_state"]
      UpperCamelCase				:   List[Any]          =							convert_state_dict(snake_case_						,snake_case_    )
  
      model.load_state_dict(snake_case_    )
      model.eval()
  
      # verify model on basic input
      UpperCamelCase				:   Any          =							VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]						,image_std=[0.5, 0.5, 0.5]    )
      UpperCamelCase				:   Optional[int]          =							prepare_video()
      UpperCamelCase				:   Tuple          =							image_processor(video[:8]						,return_tensors="""pt"""    )
  
      UpperCamelCase				:   int          =							model(**snake_case_    )
      UpperCamelCase				:   Any          =							outputs.logits
  
      UpperCamelCase				:   List[str]          =							[
          # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
          "timesformer-base-finetuned-k400",
          "timesformer-large-finetuned-k400",
          "timesformer-hr-finetuned-k400",
          # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
          "timesformer-base-finetuned-k600",
          "timesformer-large-finetuned-k600",
          "timesformer-hr-finetuned-k600",
          # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
          "timesformer-base-finetuned-ssv2",
          "timesformer-large-finetuned-ssv2",
          "timesformer-hr-finetuned-ssv2",
      ]
  
      # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
      if model_name == "timesformer-base-finetuned-k400":
          UpperCamelCase				:   str          =							torch.Size([1, 4_0_0]    )
          UpperCamelCase				:   Optional[Any]          =							torch.tensor([-0.3016, -0.7713, -0.4205]    )
      elif model_name == "timesformer-base-finetuned-k600":
          UpperCamelCase				:   str          =							torch.Size([1, 6_0_0]    )
          UpperCamelCase				:   str          =							torch.tensor([-0.7267, -0.7466, 3.2404]    )
      elif model_name == "timesformer-base-finetuned-ssv2":
          UpperCamelCase				:   int          =							torch.Size([1, 1_7_4]    )
          UpperCamelCase				:   Optional[Any]          =							torch.tensor([-0.9059, 0.6433, -3.1457]    )
      elif model_name == "timesformer-large-finetuned-k400":
          UpperCamelCase				:   List[Any]          =							torch.Size([1, 4_0_0]    )
          UpperCamelCase				:   Optional[int]          =							torch.tensor([0, 0, 0]    )
      elif model_name == "timesformer-large-finetuned-k600":
          UpperCamelCase				:   List[str]          =							torch.Size([1, 6_0_0]    )
          UpperCamelCase				:   Optional[Any]          =							torch.tensor([0, 0, 0]    )
      elif model_name == "timesformer-large-finetuned-ssv2":
          UpperCamelCase				:   Union[str, Any]          =							torch.Size([1, 1_7_4]    )
          UpperCamelCase				:   int          =							torch.tensor([0, 0, 0]    )
      elif model_name == "timesformer-hr-finetuned-k400":
          UpperCamelCase				:   int          =							torch.Size([1, 4_0_0]    )
          UpperCamelCase				:   str          =							torch.tensor([-0.9617, -3.7311, -3.7708]    )
      elif model_name == "timesformer-hr-finetuned-k600":
          UpperCamelCase				:   Optional[Any]          =							torch.Size([1, 6_0_0]    )
          UpperCamelCase				:   Optional[Any]          =							torch.tensor([2.5273, 0.7127, 1.8848]    )
      elif model_name == "timesformer-hr-finetuned-ssv2":
          UpperCamelCase				:   Union[str, Any]          =							torch.Size([1, 1_7_4]    )
          UpperCamelCase				:   str          =							torch.tensor([-3.6756, -0.7513, 0.7180]    )
      else:
          raise ValueError(f'Model name not supported. Should be one of {model_names}'    )
  
      # verify logits
      assert logits.shape == expected_shape
      assert torch.allclose(logits[0, :3]						,snake_case_						,atol=1e-4    )
      print("""Logits ok!"""    )
  
      if pytorch_dump_folder_path is not None:
          print(f'Saving model and image processor to {pytorch_dump_folder_path}'    )
          image_processor.save_pretrained(snake_case_    )
          model.save_pretrained(snake_case_    )
  
      if push_to_hub:
          print("""Pushing to the hub..."""    )
          model.push_to_hub(f'fcakyon/{model_name}'    )
  
  
  if __name__ == "__main__":
    __A :		Optional[int]      							=    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--checkpoint_url''',
        default='''https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download''',
        type=str,
        help=(
            '''URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'''
            ''' download link.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''',
        default='''''',
        type=str,
        help='''Path to the output PyTorch model directory.''',
    )
    parser.add_argument('''--model_name''', default='''timesformer-base-finetuned-k400''', type=str, help='''Name of the model.''')
    parser.add_argument(
        '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
    )
  
    __A :		Union[str, Any]      							=    parser.parse_args()
    convert_timesformer_checkpoint(
        args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
    )
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  def 					A_			(		snake_case_     :       List[str]						,snake_case_     :       Union[str, Any]=2_5_6    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[int]          =							min(img.size    )
  
      if s < target_image_size:
          raise ValueError(f'min dim for image {s} < {target_image_size}'    )
  
      UpperCamelCase				:   Dict          =							target_image_size / s
      UpperCamelCase				:   Any          =							(round(r * img.size[1]    ), round(r * img.size[0]    ))
      UpperCamelCase				:   Union[str, Any]          =							TF.resize(snake_case_						,snake_case_						,interpolation=PIL.Image.LANCZOS    )
      UpperCamelCase				:   Optional[int]          =							TF.center_crop(snake_case_						,output_size=2 * [target_image_size]    )
      UpperCamelCase				:   Optional[Any]          =							torch.unsqueeze(T.ToTensor()(snake_case_    )						,0    )
      return img
  def 					A_			(		snake_case_     :       List[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Any          =							2.0 * x - 1.0
      return x
  def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]=True						,snake_case_     :       Dict="RGB"    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Dict          =							x.detach().cpu()
      if process:
          UpperCamelCase				:   str          =							post_process_tensor(snake_case_    )
      UpperCamelCase				:   List[Any]          =							x.numpy()
      if process:
          UpperCamelCase				:   Optional[int]          =							(2_5_5 * x).astype(np.uinta    )
      UpperCamelCase				:   List[Any]          =							Image.fromarray(snake_case_    )
      if not x.mode == mode:
          UpperCamelCase				:   Any          =							x.convert(snake_case_    )
      return x
  def 					A_			(		snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   int          =							torch.clamp(snake_case_						,-1.0						,1.0    )
      UpperCamelCase				:   Optional[Any]          =							(x + 1.0) / 2.0
      UpperCamelCase				:   Dict          =							x.permute(1						,2						,0    )
      return x
  
  
  def 					A_			(		snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[Any]          =							post_process_tensor(x.squeeze()    )
      return x.permute(2						,0						,1    ).unsqueeze(0    )
  
  
  
  
  