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     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       TimesformerConfig()
  
       if "large" in model_name:
            snake_case					=       96
  
       if "hr" in model_name:
            snake_case					=       16
            snake_case					=       4_48
  
       snake_case					=       '''huggingface/label-files'''
       if "k400" in model_name:
            snake_case					=       4_00
            snake_case					=       '''kinetics400-id2label.json'''
       elif "k600" in model_name:
            snake_case					=       6_00
            snake_case					=       '''kinetics600-id2label.json'''
       elif "ssv2" in model_name:
            snake_case					=       1_74
            snake_case					=       '''something-something-v2-id2label.json'''
       else:
            raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'''       )
       snake_case					=       json.load(open(hf_hub_download(UpperCamelCase_							,UpperCamelCase_							,repo_type='''dataset'''       )							,'''r'''       )       )
       snake_case					=       {int(UpperCamelCase_       ): v for k, v in idalabel.items()}
       snake_case					=       idalabel
       snake_case					=       {v: k for k, v in idalabel.items()}
  
       return config
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if "encoder." in name:
            snake_case					=       name.replace('''encoder.'''							,''''''       )
       if "cls_token" in name:
            snake_case					=       name.replace('''cls_token'''							,'''timesformer.embeddings.cls_token'''       )
       if "pos_embed" in name:
            snake_case					=       name.replace('''pos_embed'''							,'''timesformer.embeddings.position_embeddings'''       )
       if "time_embed" in name:
            snake_case					=       name.replace('''time_embed'''							,'''timesformer.embeddings.time_embeddings'''       )
       if "patch_embed.proj" in name:
            snake_case					=       name.replace('''patch_embed.proj'''							,'''timesformer.embeddings.patch_embeddings.projection'''       )
       if "patch_embed.norm" in name:
            snake_case					=       name.replace('''patch_embed.norm'''							,'''timesformer.embeddings.norm'''       )
       if "blocks" in name:
            snake_case					=       name.replace('''blocks'''							,'''timesformer.encoder.layer'''       )
       if "attn.proj" in name:
            snake_case					=       name.replace('''attn.proj'''							,'''attention.output.dense'''       )
       if "attn" in name and "bias" not in name and "temporal" not in name:
            snake_case					=       name.replace('''attn'''							,'''attention.self'''       )
       if "attn" in name and "temporal" not in name:
            snake_case					=       name.replace('''attn'''							,'''attention.attention'''       )
       if "temporal_norm1" in name:
            snake_case					=       name.replace('''temporal_norm1'''							,'''temporal_layernorm'''       )
       if "temporal_attn.proj" in name:
            snake_case					=       name.replace('''temporal_attn'''							,'''temporal_attention.output.dense'''       )
       if "temporal_fc" in name:
            snake_case					=       name.replace('''temporal_fc'''							,'''temporal_dense'''       )
       if "norm1" in name and "temporal" not in name:
            snake_case					=       name.replace('''norm1'''							,'''layernorm_before'''       )
       if "norm2" in name:
            snake_case					=       name.replace('''norm2'''							,'''layernorm_after'''       )
       if "mlp.fc1" in name:
            snake_case					=       name.replace('''mlp.fc1'''							,'''intermediate.dense'''       )
       if "mlp.fc2" in name:
            snake_case					=       name.replace('''mlp.fc2'''							,'''output.dense'''       )
       if "norm.weight" in name and "fc" not in name and "temporal" not in name:
            snake_case					=       name.replace('''norm.weight'''							,'''timesformer.layernorm.weight'''       )
       if "norm.bias" in name and "fc" not in name and "temporal" not in name:
            snake_case					=       name.replace('''norm.bias'''							,'''timesformer.layernorm.bias'''       )
       if "head" in name:
            snake_case					=       name.replace('''head'''							,'''classifier'''       )
  
       return name
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for key in orig_state_dict.copy().keys():
            snake_case					=       orig_state_dict.pop(UpperCamelCase_       )
  
            if key.startswith('''model.'''       ):
                 snake_case					=       key.replace('''model.'''							,''''''       )
  
            if "qkv" in key:
                 snake_case					=       key.split('''.'''       )
                 snake_case					=       int(key_split[1]       )
                 snake_case					=       '''timesformer.encoder.layer.'''
                 if "temporal" in key:
                      snake_case					=       '''.temporal_attention.attention.qkv.'''
                 else:
                      snake_case					=       '''.attention.attention.qkv.'''
                 if "weight" in key:
                      snake_case					=       val
                 else:
                      snake_case					=       val
            else:
                 snake_case					=       val
  
       return orig_state_dict
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       hf_hub_download(
           repo_id='''hf-internal-testing/spaghetti-video'''							,filename='''eating_spaghetti.npy'''							,repo_type='''dataset'''       )
       snake_case					=       np.load(UpperCamelCase_       )
       return list(UpperCamelCase_       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       get_timesformer_config(UpperCamelCase_       )
  
       snake_case					=       TimesformerForVideoClassification(UpperCamelCase_       )
  
       # download original checkpoint, hosted on Google Drive
       snake_case					=       '''pytorch_model.bin'''
       gdown.cached_download(UpperCamelCase_							,UpperCamelCase_							,quiet=UpperCamelCase_       )
       snake_case					=       torch.load(UpperCamelCase_							,map_location='''cpu'''       )
       if "model" in files:
            snake_case					=       files['''model''']
       elif "module" in files:
            snake_case					=       files['''module''']
       else:
            snake_case					=       files['''model_state''']
       snake_case					=       convert_state_dict(UpperCamelCase_							,UpperCamelCase_       )
  
       model.load_state_dict(UpperCamelCase_       )
       model.eval()
  
       # verify model on basic input
       snake_case					=       VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]							,image_std=[0.5, 0.5, 0.5]       )
       snake_case					=       prepare_video()
       snake_case					=       image_processor(video[:8]							,return_tensors='''pt'''       )
  
       snake_case					=       model(**UpperCamelCase_       )
       snake_case					=       outputs.logits
  
       snake_case					=       [
           # 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":
            snake_case					=       torch.Size([1, 4_00]       )
            snake_case					=       torch.tensor([-0.3016, -0.7713, -0.4205]       )
       elif model_name == "timesformer-base-finetuned-k600":
            snake_case					=       torch.Size([1, 6_00]       )
            snake_case					=       torch.tensor([-0.7267, -0.7466, 3.2404]       )
       elif model_name == "timesformer-base-finetuned-ssv2":
            snake_case					=       torch.Size([1, 1_74]       )
            snake_case					=       torch.tensor([-0.9059, 0.6433, -3.1457]       )
       elif model_name == "timesformer-large-finetuned-k400":
            snake_case					=       torch.Size([1, 4_00]       )
            snake_case					=       torch.tensor([0, 0, 0]       )
       elif model_name == "timesformer-large-finetuned-k600":
            snake_case					=       torch.Size([1, 6_00]       )
            snake_case					=       torch.tensor([0, 0, 0]       )
       elif model_name == "timesformer-large-finetuned-ssv2":
            snake_case					=       torch.Size([1, 1_74]       )
            snake_case					=       torch.tensor([0, 0, 0]       )
       elif model_name == "timesformer-hr-finetuned-k400":
            snake_case					=       torch.Size([1, 4_00]       )
            snake_case					=       torch.tensor([-0.9617, -3.7311, -3.7708]       )
       elif model_name == "timesformer-hr-finetuned-k600":
            snake_case					=       torch.Size([1, 6_00]       )
            snake_case					=       torch.tensor([2.5273, 0.7127, 1.8848]       )
       elif model_name == "timesformer-hr-finetuned-ssv2":
            snake_case					=       torch.Size([1, 1_74]       )
            snake_case					=       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]							,UpperCamelCase_							,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(UpperCamelCase_       )
            model.save_pretrained(UpperCamelCase_       )
  
       if push_to_hub:
            print('''Pushing to the hub...'''       )
            model.push_to_hub(F'''fcakyon/{model_name}'''       )
  
  
  if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					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."
   )
  
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					parser.parse_args()
   convert_timesformer_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
   )
  
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				List[str]       =					[
       "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMForMaskedLM",
       "LayoutLMForSequenceClassification",
       "LayoutLMForTokenClassification",
       "LayoutLMForQuestionAnswering",
       "LayoutLMModel",
       "LayoutLMPreTrainedModel",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
       "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFLayoutLMForMaskedLM",
       "TFLayoutLMForSequenceClassification",
       "TFLayoutLMForTokenClassification",
       "TFLayoutLMForQuestionAnswering",
       "TFLayoutLMMainLayer",
       "TFLayoutLMModel",
       "TFLayoutLMPreTrainedModel",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )
  
  else:
   import sys
  
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  