code: 
  
  
  import argparse
  import json
  import os.path
  from collections import OrderedDict
  
  import numpy as np
  import requests
  import torch
  from flax.training.checkpoints import restore_checkpoint
  from huggingface_hub import hf_hub_download
  
  from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
  from transformers.image_utils import PILImageResampling
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->str:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Optional[int]					   =		'''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''
  
        with open(_UpperCamelCase,    '''wb'''     ) as f:
              with requests.get(_UpperCamelCase,    stream=_UpperCamelCase     ) as req:
                    for chunk in req.iter_content(chunk_size=2048     ):
                          f.write(_UpperCamelCase     )
  
  
  
  
  def 			__lowercase	(						)    ->VivitConfig:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Tuple					   =		VivitConfig()
  
        lowercase			:   Optional[int]					   =		400
        lowercase			:   Union[str, Any]					   =		'''huggingface/label-files'''
        lowercase			:   int					   =		'''kinetics400-id2label.json'''
  
        lowercase			:   str					   =		json.load(open(hf_hub_download(_UpperCamelCase,    _UpperCamelCase,    repo_type='''dataset'''     ),    '''r'''     )     )
        lowercase			:   Optional[Any]					   =		{int(_UpperCamelCase     ): v for k, v in idalabel.items()}
        lowercase			:   str					   =		idalabel
        lowercase			:   int					   =		{v: k for k, v in idalabel.items()}
        return config
  
  
  
  
  def 			__lowercase	(						)    ->Optional[int]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Optional[int]					   =		hf_hub_download(
            repo_id='''hf-internal-testing/spaghetti-video''',    filename='''eating_spaghetti_32_frames.npy''',    repo_type='''dataset'''     )
        lowercase			:   Dict					   =		np.load(_UpperCamelCase     )
        return list(_UpperCamelCase     )
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        if np.ndim(_UpperCamelCase     ) == 2:
              return transform_attention_bias(_UpperCamelCase     )
  
        elif np.ndim(_UpperCamelCase     ) == 3:
              return transform_attention_kernel(_UpperCamelCase     )
  
        else:
              raise Exception(f"""Invalid number of dimesions: {np.ndim(_UpperCamelCase     )}"""     )
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->str:
  
  
  
  
  
  
  
        """simple docstring"""
  
        return current.flatten()
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Optional[int]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        return np.reshape(_UpperCamelCase,    (current.shape[0], current.shape[1] * current.shape[2])     ).T
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->str:
  
  
  
  
  
  
  
        """simple docstring"""
  
        return np.reshape(_UpperCamelCase,    (current.shape[0] * current.shape[1], current.shape[2])     ).T
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Tuple:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   int					   =		state_dict['''optimizer''']['''target''']['''Transformer'''][f"""encoderblock_{i}"""]
  
        lowercase			:   Any					   =		f"""encoder.layer.{i}."""
        lowercase			:   Dict					   =		{
            prefix + '''intermediate.dense.bias''': state['''MlpBlock_0''']['''Dense_0''']['''bias'''],
            prefix + '''intermediate.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_0''']['''kernel''']     ),
            prefix + '''output.dense.bias''': state['''MlpBlock_0''']['''Dense_1''']['''bias'''],
            prefix + '''output.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_1''']['''kernel''']     ),
            prefix + '''layernorm_before.bias''': state['''LayerNorm_0''']['''bias'''],
            prefix + '''layernorm_before.weight''': state['''LayerNorm_0''']['''scale'''],
            prefix + '''layernorm_after.bias''': state['''LayerNorm_1''']['''bias'''],
            prefix + '''layernorm_after.weight''': state['''LayerNorm_1''']['''scale'''],
            prefix
            + '''attention.attention.query.bias''': transform_attention(
                state['''MultiHeadDotProductAttention_0''']['''query''']['''bias''']     ),
            prefix
            + '''attention.attention.query.weight''': transform_attention(
                state['''MultiHeadDotProductAttention_0''']['''query''']['''kernel''']     ),
            prefix
            + '''attention.attention.key.bias''': transform_attention(state['''MultiHeadDotProductAttention_0''']['''key''']['''bias''']     ),
            prefix
            + '''attention.attention.key.weight''': transform_attention(
                state['''MultiHeadDotProductAttention_0''']['''key''']['''kernel''']     ),
            prefix
            + '''attention.attention.value.bias''': transform_attention(
                state['''MultiHeadDotProductAttention_0''']['''value''']['''bias''']     ),
            prefix
            + '''attention.attention.value.weight''': transform_attention(
                state['''MultiHeadDotProductAttention_0''']['''value''']['''kernel''']     ),
            prefix + '''attention.output.dense.bias''': state['''MultiHeadDotProductAttention_0''']['''out''']['''bias'''],
            prefix
            + '''attention.output.dense.weight''': transform_attention_output_weight(
                state['''MultiHeadDotProductAttention_0''']['''out''']['''kernel''']     ),
        }
  
        return new_state
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->str:
  
  
  
  
  
  
  
        """simple docstring"""
  
        return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()]     )
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase=False     )    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Union[str, Any]					   =		get_n_layers(_UpperCamelCase     )
  
        lowercase			:   str					   =		OrderedDict()
  
        lowercase			:   Tuple					   =		state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
        lowercase			:   Union[str, Any]					   =		state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']
  
        lowercase			:   List[str]					   =		np.transpose(
            state_dict['''optimizer''']['''target''']['''embedding''']['''kernel'''],    (4, 3, 0, 1, 2)     )
        lowercase			:   Optional[Any]					   =		state_dict['''optimizer''']['''target''']['''embedding''']['''bias''']
  
        lowercase			:   List[Any]					   =		state_dict['''optimizer''']['''target''']['''cls''']
        lowercase			:   Optional[Any]					   =		state_dict['''optimizer''']['''target''']['''Transformer''']['''posembed_input'''][
            '''pos_embedding'''
        ]
  
        for i in range(_UpperCamelCase     ):
              new_state.update(transform_state_encoder_block(_UpperCamelCase,    _UpperCamelCase     )     )
  
        if classification_head:
              lowercase			:   int					   =		{'''vivit.''' + k: v for k, v in new_state.items()}
              lowercase			:   Optional[Any]					   =		np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''kernel''']     )
              lowercase			:   Any					   =		np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''bias''']     )
  
        return {k: torch.tensor(_UpperCamelCase     ) for k, v in new_state.items()}
  
  
  
  
  def 			__lowercase	(						)    ->VivitImageProcessor:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Optional[int]					   =		VivitImageProcessor()
  
        assert extractor.do_resize is True
        assert extractor.size == {"shortest_edge": 256}
        assert extractor.do_center_crop is True
        assert extractor.crop_size == {"width": 224, "height": 224}
        assert extractor.resample == PILImageResampling.BILINEAR
  
        # here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
        # one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
        # which effectively means no normalization (and ViViT does not overwrite those when calling this func)
        assert extractor.do_normalize is False
        assert extractor.do_rescale is True
        assert extractor.rescale_factor == 1 / 255
  
        # zero-centering = True in original implementation
        assert extractor.do_zero_centering is True
  
        return extractor
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Tuple:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   str					   =		'''checkpoint'''
  
        if not os.path.exists(_UpperCamelCase     ):
              download_checkpoint(_UpperCamelCase     )
  
        lowercase			:   Optional[Any]					   =		restore_checkpoint(_UpperCamelCase,    _UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		transform_state(_UpperCamelCase,    classification_head=_UpperCamelCase     )
  
        lowercase			:   Tuple					   =		get_vivit_config()
  
        assert config.image_size == 224
        assert config.num_frames == 32
  
        lowercase			:   Dict					   =		VivitForVideoClassification(_UpperCamelCase     )
        model.load_state_dict(_UpperCamelCase     )
        model.eval()
  
        lowercase			:   List[str]					   =		get_processor()
  
        lowercase			:   Optional[Any]					   =		prepare_video()
        lowercase			:   Tuple					   =		extractor(_UpperCamelCase,    return_tensors='''pt'''     )
  
        lowercase			:   Dict					   =		model(**_UpperCamelCase     )
  
        lowercase			:   Tuple					   =		torch.Size([1, 400]     )
        lowercase			:   Dict					   =		torch.tensor([-1.0_5_4_3, 2.0_7_6_4, -0.2_1_0_4, 0.4_4_3_9, -0.9_6_5_8]     )
  
        assert outputs.logits.shape == expected_shape
        assert torch.allclose(outputs.logits[0, :5],    _UpperCamelCase,    atol=1e-4     ), outputs.logits[0, :5]
  
        model.save_pretrained(_UpperCamelCase     )
        extractor.save_pretrained(_UpperCamelCase     )
  
  
  if __name__ == "__main__":
    __a   				=      argparse.ArgumentParser()
  
    parser.add_argument('''--output_model_name''', '''-o''', type=str, help='''Output path for the converted HuggingFace model''')
  
    __a   				=      parser.parse_args()
    convert(args.output_model_name)
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  __a   				=      {
      '''configuration_decision_transformer''': [
          '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
          '''DecisionTransformerConfig''',
      ],
  }
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __a   				=      [
        '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''DecisionTransformerGPT2Model''',
        '''DecisionTransformerGPT2PreTrainedModel''',
        '''DecisionTransformerModel''',
        '''DecisionTransformerPreTrainedModel''',
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_decision_transformer import (
        DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        DecisionTransformerConfig,
    )
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )
  
  
  else:
    import sys
  
    __a   				=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  