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 						lowerCAmelCase_		(      _snake_case   :						Any     )       ->	Tuple:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Optional[Any]			     =  "https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"
  
  	with open(_snake_case   ,     "wb"     ) as f:
  		with requests.get(_snake_case   ,     stream=_snake_case     ) as req:
  			for chunk in req.iter_content(chunk_size=2048     ):
  				f.write(_snake_case     )
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      )       ->	VivitConfig:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Tuple			     =  VivitConfig()
  
  	__magic_name__	:	Optional[int]			     =  400
  	__magic_name__	:	Any			     =  "huggingface/label-files"
  	__magic_name__	:	Tuple			     =  "kinetics400-id2label.json"
  
  	__magic_name__	:	Optional[int]			     =  json.load(open(hf_hub_download(_snake_case   ,     _snake_case   ,     repo_type="dataset"     )   ,     "r"     )     )
  	__magic_name__	:	Union[str, Any]			     =  {int(_snake_case     ): v for k, v in idalabel.items()}
  	__magic_name__	:	List[str]			     =  idalabel
  	__magic_name__	:	Tuple			     =  {v: k for k, v in idalabel.items()}
  	return config
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      )       ->	Any:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Dict			     =  hf_hub_download(
  	    repo_id="hf-internal-testing/spaghetti-video"   ,     filename="eating_spaghetti_32_frames.npy"   ,     repo_type="dataset"     )
  	__magic_name__	:	Union[str, Any]			     =  np.load(_snake_case     )
  	return list(_snake_case     )
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						np.ndarray     )       ->	Tuple:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if np.ndim(_snake_case     ) == 2:
  		return transform_attention_bias(_snake_case     )
  
  	elif np.ndim(_snake_case     ) == 3:
  		return transform_attention_kernel(_snake_case     )
  
  	else:
  		raise Exception(F'''Invalid number of dimesions: {np.ndim(_snake_case     )}'''     )
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						np.ndarray     )       ->	List[Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	return current.flatten()
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						np.ndarray     )       ->	List[str]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	return np.reshape(_snake_case   ,     (current.shape[0], current.shape[1] * current.shape[2])     ).T
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						np.ndarray     )       ->	Optional[int]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	return np.reshape(_snake_case   ,     (current.shape[0] * current.shape[1], current.shape[2])     ).T
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						List[str]   ,     _snake_case   :						Dict     )       ->	Union[str, Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Any			     =  state_dict["optimizer"]["target"]["Transformer"][F'''encoderblock_{i}''']
  
  	__magic_name__	:	Tuple			     =  F'''encoder.layer.{i}.'''
  	__magic_name__	:	Union[str, Any]			     =  {
  	    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 						lowerCAmelCase_		(      _snake_case   :						Tuple     )       ->	List[Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	return sum([1 if "encoderblock_" in k else 0 for k in state_dict["optimizer"]["target"]["Transformer"].keys()]     )
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						int   ,     _snake_case   :						Optional[int]=False     )       ->	str:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Dict			     =  get_n_layers(_snake_case     )
  
  	__magic_name__	:	Optional[int]			     =  OrderedDict()
  
  	__magic_name__	:	str			     =  state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
  	__magic_name__	:	Tuple			     =  state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]
  
  	__magic_name__	:	Optional[int]			     =  np.transpose(
  	    state_dict["optimizer"]["target"]["embedding"]["kernel"]   ,     (4, 3, 0, 1, 2)     )
  	__magic_name__	:	List[Any]			     =  state_dict["optimizer"]["target"]["embedding"]["bias"]
  
  	__magic_name__	:	Union[str, Any]			     =  state_dict["optimizer"]["target"]["cls"]
  	__magic_name__	:	List[str]			     =  state_dict["optimizer"]["target"]["Transformer"]["posembed_input"][
  	    "pos_embedding"
  	]
  
  	for i in range(_snake_case     ):
  		new_state.update(transform_state_encoder_block(_snake_case   ,     _snake_case     )     )
  
  	if classification_head:
  		__magic_name__	:	List[str]			     =  {"vivit." + k: v for k, v in new_state.items()}
  		__magic_name__	:	List[Any]			     =  np.transpose(state_dict["optimizer"]["target"]["output_projection"]["kernel"]     )
  		__magic_name__	:	str			     =  np.transpose(state_dict["optimizer"]["target"]["output_projection"]["bias"]     )
  
  	return {k: torch.tensor(_snake_case     ) for k, v in new_state.items()}
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      )       ->	VivitImageProcessor:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Optional[Any]			     =  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 						lowerCAmelCase_		(      _snake_case   :						str     )       ->	List[Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	int			     =  "checkpoint"
  
  	if not os.path.exists(_snake_case     ):
  		download_checkpoint(_snake_case     )
  
  	__magic_name__	:	Optional[Any]			     =  restore_checkpoint(_snake_case   ,     _snake_case     )
  	__magic_name__	:	str			     =  transform_state(_snake_case   ,     classification_head=_snake_case     )
  
  	__magic_name__	:	Optional[int]			     =  get_vivit_config()
  
  	assert config.image_size == 224
  	assert config.num_frames == 32
  
  	__magic_name__	:	Any			     =  VivitForVideoClassification(_snake_case     )
  	model.load_state_dict(_snake_case     )
  	model.eval()
  
  	__magic_name__	:	Union[str, Any]			     =  get_processor()
  
  	__magic_name__	:	int			     =  prepare_video()
  	__magic_name__	:	Dict			     =  extractor(_snake_case   ,     return_tensors="pt"     )
  
  	__magic_name__	:	str			     =  model(**_snake_case     )
  
  	__magic_name__	:	Dict			     =  torch.Size([1, 400]     )
  	__magic_name__	:	Union[str, Any]			     =  torch.tensor([-1.0_543, 2.0_764, -0.2_104, 0.4_439, -0.9_658]     )
  
  	assert outputs.logits.shape == expected_shape
  	assert torch.allclose(outputs.logits[0, :5]   ,     _snake_case   ,     atol=1E-4     ), outputs.logits[0, :5]
  
  	model.save_pretrained(_snake_case     )
  	extractor.save_pretrained(_snake_case     )
  
  
  if __name__ == "__main__":
  				snake_case				:	int							   =						argparse.ArgumentParser()
  
  				parser.add_argument("--output_model_name", "-o", type=str, help="Output path for the converted HuggingFace model")
  
  				snake_case				:	Any							   =						parser.parse_args()
  				convert(args.output_model_name)
  
style_context: 
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  snake_case				:	Optional[int]							   =						{
      "configuration_mobilevitv2": [
          "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "MobileViTV2Config",
          "MobileViTV2OnnxConfig",
      ],
  }
  
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				snake_case				:	Optional[int]							   =						[
  				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "MobileViTV2ForImageClassification",
  				    "MobileViTV2ForSemanticSegmentation",
  				    "MobileViTV2Model",
  				    "MobileViTV2PreTrainedModel",
  				]
  
  if TYPE_CHECKING:
  				from .configuration_mobilevitva import (
  				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
  				    MobileViTVaConfig,
  				    MobileViTVaOnnxConfig,
  				)
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_mobilevitva import (
  								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    MobileViTVaForImageClassification,
  								    MobileViTVaForSemanticSegmentation,
  								    MobileViTVaModel,
  								    MobileViTVaPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				snake_case				:	Tuple							   =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  