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 sklearn.metrics import roc_auc_score
  
  import datasets
  
  
  snake_case				:	List[Any]							   =						"\nThis metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model's predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.\n\nThis metric has three separate use cases:\n    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.\n    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.\n    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.\n"
  
  snake_case				:	Any							   =						"\nArgs:\n- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples,)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples, n_classes)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- average (`str`): Type of average, and is ignored in the binary use case. Defaults to 'macro'. Options are:\n    - `'micro'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.\n    - `'macro'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.\n    - `'weighted'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).\n    - `'samples'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.\n    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.\n- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.\n- max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.\n- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:\n    - `'ovr'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == 'macro'`, because class imbalance affects the composition of each of the 'rest' groupings.\n    - `'ovo'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == 'macro'`.\n- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in\n    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in\n    `prediction_scores` is used. Defaults to `None`.\nReturns:\n    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average='None'`. Otherwise, returns `float`.\nExamples:\n    Example 1:\n        >>> roc_auc_score = datasets.load_metric(\"roc_auc\")\n        >>> refs = [1, 0, 1, 1, 0, 0]\n        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)\n        >>> print(round(results['roc_auc'], 2))\n        0.78\n\n    Example 2:\n        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multiclass\")\n        >>> refs = [1, 0, 1, 2, 2, 0]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class='ovr')\n        >>> print(round(results['roc_auc'], 2))\n        0.85\n\n    Example 3:\n        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multilabel\")\n        >>> refs = [[1, 1, 0],\n        ...         [1, 1, 0],\n        ...         [0, 1, 0],\n        ...         [0, 0, 1],\n        ...         [0, 1, 1],\n        ...         [1, 0, 1]]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)\n        >>> print([round(res, 2) for res in results['roc_auc']])\n        [0.83, 0.38, 0.94]\n"
  
  snake_case				:	List[Any]							   =						"\\n@article{doi:10.1177/0272989X8900900307,\nauthor = {Donna Katzman McClish},\ntitle ={Analyzing a Portion of the ROC Curve},\njournal = {Medical Decision Making},\nvolume = {9},\nnumber = {3},\npages = {190-195},\nyear = {1989},\ndoi = {10.1177/0272989X8900900307},\n    note ={PMID: 2668680},\nURL = {https://doi.org/10.1177/0272989X8900900307},\neprint = {https://doi.org/10.1177/0272989X8900900307}\n}\n\n\n@article{10.1023/A:1010920819831,\nauthor = {Hand, David J. and Till, Robert J.},\ntitle = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},\nyear = {2001},\nissue_date = {November 2001},\npublisher = {Kluwer Academic Publishers},\naddress = {USA},\nvolume = {45},\nnumber = {2},\nissn = {0885-6125},\nurl = {https://doi.org/10.1023/A:1010920819831},\ndoi = {10.1023/A:1010920819831},\njournal = {Mach. Learn.},\nmonth = {oct},\npages = {171–186},\nnumpages = {16},\nkeywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}\n}\n\n\n@article{scikit-learn,\ntitle={Scikit-learn: Machine Learning in {P}ython},\nauthor={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\njournal={Journal of Machine Learning Research},\nvolume={12},\npages={2825--2830},\nyear={2011}\n}\n"
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,		_KWARGS_DESCRIPTION       )
  class    _snake_case      (  datasets.Metric       ):
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return datasets.MetricInfo(
  				    description=_DESCRIPTION		,     citation=_CITATION		,     inputs_description=_KWARGS_DESCRIPTION		,     features=datasets.Features(
  				        {
  				            "prediction_scores": datasets.Sequence(datasets.Value("float"	)	),
  				            "references": datasets.Value("int32"	),
  				        }
  				        if self.config_name == "multiclass"
  				        else {
  				            "references": datasets.Sequence(datasets.Value("int32"	)	),
  				            "prediction_scores": datasets.Sequence(datasets.Value("float"	)	),
  				        }
  				        if self.config_name == "multilabel"
  				        else {
  				            "references": datasets.Value("int32"	),
  				            "prediction_scores": datasets.Value("float"	),
  				        }	)		,     reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html"]		,     )
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a="macro"		,     _a=None		,     _a=None		,     _a="raise"		,     _a=None		,     ):
  				return {
  				    "roc_auc": roc_auc_score(
  				        _a		,     _a		,     average=_a		,     sample_weight=_a		,     max_fpr=_a		,     multi_class=_a		,     labels=_a		,     )
  				}
  