code: 
  
  
  '''simple docstring'''
  
  import inspect
  import os
  from argparse import ArgumentParser, Namespace
  from importlib import import_module
  
  import huggingface_hub
  import numpy as np
  from packaging import version
  
  from .. import (
      FEATURE_EXTRACTOR_MAPPING,
      IMAGE_PROCESSOR_MAPPING,
      PROCESSOR_MAPPING,
      TOKENIZER_MAPPING,
      AutoConfig,
      AutoFeatureExtractor,
      AutoImageProcessor,
      AutoProcessor,
      AutoTokenizer,
      is_datasets_available,
      is_tf_available,
      is_torch_available,
  )
  from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
  from . import BaseTransformersCLICommand
  
  
  if is_tf_available():
   import tensorflow as tf
  
   tf.config.experimental.enable_tensor_float_aa_execution(False)
  
  if is_torch_available():
   import torch
  
  if is_datasets_available():
   from datasets import load_dataset
  
  
  lowerCamelCase							:     Optional[int]								=			5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
  
  def _SCREAMING_SNAKE_CASE   (A ) -> List[str]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         return PTtoTFCommand(
             args.model_name						,				args.local_dir						,				args.max_error						,				args.new_weights						,				args.no_pr						,				args.push						,				args.extra_commit_description						,				args.override_model_class						,				)
  
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        @staticmethod
        def    UpperCamelCase__	(UpperCamelCase					:					ArgumentParser				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				parser.add_parser(
                   '''pt-to-tf'''     ,		help=(
                       '''CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'''
                       ''' Can also be used to validate existing weights without opening PRs, with --no-pr.'''
                   )     ,		)
               train_parser.add_argument(
                   '''--model-name'''     ,		type=UpperCamelCase     ,		required=UpperCamelCase     ,		help='''The model name, including owner/organization, as seen on the hub.'''     ,		)
               train_parser.add_argument(
                   '''--local-dir'''     ,		type=UpperCamelCase     ,		default=''''''     ,		help='''Optional local directory of the model repository. Defaults to /tmp/{model_name}'''     ,		)
               train_parser.add_argument(
                   '''--max-error'''     ,		type=UpperCamelCase     ,		default=UpperCamelCase     ,		help=(
                       f"Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."
                   )     ,		)
               train_parser.add_argument(
                   '''--new-weights'''     ,		action='''store_true'''     ,		help='''Optional flag to create new TensorFlow weights, even if they already exist.'''     ,		)
               train_parser.add_argument(
                   '''--no-pr'''     ,		action='''store_true'''     ,		help='''Optional flag to NOT open a PR with converted weights.'''				)
               train_parser.add_argument(
                   '''--push'''     ,		action='''store_true'''     ,		help='''Optional flag to push the weights directly to `main` (requires permissions)'''     ,		)
               train_parser.add_argument(
                   '''--extra-commit-description'''     ,		type=UpperCamelCase     ,		default=''''''     ,		help='''Optional additional commit description to use when opening a PR (e.g. to tag the owner).'''     ,		)
               train_parser.add_argument(
                   '''--override-model-class'''     ,		type=UpperCamelCase     ,		default=UpperCamelCase     ,		help='''If you think you know better than the auto-detector, you can specify the model class here. '''
                   '''Can be either an AutoModel class or a specific model class like BertForSequenceClassification.'''     ,		)
               train_parser.set_defaults(func=UpperCamelCase				)
  
  
  
        @staticmethod
        def    UpperCamelCase__	(UpperCamelCase					:					str     ,		UpperCamelCase					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				set(pt_outputs.keys()				)
               lowercase__       =				set(tf_outputs.keys()				)
               if pt_out_attrs != tf_out_attrs:
                      raise ValueError(
                          f"The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"
                          f" {tf_out_attrs})"				)
  
               # 2. For each output attribute, computes the difference
               def _find_pt_tf_differences(UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					Optional[Any]=""				):
                      # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
                      # recursivelly, keeping the name of the attribute.
                      if isinstance(UpperCamelCase     ,		torch.Tensor				):
                             lowercase__       =				np.max(np.abs(pt_out.numpy() - tf_out.numpy()				)				)
                             lowercase__       =				tensor_difference
                      else:
                             lowercase__       =				attr_name
                             for i, pt_item in enumerate(UpperCamelCase				):
                                    # If it is a named attribute, we keep the name. Otherwise, just its index.
                                    if isinstance(UpperCamelCase     ,		UpperCamelCase				):
                                           lowercase__       =				root_name + pt_item
                                           lowercase__       =				tf_out[pt_item]
                                           lowercase__       =				pt_out[pt_item]
                                    else:
                                           lowercase__       =				root_name + f"[{i}]"
                                           lowercase__       =				tf_out[i]
                                    lowercase__       =				_find_pt_tf_differences(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)
  
                      return differences
  
               return _find_pt_tf_differences(UpperCamelCase     ,		UpperCamelCase     ,		{}				)
  
  
  
        def __init__(self					:					Optional[Any]     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					float     ,		UpperCamelCase					:					bool     ,		UpperCamelCase					:					bool     ,		UpperCamelCase					:					bool     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					str     ,		*UpperCamelCase					:					Tuple     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				logging.get_logger('''transformers-cli/pt_to_tf'''				)
               lowercase__       =				model_name
               lowercase__       =				local_dir if local_dir else os.path.join('''/tmp'''     ,		UpperCamelCase				)
               lowercase__       =				max_error
               lowercase__       =				new_weights
               lowercase__       =				no_pr
               lowercase__       =				push
               lowercase__       =				extra_commit_description
               lowercase__       =				override_model_class
  
  
  
        def    UpperCamelCase__	(self					:					List[str]     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               def _get_audio_input():
                      lowercase__       =				load_dataset('''hf-internal-testing/librispeech_asr_dummy'''     ,		'''clean'''     ,		split='''validation'''				)
                      lowercase__       =				ds.sort('''id'''				).select(range(2				)				)[:2]['''audio''']
                      lowercase__       =				[x['''array'''] for x in speech_samples]
                      return raw_samples
  
               lowercase__       =				type(pt_model.config				)
               if model_config_class in PROCESSOR_MAPPING:
                      lowercase__       =				AutoProcessor.from_pretrained(self._local_dir				)
                      if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
                             lowercase__       =				processor.tokenizer.eos_token
               elif model_config_class in IMAGE_PROCESSOR_MAPPING:
                      lowercase__       =				AutoImageProcessor.from_pretrained(self._local_dir				)
               elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
                      lowercase__       =				AutoFeatureExtractor.from_pretrained(self._local_dir				)
               elif model_config_class in TOKENIZER_MAPPING:
                      lowercase__       =				AutoTokenizer.from_pretrained(self._local_dir				)
                      if processor.pad_token is None:
                             lowercase__       =				processor.eos_token
               else:
                      raise ValueError(f"Unknown data processing type (model config type: {model_config_class})"				)
  
               lowercase__       =				set(inspect.signature(pt_model.forward				).parameters.keys()				)
               lowercase__       =				{}
               if "input_ids" in model_forward_signature:
                      processor_inputs.update(
                          {
                              '''text''': ['''Hi there!''', '''I am a batch with more than one row and different input lengths.'''],
                              '''padding''': True,
                              '''truncation''': True,
                          }				)
               if "pixel_values" in model_forward_signature:
                      lowercase__       =				load_dataset('''cifar10'''     ,		'''plain_text'''     ,		split='''test'''				)[:2]['''img''']
                      processor_inputs.update({'''images''': sample_images}				)
               if "input_features" in model_forward_signature:
                      lowercase__       =				inspect.signature(processor.feature_extractor				).parameters
                      # Pad to the largest input length by default but take feature extractor default
                      # padding value if it exists e.g. "max_length" and is not False or None
                      if "padding" in feature_extractor_signature:
                             lowercase__       =				feature_extractor_signature['''padding'''].default
                             if default_strategy is not False and default_strategy is not None:
                                    lowercase__       =				default_strategy
                             else:
                                    lowercase__       =				True
                      else:
                             lowercase__       =				True
                      processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': padding_strategy}				)
               if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
                      processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': True}				)
               lowercase__       =				processor(**UpperCamelCase     ,		return_tensors='''pt'''				)
               lowercase__       =				processor(**UpperCamelCase     ,		return_tensors='''tf'''				)
  
               # Extra input requirements, in addition to the input modality
               if (
                   config.is_encoder_decoder
                   or (hasattr(UpperCamelCase     ,		'''encoder'''				) and hasattr(UpperCamelCase     ,		'''decoder'''				))
                   or "decoder_input_ids" in tf_dummy_inputs
               ):
                      lowercase__       =				np.asarray([[1], [1]]     ,		dtype=UpperCamelCase				) * (pt_model.config.decoder_start_token_id or 0)
                      pt_input.update({'''decoder_input_ids''': torch.tensor(UpperCamelCase				)}				)
                      tf_input.update({'''decoder_input_ids''': tf.convert_to_tensor(UpperCamelCase				)}				)
  
               return pt_input, tf_input
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					str				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if version.parse(huggingface_hub.__version__				) < version.parse('''0.9.0'''				):
                      raise ImportError(
                          '''The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'''
                          ''' installation.'''				)
               else:
                      from huggingface_hub import Repository, create_commit
                      from huggingface_hub._commit_api import CommitOperationAdd
  
               # Fetch remote data
               lowercase__       =				Repository(local_dir=self._local_dir     ,		clone_from=self._model_name				)
  
               # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
               lowercase__       =				AutoConfig.from_pretrained(self._local_dir				)
               lowercase__       =				config.architectures
               if self._override_model_class is not None:
                      if self._override_model_class.startswith('''TF'''				):
                             lowercase__       =				[self._override_model_class[2:]]
                      else:
                             lowercase__       =				[self._override_model_class]
                      try:
                             lowercase__       =				getattr(import_module('''transformers'''				)     ,		architectures[0]				)
                      except AttributeError:
                             raise ValueError(f"Model class {self._override_model_class} not found in transformers."				)
                      try:
                             lowercase__       =				getattr(import_module('''transformers'''				)     ,		'''TF''' + architectures[0]				)
                      except AttributeError:
                             raise ValueError(f"TF model class TF{self._override_model_class} not found in transformers."				)
               elif architectures is None:  # No architecture defined -- use auto classes
                      lowercase__       =				getattr(import_module('''transformers'''				)     ,		'''AutoModel'''				)
                      lowercase__       =				getattr(import_module('''transformers'''				)     ,		'''TFAutoModel'''				)
                      self._logger.warning('''No detected architecture, using AutoModel/TFAutoModel'''				)
               else:  # Architecture defined -- use it
                      if len(UpperCamelCase				) > 1:
                             raise ValueError(f"More than one architecture was found, aborting. (architectures = {architectures})"				)
                      self._logger.warning(f"Detected architecture: {architectures[0]}"				)
                      lowercase__       =				getattr(import_module('''transformers'''				)     ,		architectures[0]				)
                      try:
                             lowercase__       =				getattr(import_module('''transformers'''				)     ,		'''TF''' + architectures[0]				)
                      except AttributeError:
                             raise AttributeError(f"The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."				)
  
          # Check the TF dummy inputs to see what keys we need in the forward pass
               lowercase__       =				tf_class.from_config(UpperCamelCase				)
               lowercase__       =				tf_from_pt_model.dummy_inputs
  
               del tf_from_pt_model  # Try to keep only one model in memory at a time
  
               # Load the model and get some basic inputs
               lowercase__       =				pt_class.from_pretrained(self._local_dir				)
               pt_model.eval()
  
               lowercase__    ,lowercase__       =				self.get_inputs(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)
  
               with torch.no_grad():
                      lowercase__       =				pt_model(**UpperCamelCase     ,		output_hidden_states=UpperCamelCase				)
               del pt_model  # will no longer be used, and may have a large memory footprint
  
               lowercase__       =				tf_class.from_pretrained(self._local_dir     ,		from_pt=UpperCamelCase				)
               lowercase__       =				tf_from_pt_model(**UpperCamelCase     ,		output_hidden_states=UpperCamelCase     ,		training=UpperCamelCase				)
  
               # Confirms that cross loading PT weights into TF worked.
               lowercase__       =				self.find_pt_tf_differences(UpperCamelCase     ,		UpperCamelCase				)
               lowercase__       =				{k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
               lowercase__       =				{k: v for k, v in crossload_differences.items() if '''hidden''' in k}
               if len(UpperCamelCase				) == 0 and architectures is not None:
                      raise ValueError(
                          f"Something went wrong -- the config file has architectures ({architectures}), but no model head"
                          ''' output was found. All outputs start with \'hidden\''''				)
               lowercase__       =				max(output_differences.values()				) if output_differences else 0.0
               lowercase__       =				max(hidden_differences.values()				)
               if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
                      raise ValueError(
                          '''The cross-loaded TensorFlow model has different outputs, something went wrong!\n'''
                          + f"\nList of maximum output differences above the threshold ({self._max_error}):\n"
                          + '''\n'''.join([f"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error]				)
                          + f"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
                          + '''\n'''.join([f"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]				)				)
  
               # Save the weights in a TF format (if needed) and confirms that the results are still good
               lowercase__       =				os.path.join(self._local_dir     ,		UpperCamelCase				)
               lowercase__       =				os.path.join(self._local_dir     ,		UpperCamelCase				)
               if (not os.path.exists(UpperCamelCase				) and not os.path.exists(UpperCamelCase				)) or self._new_weights:
                      tf_from_pt_model.save_pretrained(self._local_dir				)
               del tf_from_pt_model  # will no longer be used, and may have a large memory footprint
  
               lowercase__       =				tf_class.from_pretrained(self._local_dir				)
               lowercase__       =				tf_model(**UpperCamelCase     ,		output_hidden_states=UpperCamelCase				)
  
               lowercase__       =				self.find_pt_tf_differences(UpperCamelCase     ,		UpperCamelCase				)
               lowercase__       =				{k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
               lowercase__       =				{k: v for k, v in conversion_differences.items() if '''hidden''' in k}
               if len(UpperCamelCase				) == 0 and architectures is not None:
                      raise ValueError(
                          f"Something went wrong -- the config file has architectures ({architectures}), but no model head"
                          ''' output was found. All outputs start with \'hidden\''''				)
               lowercase__       =				max(output_differences.values()				) if output_differences else 0.0
               lowercase__       =				max(hidden_differences.values()				)
               if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
                      raise ValueError(
                          '''The converted TensorFlow model has different outputs, something went wrong!\n'''
                          + f"\nList of maximum output differences above the threshold ({self._max_error}):\n"
                          + '''\n'''.join([f"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error]				)
                          + f"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
                          + '''\n'''.join([f"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]				)				)
  
               lowercase__       =				'''Update TF weights''' if self._new_weights else '''Add TF weights'''
               if self._push:
                      repo.git_add(auto_lfs_track=UpperCamelCase				)
                      repo.git_commit(UpperCamelCase				)
                      repo.git_push(blocking=UpperCamelCase				)  # this prints a progress bar with the upload
                      self._logger.warning(f"TF weights pushed into {self._model_name}"				)
               elif not self._no_pr:
                      self._logger.warning('''Uploading the weights into a new PR...'''				)
                      lowercase__       =				(
                          '''Model converted by the [`transformers`\' `pt_to_tf`'''
                          ''' CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). '''
                          '''All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n'''
                          f"Maximum crossload output difference={max_crossload_output_diff:.3e}; "
                          f"Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"
                          f"Maximum conversion output difference={max_conversion_output_diff:.3e}; "
                          f"Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"
                      )
                      if self._max_error > MAX_ERROR:
                             commit_descrition += (
                                 f"\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"
                             )
                      if self._extra_commit_description:
                             commit_descrition += "\n\n" + self._extra_commit_description
  
                      # sharded model -> adds all related files (index and .h5 shards)
                      if os.path.exists(UpperCamelCase				):
                             lowercase__       =				[
                                 CommitOperationAdd(path_in_repo=UpperCamelCase     ,		path_or_fileobj=UpperCamelCase				)
                             ]
                             for shard_path in tf.io.gfile.glob(self._local_dir + '''/tf_model-*.h5'''				):
                                    operations += [
                                        CommitOperationAdd(path_in_repo=os.path.basename(UpperCamelCase				)     ,		path_or_fileobj=UpperCamelCase				)
                                    ]
                      else:
                             lowercase__       =				[CommitOperationAdd(path_in_repo=UpperCamelCase     ,		path_or_fileobj=UpperCamelCase				)]
  
                      lowercase__       =				create_commit(
                          repo_id=self._model_name     ,		operations=UpperCamelCase     ,		commit_message=UpperCamelCase     ,		commit_description=UpperCamelCase     ,		repo_type='''model'''     ,		create_pr=UpperCamelCase     ,		).pr_url
                      self._logger.warning(f"PR open in {hub_pr_url}"				)
style_context: 
  
  
  '''simple docstring'''
  
  import copy
  import inspect
  import unittest
  
  import numpy as np
  from huggingface_hub import hf_hub_download
  
  from transformers import VivitConfig
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  
  
  if is_torch_available():
   import torch
   from torch import nn
  
   from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
   from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
   from transformers import VivitImageProcessor
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def __init__(self					:					int     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					str=2     ,		UpperCamelCase					:					Union[str, Any]=True     ,		UpperCamelCase					:					Dict=True     ,		UpperCamelCase					:					List[Any]=10     ,		UpperCamelCase					:					Optional[Any]=10     ,		UpperCamelCase					:					Optional[int]=8     ,		UpperCamelCase					:					int=[2, 4, 4]     ,		UpperCamelCase					:					Dict=3     ,		UpperCamelCase					:					Union[str, Any]=768     ,		UpperCamelCase					:					Any=5     ,		UpperCamelCase					:					List[str]=4     ,		UpperCamelCase					:					Optional[int]=37     ,		UpperCamelCase					:					List[Any]="gelu_fast"     ,		UpperCamelCase					:					List[Any]=0.0     ,		UpperCamelCase					:					str=0.0     ,		UpperCamelCase					:					Tuple=0.02     ,		UpperCamelCase					:					Any=1E-06     ,		UpperCamelCase					:					str=True     ,		UpperCamelCase					:					List[str]=None     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				parent
               lowercase__       =				batch_size
               lowercase__       =				is_training
               lowercase__       =				use_labels
               lowercase__       =				num_labels
               lowercase__       =				image_size
               lowercase__       =				num_frames
               lowercase__       =				tubelet_size
               lowercase__       =				num_channels
               lowercase__       =				hidden_size
               lowercase__       =				num_hidden_layers
               lowercase__       =				num_attention_heads
               lowercase__       =				intermediate_size
               lowercase__       =				hidden_act
               lowercase__       =				hidden_dropout_prob
               lowercase__       =				attention_probs_dropout_prob
               lowercase__       =				initializer_range
               lowercase__       =				layer_norm_eps
               lowercase__       =				qkv_bias
               lowercase__       =				scope
  
               lowercase__       =				(
                   (self.image_size // self.tubelet_size[2])
                   * (self.image_size // self.tubelet_size[1])
                   * (self.num_frames // self.tubelet_size[0])
               ) + 1  # CLS token
  
  
  
        def    UpperCamelCase__	(self					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				floats_tensor(
                   [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]				)
  
               lowercase__       =				None
               if self.use_labels:
                      lowercase__       =				ids_tensor([self.batch_size]     ,		self.num_labels				)
  
               lowercase__       =				self.get_config()
  
               return config, pixel_values, labels
  
  
  
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				VivitConfig(
                   num_frames=self.num_frames     ,		image_size=self.image_size     ,		tubelet_size=self.tubelet_size     ,		num_channels=self.num_channels     ,		hidden_size=self.hidden_size     ,		num_hidden_layers=self.num_hidden_layers     ,		num_attention_heads=self.num_attention_heads     ,		intermediate_size=self.intermediate_size     ,		hidden_act=self.hidden_act     ,		hidden_dropout_prob=self.hidden_dropout_prob     ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,		initializer_range=self.initializer_range     ,		layer_norm_eps=self.layer_norm_eps     ,		qkv_bias=self.qkv_bias     ,		)
               lowercase__       =				self.num_labels
               return config
  
  
  
        def    UpperCamelCase__	(self					:					Optional[int]     ,		UpperCamelCase					:					Optional[Any]     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					Dict				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				VivitModel(config=UpperCamelCase				)
               model.to(UpperCamelCase				)
               model.eval()
               lowercase__       =				model(UpperCamelCase				)
               self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size)				)
  
  
  
        def    UpperCamelCase__	(self					:					int     ,		UpperCamelCase					:					Union[str, Any]     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				VivitForVideoClassification(UpperCamelCase				)
               model.to(UpperCamelCase				)
               model.eval()
  
               lowercase__       =				model(UpperCamelCase				)
  
               # verify the logits shape
               lowercase__       =				torch.Size((self.batch_size, self.num_labels)				)
               self.parent.assertEqual(result.logits.shape     ,		UpperCamelCase				)
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Dict				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.prepare_config_and_inputs()
               lowercase__    ,lowercase__    ,lowercase__       =				config_and_inputs
               lowercase__       =				{'''pixel_values''': pixel_values}
               return config, inputs_dict
  
  
  
  
  
  
  @require_torch
  class 						__lowerCAmelCase		(lowercase_						,  unittest.TestCase  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  List[str]						      =							(VivitModel, VivitForVideoClassification) if is_torch_available() else ()
  
        lowerCAmelCase__      :  Tuple						      =							False
        lowerCAmelCase__      :  Optional[Any]						      =							False
        lowerCAmelCase__      :  str						      =							False
        lowerCAmelCase__      :  Any						      =							False
  
  
  
        def    UpperCamelCase__	(self					:					Dict				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				VivitModelTester(self				)
               lowercase__       =				ConfigTester(self     ,		config_class=UpperCamelCase     ,		has_text_modality=UpperCamelCase     ,		hidden_size=37				)
  
  
  
        def    UpperCamelCase__	(self					:					List[str]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					Tuple=False				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				copy.deepcopy(UpperCamelCase				)
  
               if return_labels:
                      if model_class in get_values(UpperCamelCase				):
                             lowercase__       =				torch.zeros(
                                 self.model_tester.batch_size     ,		dtype=torch.long     ,		device=UpperCamelCase				)
  
               return inputs_dict
  
  
  
        def    UpperCamelCase__	(self					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               self.config_tester.run_common_tests()
  
  
  
        @unittest.skip(reason='''Vivit does not use inputs_embeds'''				)
        def    UpperCamelCase__	(self					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               pass
  
  
  
        def    UpperCamelCase__	(self					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__    ,lowercase__       =				self.model_tester.prepare_config_and_inputs_for_common()
  
               for model_class in self.all_model_classes:
                      lowercase__       =				model_class(UpperCamelCase				)
                      self.assertIsInstance(model.get_input_embeddings()     ,		(nn.Module)				)
                      lowercase__       =				model.get_output_embeddings()
                      self.assertTrue(x is None or isinstance(UpperCamelCase     ,		nn.Linear				)				)
  
  
  
        def    UpperCamelCase__	(self					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__    ,lowercase__       =				self.model_tester.prepare_config_and_inputs_for_common()
  
               for model_class in self.all_model_classes:
                      lowercase__       =				model_class(UpperCamelCase				)
                      lowercase__       =				inspect.signature(model.forward				)
                      # signature.parameters is an OrderedDict => so arg_names order is deterministic
                      lowercase__       =				[*signature.parameters.keys()]
  
                      lowercase__       =				['''pixel_values''', '''head_mask''']
                      self.assertListEqual(arg_names[:2]     ,		UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					str				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_model(*UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_for_video_classification(*UpperCamelCase				)
  
  
  
        @slow
        def    UpperCamelCase__	(self					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                      lowercase__       =				VivitModel.from_pretrained(UpperCamelCase				)
                      self.assertIsNotNone(UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__    ,lowercase__       =				self.model_tester.prepare_config_and_inputs_for_common()
               lowercase__       =				True
  
               for model_class in self.all_model_classes:
                      lowercase__       =				self.model_tester.seq_length
  
                      lowercase__       =				True
                      lowercase__       =				False
                      lowercase__       =				True
                      lowercase__       =				model_class(UpperCamelCase				)
                      model.to(UpperCamelCase				)
                      model.eval()
                      with torch.no_grad():
                             lowercase__       =				model(**self._prepare_for_class(UpperCamelCase     ,		UpperCamelCase				)				)
                      lowercase__       =				outputs.attentions
                      self.assertEqual(len(UpperCamelCase				)     ,		self.model_tester.num_hidden_layers				)
  
                      # check that output_attentions also work using config
                      del inputs_dict["output_attentions"]
                      lowercase__       =				True
                      lowercase__       =				model_class(UpperCamelCase				)
                      model.to(UpperCamelCase				)
                      model.eval()
                      with torch.no_grad():
                             lowercase__       =				model(**self._prepare_for_class(UpperCamelCase     ,		UpperCamelCase				)				)
                      lowercase__       =				outputs.attentions
                      self.assertEqual(len(UpperCamelCase				)     ,		self.model_tester.num_hidden_layers				)
  
                      self.assertListEqual(
                          list(attentions[0].shape[-3:]				)     ,		[self.model_tester.num_attention_heads, seq_len, seq_len]     ,		)
                      lowercase__       =				len(UpperCamelCase				)
  
                      # Check attention is always last and order is fine
                      lowercase__       =				True
                      lowercase__       =				True
                      lowercase__       =				model_class(UpperCamelCase				)
                      model.to(UpperCamelCase				)
                      model.eval()
                      with torch.no_grad():
                             lowercase__       =				model(**self._prepare_for_class(UpperCamelCase     ,		UpperCamelCase				)				)
  
                      self.assertEqual(out_len + 1     ,		len(UpperCamelCase				)				)
  
                      lowercase__       =				outputs.attentions
  
                      self.assertEqual(len(UpperCamelCase				)     ,		self.model_tester.num_hidden_layers				)
  
                      self.assertListEqual(
                          list(self_attentions[0].shape[-3:]				)     ,		[self.model_tester.num_attention_heads, seq_len, seq_len]     ,		)
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               def check_hidden_states_output(UpperCamelCase					:					Dict     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					Any				):
                      lowercase__       =				model_class(UpperCamelCase				)
                      model.to(UpperCamelCase				)
                      model.eval()
  
                      with torch.no_grad():
                             lowercase__       =				model(**self._prepare_for_class(UpperCamelCase     ,		UpperCamelCase				)				)
  
                      lowercase__       =				outputs.hidden_states
                      lowercase__       =				self.model_tester.num_hidden_layers + 1
                      self.assertEqual(len(UpperCamelCase				)     ,		UpperCamelCase				)
  
                      lowercase__       =				self.model_tester.seq_length
  
                      self.assertListEqual(
                          list(hidden_states[0].shape[-2:]				)     ,		[seq_length, self.model_tester.hidden_size]     ,		)
  
               lowercase__    ,lowercase__       =				self.model_tester.prepare_config_and_inputs_for_common()
  
               for model_class in self.all_model_classes:
                      lowercase__       =				True
                      check_hidden_states_output(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)
  
                      # check that output_hidden_states also work using config
                      del inputs_dict["output_hidden_states"]
                      lowercase__       =				True
  
                      check_hidden_states_output(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)
  
  
  
  
  
  
        @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
        def    UpperCamelCase__	(self					:					str				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               pass
  
  
  
  
  
  
  def _SCREAMING_SNAKE_CASE   () -> List[Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				hf_hub_download(
             repo_id='''hf-internal-testing/spaghetti-video'''						,				filename='''eating_spaghetti_32_frames.npy'''						,				repo_type='''dataset''' )
         lowercase__       =				np.load(A )
         return list(A )
  
  @require_torch
  @require_vision
  class 						__lowerCAmelCase		(unittest.TestCase  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        @cached_property
        def    UpperCamelCase__	(self					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return VivitImageProcessor() if is_vision_available() else None
  
  
  
  
  
  
        @slow
        def    UpperCamelCase__	(self					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400'''				).to(UpperCamelCase				)
  
               lowercase__       =				self.default_image_processor
               lowercase__       =				prepare_video()
               lowercase__       =				image_processor(UpperCamelCase     ,		return_tensors='''pt'''				).to(UpperCamelCase				)
  
               # forward pass
               with torch.no_grad():
                      lowercase__       =				model(**UpperCamelCase				)
  
               # verify the logits
               lowercase__       =				torch.Size((1, 400)				)
               self.assertEqual(outputs.logits.shape     ,		UpperCamelCase				)
  
               # taken from original model
               lowercase__       =				torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]				).to(UpperCamelCase				)
  
               self.assertTrue(torch.allclose(outputs.logits[0, :5]     ,		UpperCamelCase     ,		atol=1E-4				)				)
  
  
  
  
  