code: 
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowerCamelCase							:     Optional[Any]								=			{
      'configuration_mobilevitv2': [
          'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'MobileViTV2Config',
          'MobileViTV2OnnxConfig',
      ],
  }
  
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   lowerCamelCase							:     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
  
   lowerCamelCase							:     Dict								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
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				)				)
  
  
  
  
  