code: 
  
  
  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 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case    ,      __snake_case=2    ,      __snake_case=True    ,      __snake_case=True    ,      __snake_case=1_0    ,      __snake_case=1_0    ,      __snake_case=8    ,      __snake_case=[2, 4, 4]    ,      __snake_case=3    ,      __snake_case=7_6_8    ,      __snake_case=5    ,      __snake_case=4    ,      __snake_case=3_7    ,      __snake_case="gelu_fast"    ,      __snake_case=0.0    ,      __snake_case=0.0    ,      __snake_case=0.02    ,      __snake_case=1E-06    ,      __snake_case=True    ,      __snake_case=None    ,      ):
           snake_case					=       parent
           snake_case					=       batch_size
           snake_case					=       is_training
           snake_case					=       use_labels
           snake_case					=       num_labels
           snake_case					=       image_size
           snake_case					=       num_frames
           snake_case					=       tubelet_size
           snake_case					=       num_channels
           snake_case					=       hidden_size
           snake_case					=       num_hidden_layers
           snake_case					=       num_attention_heads
           snake_case					=       intermediate_size
           snake_case					=       hidden_act
           snake_case					=       hidden_dropout_prob
           snake_case					=       attention_probs_dropout_prob
           snake_case					=       initializer_range
           snake_case					=       layer_norm_eps
           snake_case					=       qkv_bias
           snake_case					=       scope
  
           snake_case					=       (
               (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 	a_    ( self					):
           snake_case					=       floats_tensor(
               [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]					)
  
           snake_case					=       None
           if self.use_labels:
                snake_case					=       ids_tensor([self.batch_size]    ,      self.num_labels					)
  
           snake_case					=       self.get_config()
  
           return config, pixel_values, labels
      def 	a_    ( self					):
           snake_case					=       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    ,      )
           snake_case					=       self.num_labels
           return config
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           snake_case					=       VivitModel(config=UpperCamelCase__					)
           model.to(UpperCamelCase__					)
           model.eval()
           snake_case					=       model(UpperCamelCase__					)
           self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, self.seq_length, self.hidden_size)					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           snake_case					=       VivitForVideoClassification(UpperCamelCase__					)
           model.to(UpperCamelCase__					)
           model.eval()
  
           snake_case					=       model(UpperCamelCase__					)
  
           # verify the logits shape
           snake_case					=       torch.Size((self.batch_size, self.num_labels)					)
           self.parent.assertEqual(result.logits.shape    ,      UpperCamelCase__					)
  
  
  
  
      def 	a_    ( self					):
           snake_case					=       self.prepare_config_and_inputs()
           snake_case					=       config_and_inputs
           snake_case					=       {'''pixel_values''': pixel_values}
           return config, inputs_dict
  
  
  
  
  
  
  @require_torch
  class 						A__      (					__SCREAMING_SNAKE_CASE ,      unittest.TestCase			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	(VivitModel, VivitForVideoClassification) if is_torch_available() else ()
  
      __magic_name__    						=	False
      __magic_name__    						=	False
      __magic_name__    						=	False
      __magic_name__    						=	False
      def 	a_    ( self					):
           snake_case					=       VivitModelTester(self					)
           snake_case					=       ConfigTester(self    ,      config_class=UpperCamelCase__    ,      has_text_modality=UpperCamelCase__    ,      hidden_size=3_7					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case=False					):
           snake_case					=       copy.deepcopy(UpperCamelCase__					)
  
           if return_labels:
                if model_class in get_values(UpperCamelCase__					):
                     snake_case					=       torch.zeros(
                         self.model_tester.batch_size    ,      dtype=torch.long    ,      device=UpperCamelCase__					)
  
           return inputs_dict
      def 	a_    ( self					):
           self.config_tester.run_common_tests()
      @unittest.skip(reason='''Vivit does not use inputs_embeds'''					)
      def 	a_    ( self					):
           pass
      def 	a_    ( self					):
           snake_case					=       self.model_tester.prepare_config_and_inputs_for_common()
  
           for model_class in self.all_model_classes:
                snake_case					=       model_class(UpperCamelCase__					)
                self.assertIsInstance(model.get_input_embeddings()    ,      (nn.Module)					)
                snake_case					=       model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(UpperCamelCase__    ,      nn.Linear					)					)
      def 	a_    ( self					):
           snake_case					=       self.model_tester.prepare_config_and_inputs_for_common()
  
           for model_class in self.all_model_classes:
                snake_case					=       model_class(UpperCamelCase__					)
                snake_case					=       inspect.signature(model.forward					)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                snake_case					=       [*signature.parameters.keys()]
  
                snake_case					=       ['''pixel_values''', '''head_mask''']
                self.assertListEqual(arg_names[:2]    ,      UpperCamelCase__					)
      def 	a_    ( self					):
           snake_case					=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__					)
      def 	a_    ( self					):
           snake_case					=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_video_classification(*UpperCamelCase__					)
      @slow
      def 	a_    ( self					):
  
  
  
  
  
  
  
           for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case					=       VivitModel.from_pretrained(UpperCamelCase__					)
                self.assertIsNotNone(UpperCamelCase__					)
      def 	a_    ( self					):
           snake_case					=       self.model_tester.prepare_config_and_inputs_for_common()
           snake_case					=       True
  
           for model_class in self.all_model_classes:
                snake_case					=       self.model_tester.seq_length
  
                snake_case					=       True
                snake_case					=       False
                snake_case					=       True
                snake_case					=       model_class(UpperCamelCase__					)
                model.to(UpperCamelCase__					)
                model.eval()
                with torch.no_grad():
                     snake_case					=       model(**self._prepare_for_class(UpperCamelCase__    ,      UpperCamelCase__					)					)
                snake_case					=       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"]
                snake_case					=       True
                snake_case					=       model_class(UpperCamelCase__					)
                model.to(UpperCamelCase__					)
                model.eval()
                with torch.no_grad():
                     snake_case					=       model(**self._prepare_for_class(UpperCamelCase__    ,      UpperCamelCase__					)					)
                snake_case					=       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]    ,      )
                snake_case					=       len(UpperCamelCase__					)
  
                # Check attention is always last and order is fine
                snake_case					=       True
                snake_case					=       True
                snake_case					=       model_class(UpperCamelCase__					)
                model.to(UpperCamelCase__					)
                model.eval()
                with torch.no_grad():
                     snake_case					=       model(**self._prepare_for_class(UpperCamelCase__    ,      UpperCamelCase__					)					)
  
                self.assertEqual(out_len + 1    ,      len(UpperCamelCase__					)					)
  
                snake_case					=       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 	a_    ( self					):
  
  
  
  
  
  
  
           def check_hidden_states_output(__snake_case    ,      __snake_case    ,      __snake_case					):
                snake_case					=       model_class(UpperCamelCase__					)
                model.to(UpperCamelCase__					)
                model.eval()
  
                with torch.no_grad():
                     snake_case					=       model(**self._prepare_for_class(UpperCamelCase__    ,      UpperCamelCase__					)					)
  
                snake_case					=       outputs.hidden_states
                snake_case					=       self.model_tester.num_hidden_layers + 1
                self.assertEqual(len(UpperCamelCase__					)    ,      UpperCamelCase__					)
  
                snake_case					=       self.model_tester.seq_length
  
                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]					)    ,      [seq_length, self.model_tester.hidden_size]    ,      )
  
           snake_case					=       self.model_tester.prepare_config_and_inputs_for_common()
  
           for model_class in self.all_model_classes:
                snake_case					=       True
                check_hidden_states_output(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__					)
  
                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                snake_case					=       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 	a_    ( self					):
           pass
  
  
  
  
  
  
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       hf_hub_download(
           repo_id='''hf-internal-testing/spaghetti-video'''							,filename='''eating_spaghetti_32_frames.npy'''							,repo_type='''dataset'''       )
       snake_case					=       np.load(SCREAMING_SNAKE_CASE__       )
       return list(SCREAMING_SNAKE_CASE__       )
  
  @require_torch
  @require_vision
  class 						A__      (					unittest.TestCase			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      @cached_property
      def 	a_    ( self					):
           return VivitImageProcessor() if is_vision_available() else None
  
  
  
  
      @slow
      def 	a_    ( self					):
           snake_case					=       VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400'''					).to(UpperCamelCase__					)
  
           snake_case					=       self.default_image_processor
           snake_case					=       prepare_video()
           snake_case					=       image_processor(UpperCamelCase__    ,      return_tensors='''pt'''					).to(UpperCamelCase__					)
  
           # forward pass
           with torch.no_grad():
                snake_case					=       model(**UpperCamelCase__					)
  
           # verify the logits
           snake_case					=       torch.Size((1, 4_0_0)					)
           self.assertEqual(outputs.logits.shape    ,      UpperCamelCase__					)
  
           # taken from original model
           snake_case					=       torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]					).to(UpperCamelCase__					)
  
           self.assertTrue(torch.allclose(outputs.logits[0, :5]    ,      UpperCamelCase__    ,      atol=1E-4					)					)
  
  
  
  
style_context: 
  
  
  import torch
  
  from accelerate import Accelerator, DistributedType
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __enter__( self					):
           if self.enabled:
                snake_case					=       self.model.no_sync()
                self.model_sync_obj.__enter__()
  
           return self
      def __exit__( self    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           if self.enabled:
                # Average all models on exit
                self._sync_and_avg_model_params()
                self.model_sync_obj.__exit__(__snake_case    ,      __snake_case    ,      __snake_case					)
      def __init__( self    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case = True					):
           if accelerator.distributed_type not in [
               DistributedType.NO,
               DistributedType.MULTI_CPU,
               DistributedType.MULTI_GPU,
           ]:
                raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''					)
           snake_case					=       enabled and accelerator.distributed_type != DistributedType.NO
           snake_case					=       0
           if self.enabled:
                snake_case					=       accelerator
                snake_case					=       model
                snake_case					=       local_sgd_steps
      def 	a_    ( self					):
           self.num_steps += 1
           if not self.enabled:
                return
  
           if self.num_steps % self.local_sgd_steps == 0:
                self._sync_and_avg_model_params()
  
  
  
  
      def 	a_    ( self					):
           self.accelerator.wait_for_everyone()
           with self.accelerator.autocast():
                for param in self.model.parameters():
                     snake_case					=       self.accelerator.reduce(param.data    ,      reduction='''mean'''					)
  
  
  
  
  