






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  snake_case		:


   '''simple docstring'''

   def __init__(     self			:					List[Any],	_lowerCamelCase			:					List[Any],	_lowerCamelCase			:					int=2,	_lowerCamelCase			:					Dict=True,	_lowerCamelCase			:					Any=True,	_lowerCamelCase			:					Any=10,	_lowerCamelCase			:					List[Any]=10,	_lowerCamelCase			:					Optional[int]=8,	_lowerCamelCase			:					Optional[int]=[2, 4, 4],	_lowerCamelCase			:					int=3,	_lowerCamelCase			:					Optional[int]=7_68,	_lowerCamelCase			:					str=5,	_lowerCamelCase			:					str=4,	_lowerCamelCase			:					Dict=37,	_lowerCamelCase			:					Tuple="gelu_fast",	_lowerCamelCase			:					Any=0.0,	_lowerCamelCase			:					Union[str, Any]=0.0,	_lowerCamelCase			:					Tuple=0.02,	_lowerCamelCase			:					Tuple=1e-06,	_lowerCamelCase			:					Union[str, Any]=True,	_lowerCamelCase			:					Union[str, Any]=None,	):

     '''simple docstring'''
     __A						      =			parent
     __A						      =			batch_size
     __A						      =			is_training
     __A						      =			use_labels
     __A						      =			num_labels
     __A						      =			image_size
     __A						      =			num_frames
     __A						      =			tubelet_size
     __A						      =			num_channels
     __A						      =			hidden_size
     __A						      =			num_hidden_layers
     __A						      =			num_attention_heads
     __A						      =			intermediate_size
     __A						      =			hidden_act
     __A						      =			hidden_dropout_prob
     __A						      =			attention_probs_dropout_prob
     __A						      =			initializer_range
     __A						      =			layer_norm_eps
     __A						      =			qkv_bias
     __A						      =			scope

     __A						      =			(
         (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     _SCREAMING_SNAKE_CASE    (     self			:					List[Any]  ):

     '''simple docstring'''
     __A						      =			floats_tensor(
         [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]  )

     __A						      =			None
     if self.use_labels:
       __A						      =			ids_tensor([self.batch_size],	self.num_labels  )

     __A						      =			self.get_config()

     return config, pixel_values, labels


   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     __A						      =			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,	)
     __A						      =			self.num_labels
     return config


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any],	_lowerCamelCase			:					Any,	_lowerCamelCase			:					List[str],	_lowerCamelCase			:					List[Any]  ):

     '''simple docstring'''
     __A						      =			VivitModel(config=_lowerCamelCase  )
     model.to(_lowerCamelCase  )
     model.eval()
     __A						      =			model(_lowerCamelCase  )
     self.parent.assertEqual(result.last_hidden_state.shape,	(self.batch_size, self.seq_length, self.hidden_size)  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str],	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					Dict,	_lowerCamelCase			:					List[str]  ):

     '''simple docstring'''
     __A						      =			VivitForVideoClassification(_lowerCamelCase  )
     model.to(_lowerCamelCase  )
     model.eval()

     __A						      =			model(_lowerCamelCase  )

     # verify the logits shape
     __A						      =			torch.Size((self.batch_size, self.num_labels)  )
     self.parent.assertEqual(result.logits.shape,	_lowerCamelCase  )






   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any]  ):

     '''simple docstring'''
     __A						      =			self.prepare_config_and_inputs()
     __A      ,   __A      ,   __A						      =			config_and_inputs
     __A						      =			{'''pixel_values''': pixel_values}
     return config, inputs_dict





@require_torch
class  snake_case		(							_lowerCAmelCase       ,     unittest.TestCase						):


   '''simple docstring'''

   A_						:     List[str]									=			(VivitModel, VivitForVideoClassification) if is_torch_available() else ()

   A_						:     Optional[Any]									=			False
   A_						:     List[str]									=			False
   A_						:     Optional[Any]									=			False
   A_						:     str									=			False


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''simple docstring'''
     __A						      =			VivitModelTester(self  )
     __A						      =			ConfigTester(self,	config_class=_lowerCamelCase,	has_text_modality=_lowerCamelCase,	hidden_size=37  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str],	_lowerCamelCase			:					Optional[int],	_lowerCamelCase			:					Optional[Any],	_lowerCamelCase			:					Optional[Any]=False  ):

     '''simple docstring'''
     __A						      =			copy.deepcopy(_lowerCamelCase  )

     if return_labels:
       if model_class in get_values(_lowerCamelCase  ):
         __A						      =			torch.zeros(
             self.model_tester.batch_size,	dtype=torch.long,	device=_lowerCamelCase  )

     return inputs_dict


   def     _SCREAMING_SNAKE_CASE    (     self			:					Union[str, Any]  ):

     '''simple docstring'''
     self.config_tester.run_common_tests()


   @unittest.skip(reason='''Vivit does not use inputs_embeds'''  )
   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     pass


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int]  ):

     '''simple docstring'''
     __A      ,   __A						      =			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       __A						      =			model_class(_lowerCamelCase  )
       self.assertIsInstance(model.get_input_embeddings(),	(nn.Module)  )
       __A						      =			model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(_lowerCamelCase,	nn.Linear  )  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     __A      ,   __A						      =			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       __A						      =			model_class(_lowerCamelCase  )
       __A						      =			inspect.signature(model.forward  )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __A						      =			[*signature.parameters.keys()]

       __A						      =			['''pixel_values''', '''head_mask''']
       self.assertListEqual(arg_names[:2],	_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any]  ):

     '''simple docstring'''
     __A						      =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     __A						      =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_video_classification(*_lowerCamelCase  )


   @slow
   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int]  ):

     '''simple docstring'''
     for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __A						      =			VivitModel.from_pretrained(_lowerCamelCase  )
       self.assertIsNotNone(_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Any  ):

     '''simple docstring'''
     __A      ,   __A						      =			self.model_tester.prepare_config_and_inputs_for_common()
     __A						      =			True

     for model_class in self.all_model_classes:
       __A						      =			self.model_tester.seq_length

       __A						      =			True
       __A						      =			False
       __A						      =			True
       __A						      =			model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       with torch.no_grad():
         __A						      =			model(**self._prepare_for_class(_lowerCamelCase,	_lowerCamelCase  )  )
       __A						      =			outputs.attentions
       self.assertEqual(len(_lowerCamelCase  ),	self.model_tester.num_hidden_layers  )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       __A						      =			True
       __A						      =			model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       with torch.no_grad():
         __A						      =			model(**self._prepare_for_class(_lowerCamelCase,	_lowerCamelCase  )  )
       __A						      =			outputs.attentions
       self.assertEqual(len(_lowerCamelCase  ),	self.model_tester.num_hidden_layers  )

       self.assertListEqual(
           list(attentions[0].shape[-3:]  ),	[self.model_tester.num_attention_heads, seq_len, seq_len],	)
       __A						      =			len(_lowerCamelCase  )

       # Check attention is always last and order is fine
       __A						      =			True
       __A						      =			True
       __A						      =			model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       with torch.no_grad():
         __A						      =			model(**self._prepare_for_class(_lowerCamelCase,	_lowerCamelCase  )  )

       self.assertEqual(out_len + 1,	len(_lowerCamelCase  )  )

       __A						      =			outputs.attentions

       self.assertEqual(len(_lowerCamelCase  ),	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     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     def check_hidden_states_output(_lowerCamelCase			:					str,	_lowerCamelCase			:					Optional[Any],	_lowerCamelCase			:					Union[str, Any]  ):
       __A						      =			model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()

       with torch.no_grad():
         __A						      =			model(**self._prepare_for_class(_lowerCamelCase,	_lowerCamelCase  )  )

       __A						      =			outputs.hidden_states
       __A						      =			self.model_tester.num_hidden_layers + 1
       self.assertEqual(len(_lowerCamelCase  ),	_lowerCamelCase  )

       __A						      =			self.model_tester.seq_length

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]  ),	[seq_length, self.model_tester.hidden_size],	)

     __A      ,   __A						      =			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       __A						      =			True
       check_hidden_states_output(_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase  )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __A						      =			True

       check_hidden_states_output(_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase  )






   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
   def     _SCREAMING_SNAKE_CASE    (     self			:					int  ):

     '''simple docstring'''
     pass





def  lowerCAmelCase    (   ):




  """simple docstring"""







  __A						      =			hf_hub_download(
      repo_id='''hf-internal-testing/spaghetti-video'''		,	filename='''eating_spaghetti_32_frames.npy'''		,	repo_type='''dataset'''			)
  __A						      =			np.load(__UpperCamelCase			)
  return list(__UpperCamelCase			)





@require_torch
@require_vision
class  snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   @cached_property
   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any]  ):

     '''simple docstring'''
     return VivitImageProcessor() if is_vision_available() else None






   @slow
   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''simple docstring'''
     __A						      =			VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400'''  ).to(_lowerCamelCase  )

     __A						      =			self.default_image_processor
     __A						      =			prepare_video()
     __A						      =			image_processor(_lowerCamelCase,	return_tensors='''pt'''  ).to(_lowerCamelCase  )

     # forward pass
     with torch.no_grad():
       __A						      =			model(**_lowerCamelCase  )

     # verify the logits
     __A						      =			torch.Size((1, 4_00)  )
     self.assertEqual(outputs.logits.shape,	_lowerCamelCase  )

     # taken from original model
     __A						      =			torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]  ).to(_lowerCamelCase  )

     self.assertTrue(torch.allclose(outputs.logits[0, :5],	_lowerCamelCase,	atol=1e-4  )  )












"""simple docstring"""


from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if is_torch_available():
    import torch

    if isinstance(__UpperCamelCase		,	torch.Tensor			):
      return True

  return isinstance(__UpperCamelCase		,	np.ndarray			)





class  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     __A						      =			fields(self  )

     # Safety and consistency checks
     if not len(_lowerCamelCase  ):
       raise ValueError(f'{self.__class__.__name__} has no fields.'  )

     __A						      =			getattr(self,	class_fields[0].name  )
     __A						      =			all(getattr(self,	field.name  ) is None for field in class_fields[1:]  )

     if other_fields_are_none and isinstance(_lowerCamelCase,	_lowerCamelCase  ):
       for key, value in first_field.items():
         __A						      =			value
     else:
       for field in class_fields:
         __A						      =			getattr(self,	field.name  )
         if v is not None:
           __A						      =			v


   def __delitem__(     self			:					Dict,	*_lowerCamelCase			:					int,	**_lowerCamelCase			:					Any  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any],	*_lowerCamelCase			:					List[str],	**_lowerCamelCase			:					str  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Any,	*_lowerCamelCase			:					Optional[Any],	**_lowerCamelCase			:					str  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``pop`` on a {self.__class__.__name__} instance.'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str],	*_lowerCamelCase			:					Any,	**_lowerCamelCase			:					Optional[int]  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``update`` on a {self.__class__.__name__} instance.'  )


   def __getitem__(     self			:					int,	_lowerCamelCase			:					List[Any]  ):

     '''simple docstring'''
     if isinstance(_lowerCamelCase,	_lowerCamelCase  ):
       __A						      =			dict(self.items()  )
       return inner_dict[k]
     else:
       return self.to_tuple()[k]


   def __setattr__(     self			:					Optional[int],	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					Union[str, Any]  ):

     '''simple docstring'''
     if name in self.keys() and value is not None:
       # Don't call self.__setitem__ to avoid recursion errors
       super().__setitem__(_lowerCamelCase,	_lowerCamelCase  )
     super().__setattr__(_lowerCamelCase,	_lowerCamelCase  )


   def __setitem__(     self			:					List[str],	_lowerCamelCase			:					Optional[int],	_lowerCamelCase			:					str  ):

     '''simple docstring'''
     # Will raise a KeyException if needed
     super().__setitem__(_lowerCamelCase,	_lowerCamelCase  )
     # Don't call self.__setattr__ to avoid recursion errors
     super().__setattr__(_lowerCamelCase,	_lowerCamelCase  )






   def     _SCREAMING_SNAKE_CASE    (     self			:					Any  ):

     '''simple docstring'''
     return tuple(self[k] for k in self.keys()  )




