code: 
  
  from __future__ import annotations
  
  import inspect
  import unittest
  from typing import List, Tuple
  
  from transformers import ConvNextConfig
  from transformers.testing_utils import require_tf, require_vision, slow
  from transformers.utils import cached_property, is_tf_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
   import tensorflow as tf
  
   from transformers import TFConvNextForImageClassification, TFConvNextModel
  
  
  if is_vision_available():
   from PIL import Image
  
   from transformers import ConvNextImageProcessor
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __init__(	self			:    List[Any]    ,				UpperCAmelCase_			:    Optional[int]    ,				UpperCAmelCase_			:    List[str]=13    ,				UpperCAmelCase_			:    Union[str, Any]=32    ,				UpperCAmelCase_			:    Any=3    ,				UpperCAmelCase_			:    Optional[Any]=4    ,				UpperCAmelCase_			:    Union[str, Any]=[10, 20, 30, 40]    ,				UpperCAmelCase_			:    Optional[Any]=[2, 2, 3, 2]    ,				UpperCAmelCase_			:    Tuple=True    ,				UpperCAmelCase_			:    Tuple=True    ,				UpperCAmelCase_			:    Optional[Any]=37    ,				UpperCAmelCase_			:    List[str]="gelu"    ,				UpperCAmelCase_			:    Any=10    ,				UpperCAmelCase_			:    List[str]=0.02    ,				UpperCAmelCase_			:    Tuple=3    ,				UpperCAmelCase_			:    str=None    ,				):
          lowerCAmelCase				:			List[Any]    			=      parent
          lowerCAmelCase				:			int    			=      batch_size
          lowerCAmelCase				:			Tuple    			=      image_size
          lowerCAmelCase				:			Optional[int]    			=      num_channels
          lowerCAmelCase				:			Dict    			=      num_stages
          lowerCAmelCase				:			str    			=      hidden_sizes
          lowerCAmelCase				:			Any    			=      depths
          lowerCAmelCase				:			List[Any]    			=      is_training
          lowerCAmelCase				:			Any    			=      use_labels
          lowerCAmelCase				:			Optional[Any]    			=      intermediate_size
          lowerCAmelCase				:			Union[str, Any]    			=      hidden_act
          lowerCAmelCase				:			Any    			=      type_sequence_label_size
          lowerCAmelCase				:			List[Any]    			=      initializer_range
          lowerCAmelCase				:			str    			=      scope
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ):
          lowerCAmelCase				:			Dict    			=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )
  
          lowerCAmelCase				:			List[str]    			=      None
          if self.use_labels:
              lowerCAmelCase				:			Optional[Any]    			=      ids_tensor([self.batch_size]    ,				self.type_sequence_label_size    )
  
          lowerCAmelCase				:			Dict    			=      self.get_config()
  
          return config, pixel_values, labels
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ):
          return ConvNextConfig(
              num_channels=self.num_channels    ,				hidden_sizes=self.hidden_sizes    ,				depths=self.depths    ,				num_stages=self.num_stages    ,				hidden_act=self.hidden_act    ,				is_decoder=UpperCAmelCase_    ,				initializer_range=self.initializer_range    ,				)
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Optional[int]    ):
          lowerCAmelCase				:			Tuple    			=      TFConvNextModel(config=UpperCAmelCase_    )
          lowerCAmelCase				:			str    			=      model(UpperCAmelCase_    ,				training=UpperCAmelCase_    )
          # expected last hidden states: B, C, H // 32, W // 32
          self.parent.assertEqual(
              result.last_hidden_state.shape    ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,				)
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Dict    ):
          lowerCAmelCase				:			List[str]    			=      self.type_sequence_label_size
          lowerCAmelCase				:			Dict    			=      TFConvNextForImageClassification(UpperCAmelCase_    )
          lowerCAmelCase				:			Dict    			=      model(UpperCAmelCase_    ,				labels=UpperCAmelCase_    ,				training=UpperCAmelCase_    )
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Union[str, Any]    ):
          lowerCAmelCase				:			Optional[Any]    			=      self.prepare_config_and_inputs()
          lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase				:			str    			=      config_and_inputs
          lowerCAmelCase				:			int    			=      {'pixel_values': pixel_values}
          return config, inputs_dict
  
  
  
  
  
  
  @require_tf
  class    __A				(							lowerCAmelCase						,						lowerCAmelCase						,						unittest.TestCase ):
      lowerCAmelCase_							:		List[str]						=							(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
      lowerCAmelCase_							:		Optional[Any]						=							(
          {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
          if is_tf_available()
          else {}
      )
  
      lowerCAmelCase_							:		Optional[int]						=							False
      lowerCAmelCase_							:		List[Any]						=							False
      lowerCAmelCase_							:		Any						=							False
      lowerCAmelCase_							:		str						=							False
      lowerCAmelCase_							:		str						=							False
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Dict    ):
          lowerCAmelCase				:			str    			=      TFConvNextModelTester(self    )
          lowerCAmelCase				:			List[Any]    			=      ConfigTester(
              self    ,				config_class=UpperCAmelCase_    ,				has_text_modality=UpperCAmelCase_    ,				hidden_size=37    ,				)
  
  
  
  
  
  
      @unittest.skip(reason='ConvNext does not use inputs_embeds'    )
      def 	lowercase__ (	self			:    List[str]    ):
          pass
  
  
  
  
  
  
      @unittest.skipIf(
          not is_tf_available() or len(tf.config.list_physical_devices('GPU'    )    ) == 0    ,				reason='TF does not support backprop for grouped convolutions on CPU.'    ,				)
      @slow
      def 	lowercase__ (	self			:    Optional[Any]    ):
          super().test_keras_fit()
  
  
  
  
  
  
      @unittest.skip(reason='ConvNext does not support input and output embeddings'    )
      def 	lowercase__ (	self			:    List[str]    ):
          pass
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ):
          lowerCAmelCase			,  lowerCAmelCase				:			List[str]    			=      self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              lowerCAmelCase				:			Tuple    			=      model_class(UpperCAmelCase_    )
              lowerCAmelCase				:			Tuple    			=      inspect.signature(model.call    )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              lowerCAmelCase				:			Optional[int]    			=      [*signature.parameters.keys()]
  
              lowerCAmelCase				:			Dict    			=      ['pixel_values']
              self.assertListEqual(arg_names[:1]    ,				UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ):
          lowerCAmelCase				:			Dict    			=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCAmelCase_    )
  
  
  
  
  
  
      @unittest.skipIf(
          not is_tf_available() or len(tf.config.list_physical_devices('GPU'    )    ) == 0    ,				reason='TF does not support backprop for grouped convolutions on CPU.'    ,				)
      def 	lowercase__ (	self			:    List[str]    ):
          super().test_dataset_conversion()
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ):
          def check_hidden_states_output(UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    List[str]    ):
              lowerCAmelCase				:			Optional[int]    			=      model_class(UpperCAmelCase_    )
  
              lowerCAmelCase				:			Dict    			=      model(**self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    )    )
              lowerCAmelCase				:			List[Any]    			=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
              lowerCAmelCase				:			List[str]    			=      self.model_tester.num_stages
              self.assertEqual(len(UpperCAmelCase_    )    ,				expected_num_stages + 1    )
  
              # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]    )    ,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,				)
  
          lowerCAmelCase			,  lowerCAmelCase				:			Optional[Any]    			=      self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              lowerCAmelCase				:			List[str]    			=      True
              check_hidden_states_output(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              lowerCAmelCase				:			List[str]    			=      True
  
              check_hidden_states_output(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          lowerCAmelCase			,  lowerCAmelCase				:			List[Any]    			=      self.model_tester.prepare_config_and_inputs_for_common()
  
          def check_equivalence(UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    Union[str, Any]={}    ):
              lowerCAmelCase				:			Union[str, Any]    			=      model(UpperCAmelCase_    ,				return_dict=UpperCAmelCase_    ,				**UpperCAmelCase_    )
              lowerCAmelCase				:			List[str]    			=      model(UpperCAmelCase_    ,				return_dict=UpperCAmelCase_    ,				**UpperCAmelCase_    ).to_tuple()
  
              def recursive_check(UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Tuple    ):
                  if isinstance(UpperCAmelCase_    ,				(List, Tuple)    ):
                      for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase_    ,				UpperCAmelCase_    ):
                          recursive_check(UpperCAmelCase_    ,				UpperCAmelCase_    )
                  elif tuple_object is None:
                      return
                  else:
                      self.assertTrue(
                          all(tf.equal(UpperCAmelCase_    ,				UpperCAmelCase_    )    )    ,				msg=(
                              'Tuple and dict output are not equal. Difference:'
                              f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object    )    )}"
                          )    ,				)
  
                  recursive_check(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
          for model_class in self.all_model_classes:
              lowerCAmelCase				:			Union[str, Any]    			=      model_class(UpperCAmelCase_    )
  
              lowerCAmelCase				:			Tuple    			=      self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    )
              lowerCAmelCase				:			Union[str, Any]    			=      self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    )
              check_equivalence(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Tuple    			=      self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    ,				return_labels=UpperCAmelCase_    )
              lowerCAmelCase				:			Tuple    			=      self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    ,				return_labels=UpperCAmelCase_    )
              check_equivalence(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Union[str, Any]    			=      self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    )
              lowerCAmelCase				:			Optional[Any]    			=      self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    )
              check_equivalence(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				{'output_hidden_states': True}    )
  
              lowerCAmelCase				:			Union[str, Any]    			=      self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    ,				return_labels=UpperCAmelCase_    )
              lowerCAmelCase				:			int    			=      self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    ,				return_labels=UpperCAmelCase_    )
              check_equivalence(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				{'output_hidden_states': True}    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ):
          lowerCAmelCase				:			Any    			=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_    )
  
  
  
  
  
  
      @slow
      def 	lowercase__ (	self			:    Union[str, Any]    ):
          lowerCAmelCase				:			Dict    			=      TFConvNextModel.from_pretrained('facebook/convnext-tiny-224'    )
          self.assertIsNotNone(UpperCAmelCase_    )
  
  
  
  def 		SCREAMING_SNAKE_CASE__				(							)					-> Union[str, Any]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Dict    			=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
      return image
  
  
  
  
  
  
  @require_tf
  @require_vision
  class    __A				(							unittest.TestCase ):
  
  
  
  
  
  
      @cached_property
      def 	lowercase__ (	self			:    Optional[int]    ):
          return ConvNextImageProcessor.from_pretrained('facebook/convnext-tiny-224'    ) if is_vision_available() else None
  
  
  
  
  
  
      @slow
      def 	lowercase__ (	self			:    int    ):
          lowerCAmelCase				:			Tuple    			=      TFConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224'    )
  
          lowerCAmelCase				:			Optional[Any]    			=      self.default_image_processor
          lowerCAmelCase				:			str    			=      prepare_img()
          lowerCAmelCase				:			Dict    			=      image_processor(images=UpperCAmelCase_    ,				return_tensors='tf'    )
  
          # forward pass
          lowerCAmelCase				:			List[Any]    			=      model(**UpperCAmelCase_    )
  
          # verify the logits
          lowerCAmelCase				:			List[Any]    			=      tf.TensorShape((1, 1000)    )
          self.assertEqual(outputs.logits.shape    ,				UpperCAmelCase_    )
  
          lowerCAmelCase				:			Any    			=      tf.constant([-0.02_60, -0.47_39, 0.19_11]    )
  
          tf.debugging.assert_near(outputs.logits[0, :3]    ,				UpperCAmelCase_    ,				atol=1E-4    )
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
  
  
  __A   :     Dict      =			{}
  
  try:
   if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     List[Any]      =			['''BarthezTokenizer''']
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     List[str]      =			['''BarthezTokenizerFast''']
  
  
  if TYPE_CHECKING:
   try:
    if not is_sentencepiece_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_barthez import BarthezTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_barthez_fast import BarthezTokenizerFast
  
  else:
   import sys
  
   __A   :     Tuple      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  