code: 
  """simple docstring"""
  
  
  
  
  
  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_			:
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_       ,     lowercase_=13       ,     lowercase_=32       ,     lowercase_=3       ,     lowercase_=4       ,     lowercase_=[10, 20, 30, 40]       ,     lowercase_=[2, 2, 3, 2]       ,     lowercase_=True       ,     lowercase_=True       ,     lowercase_=37       ,     lowercase_="gelu"       ,     lowercase_=10       ,     lowercase_=0.02       ,     lowercase_=3       ,     lowercase_=None       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					parent
       UpperCAmelCase_      :      Union[str, Any]      =					batch_size
       UpperCAmelCase_      :      Union[str, Any]      =					image_size
       UpperCAmelCase_      :      Tuple      =					num_channels
       UpperCAmelCase_      :      Optional[Any]      =					num_stages
       UpperCAmelCase_      :      Union[str, Any]      =					hidden_sizes
       UpperCAmelCase_      :      List[str]      =					depths
       UpperCAmelCase_      :      str      =					is_training
       UpperCAmelCase_      :      Dict      =					use_labels
       UpperCAmelCase_      :      Any      =					intermediate_size
       UpperCAmelCase_      :      Tuple      =					hidden_act
       UpperCAmelCase_      :      int      =					type_sequence_label_size
       UpperCAmelCase_      :      Any      =					initializer_range
       UpperCAmelCase_      :      List[Any]      =					scope
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Any      =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)
  
       UpperCAmelCase_      :      Optional[Any]      =					None
       if self.use_labels:
          UpperCAmelCase_      :      List[Any]      =					ids_tensor([self.batch_size]       ,     self.type_sequence_label_size						)
  
       UpperCAmelCase_      :      List[Any]      =					self.get_config()
  
       return config, pixel_values, labels
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       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=lowercase_       ,     initializer_range=self.initializer_range       ,     )
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Any      =					TFConvNextModel(config=lowercase_						)
       UpperCAmelCase_      :      int      =					model(lowercase_       ,     training=lowercase_						)
       # 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  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					self.type_sequence_label_size
       UpperCAmelCase_      :      Any      =					TFConvNextForImageClassification(lowercase_						)
       UpperCAmelCase_      :      List[Any]      =					model(lowercase_       ,     labels=lowercase_       ,     training=lowercase_						)
       self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.type_sequence_label_size)						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					self.prepare_config_and_inputs()
       UpperCAmelCase_      :      Union[str, Any]      =					config_and_inputs
       UpperCAmelCase_      :      Dict      =					{"pixel_values": pixel_values}
       return config, inputs_dict
  
  
  
  
  @require_tf
  class A_			(lowercase__       ,lowercase__       ,unittest.TestCase   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		Tuple						  =	(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
    SCREAMING_SNAKE_CASE__    :		str						  =	(
        {"""feature-extraction""": TFConvNextModel, """image-classification""": TFConvNextForImageClassification}
        if is_tf_available()
        else {}
    )
  
    SCREAMING_SNAKE_CASE__    :		int						  =	False
    SCREAMING_SNAKE_CASE__    :		List[Any]						  =	False
    SCREAMING_SNAKE_CASE__    :		Tuple						  =	False
    SCREAMING_SNAKE_CASE__    :		Optional[Any]						  =	False
    SCREAMING_SNAKE_CASE__    :		int						  =	False
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[Any]      =					TFConvNextModelTester(self						)
       UpperCAmelCase_      :      Union[str, Any]      =					ConfigTester(
           self       ,     config_class=lowercase_       ,     has_text_modality=lowercase_       ,     hidden_size=37       ,     )
  
  
  
  
  
  
  
    @unittest.skip(reason="ConvNext does not use inputs_embeds"						)
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       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  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       super().test_keras_fit()
  
  
  
  
  
  
  
    @unittest.skip(reason="ConvNext does not support input and output embeddings"						)
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       pass
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					self.model_tester.prepare_config_and_inputs_for_common()
  
       for model_class in self.all_model_classes:
          UpperCAmelCase_      :      Dict      =					model_class(lowercase_						)
          UpperCAmelCase_      :      str      =					inspect.signature(model.call						)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          UpperCAmelCase_      :      List[Any]      =					[*signature.parameters.keys()]
  
          UpperCAmelCase_      :      str      =					["pixel_values"]
          self.assertListEqual(arg_names[:1]       ,     lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase_						)
  
  
  
  
  
  
  
    @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  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       super().test_dataset_conversion()
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       def check_hidden_states_output(lowercase_       ,     lowercase_       ,     lowercase_						):
          UpperCAmelCase_      :      Union[str, Any]      =					model_class(lowercase_						)
  
          UpperCAmelCase_      :      Tuple      =					model(**self._prepare_for_class(lowercase_       ,     lowercase_						)						)
          UpperCAmelCase_      :      Optional[int]      =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
          UpperCAmelCase_      :      Tuple      =					self.model_tester.num_stages
          self.assertEqual(len(lowercase_						)       ,     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]       ,     )
  
       UpperCAmelCase_      :      List[Any]      =					self.model_tester.prepare_config_and_inputs_for_common()
  
       for model_class in self.all_model_classes:
          UpperCAmelCase_      :      List[str]      =					True
          check_hidden_states_output(lowercase_       ,     lowercase_       ,     lowercase_						)
  
          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          UpperCAmelCase_      :      Any      =					True
  
          check_hidden_states_output(lowercase_       ,     lowercase_       ,     lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      int      =					self.model_tester.prepare_config_and_inputs_for_common()
  
       def check_equivalence(lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_={}						):
          UpperCAmelCase_      :      List[str]      =					model(lowercase_       ,     return_dict=lowercase_       ,     **lowercase_						)
          UpperCAmelCase_      :      Dict      =					model(lowercase_       ,     return_dict=lowercase_       ,     **lowercase_						).to_tuple()
  
          def recursive_check(lowercase_       ,     lowercase_						):
             if isinstance(lowercase_       ,     (List, Tuple)						):
                for tuple_iterable_value, dict_iterable_value in zip(lowercase_       ,     lowercase_						):
                   recursive_check(lowercase_       ,     lowercase_						)
             elif tuple_object is None:
                return
             else:
                self.assertTrue(
                    all(tf.equal(lowercase_       ,     lowercase_						)						)       ,     msg=(
                        "Tuple and dict output are not equal. Difference:"
                        F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object						)						)}"""
                    )       ,     )
  
             recursive_check(lowercase_       ,     lowercase_						)
  
       for model_class in self.all_model_classes:
          UpperCAmelCase_      :      int      =					model_class(lowercase_						)
  
          UpperCAmelCase_      :      int      =					self._prepare_for_class(lowercase_       ,     lowercase_						)
          UpperCAmelCase_      :      Tuple      =					self._prepare_for_class(lowercase_       ,     lowercase_						)
          check_equivalence(lowercase_       ,     lowercase_       ,     lowercase_						)
  
          UpperCAmelCase_      :      List[str]      =					self._prepare_for_class(lowercase_       ,     lowercase_       ,     return_labels=lowercase_						)
          UpperCAmelCase_      :      List[Any]      =					self._prepare_for_class(lowercase_       ,     lowercase_       ,     return_labels=lowercase_						)
          check_equivalence(lowercase_       ,     lowercase_       ,     lowercase_						)
  
          UpperCAmelCase_      :      List[Any]      =					self._prepare_for_class(lowercase_       ,     lowercase_						)
          UpperCAmelCase_      :      int      =					self._prepare_for_class(lowercase_       ,     lowercase_						)
          check_equivalence(lowercase_       ,     lowercase_       ,     lowercase_       ,     {"output_hidden_states": True}						)
  
          UpperCAmelCase_      :      int      =					self._prepare_for_class(lowercase_       ,     lowercase_       ,     return_labels=lowercase_						)
          UpperCAmelCase_      :      Optional[int]      =					self._prepare_for_class(lowercase_       ,     lowercase_       ,     return_labels=lowercase_						)
          check_equivalence(lowercase_       ,     lowercase_       ,     lowercase_       ,     {"output_hidden_states": True}						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      str      =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowercase_						)
  
  
  
  
  
  
  
    @slow
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Tuple      =					TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"						)
       self.assertIsNotNone(lowercase_						)
  
  
  
  
  
  
  
  def 			__a  (	):
     UpperCAmelCase_      :      str      =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
     return image
  
  
  @require_tf
  @require_vision
  class A_			(unittest.TestCase   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @cached_property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"						) if is_vision_available() else None
  
  
  
  
  
  
  
    @slow
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"						)
  
       UpperCAmelCase_      :      str      =					self.default_image_processor
       UpperCAmelCase_      :      int      =					prepare_img()
       UpperCAmelCase_      :      str      =					image_processor(images=lowercase_       ,     return_tensors="tf"						)
  
       # forward pass
       UpperCAmelCase_      :      int      =					model(**lowercase_						)
  
       # verify the logits
       UpperCAmelCase_      :      Optional[int]      =					tf.TensorShape((1, 1000)						)
       self.assertEqual(outputs.logits.shape       ,     lowercase_						)
  
       UpperCAmelCase_      :      Union[str, Any]      =					tf.constant([-0.02_60, -0.47_39, 0.19_11]						)
  
       tf.debugging.assert_near(outputs.logits[0, :3]       ,     lowercase_       ,     atol=1E-4						)
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from __future__ import annotations
  
  from typing import Any
  
  
  class A_			:
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_       ,     lowercase_						):  # noqa: A002
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					item
       UpperCAmelCase_      :      Optional[Any]      =					next
  
  
  
  
  class A_			:
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Node | None      =					None
       UpperCAmelCase_      :      Optional[int]      =					0
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      int      =					Node(lowercase_       ,     self.head						)
       self.size += 1
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       # Switched 'self.is_empty()' to 'self.head is None'
       # because mypy was considering the possibility that 'self.head'
       # can be None in below else part and giving error
       if self.head is None:
          return None
       else:
          UpperCAmelCase_      :      int      =					self.head.item
          UpperCAmelCase_      :      str      =					self.head.next
          self.size -= 1
          return item
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return self.head is None
  
  
  
  
  
  
  
    def __str__(	self						):
  
       """simple docstring"""
       if self.is_empty():
          return ""
       else:
          UpperCAmelCase_      :      str      =					self.head
          UpperCAmelCase_      :      str      =					""
          UpperCAmelCase_      :      list[str]      =					[]
          while iterate:
             item_list.append(str(iterate.item						)						)
             UpperCAmelCase_      :      Tuple      =					iterate.next
  
          UpperCAmelCase_      :      Optional[Any]      =					" --> ".join(lowercase_						)
  
          return item_str
  
  
  
  
  
  
  
    def __len__(	self						):
  
       """simple docstring"""
       return self.size
  
  
  