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     __UpperCamelCase				:
  
  
  		def __init__(self				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	str  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=1_3  ,  __SCREAMING_SNAKE_CASE				:	Tuple=3_2  ,  __SCREAMING_SNAKE_CASE				:	Tuple=3  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=4  ,  __SCREAMING_SNAKE_CASE				:	str=[1_0, 2_0, 3_0, 4_0]  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=[2, 2, 3, 2]  ,  __SCREAMING_SNAKE_CASE				:	str=True  ,  __SCREAMING_SNAKE_CASE				:	str=True  ,  __SCREAMING_SNAKE_CASE				:	Dict=3_7  ,  __SCREAMING_SNAKE_CASE				:	int="gelu"  ,  __SCREAMING_SNAKE_CASE				:	Tuple=1_0  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=0.0_2  ,  __SCREAMING_SNAKE_CASE				:	Tuple=3  ,  __SCREAMING_SNAKE_CASE				:	str=None  ,  ):
  							A    =      parent
  							A    =      batch_size
  							A    =      image_size
  							A    =      num_channels
  							A    =      num_stages
  							A    =      hidden_sizes
  							A    =      depths
  							A    =      is_training
  							A    =      use_labels
  							A    =      intermediate_size
  							A    =      hidden_act
  							A    =      type_sequence_label_size
  							A    =      initializer_range
  							A    =      scope
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any):
  							A    =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
  
  							A    =      None
  							if self.use_labels:
  												A    =      ids_tensor([self.batch_size]  ,  self.type_sequence_label_size)
  
  							A    =      self.get_config()
  
  							return config, pixel_values, labels
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
  							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=__SCREAMING_SNAKE_CASE  ,  initializer_range=self.initializer_range  ,  )
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							A    =      TFConvNextModel(config=__SCREAMING_SNAKE_CASE)
  							A    =      model(__SCREAMING_SNAKE_CASE  ,  training=__SCREAMING_SNAKE_CASE)
  							# 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 // 3_2, self.image_size // 3_2)  ,  )
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	int  ,  __SCREAMING_SNAKE_CASE				:	str  ,  __SCREAMING_SNAKE_CASE				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]):
  							A    =      self.type_sequence_label_size
  							A    =      TFConvNextForImageClassification(__SCREAMING_SNAKE_CASE)
  							A    =      model(__SCREAMING_SNAKE_CASE  ,  labels=__SCREAMING_SNAKE_CASE  ,  training=__SCREAMING_SNAKE_CASE)
  							self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.type_sequence_label_size))
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
  							A    =      self.prepare_config_and_inputs()
  							A    ,					A    ,					A    =      config_and_inputs
  							A    =      {"pixel_values": pixel_values}
  							return config, inputs_dict
  
  
  
  
  
  @require_tf
  class     __UpperCamelCase				(     _A  ,	_A  ,	unittest.TestCase		):
  		SCREAMING_SNAKE_CASE              =					(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
  		SCREAMING_SNAKE_CASE              =					(
  		    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
  		    if is_tf_available()
  		    else {}
  		)
  
  		SCREAMING_SNAKE_CASE              =					False
  		SCREAMING_SNAKE_CASE              =					False
  		SCREAMING_SNAKE_CASE              =					False
  		SCREAMING_SNAKE_CASE              =					False
  		SCREAMING_SNAKE_CASE              =					False
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any):
  							A    =      TFConvNextModelTester(self)
  							A    =      ConfigTester(
  							    self  ,  config_class=__SCREAMING_SNAKE_CASE  ,  has_text_modality=__SCREAMING_SNAKE_CASE  ,  hidden_size=3_7  ,  )
  
  
  		@unittest.skip(reason="ConvNext does not use inputs_embeds")
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
  							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       SCREAMING_SNAKE_CASE__     (self				:	int):
  							super().test_keras_fit()
  
  
  		@unittest.skip(reason="ConvNext does not support input and output embeddings")
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
  							pass
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
  							A    ,					A    =      self.model_tester.prepare_config_and_inputs_for_common()
  
  							for model_class in self.all_model_classes:
  												A    =      model_class(__SCREAMING_SNAKE_CASE)
  												A    =      inspect.signature(model.call)
  												# signature.parameters is an OrderedDict => so arg_names order is deterministic
  												A    =      [*signature.parameters.keys()]
  
  												A    =      ["pixel_values"]
  												self.assertListEqual(arg_names[:1]  ,  __SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
  							A    =      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)
  
  
  		@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       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
  							super().test_dataset_conversion()
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	str):
  							def check_hidden_states_output(__SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	int):
  												A    =      model_class(__SCREAMING_SNAKE_CASE)
  
  												A    =      model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE))
  												A    =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
  												A    =      self.model_tester.num_stages
  												self.assertEqual(len(__SCREAMING_SNAKE_CASE)  ,  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]  ,  )
  
  							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(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  
  												# check that output_hidden_states also work using config
  												del inputs_dict["output_hidden_states"]
  												A    =      True
  
  												check_hidden_states_output(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any):
  							A    ,					A    =      self.model_tester.prepare_config_and_inputs_for_common()
  
  							def check_equivalence(__SCREAMING_SNAKE_CASE				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Any={}):
  												A    =      model(__SCREAMING_SNAKE_CASE  ,  return_dict=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)
  												A    =      model(__SCREAMING_SNAKE_CASE  ,  return_dict=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE).to_tuple()
  
  												def recursive_check(__SCREAMING_SNAKE_CASE				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]):
  																	if isinstance(__SCREAMING_SNAKE_CASE  ,  (List, Tuple)):
  																						for tuple_iterable_value, dict_iterable_value in zip(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																											recursive_check(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  																	elif tuple_object is None:
  																						return
  																	else:
  																						self.assertTrue(
  																						    all(tf.equal(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE))  ,  msg=(
  																						        "Tuple and dict output are not equal. Difference:"
  																						        F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}"""
  																						    )  ,  )
  
  																	recursive_check(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  
  							for model_class in self.all_model_classes:
  												A    =      model_class(__SCREAMING_SNAKE_CASE)
  
  												A    =      self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  												A    =      self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  												check_equivalence(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  
  												A    =      self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  return_labels=__SCREAMING_SNAKE_CASE)
  												A    =      self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  return_labels=__SCREAMING_SNAKE_CASE)
  												check_equivalence(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  
  												A    =      self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  												A    =      self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  												check_equivalence(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  {"output_hidden_states": True})
  
  												A    =      self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  return_labels=__SCREAMING_SNAKE_CASE)
  												A    =      self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  return_labels=__SCREAMING_SNAKE_CASE)
  												check_equivalence(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  {"output_hidden_states": True})
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	int):
  							A    =      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE)
  
  
  		@slow
  		def       SCREAMING_SNAKE_CASE__     (self				:	int):
  							A    =      TFConvNextModel.from_pretrained("facebook/convnext-tiny-224")
  							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
  
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					A    =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
  					return image
  
  
  
  
  
  @require_tf
  @require_vision
  class     __UpperCamelCase				(     unittest.TestCase		):
  
  
  		@cached_property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple):
  							return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224") if is_vision_available() else None
  
  
  		@slow
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any):
  							A    =      TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224")
  
  							A    =      self.default_image_processor
  							A    =      prepare_img()
  							A    =      image_processor(images=__SCREAMING_SNAKE_CASE  ,  return_tensors="tf")
  
  							# forward pass
  							A    =      model(**__SCREAMING_SNAKE_CASE)
  
  							# verify the logits
  							A    =      tf.TensorShape((1, 1_0_0_0))
  							self.assertEqual(outputs.logits.shape  ,  __SCREAMING_SNAKE_CASE)
  
  							A    =      tf.constant([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1])
  
  							tf.debugging.assert_near(outputs.logits[0, :3]  ,  __SCREAMING_SNAKE_CASE  ,  atol=1E-4)
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  __A  :					List[str]												=	logging.get_logger(__name__)
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  		SCREAMING_SNAKE_CASE              =					"mt5"
  		SCREAMING_SNAKE_CASE              =					["past_key_values"]
  
  
  		def __init__(self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=2_5_0_1_1_2  ,  __SCREAMING_SNAKE_CASE				:	int=5_1_2  ,  __SCREAMING_SNAKE_CASE				:	Tuple=6_4  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=1_0_2_4  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=8  ,  __SCREAMING_SNAKE_CASE				:	Any=None  ,  __SCREAMING_SNAKE_CASE				:	Tuple=6  ,  __SCREAMING_SNAKE_CASE				:	int=3_2  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=1_2_8  ,  __SCREAMING_SNAKE_CASE				:	Tuple=0.1  ,  __SCREAMING_SNAKE_CASE				:	Tuple=1E-6  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=1.0  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]="gated-gelu"  ,  __SCREAMING_SNAKE_CASE				:	List[str]=True  ,  __SCREAMING_SNAKE_CASE				:	str=True  ,  __SCREAMING_SNAKE_CASE				:	int="T5Tokenizer"  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=False  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=0  ,  __SCREAMING_SNAKE_CASE				:	Tuple=1  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0  ,  **__SCREAMING_SNAKE_CASE				:	str  ,  ):
  							super().__init__(
  							    is_encoder_decoder=__SCREAMING_SNAKE_CASE  ,  tokenizer_class=__SCREAMING_SNAKE_CASE  ,  tie_word_embeddings=__SCREAMING_SNAKE_CASE  ,  pad_token_id=__SCREAMING_SNAKE_CASE  ,  eos_token_id=__SCREAMING_SNAKE_CASE  ,  decoder_start_token_id=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE  ,  )
  							A    =      vocab_size
  							A    =      d_model
  							A    =      d_kv
  							A    =      d_ff
  							A    =      num_layers
  							A    =      (
  							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
  							)  # default = symmetry
  							A    =      num_heads
  							A    =      relative_attention_num_buckets
  							A    =      relative_attention_max_distance
  							A    =      dropout_rate
  							A    =      layer_norm_epsilon
  							A    =      initializer_factor
  							A    =      feed_forward_proj
  							A    =      use_cache
  
  							A    =      self.feed_forward_proj.split("-")
  							A    =      act_info[-1]
  							A    =      act_info[0] == "gated"
  
  							if len(__SCREAMING_SNAKE_CASE) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE) > 2:
  												raise ValueError(
  												    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
  												    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
  												    "'gated-gelu' or 'relu'")
  
  							# for backwards compatibility
  							if feed_forward_proj == "gated-gelu":
  												A    =      "gelu_new"
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
  							return self.d_model
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
  							return self.num_heads
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
  							return self.num_layers
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  
  
  		@property
  		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
  		def       SCREAMING_SNAKE_CASE__     (self				:	int):
  							A    =      {
  							    "input_ids": {0: "batch", 1: "encoder_sequence"},
  							    "attention_mask": {0: "batch", 1: "encoder_sequence"},
  							}
  							if self.use_past:
  												A    =      "past_encoder_sequence + sequence"
  												A    =      {0: "batch"}
  												A    =      {0: "batch", 1: "past_decoder_sequence + sequence"}
  							else:
  												A    =      {0: "batch", 1: "decoder_sequence"}
  												A    =      {0: "batch", 1: "decoder_sequence"}
  
  							if self.use_past:
  												self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE  ,  direction="inputs")
  
  							return common_inputs
  
  
  		@property
  		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
  							return 1_3
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	int):
  							return 5E-4
  
  
  
  
  
  