"""simple docstring"""



from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments






"""simple docstring"""



from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor
class 						__UpperCAmelCase    :







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=3   ,      snake_case_=32   ,      snake_case_=3   ,      snake_case_=10   ,      snake_case_=[10, 20, 30, 40]   ,      snake_case_=[1, 1, 2, 1]   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_="relu"   ,      snake_case_=3   ,      snake_case_=None   ,      ):







										'''simple docstring'''


										A__       :     Tuple								=			parent
										A__       :     int								=			batch_size
										A__       :     List[Any]								=			image_size
										A__       :     Any								=			num_channels
										A__       :     Tuple								=			embeddings_size
										A__       :     Union[str, Any]								=			hidden_sizes
										A__       :     Tuple								=			depths
										A__       :     Union[str, Any]								=			is_training
										A__       :     Any								=			use_labels
										A__       :     Optional[Any]								=			hidden_act
										A__       :     str								=			num_labels
										A__       :     Optional[Any]								=			scope
										A__       :     List[Any]								=			len(snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

										A__       :     str								=			None
										if self.use_labels:
																	A__       :     Tuple								=			ids_tensor([self.batch_size]   ,      self.num_labels							)

										A__       :     Dict								=			self.get_config()
										return config, pixel_values, labels





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return RegNetConfig(
										    num_channels=self.num_channels   ,      embeddings_size=self.embeddings_size   ,      hidden_sizes=self.hidden_sizes   ,      depths=self.depths   ,      hidden_act=self.hidden_act   ,      num_labels=self.num_labels   ,      )





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			TFRegNetModel(config=snake_case_							)
										A__       :     Optional[int]								=			model(snake_case_   ,      training=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 // 32, self.image_size // 32)   ,      )





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Optional[int]								=			self.num_labels
										A__       :     Tuple								=			TFRegNetForImageClassification(snake_case_							)
										A__       :     Optional[int]								=			model(snake_case_   ,      labels=snake_case_   ,      training=snake_case_							)
										self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_labels)							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			self.prepare_config_and_inputs()
										A__				,							A__				,							A__       :     Optional[Any]								=			config_and_inputs
										A__       :     str								=			{"""pixel_values""": pixel_values}
										return config, inputs_dict





@require_tf
class 						__UpperCAmelCase    (__A							,   __A							,   unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						str					     =				(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
			_UpperCamelCase  :						Tuple					     =				(
			    {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
			    if is_tf_available()
			    else {}
			)

			_UpperCamelCase  :						str					     =				False
			_UpperCamelCase  :						int					     =				False
			_UpperCamelCase  :						Tuple					     =				False
			_UpperCamelCase  :						Any					     =				False
			_UpperCamelCase  :						Union[str, Any]					     =				False





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Dict								=			TFRegNetModelTester(self							)
										A__       :     str								=			ConfigTester(self   ,      config_class=snake_case_   ,      has_text_modality=snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return





			@unittest.skip(reason="""RegNet does not use inputs_embeds"""							)
			def 		lowerCamelCase					(			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 		lowerCamelCase					(			self							):







										'''simple docstring'''


										super().test_keras_fit()





			@unittest.skip(reason="""RegNet does not support input and output embeddings"""							)
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										pass





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__       :     int								=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	A__       :     int								=			model_class(snake_case_							)
																	A__       :     Optional[int]								=			inspect.signature(model.call							)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	A__       :     Union[str, Any]								=			[*signature.parameters.keys()]

																	A__       :     Tuple								=			["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										def check_hidden_states_output(snake_case_   ,      snake_case_   ,      snake_case_							):
																	A__       :     str								=			model_class(snake_case_							)
																	A__       :     Union[str, Any]								=			model(**self._prepare_for_class(snake_case_   ,      snake_case_							)   ,      training=snake_case_							)

																	A__       :     Optional[int]								=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																	A__       :     Union[str, Any]								=			self.model_tester.num_stages
																	self.assertEqual(len(snake_case_							)   ,      expected_num_stages + 1							)

																	# RegNet'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 // 2, self.model_tester.image_size // 2]   ,      )

										A__				,							A__       :     int								=			self.model_tester.prepare_config_and_inputs_for_common()
										A__       :     Optional[int]								=			["""basic""", """bottleneck"""]
										for model_class in self.all_model_classes:
																	for layer_type in layers_type:
																								A__       :     Optional[Any]								=			layer_type
																								A__       :     Dict								=			True
																								check_hidden_states_output(snake_case_   ,      snake_case_   ,      snake_case_							)

																								# check that output_hidden_states also work using config
																								del inputs_dict["output_hidden_states"]
																								A__       :     List[Any]								=			True

																								check_hidden_states_output(snake_case_   ,      snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__       :     Optional[Any]								=			self.model_tester.prepare_config_and_inputs_for_common()

										def check_equivalence(snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_={}							):
																	A__       :     List[str]								=			model(snake_case_   ,      return_dict=snake_case_   ,      **snake_case_							)
																	A__       :     Union[str, Any]								=			model(snake_case_   ,      return_dict=snake_case_   ,      **snake_case_							).to_tuple()

																	def recursive_check(snake_case_   ,      snake_case_							):
																								if isinstance(snake_case_   ,      (List, Tuple)							):
																															for tuple_iterable_value, dict_iterable_value in zip(snake_case_   ,      snake_case_							):
																																						recursive_check(snake_case_   ,      snake_case_							)
																								elif tuple_object is None:
																															return
																								else:
																															self.assertTrue(
																															    all(tf.equal(snake_case_   ,      snake_case_							)							)   ,      msg=(
																															        """Tuple and dict output are not equal. Difference:"""
																															        F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object							)							)}'''
																															    )   ,      )

																								recursive_check(snake_case_   ,      snake_case_							)

										for model_class in self.all_model_classes:
																	A__       :     Any								=			model_class(snake_case_							)

																	A__       :     Dict								=			self._prepare_for_class(snake_case_   ,      snake_case_							)
																	A__       :     List[Any]								=			self._prepare_for_class(snake_case_   ,      snake_case_							)
																	check_equivalence(snake_case_   ,      snake_case_   ,      snake_case_							)

																	A__       :     Tuple								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)
																	A__       :     List[str]								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)
																	check_equivalence(snake_case_   ,      snake_case_   ,      snake_case_							)

																	A__       :     Optional[int]								=			self._prepare_for_class(snake_case_   ,      snake_case_							)
																	A__       :     List[str]								=			self._prepare_for_class(snake_case_   ,      snake_case_							)
																	check_equivalence(snake_case_   ,      snake_case_   ,      snake_case_   ,      {"""output_hidden_states""": True}							)

																	A__       :     Dict								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)
																	A__       :     Union[str, Any]								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)
																	check_equivalence(snake_case_   ,      snake_case_   ,      snake_case_   ,      {"""output_hidden_states""": True}							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Tuple								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*snake_case_							)





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	A__       :     Any								=			TFRegNetModel.from_pretrained(snake_case_							)
																	self.assertIsNotNone(snake_case_							)





def  _A(		):
							A__       :     Optional[int]								=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
							return image



@require_tf
@require_vision
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''





			@cached_property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return (
										    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
										    if is_vision_available()
										    else None
										)





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     int								=			TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

										A__       :     str								=			self.default_image_processor
										A__       :     Any								=			prepare_img()
										A__       :     Tuple								=			image_processor(images=snake_case_   ,      return_tensors="""tf"""							)

										# forward pass
										A__       :     List[Any]								=			model(**snake_case_   ,      training=snake_case_							)

										# verify the logits
										A__       :     Tuple								=			tf.TensorShape((1, 1_000)							)
										self.assertEqual(outputs.logits.shape   ,      snake_case_							)

										A__       :     Tuple								=			tf.constant([-0.41_80, -1.50_51, -3.48_36]							)

										tf.debugging.assert_near(outputs.logits[0, :3]   ,      snake_case_   ,      atol=1E-4							)





