


"""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 				lowerCamelCase							:


							"""simple docstring"""






							def __init__(    self			:  Union[str, Any]		,			lowerCamelCase_			:  str		,			lowerCamelCase_			:  Dict=13		,			lowerCamelCase_			:  int=32		,			lowerCamelCase_			:  List[Any]=3		,			lowerCamelCase_			:  Any=4		,			lowerCamelCase_			:  Optional[Any]=[10, 20, 30, 40]		,			lowerCamelCase_			:  int=[2, 2, 3, 2]		,			lowerCamelCase_			:  Dict=True		,			lowerCamelCase_			:  Dict=True		,			lowerCamelCase_			:  Any=37		,			lowerCamelCase_			:  Tuple="gelu"		,			lowerCamelCase_			:  Union[str, Any]=10		,			lowerCamelCase_			:  str=0.02		,			lowerCamelCase_			:  Union[str, Any]=3		,			lowerCamelCase_			:  int=None		,			):
													UpperCAmelCase__												=     parent
													UpperCAmelCase__												=     batch_size
													UpperCAmelCase__												=     image_size
													UpperCAmelCase__												=     num_channels
													UpperCAmelCase__												=     num_stages
													UpperCAmelCase__												=     hidden_sizes
													UpperCAmelCase__												=     depths
													UpperCAmelCase__												=     is_training
													UpperCAmelCase__												=     use_labels
													UpperCAmelCase__												=     intermediate_size
													UpperCAmelCase__												=     hidden_act
													UpperCAmelCase__												=     type_sequence_label_size
													UpperCAmelCase__												=     initializer_range
													UpperCAmelCase__												=     scope






							def        lowerCAmelCase  (    self			:  List[Any]					):
													UpperCAmelCase__												=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

													UpperCAmelCase__												=     None
													if self.use_labels:
																			UpperCAmelCase__												=     ids_tensor([self.batch_size]		,			self.type_sequence_label_size					)

													UpperCAmelCase__												=     self.get_config()

													return config, pixel_values, labels






							def        lowerCAmelCase  (    self			:  Dict					):
													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=lowerCamelCase_		,			initializer_range=self.initializer_range		,			)






							def        lowerCAmelCase  (    self			:  Optional[Any]		,			lowerCamelCase_			:  Optional[Any]		,			lowerCamelCase_			:  Optional[int]		,			lowerCamelCase_			:  Optional[int]					):
													UpperCAmelCase__												=     TFConvNextModel(config=lowerCamelCase_					)
													UpperCAmelCase__												=     model(lowerCamelCase_		,			training=lowerCamelCase_					)
													# 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			:  List[Any]		,			lowerCamelCase_			:  List[str]		,			lowerCamelCase_			:  Optional[int]		,			lowerCamelCase_			:  Optional[Any]					):
													UpperCAmelCase__												=     self.type_sequence_label_size
													UpperCAmelCase__												=     TFConvNextForImageClassification(lowerCamelCase_					)
													UpperCAmelCase__												=     model(lowerCamelCase_		,			labels=lowerCamelCase_		,			training=lowerCamelCase_					)
													self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.type_sequence_label_size)					)






							def        lowerCAmelCase  (    self			:  Union[str, Any]					):
													UpperCAmelCase__												=     self.prepare_config_and_inputs()
													UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__												=     config_and_inputs
													UpperCAmelCase__												=     {'''pixel_values''': pixel_values}
													return config, inputs_dict




@require_tf
class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE					,       unittest.TestCase			):


							"""simple docstring"""
							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        lowerCAmelCase  (    self			:  Tuple					):
													UpperCAmelCase__												=     TFConvNextModelTester(self					)
													UpperCAmelCase__												=     ConfigTester(
													    self		,			config_class=lowerCamelCase_		,			has_text_modality=lowerCamelCase_		,			hidden_size=37		,			)






							@unittest.skip(reason='''ConvNext does not use inputs_embeds'''					)
							def        lowerCAmelCase  (    self			:  Optional[int]					):
													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			:  str					):
													super().test_keras_fit()






							@unittest.skip(reason='''ConvNext does not support input and output embeddings'''					)
							def        lowerCAmelCase  (    self			:  List[str]					):
													pass






							def        lowerCAmelCase  (    self			:  Optional[int]					):
													UpperCAmelCase__						,					UpperCAmelCase__												=     self.model_tester.prepare_config_and_inputs_for_common()

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

																			UpperCAmelCase__												=     ['''pixel_values''']
																			self.assertListEqual(arg_names[:1]		,			lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Optional[int]					):
													UpperCAmelCase__												=     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowerCamelCase_					)






							@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        lowerCAmelCase  (    self			:  Dict					):
													super().test_dataset_conversion()






							def        lowerCAmelCase  (    self			:  str					):
													def check_hidden_states_output(lowerCamelCase_			:  Any		,			lowerCamelCase_			:  Dict		,			lowerCamelCase_			:  Dict					):
																			UpperCAmelCase__												=     model_class(lowerCamelCase_					)

																			UpperCAmelCase__												=     model(**self._prepare_for_class(lowerCamelCase_		,			lowerCamelCase_					)					)
																			UpperCAmelCase__												=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																			UpperCAmelCase__												=     self.model_tester.num_stages
																			self.assertEqual(len(lowerCamelCase_					)		,			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__						,					UpperCAmelCase__												=     self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			UpperCAmelCase__												=     True
																			check_hidden_states_output(lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_					)

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

																			check_hidden_states_output(lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  List[Any]					):
													UpperCAmelCase__						,					UpperCAmelCase__												=     self.model_tester.prepare_config_and_inputs_for_common()

													def check_equivalence(lowerCamelCase_			:  Optional[Any]		,			lowerCamelCase_			:  List[str]		,			lowerCamelCase_			:  str		,			lowerCamelCase_			:  Dict={}					):
																			UpperCAmelCase__												=     model(lowerCamelCase_		,			return_dict=lowerCamelCase_		,			**lowerCamelCase_					)
																			UpperCAmelCase__												=     model(lowerCamelCase_		,			return_dict=lowerCamelCase_		,			**lowerCamelCase_					).to_tuple()

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

																									recursive_check(lowerCamelCase_		,			lowerCamelCase_					)

													for model_class in self.all_model_classes:
																			UpperCAmelCase__												=     model_class(lowerCamelCase_					)

																			UpperCAmelCase__												=     self._prepare_for_class(lowerCamelCase_		,			lowerCamelCase_					)
																			UpperCAmelCase__												=     self._prepare_for_class(lowerCamelCase_		,			lowerCamelCase_					)
																			check_equivalence(lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_					)

																			UpperCAmelCase__												=     self._prepare_for_class(lowerCamelCase_		,			lowerCamelCase_		,			return_labels=lowerCamelCase_					)
																			UpperCAmelCase__												=     self._prepare_for_class(lowerCamelCase_		,			lowerCamelCase_		,			return_labels=lowerCamelCase_					)
																			check_equivalence(lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_					)

																			UpperCAmelCase__												=     self._prepare_for_class(lowerCamelCase_		,			lowerCamelCase_					)
																			UpperCAmelCase__												=     self._prepare_for_class(lowerCamelCase_		,			lowerCamelCase_					)
																			check_equivalence(lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			{'''output_hidden_states''': True}					)

																			UpperCAmelCase__												=     self._prepare_for_class(lowerCamelCase_		,			lowerCamelCase_		,			return_labels=lowerCamelCase_					)
																			UpperCAmelCase__												=     self._prepare_for_class(lowerCamelCase_		,			lowerCamelCase_		,			return_labels=lowerCamelCase_					)
																			check_equivalence(lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			{'''output_hidden_states''': True}					)






							def        lowerCAmelCase  (    self			:  Any					):
													UpperCAmelCase__												=     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_					)






							@slow
							def        lowerCAmelCase  (    self			:  int					):
													UpperCAmelCase__												=     TFConvNextModel.from_pretrained('''facebook/convnext-tiny-224'''					)
													self.assertIsNotNone(lowerCamelCase_					)






def        a__			(							):





						"""simple docstring"""

						UpperCAmelCase__												=     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
						return image


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


							"""simple docstring"""






							@cached_property
							def        lowerCAmelCase  (    self			:  Any					):
													return ConvNextImageProcessor.from_pretrained('''facebook/convnext-tiny-224'''					) if is_vision_available() else None






							@slow
							def        lowerCAmelCase  (    self			:  Union[str, Any]					):
													UpperCAmelCase__												=     TFConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''					)

													UpperCAmelCase__												=     self.default_image_processor
													UpperCAmelCase__												=     prepare_img()
													UpperCAmelCase__												=     image_processor(images=lowerCamelCase_		,			return_tensors='''tf'''					)

													# forward pass
													UpperCAmelCase__												=     model(**lowerCamelCase_					)

													# verify the logits
													UpperCAmelCase__												=     tf.TensorShape((1, 1000)					)
													self.assertEqual(outputs.logits.shape		,			lowerCamelCase_					)

													UpperCAmelCase__												=     tf.constant([-0.0260, -0.4739, 0.1911]					)

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



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__a      			= {}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BarthezTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["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      			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)