





import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
				from transformers.models.electra.modeling_flax_electra import (
				    FlaxElectraForCausalLM,
				    FlaxElectraForMaskedLM,
				    FlaxElectraForMultipleChoice,
				    FlaxElectraForPreTraining,
				    FlaxElectraForQuestionAnswering,
				    FlaxElectraForSequenceClassification,
				    FlaxElectraForTokenClassification,
				    FlaxElectraModel,
				)




class 	_UpperCAmelCase						( unittest.TestCase       ):




					def __init__(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=13     ,   __lowerCAmelCase=7     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=99     ,   __lowerCAmelCase=24     ,   __lowerCAmelCase=32     ,   __lowerCAmelCase=5     ,   __lowerCAmelCase=4     ,   __lowerCAmelCase=37     ,   __lowerCAmelCase="gelu"     ,   __lowerCAmelCase=0.1     ,   __lowerCAmelCase=0.1     ,   __lowerCAmelCase=512     ,   __lowerCAmelCase=16     ,   __lowerCAmelCase=2     ,   __lowerCAmelCase=0.0_2     ,   __lowerCAmelCase=4     ,   )    ->	Any:
							UpperCamelCase__								=						parent
							UpperCamelCase__								=						batch_size
							UpperCamelCase__								=						seq_length
							UpperCamelCase__								=						is_training
							UpperCamelCase__								=						use_attention_mask
							UpperCamelCase__								=						use_token_type_ids
							UpperCamelCase__								=						use_labels
							UpperCamelCase__								=						vocab_size
							UpperCamelCase__								=						hidden_size
							UpperCamelCase__								=						embedding_size
							UpperCamelCase__								=						num_hidden_layers
							UpperCamelCase__								=						num_attention_heads
							UpperCamelCase__								=						intermediate_size
							UpperCamelCase__								=						hidden_act
							UpperCamelCase__								=						hidden_dropout_prob
							UpperCamelCase__								=						attention_probs_dropout_prob
							UpperCamelCase__								=						max_position_embeddings
							UpperCamelCase__								=						type_vocab_size
							UpperCamelCase__								=						type_sequence_label_size
							UpperCamelCase__								=						initializer_range
							UpperCamelCase__								=						num_choices




					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__								=						ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size							)

							UpperCamelCase__								=						None
							if self.use_attention_mask:
									UpperCamelCase__								=						random_attention_mask([self.batch_size, self.seq_length]							)

							UpperCamelCase__								=						None
							if self.use_token_type_ids:
									UpperCamelCase__								=						ids_tensor([self.batch_size, self.seq_length]     ,   self.type_vocab_size							)

							UpperCamelCase__								=						ElectraConfig(
							    vocab_size=self.vocab_size     ,   hidden_size=self.hidden_size     ,   embedding_size=self.embedding_size     ,   num_hidden_layers=self.num_hidden_layers     ,   num_attention_heads=self.num_attention_heads     ,   intermediate_size=self.intermediate_size     ,   hidden_act=self.hidden_act     ,   hidden_dropout_prob=self.hidden_dropout_prob     ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,   max_position_embeddings=self.max_position_embeddings     ,   type_vocab_size=self.type_vocab_size     ,   initializer_range=self.initializer_range     ,   )

							return config, input_ids, token_type_ids, attention_mask




					def 						snake_case_		(    self							)    ->	str:
							UpperCamelCase__								=						self.prepare_config_and_inputs()
							UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__								=						config_and_inputs
							UpperCamelCase__								=						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
							return config, inputs_dict




@require_flax
class 	_UpperCAmelCase						( __UpperCAmelCase			,							unittest.TestCase       ):
					__A        = True

					__A        = (
					    (
					        FlaxElectraModel,
					        FlaxElectraForCausalLM,
					        FlaxElectraForMaskedLM,
					        FlaxElectraForPreTraining,
					        FlaxElectraForTokenClassification,
					        FlaxElectraForQuestionAnswering,
					        FlaxElectraForMultipleChoice,
					        FlaxElectraForSequenceClassification,
					    )
					    if is_flax_available()
					    else ()
					)




					def 						snake_case_		(    self							)    ->	str:
							UpperCamelCase__								=						FlaxElectraModelTester(self							)




					@slow
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							for model_class_name in self.all_model_classes:
									if model_class_name == FlaxElectraForMaskedLM:
											UpperCamelCase__								=						model_class_name.from_pretrained("""google/electra-small-generator"""							)
									else:
											UpperCamelCase__								=						model_class_name.from_pretrained("""google/electra-small-discriminator"""							)
									UpperCamelCase__								=						model(np.ones((1, 1)							)							)
									self.assertIsNotNone(UpperCAmelCase_							)











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      :





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_							,			UpperCAmelCase_=13							,			UpperCAmelCase_=32							,			UpperCAmelCase_=3							,			UpperCAmelCase_=4							,			UpperCAmelCase_=[10, 20, 30, 40]							,			UpperCAmelCase_=[2, 2, 3, 2]							,			UpperCAmelCase_=True							,			UpperCAmelCase_=True							,			UpperCAmelCase_=37							,			UpperCAmelCase_="gelu"							,			UpperCAmelCase_=10							,			UpperCAmelCase_=0.02							,			UpperCAmelCase_=3							,			UpperCAmelCase_=None							,			):
											lowerCAmelCase =		parent
											lowerCAmelCase =		batch_size
											lowerCAmelCase =		image_size
											lowerCAmelCase =		num_channels
											lowerCAmelCase =		num_stages
											lowerCAmelCase =		hidden_sizes
											lowerCAmelCase =		depths
											lowerCAmelCase =		is_training
											lowerCAmelCase =		use_labels
											lowerCAmelCase =		intermediate_size
											lowerCAmelCase =		hidden_act
											lowerCAmelCase =		type_sequence_label_size
											lowerCAmelCase =		initializer_range
											lowerCAmelCase =		scope



				def    __snake_case		(			self    ):
											lowerCAmelCase =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

											lowerCAmelCase =		self.get_config()

											return config, pixel_values, labels



				def    __snake_case		(			self    ):
											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    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		TFConvNextModel(config=UpperCAmelCase_    )
											lowerCAmelCase =		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    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		self.type_sequence_label_size
											lowerCAmelCase =		TFConvNextForImageClassification(UpperCAmelCase_    )
											lowerCAmelCase =		model(UpperCAmelCase_							,			labels=UpperCAmelCase_							,			training=UpperCAmelCase_    )
											self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)    )






				def    __snake_case		(			self    ):
											lowerCAmelCase =		self.prepare_config_and_inputs()
											lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase =		config_and_inputs
											lowerCAmelCase =		{'''pixel_values''': pixel_values}
											return config, inputs_dict




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





				'''simple docstring'''



				__a				:			Union[str, Any]					    =(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
				__a				:			Optional[int]					    =(
				    {"""feature-extraction""": TFConvNextModel, """image-classification""": TFConvNextForImageClassification}
				    if is_tf_available()
				    else {}
				)

				__a				:			Any					    =False
				__a				:			int					    =False
				__a				:			Union[str, Any]					    =False
				__a				:			Optional[Any]					    =False
				__a				:			List[Any]					    =False



				def    __snake_case		(			self    ):
											lowerCAmelCase =		TFConvNextModelTester(self    )
											lowerCAmelCase =		ConfigTester(
											    self							,			config_class=UpperCAmelCase_							,			has_text_modality=UpperCAmelCase_							,			hidden_size=37							,			)



				@unittest.skip(reason='''ConvNext does not use inputs_embeds'''    )
				def    __snake_case		(			self    ):
											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    __snake_case		(			self    ):
											super().test_keras_fit()



				@unittest.skip(reason='''ConvNext does not support input and output embeddings'''    )
				def    __snake_case		(			self    ):
											pass



				def    __snake_case		(			self    ):
											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs_for_common()

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

																		lowerCAmelCase =		['''pixel_values''']
																		self.assertListEqual(arg_names[:1]							,			UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											lowerCAmelCase =		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    __snake_case		(			self    ):
											super().test_dataset_conversion()



				def    __snake_case		(			self    ):
											def check_hidden_states_output(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
																		lowerCAmelCase =		model_class(UpperCAmelCase_    )

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

																		lowerCAmelCase =		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 =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		lowerCAmelCase =		True
																		check_hidden_states_output(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

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

																		check_hidden_states_output(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs_for_common()

											def check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_={}    ):
																		lowerCAmelCase =		model(UpperCAmelCase_							,			return_dict=UpperCAmelCase_							,			**UpperCAmelCase_    )
																		lowerCAmelCase =		model(UpperCAmelCase_							,			return_dict=UpperCAmelCase_							,			**UpperCAmelCase_    ).to_tuple()

																		def recursive_check(UpperCAmelCase_							,			UpperCAmelCase_    ):
																									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 =		model_class(UpperCAmelCase_    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			{'''output_hidden_states''': True}    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			{'''output_hidden_states''': True}    )



				def    __snake_case		(			self    ):
											lowerCAmelCase =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_    )






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




def    UpperCAmelCase							(  ):
							lowerCAmelCase =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
							return image


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





				'''simple docstring'''



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






				@slow
				def    __snake_case		(			self    ):
											lowerCAmelCase =		TFConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''    )

											lowerCAmelCase =		self.default_image_processor
											lowerCAmelCase =		prepare_img()
											lowerCAmelCase =		image_processor(images=UpperCAmelCase_							,			return_tensors='''tf'''    )

											# forward pass
											lowerCAmelCase =		model(**UpperCAmelCase_    )

											# verify the logits
											lowerCAmelCase =		tf.TensorShape((1, 10_00)    )
											self.assertEqual(outputs.logits.shape							,			UpperCAmelCase_    )

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

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



