





"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFDebertaForMaskedLM,
						    TFDebertaForQuestionAnswering,
						    TFDebertaForSequenceClassification,
						    TFDebertaForTokenClassification,
						    TFDebertaModel,
						)




class 					_a		:






				def __init__(					self						:       Tuple,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Optional[Any]=1_3,					lowerCAmelCase__						:       List[str]=7,					lowerCAmelCase__						:       Optional[Any]=True,					lowerCAmelCase__						:       str=True,					lowerCAmelCase__						:       Optional[Any]=True,					lowerCAmelCase__						:       Optional[Any]=True,					lowerCAmelCase__						:       Any=9_9,					lowerCAmelCase__						:       List[str]=3_2,					lowerCAmelCase__						:       Optional[int]=2,					lowerCAmelCase__						:       str=4,					lowerCAmelCase__						:       Optional[int]=3_7,					lowerCAmelCase__						:       Dict="gelu",					lowerCAmelCase__						:       Optional[int]=0.1,					lowerCAmelCase__						:       Dict=0.1,					lowerCAmelCase__						:       Tuple=5_1_2,					lowerCAmelCase__						:       Optional[int]=1_6,					lowerCAmelCase__						:       Optional[int]=2,					lowerCAmelCase__						:       Optional[Any]=0.02,					lowerCAmelCase__						:       Dict=3,					lowerCAmelCase__						:       Tuple=4,					lowerCAmelCase__						:       Optional[Any]=None,					)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       parent
										_UpperCamelCase     :   Tuple      =       1_3
										_UpperCamelCase     :   Optional[Any]      =       7
										_UpperCamelCase     :   Optional[Any]      =       True
										_UpperCamelCase     :   str      =       True
										_UpperCamelCase     :   List[str]      =       True
										_UpperCamelCase     :   Union[str, Any]      =       True
										_UpperCamelCase     :   List[str]      =       9_9
										_UpperCamelCase     :   Any      =       3_2
										_UpperCamelCase     :   Dict      =       2
										_UpperCamelCase     :   List[str]      =       4
										_UpperCamelCase     :   List[str]      =       3_7
										_UpperCamelCase     :   List[Any]      =       '''gelu'''
										_UpperCamelCase     :   Optional[Any]      =       0.1
										_UpperCamelCase     :   List[Any]      =       0.1
										_UpperCamelCase     :   Any      =       5_1_2
										_UpperCamelCase     :   Union[str, Any]      =       1_6
										_UpperCamelCase     :   List[str]      =       False
										_UpperCamelCase     :   Any      =       -1
										_UpperCamelCase     :   int      =       True
										_UpperCamelCase     :   Optional[Any]      =       2
										_UpperCamelCase     :   Optional[int]      =       0.02
										_UpperCamelCase     :   Optional[Any]      =       3
										_UpperCamelCase     :   str      =       4
										_UpperCamelCase     :   int      =       None






				def 							snake_case      (					self						:       Optional[int]				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       ids_tensor([self.batch_size, self.seq_length],					self.vocab_size				)

										_UpperCamelCase     :   Optional[int]      =       None
										if self.use_input_mask:
																_UpperCamelCase     :   Any      =       random_attention_mask([self.batch_size, self.seq_length]				)

										_UpperCamelCase     :   Union[str, Any]      =       None
										if self.use_token_type_ids:
																_UpperCamelCase     :   int      =       ids_tensor([self.batch_size, self.seq_length],					self.type_vocab_size				)

										_UpperCamelCase     :   Dict      =       None
										_UpperCamelCase     :   Optional[int]      =       None
										_UpperCamelCase     :   str      =       None
										if self.use_labels:
																_UpperCamelCase     :   Dict      =       ids_tensor([self.batch_size],					self.type_sequence_label_size				)
																_UpperCamelCase     :   Any      =       ids_tensor([self.batch_size, self.seq_length],					self.num_labels				)

										_UpperCamelCase     :   str      =       DebertaConfig(
										    vocab_size=self.vocab_size,					hidden_size=self.hidden_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,					relative_attention=self.relative_attention,					max_relative_positions=self.max_relative_positions,					position_biased_input=self.position_biased_input,					initializer_range=self.initializer_range,					return_dict=lowerCAmelCase__,					)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






				def 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Dict				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       TFDebertaModel(config=lowerCAmelCase__				)
										_UpperCamelCase     :   List[str]      =       {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

										_UpperCamelCase     :   int      =       [input_ids, input_mask]
										_UpperCamelCase     :   Union[str, Any]      =       model(lowerCAmelCase__				)

										_UpperCamelCase     :   List[str]      =       model(lowerCAmelCase__				)

										self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)				)






				def 							snake_case      (					self						:       Optional[Any],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       List[Any]				)      -> Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       TFDebertaForMaskedLM(config=lowerCAmelCase__				)
										_UpperCamelCase     :   Tuple      =       {
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}
										_UpperCamelCase     :   Optional[Any]      =       model(lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.vocab_size)				)






				def 							snake_case      (					self						:       Union[str, Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       str				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       self.num_labels
										_UpperCamelCase     :   int      =       TFDebertaForSequenceClassification(config=lowerCAmelCase__				)
										_UpperCamelCase     :   Union[str, Any]      =       {
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}

										_UpperCamelCase     :   Any      =       model(lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)				)






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       int				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       self.num_labels
										_UpperCamelCase     :   Optional[int]      =       TFDebertaForTokenClassification(config=lowerCAmelCase__				)
										_UpperCamelCase     :   Tuple      =       {
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}
										_UpperCamelCase     :   Optional[Any]      =       model(lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.num_labels)				)






				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       Tuple				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       TFDebertaForQuestionAnswering(config=lowerCAmelCase__				)
										_UpperCamelCase     :   str      =       {
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}

										_UpperCamelCase     :   List[str]      =       model(lowerCAmelCase__				)
										self.parent.assertEqual(result.start_logits.shape,					(self.batch_size, self.seq_length)				)
										self.parent.assertEqual(result.end_logits.shape,					(self.batch_size, self.seq_length)				)






				def 							snake_case      (					self						:       List[Any]				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       self.prepare_config_and_inputs()
										(
										    (
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				
										)     :   List[Any]      =       config_and_inputs
										_UpperCamelCase     :   Dict      =       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
										return config, inputs_dict




@require_tf
class 					_a		( _lowerCAmelCase      ,							_lowerCAmelCase      ,							unittest.TestCase							):
				UpperCamelCase								=       (
				    (
				        TFDebertaModel,
				        TFDebertaForMaskedLM,
				        TFDebertaForQuestionAnswering,
				        TFDebertaForSequenceClassification,
				        TFDebertaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				UpperCamelCase								=       (
				    {
				        '''feature-extraction''': TFDebertaModel,
				        '''fill-mask''': TFDebertaForMaskedLM,
				        '''question-answering''': TFDebertaForQuestionAnswering,
				        '''text-classification''': TFDebertaForSequenceClassification,
				        '''token-classification''': TFDebertaForTokenClassification,
				        '''zero-shot''': TFDebertaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				UpperCamelCase								=       False
				UpperCamelCase								=       False






				def 							snake_case      (					self						:       List[Any]				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       TFDebertaModelTester(self				)
										_UpperCamelCase     :   int      =       ConfigTester(self,					config_class=lowerCAmelCase__,					hidden_size=3_7				)






				def 							snake_case      (					self						:       Optional[int]				)      -> List[Any]:




										'''simple docstring'''



										self.config_tester.run_common_tests()






				def 							snake_case      (					self						:       Dict				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       Tuple				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       List[Any]				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__				)






				@slow
				def 							snake_case      (					self						:       Optional[int]				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''				)
										self.assertIsNotNone(lowerCAmelCase__				)




@require_tf
class 					_a		( unittest.TestCase							):






				@unittest.skip(reason='''Model not available yet'''				)
				def 							snake_case      (					self						:       Tuple				)      -> Optional[Any]:




										'''simple docstring'''



										pass






				@slow
				def 							snake_case      (					self						:       Any				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''				)
										_UpperCamelCase     :   List[Any]      =       tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]				)
										_UpperCamelCase     :   str      =       tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
										_UpperCamelCase     :   Tuple      =       model(lowerCAmelCase__,					attention_mask=lowerCAmelCase__				)[0]

										_UpperCamelCase     :   Union[str, Any]      =       tf.constant(
										    [
										        [
										            [-0.59_855_896, -0.80_552_566, -0.8_462_135],
										            [1.4_484_025, -0.93_483_794, -0.80_593_085],
										            [0.3_122_741, 0.00_316_059, -1.4_131_377],
										        ]
										    ]				)
										tf.debugging.assert_near(output[:, 1:4, 1:4],					lowerCAmelCase__,					atol=1e-4				)










"""simple docstring"""
import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
		from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor






class    _UpperCAmelCase  :


					def __init__(   self     :Optional[Any]	,			__UpperCamelCase     :Dict	,			__UpperCamelCase     :Optional[Any]=3	,			__UpperCamelCase     :Optional[int]=32	,			__UpperCamelCase     :str=3	,			__UpperCamelCase     :Union[str, Any]=10	,			__UpperCamelCase     :Union[str, Any]=[10, 20, 30, 40]	,			__UpperCamelCase     :int=[1, 1, 2, 1]	,			__UpperCamelCase     :Dict=True	,			__UpperCamelCase     :Optional[Any]=True	,			__UpperCamelCase     :Any="relu"	,			__UpperCamelCase     :Optional[Any]=3	,			__UpperCamelCase     :int=None	,			__UpperCamelCase     :str=["stage2", "stage3", "stage4"]	,			__UpperCamelCase     :str=[2, 3, 4]	,			):
												A						=    parent
												A						=    batch_size
												A						=    image_size
												A						=    num_channels
												A						=    embeddings_size
												A						=    hidden_sizes
												A						=    depths
												A						=    is_training
												A						=    use_labels
												A						=    hidden_act
												A						=    num_labels
												A						=    scope
												A						=    len(__UpperCamelCase						)
												A						=    out_features
												A						=    out_indices


					def       lowerCamelCase			(   self     :List[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.num_labels						)

												A						=    self.get_config()

												return config, pixel_values, labels


					def       lowerCamelCase			(   self     :Optional[Any]						):
												return ResNetConfig(
												    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	,			out_features=self.out_features	,			out_indices=self.out_indices	,			)


					def       lowerCamelCase			(   self     :int	,			__UpperCamelCase     :Optional[Any]	,			__UpperCamelCase     :str	,			__UpperCamelCase     :List[Any]						):
												A						=    ResNetModel(config=__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												A						=    model(__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       lowerCamelCase			(   self     :int	,			__UpperCamelCase     :Optional[Any]	,			__UpperCamelCase     :str	,			__UpperCamelCase     :str						):
												A						=    self.num_labels
												A						=    ResNetForImageClassification(__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												A						=    model(__UpperCamelCase	,			labels=__UpperCamelCase						)
												self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.num_labels)						)


					def       lowerCamelCase			(   self     :Dict	,			__UpperCamelCase     :Optional[Any]	,			__UpperCamelCase     :Optional[int]	,			__UpperCamelCase     :str						):
												A						=    ResNetBackbone(config=__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												A						=    model(__UpperCamelCase						)

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps						)	,			len(config.out_features						)						)
												self.parent.assertListEqual(list(result.feature_maps[0].shape						)	,			[self.batch_size, self.hidden_sizes[1], 4, 4]						)

												# verify channels
												self.parent.assertEqual(len(model.channels						)	,			len(config.out_features						)						)
												self.parent.assertListEqual(model.channels	,			config.hidden_sizes[1:]						)

												# verify backbone works with out_features=None
												A						=    None
												A						=    ResNetBackbone(config=__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												A						=    model(__UpperCamelCase						)

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps						)	,			1						)
												self.parent.assertListEqual(list(result.feature_maps[0].shape						)	,			[self.batch_size, self.hidden_sizes[-1], 1, 1]						)

												# verify channels
												self.parent.assertEqual(len(model.channels						)	,			1						)
												self.parent.assertListEqual(model.channels	,			[config.hidden_sizes[-1]]						)


					def       lowerCamelCase			(   self     :List[Any]						):
												A						=    self.prepare_config_and_inputs()
												A,      A,      A						=    config_and_inputs
												A						=    {"pixel_values": pixel_values}
												return config, inputs_dict







@require_torch
class    _UpperCAmelCase  (      lowercase_	,				lowercase_	,				unittest.TestCase					):
					UpperCamelCase						=				(
					    (
					        ResNetModel,
					        ResNetForImageClassification,
					        ResNetBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					UpperCamelCase						=				(
					    {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
					    if is_torch_available()
					    else {}
					)

					UpperCamelCase						=				True
					UpperCamelCase						=				False
					UpperCamelCase						=				False
					UpperCamelCase						=				False
					UpperCamelCase						=				False


					def       lowerCamelCase			(   self     :List[Any]						):
												A						=    ResNetModelTester(self						)
												A						=    ConfigTester(self	,			config_class=__UpperCamelCase	,			has_text_modality=__UpperCamelCase						)


					def       lowerCamelCase			(   self     :List[Any]						):
												self.create_and_test_config_common_properties()
												self.config_tester.create_and_test_config_to_json_string()
												self.config_tester.create_and_test_config_to_json_file()
												self.config_tester.create_and_test_config_from_and_save_pretrained()
												self.config_tester.create_and_test_config_with_num_labels()
												self.config_tester.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()


					def       lowerCamelCase			(   self     :Union[str, Any]						):
												return


					@unittest.skip(reason="ResNet does not use inputs_embeds"						)
					def       lowerCamelCase			(   self     :Optional[int]						):
												pass


					@unittest.skip(reason="ResNet does not support input and output embeddings"						)
					def       lowerCamelCase			(   self     :Optional[Any]						):
												pass


					def       lowerCamelCase			(   self     :List[Any]						):
												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()

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

																			A						=    ["pixel_values"]
																			self.assertListEqual(arg_names[:1]	,			__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Tuple						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Union[str, Any]						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Optional[Any]						):
												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A						=    model_class(config=__UpperCamelCase						)
																			for name, module in model.named_modules():
																										if isinstance(__UpperCamelCase	,			(nn.BatchNormad, nn.GroupNorm)						):
																																	self.assertTrue(
																																	    torch.all(module.weight == 1						)	,			msg=f"Parameter {name} of model {model_class} seems not properly initialized"	,			)
																																	self.assertTrue(
																																	    torch.all(module.bias == 0						)	,			msg=f"Parameter {name} of model {model_class} seems not properly initialized"	,			)


					def       lowerCamelCase			(   self     :Optional[Any]						):
												def check_hidden_states_output(__UpperCamelCase     :int	,			__UpperCamelCase     :Any	,			__UpperCamelCase     :Union[str, Any]						):
																			A						=    model_class(__UpperCamelCase						)
																			model.to(__UpperCamelCase						)
																			model.eval()

																			with torch.no_grad():
																										A						=    model(**self._prepare_for_class(__UpperCamelCase	,			__UpperCamelCase						)						)

																			A						=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																			A						=    self.model_tester.num_stages
																			self.assertEqual(len(__UpperCamelCase						)	,			expected_num_stages + 1						)

																			# ResNet'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()
												A						=    ["basic", "bottleneck"]
												for model_class in self.all_model_classes:
																			for layer_type in layers_type:
																										A						=    layer_type
																										A						=    True
																										check_hidden_states_output(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase						)

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

																										check_hidden_states_output(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase						)


					@unittest.skip(reason="ResNet does not use feedforward chunking"						)
					def       lowerCamelCase			(   self     :Tuple						):
												pass


					def       lowerCamelCase			(   self     :Optional[int]						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase						)


					@slow
					def       lowerCamelCase			(   self     :Union[str, Any]						):
												for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			A						=    ResNetModel.from_pretrained(__UpperCamelCase						)
																			self.assertIsNotNone(__UpperCamelCase						)







def    A__							(     ):
							A						=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
							return image






@require_torch
@require_vision
class    _UpperCAmelCase  (      unittest.TestCase					):


					@cached_property
					def       lowerCamelCase			(   self     :List[Any]						):
												return (
												    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
												    if is_vision_available()
												    else None
												)


					@slow
					def       lowerCamelCase			(   self     :Dict						):
												A						=    ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(__UpperCamelCase						)

												A						=    self.default_image_processor
												A						=    prepare_img()
												A						=    image_processor(images=__UpperCamelCase	,			return_tensors="pt"						).to(__UpperCamelCase						)

												# forward pass
												with torch.no_grad():
																			A						=    model(**__UpperCamelCase						)

												# verify the logits
												A						=    torch.Size((1, 10_00)						)
												self.assertEqual(outputs.logits.shape	,			__UpperCamelCase						)

												A						=    torch.tensor([-11.1_069, -9.7_877, -8.3_777]						).to(__UpperCamelCase						)

												self.assertTrue(torch.allclose(outputs.logits[0, :3]	,			__UpperCamelCase	,			atol=1e-4						)						)







@require_torch
class    _UpperCAmelCase  (      lowercase_	,				unittest.TestCase					):
					UpperCamelCase						=				(ResNetBackbone,) if is_torch_available() else ()
					UpperCamelCase						=				False
					UpperCamelCase						=				ResNetConfig


					def       lowerCamelCase			(   self     :Dict						):
												A						=    ResNetModelTester(self						)


