import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
A__           =							{"""LayoutLMv2Config""", """LayoutLMv3Config"""}
@is_pipeline_test
class   _lowerCAmelCase		(     unittest.TestCase				):
							_UpperCAmelCase						    =     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							_UpperCAmelCase						    =     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														_UpperCAmelCase						    =     {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														_UpperCAmelCase						    =     {
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}







							def 				snake_case  (				self     :    Dict	,					__snake_case     :    Optional[int]	,					__snake_case     :    Optional[Any]	,					__snake_case     :    Dict		):
										lowerCamelCase						:Any						  =     ZeroShotClassificationPipeline(
										    model=__snake_case	,					tokenizer=__snake_case	,					candidate_labels=['''polics''', '''health''']		)
										return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]







							def 				snake_case  (				self     :    int	,					__snake_case     :    str	,					__snake_case     :    List[Any]		):
										lowerCamelCase						:List[str]						  =     classifier('''Who are you voting for in 2020?'''	,					candidate_labels='''politics'''		)
										self.assertEqual(__snake_case	,					{'''sequence''': ANY(__snake_case		), '''labels''': [ANY(__snake_case		)], '''scores''': [ANY(__snake_case		)]}		)

										# No kwarg
										lowerCamelCase						:Any						  =     classifier('''Who are you voting for in 2020?'''	,					['''politics''']		)
										self.assertEqual(__snake_case	,					{'''sequence''': ANY(__snake_case		), '''labels''': [ANY(__snake_case		)], '''scores''': [ANY(__snake_case		)]}		)

										lowerCamelCase						:Any						  =     classifier('''Who are you voting for in 2020?'''	,					candidate_labels=['''politics''']		)
										self.assertEqual(__snake_case	,					{'''sequence''': ANY(__snake_case		), '''labels''': [ANY(__snake_case		)], '''scores''': [ANY(__snake_case		)]}		)

										lowerCamelCase						:int						  =     classifier('''Who are you voting for in 2020?'''	,					candidate_labels='''politics, public health'''		)
										self.assertEqual(
										    __snake_case	,					{'''sequence''': ANY(__snake_case		), '''labels''': [ANY(__snake_case		), ANY(__snake_case		)], '''scores''': [ANY(__snake_case		), ANY(__snake_case		)]}		)
										self.assertAlmostEqual(sum(nested_simplify(outputs['''scores''']		)		)	,					1.0		)

										lowerCamelCase						:Dict						  =     classifier('''Who are you voting for in 2020?'''	,					candidate_labels=['''politics''', '''public health''']		)
										self.assertEqual(
										    __snake_case	,					{'''sequence''': ANY(__snake_case		), '''labels''': [ANY(__snake_case		), ANY(__snake_case		)], '''scores''': [ANY(__snake_case		), ANY(__snake_case		)]}		)
										self.assertAlmostEqual(sum(nested_simplify(outputs['''scores''']		)		)	,					1.0		)

										lowerCamelCase						:Optional[Any]						  =     classifier(
										    '''Who are you voting for in 2020?'''	,					candidate_labels='''politics'''	,					hypothesis_template='''This text is about {}'''		)
										self.assertEqual(__snake_case	,					{'''sequence''': ANY(__snake_case		), '''labels''': [ANY(__snake_case		)], '''scores''': [ANY(__snake_case		)]}		)

										# https://github.com/huggingface/transformers/issues/13846
										lowerCamelCase						:List[str]						  =     classifier(['''I am happy''']	,					['''positive''', '''negative''']		)
										self.assertEqual(
										    __snake_case	,					[
										        {'''sequence''': ANY(__snake_case		), '''labels''': [ANY(__snake_case		), ANY(__snake_case		)], '''scores''': [ANY(__snake_case		), ANY(__snake_case		)]}
										        for i in range(1		)
										    ]	,					)
										lowerCamelCase						:str						  =     classifier(['''I am happy''', '''I am sad''']	,					['''positive''', '''negative''']		)
										self.assertEqual(
										    __snake_case	,					[
										        {'''sequence''': ANY(__snake_case		), '''labels''': [ANY(__snake_case		), ANY(__snake_case		)], '''scores''': [ANY(__snake_case		), ANY(__snake_case		)]}
										        for i in range(2		)
										    ]	,					)

										with self.assertRaises(__snake_case		):
													classifier(''''''	,					candidate_labels='''politics'''		)

										with self.assertRaises(__snake_case		):
													classifier(__snake_case	,					candidate_labels='''politics'''		)

										with self.assertRaises(__snake_case		):
													classifier('''Who are you voting for in 2020?'''	,					candidate_labels=''''''		)

										with self.assertRaises(__snake_case		):
													classifier('''Who are you voting for in 2020?'''	,					candidate_labels=__snake_case		)

										with self.assertRaises(__snake_case		):
													classifier(
													    '''Who are you voting for in 2020?'''	,					candidate_labels='''politics'''	,					hypothesis_template='''Not formatting template'''	,					)

										with self.assertRaises(__snake_case		):
													classifier(
													    '''Who are you voting for in 2020?'''	,					candidate_labels='''politics'''	,					hypothesis_template=__snake_case	,					)

										self.run_entailment_id(__snake_case		)







							def 				snake_case  (				self     :    Dict	,					__snake_case     :    Pipeline		):
										lowerCamelCase						:int						  =     zero_shot_classifier.model.config
										lowerCamelCase						:Optional[int]						  =     config.labelaid
										lowerCamelCase						:Optional[Any]						  =     zero_shot_classifier.entailment_id

										lowerCamelCase						:List[Any]						  =     {'''LABEL_0''': 0, '''LABEL_1''': 1, '''LABEL_2''': 2}
										self.assertEqual(zero_shot_classifier.entailment_id	,					-1		)

										lowerCamelCase						:Any						  =     {'''entailment''': 0, '''neutral''': 1, '''contradiction''': 2}
										self.assertEqual(zero_shot_classifier.entailment_id	,					0		)

										lowerCamelCase						:Tuple						  =     {'''ENTAIL''': 0, '''NON-ENTAIL''': 1}
										self.assertEqual(zero_shot_classifier.entailment_id	,					0		)

										lowerCamelCase						:List[Any]						  =     {'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
										self.assertEqual(zero_shot_classifier.entailment_id	,					2		)

										lowerCamelCase						:Any						  =     original_labelaid
										self.assertEqual(__snake_case	,					zero_shot_classifier.entailment_id		)







							@require_torch
							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:str						  =     pipeline(
										    '''zero-shot-classification'''	,					model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''	,					framework='''pt'''	,					)
										# There was a regression in 4.10 for this
										# Adding a test so we don't make the mistake again.
										# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
										zero_shot_classifier(
										    '''Who are you voting for in 2020?''' * 100	,					candidate_labels=['''politics''', '''public health''', '''science''']		)







							@require_torch
							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Optional[int]						  =     pipeline(
										    '''zero-shot-classification'''	,					model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''	,					framework='''pt'''	,					)
										lowerCamelCase						:Optional[int]						  =     zero_shot_classifier(
										    '''Who are you voting for in 2020?'''	,					candidate_labels=['''politics''', '''public health''', '''science''']		)

										self.assertEqual(
										    nested_simplify(__snake_case		)	,					{
										        '''sequence''': '''Who are you voting for in 2020?''',
										        '''labels''': ['''science''', '''public health''', '''politics'''],
										        '''scores''': [0.3_3_3, 0.3_3_3, 0.3_3_3],
										    }	,					)







							@require_tf
							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:Any						  =     pipeline(
										    '''zero-shot-classification'''	,					model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''	,					framework='''tf'''	,					)
										lowerCamelCase						:Union[str, Any]						  =     zero_shot_classifier(
										    '''Who are you voting for in 2020?'''	,					candidate_labels=['''politics''', '''public health''', '''science''']		)

										self.assertEqual(
										    nested_simplify(__snake_case		)	,					{
										        '''sequence''': '''Who are you voting for in 2020?''',
										        '''labels''': ['''science''', '''public health''', '''politics'''],
										        '''scores''': [0.3_3_3, 0.3_3_3, 0.3_3_3],
										    }	,					)







							@slow
							@require_torch
							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Union[str, Any]						  =     pipeline('''zero-shot-classification'''	,					model='''roberta-large-mnli'''	,					framework='''pt'''		)
										lowerCamelCase						:Optional[int]						  =     zero_shot_classifier(
										    '''Who are you voting for in 2020?'''	,					candidate_labels=['''politics''', '''public health''', '''science''']		)

										self.assertEqual(
										    nested_simplify(__snake_case		)	,					{
										        '''sequence''': '''Who are you voting for in 2020?''',
										        '''labels''': ['''politics''', '''public health''', '''science'''],
										        '''scores''': [0.9_7_6, 0.0_1_5, 0.0_0_9],
										    }	,					)
										lowerCamelCase						:List[str]						  =     zero_shot_classifier(
										    '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
										    ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
										    ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
										    ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
										    ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
										    ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
										    ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
										    ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
										    ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
										    ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
										    ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
										    ''' large and limited training data.'''	,					candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']	,					multi_label=__snake_case	,					)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					{
										        '''sequence''': (
										            '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
										            ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
										            ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
										            ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
										            ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
										            ''' superior in quality while being more parallelizable and requiring significantly less time to'''
										            ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
										            ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
										            ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
										            ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
										            ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
										            ''' other tasks by applying it successfully to English constituency parsing both with large and'''
										            ''' limited training data.'''
										        ),
										        '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
										        '''scores''': [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
										    }	,					)







							@slow
							@require_tf
							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:Optional[Any]						  =     pipeline('''zero-shot-classification'''	,					model='''roberta-large-mnli'''	,					framework='''tf'''		)
										lowerCamelCase						:Optional[Any]						  =     zero_shot_classifier(
										    '''Who are you voting for in 2020?'''	,					candidate_labels=['''politics''', '''public health''', '''science''']		)

										self.assertEqual(
										    nested_simplify(__snake_case		)	,					{
										        '''sequence''': '''Who are you voting for in 2020?''',
										        '''labels''': ['''politics''', '''public health''', '''science'''],
										        '''scores''': [0.9_7_6, 0.0_1_5, 0.0_0_9],
										    }	,					)
										lowerCamelCase						:int						  =     zero_shot_classifier(
										    '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
										    ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
										    ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
										    ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
										    ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
										    ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
										    ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
										    ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
										    ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
										    ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
										    ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
										    ''' large and limited training data.'''	,					candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']	,					multi_label=__snake_case	,					)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					{
										        '''sequence''': (
										            '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
										            ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
										            ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
										            ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
										            ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
										            ''' superior in quality while being more parallelizable and requiring significantly less time to'''
										            ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
										            ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
										            ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
										            ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
										            ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
										            ''' other tasks by applying it successfully to English constituency parsing both with large and'''
										            ''' limited training data.'''
										        ),
										        '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
										        '''scores''': [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
										    }	,					)





import inspect
import unittest

from transformers import YolosConfig
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_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import YolosForObjectDetection, YolosModel
			from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor
class   _lowerCAmelCase		:







							def __init__(				self     :    Any	,					__snake_case     :    Optional[int]	,					__snake_case     :    int=13	,					__snake_case     :    str=[30, 30]	,					__snake_case     :    Tuple=2	,					__snake_case     :    Optional[Any]=3	,					__snake_case     :    int=True	,					__snake_case     :    Tuple=True	,					__snake_case     :    List[Any]=32	,					__snake_case     :    int=5	,					__snake_case     :    Optional[Any]=4	,					__snake_case     :    Union[str, Any]=37	,					__snake_case     :    str="gelu"	,					__snake_case     :    Tuple=0.1	,					__snake_case     :    List[Any]=0.1	,					__snake_case     :    Union[str, Any]=10	,					__snake_case     :    str=0.0_2	,					__snake_case     :    Union[str, Any]=3	,					__snake_case     :    Union[str, Any]=None	,					__snake_case     :    List[str]=8	,					__snake_case     :    Any=10	,					):
										lowerCamelCase						:Optional[Any]						  =     parent
										lowerCamelCase						:List[Any]						  =     batch_size
										lowerCamelCase						:Any						  =     image_size
										lowerCamelCase						:Union[str, Any]						  =     patch_size
										lowerCamelCase						:Any						  =     num_channels
										lowerCamelCase						:List[Any]						  =     is_training
										lowerCamelCase						:Optional[Any]						  =     use_labels
										lowerCamelCase						:Any						  =     hidden_size
										lowerCamelCase						:List[Any]						  =     num_hidden_layers
										lowerCamelCase						:List[str]						  =     num_attention_heads
										lowerCamelCase						:Tuple						  =     intermediate_size
										lowerCamelCase						:List[str]						  =     hidden_act
										lowerCamelCase						:List[str]						  =     hidden_dropout_prob
										lowerCamelCase						:Any						  =     attention_probs_dropout_prob
										lowerCamelCase						:List[Any]						  =     type_sequence_label_size
										lowerCamelCase						:Optional[int]						  =     initializer_range
										lowerCamelCase						:List[Any]						  =     num_labels
										lowerCamelCase						:Any						  =     scope
										lowerCamelCase						:Union[str, Any]						  =     n_targets
										lowerCamelCase						:Optional[Any]						  =     num_detection_tokens
										# we set the expected sequence length (which is used in several tests)
										# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
										lowerCamelCase						:Tuple						  =     (image_size[1] // patch_size) * (image_size[0] // patch_size)
										lowerCamelCase						:str						  =     num_patches + 1 + self.num_detection_tokens







							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:str						  =     floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]		)

										lowerCamelCase						:List[str]						  =     None
										if self.use_labels:
													# labels is a list of Dict (each Dict being the labels for a given example in the batch)
													lowerCamelCase						:Optional[int]						  =     []
													for i in range(self.batch_size		):
																lowerCamelCase						:List[str]						  =     {}
																lowerCamelCase						:Tuple						  =     torch.randint(
																    high=self.num_labels	,					size=(self.n_targets,)	,					device=__snake_case		)
																lowerCamelCase						:List[str]						  =     torch.rand(self.n_targets	,					4	,					device=__snake_case		)
																labels.append(__snake_case		)

										lowerCamelCase						:str						  =     self.get_config()

										return config, pixel_values, labels







							def 				snake_case  (				self     :    Union[str, Any]		):
										return YolosConfig(
										    image_size=self.image_size	,					patch_size=self.patch_size	,					num_channels=self.num_channels	,					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	,					is_decoder=__snake_case	,					initializer_range=self.initializer_range	,					num_detection_tokens=self.num_detection_tokens	,					num_labels=self.num_labels	,					)







							def 				snake_case  (				self     :    Tuple	,					__snake_case     :    Tuple	,					__snake_case     :    Tuple	,					__snake_case     :    Any		):
										lowerCamelCase						:Optional[Any]						  =     YolosModel(config=__snake_case		)
										model.to(__snake_case		)
										model.eval()
										lowerCamelCase						:Union[str, Any]						  =     model(__snake_case		)
										self.parent.assertEqual(
										    result.last_hidden_state.shape	,					(self.batch_size, self.expected_seq_len, self.hidden_size)		)







							def 				snake_case  (				self     :    Dict	,					__snake_case     :    str	,					__snake_case     :    Optional[int]	,					__snake_case     :    Optional[Any]		):
										lowerCamelCase						:int						  =     YolosForObjectDetection(__snake_case		)
										model.to(__snake_case		)
										model.eval()

										lowerCamelCase						:str						  =     model(pixel_values=__snake_case		)
										lowerCamelCase						:Any						  =     model(__snake_case		)

										self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.num_detection_tokens, self.num_labels + 1)		)
										self.parent.assertEqual(result.pred_boxes.shape	,					(self.batch_size, self.num_detection_tokens, 4)		)

										lowerCamelCase						:int						  =     model(pixel_values=__snake_case	,					labels=__snake_case		)

										self.parent.assertEqual(result.loss.shape	,					()		)
										self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.num_detection_tokens, self.num_labels + 1)		)
										self.parent.assertEqual(result.pred_boxes.shape	,					(self.batch_size, self.num_detection_tokens, 4)		)







							def 				snake_case  (				self     :    int		):
										lowerCamelCase						:List[Any]						  =     self.prepare_config_and_inputs()
										lowerCamelCase  ,    lowerCamelCase  ,    lowerCamelCase						:str						  =     config_and_inputs
										lowerCamelCase						:Union[str, Any]						  =     {'''pixel_values''': pixel_values}
										return config, inputs_dict


@require_torch
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
							_UpperCAmelCase						    =     (
							    {'feature-extraction': YolosModel, 'object-detection': YolosForObjectDetection} if is_torch_available() else {}
							)

							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False







							def 				snake_case  (				self     :    Any	,					__snake_case     :    List[Any]	,					__snake_case     :    Tuple	,					__snake_case     :    Dict=False		):
										lowerCamelCase						:Optional[int]						  =     super()._prepare_for_class(__snake_case	,					__snake_case	,					return_labels=__snake_case		)

										if return_labels:
													if model_class.__name__ == "YolosForObjectDetection":
																lowerCamelCase						:Dict						  =     []
																for i in range(self.model_tester.batch_size		):
																			lowerCamelCase						:Optional[Any]						  =     {}
																			lowerCamelCase						:List[Any]						  =     torch.ones(
																			    size=(self.model_tester.n_targets,)	,					device=__snake_case	,					dtype=torch.long		)
																			lowerCamelCase						:str						  =     torch.ones(
																			    self.model_tester.n_targets	,					4	,					device=__snake_case	,					dtype=torch.float		)
																			labels.append(__snake_case		)
																lowerCamelCase						:Union[str, Any]						  =     labels

										return inputs_dict







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:Union[str, Any]						  =     YolosModelTester(self		)
										lowerCamelCase						:Dict						  =     ConfigTester(self	,					config_class=__snake_case	,					has_text_modality=__snake_case	,					hidden_size=37		)







							def 				snake_case  (				self     :    Union[str, Any]		):
										self.config_tester.run_common_tests()







							def 				snake_case  (				self     :    Optional[Any]		):
										# YOLOS does not use inputs_embeds
										pass







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase  ,    lowerCamelCase						:str						  =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase						:Optional[int]						  =     model_class(__snake_case		)
													self.assertIsInstance(model.get_input_embeddings()	,					(nn.Module)		)
													lowerCamelCase						:str						  =     model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(__snake_case	,					nn.Linear		)		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase  ,    lowerCamelCase						:str						  =     self.model_tester.prepare_config_and_inputs_for_common()

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

													lowerCamelCase						:Tuple						  =     ['''pixel_values''']
													self.assertListEqual(arg_names[:1]	,					__snake_case		)







							def 				snake_case  (				self     :    int		):
										lowerCamelCase						:List[str]						  =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__snake_case		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase  ,    lowerCamelCase						:List[Any]						  =     self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase						:int						  =     True

										# in YOLOS, the seq_len is different
										lowerCamelCase						:str						  =     self.model_tester.expected_seq_len
										for model_class in self.all_model_classes:
													lowerCamelCase						:str						  =     True
													lowerCamelCase						:Tuple						  =     False
													lowerCamelCase						:Optional[int]						  =     True
													lowerCamelCase						:int						  =     model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()
													with torch.no_grad():
																lowerCamelCase						:str						  =     model(**self._prepare_for_class(__snake_case	,					__snake_case		)		)
													lowerCamelCase						:str						  =     outputs.attentions
													self.assertEqual(len(__snake_case		)	,					self.model_tester.num_hidden_layers		)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													lowerCamelCase						:Optional[Any]						  =     True
													lowerCamelCase						:str						  =     model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()
													with torch.no_grad():
																lowerCamelCase						:Tuple						  =     model(**self._prepare_for_class(__snake_case	,					__snake_case		)		)
													lowerCamelCase						:Tuple						  =     outputs.attentions
													self.assertEqual(len(__snake_case		)	,					self.model_tester.num_hidden_layers		)

													self.assertListEqual(
													    list(attentions[0].shape[-3:]		)	,					[self.model_tester.num_attention_heads, seq_len, seq_len]	,					)
													lowerCamelCase						:Optional[int]						  =     len(__snake_case		)

													# Check attention is always last and order is fine
													lowerCamelCase						:Union[str, Any]						  =     True
													lowerCamelCase						:List[Any]						  =     True
													lowerCamelCase						:Tuple						  =     model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()
													with torch.no_grad():
																lowerCamelCase						:int						  =     model(**self._prepare_for_class(__snake_case	,					__snake_case		)		)

													lowerCamelCase						:Dict						  =     1
													self.assertEqual(out_len + added_hidden_states	,					len(__snake_case		)		)

													lowerCamelCase						:Dict						  =     outputs.attentions

													self.assertEqual(len(__snake_case		)	,					self.model_tester.num_hidden_layers		)
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]		)	,					[self.model_tester.num_attention_heads, seq_len, seq_len]	,					)







							def 				snake_case  (				self     :    List[str]		):
										def check_hidden_states_output(__snake_case     :    Union[str, Any]	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    Tuple		):
													lowerCamelCase						:Union[str, Any]						  =     model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()

													with torch.no_grad():
																lowerCamelCase						:Any						  =     model(**self._prepare_for_class(__snake_case	,					__snake_case		)		)

													lowerCamelCase						:Optional[Any]						  =     outputs.hidden_states

													lowerCamelCase						:Any						  =     getattr(
													    self.model_tester	,					'''expected_num_hidden_layers'''	,					self.model_tester.num_hidden_layers + 1		)
													self.assertEqual(len(__snake_case		)	,					__snake_case		)

													# YOLOS has a different seq_length
													lowerCamelCase						:List[str]						  =     self.model_tester.expected_seq_len

													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]		)	,					[seq_length, self.model_tester.hidden_size]	,					)

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

										for model_class in self.all_model_classes:
													lowerCamelCase						:Union[str, Any]						  =     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"]
													lowerCamelCase						:Any						  =     True

													check_hidden_states_output(__snake_case	,					__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Optional[Any]						  =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_object_detection(*__snake_case		)







							@slow
							def 				snake_case  (				self     :    Dict		):
										for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase						:Tuple						  =     YolosModel.from_pretrained(__snake_case		)
													self.assertIsNotNone(__snake_case		)





def  _lowerCamelCase					(   ):
			lowerCamelCase						:int						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
			return image


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







							@cached_property
							def 				snake_case  (				self     :    Tuple		):
										return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''		) if is_vision_available() else None







							@slow
							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Union[str, Any]						  =     YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''		).to(__snake_case		)

										lowerCamelCase						:Optional[Any]						  =     self.default_image_processor
										lowerCamelCase						:str						  =     prepare_img()
										lowerCamelCase						:Dict						  =     image_processor(images=__snake_case	,					return_tensors='''pt'''		).to(__snake_case		)

										# forward pass
										with torch.no_grad():
													lowerCamelCase						:Optional[Any]						  =     model(inputs.pixel_values		)

										# verify outputs
										lowerCamelCase						:int						  =     torch.Size((1, 100, 92)		)
										self.assertEqual(outputs.logits.shape	,					__snake_case		)

										lowerCamelCase						:Any						  =     torch.tensor(
										    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]	,					device=__snake_case	,					)
										lowerCamelCase						:Any						  =     torch.tensor(
										    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]	,					device=__snake_case		)
										self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]	,					__snake_case	,					atol=1e-4		)		)
										self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]	,					__snake_case	,					atol=1e-4		)		)

										# verify postprocessing
										lowerCamelCase						:List[str]						  =     image_processor.post_process_object_detection(
										    __snake_case	,					threshold=0.3	,					target_sizes=[image.size[::-1]]		)[0]
										lowerCamelCase						:List[str]						  =     torch.tensor([0.9_9_9_4, 0.9_7_9_0, 0.9_9_6_4, 0.9_9_7_2, 0.9_8_6_1]		).to(__snake_case		)
										lowerCamelCase						:str						  =     [75, 75, 17, 63, 17]
										lowerCamelCase						:Tuple						  =     torch.tensor([3_3_5.0_6_0_9, 7_9.3_8_4_8, 3_7_5.4_2_1_6, 1_8_7.2_4_9_5]		).to(__snake_case		)

										self.assertEqual(len(results['''scores''']		)	,					5		)
										self.assertTrue(torch.allclose(results['''scores''']	,					__snake_case	,					atol=1e-4		)		)
										self.assertSequenceEqual(results['''labels'''].tolist()	,					__snake_case		)
										self.assertTrue(torch.allclose(results['''boxes'''][0, :]	,					__snake_case		)		)



