





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				:    List[Any]       =   {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}

@is_pipeline_test
class 	A_       (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      _lowercase				(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				ZeroShotClassificationPipeline(
				    model=snake_case_							,					tokenizer=snake_case_							,					candidate_labels=['''polics''', '''health''']		)
				return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
	def      _lowercase				(    self							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				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
				UpperCAmelCase   		=				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_		)]}		)

				UpperCAmelCase   		=				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_		)]}		)

				UpperCAmelCase   		=				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		)

				UpperCAmelCase   		=				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		)

				UpperCAmelCase   		=				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
				UpperCAmelCase   		=				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		)
				    ]							,					)
				UpperCAmelCase   		=				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      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				zero_shot_classifier.model.config
				UpperCAmelCase   		=				config.labelaid
				UpperCAmelCase   		=				zero_shot_classifier.entailment_id

				UpperCAmelCase   		=				{'''LABEL_0''': 0, '''LABEL_1''': 1, '''LABEL_2''': 2}
				self.assertEqual(zero_shot_classifier.entailment_id							,					-1		)

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

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

				UpperCAmelCase   		=				{'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
				self.assertEqual(zero_shot_classifier.entailment_id							,					2		)

				UpperCAmelCase   		=				original_labelaid
				self.assertEqual(snake_case_							,					zero_shot_classifier.entailment_id		)
	@require_torch
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				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?''' * 1_0_0							,					candidate_labels=['''politics''', '''public health''', '''science''']		)
	@require_torch
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				pipeline(
				    '''zero-shot-classification'''							,					model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''							,					framework='''pt'''							,					)
				UpperCAmelCase   		=				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_33, 0.3_33, 0.3_33],
				    }							,					)
	@require_tf
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				pipeline(
				    '''zero-shot-classification'''							,					model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''							,					framework='''tf'''							,					)
				UpperCAmelCase   		=				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_33, 0.3_33, 0.3_33],
				    }							,					)
	@slow
	@require_torch
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				pipeline('''zero-shot-classification'''							,					model='''roberta-large-mnli'''							,					framework='''pt'''		)
				UpperCAmelCase   		=				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_76, 0.0_15, 0.0_09],
				    }							,					)
				UpperCAmelCase   		=				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_17, 0.7_13, 0.0_18, 0.0_18],
				    }							,					)







	@slow
	@require_tf
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				pipeline('''zero-shot-classification'''							,					model='''roberta-large-mnli'''							,					framework='''tf'''		)
				UpperCAmelCase   		=				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_76, 0.0_15, 0.0_09],
				    }							,					)
				UpperCAmelCase   		=				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_17, 0.7_13, 0.0_18, 0.0_18],
				    }							,					)










'''simple docstring'''




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

  if is_vision_available():
    from transformers import MaskFormerImageProcessor

if is_vision_available():
  from PIL import Image







class   A_	:




 def __init__(					self       :    str	,			snake_case_       :    int	,			snake_case_       :    Union[str, Any]=2	,			snake_case_       :    List[Any]=True	,			snake_case_       :    str=False	,			snake_case_       :    str=1_0	,			snake_case_       :    str=3	,			snake_case_       :    Dict=3_2 * 4	,			snake_case_       :    Any=3_2 * 6	,			snake_case_       :    Optional[Any]=4	,			snake_case_       :    Optional[int]=3_2	,			):
      _UpperCAmelCase     							=   parent
      _UpperCAmelCase     							=   batch_size
      _UpperCAmelCase     							=   is_training
      _UpperCAmelCase     							=   use_auxiliary_loss
      _UpperCAmelCase     							=   num_queries
      _UpperCAmelCase     							=   num_channels
      _UpperCAmelCase     							=   min_size
      _UpperCAmelCase     							=   max_size
      _UpperCAmelCase     							=   num_labels
      _UpperCAmelCase     							=   mask_feature_size




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]    ).to(
          snake_case_    )

      _UpperCAmelCase     							=   torch.ones([self.batch_size, self.min_size, self.max_size]	,			device=snake_case_    )

      _UpperCAmelCase     							=   (
          torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]	,			device=snake_case_    ) > 0.5
      ).float()
      _UpperCAmelCase     							=   (torch.rand((self.batch_size, self.num_labels)	,			device=snake_case_    ) > 0.5).long()

      _UpperCAmelCase     							=   self.get_config()
      return config, pixel_values, pixel_mask, mask_labels, class_labels




 def      lowercase	(					self       :    List[Any]    ):
      return MaskFormerConfig.from_backbone_and_decoder_configs(
          backbone_config=SwinConfig(
              depths=[1, 1, 1, 1]	,			)	,			decoder_config=DetrConfig(
              decoder_ffn_dim=1_2_8	,			num_queries=self.num_queries	,			decoder_attention_heads=2	,			d_model=self.mask_feature_size	,			)	,			mask_feature_size=self.mask_feature_size	,			fpn_feature_size=self.mask_feature_size	,			num_channels=self.num_channels	,			num_labels=self.num_labels	,			)




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   self.prepare_config_and_inputs()
      _UpperCAmelCase     							=   {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
      return config, inputs_dict




 def      lowercase	(					self       :    List[Any]	,			snake_case_       :    Optional[Any]	,			snake_case_       :    Optional[Any]    ):
      _UpperCAmelCase     							=   output.encoder_hidden_states
      _UpperCAmelCase     							=   output.pixel_decoder_hidden_states
      _UpperCAmelCase     							=   output.transformer_decoder_hidden_states

      self.parent.assertTrue(len(snake_case_    )	,			len(config.backbone_config.depths    )    )
      self.parent.assertTrue(len(snake_case_    )	,			len(config.backbone_config.depths    )    )
      self.parent.assertTrue(len(snake_case_    )	,			config.decoder_config.decoder_layers    )




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    str	,			snake_case_       :    Optional[int]	,			snake_case_       :    Any	,			snake_case_       :    Optional[Any]=False    ):
      with torch.no_grad():
           _UpperCAmelCase     							=   MaskFormerModel(config=snake_case_    )
           model.to(snake_case_    )
           model.eval()

           _UpperCAmelCase     							=   model(pixel_values=snake_case_	,			pixel_mask=snake_case_    )
           _UpperCAmelCase     							=   model(snake_case_	,			output_hidden_states=snake_case_    )
      # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
      # encoder and pixel decoder
      self.parent.assertEqual(
          output.transformer_decoder_last_hidden_state.shape	,			(self.batch_size, self.num_queries, self.mask_feature_size)	,			)
      # let's ensure the other two hidden state exists
      self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None    )
      self.parent.assertTrue(output.encoder_last_hidden_state is not None    )

      if output_hidden_states:
           self.check_output_hidden_state(snake_case_	,			snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    List[str]	,			snake_case_       :    List[Any]	,			snake_case_       :    int	,			snake_case_       :    str	,			snake_case_       :    List[Any]    ):
      _UpperCAmelCase     							=   MaskFormerForInstanceSegmentation(config=snake_case_    )
      model.to(snake_case_    )
      model.eval()

      def comm_check_on_output(snake_case_       :    int    ):
           # let's still check that all the required stuff is there
           self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None    )
           self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None    )
           self.parent.assertTrue(result.encoder_last_hidden_state is not None    )
           # okay, now we need to check the logits shape
           # due to the encoder compression, masks have a //4 spatial size
           self.parent.assertEqual(
               result.masks_queries_logits.shape	,			(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)	,			)
           # + 1 for null class
           self.parent.assertEqual(
               result.class_queries_logits.shape	,			(self.batch_size, self.num_queries, self.num_labels + 1)    )

      with torch.no_grad():
           _UpperCAmelCase     							=   model(pixel_values=snake_case_	,			pixel_mask=snake_case_    )
           _UpperCAmelCase     							=   model(snake_case_    )

           comm_check_on_output(snake_case_    )

           _UpperCAmelCase     							=   model(
               pixel_values=snake_case_	,			pixel_mask=snake_case_	,			mask_labels=snake_case_	,			class_labels=snake_case_    )

      comm_check_on_output(snake_case_    )

      self.parent.assertTrue(result.loss is not None    )
      self.parent.assertEqual(result.loss.shape	,			torch.Size([1]    )    )







@require_torch
class   A_	(  lowerCAmelCase_							,						lowerCAmelCase_							,						unittest.TestCase			):
 _lowerCamelCase  : Dict           =							(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
 _lowerCamelCase  : Tuple           =							(
     {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
     if is_torch_available()
     else {}
 )

 _lowerCamelCase  : Optional[Any]           =							False
 _lowerCamelCase  : Dict           =							False
 _lowerCamelCase  : Any           =							False
 _lowerCamelCase  : List[Any]           =							False




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   MaskFormerModelTester(self    )
      _UpperCAmelCase     							=   ConfigTester(self	,			config_class=snake_case_	,			has_text_modality=snake_case_    )




 def      lowercase	(					self       :    Optional[Any]    ):
      self.config_tester.run_common_tests()




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase							,  _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskformer_model(snake_case_	,			**snake_case_	,			output_hidden_states=snake_case_    )




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*snake_case_    )




 @unittest.skip(reason="MaskFormer does not use inputs_embeds"    )
 def      lowercase	(					self       :    Any    ):
      pass




 @unittest.skip(reason="MaskFormer does not have a get_input_embeddings method"    )
 def      lowercase	(					self       :    List[str]    ):
      pass




 @unittest.skip(reason="MaskFormer is not a generative model"    )
 def      lowercase	(					self       :    List[str]    ):
      pass




 @unittest.skip(reason="MaskFormer does not use token embeddings"    )
 def      lowercase	(					self       :    List[Any]    ):
      pass




 @require_torch_multi_gpu
 @unittest.skip(
     reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`"    )
 def      lowercase	(					self       :    Any    ):
      pass




 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."    )
 def      lowercase	(					self       :    Union[str, Any]    ):
      pass




 def      lowercase	(					self       :    List[str]    ):
      _UpperCAmelCase							,  _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs_for_common()

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

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




 @slow
 def      lowercase	(					self       :    Optional[int]    ):
      for model_name in ["facebook/maskformer-swin-small-coco"]:
           _UpperCAmelCase     							=   MaskFormerModel.from_pretrained(snake_case_    )
           self.assertIsNotNone(snake_case_    )




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   (self.model_tester.min_size,) * 2
      _UpperCAmelCase     							=   {
          "pixel_values": torch.randn((2, 3, *size)	,			device=snake_case_    ),
          "mask_labels": torch.randn((2, 1_0, *size)	,			device=snake_case_    ),
          "class_labels": torch.zeros(2	,			1_0	,			device=snake_case_    ).long(),
      }

      _UpperCAmelCase     							=   MaskFormerForInstanceSegmentation(MaskFormerConfig()    ).to(snake_case_    )
      _UpperCAmelCase     							=   model(**snake_case_    )
      self.assertTrue(outputs.loss is not None    )




 def      lowercase	(					self       :    Dict    ):
      _UpperCAmelCase							,  _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskformer_model(snake_case_	,			**snake_case_	,			output_hidden_states=snake_case_    )




 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase							,  _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _UpperCAmelCase     							=   model_class(snake_case_    ).to(snake_case_    )
           _UpperCAmelCase     							=   model(**snake_case_	,			output_attentions=snake_case_    )
           self.assertTrue(outputs.attentions is not None    )




 def      lowercase	(					self       :    int    ):
      if not self.model_tester.is_training:
           return
      # only MaskFormerForInstanceSegmentation has the loss
      _UpperCAmelCase     							=   self.all_model_classes[1]
      _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs()

      _UpperCAmelCase     							=   model_class(snake_case_    )
      model.to(snake_case_    )
      model.train()

      _UpperCAmelCase     							=   model(snake_case_	,			mask_labels=snake_case_	,			class_labels=snake_case_    ).loss
      loss.backward()




 def      lowercase	(					self       :    int    ):
      # only MaskFormerForInstanceSegmentation has the loss
      _UpperCAmelCase     							=   self.all_model_classes[1]
      _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs()
      _UpperCAmelCase     							=   True
      _UpperCAmelCase     							=   True

      _UpperCAmelCase     							=   model_class(snake_case_    )
      model.to(snake_case_    )
      model.train()

      _UpperCAmelCase     							=   model(snake_case_	,			mask_labels=snake_case_	,			class_labels=snake_case_    )

      _UpperCAmelCase     							=   outputs.encoder_hidden_states[0]
      encoder_hidden_states.retain_grad()

      _UpperCAmelCase     							=   outputs.pixel_decoder_hidden_states[0]
      pixel_decoder_hidden_states.retain_grad()
      # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
      _UpperCAmelCase     							=   outputs.transformer_decoder_hidden_states[0]
      transformer_decoder_hidden_states.retain_grad()

      _UpperCAmelCase     							=   outputs.attentions[0]
      attentions.retain_grad()

      outputs.loss.backward(retain_graph=snake_case_    )

      self.assertIsNotNone(encoder_hidden_states.grad    )
      self.assertIsNotNone(pixel_decoder_hidden_states.grad    )
      self.assertIsNotNone(transformer_decoder_hidden_states.grad    )
      self.assertIsNotNone(attentions.grad    )





__SCREAMING_SNAKE_CASE     :Dict       =				1e-4
def 	UpperCAmelCase_							(					)		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase     							=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
     return image







@require_vision
@slow
class   A_	(  unittest.TestCase			):




 @cached_property
 def      lowercase	(					self       :    Dict    ):
      return (
          MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco"    )
          if is_vision_available()
          else None
      )




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco"    ).to(snake_case_    )
      _UpperCAmelCase     							=   self.default_image_processor
      _UpperCAmelCase     							=   prepare_img()
      _UpperCAmelCase     							=   image_processor(snake_case_	,			return_tensors="pt"    ).to(snake_case_    )
      _UpperCAmelCase     							=   inputs["pixel_values"].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0    )
      # check size
      self.assertEqual(snake_case_	,			(1, 3, 8_0_0, 1_0_8_8)    )

      with torch.no_grad():
           _UpperCAmelCase     							=   model(**snake_case_    )

      _UpperCAmelCase     							=   torch.tensor(
          [[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]]    ).to(snake_case_    )
      self.assertTrue(
          torch.allclose(
              outputs.encoder_last_hidden_state[0, 0, :3, :3]	,			snake_case_	,			atol=snake_case_    )    )

      _UpperCAmelCase     							=   torch.tensor(
          [[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]]    ).to(snake_case_    )
      self.assertTrue(
          torch.allclose(
              outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]	,			snake_case_	,			atol=snake_case_    )    )

      _UpperCAmelCase     							=   torch.tensor(
          [[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]]    ).to(snake_case_    )
      self.assertTrue(
          torch.allclose(
              outputs.transformer_decoder_last_hidden_state[0, :3, :3]	,			snake_case_	,			atol=snake_case_    )    )




 def      lowercase	(					self       :    Tuple    ):
      _UpperCAmelCase     							=   (
          MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"    )
          .to(snake_case_    )
          .eval()
      )
      _UpperCAmelCase     							=   self.default_image_processor
      _UpperCAmelCase     							=   prepare_img()
      _UpperCAmelCase     							=   image_processor(snake_case_	,			return_tensors="pt"    ).to(snake_case_    )
      _UpperCAmelCase     							=   inputs["pixel_values"].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0    )
      # check size
      self.assertEqual(snake_case_	,			(1, 3, 8_0_0, 1_0_8_8)    )

      with torch.no_grad():
           _UpperCAmelCase     							=   model(**snake_case_    )
      # masks_queries_logits
      _UpperCAmelCase     							=   outputs.masks_queries_logits
      self.assertEqual(
          masks_queries_logits.shape	,			(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	,			)
      _UpperCAmelCase     							=   [
          [-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
          [-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
          [-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
      ]
      _UpperCAmelCase     							=   torch.tensor(snake_case_    ).to(snake_case_    )
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,			snake_case_	,			atol=snake_case_    )    )
      # class_queries_logits
      _UpperCAmelCase     							=   outputs.class_queries_logits
      self.assertEqual(
          class_queries_logits.shape	,			(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)    )
      _UpperCAmelCase     							=   torch.tensor(
          [
              [1.6_512e00, -5.2_572e00, -3.3_519e00],
              [3.6_169e-02, -5.9_025e00, -2.9_313e00],
              [1.0_766e-04, -7.7_630e00, -5.1_263e00],
          ]    ).to(snake_case_    )
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,			snake_case_	,			atol=snake_case_    )    )




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   (
          MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff"    )
          .to(snake_case_    )
          .eval()
      )
      _UpperCAmelCase     							=   self.default_image_processor
      _UpperCAmelCase     							=   prepare_img()
      _UpperCAmelCase     							=   image_processor(snake_case_	,			return_tensors="pt"    ).to(snake_case_    )
      _UpperCAmelCase     							=   inputs["pixel_values"].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0    )
      # check size
      self.assertEqual(snake_case_	,			(1, 3, 8_0_0, 1_0_8_8)    )

      with torch.no_grad():
           _UpperCAmelCase     							=   model(**snake_case_    )
      # masks_queries_logits
      _UpperCAmelCase     							=   outputs.masks_queries_logits
      self.assertEqual(
          masks_queries_logits.shape	,			(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	,			)
      _UpperCAmelCase     							=   [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
      _UpperCAmelCase     							=   torch.tensor(snake_case_    ).to(snake_case_    )
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,			snake_case_	,			atol=snake_case_    )    )
      # class_queries_logits
      _UpperCAmelCase     							=   outputs.class_queries_logits
      self.assertEqual(
          class_queries_logits.shape	,			(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)    )
      _UpperCAmelCase     							=   torch.tensor(
          [[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]]    ).to(snake_case_    )
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,			snake_case_	,			atol=snake_case_    )    )




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   (
          MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"    )
          .to(snake_case_    )
          .eval()
      )
      _UpperCAmelCase     							=   self.default_image_processor

      _UpperCAmelCase     							=   image_processor(
          [np.zeros((3, 8_0_0, 1_3_3_3)    ), np.zeros((3, 8_0_0, 1_3_3_3)    )]	,			segmentation_maps=[np.zeros((3_8_4, 3_8_4)    ).astype(np.floataa    ), np.zeros((3_8_4, 3_8_4)    ).astype(np.floataa    )]	,			return_tensors="pt"	,			)

      _UpperCAmelCase     							=   inputs["pixel_values"].to(snake_case_    )
      _UpperCAmelCase     							=   [el.to(snake_case_    ) for el in inputs["mask_labels"]]
      _UpperCAmelCase     							=   [el.to(snake_case_    ) for el in inputs["class_labels"]]

      with torch.no_grad():
           _UpperCAmelCase     							=   model(**snake_case_    )

      self.assertTrue(outputs.loss is not None    )




