
import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
					from transformers.models.mobilevitva.modeling_mobilevitva import (
					    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
					    make_divisible,
					)


if is_vision_available():
					from PIL import Image

					from transformers import MobileViTImageProcessor





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= self.config_class(**self.inputs_dict )
					self.parent.assertTrue(hasattr(A							,"width_multiplier" ) )





class 							UpperCAmelCase      :




	'''simple docstring'''





	def __init__(       self					: List[str]							,A					: List[Any]							,A					: Optional[int]=13							,A					: Dict=64							,A					: Optional[Any]=2							,A					: Optional[int]=3							,A					: int="swish"							,A					: Tuple=3							,A					: Tuple=32							,A					: int=0.1							,A					: Any=0.02							,A					: Any=True							,A					: Optional[int]=True							,A					: Tuple=10							,A					: Any=None							,A					: Any=0.25							,A					: Tuple=0.0							,A					: Optional[int]=0.0							,):
					__A   			= parent
					__A   			= batch_size
					__A   			= image_size
					__A   			= patch_size
					__A   			= num_channels
					__A   			= make_divisible(5_12 * width_multiplier							,divisor=8 )
					__A   			= hidden_act
					__A   			= conv_kernel_size
					__A   			= output_stride
					__A   			= classifier_dropout_prob
					__A   			= use_labels
					__A   			= is_training
					__A   			= num_labels
					__A   			= initializer_range
					__A   			= scope
					__A   			= width_multiplier
					__A   			= ffn_dropout
					__A   			= attn_dropout






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

					__A   			= None
					__A   			= None
					if self.use_labels:
									__A   			= ids_tensor([self.batch_size]							,self.num_labels )
									__A   			= ids_tensor([self.batch_size, self.image_size, self.image_size]							,self.num_labels )

					__A   			= self.get_config()

					return config, pixel_values, labels, pixel_labels






	def 				UpperCamelCase_  (       self					: List[Any] ):
					return MobileViTVaConfig(
					    image_size=self.image_size							,patch_size=self.patch_size							,num_channels=self.num_channels							,hidden_act=self.hidden_act							,conv_kernel_size=self.conv_kernel_size							,output_stride=self.output_stride							,classifier_dropout_prob=self.classifier_dropout_prob							,initializer_range=self.initializer_range							,width_multiplier=self.width_multiplier							,ffn_dropout=self.ffn_dropout_prob							,attn_dropout=self.attn_dropout_prob							,)






	def 				UpperCamelCase_  (       self					: int							,A					: Any							,A					: Any							,A					: Union[str, Any]							,A					: Optional[int] ):
					__A   			= MobileViTVaModel(config=A )
					model.to(A )
					model.eval()
					__A   			= model(A )
					self.parent.assertEqual(
					    result.last_hidden_state.shape							,(
					        self.batch_size,
					        self.last_hidden_size,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    )							,)






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Optional[Any]							,A					: Optional[Any]							,A					: int							,A					: Tuple ):
					__A   			= self.num_labels
					__A   			= MobileViTVaForImageClassification(A )
					model.to(A )
					model.eval()
					__A   			= model(A							,labels=A )
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels) )






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Dict							,A					: List[str]							,A					: Union[str, Any]							,A					: int ):
					__A   			= self.num_labels
					__A   			= MobileViTVaForSemanticSegmentation(A )
					model.to(A )
					model.eval()
					__A   			= model(A )
					self.parent.assertEqual(
					    result.logits.shape							,(
					        self.batch_size,
					        self.num_labels,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    )							,)
					__A   			= model(A							,labels=A )
					self.parent.assertEqual(
					    result.logits.shape							,(
					        self.batch_size,
					        self.num_labels,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    )							,)







	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.prepare_config_and_inputs()
					__A			,							__A			,							__A			,							__A   			= config_and_inputs
					__A   			= {"pixel_values": pixel_values}
					return config, inputs_dict





@require_torch
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     (
	    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
	    if is_torch_available()
	    else ()
	)

	snake_case_					    =     (
	    {
	        "feature-extraction": MobileViTVaModel,
	        "image-classification": MobileViTVaForImageClassification,
	        "image-segmentation": MobileViTVaForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	snake_case_					    =     False
	snake_case_					    =     False
	snake_case_					    =     False
	snake_case_					    =     False






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= MobileViTVaModelTester(self )
					__A   			= MobileViTVaConfigTester(self							,config_class=A							,has_text_modality=A )






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






	@unittest.skip(reason="MobileViTV2 does not use inputs_embeds" )
	def 				UpperCamelCase_  (       self					: List[str] ):
					pass






	@unittest.skip(reason="MobileViTV2 does not support input and output embeddings" )
	def 				UpperCamelCase_  (       self					: List[Any] ):
					pass






	@unittest.skip(reason="MobileViTV2 does not output attentions" )
	def 				UpperCamelCase_  (       self					: List[Any] ):
					pass






	@require_torch_multi_gpu
	@unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run." )
	def 				UpperCamelCase_  (       self					: int ):
					pass






	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
	def 				UpperCamelCase_  (       self					: Optional[int] ):
					pass






	def 				UpperCamelCase_  (       self					: Dict ):
					__A			,							__A   			= self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									__A   			= model_class(A )
									__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]							,A )






	def 				UpperCamelCase_  (       self					: Any ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					def check_hidden_states_output(A					: Dict							,A					: Optional[int]							,A					: Any ):
									__A   			= model_class(A )
									model.to(A )
									model.eval()

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

									__A   			= outputs.hidden_states

									__A   			= 5
									self.assertEqual(len(A )							,A )

									# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
									# with the width and height being successively divided by 2.
									__A   			= 2
									for i in range(len(A ) ):
													self.assertListEqual(
													    list(hidden_states[i].shape[-2:] )							,[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]							,)
													divisor *= 2

									self.assertEqual(self.model_tester.output_stride							,divisor // 2 )

					__A			,							__A   			= self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									__A   			= True
									check_hidden_states_output(A							,A							,A )

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

									check_hidden_states_output(A							,A							,A )






	def 				UpperCamelCase_  (       self					: Any ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*A )






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*A )







	@slow
	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A   			= MobileViTVaModel.from_pretrained(A )
									self.assertIsNotNone(A )





def 						UpperCAmelCase			(	)		->	Union[str, Any]:







				"""simple docstring"""



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





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




	'''simple docstring'''





	@cached_property
	def 				UpperCamelCase_  (       self					: Any ):
					return (
					    MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256" )
					    if is_vision_available()
					    else None
					)






	@slow
	def 				UpperCamelCase_  (       self					: str ):
					__A   			= MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256" ).to(
					    A )

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

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

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

					__A   			= torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01] ).to(A )

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






	@slow
	def 				UpperCamelCase_  (       self					: int ):
					__A   			= MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3" )
					__A   			= model.to(A )

					__A   			= MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3" )

					__A   			= prepare_img()
					__A   			= image_processor(images=A							,return_tensors="pt" ).to(A )

					# forward pass
					with torch.no_grad():
									__A   			= model(**A )
					__A   			= outputs.logits

					# verify the logits
					__A   			= torch.Size((1, 21, 32, 32) )
					self.assertEqual(logits.shape							,A )

					__A   			= torch.tensor(
					    [
					        [[7.08_63, 7.15_25, 6.82_01], [6.69_31, 6.87_70, 6.89_33], [6.29_78, 7.03_66, 6.96_36]],
					        [[-3.71_34, -3.67_12, -3.66_75], [-3.58_25, -3.35_49, -3.47_77], [-3.34_35, -3.39_79, -3.28_57]],
					        [[-2.93_29, -2.80_03, -2.73_69], [-3.05_64, -2.47_80, -2.02_07], [-2.68_89, -1.92_98, -1.76_40]],
					    ]							,device=A							,)

					self.assertTrue(torch.allclose(logits[0, :3, :3, :3]							,A							,atol=1E-4 ) )







	@slow
	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3" )
					__A   			= model.to(A )

					__A   			= MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3" )

					__A   			= prepare_img()
					__A   			= image_processor(images=A							,return_tensors="pt" ).to(A )

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

					__A   			= outputs.logits.detach().cpu()

					__A   			= image_processor.post_process_semantic_segmentation(outputs=A							,target_sizes=[(50, 60)] )
					__A   			= torch.Size((50, 60) )
					self.assertEqual(segmentation[0].shape							,A )

					__A   			= image_processor.post_process_semantic_segmentation(outputs=A )
					__A   			= torch.Size((32, 32) )
					self.assertEqual(segmentation[0].shape							,A )





'''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        lowerCAmelCase_:
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=2  ,__UpperCAmelCase=True  ,__UpperCAmelCase=False  ,__UpperCAmelCase=10  ,__UpperCAmelCase=3  ,__UpperCAmelCase=32 * 4  ,__UpperCAmelCase=32 * 6  ,__UpperCAmelCase=4  ,__UpperCAmelCase=32  ,)    ->		str:
      lowerCAmelCase__ :  Optional[int]							      =					parent
      lowerCAmelCase__ :  Optional[int]							      =					batch_size
      lowerCAmelCase__ :  Optional[int]							      =					is_training
      lowerCAmelCase__ :  Dict							      =					use_auxiliary_loss
      lowerCAmelCase__ :  Union[str, Any]							      =					num_queries
      lowerCAmelCase__ :  str							      =					num_channels
      lowerCAmelCase__ :  List[str]							      =					min_size
      lowerCAmelCase__ :  int							      =					max_size
      lowerCAmelCase__ :  Optional[Any]							      =					num_labels
      lowerCAmelCase__ :  List[Any]							      =					mask_feature_size




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__ :  str							      =					floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]      ).to(
          __UpperCAmelCase      )

      lowerCAmelCase__ :  str							      =					torch.ones([self.batch_size, self.min_size, self.max_size]  ,device=__UpperCAmelCase      )

      lowerCAmelCase__ :  Any							      =					(
          torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]  ,device=__UpperCAmelCase      ) > 0.5
      ).float()
      lowerCAmelCase__ :  Optional[int]							      =					(torch.rand((self.batch_size, self.num_labels)  ,device=__UpperCAmelCase      ) > 0.5).long()

      lowerCAmelCase__ :  Any							      =					self.get_config()
      return config, pixel_values, pixel_mask, mask_labels, class_labels




  def 							UpperCAmelCase_	(    self      )    ->		Dict:
      return MaskFormerConfig.from_backbone_and_decoder_configs(
          backbone_config=SwinConfig(
              depths=[1, 1, 1, 1]  ,)  ,decoder_config=DetrConfig(
              decoder_ffn_dim=128  ,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 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  List[str]							      =					self.prepare_config_and_inputs()
      lowerCAmelCase__ :  List[str]							      =					{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
      return config, inputs_dict




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Any:
      lowerCAmelCase__ :  Optional[int]							      =					output.encoder_hidden_states
      lowerCAmelCase__ :  Optional[int]							      =					output.pixel_decoder_hidden_states
      lowerCAmelCase__ :  Dict							      =					output.transformer_decoder_hidden_states

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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=False      )    ->		Optional[Any]:
      with torch.no_grad():
          lowerCAmelCase__ :  int							      =					MaskFormerModel(config=__UpperCAmelCase      )
          model.to(__UpperCAmelCase      )
          model.eval()

          lowerCAmelCase__ :  str							      =					model(pixel_values=__UpperCAmelCase  ,pixel_mask=__UpperCAmelCase      )
          lowerCAmelCase__ :  int							      =					model(__UpperCAmelCase  ,output_hidden_states=__UpperCAmelCase      )
      # 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(__UpperCAmelCase  ,__UpperCAmelCase      )






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Optional[int]:
      lowerCAmelCase__ :  Dict							      =					MaskFormerForInstanceSegmentation(config=__UpperCAmelCase      )
      model.to(__UpperCAmelCase      )
      model.eval()

      def comm_check_on_output(__UpperCAmelCase      ):
          # 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():
          lowerCAmelCase__ :  List[Any]							      =					model(pixel_values=__UpperCAmelCase  ,pixel_mask=__UpperCAmelCase      )
          lowerCAmelCase__ :  Dict							      =					model(__UpperCAmelCase      )

          comm_check_on_output(__UpperCAmelCase      )

          lowerCAmelCase__ :  Optional[int]							      =					model(
              pixel_values=__UpperCAmelCase  ,pixel_mask=__UpperCAmelCase  ,mask_labels=__UpperCAmelCase  ,class_labels=__UpperCAmelCase      )

      comm_check_on_output(__UpperCAmelCase      )

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







@require_torch
class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, unittest.TestCase     ):
  '''simple docstring'''




  __lowercase							:      Optional[Any]				=      (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
  __lowercase							:      int				=      (
      {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
      if is_torch_available()
      else {}
  )

  __lowercase							:      Union[str, Any]				=      False
  __lowercase							:      Dict				=      False
  __lowercase							:      Tuple				=      False
  __lowercase							:      List[Any]				=      False




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  str							      =					MaskFormerModelTester(self      )
      lowerCAmelCase__ :  List[Any]							      =					ConfigTester(self  ,config_class=__UpperCAmelCase  ,has_text_modality=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      self.config_tester.run_common_tests()




  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__      ,   lowerCAmelCase__ :  str							      =					self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskformer_model(__UpperCAmelCase  ,**__UpperCAmelCase  ,output_hidden_states=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  List[str]							      =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__UpperCAmelCase      )




  @unittest.skip(reason="""MaskFormer does not use inputs_embeds"""      )
  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      pass




  @unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""      )
  def 							UpperCAmelCase_	(    self      )    ->		str:
      pass




  @unittest.skip(reason="""MaskFormer is not a generative model"""      )
  def 							UpperCAmelCase_	(    self      )    ->		Any:
      pass




  @unittest.skip(reason="""MaskFormer does not use token embeddings"""      )
  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      pass




  @require_torch_multi_gpu
  @unittest.skip(
      reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""      )
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      pass




  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""      )
  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      pass




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					self.model_tester.prepare_config_and_inputs_for_common()

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

          lowerCAmelCase__ :  Tuple							      =					["""pixel_values"""]
          self.assertListEqual(arg_names[:1]  ,__UpperCAmelCase      )




  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      for model_name in ["facebook/maskformer-swin-small-coco"]:
          lowerCAmelCase__ :  List[str]							      =					MaskFormerModel.from_pretrained(__UpperCAmelCase      )
          self.assertIsNotNone(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__ :  List[Any]							      =					(self.model_tester.min_size,) * 2
      lowerCAmelCase__ :  Any							      =					{
          """pixel_values""": torch.randn((2, 3, *size)  ,device=__UpperCAmelCase      ),
          """mask_labels""": torch.randn((2, 10, *size)  ,device=__UpperCAmelCase      ),
          """class_labels""": torch.zeros(2  ,10  ,device=__UpperCAmelCase      ).long(),
      }

      lowerCAmelCase__ :  Tuple							      =					MaskFormerForInstanceSegmentation(MaskFormerConfig()      ).to(__UpperCAmelCase      )
      lowerCAmelCase__ :  Union[str, Any]							      =					model(**__UpperCAmelCase      )
      self.assertTrue(outputs.loss is not None      )




  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__      ,   lowerCAmelCase__ :  List[str]							      =					self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskformer_model(__UpperCAmelCase  ,**__UpperCAmelCase  ,output_hidden_states=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          lowerCAmelCase__ :  int							      =					model_class(__UpperCAmelCase      ).to(__UpperCAmelCase      )
          lowerCAmelCase__ :  List[Any]							      =					model(**__UpperCAmelCase  ,output_attentions=__UpperCAmelCase      )
          self.assertTrue(outputs.attentions is not None      )




  def 							UpperCAmelCase_	(    self      )    ->		int:
      if not self.model_tester.is_training:
          return
      # only MaskFormerForInstanceSegmentation has the loss
      lowerCAmelCase__ :  Dict							      =					self.all_model_classes[1]
      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					self.model_tester.prepare_config_and_inputs()

      lowerCAmelCase__ :  List[Any]							      =					model_class(__UpperCAmelCase      )
      model.to(__UpperCAmelCase      )
      model.train()

      lowerCAmelCase__ :  List[str]							      =					model(__UpperCAmelCase  ,mask_labels=__UpperCAmelCase  ,class_labels=__UpperCAmelCase      ).loss
      loss.backward()






  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      # only MaskFormerForInstanceSegmentation has the loss
      lowerCAmelCase__ :  Tuple							      =					self.all_model_classes[1]
      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					self.model_tester.prepare_config_and_inputs()
      lowerCAmelCase__ :  Union[str, Any]							      =					True
      lowerCAmelCase__ :  Tuple							      =					True

      lowerCAmelCase__ :  Optional[Any]							      =					model_class(__UpperCAmelCase      )
      model.to(__UpperCAmelCase      )
      model.train()

      lowerCAmelCase__ :  Dict							      =					model(__UpperCAmelCase  ,mask_labels=__UpperCAmelCase  ,class_labels=__UpperCAmelCase      )

      lowerCAmelCase__ :  Optional[Any]							      =					outputs.encoder_hidden_states[0]
      encoder_hidden_states.retain_grad()

      lowerCAmelCase__ :  str							      =					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
      lowerCAmelCase__ :  Union[str, Any]							      =					outputs.transformer_decoder_hidden_states[0]
      transformer_decoder_hidden_states.retain_grad()

      lowerCAmelCase__ :  List[Any]							      =					outputs.attentions[0]
      attentions.retain_grad()

      outputs.loss.backward(retain_graph=__UpperCAmelCase      )

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







_lowerCAmelCase      						=	1e-4
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
    return image






@require_vision
@slow
class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  @cached_property
  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      return (
          MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""      )
          if is_vision_available()
          else None
      )




  def 							UpperCAmelCase_	(    self      )    ->		Any:
      lowerCAmelCase__ :  Any							      =					MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""      ).to(__UpperCAmelCase      )
      lowerCAmelCase__ :  str							      =					self.default_image_processor
      lowerCAmelCase__ :  str							      =					prepare_img()
      lowerCAmelCase__ :  Optional[int]							      =					image_processor(__UpperCAmelCase  ,return_tensors="""pt"""      ).to(__UpperCAmelCase      )
      lowerCAmelCase__ :  Dict							      =					inputs["""pixel_values"""].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0      )
      # check size
      self.assertEqual(__UpperCAmelCase  ,(1, 3, 800, 1088)      )

      with torch.no_grad():
          lowerCAmelCase__ :  Union[str, Any]							      =					model(**__UpperCAmelCase      )

      lowerCAmelCase__ :  Optional[Any]							      =					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(__UpperCAmelCase      )
      self.assertTrue(
          torch.allclose(
              outputs.encoder_last_hidden_state[0, 0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )

      lowerCAmelCase__ :  Dict							      =					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(__UpperCAmelCase      )
      self.assertTrue(
          torch.allclose(
              outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )

      lowerCAmelCase__ :  Optional[int]							      =					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(__UpperCAmelCase      )
      self.assertTrue(
          torch.allclose(
              outputs.transformer_decoder_last_hidden_state[0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  List[Any]							      =					(
          MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""      )
          .to(__UpperCAmelCase      )
          .eval()
      )
      lowerCAmelCase__ :  Optional[Any]							      =					self.default_image_processor
      lowerCAmelCase__ :  List[str]							      =					prepare_img()
      lowerCAmelCase__ :  str							      =					image_processor(__UpperCAmelCase  ,return_tensors="""pt"""      ).to(__UpperCAmelCase      )
      lowerCAmelCase__ :  Optional[int]							      =					inputs["""pixel_values"""].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0      )
      # check size
      self.assertEqual(__UpperCAmelCase  ,(1, 3, 800, 1088)      )

      with torch.no_grad():
          lowerCAmelCase__ :  List[Any]							      =					model(**__UpperCAmelCase      )
      # masks_queries_logits
      lowerCAmelCase__ :  Optional[int]							      =					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)  ,)
      lowerCAmelCase__ :  Optional[int]							      =					[
          [-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],
      ]
      lowerCAmelCase__ :  Optional[int]							      =					torch.tensor(__UpperCAmelCase      ).to(__UpperCAmelCase      )
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )
      # class_queries_logits
      lowerCAmelCase__ :  Tuple							      =					outputs.class_queries_logits
      self.assertEqual(
          class_queries_logits.shape  ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)      )
      lowerCAmelCase__ :  Union[str, Any]							      =					torch.tensor(
          [
              [1.65_12E00, -5.25_72E00, -3.35_19E00],
              [3.61_69E-02, -5.90_25E00, -2.93_13E00],
              [1.07_66E-04, -7.76_30E00, -5.12_63E00],
          ]      ).to(__UpperCAmelCase      )
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )




  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__ :  List[Any]							      =					(
          MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""      )
          .to(__UpperCAmelCase      )
          .eval()
      )
      lowerCAmelCase__ :  Optional[Any]							      =					self.default_image_processor
      lowerCAmelCase__ :  int							      =					prepare_img()
      lowerCAmelCase__ :  Optional[Any]							      =					image_processor(__UpperCAmelCase  ,return_tensors="""pt"""      ).to(__UpperCAmelCase      )
      lowerCAmelCase__ :  str							      =					inputs["""pixel_values"""].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0      )
      # check size
      self.assertEqual(__UpperCAmelCase  ,(1, 3, 800, 1088)      )

      with torch.no_grad():
          lowerCAmelCase__ :  str							      =					model(**__UpperCAmelCase      )
      # masks_queries_logits
      lowerCAmelCase__ :  Optional[Any]							      =					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)  ,)
      lowerCAmelCase__ :  int							      =					[[-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]]
      lowerCAmelCase__ :  List[str]							      =					torch.tensor(__UpperCAmelCase      ).to(__UpperCAmelCase      )
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )
      # class_queries_logits
      lowerCAmelCase__ :  Optional[Any]							      =					outputs.class_queries_logits
      self.assertEqual(
          class_queries_logits.shape  ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)      )
      lowerCAmelCase__ :  Tuple							      =					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(__UpperCAmelCase      )
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )






  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  str							      =					(
          MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""      )
          .to(__UpperCAmelCase      )
          .eval()
      )
      lowerCAmelCase__ :  Dict							      =					self.default_image_processor

      lowerCAmelCase__ :  Union[str, Any]							      =					image_processor(
          [np.zeros((3, 800, 1333)      ), np.zeros((3, 800, 1333)      )]  ,segmentation_maps=[np.zeros((384, 384)      ).astype(np.floataa      ), np.zeros((384, 384)      ).astype(np.floataa      )]  ,return_tensors="""pt"""  ,)

      lowerCAmelCase__ :  Tuple							      =					inputs["""pixel_values"""].to(__UpperCAmelCase      )
      lowerCAmelCase__ :  Union[str, Any]							      =					[el.to(__UpperCAmelCase      ) for el in inputs["""mask_labels"""]]
      lowerCAmelCase__ :  Union[str, Any]							      =					[el.to(__UpperCAmelCase      ) for el in inputs["""class_labels"""]]

      with torch.no_grad():
          lowerCAmelCase__ :  Any							      =					model(**__UpperCAmelCase      )

      self.assertTrue(outputs.loss is not None      )


