


'''simple docstring'''

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  ["image_processor", "tokenizer"]
  _lowerCamelCase		    =  "LayoutLMv3ImageProcessor"
  _lowerCamelCase		    =  ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






  def __init__(    self			,  UpperCamelCase=None			,  UpperCamelCase=None			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  None
   if "feature_extractor" in kwargs:
    warnings.warn(
        "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
        " instead."			,  UpperCamelCase			,  )
    lowerCamelCase_      =  kwargs.pop("feature_extractor"     )

   lowerCamelCase_      =  image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
    raise ValueError("You need to specify an `image_processor`."     )
   if tokenizer is None:
    raise ValueError("You need to specify a `tokenizer`."     )

   super().__init__(UpperCamelCase			,  UpperCamelCase     )






  def __call__(    self			,  UpperCamelCase			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = True			,  UpperCamelCase = False			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = 0			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = False			,  UpperCamelCase = False			,  UpperCamelCase = False			,  UpperCamelCase = False			,  UpperCamelCase = True			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   # verify input
   if self.image_processor.apply_ocr and (boxes is not None):
    raise ValueError(
        "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."     )

   if self.image_processor.apply_ocr and (word_labels is not None):
    raise ValueError(
        "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."     )

   # first, apply the image processor
   lowerCamelCase_      =  self.image_processor(images=UpperCamelCase			,  return_tensors=UpperCamelCase     )

   # second, apply the tokenizer
   if text is not None and self.image_processor.apply_ocr and text_pair is None:
    if isinstance(UpperCamelCase			,  UpperCamelCase     ):
     lowerCamelCase_      =  [text]  # add batch dimension (as the image processor always adds a batch dimension)
    lowerCamelCase_      =  features["words"]

   lowerCamelCase_      =  self.tokenizer(
       text=text if text is not None else features["words"]			,  text_pair=text_pair if text_pair is not None else None			,  boxes=boxes if boxes is not None else features["boxes"]			,  word_labels=UpperCamelCase			,  add_special_tokens=UpperCamelCase			,  padding=UpperCamelCase			,  truncation=UpperCamelCase			,  max_length=UpperCamelCase			,  stride=UpperCamelCase			,  pad_to_multiple_of=UpperCamelCase			,  return_token_type_ids=UpperCamelCase			,  return_attention_mask=UpperCamelCase			,  return_overflowing_tokens=UpperCamelCase			,  return_special_tokens_mask=UpperCamelCase			,  return_offsets_mapping=UpperCamelCase			,  return_length=UpperCamelCase			,  verbose=UpperCamelCase			,  return_tensors=UpperCamelCase			,  **UpperCamelCase			,  )

   # add pixel values
   lowerCamelCase_      =  features.pop("pixel_values"     )
   if return_overflowing_tokens is True:
    lowerCamelCase_      =  self.get_overflowing_images(UpperCamelCase			,  encoded_inputs["overflow_to_sample_mapping"]     )
   lowerCamelCase_      =  images

   return encoded_inputs






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
   lowerCamelCase_      =  []
   for sample_idx in overflow_to_sample_mapping:
    images_with_overflow.append(images[sample_idx]     )

   if len(UpperCamelCase     ) != len(UpperCamelCase     ):
    raise ValueError(
        "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
        f''' {len(UpperCamelCase     )} and {len(UpperCamelCase     )}'''     )

   return images_with_overflow






  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   return self.tokenizer.batch_decode(*UpperCamelCase			,  **UpperCamelCase     )






  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   return self.tokenizer.decode(*UpperCamelCase			,  **UpperCamelCase     )






  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   return ["input_ids", "bbox", "attention_mask", "pixel_values"]






  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,  UpperCamelCase			,  )
   return self.image_processor_class







  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,  UpperCamelCase			,  )
   return self.image_processor








import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, 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 MaskaFormerForUniversalSegmentation, MaskaFormerModel

						if is_vision_available():
												from transformers import MaskaFormerImageProcessor

if is_vision_available():
						from PIL import Image



class 				__snake_case						:




							def __init__(       self     :       Optional[Any]				,				A_     :       Dict				,				A_     :       Optional[Any]=2				,				A_     :       List[str]=True				,				A_     :       Dict=False				,				A_     :       Union[str, Any]=1_0				,				A_     :       Optional[Any]=3				,				A_     :       str=3_2 * 8				,				A_     :       List[str]=3_2 * 8				,				A_     :       Dict=4				,				A_     :       List[Any]=6_4				,				):
									lowerCAmelCase_			:     Any									= parent
									lowerCAmelCase_			:     Union[str, Any]									= batch_size
									lowerCAmelCase_			:     List[Any]									= is_training
									lowerCAmelCase_			:     int									= use_auxiliary_loss
									lowerCAmelCase_			:     str									= num_queries
									lowerCAmelCase_			:     Any									= num_channels
									lowerCAmelCase_			:     Union[str, Any]									= min_size
									lowerCAmelCase_			:     Optional[int]									= max_size
									lowerCAmelCase_			:     List[str]									= num_labels
									lowerCAmelCase_			:     str									= hidden_dim
									lowerCAmelCase_			:     List[str]									= hidden_dim




							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     str									= floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
									    A_)

									lowerCAmelCase_			:     Optional[int]									= torch.ones([self.batch_size, self.min_size, self.max_size]				,				device=A_)

									lowerCAmelCase_			:     List[str]									= (
									    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]				,				device=A_) > 0.5
									).float()
									lowerCAmelCase_			:     Any									= (torch.rand((self.batch_size, self.num_labels)				,				device=A_) > 0.5).long()

									lowerCAmelCase_			:     Optional[int]									= self.get_config()
									return config, pixel_values, pixel_mask, mask_labels, class_labels




							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									lowerCAmelCase_			:     Optional[Any]									= MaskaFormerConfig(
									    hidden_size=self.hidden_dim				,				)
									lowerCAmelCase_			:     Dict									= self.num_queries
									lowerCAmelCase_			:     str									= self.num_labels

									lowerCAmelCase_			:     str									= [1, 1, 1, 1]
									lowerCAmelCase_			:     Dict									= self.num_channels

									lowerCAmelCase_			:     List[str]									= 6_4
									lowerCAmelCase_			:     Union[str, Any]									= 1_2_8
									lowerCAmelCase_			:     str									= self.hidden_dim
									lowerCAmelCase_			:     Optional[Any]									= self.hidden_dim
									lowerCAmelCase_			:     Any									= self.hidden_dim
									return config




							def    UpperCAmelCase__			(       self     :       Union[str, Any]):
									lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= self.prepare_config_and_inputs()
									lowerCAmelCase_			:     List[str]									= {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
									return config, inputs_dict




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       List[Any]				,				A_     :       Tuple):
									lowerCAmelCase_			:     Any									= output.encoder_hidden_states
									lowerCAmelCase_			:     int									= output.pixel_decoder_hidden_states
									lowerCAmelCase_			:     Union[str, Any]									= output.transformer_decoder_hidden_states

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




							def    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       int				,				A_     :       Union[str, Any]				,				A_     :       Union[str, Any]				,				A_     :       str=False):
									with torch.no_grad():
											lowerCAmelCase_			:     Union[str, Any]									= MaskaFormerModel(config=A_)
											model.to(A_)
											model.eval()

											lowerCAmelCase_			:     List[Any]									= model(pixel_values=A_				,				pixel_mask=A_)
											lowerCAmelCase_			:     Any									= model(A_				,				output_hidden_states=A_)

									self.parent.assertEqual(
									    output.transformer_decoder_last_hidden_state.shape				,				(self.batch_size, self.num_queries, self.hidden_dim)				,				)
									# 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(A_				,				A_)







							def    UpperCAmelCase__			(       self     :       List[Any]				,				A_     :       Union[str, Any]				,				A_     :       List[str]				,				A_     :       List[Any]				,				A_     :       Tuple				,				A_     :       Any):
									lowerCAmelCase_			:     Any									= MaskaFormerForUniversalSegmentation(config=A_)
									model.to(A_)
									model.eval()

									def comm_check_on_output(A_     :       Optional[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():
											lowerCAmelCase_			:     List[Any]									= model(pixel_values=A_				,				pixel_mask=A_)
											lowerCAmelCase_			:     List[Any]									= model(A_)

											comm_check_on_output(A_)

											lowerCAmelCase_			:     Any									= model(
											    pixel_values=A_				,				pixel_mask=A_				,				mask_labels=A_				,				class_labels=A_)

									comm_check_on_output(A_)

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





@require_torch
class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						,unittest.TestCase						):
							_a			      = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							_a			      = {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

							_a			      = False
							_a			      = False
							_a			      = False
							_a			      = False




							def    UpperCAmelCase__			(       self     :       Any):
									lowerCAmelCase_			:     Tuple									= MaskaFormerModelTester(self)
									lowerCAmelCase_			:     Optional[Any]									= ConfigTester(self				,				config_class=A_				,				has_text_modality=A_)




							def    UpperCAmelCase__			(       self     :       Optional[int]):
									self.config_tester.run_common_tests()




							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									lowerCAmelCase_					,  lowerCAmelCase_			:     Dict									= self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskaformer_model(A_				,				**A_				,				output_hidden_states=A_)




							def    UpperCAmelCase__			(       self     :       List[Any]):
									lowerCAmelCase_			:     int									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*A_)




							@unittest.skip(reason='''Mask2Former does not use inputs_embeds''')
							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									pass




							@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method''')
							def    UpperCAmelCase__			(       self     :       str):
									pass




							@unittest.skip(reason='''Mask2Former is not a generative model''')
							def    UpperCAmelCase__			(       self     :       int):
									pass




							@unittest.skip(reason='''Mask2Former does not use token embeddings''')
							def    UpperCAmelCase__			(       self     :       Tuple):
									pass




							@require_torch_multi_gpu
							@unittest.skip(
							    reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''')
							def    UpperCAmelCase__			(       self     :       List[str]):
									pass




							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
							def    UpperCAmelCase__			(       self     :       Tuple):
									pass




							def    UpperCAmelCase__			(       self     :       Dict):
									lowerCAmelCase_					,  lowerCAmelCase_			:     List[str]									= self.model_tester.prepare_config_and_inputs_for_common()

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

											lowerCAmelCase_			:     Dict									= ['''pixel_values''']
											self.assertListEqual(arg_names[:1]				,				A_)




							@slow
							def    UpperCAmelCase__			(       self     :       List[str]):
									for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
											lowerCAmelCase_			:     List[str]									= MaskaFormerModel.from_pretrained(A_)
											self.assertIsNotNone(A_)




							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									lowerCAmelCase_			:     Optional[int]									= (self.model_tester.min_size,) * 2
									lowerCAmelCase_			:     str									= {
									    '''pixel_values''': torch.randn((2, 3, *size)				,				device=A_),
									    '''mask_labels''': torch.randn((2, 1_0, *size)				,				device=A_),
									    '''class_labels''': torch.zeros(2				,				1_0				,				device=A_).long(),
									}
									lowerCAmelCase_			:     Union[str, Any]									= self.model_tester.get_config()

									lowerCAmelCase_			:     Any									= MaskaFormerForUniversalSegmentation(A_).to(A_)
									lowerCAmelCase_			:     Union[str, Any]									= model(**A_)
									self.assertTrue(outputs.loss is not None)




							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_					,  lowerCAmelCase_			:     Dict									= self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskaformer_model(A_				,				**A_				,				output_hidden_states=A_)




							def    UpperCAmelCase__			(       self     :       Union[str, Any]):
									lowerCAmelCase_					,  lowerCAmelCase_			:     Tuple									= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											lowerCAmelCase_			:     Union[str, Any]									= model_class(A_).to(A_)
											lowerCAmelCase_			:     List[str]									= model(**A_				,				output_attentions=A_)
											self.assertTrue(outputs.attentions is not None)




							def    UpperCAmelCase__			(       self     :       List[Any]):
									if not self.model_tester.is_training:
											return

									lowerCAmelCase_			:     Dict									= self.all_model_classes[1]
									lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     str									= self.model_tester.prepare_config_and_inputs()

									lowerCAmelCase_			:     Optional[Any]									= model_class(A_)
									model.to(A_)
									model.train()

									lowerCAmelCase_			:     List[Any]									= model(A_				,				mask_labels=A_				,				class_labels=A_).loss
									loss.backward()







							def    UpperCAmelCase__			(       self     :       str):
									lowerCAmelCase_			:     str									= self.all_model_classes[1]
									lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= self.model_tester.prepare_config_and_inputs()
									lowerCAmelCase_			:     Optional[Any]									= True
									lowerCAmelCase_			:     Dict									= True

									lowerCAmelCase_			:     Any									= model_class(A_).to(A_)
									model.train()

									lowerCAmelCase_			:     List[str]									= model(A_				,				mask_labels=A_				,				class_labels=A_)

									lowerCAmelCase_			:     int									= outputs.encoder_hidden_states[0]
									encoder_hidden_states.retain_grad()

									lowerCAmelCase_			:     str									= outputs.pixel_decoder_hidden_states[0]
									pixel_decoder_hidden_states.retain_grad()

									lowerCAmelCase_			:     Dict									= outputs.transformer_decoder_hidden_states[0]
									transformer_decoder_hidden_states.retain_grad()

									lowerCAmelCase_			:     str									= outputs.attentions[0]
									attentions.retain_grad()

									outputs.loss.backward(retain_graph=A_)

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





A__  :  int								=					1E-4


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     Optional[int]									= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
		return image





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




							@cached_property
							def    UpperCAmelCase__			(       self     :       Tuple):
									return "facebook/mask2former-swin-small-coco-instance"




							@cached_property
							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints) if is_vision_available() else None




							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     Any									= MaskaFormerModel.from_pretrained(self.model_checkpoints).to(A_)
									lowerCAmelCase_			:     str									= self.default_image_processor
									lowerCAmelCase_			:     Tuple									= prepare_img()
									lowerCAmelCase_			:     Any									= image_processor(A_				,				return_tensors='''pt''').to(A_)
									lowerCAmelCase_			:     str									= 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(A_				,				(1, 3, 3_8_4, 3_8_4))

									with torch.no_grad():
											lowerCAmelCase_			:     Optional[Any]									= model(**A_)

									lowerCAmelCase_			:     List[str]									= torch.tensor(
									    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]).to(A_)
									self.assertTrue(
									    torch.allclose(
									        outputs.encoder_last_hidden_state[0, 0, :3, :3]				,				A_				,				atol=A_))

									lowerCAmelCase_			:     Union[str, Any]									= torch.tensor(
									    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]).to(A_)
									self.assertTrue(
									    torch.allclose(
									        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]				,				A_				,				atol=A_))

									lowerCAmelCase_			:     Optional[int]									= torch.tensor(
									    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]).to(A_)
									self.assertTrue(
									    torch.allclose(
									        outputs.transformer_decoder_last_hidden_state[0, :3, :3]				,				A_				,				atol=A_))




							def    UpperCAmelCase__			(       self     :       List[Any]):
									lowerCAmelCase_			:     List[str]									= MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(A_).eval()
									lowerCAmelCase_			:     Optional[int]									= self.default_image_processor
									lowerCAmelCase_			:     List[Any]									= prepare_img()
									lowerCAmelCase_			:     Tuple									= image_processor(A_				,				return_tensors='''pt''').to(A_)
									lowerCAmelCase_			:     Union[str, Any]									= 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(A_				,				(1, 3, 3_8_4, 3_8_4))

									with torch.no_grad():
											lowerCAmelCase_			:     Tuple									= model(**A_)
									# masks_queries_logits
									lowerCAmelCase_			:     int									= outputs.masks_queries_logits
									self.assertEqual(
									    masks_queries_logits.shape				,				(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4))
									lowerCAmelCase_			:     Tuple									= [
									    [-8.7839, -9.0056, -8.8121],
									    [-7.4104, -7.0313, -6.5401],
									    [-6.6105, -6.3427, -6.4675],
									]
									lowerCAmelCase_			:     Optional[Any]									= torch.tensor(A_).to(A_)
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]				,				A_				,				atol=A_))
									# class_queries_logits
									lowerCAmelCase_			:     Dict									= outputs.class_queries_logits
									self.assertEqual(class_queries_logits.shape				,				(1, model.config.num_queries, model.config.num_labels + 1))
									lowerCAmelCase_			:     Any									= torch.tensor(
									    [
									        [1.8324, -8.0835, -4.1922],
									        [0.8450, -9.0050, -3.6053],
									        [0.3045, -7.7293, -3.0275],
									    ]).to(A_)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]				,				A_				,				atol=A_))







							def    UpperCAmelCase__			(       self     :       Any):
									lowerCAmelCase_			:     List[str]									= MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(A_).eval()
									lowerCAmelCase_			:     Optional[Any]									= self.default_image_processor

									lowerCAmelCase_			:     Optional[Any]									= 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'''				,				)

									lowerCAmelCase_			:     Dict									= inputs['''pixel_values'''].to(A_)
									lowerCAmelCase_			:     Tuple									= [el.to(A_) for el in inputs['''mask_labels''']]
									lowerCAmelCase_			:     str									= [el.to(A_) for el in inputs['''class_labels''']]

									with torch.no_grad():
											lowerCAmelCase_			:     int									= model(**A_)

									self.assertTrue(outputs.loss is not None)





