





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__       : Optional[Any]         =       {
    '''configuration_mask2former''': [
        '''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Mask2FormerConfig''',
    ],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__       : List[Any]         =       ['''Mask2FormerImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__       : List[str]         =       [
      '''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''Mask2FormerForUniversalSegmentation''',
      '''Mask2FormerModel''',
      '''Mask2FormerPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_maskaformer import MaskaFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_maskaformer import (
        MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        MaskaFormerForUniversalSegmentation,
        MaskaFormerModel,
        MaskaFormerPreTrainedModel,
    )


else:
  import sys

  lowercase__       : List[str]         =       _LazyModule(__name__, globals()['''__file__'''], _import_structure)





'''simple docstring'''






import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import (
          MODEL_MAPPING,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
      )
      from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      import PIL
      from PIL import Image

      from transformers import BeitImageProcessor






class        lowercase_      :





     def __init__(     self     :					Union[str, Any]     ,							__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					Any=100     ,							__lowerCamelCase     :					str=13     ,							__lowerCamelCase     :					str=30     ,							__lowerCamelCase     :					int=2     ,							__lowerCamelCase     :					List[Any]=3     ,							__lowerCamelCase     :					str=True     ,							__lowerCamelCase     :					Optional[int]=True     ,							__lowerCamelCase     :					List[Any]=32     ,							__lowerCamelCase     :					Union[str, Any]=4     ,							__lowerCamelCase     :					str=4     ,							__lowerCamelCase     :					Tuple=37     ,							__lowerCamelCase     :					List[Any]="gelu"     ,							__lowerCamelCase     :					Optional[Any]=0.1     ,							__lowerCamelCase     :					List[str]=0.1     ,							__lowerCamelCase     :					Union[str, Any]=10     ,							__lowerCamelCase     :					int=0.0_2     ,							__lowerCamelCase     :					Tuple=3     ,							__lowerCamelCase     :					int=None     ,							__lowerCamelCase     :					Tuple=[0, 1, 2, 3]     ,							):
           snake_case__       :   Optional[int]	      =    parent
           snake_case__       :   Union[str, Any]	      =    100
           snake_case__       :   Union[str, Any]	      =    batch_size
           snake_case__       :   Dict	      =    image_size
           snake_case__       :   Tuple	      =    patch_size
           snake_case__       :   Dict	      =    num_channels
           snake_case__       :   List[Any]	      =    is_training
           snake_case__       :   Optional[Any]	      =    use_labels
           snake_case__       :   Optional[int]	      =    hidden_size
           snake_case__       :   Optional[int]	      =    num_hidden_layers
           snake_case__       :   Optional[Any]	      =    num_attention_heads
           snake_case__       :   Union[str, Any]	      =    intermediate_size
           snake_case__       :   str	      =    hidden_act
           snake_case__       :   Tuple	      =    hidden_dropout_prob
           snake_case__       :   Dict	      =    attention_probs_dropout_prob
           snake_case__       :   List[Any]	      =    type_sequence_label_size
           snake_case__       :   Dict	      =    initializer_range
           snake_case__       :   str	      =    scope
           snake_case__       :   Optional[Any]	      =    out_indices
           snake_case__       :   List[Any]	      =    num_labels

           # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           snake_case__       :   Optional[Any]	      =    (image_size // patch_size) ** 2
           snake_case__       :   Dict	      =    num_patches + 1





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   Tuple	      =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

           snake_case__       :   Tuple	      =    None
           snake_case__       :   str	      =    None
           if self.use_labels:
                 snake_case__       :   Union[str, Any]	      =    ids_tensor([self.batch_size]     ,							self.type_sequence_label_size			)
                 snake_case__       :   Optional[int]	      =    ids_tensor([self.batch_size, self.image_size, self.image_size]     ,							self.num_labels			)

           snake_case__       :   str	      =    self.get_config()

           return config, pixel_values, labels, pixel_labels





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           return BeitConfig(
               vocab_size=self.vocab_size     ,							image_size=self.image_size     ,							patch_size=self.patch_size     ,							num_channels=self.num_channels     ,							hidden_size=self.hidden_size     ,							num_hidden_layers=self.num_hidden_layers     ,							num_attention_heads=self.num_attention_heads     ,							intermediate_size=self.intermediate_size     ,							hidden_act=self.hidden_act     ,							hidden_dropout_prob=self.hidden_dropout_prob     ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,							is_decoder=__lowerCamelCase     ,							initializer_range=self.initializer_range     ,							out_indices=self.out_indices     ,							)





     def 			_lowerCAmelCase      (     self     :					int     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					int			):
           snake_case__       :   Tuple	      =    BeitModel(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   int	      =    model(__lowerCamelCase			)
           self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)			)





     def 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Optional[int]			):
           snake_case__       :   Optional[int]	      =    BeitForMaskedImageModeling(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   Dict	      =    model(__lowerCamelCase			)
           self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length - 1, self.vocab_size)			)





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					Any			):
           snake_case__       :   List[Any]	      =    self.type_sequence_label_size
           snake_case__       :   int	      =    BeitForImageClassification(__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   List[Any]	      =    model(__lowerCamelCase     ,							labels=__lowerCamelCase			)
           self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.type_sequence_label_size)			)

           # test greyscale images
           snake_case__       :   Union[str, Any]	      =    1
           snake_case__       :   Any	      =    BeitForImageClassification(__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()

           snake_case__       :   Union[str, Any]	      =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
           snake_case__       :   str	      =    model(__lowerCamelCase     ,							labels=__lowerCamelCase			)
           self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.type_sequence_label_size)			)





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Any     ,							__lowerCamelCase     :					List[Any]			):
           snake_case__       :   str	      =    self.num_labels
           snake_case__       :   int	      =    BeitForSemanticSegmentation(__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   List[Any]	      =    model(__lowerCamelCase			)
           self.parent.assertEqual(
               result.logits.shape     ,							(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)			)
           snake_case__       :   Dict	      =    model(__lowerCamelCase     ,							labels=__lowerCamelCase			)
           self.parent.assertEqual(
               result.logits.shape     ,							(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)			)





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






@require_torch
class        lowercase_      (						lowerCAmelCase_						,       lowerCAmelCase_						,       unittest.TestCase							):
     A_											=			(
         (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     A_											=			(
         {
             "feature-extraction": BeitModel,
             "image-classification": BeitForImageClassification,
             "image-segmentation": BeitForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     A_											=			False
     A_											=			False
     A_											=			False





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   Any	      =    BeitModelTester(self			)
           snake_case__       :   List[str]	      =    ConfigTester(self     ,							config_class=__lowerCamelCase     ,							has_text_modality=__lowerCamelCase     ,							hidden_size=37			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           self.config_tester.run_common_tests()





     @unittest.skip(reason='BEiT does not use inputs_embeds'			)
     def 			_lowerCAmelCase      (     self     :					Tuple			):
           pass





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





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__      ,							snake_case__       :   str	      =    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 snake_case__       :   Optional[Any]	      =    model_class(__lowerCamelCase			)
                 self.assertIsInstance(model.get_input_embeddings()     ,							(nn.Module)			)
                 snake_case__       :   Dict	      =    model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(__lowerCamelCase     ,							nn.Linear			)			)





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__      ,							snake_case__       :   Optional[Any]	      =    self.model_tester.prepare_config_and_inputs_for_common()

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

                 snake_case__       :   List[str]	      =    ['pixel_values']
                 self.assertListEqual(arg_names[:1]     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   Union[str, Any]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   List[Any]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   Any	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   List[str]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           if not self.model_tester.is_training:
                 return

           snake_case__      ,							snake_case__       :   Any	      =    self.model_tester.prepare_config_and_inputs_for_common()
           snake_case__       :   str	      =    True

           for model_class in self.all_model_classes:
                 # we don't test BeitForMaskedImageModeling
                 if model_class in [*get_values(__lowerCamelCase			), BeitForMaskedImageModeling]:
                       continue

                 snake_case__       :   int	      =    model_class(__lowerCamelCase			)
                 model.to(__lowerCamelCase			)
                 model.train()
                 snake_case__       :   Dict	      =    self._prepare_for_class(__lowerCamelCase     ,							__lowerCamelCase     ,							return_labels=__lowerCamelCase			)
                 snake_case__       :   Optional[Any]	      =    model(**__lowerCamelCase			).loss
                 loss.backward()





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__      ,							snake_case__       :   Tuple	      =    self.model_tester.prepare_config_and_inputs_for_common()
           if not self.model_tester.is_training:
                 return

           snake_case__       :   List[str]	      =    False
           snake_case__       :   List[Any]	      =    True

           for model_class in self.all_model_classes:
                 # we don't test BeitForMaskedImageModeling
                 if (
                     model_class in [*get_values(__lowerCamelCase			), BeitForMaskedImageModeling]
                     or not model_class.supports_gradient_checkpointing
                 ):
                       continue

                 snake_case__       :   Any	      =    model_class(__lowerCamelCase			)
                 model.gradient_checkpointing_enable()
                 model.to(__lowerCamelCase			)
                 model.train()
                 snake_case__       :   Optional[int]	      =    self._prepare_for_class(__lowerCamelCase     ,							__lowerCamelCase     ,							return_labels=__lowerCamelCase			)
                 snake_case__       :   int	      =    model(**__lowerCamelCase			).loss
                 loss.backward()





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__      ,							snake_case__       :   Optional[Any]	      =    self.model_tester.prepare_config_and_inputs_for_common()

           snake_case__       :   Tuple	      =    _config_zero_init(__lowerCamelCase			)
           for model_class in self.all_model_classes:
                 snake_case__       :   List[str]	      =    model_class(config=__lowerCamelCase			)
                 for name, param in model.named_parameters():
                       # we skip lambda parameters as these require special initial values
                       # determined by config.layer_scale_init_value
                       if "lambda" in name:
                             continue
                       if param.requires_grad:
                             self.assertIn(
                                 ((param.data.mean() * 1E9).round() / 1E9).item()     ,							[0.0, 1.0]     ,							msg=F"Parameter {name} of model {model_class} seems not properly initialized"     ,							)





     @slow
     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case__       :   str	      =    BeitModel.from_pretrained(__lowerCamelCase			)
                 self.assertIsNotNone(__lowerCamelCase			)






def 		UpperCamelCase__      (		)    ->   int:
      snake_case__       :   Any	      =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
      return image






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





     @cached_property
     def 			_lowerCAmelCase      (     self     :					List[str]			):
           return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'			) if is_vision_available() else None





     @slow
     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   Tuple	      =    BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'			).to(__lowerCamelCase			)

           snake_case__       :   Any	      =    self.default_image_processor
           snake_case__       :   List[str]	      =    prepare_img()
           snake_case__       :   Any	      =    image_processor(images=__lowerCamelCase     ,							return_tensors='pt'			).pixel_values.to(__lowerCamelCase			)

           # prepare bool_masked_pos
           snake_case__       :   Optional[Any]	      =    torch.ones((1, 196)     ,							dtype=torch.bool			).to(__lowerCamelCase			)

           # forward pass
           with torch.no_grad():
                 snake_case__       :   Union[str, Any]	      =    model(pixel_values=__lowerCamelCase     ,							bool_masked_pos=__lowerCamelCase			)
           snake_case__       :   str	      =    outputs.logits

           # verify the logits
           snake_case__       :   Dict	      =    torch.Size((1, 196, 8192)			)
           self.assertEqual(logits.shape     ,							__lowerCamelCase			)

           snake_case__       :   int	      =    torch.tensor(
               [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]			).to(__lowerCamelCase			)

           self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]     ,							__lowerCamelCase     ,							atol=1E-2			)			)





     @slow
     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   Optional[int]	      =    BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'			).to(__lowerCamelCase			)

           snake_case__       :   List[str]	      =    self.default_image_processor
           snake_case__       :   int	      =    prepare_img()
           snake_case__       :   Optional[Any]	      =    image_processor(images=__lowerCamelCase     ,							return_tensors='pt'			).to(__lowerCamelCase			)

           # forward pass
           with torch.no_grad():
                 snake_case__       :   Union[str, Any]	      =    model(**__lowerCamelCase			)
           snake_case__       :   Dict	      =    outputs.logits

           # verify the logits
           snake_case__       :   List[Any]	      =    torch.Size((1, 1000)			)
           self.assertEqual(logits.shape     ,							__lowerCamelCase			)

           snake_case__       :   Union[str, Any]	      =    torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]			).to(__lowerCamelCase			)

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

           snake_case__       :   Any	      =    281
           self.assertEqual(logits.argmax(-1			).item()     ,							__lowerCamelCase			)





     @slow
     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   Any	      =    BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'			).to(
               __lowerCamelCase			)

           snake_case__       :   int	      =    self.default_image_processor
           snake_case__       :   Tuple	      =    prepare_img()
           snake_case__       :   List[Any]	      =    image_processor(images=__lowerCamelCase     ,							return_tensors='pt'			).to(__lowerCamelCase			)

           # forward pass
           with torch.no_grad():
                 snake_case__       :   Union[str, Any]	      =    model(**__lowerCamelCase			)
           snake_case__       :   Dict	      =    outputs.logits

           # verify the logits
           snake_case__       :   Optional[int]	      =    torch.Size((1, 21841)			)
           self.assertEqual(logits.shape     ,							__lowerCamelCase			)

           snake_case__       :   Dict	      =    torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]			).to(__lowerCamelCase			)

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

           snake_case__       :   Optional[Any]	      =    2396
           self.assertEqual(logits.argmax(-1			).item()     ,							__lowerCamelCase			)





     @slow
     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   Optional[Any]	      =    BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'			)
           snake_case__       :   str	      =    model.to(__lowerCamelCase			)

           snake_case__       :   List[str]	      =    BeitImageProcessor(do_resize=__lowerCamelCase     ,							size=640     ,							do_center_crop=__lowerCamelCase			)

           snake_case__       :   Tuple	      =    load_dataset('hf-internal-testing/fixtures_ade20k'     ,							split='test'			)
           snake_case__       :   Union[str, Any]	      =    Image.open(ds[0]['file']			)
           snake_case__       :   Tuple	      =    image_processor(images=__lowerCamelCase     ,							return_tensors='pt'			).to(__lowerCamelCase			)

           # forward pass
           with torch.no_grad():
                 snake_case__       :   List[str]	      =    model(**__lowerCamelCase			)
           snake_case__       :   Any	      =    outputs.logits

           # verify the logits
           snake_case__       :   Optional[int]	      =    torch.Size((1, 150, 160, 160)			)
           self.assertEqual(logits.shape     ,							__lowerCamelCase			)

           snake_case__       :   Tuple	      =    version.parse(PIL.__version__			) < version.parse('9.0.0'			)

           if is_pillow_less_than_a:
                 snake_case__       :   Optional[Any]	      =    torch.tensor(
                     [
                         [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
                         [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
                         [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
                     ]     ,							device=__lowerCamelCase     ,							)
           else:
                 snake_case__       :   Union[str, Any]	      =    torch.tensor(
                     [
                         [[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
                         [[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
                         [[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
                     ]     ,							device=__lowerCamelCase     ,							)

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





     @slow
     def 			_lowerCAmelCase      (     self     :					str			):
           snake_case__       :   Tuple	      =    BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'			)
           snake_case__       :   int	      =    model.to(__lowerCamelCase			)

           snake_case__       :   Optional[Any]	      =    BeitImageProcessor(do_resize=__lowerCamelCase     ,							size=640     ,							do_center_crop=__lowerCamelCase			)

           snake_case__       :   Tuple	      =    load_dataset('hf-internal-testing/fixtures_ade20k'     ,							split='test'			)
           snake_case__       :   Optional[int]	      =    Image.open(ds[0]['file']			)
           snake_case__       :   Optional[int]	      =    image_processor(images=__lowerCamelCase     ,							return_tensors='pt'			).to(__lowerCamelCase			)

           # forward pass
           with torch.no_grad():
                 snake_case__       :   Optional[int]	      =    model(**__lowerCamelCase			)

           snake_case__       :   Tuple	      =    outputs.logits.detach().cpu()

           snake_case__       :   Any	      =    image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase     ,							target_sizes=[(500, 300)]			)
           snake_case__       :   List[Any]	      =    torch.Size((500, 300)			)
           self.assertEqual(segmentation[0].shape     ,							__lowerCamelCase			)

           snake_case__       :   Tuple	      =    image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase			)
           snake_case__       :   Optional[Any]	      =    torch.Size((160, 160)			)
           self.assertEqual(segmentation[0].shape     ,							__lowerCamelCase			)
