

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 			_A		:

  def __init__(					self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=2			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE=False			,       _SCREAMING_SNAKE_CASE=10			,       _SCREAMING_SNAKE_CASE=3			,       _SCREAMING_SNAKE_CASE=32 * 8			,       _SCREAMING_SNAKE_CASE=32 * 8			,       _SCREAMING_SNAKE_CASE=4			,       _SCREAMING_SNAKE_CASE=64			,       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Any     							=  parent
        SCREAMING_SNAKE_CASE_					:	str     							=  batch_size
        SCREAMING_SNAKE_CASE_					:	Tuple     							=  is_training
        SCREAMING_SNAKE_CASE_					:	Tuple     							=  use_auxiliary_loss
        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  num_queries
        SCREAMING_SNAKE_CASE_					:	Dict     							=  num_channels
        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  min_size
        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  max_size
        SCREAMING_SNAKE_CASE_					:	Dict     							=  num_labels
        SCREAMING_SNAKE_CASE_					:	List[Any]     							=  hidden_dim
        SCREAMING_SNAKE_CASE_					:	str     							=  hidden_dim

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	List[Any]     							=  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
            __lowerCAmelCase       )

        SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  torch.ones([self.batch_size, self.min_size, self.max_size]			,       device=__lowerCAmelCase       )

        SCREAMING_SNAKE_CASE_					:	Tuple     							=  (
            torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]			,       device=__lowerCAmelCase       ) > 0.5
        ).float()
        SCREAMING_SNAKE_CASE_					:	Tuple     							=  (torch.rand((self.batch_size, self.num_labels)			,       device=__lowerCAmelCase       ) > 0.5).long()

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

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	str     							=  MaskaFormerConfig(
            hidden_size=self.hidden_dim			,       )
        SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  self.num_queries
        SCREAMING_SNAKE_CASE_					:	Dict     							=  self.num_labels

        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  [1, 1, 1, 1]
        SCREAMING_SNAKE_CASE_					:	Any     							=  self.num_channels

        SCREAMING_SNAKE_CASE_					:	List[Any]     							=  64
        SCREAMING_SNAKE_CASE_					:	List[str]     							=  128
        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  self.hidden_dim
        SCREAMING_SNAKE_CASE_					:	List[str]     							=  self.hidden_dim
        SCREAMING_SNAKE_CASE_					:	str     							=  self.hidden_dim
        return config

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_					:	Any     							=  self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE_					:	str     							=  {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
        return config, inputs_dict

  def 			UpperCAmelCase     (					self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Dict     							=  output.encoder_hidden_states
        SCREAMING_SNAKE_CASE_					:	Dict     							=  output.pixel_decoder_hidden_states
        SCREAMING_SNAKE_CASE_					:	List[str]     							=  output.transformer_decoder_hidden_states

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

  def 			UpperCAmelCase     (					self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=False       ):





        """simple docstring"""







        with torch.no_grad():
              SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  MaskaFormerModel(config=__lowerCAmelCase       )
              model.to(__lowerCAmelCase       )
              model.eval()

              SCREAMING_SNAKE_CASE_					:	Dict     							=  model(pixel_values=__lowerCAmelCase			,       pixel_mask=__lowerCAmelCase       )
              SCREAMING_SNAKE_CASE_					:	List[str]     							=  model(__lowerCAmelCase			,       output_hidden_states=__lowerCAmelCase       )

        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(__lowerCAmelCase			,       __lowerCAmelCase       )



  def 			UpperCAmelCase     (					self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  MaskaFormerForUniversalSegmentation(config=__lowerCAmelCase       )
        model.to(__lowerCAmelCase       )
        model.eval()

        def comm_check_on_output(_SCREAMING_SNAKE_CASE       ):
              # 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():
              SCREAMING_SNAKE_CASE_					:	int     							=  model(pixel_values=__lowerCAmelCase			,       pixel_mask=__lowerCAmelCase       )
              SCREAMING_SNAKE_CASE_					:	List[Any]     							=  model(__lowerCAmelCase       )

              comm_check_on_output(__lowerCAmelCase       )

              SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  model(
                  pixel_values=__lowerCAmelCase			,       pixel_mask=__lowerCAmelCase			,       mask_labels=__lowerCAmelCase			,       class_labels=__lowerCAmelCase       )

        comm_check_on_output(__lowerCAmelCase       )

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

@require_torch
class 			_A		(	__magic_name__ ,  __magic_name__ ,  unittest.TestCase):
  SCREAMING_SNAKE_CASE     :   List[str]							=	(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE     :   str							=	{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

  SCREAMING_SNAKE_CASE     :   Any							=	False
  SCREAMING_SNAKE_CASE     :   Optional[Any]							=	False
  SCREAMING_SNAKE_CASE     :   Dict							=	False
  SCREAMING_SNAKE_CASE     :   Any							=	False

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	str     							=  MaskaFormerModelTester(self       )
        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  ConfigTester(self			,       config_class=__lowerCAmelCase			,       has_text_modality=__lowerCAmelCase       )

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        self.config_tester.run_common_tests()

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_					:	Any     							=  self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(__lowerCAmelCase			,       **__lowerCAmelCase			,       output_hidden_states=__lowerCAmelCase       )

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	str     							=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__lowerCAmelCase       )

  @unittest.skip(reason='Mask2Former does not use inputs_embeds'       )
  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        pass

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





        """simple docstring"""







        pass

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





        """simple docstring"""







        pass

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





        """simple docstring"""







        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       ):





        """simple docstring"""







        pass

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





        """simple docstring"""







        pass

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_					:	Any     							=  self.model_tester.prepare_config_and_inputs_for_common()

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

              SCREAMING_SNAKE_CASE_					:	List[Any]     							=  ['pixel_values']
              self.assertListEqual(arg_names[:1]			,       __lowerCAmelCase       )

  @slow
  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
              SCREAMING_SNAKE_CASE_					:	Dict     							=  MaskaFormerModel.from_pretrained(__lowerCAmelCase       )
              self.assertIsNotNone(__lowerCAmelCase       )

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Tuple     							=  (self.model_tester.min_size,) * 2
        SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  {
            'pixel_values': torch.randn((2, 3, *size)			,       device=__lowerCAmelCase       ),
            'mask_labels': torch.randn((2, 10, *size)			,       device=__lowerCAmelCase       ),
            'class_labels': torch.zeros(2			,       10			,       device=__lowerCAmelCase       ).long(),
        }
        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  self.model_tester.get_config()

        SCREAMING_SNAKE_CASE_					:	Any     							=  MaskaFormerForUniversalSegmentation(__lowerCAmelCase       ).to(__lowerCAmelCase       )
        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  model(**__lowerCAmelCase       )
        self.assertTrue(outputs.loss is not None       )

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_					:	List[Any]     							=  self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(__lowerCAmelCase			,       **__lowerCAmelCase			,       output_hidden_states=__lowerCAmelCase       )

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_					:	Dict     							=  model_class(__lowerCAmelCase       ).to(__lowerCAmelCase       )
              SCREAMING_SNAKE_CASE_					:	Tuple     							=  model(**__lowerCAmelCase			,       output_attentions=__lowerCAmelCase       )
              self.assertTrue(outputs.attentions is not None       )

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        if not self.model_tester.is_training:
              return

        SCREAMING_SNAKE_CASE_					:	int     							=  self.all_model_classes[1]
        SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_					:	Any     							=  self.model_tester.prepare_config_and_inputs()

        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  model_class(__lowerCAmelCase       )
        model.to(__lowerCAmelCase       )
        model.train()

        SCREAMING_SNAKE_CASE_					:	Any     							=  model(__lowerCAmelCase			,       mask_labels=__lowerCAmelCase			,       class_labels=__lowerCAmelCase       ).loss
        loss.backward()



  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Dict     							=  self.all_model_classes[1]
        SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_					:	Tuple     							=  self.model_tester.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE_					:	Any     							=  True
        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  True

        SCREAMING_SNAKE_CASE_					:	Any     							=  model_class(__lowerCAmelCase       ).to(__lowerCAmelCase       )
        model.train()

        SCREAMING_SNAKE_CASE_					:	Dict     							=  model(__lowerCAmelCase			,       mask_labels=__lowerCAmelCase			,       class_labels=__lowerCAmelCase       )

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

        SCREAMING_SNAKE_CASE_					:	Dict     							=  outputs.pixel_decoder_hidden_states[0]
        pixel_decoder_hidden_states.retain_grad()

        SCREAMING_SNAKE_CASE_					:	int     							=  outputs.transformer_decoder_hidden_states[0]
        transformer_decoder_hidden_states.retain_grad()

        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  outputs.attentions[0]
        attentions.retain_grad()

        outputs.loss.backward(retain_graph=__lowerCAmelCase       )

        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  : Dict      =	1e-4

def  A_	(				):

      """simple docstring"""



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



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

  @cached_property
  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        return "facebook/mask2former-swin-small-coco-instance"

  @cached_property
  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints       ) if is_vision_available() else None

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	str     							=  MaskaFormerModel.from_pretrained(self.model_checkpoints       ).to(__lowerCAmelCase       )
        SCREAMING_SNAKE_CASE_					:	Dict     							=  self.default_image_processor
        SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  prepare_img()
        SCREAMING_SNAKE_CASE_					:	Tuple     							=  image_processor(__lowerCAmelCase			,       return_tensors='pt'       ).to(__lowerCAmelCase       )
        SCREAMING_SNAKE_CASE_					:	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(__lowerCAmelCase			,       (1, 3, 384, 384)       )

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

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

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

        SCREAMING_SNAKE_CASE_					:	List[Any]     							=  torch.tensor(
            [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]       ).to(__lowerCAmelCase       )
        self.assertTrue(
            torch.allclose(
                outputs.transformer_decoder_last_hidden_state[0, :3, :3]			,       __lowerCAmelCase			,       atol=__lowerCAmelCase       )       )

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(__lowerCAmelCase       ).eval()
        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  self.default_image_processor
        SCREAMING_SNAKE_CASE_					:	List[Any]     							=  prepare_img()
        SCREAMING_SNAKE_CASE_					:	Tuple     							=  image_processor(__lowerCAmelCase			,       return_tensors='pt'       ).to(__lowerCAmelCase       )
        SCREAMING_SNAKE_CASE_					:	List[Any]     							=  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(__lowerCAmelCase			,       (1, 3, 384, 384)       )

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



  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	str     							=  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(__lowerCAmelCase       ).eval()
        SCREAMING_SNAKE_CASE_					:	List[Any]     							=  self.default_image_processor

        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  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'			,       )

        SCREAMING_SNAKE_CASE_					:	List[str]     							=  inputs['pixel_values'].to(__lowerCAmelCase       )
        SCREAMING_SNAKE_CASE_					:	str     							=  [el.to(__lowerCAmelCase       ) for el in inputs['mask_labels']]
        SCREAMING_SNAKE_CASE_					:	Any     							=  [el.to(__lowerCAmelCase       ) for el in inputs['class_labels']]

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

        self.assertTrue(outputs.loss is not None       )













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A       :   Dict					= {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Dict					= ['''LayoutXLMTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Dict					= ['''LayoutXLMTokenizerFast''']

if TYPE_CHECKING:
					from .processing_layoutxlm import LayoutXLMProcessor

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutxlm import LayoutXLMTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
					import sys

					A       :   int					= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



