


'''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    __snake_case       :







    """simple docstring"""



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

    def __lowercase   (   self    :  Any		)   ->  Dict:
      lowerCAmelCase_			:      str			=   floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
          lowerCamelCase		)

      lowerCAmelCase_			:      Any			=   torch.ones([self.batch_size, self.min_size, self.max_size]    ,						device=lowerCamelCase		)

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

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

    def __lowercase   (   self    :  Any		)   ->  Dict:
      return MaskFormerConfig.from_backbone_and_decoder_configs(
          backbone_config=SwinConfig(
              depths=[1, 1, 1, 1]    ,						)    ,						decoder_config=DetrConfig(
              decoder_ffn_dim=1_28    ,						num_queries=self.num_queries    ,						decoder_attention_heads=2    ,						d_model=self.mask_feature_size    ,						)    ,						mask_feature_size=self.mask_feature_size    ,						fpn_feature_size=self.mask_feature_size    ,						num_channels=self.num_channels    ,						num_labels=self.num_labels    ,						)

    def __lowercase   (   self    :  str		)   ->  str:
      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Dict			=   self.prepare_config_and_inputs()
      lowerCAmelCase_			:      int			=   {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
      return config, inputs_dict

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  Union[str, Any]		)   ->  Dict:
      lowerCAmelCase_			:      List[str]			=   output.encoder_hidden_states
      lowerCAmelCase_			:      Any			=   output.pixel_decoder_hidden_states
      lowerCAmelCase_			:      Union[str, Any]			=   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_config.decoder_layers		)

    def __lowercase   (   self    :  str    ,						lowerCamelCase    :  Union[str, Any]    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  Optional[int]    ,						lowerCamelCase    :  List[str]=False		)   ->  Any:
      with torch.no_grad():
        lowerCAmelCase_			:      List[Any]			=   MaskFormerModel(config=lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()

        lowerCAmelCase_			:      List[Any]			=   model(pixel_values=lowerCamelCase    ,						pixel_mask=lowerCamelCase		)
        lowerCAmelCase_			:      Optional[Any]			=   model(lowerCamelCase    ,						output_hidden_states=lowerCamelCase		)
      # 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(lowerCamelCase    ,						lowerCamelCase		)





    def __lowercase   (   self    :  List[str]    ,						lowerCamelCase    :  Optional[Any]    ,						lowerCamelCase    :  Optional[int]    ,						lowerCamelCase    :  Union[str, Any]    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  int		)   ->  List[Any]:
      lowerCAmelCase_			:      List[str]			=   MaskFormerForInstanceSegmentation(config=lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()

      def comm_check_on_output(lowerCamelCase    :  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=lowerCamelCase    ,						pixel_mask=lowerCamelCase		)
        lowerCAmelCase_			:      Any			=   model(lowerCamelCase		)

        comm_check_on_output(lowerCamelCase		)

        lowerCAmelCase_			:      Any			=   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    __snake_case       (   _SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE	,unittest.TestCase):







    """simple docstring"""



    lowercase    =					(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
    lowercase    =					(
        {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
        if is_torch_available()
        else {}
    )

    lowercase    =					False
    lowercase    =					False
    lowercase    =					False
    lowercase    =					False

    def __lowercase   (   self    :  List[Any]		)   ->  List[Any]:
      lowerCAmelCase_			:      int			=   MaskFormerModelTester(self		)
      lowerCAmelCase_			:      Optional[Any]			=   ConfigTester(self    ,						config_class=lowerCamelCase    ,						has_text_modality=lowerCamelCase		)

    def __lowercase   (   self    :  Any		)   ->  Any:
      self.config_tester.run_common_tests()

    def __lowercase   (   self    :  List[Any]		)   ->  str:
      lowerCAmelCase_,							lowerCAmelCase_			:      List[str]			=   self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskformer_model(lowerCamelCase    ,						**lowerCamelCase    ,						output_hidden_states=lowerCamelCase		)

    def __lowercase   (   self    :  Any		)   ->  Any:
      lowerCAmelCase_			:      Optional[int]			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*lowerCamelCase		)

    @unittest.skip(reason="""MaskFormer does not use inputs_embeds"""		)
    def __lowercase   (   self    :  str		)   ->  Union[str, Any]:
      pass

    @unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""		)
    def __lowercase   (   self    :  Union[str, Any]		)   ->  List[Any]:
      pass

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

    @unittest.skip(reason="""MaskFormer does not use token embeddings"""		)
    def __lowercase   (   self    :  Optional[int]		)   ->  Tuple:
      pass

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

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

    def __lowercase   (   self    :  int		)   ->  List[Any]:
      lowerCAmelCase_,							lowerCAmelCase_			:      Tuple			=   self.model_tester.prepare_config_and_inputs_for_common()

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

        lowerCAmelCase_			:      Tuple			=   ["""pixel_values"""]
        self.assertListEqual(arg_names[:1]    ,						lowerCamelCase		)

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

    def __lowercase   (   self    :  Tuple		)   ->  int:
      lowerCAmelCase_			:      str			=   (self.model_tester.min_size,) * 2
      lowerCAmelCase_			:      int			=   {
          """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(),
      }

      lowerCAmelCase_			:      Union[str, Any]			=   MaskFormerForInstanceSegmentation(MaskFormerConfig()		).to(lowerCamelCase		)
      lowerCAmelCase_			:      List[str]			=   model(**lowerCamelCase		)
      self.assertTrue(outputs.loss is not None		)

    def __lowercase   (   self    :  List[str]		)   ->  Tuple:
      lowerCAmelCase_,							lowerCAmelCase_			:      Dict			=   self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskformer_model(lowerCamelCase    ,						**lowerCamelCase    ,						output_hidden_states=lowerCamelCase		)

    def __lowercase   (   self    :  List[str]		)   ->  List[str]:
      lowerCAmelCase_,							lowerCAmelCase_			:      List[str]			=   self.model_tester.prepare_config_and_inputs_for_common()

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

    def __lowercase   (   self    :  str		)   ->  List[Any]:
      if not self.model_tester.is_training:
        return
      # only MaskFormerForInstanceSegmentation has the loss
      lowerCAmelCase_			:      str			=   self.all_model_classes[1]
      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Dict			=   self.model_tester.prepare_config_and_inputs()

      lowerCAmelCase_			:      Dict			=   model_class(lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.train()

      lowerCAmelCase_			:      Optional[Any]			=   model(lowerCamelCase    ,						mask_labels=lowerCamelCase    ,						class_labels=lowerCamelCase		).loss
      loss.backward()





    def __lowercase   (   self    :  Tuple		)   ->  int:
      # only MaskFormerForInstanceSegmentation has the loss
      lowerCAmelCase_			:      str			=   self.all_model_classes[1]
      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Any			=   self.model_tester.prepare_config_and_inputs()
      lowerCAmelCase_			:      int			=   True
      lowerCAmelCase_			:      Dict			=   True

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

      lowerCAmelCase_			:      Dict			=   model(lowerCamelCase    ,						mask_labels=lowerCamelCase    ,						class_labels=lowerCamelCase		)

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

      lowerCAmelCase_			:      Tuple			=   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_			:      str			=   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		)







__A					:  Dict											=      1E-4







def 				UpperCamelCase_			(    ):
  '''simple docstring'''





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






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







    """simple docstring"""



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

    def __lowercase   (   self    :  Any		)   ->  Tuple:
      lowerCAmelCase_			:      Dict			=   MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""		).to(lowerCamelCase		)
      lowerCAmelCase_			:      str			=   self.default_image_processor
      lowerCAmelCase_			:      Optional[int]			=   prepare_img()
      lowerCAmelCase_			:      str			=   image_processor(lowerCamelCase    ,						return_tensors="""pt"""		).to(lowerCamelCase		)
      lowerCAmelCase_			:      Optional[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, 8_00, 10_88)		)

      with torch.no_grad():
        lowerCAmelCase_			:      Any			=   model(**lowerCamelCase		)

      lowerCAmelCase_			:      Optional[Any]			=   torch.tensor(
          [[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]]		).to(lowerCamelCase		)
      self.assertTrue(
          torch.allclose(
              outputs.encoder_last_hidden_state[0, 0, :3, :3]    ,						lowerCamelCase    ,						atol=lowerCamelCase		)		)

      lowerCAmelCase_			:      Optional[Any]			=   torch.tensor(
          [[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]]		).to(lowerCamelCase		)
      self.assertTrue(
          torch.allclose(
              outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]    ,						lowerCamelCase    ,						atol=lowerCamelCase		)		)

      lowerCAmelCase_			:      Tuple			=   torch.tensor(
          [[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]]		).to(lowerCamelCase		)
      self.assertTrue(
          torch.allclose(
              outputs.transformer_decoder_last_hidden_state[0, :3, :3]    ,						lowerCamelCase    ,						atol=lowerCamelCase		)		)

    def __lowercase   (   self    :  Union[str, Any]		)   ->  Dict:
      lowerCAmelCase_			:      str			=   (
          MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""		)
          .to(lowerCamelCase		)
          .eval()
      )
      lowerCAmelCase_			:      Tuple			=   self.default_image_processor
      lowerCAmelCase_			:      Any			=   prepare_img()
      lowerCAmelCase_			:      Any			=   image_processor(lowerCamelCase    ,						return_tensors="""pt"""		).to(lowerCamelCase		)
      lowerCAmelCase_			:      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, 8_00, 10_88)		)

      with torch.no_grad():
        lowerCAmelCase_			:      Union[str, Any]			=   model(**lowerCamelCase		)
      # masks_queries_logits
      lowerCAmelCase_			:      Tuple			=   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_			:      Union[str, Any]			=   [
          [-1.3_737_124, -1.7_724_937, -1.9_364_233],
          [-1.5_977_281, -1.9_867_939, -2.1_523_695],
          [-1.5_795_398, -1.9_269_832, -2.093_942],
      ]
      lowerCAmelCase_			:      Any			=   torch.tensor(lowerCamelCase		).to(lowerCamelCase		)
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,						lowerCamelCase    ,						atol=lowerCamelCase		)		)
      # 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_			:      List[str]			=   torch.tensor(
          [
              [1.6_512E00, -5.2_572E00, -3.3_519E00],
              [3.6_169E-02, -5.9_025E00, -2.9_313E00],
              [1.0_766E-04, -7.7_630E00, -5.1_263E00],
          ]		).to(lowerCamelCase		)
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,						lowerCamelCase    ,						atol=lowerCamelCase		)		)

    def __lowercase   (   self    :  int		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Optional[int]			=   (
          MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""		)
          .to(lowerCamelCase		)
          .eval()
      )
      lowerCAmelCase_			:      Union[str, Any]			=   self.default_image_processor
      lowerCAmelCase_			:      int			=   prepare_img()
      lowerCAmelCase_			:      Union[str, Any]			=   image_processor(lowerCamelCase    ,						return_tensors="""pt"""		).to(lowerCamelCase		)
      lowerCAmelCase_			:      Optional[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, 8_00, 10_88)		)

      with torch.no_grad():
        lowerCAmelCase_			:      Tuple			=   model(**lowerCamelCase		)
      # masks_queries_logits
      lowerCAmelCase_			:      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_			:      Optional[Any]			=   [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
      lowerCAmelCase_			:      Tuple			=   torch.tensor(lowerCamelCase		).to(lowerCamelCase		)
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,						lowerCamelCase    ,						atol=lowerCamelCase		)		)
      # 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_			:      Optional[Any]			=   torch.tensor(
          [[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]]		).to(lowerCamelCase		)
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,						lowerCamelCase    ,						atol=lowerCamelCase		)		)





    def __lowercase   (   self    :  List[str]		)   ->  str:
      lowerCAmelCase_			:      Dict			=   (
          MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""		)
          .to(lowerCamelCase		)
          .eval()
      )
      lowerCAmelCase_			:      str			=   self.default_image_processor

      lowerCAmelCase_			:      Dict			=   image_processor(
          [np.zeros((3, 8_00, 13_33)		), np.zeros((3, 8_00, 13_33)		)]    ,						segmentation_maps=[np.zeros((3_84, 3_84)		).astype(np.floataa		), np.zeros((3_84, 3_84)		).astype(np.floataa		)]    ,						return_tensors="""pt"""    ,						)

      lowerCAmelCase_			:      Optional[int]			=   inputs["""pixel_values"""].to(lowerCamelCase		)
      lowerCAmelCase_			:      int			=   [el.to(lowerCamelCase		) for el in inputs["""mask_labels"""]]
      lowerCAmelCase_			:      Optional[Any]			=   [el.to(lowerCamelCase		) for el in inputs["""class_labels"""]]

      with torch.no_grad():
        lowerCAmelCase_			:      str			=   model(**lowerCamelCase		)

      self.assertTrue(outputs.loss is not None		)







'''simple docstring'''






def 				UpperCamelCase_			(    A__					:   list[list[float]]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[list[float]]			=   []
  for data in source_data:
    for i, el in enumerate(A__					):
      if len(A__					) < i + 1:
        data_lists.append([]					)
      data_lists[i].append(float(A__					)					)
  return data_lists







def 				UpperCamelCase_			(    A__					:   list[list[float]]							,    A__					:   list[int]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[list[float]]			=   []
  for dlist, weight in zip(A__							,    A__					):
    lowerCAmelCase_			:      Tuple			=   min(A__					)
    lowerCAmelCase_			:      str			=   max(A__					)

    lowerCAmelCase_			:      list[float]			=   []
    # for weight 0 score is 1 - actual score
    if weight == 0:
      for item in dlist:
        try:
          score.append(1 - ((item - mind) / (maxd - mind))					)
        except ZeroDivisionError:
          score.append(1					)

    elif weight == 1:
      for item in dlist:
        try:
          score.append((item - mind) / (maxd - mind)					)
        except ZeroDivisionError:
          score.append(0					)

        # weight not 0 or 1
    else:
      lowerCAmelCase_			:      List[Any]			=   f'Invalid weight of {weight:f} provided'
      raise ValueError(A__					)

    score_lists.append(A__					)

  return score_lists







def 				UpperCamelCase_			(    A__					:   list[list[float]]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[float]			=   [0 for i in range(len(score_lists[0]					)					)]

  for slist in score_lists:
    for j, ele in enumerate(A__					):
      lowerCAmelCase_			:      List[Any]			=   final_scores[j] + ele

  return final_scores







def 				UpperCamelCase_			(    A__					:   list[list[float]]							,    A__					:   list[int]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Optional[Any]			=   get_data(A__					)
  lowerCAmelCase_			:      Tuple			=   calculate_each_score(A__							,    A__					)
  lowerCAmelCase_			:      Optional[int]			=   generate_final_scores(A__					)

  # append scores to source data
  for i, ele in enumerate(A__					):
    source_data[i].append(A__					)

  return source_data



