




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   __a:






    """simple docstring"""





    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 * 4       ,_SCREAMING_SNAKE_CASE=32 * 6       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=32       ,)			->		Union[str, Any]:
         UpperCAmelCase_      :							Union[str, Any]       =      parent
         UpperCAmelCase_      :							Any       =      batch_size
         UpperCAmelCase_      :							Any       =      is_training
         UpperCAmelCase_      :							List[Any]       =      use_auxiliary_loss
         UpperCAmelCase_      :							Union[str, Any]       =      num_queries
         UpperCAmelCase_      :							List[Any]       =      num_channels
         UpperCAmelCase_      :							Any       =      min_size
         UpperCAmelCase_      :							Union[str, Any]       =      max_size
         UpperCAmelCase_      :							int       =      num_labels
         UpperCAmelCase_      :							Any       =      mask_feature_size





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Tuple       =      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]  ).to(
             _SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Dict       =      torch.ones([self.batch_size, self.min_size, self.max_size]       ,device=_SCREAMING_SNAKE_CASE  )

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

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





    def 							a__				(				self  )			->		Union[str, Any]:
         return MaskFormerConfig.from_backbone_and_decoder_configs(
             backbone_config=SwinConfig(
                 depths=[1, 1, 1, 1]       ,)       ,decoder_config=DetrConfig(
                 decoder_ffn_dim=128       ,num_queries=self.num_queries       ,decoder_attention_heads=2       ,d_model=self.mask_feature_size       ,)       ,mask_feature_size=self.mask_feature_size       ,fpn_feature_size=self.mask_feature_size       ,num_channels=self.num_channels       ,num_labels=self.num_labels       ,)





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Optional[int]       =      self.prepare_config_and_inputs()
         UpperCAmelCase_      :							Dict       =      {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
         return config, inputs_dict





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Optional[Any]:
         UpperCAmelCase_      :							List[str]       =      output.encoder_hidden_states
         UpperCAmelCase_      :							Any       =      output.pixel_decoder_hidden_states
         UpperCAmelCase_      :							List[Any]       =      output.transformer_decoder_hidden_states

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





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=False  )			->		Tuple:
         with torch.no_grad():
              UpperCAmelCase_      :							int       =      MaskFormerModel(config=_SCREAMING_SNAKE_CASE  )
              model.to(_SCREAMING_SNAKE_CASE  )
              model.eval()

              UpperCAmelCase_      :							str       =      model(pixel_values=_SCREAMING_SNAKE_CASE       ,pixel_mask=_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							int       =      model(_SCREAMING_SNAKE_CASE       ,output_hidden_states=_SCREAMING_SNAKE_CASE  )
         # 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(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         UpperCAmelCase_      :							List[str]       =      MaskFormerForInstanceSegmentation(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         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():
              UpperCAmelCase_      :							int       =      model(pixel_values=_SCREAMING_SNAKE_CASE       ,pixel_mask=_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							List[str]       =      model(_SCREAMING_SNAKE_CASE  )

              comm_check_on_output(_SCREAMING_SNAKE_CASE  )

              UpperCAmelCase_      :							Optional[int]       =      model(
                  pixel_values=_SCREAMING_SNAKE_CASE       ,pixel_mask=_SCREAMING_SNAKE_CASE       ,mask_labels=_SCREAMING_SNAKE_CASE       ,class_labels=_SCREAMING_SNAKE_CASE  )

         comm_check_on_output(_SCREAMING_SNAKE_CASE  )

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


@require_torch
class   __a(					_a    ,						_a    ,						unittest.TestCase       ):






    """simple docstring"""





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

    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							str       =      MaskFormerModelTester(self  )
         UpperCAmelCase_      :							List[Any]       =      ConfigTester(self       ,config_class=_SCREAMING_SNAKE_CASE       ,has_text_modality=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[Any]:
         self.config_tester.run_common_tests()





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





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Union[str, Any]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_SCREAMING_SNAKE_CASE  )





    @unittest.skip(reason='''MaskFormer does not use inputs_embeds'''  )
    def 							a__				(				self  )			->		int:
         pass





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





    @unittest.skip(reason='''MaskFormer is not a generative model'''  )
    def 							a__				(				self  )			->		List[Any]:
         pass





    @unittest.skip(reason='''MaskFormer does not use token embeddings'''  )
    def 							a__				(				self  )			->		str:
         pass





    @require_torch_multi_gpu
    @unittest.skip(
        reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''  )
    def 							a__				(				self  )			->		Tuple:
         pass





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





    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							int       =      self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase_      :							str       =      ['''pixel_values''']
              self.assertListEqual(arg_names[:1]       ,_SCREAMING_SNAKE_CASE  )





    @slow
    def 							a__				(				self  )			->		Any:
         for model_name in ["facebook/maskformer-swin-small-coco"]:
              UpperCAmelCase_      :							Optional[int]       =      MaskFormerModel.from_pretrained(_SCREAMING_SNAKE_CASE  )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							Optional[int]       =      (self.model_tester.min_size,) * 2
         UpperCAmelCase_      :							Optional[int]       =      {
             '''pixel_values''': torch.randn((2, 3, *size)       ,device=_SCREAMING_SNAKE_CASE  ),
             '''mask_labels''': torch.randn((2, 10, *size)       ,device=_SCREAMING_SNAKE_CASE  ),
             '''class_labels''': torch.zeros(2       ,10       ,device=_SCREAMING_SNAKE_CASE  ).long(),
         }

         UpperCAmelCase_      :							Union[str, Any]       =      MaskFormerForInstanceSegmentation(MaskFormerConfig()  ).to(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      model(**_SCREAMING_SNAKE_CASE  )
         self.assertTrue(outputs.loss is not None  )





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





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							Any       =      self.model_tester.prepare_config_and_inputs_for_common()

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





    def 							a__				(				self  )			->		List[Any]:
         if not self.model_tester.is_training:
              return
         # only MaskFormerForInstanceSegmentation has the loss
         UpperCAmelCase_      :							Any       =      self.all_model_classes[1]
         UpperCAmelCase_      :							int       =      self.model_tester.prepare_config_and_inputs()

         UpperCAmelCase_      :							Dict       =      model_class(_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.train()

         UpperCAmelCase_      :							List[Any]       =      model(_SCREAMING_SNAKE_CASE       ,mask_labels=_SCREAMING_SNAKE_CASE       ,class_labels=_SCREAMING_SNAKE_CASE  ).loss
         loss.backward()





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

         UpperCAmelCase_      :							str       =      model_class(_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.train()

         UpperCAmelCase_      :							List[Any]       =      model(_SCREAMING_SNAKE_CASE       ,mask_labels=_SCREAMING_SNAKE_CASE       ,class_labels=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Dict       =      outputs.encoder_hidden_states[0]
         encoder_hidden_states.retain_grad()

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

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

         outputs.loss.backward(retain_graph=_SCREAMING_SNAKE_CASE  )

         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          =			1E-4







def lowerCamelCase__       (      ):

     '''simple docstring'''


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



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






    """simple docstring"""





    @cached_property
    def 							a__				(				self  )			->		Any:
         return (
             MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''  )
             if is_vision_available()
             else None
         )





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							List[Any]       =      MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''  ).to(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[Any]       =      self.default_image_processor
         UpperCAmelCase_      :							int       =      prepare_img()
         UpperCAmelCase_      :							List[Any]       =      image_processor(_SCREAMING_SNAKE_CASE       ,return_tensors='''pt'''  ).to(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							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(_SCREAMING_SNAKE_CASE       ,(1, 3, 800, 1_088)  )

         with torch.no_grad():
              UpperCAmelCase_      :							int       =      model(**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      torch.tensor(
             [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]]  ).to(_SCREAMING_SNAKE_CASE  )
         self.assertTrue(
             torch.allclose(
                 outputs.encoder_last_hidden_state[0, 0, :3, :3]       ,_SCREAMING_SNAKE_CASE       ,atol=_SCREAMING_SNAKE_CASE  )  )

         UpperCAmelCase_      :							Dict       =      torch.tensor(
             [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]]  ).to(_SCREAMING_SNAKE_CASE  )
         self.assertTrue(
             torch.allclose(
                 outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]       ,_SCREAMING_SNAKE_CASE       ,atol=_SCREAMING_SNAKE_CASE  )  )

         UpperCAmelCase_      :							Optional[int]       =      torch.tensor(
             [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]]  ).to(_SCREAMING_SNAKE_CASE  )
         self.assertTrue(
             torch.allclose(
                 outputs.transformer_decoder_last_hidden_state[0, :3, :3]       ,_SCREAMING_SNAKE_CASE       ,atol=_SCREAMING_SNAKE_CASE  )  )





    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							Union[str, Any]       =      (
             MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''  )
             .to(_SCREAMING_SNAKE_CASE  )
             .eval()
         )
         UpperCAmelCase_      :							Any       =      self.default_image_processor
         UpperCAmelCase_      :							Optional[Any]       =      prepare_img()
         UpperCAmelCase_      :							Tuple       =      image_processor(_SCREAMING_SNAKE_CASE       ,return_tensors='''pt'''  ).to(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Dict       =      inputs['''pixel_values'''].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0  )
         # check size
         self.assertEqual(_SCREAMING_SNAKE_CASE       ,(1, 3, 800, 1_088)  )

         with torch.no_grad():
              UpperCAmelCase_      :							Any       =      model(**_SCREAMING_SNAKE_CASE  )
         # masks_queries_logits
         UpperCAmelCase_      :							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)       ,)
         UpperCAmelCase_      :							str       =      [
             [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
             [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
             [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
         ]
         UpperCAmelCase_      :							Tuple       =      torch.tensor(_SCREAMING_SNAKE_CASE  ).to(_SCREAMING_SNAKE_CASE  )
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,_SCREAMING_SNAKE_CASE       ,atol=_SCREAMING_SNAKE_CASE  )  )
         # class_queries_logits
         UpperCAmelCase_      :							Union[str, Any]       =      outputs.class_queries_logits
         self.assertEqual(
             class_queries_logits.shape       ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)  )
         UpperCAmelCase_      :							str       =      torch.tensor(
             [
                 [1.65_12e00, -5.25_72e00, -3.35_19e00],
                 [3.61_69e-02, -5.90_25e00, -2.93_13e00],
                 [1.07_66e-04, -7.76_30e00, -5.12_63e00],
             ]  ).to(_SCREAMING_SNAKE_CASE  )
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,_SCREAMING_SNAKE_CASE       ,atol=_SCREAMING_SNAKE_CASE  )  )





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Any       =      (
             MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''  )
             .to(_SCREAMING_SNAKE_CASE  )
             .eval()
         )
         UpperCAmelCase_      :							Optional[int]       =      self.default_image_processor
         UpperCAmelCase_      :							Any       =      prepare_img()
         UpperCAmelCase_      :							Tuple       =      image_processor(_SCREAMING_SNAKE_CASE       ,return_tensors='''pt'''  ).to(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, 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(_SCREAMING_SNAKE_CASE       ,(1, 3, 800, 1_088)  )

         with torch.no_grad():
              UpperCAmelCase_      :							int       =      model(**_SCREAMING_SNAKE_CASE  )
         # masks_queries_logits
         UpperCAmelCase_      :							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)       ,)
         UpperCAmelCase_      :							str       =      [[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
         UpperCAmelCase_      :							Any       =      torch.tensor(_SCREAMING_SNAKE_CASE  ).to(_SCREAMING_SNAKE_CASE  )
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,_SCREAMING_SNAKE_CASE       ,atol=_SCREAMING_SNAKE_CASE  )  )
         # class_queries_logits
         UpperCAmelCase_      :							Tuple       =      outputs.class_queries_logits
         self.assertEqual(
             class_queries_logits.shape       ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)  )
         UpperCAmelCase_      :							Optional[Any]       =      torch.tensor(
             [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]]  ).to(_SCREAMING_SNAKE_CASE  )
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,_SCREAMING_SNAKE_CASE       ,atol=_SCREAMING_SNAKE_CASE  )  )





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							int       =      (
             MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''  )
             .to(_SCREAMING_SNAKE_CASE  )
             .eval()
         )
         UpperCAmelCase_      :							Union[str, Any]       =      self.default_image_processor

         UpperCAmelCase_      :							Union[str, Any]       =      image_processor(
             [np.zeros((3, 800, 1_333)  ), np.zeros((3, 800, 1_333)  )]       ,segmentation_maps=[np.zeros((384, 384)  ).astype(np.floataa  ), np.zeros((384, 384)  ).astype(np.floataa  )]       ,return_tensors='''pt'''       ,)

         UpperCAmelCase_      :							List[Any]       =      inputs['''pixel_values'''].to(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      [el.to(_SCREAMING_SNAKE_CASE  ) for el in inputs['''mask_labels''']]
         UpperCAmelCase_      :							Optional[Any]       =      [el.to(_SCREAMING_SNAKE_CASE  ) for el in inputs['''class_labels''']]

         with torch.no_grad():
              UpperCAmelCase_      :							Dict       =      model(**_SCREAMING_SNAKE_CASE  )

         self.assertTrue(outputs.loss is not None  )





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


__a          =			logging.get_logger(__name__)







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if isinstance(_lowercase			,						(list, tuple)				) and isinstance(videos[0]			,						(list, tuple)				) and is_valid_image(videos[0][0]				):
          return videos

     elif isinstance(_lowercase			,						(list, tuple)				) and is_valid_image(videos[0]				):
          return [videos]

     elif is_valid_image(_lowercase				):
          return [[videos]]

     raise ValueError(f'''Could not make batched video from {videos}'''				)



class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				['''pixel_values''']





    def __init__(				self       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = 1 / 255       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		None:
         super().__init__(**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      size if size is not None else {'''shortest_edge''': 224}
         UpperCAmelCase_      :							Any       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         UpperCAmelCase_      :							List[str]       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,param_name='''crop_size'''  )

         UpperCAmelCase_      :							str       =      do_resize
         UpperCAmelCase_      :							Union[str, Any]       =      size
         UpperCAmelCase_      :							int       =      do_center_crop
         UpperCAmelCase_      :							List[str]       =      crop_size
         UpperCAmelCase_      :							Optional[int]       =      resample
         UpperCAmelCase_      :							List[Any]       =      do_rescale
         UpperCAmelCase_      :							Tuple       =      rescale_factor
         UpperCAmelCase_      :							Optional[Any]       =      do_normalize
         UpperCAmelCase_      :							int       =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase_      :							Optional[int]       =      image_std if image_std is not None else IMAGENET_STANDARD_STD





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		np.ndarray:
         UpperCAmelCase_      :							Optional[int]       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         if "shortest_edge" in size:
              UpperCAmelCase_      :							Dict       =      get_resize_output_image_size(_SCREAMING_SNAKE_CASE       ,size['''shortest_edge''']       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         elif "height" in size and "width" in size:
              UpperCAmelCase_      :							Tuple       =      (size['''height'''], size['''width'''])
         else:
              raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''  )
         return resize(_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE       ,resample=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		np.ndarray:
         UpperCAmelCase_      :							str       =      get_size_dict(_SCREAMING_SNAKE_CASE  )
         if "height" not in size or "width" not in size:
              raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''  )
         return center_crop(_SCREAMING_SNAKE_CASE       ,size=(size['''height'''], size['''width'''])       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		Dict:
         return rescale(_SCREAMING_SNAKE_CASE       ,scale=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		np.ndarray:
         return normalize(_SCREAMING_SNAKE_CASE       ,mean=_SCREAMING_SNAKE_CASE       ,std=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST       ,)			->		np.ndarray:
         if do_resize and size is None or resample is None:
              raise ValueError('''Size and resample must be specified if do_resize is True.'''  )

         if do_center_crop and crop_size is None:
              raise ValueError('''Crop size must be specified if do_center_crop is True.'''  )

         if do_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

         if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('''Image mean and std must be specified if do_normalize is True.'''  )

         # All transformations expect numpy arrays.
         UpperCAmelCase_      :							Any       =      to_numpy_array(_SCREAMING_SNAKE_CASE  )

         if do_resize:
              UpperCAmelCase_      :							Union[str, Any]       =      self.resize(image=_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE       ,resample=_SCREAMING_SNAKE_CASE  )

         if do_center_crop:
              UpperCAmelCase_      :							Optional[int]       =      self.center_crop(_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE  )

         if do_rescale:
              UpperCAmelCase_      :							str       =      self.rescale(image=_SCREAMING_SNAKE_CASE       ,scale=_SCREAMING_SNAKE_CASE  )

         if do_normalize:
              UpperCAmelCase_      :							List[Any]       =      self.normalize(image=_SCREAMING_SNAKE_CASE       ,mean=_SCREAMING_SNAKE_CASE       ,std=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[int]       =      to_channel_dimension_format(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
         return image





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST       ,**_SCREAMING_SNAKE_CASE       ,)			->		PIL.Image.Image:
         UpperCAmelCase_      :							Dict       =      do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_      :							int       =      resample if resample is not None else self.resample
         UpperCAmelCase_      :							List[str]       =      do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_      :							Optional[Any]       =      do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_      :							List[Any]       =      rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_      :							Tuple       =      do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_      :							Optional[int]       =      image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_      :							Optional[int]       =      image_std if image_std is not None else self.image_std

         UpperCAmelCase_      :							List[str]       =      size if size is not None else self.size
         UpperCAmelCase_      :							Optional[int]       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_      :							Any       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,param_name='''crop_size'''  )

         if not valid_images(_SCREAMING_SNAKE_CASE  ):
              raise ValueError(
                  '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                  '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

         UpperCAmelCase_      :							List[Any]       =      make_batched(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							str       =      [
             [
                 self._preprocess_image(
                     image=_SCREAMING_SNAKE_CASE       ,do_resize=_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE       ,resample=_SCREAMING_SNAKE_CASE       ,do_center_crop=_SCREAMING_SNAKE_CASE       ,crop_size=_SCREAMING_SNAKE_CASE       ,do_rescale=_SCREAMING_SNAKE_CASE       ,rescale_factor=_SCREAMING_SNAKE_CASE       ,do_normalize=_SCREAMING_SNAKE_CASE       ,image_mean=_SCREAMING_SNAKE_CASE       ,image_std=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,)
                 for img in video
             ]
             for video in videos
         ]

         UpperCAmelCase_      :							Any       =      {'''pixel_values''': videos}
         return BatchFeature(data=_SCREAMING_SNAKE_CASE       ,tensor_type=_SCREAMING_SNAKE_CASE  )