



'''simple docstring'''





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 	lowerCAmelCase_:
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase=2  ,__UpperCAmelCase=True  ,__UpperCAmelCase=False  ,__UpperCAmelCase=10  ,__UpperCAmelCase=3  ,__UpperCAmelCase=32 * 8  ,__UpperCAmelCase=32 * 8  ,__UpperCAmelCase=4  ,__UpperCAmelCase=64  ,)   ->		Tuple:
      lowerCAmelCase__			:  List[Any]											= parent
      lowerCAmelCase__			:  Union[str, Any]											= batch_size
      lowerCAmelCase__			:  List[str]											= is_training
      lowerCAmelCase__			:  Optional[int]											= use_auxiliary_loss
      lowerCAmelCase__			:  Dict											= num_queries
      lowerCAmelCase__			:  Optional[int]											= num_channels
      lowerCAmelCase__			:  Optional[Any]											= min_size
      lowerCAmelCase__			:  str											= max_size
      lowerCAmelCase__			:  Any											= num_labels
      lowerCAmelCase__			:  Optional[Any]											= hidden_dim
      lowerCAmelCase__			:  Any											= hidden_dim




  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__			:  Tuple											= floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]				).to(
          __UpperCAmelCase				)

      lowerCAmelCase__			:  str											= torch.ones([self.batch_size, self.min_size, self.max_size]  ,device=__UpperCAmelCase				)

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

      lowerCAmelCase__			:  Any											= self.get_config()
      return config, pixel_values, pixel_mask, mask_labels, class_labels




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  int											= MaskaFormerConfig(
          hidden_size=self.hidden_dim  ,)
      lowerCAmelCase__			:  int											= self.num_queries
      lowerCAmelCase__			:  Optional[int]											= self.num_labels

      lowerCAmelCase__			:  Optional[Any]											= [1, 1, 1, 1]
      lowerCAmelCase__			:  Optional[int]											= self.num_channels

      lowerCAmelCase__			:  Dict											= 64
      lowerCAmelCase__			:  Any											= 128
      lowerCAmelCase__			:  Dict											= self.hidden_dim
      lowerCAmelCase__			:  List[str]											= self.hidden_dim
      lowerCAmelCase__			:  List[str]											= self.hidden_dim
      return config




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__			:  Union[str, Any]											= self.prepare_config_and_inputs()
      lowerCAmelCase__			:  Dict											= {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
      return config, inputs_dict




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Optional[Any]:
      lowerCAmelCase__			:  Optional[Any]											= output.encoder_hidden_states
      lowerCAmelCase__			:  int											= output.pixel_decoder_hidden_states
      lowerCAmelCase__			:  Any											= output.transformer_decoder_hidden_states

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




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=False				)   ->		Optional[Any]:
      with torch.no_grad():
          lowerCAmelCase__			:  str											= MaskaFormerModel(config=__UpperCAmelCase				)
          model.to(__UpperCAmelCase				)
          model.eval()

          lowerCAmelCase__			:  Tuple											= model(pixel_values=__UpperCAmelCase  ,pixel_mask=__UpperCAmelCase				)
          lowerCAmelCase__			:  List[str]											= model(__UpperCAmelCase  ,output_hidden_states=__UpperCAmelCase				)

      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(__UpperCAmelCase  ,__UpperCAmelCase				)






  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Any:
      lowerCAmelCase__			:  Dict											= MaskaFormerForUniversalSegmentation(config=__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.eval()

      def comm_check_on_output(__UpperCAmelCase				):
          # 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__			:  Optional[Any]											= model(pixel_values=__UpperCAmelCase  ,pixel_mask=__UpperCAmelCase				)
          lowerCAmelCase__			:  Dict											= model(__UpperCAmelCase				)

          comm_check_on_output(__UpperCAmelCase				)

          lowerCAmelCase__			:  str											= model(
              pixel_values=__UpperCAmelCase  ,pixel_mask=__UpperCAmelCase  ,mask_labels=__UpperCAmelCase  ,class_labels=__UpperCAmelCase				)

      comm_check_on_output(__UpperCAmelCase				)

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







@require_torch
class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				int           =				(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  __lowercase							:				Optional[Any]           =				{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

  __lowercase							:				List[Any]           =				False
  __lowercase							:				Union[str, Any]           =				False
  __lowercase							:				Tuple           =				False
  __lowercase							:				Dict           =				False




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  Tuple											= MaskaFormerModelTester(self				)
      lowerCAmelCase__			:  Dict											= ConfigTester(self  ,config_class=__UpperCAmelCase  ,has_text_modality=__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      self.config_tester.run_common_tests()




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskaformer_model(__UpperCAmelCase  ,**__UpperCAmelCase  ,output_hidden_states=__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__			:  List[str]											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__UpperCAmelCase				)




  @unittest.skip(reason="""Mask2Former does not use inputs_embeds"""				)
  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      pass




  @unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""				)
  def 							UpperCAmelCase_     (   self				)   ->		str:
      pass




  @unittest.skip(reason="""Mask2Former is not a generative model"""				)
  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      pass




  @unittest.skip(reason="""Mask2Former does not use token embeddings"""				)
  def 							UpperCAmelCase_     (   self				)   ->		str:
      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				)   ->		Union[str, Any]:
      pass




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




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__				,       lowerCAmelCase__			:  str											= self.model_tester.prepare_config_and_inputs_for_common()

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

          lowerCAmelCase__			:  str											= ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]  ,__UpperCAmelCase				)




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
          lowerCAmelCase__			:  int											= MaskaFormerModel.from_pretrained(__UpperCAmelCase				)
          self.assertIsNotNone(__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      lowerCAmelCase__			:  str											= (self.model_tester.min_size,) * 2
      lowerCAmelCase__			:  Optional[int]											= {
          """pixel_values""": torch.randn((2, 3, *size)  ,device=__UpperCAmelCase				),
          """mask_labels""": torch.randn((2, 10, *size)  ,device=__UpperCAmelCase				),
          """class_labels""": torch.zeros(2  ,10  ,device=__UpperCAmelCase				).long(),
      }
      lowerCAmelCase__			:  Optional[int]											= self.model_tester.get_config()

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




  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__				,       lowerCAmelCase__			:  Optional[int]											= self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskaformer_model(__UpperCAmelCase  ,**__UpperCAmelCase  ,output_hidden_states=__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__				,       lowerCAmelCase__			:  List[str]											= self.model_tester.prepare_config_and_inputs_for_common()

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




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      if not self.model_tester.is_training:
          return

      lowerCAmelCase__			:  int											= self.all_model_classes[1]
      lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__			:  List[str]											= self.model_tester.prepare_config_and_inputs()

      lowerCAmelCase__			:  str											= model_class(__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.train()

      lowerCAmelCase__			:  Optional[Any]											= model(__UpperCAmelCase  ,mask_labels=__UpperCAmelCase  ,class_labels=__UpperCAmelCase				).loss
      loss.backward()






  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  Union[str, Any]											= self.all_model_classes[1]
      lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__			:  Union[str, Any]											= self.model_tester.prepare_config_and_inputs()
      lowerCAmelCase__			:  Dict											= True
      lowerCAmelCase__			:  Tuple											= True

      lowerCAmelCase__			:  int											= model_class(__UpperCAmelCase				).to(__UpperCAmelCase				)
      model.train()

      lowerCAmelCase__			:  str											= model(__UpperCAmelCase  ,mask_labels=__UpperCAmelCase  ,class_labels=__UpperCAmelCase				)

      lowerCAmelCase__			:  Tuple											= outputs.encoder_hidden_states[0]
      encoder_hidden_states.retain_grad()

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

      lowerCAmelCase__			:  List[str]											= outputs.transformer_decoder_hidden_states[0]
      transformer_decoder_hidden_states.retain_grad()

      lowerCAmelCase__			:  List[Any]											= outputs.attentions[0]
      attentions.retain_grad()

      outputs.loss.backward(retain_graph=__UpperCAmelCase				)

      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										=     1e-4
def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





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






@require_vision
@slow
class 	lowerCAmelCase_(    unittest.TestCase					):
  '''simple docstring'''




  @cached_property
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      return "facebook/mask2former-swin-small-coco-instance"




  @cached_property
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints				) if is_vision_available() else None




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  Any											= MaskaFormerModel.from_pretrained(self.model_checkpoints				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  List[str]											= self.default_image_processor
      lowerCAmelCase__			:  str											= prepare_img()
      lowerCAmelCase__			:  Union[str, Any]											= image_processor(__UpperCAmelCase  ,return_tensors="""pt"""				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  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(__UpperCAmelCase  ,(1, 3, 384, 384)				)

      with torch.no_grad():
          lowerCAmelCase__			:  List[Any]											= model(**__UpperCAmelCase				)

      lowerCAmelCase__			:  Tuple											= torch.tensor(
          [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]				).to(__UpperCAmelCase				)
      self.assertTrue(
          torch.allclose(
              outputs.encoder_last_hidden_state[0, 0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase				)				)

      lowerCAmelCase__			:  List[Any]											= torch.tensor(
          [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]				).to(__UpperCAmelCase				)
      self.assertTrue(
          torch.allclose(
              outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase				)				)

      lowerCAmelCase__			:  Optional[Any]											= torch.tensor(
          [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]				).to(__UpperCAmelCase				)
      self.assertTrue(
          torch.allclose(
              outputs.transformer_decoder_last_hidden_state[0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase				)				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      lowerCAmelCase__			:  Any											= MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(__UpperCAmelCase				).eval()
      lowerCAmelCase__			:  List[str]											= self.default_image_processor
      lowerCAmelCase__			:  str											= prepare_img()
      lowerCAmelCase__			:  List[Any]											= image_processor(__UpperCAmelCase  ,return_tensors="""pt"""				).to(__UpperCAmelCase				)
      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(__UpperCAmelCase  ,(1, 3, 384, 384)				)

      with torch.no_grad():
          lowerCAmelCase__			:  Dict											= model(**__UpperCAmelCase				)
      # masks_queries_logits
      lowerCAmelCase__			:  str											= outputs.masks_queries_logits
      self.assertEqual(
          masks_queries_logits.shape  ,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)				)
      lowerCAmelCase__			:  List[Any]											= [
          [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
          [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
          [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
      ]
      lowerCAmelCase__			:  int											= torch.tensor(__UpperCAmelCase				).to(__UpperCAmelCase				)
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase				)				)
      # class_queries_logits
      lowerCAmelCase__			:  str											= outputs.class_queries_logits
      self.assertEqual(class_queries_logits.shape  ,(1, model.config.num_queries, model.config.num_labels + 1)				)
      lowerCAmelCase__			:  Optional[Any]											= torch.tensor(
          [
              [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
              [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
              [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
          ]				).to(__UpperCAmelCase				)
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase				)				)






  def 							UpperCAmelCase_     (   self				)   ->		int:
      lowerCAmelCase__			:  Tuple											= MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(__UpperCAmelCase				).eval()
      lowerCAmelCase__			:  List[Any]											= self.default_image_processor

      lowerCAmelCase__			:  List[str]											= 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"""  ,)

      lowerCAmelCase__			:  Any											= inputs["""pixel_values"""].to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Union[str, Any]											= [el.to(__UpperCAmelCase				) for el in inputs["""mask_labels"""]]
      lowerCAmelCase__			:  Optional[Any]											= [el.to(__UpperCAmelCase				) for el in inputs["""class_labels"""]]

      with torch.no_grad():
          lowerCAmelCase__			:  int											= model(**__UpperCAmelCase				)

      self.assertTrue(outputs.loss is not None				)







'''simple docstring'''





from PIL import Image
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= image.size
    lowerCAmelCase__			:  int											= 0
    lowerCAmelCase__			:  int											= image.load()
    for i in range(UpperCamelCase      ):
        for j in range(UpperCamelCase      ):
            lowerCAmelCase__			:  int											= pixels[j, i]
            mean += pixel
    mean //= width * height

    for j in range(UpperCamelCase      ):
        for i in range(UpperCamelCase      ):
            lowerCAmelCase__			:  Dict											= 255 if pixels[i, j] > mean else 0
    return image


if __name__ == "__main__":
 _lowerCAmelCase										=     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
 image.save('''output_image_path''')


