






'''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        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    ,	)						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      parent
        UpperCamelCase       						=      batch_size
        UpperCamelCase       						=      is_training
        UpperCamelCase       						=      use_auxiliary_loss
        UpperCamelCase       						=      num_queries
        UpperCamelCase       						=      num_channels
        UpperCamelCase       						=      min_size
        UpperCamelCase       						=      max_size
        UpperCamelCase       						=      num_labels
        UpperCamelCase       						=      hidden_dim
        UpperCamelCase       						=      hidden_dim

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]   ).to(
            _SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      torch.ones([self.batch_size, self.min_size, self.max_size]    ,	device=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      (
            torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]    ,	device=_SCREAMING_SNAKE_CASE   ) > 0.5
        ).float()
        UpperCamelCase       						=      (torch.rand((self.batch_size, self.num_labels)    ,	device=_SCREAMING_SNAKE_CASE   ) > 0.5).long()

        UpperCamelCase       						=      self.get_config()
        return config, pixel_values, pixel_mask, mask_labels, class_labels

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      MaskaFormerConfig(
            hidden_size=self.hidden_dim    ,	)
        UpperCamelCase       						=      self.num_queries
        UpperCamelCase       						=      self.num_labels

        UpperCamelCase       						=      [1, 1, 1, 1]
        UpperCamelCase       						=      self.num_channels

        UpperCamelCase       						=      64
        UpperCamelCase       						=      128
        UpperCamelCase       						=      self.hidden_dim
        UpperCamelCase       						=      self.hidden_dim
        UpperCamelCase       						=      self.hidden_dim
        return config

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      self.prepare_config_and_inputs()
        UpperCamelCase       						=      {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
        return config, inputs_dict

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      output.encoder_hidden_states
        UpperCamelCase       						=      output.pixel_decoder_hidden_states
        UpperCamelCase       						=      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_layers   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=False   )						->   Tuple:



        """simple docstring"""






        with torch.no_grad():
              UpperCamelCase       						=      MaskaFormerModel(config=_SCREAMING_SNAKE_CASE   )
              model.to(_SCREAMING_SNAKE_CASE   )
              model.eval()

              UpperCamelCase       						=      model(pixel_values=_SCREAMING_SNAKE_CASE    ,	pixel_mask=_SCREAMING_SNAKE_CASE   )
              UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	output_hidden_states=_SCREAMING_SNAKE_CASE   )

        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(_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]:



        """simple docstring"""






        UpperCamelCase       						=      MaskaFormerForUniversalSegmentation(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       						=      model(pixel_values=_SCREAMING_SNAKE_CASE    ,	pixel_mask=_SCREAMING_SNAKE_CASE   )
              UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE   )

              comm_check_on_output(_SCREAMING_SNAKE_CASE   )

              UpperCamelCase       						=      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_		(    lowerCamelCase			, lowerCamelCase			, unittest.TestCase       ):
  lowercase  	=   (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  lowercase  	=   {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

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

  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      MaskaFormerModelTester(self   )
        UpperCamelCase       						=      ConfigTester(self    ,	config_class=_SCREAMING_SNAKE_CASE    ,	has_text_modality=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        self.config_tester.run_common_tests()

  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE    ,	output_hidden_states=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_SCREAMING_SNAKE_CASE   )

  @unittest.skip(reason="""Mask2Former does not use inputs_embeds"""   )
  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        pass

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



        """simple docstring"""






        pass

  @unittest.skip(reason="""Mask2Former is not a generative model"""   )
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        pass

  @unittest.skip(reason="""Mask2Former does not use token embeddings"""   )
  def 						A__		(   self   )						->   Tuple:



        """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 						A__		(   self   )						->   str:



        """simple docstring"""






        pass

  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""   )
  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        pass

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCamelCase       						=      ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]    ,	_SCREAMING_SNAKE_CASE   )

  @slow
  def 						A__		(   self   )						->   str:



        """simple docstring"""






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

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      (self.model_tester.min_size,) * 2
        UpperCamelCase       						=      {
            """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       						=      self.model_tester.get_config()

        UpperCamelCase       						=      MaskaFormerForUniversalSegmentation(_SCREAMING_SNAKE_CASE   ).to(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      model(**_SCREAMING_SNAKE_CASE   )
        self.assertTrue(outputs.loss is not None   )

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE    ,	output_hidden_states=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase       						=      model_class(_SCREAMING_SNAKE_CASE   ).to(_SCREAMING_SNAKE_CASE   )
              UpperCamelCase       						=      model(**_SCREAMING_SNAKE_CASE    ,	output_attentions=_SCREAMING_SNAKE_CASE   )
              self.assertTrue(outputs.attentions is not None   )

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        if not self.model_tester.is_training:
              return

        UpperCamelCase       						=      self.all_model_classes[1]
        UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()

        UpperCamelCase       						=      model_class(_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.train()

        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	mask_labels=_SCREAMING_SNAKE_CASE    ,	class_labels=_SCREAMING_SNAKE_CASE   ).loss
        loss.backward()


  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.all_model_classes[1]
        UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        UpperCamelCase       						=      True
        UpperCamelCase       						=      True

        UpperCamelCase       						=      model_class(_SCREAMING_SNAKE_CASE   ).to(_SCREAMING_SNAKE_CASE   )
        model.train()

        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	mask_labels=_SCREAMING_SNAKE_CASE    ,	class_labels=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      outputs.encoder_hidden_states[0]
        encoder_hidden_states.retain_grad()

        UpperCamelCase       						=      outputs.pixel_decoder_hidden_states[0]
        pixel_decoder_hidden_states.retain_grad()

        UpperCamelCase       						=      outputs.transformer_decoder_hidden_states[0]
        transformer_decoder_hidden_states.retain_grad()

        UpperCamelCase       						=      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   )


SCREAMING_SNAKE_CASE__											=     1E-4







def 					lowercase__				(   )->				Dict:
      UpperCamelCase       						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
      return image






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

  @cached_property
  def 						A__		(   self   )						->   Any:



        """simple docstring"""






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

  @cached_property
  def 						A__		(   self   )						->   Any:



        """simple docstring"""






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

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      MaskaFormerModel.from_pretrained(self.model_checkpoints   ).to(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      self.default_image_processor
        UpperCamelCase       						=      prepare_img()
        UpperCamelCase       						=      image_processor(_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   ).to(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      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, 384, 384)   )

        with torch.no_grad():
              UpperCamelCase       						=      model(**_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      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(_SCREAMING_SNAKE_CASE   )
        self.assertTrue(
            torch.allclose(
                outputs.encoder_last_hidden_state[0, 0, :3, :3]    ,	_SCREAMING_SNAKE_CASE    ,	atol=_SCREAMING_SNAKE_CASE   )   )

        UpperCamelCase       						=      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(_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       						=      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(_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   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(_SCREAMING_SNAKE_CASE   ).eval()
        UpperCamelCase       						=      self.default_image_processor
        UpperCamelCase       						=      prepare_img()
        UpperCamelCase       						=      image_processor(_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   ).to(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      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, 384, 384)   )

        with torch.no_grad():
              UpperCamelCase       						=      model(**_SCREAMING_SNAKE_CASE   )
        # masks_queries_logits
        UpperCamelCase       						=      outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape    ,	(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)   )
        UpperCamelCase       						=      [
            [-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],
        ]
        UpperCamelCase       						=      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       						=      outputs.class_queries_logits
        self.assertEqual(class_queries_logits.shape    ,	(1, model.config.num_queries, model.config.num_labels + 1)   )
        UpperCamelCase       						=      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(_SCREAMING_SNAKE_CASE   )
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,	_SCREAMING_SNAKE_CASE    ,	atol=_SCREAMING_SNAKE_CASE   )   )


  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(_SCREAMING_SNAKE_CASE   ).eval()
        UpperCamelCase       						=      self.default_image_processor

        UpperCamelCase       						=      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"""    ,	)

        UpperCamelCase       						=      inputs["""pixel_values"""].to(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      [el.to(_SCREAMING_SNAKE_CASE   ) for el in inputs["""mask_labels"""]]
        UpperCamelCase       						=      [el.to(_SCREAMING_SNAKE_CASE   ) for el in inputs["""class_labels"""]]

        with torch.no_grad():
              UpperCamelCase       						=      model(**_SCREAMING_SNAKE_CASE   )

        self.assertTrue(outputs.loss is not None   )








'''simple docstring'''

from math import factorial







def 					lowercase__				(   __UpperCamelCase = 20					)->				int:
      UpperCamelCase       						=      2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
      # 2, 3,...
      UpperCamelCase       						=      n // 2

      return int(factorial(__UpperCamelCase					) / (factorial(__UpperCamelCase					) * factorial(n - k					))					)


if __name__ == "__main__":
     import sys

     if len(sys.argv) == 1:
          print(solution(2_0))
     else:
          try:
               SCREAMING_SNAKE_CASE__											=     int(sys.argv[1])
               print(solution(n))
          except ValueError:
               print('Invalid entry - please enter a number.')
