

'''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 							lowercase_						:

  '''simple docstring'''





  def __init__(  self ,			a_ ,			a_=2 ,			a_=True ,			a_=False ,			a_=1_0 ,			a_=3 ,			a_=3_2 * 8 ,			a_=3_2 * 8 ,			a_=4 ,			a_=6_4 ,			)				->					Optional[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     snake_case_      (  self  )				->					Tuple:


    """simple docstring"""
    UpperCAmelCase    				=						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]  ).to(
        a_  )

    UpperCAmelCase    				=						torch.ones([self.batch_size, self.min_size, self.max_size] ,			device=a_  )

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

    UpperCAmelCase    				=						self.get_config()
    return config, pixel_values, pixel_mask, mask_labels, class_labels
  def     snake_case_      (  self  )				->					Tuple:


    """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    				=						6_4
    UpperCAmelCase    				=						1_2_8
    UpperCAmelCase    				=						self.hidden_dim
    UpperCAmelCase    				=						self.hidden_dim
    UpperCAmelCase    				=						self.hidden_dim
    return config
  def     snake_case_      (  self  )				->					Optional[int]:


    """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     snake_case_      (  self ,			a_ ,			a_  )				->					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(a_  ) ,			len(config.backbone_config.depths  )  )
    self.parent.assertTrue(len(a_  ) ,			len(config.backbone_config.depths  )  )
    self.parent.assertTrue(len(a_  ) ,			config.decoder_layers  )
  def     snake_case_      (  self ,			a_ ,			a_ ,			a_ ,			a_=False  )				->					Optional[Any]:


    """simple docstring"""
    with torch.no_grad():
      UpperCAmelCase    				=						MaskaFormerModel(config=a_  )
      model.to(a_  )
      model.eval()

      UpperCAmelCase    				=						model(pixel_values=a_ ,			pixel_mask=a_  )
      UpperCAmelCase    				=						model(a_ ,			output_hidden_states=a_  )

    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(a_ ,			a_  )





  def     snake_case_      (  self ,			a_ ,			a_ ,			a_ ,			a_ ,			a_  )				->					Tuple:


    """simple docstring"""
    UpperCAmelCase    				=						MaskaFormerForUniversalSegmentation(config=a_  )
    model.to(a_  )
    model.eval()

    def comm_check_on_output(a_  ):
      # 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=a_ ,			pixel_mask=a_  )
      UpperCAmelCase    				=						model(a_  )

      comm_check_on_output(a_  )

      UpperCAmelCase    				=						model(
          pixel_values=a_ ,			pixel_mask=a_ ,			mask_labels=a_ ,			class_labels=a_  )

    comm_check_on_output(a_  )

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




@require_torch
class 							lowercase_						(		a			,							a			,							unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			Tuple     	= (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  __lowerCAmelCase       :			List[Any]     	= {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

  __lowerCAmelCase       :			Dict     	= False
  __lowerCAmelCase       :			int     	= False
  __lowerCAmelCase       :			Optional[int]     	= False
  __lowerCAmelCase       :			Optional[Any]     	= False
  def     snake_case_      (  self  )				->					Tuple:


    """simple docstring"""
    UpperCAmelCase    				=						MaskaFormerModelTester(self  )
    UpperCAmelCase    				=						ConfigTester(self ,			config_class=a_ ,			has_text_modality=a_  )
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    self.config_tester.run_common_tests()
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase				,   UpperCAmelCase    				=						self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.create_and_check_maskaformer_model(a_ ,			**a_ ,			output_hidden_states=a_  )
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*a_  )
  @unittest.skip(reason='Mask2Former does not use inputs_embeds'  )
  def     snake_case_      (  self  )				->					Tuple:


    """simple docstring"""
    pass
  @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'  )
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    pass
  @unittest.skip(reason='Mask2Former is not a generative model'  )
  def     snake_case_      (  self  )				->					List[Any]:


    """simple docstring"""
    pass
  @unittest.skip(reason='Mask2Former does not use token embeddings'  )
  def     snake_case_      (  self  )				->					Dict:


    """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     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    pass
  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'  )
  def     snake_case_      (  self  )				->					Optional[Any]:


    """simple docstring"""
    pass
  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    UpperCAmelCase				,   UpperCAmelCase    				=						self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      UpperCAmelCase    				=						model_class(a_  )
      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] ,			a_  )
  @slow
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
      UpperCAmelCase    				=						MaskaFormerModel.from_pretrained(a_  )
      self.assertIsNotNone(a_  )
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						(self.model_tester.min_size,) * 2
    UpperCAmelCase    				=						{
        'pixel_values': torch.randn((2, 3, *size) ,			device=a_  ),
        'mask_labels': torch.randn((2, 1_0, *size) ,			device=a_  ),
        'class_labels': torch.zeros(2 ,			1_0 ,			device=a_  ).long(),
    }
    UpperCAmelCase    				=						self.model_tester.get_config()

    UpperCAmelCase    				=						MaskaFormerForUniversalSegmentation(a_  ).to(a_  )
    UpperCAmelCase    				=						model(**a_  )
    self.assertTrue(outputs.loss is not None  )
  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    UpperCAmelCase				,   UpperCAmelCase    				=						self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.create_and_check_maskaformer_model(a_ ,			**a_ ,			output_hidden_states=a_  )
  def     snake_case_      (  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(a_  ).to(a_  )
      UpperCAmelCase    				=						model(**a_ ,			output_attentions=a_  )
      self.assertTrue(outputs.attentions is not None  )
  def     snake_case_      (  self  )				->					List[Any]:


    """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(a_  )
    model.to(a_  )
    model.train()

    UpperCAmelCase    				=						model(a_ ,			mask_labels=a_ ,			class_labels=a_  ).loss
    loss.backward()





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


    """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(a_  ).to(a_  )
    model.train()

    UpperCAmelCase    				=						model(a_ ,			mask_labels=a_ ,			class_labels=a_  )

    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=a_  )

    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					:			List[str]							=    1E-4


def    lowerCamelCase__		(    ):
  UpperCAmelCase    				=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
  return image



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

  '''simple docstring'''





  @cached_property
  def     snake_case_      (  self  )				->					Optional[Any]:


    """simple docstring"""
    return "facebook/mask2former-swin-small-coco-instance"
  @cached_property
  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints  ) if is_vision_available() else None
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						MaskaFormerModel.from_pretrained(self.model_checkpoints  ).to(a_  )
    UpperCAmelCase    				=						self.default_image_processor
    UpperCAmelCase    				=						prepare_img()
    UpperCAmelCase    				=						image_processor(a_ ,			return_tensors='pt'  ).to(a_  )
    UpperCAmelCase    				=						inputs['pixel_values'].shape
    # check size is divisible by 32
    self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0  )
    # check size
    self.assertEqual(a_ ,			(1, 3, 3_8_4, 3_8_4)  )

    with torch.no_grad():
      UpperCAmelCase    				=						model(**a_  )

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

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

    UpperCAmelCase    				=						torch.tensor(
        [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]  ).to(a_  )
    self.assertTrue(
        torch.allclose(
            outputs.transformer_decoder_last_hidden_state[0, :3, :3] ,			a_ ,			atol=a_  )  )
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints  ).to(a_  ).eval()
    UpperCAmelCase    				=						self.default_image_processor
    UpperCAmelCase    				=						prepare_img()
    UpperCAmelCase    				=						image_processor(a_ ,			return_tensors='pt'  ).to(a_  )
    UpperCAmelCase    				=						inputs['pixel_values'].shape
    # check size is divisible by 32
    self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0  )
    # check size
    self.assertEqual(a_ ,			(1, 3, 3_8_4, 3_8_4)  )

    with torch.no_grad():
      UpperCAmelCase    				=						model(**a_  )
    # 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    				=						[
        [-8.7839, -9.0056, -8.8121],
        [-7.4104, -7.0313, -6.5401],
        [-6.6105, -6.3427, -6.4675],
    ]
    UpperCAmelCase    				=						torch.tensor(a_  ).to(a_  )
    self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,			a_ ,			atol=a_  )  )
    # 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(
        [
            [1.8324, -8.0835, -4.1922],
            [0.8450, -9.0050, -3.6053],
            [0.3045, -7.7293, -3.0275],
        ]  ).to(a_  )
    self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,			a_ ,			atol=a_  )  )





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


    """simple docstring"""
    UpperCAmelCase    				=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints  ).to(a_  ).eval()
    UpperCAmelCase    				=						self.default_image_processor

    UpperCAmelCase    				=						image_processor(
        [np.zeros((3, 8_0_0, 1_3_3_3)  ), np.zeros((3, 8_0_0, 1_3_3_3)  )] ,			segmentation_maps=[np.zeros((3_8_4, 3_8_4)  ).astype(np.floataa  ), np.zeros((3_8_4, 3_8_4)  ).astype(np.floataa  )] ,			return_tensors='pt' ,			)

    UpperCAmelCase    				=						inputs['pixel_values'].to(a_  )
    UpperCAmelCase    				=						[el.to(a_  ) for el in inputs['mask_labels']]
    UpperCAmelCase    				=						[el.to(a_  ) for el in inputs['class_labels']]

    with torch.no_grad():
      UpperCAmelCase    				=						model(**a_  )

    self.assertTrue(outputs.loss is not None  )





'''simple docstring'''




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : list[int]							, SCREAMING_SNAKE_CASE   : list[int]							, SCREAMING_SNAKE_CASE   : int    ):
  return not any(
      neighbour == 1 and colored_vertices[i] == color
      for i, neighbour in enumerate(SCREAMING_SNAKE_CASE    )    )


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : list[list[int]]							, SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : list[int]							, SCREAMING_SNAKE_CASE   : int    ):

  # Base Case
  if index == len(SCREAMING_SNAKE_CASE    ):
    return True

  # Recursive Step
  for i in range(SCREAMING_SNAKE_CASE    ):
    if valid_coloring(graph[index]							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    ):
      # Color current vertex
      UpperCAmelCase    				=						i
      # Validate coloring
      if util_color(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, index + 1    ):
        return True
      # Backtrack
      UpperCAmelCase    				=						-1
  return False



def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : list[list[int]]							, SCREAMING_SNAKE_CASE   : int    ):
  UpperCAmelCase    				=						[-1] * len(SCREAMING_SNAKE_CASE    )

  if util_color(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, 0    ):
    return colored_vertices

  return []


