

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     :




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase=2							,   lowerCAmelCase=True							,   lowerCAmelCase=False							,   lowerCAmelCase=10							,   lowerCAmelCase=3							,   lowerCAmelCase=32 * 8							,   lowerCAmelCase=32 * 8							,   lowerCAmelCase=4							,   lowerCAmelCase=64							,   )  -> str:
        '''simple docstring'''






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




  def   A__     (				self					)  -> str:
        '''simple docstring'''






        _lowercase	       =floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]					).to(
            lowerCAmelCase					)

        _lowercase	       =torch.ones([self.batch_size, self.min_size, self.max_size]							,   device=lowerCAmelCase					)

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

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




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






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

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

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




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        _lowercase       ,       _lowercase       ,       _lowercase       ,       _lowercase       ,       _lowercase	       =self.prepare_config_and_inputs()
        _lowercase	       ={'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
        return config, inputs_dict




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase					)  -> List[str]:
        '''simple docstring'''






        _lowercase	       =output.encoder_hidden_states
        _lowercase	       =output.pixel_decoder_hidden_states
        _lowercase	       =output.transformer_decoder_hidden_states

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




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase=False					)  -> int:
        '''simple docstring'''






        with torch.no_grad():
              _lowercase	       =MaskaFormerModel(config=lowerCAmelCase					)
              model.to(lowerCAmelCase					)
              model.eval()

              _lowercase	       =model(pixel_values=lowerCAmelCase							,   pixel_mask=lowerCAmelCase					)
              _lowercase	       =model(lowerCAmelCase							,   output_hidden_states=lowerCAmelCase					)

        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(lowerCAmelCase							,   lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






        _lowercase	       =MaskaFormerForUniversalSegmentation(config=lowerCAmelCase					)
        model.to(lowerCAmelCase					)
        model.eval()

        def comm_check_on_output(lowerCAmelCase					):
              # 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():
              _lowercase	       =model(pixel_values=lowerCAmelCase							,   pixel_mask=lowerCAmelCase					)
              _lowercase	       =model(lowerCAmelCase					)

              comm_check_on_output(lowerCAmelCase					)

              _lowercase	       =model(
                  pixel_values=lowerCAmelCase							,   pixel_mask=lowerCAmelCase							,   mask_labels=lowerCAmelCase							,   class_labels=lowerCAmelCase					)

        comm_check_on_output(lowerCAmelCase					)

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





@require_torch
class  __lowerCAmelCase     (					lowercase_			,			lowercase_			,			unittest.TestCase							):
  _a									=  (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  _a									=  {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

  _a									=  False
  _a									=  False
  _a									=  False
  _a									=  False




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






        _lowercase	       =MaskaFormerModelTester(self					)
        _lowercase	       =ConfigTester(self							,   config_class=lowerCAmelCase							,   has_text_modality=lowerCAmelCase					)




  def   A__     (				self					)  -> Dict:
        '''simple docstring'''






        self.config_tester.run_common_tests()




  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        _lowercase       ,       _lowercase	       =self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase							,   **lowerCAmelCase							,   output_hidden_states=lowerCAmelCase					)




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






        _lowercase	       =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCAmelCase					)




  @unittest.skip(reason='Mask2Former does not use inputs_embeds'					)
  def   A__     (				self					)  -> str:
        '''simple docstring'''






        pass




  @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'					)
  def   A__     (				self					)  -> str:
        '''simple docstring'''






        pass




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






        pass




  @unittest.skip(reason='Mask2Former does not use token embeddings'					)
  def   A__     (				self					)  -> Union[str, Any]:
        '''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					)  -> List[Any]:
        '''simple docstring'''






        pass




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






        _lowercase       ,       _lowercase	       =self.model_tester.prepare_config_and_inputs_for_common()

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

              _lowercase	       =['pixel_values']
              self.assertListEqual(arg_names[:1]							,   lowerCAmelCase					)




  @slow
  def   A__     (				self					)  -> Dict:
        '''simple docstring'''






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




  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        _lowercase	       =(self.model_tester.min_size,) * 2
        _lowercase	       ={
            'pixel_values': torch.randn((2, 3, *size)							,   device=lowerCAmelCase					),
            'mask_labels': torch.randn((2, 10, *size)							,   device=lowerCAmelCase					),
            'class_labels': torch.zeros(2							,   10							,   device=lowerCAmelCase					).long(),
        }
        _lowercase	       =self.model_tester.get_config()

        _lowercase	       =MaskaFormerForUniversalSegmentation(lowerCAmelCase					).to(lowerCAmelCase					)
        _lowercase	       =model(**lowerCAmelCase					)
        self.assertTrue(outputs.loss is not None					)




  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        _lowercase       ,       _lowercase	       =self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase							,   **lowerCAmelCase							,   output_hidden_states=lowerCAmelCase					)




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






        _lowercase       ,       _lowercase	       =self.model_tester.prepare_config_and_inputs_for_common()

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




  def   A__     (				self					)  -> Dict:
        '''simple docstring'''






        if not self.model_tester.is_training:
              return

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

        _lowercase	       =model_class(lowerCAmelCase					)
        model.to(lowerCAmelCase					)
        model.train()

        _lowercase	       =model(lowerCAmelCase							,   mask_labels=lowerCAmelCase							,   class_labels=lowerCAmelCase					).loss
        loss.backward()




  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






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

        _lowercase	       =model_class(lowerCAmelCase					).to(lowerCAmelCase					)
        model.train()

        _lowercase	       =model(lowerCAmelCase							,   mask_labels=lowerCAmelCase							,   class_labels=lowerCAmelCase					)

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

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

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

        _lowercase	       =outputs.attentions[0]
        attentions.retain_grad()

        outputs.loss.backward(retain_graph=lowerCAmelCase					)

        self.assertIsNotNone(encoder_hidden_states.grad					)
        self.assertIsNotNone(pixel_decoder_hidden_states.grad					)
        self.assertIsNotNone(transformer_decoder_hidden_states.grad					)
        self.assertIsNotNone(attentions.grad					)
lowercase_									=   1e-4
def      a      (			)    ->      Any:





      """simple docstring"""



      _lowercase	       =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
      return image







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




  @cached_property
  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






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




  @cached_property
  def   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






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




  def   A__     (				self					)  -> Any:
        '''simple docstring'''






        _lowercase	       =MaskaFormerModel.from_pretrained(self.model_checkpoints					).to(lowerCAmelCase					)
        _lowercase	       =self.default_image_processor
        _lowercase	       =prepare_img()
        _lowercase	       =image_processor(lowerCAmelCase							,   return_tensors='pt'					).to(lowerCAmelCase					)
        _lowercase	       =inputs['pixel_values'].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
        # check size
        self.assertEqual(lowerCAmelCase							,   (1, 3, 384, 384)					)

        with torch.no_grad():
              _lowercase	       =model(**lowerCAmelCase					)

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

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

        _lowercase	       =torch.tensor(
            [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]					).to(lowerCAmelCase					)
        self.assertTrue(
            torch.allclose(
                outputs.transformer_decoder_last_hidden_state[0, :3, :3]							,   lowerCAmelCase							,   atol=lowerCAmelCase					)					)




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






        _lowercase	       =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints					).to(lowerCAmelCase					).eval()
        _lowercase	       =self.default_image_processor
        _lowercase	       =prepare_img()
        _lowercase	       =image_processor(lowerCAmelCase							,   return_tensors='pt'					).to(lowerCAmelCase					)
        _lowercase	       =inputs['pixel_values'].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
        # check size
        self.assertEqual(lowerCAmelCase							,   (1, 3, 384, 384)					)

        with torch.no_grad():
              _lowercase	       =model(**lowerCAmelCase					)
        # masks_queries_logits
        _lowercase	       =outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape							,   (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)					)
        _lowercase	       =[
            [-8.7839, -9.0056, -8.8121],
            [-7.4104, -7.0313, -6.5401],
            [-6.6105, -6.3427, -6.4675],
        ]
        _lowercase	       =torch.tensor(lowerCAmelCase					).to(lowerCAmelCase					)
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]							,   lowerCAmelCase							,   atol=lowerCAmelCase					)					)
        # class_queries_logits
        _lowercase	       =outputs.class_queries_logits
        self.assertEqual(class_queries_logits.shape							,   (1, model.config.num_queries, model.config.num_labels + 1)					)
        _lowercase	       =torch.tensor(
            [
                [1.8324, -8.0835, -4.1922],
                [0.8450, -9.0050, -3.6053],
                [0.3045, -7.7293, -3.0275],
            ]					).to(lowerCAmelCase					)
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]							,   lowerCAmelCase							,   atol=lowerCAmelCase					)					)




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        _lowercase	       =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints					).to(lowerCAmelCase					).eval()
        _lowercase	       =self.default_image_processor

        _lowercase	       =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'							,   )

        _lowercase	       =inputs['pixel_values'].to(lowerCAmelCase					)
        _lowercase	       =[el.to(lowerCAmelCase					) for el in inputs['mask_labels']]
        _lowercase	       =[el.to(lowerCAmelCase					) for el in inputs['class_labels']]

        with torch.no_grad():
              _lowercase	       =model(**lowerCAmelCase					)

        self.assertTrue(outputs.loss is not None					)





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							if edge <= 0 or not isinstance(UpperCamelCase__   ,							UpperCamelCase__      ):
														raise ValueError('Length must be a positive.'      )
							return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							if edge <= 0 or not isinstance(UpperCamelCase__   ,							UpperCamelCase__      ):
														raise ValueError('Length must be a positive.'      )
							return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




