





'''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				:			List[str]				,lowerCamelCase				:			List[str]				,lowerCamelCase				:			Optional[int]=2				,lowerCamelCase				:			List[str]=True				,lowerCamelCase				:			List[Any]=False				,lowerCamelCase				:			List[Any]=10				,lowerCamelCase				:			str=3				,lowerCamelCase				:			Optional[Any]=32 * 8				,lowerCamelCase				:			Optional[int]=32 * 8				,lowerCamelCase				:			Any=4				,lowerCamelCase				:			Any=64				,):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]    ).to(
          UpperCamelCase_    )

      __SCREAMING_SNAKE_CASE      							=       torch.ones([self.batch_size, self.min_size, self.max_size]				,device=UpperCamelCase_    )

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

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





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

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

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

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





   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			str				,lowerCamelCase				:			List[str]				,lowerCamelCase				:			Any    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       output.encoder_hidden_states
      __SCREAMING_SNAKE_CASE      							=       output.pixel_decoder_hidden_states
      __SCREAMING_SNAKE_CASE      							=       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				:			int				,lowerCamelCase				:			Tuple				,lowerCamelCase				:			List[Any]				,lowerCamelCase				:			Dict				,lowerCamelCase				:			Union[str, Any]=False    ):

      '''simple docstring'''

      with torch.no_grad():
         __SCREAMING_SNAKE_CASE      							=       MaskaFormerModel(config=UpperCamelCase_    )
         model.to(UpperCamelCase_    )
         model.eval()

         __SCREAMING_SNAKE_CASE      							=       model(pixel_values=UpperCamelCase_				,pixel_mask=UpperCamelCase_    )
         __SCREAMING_SNAKE_CASE      							=       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				:			Any				,lowerCamelCase				:			Dict				,lowerCamelCase				:			Optional[Any]				,lowerCamelCase				:			Tuple				,lowerCamelCase				:			Tuple				,lowerCamelCase				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       MaskaFormerForUniversalSegmentation(config=UpperCamelCase_    )
      model.to(UpperCamelCase_    )
      model.eval()

      def comm_check_on_output(lowerCamelCase				:			List[Any]    ):
         # 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():
         __SCREAMING_SNAKE_CASE      							=       model(pixel_values=UpperCamelCase_				,pixel_mask=UpperCamelCase_    )
         __SCREAMING_SNAKE_CASE      							=       model(UpperCamelCase_    )

         comm_check_on_output(UpperCamelCase_    )

         __SCREAMING_SNAKE_CASE      							=       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 __a (   _snake_case,   _snake_case,   unittest.TestCase			):
   __UpperCamelCase   : List[Any]	      =	(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
   __UpperCamelCase   : Tuple	      =	{"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

   __UpperCamelCase   : List[str]	      =	False
   __UpperCamelCase   : Optional[int]	      =	False
   __UpperCamelCase   : Tuple	      =	False
   __UpperCamelCase   : Optional[Any]	      =	False





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       MaskaFormerModelTester(self    )
      __SCREAMING_SNAKE_CASE      							=       ConfigTester(self				,config_class=UpperCamelCase_				,has_text_modality=UpperCamelCase_    )





   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      self.config_tester.run_common_tests()





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       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				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       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				:			Tuple    ):

      '''simple docstring'''

      pass





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

      '''simple docstring'''

      pass





   @unittest.skip(reason="""Mask2Former is not a generative model"""    )
   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      pass





   @unittest.skip(reason="""Mask2Former does not use token embeddings"""    )
   def      UpperCAmelCase__     (					self				:			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      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      pass





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

      '''simple docstring'''

      pass





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.model_tester.prepare_config_and_inputs_for_common()

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

         __SCREAMING_SNAKE_CASE      							=       ["pixel_values"]
         self.assertListEqual(arg_names[:1]				,UpperCamelCase_    )





   @slow
   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE      							=       MaskaFormerForUniversalSegmentation(UpperCamelCase_    ).to(UpperCamelCase_    )
      __SCREAMING_SNAKE_CASE      							=       model(**UpperCamelCase_    )
      self.assertTrue(outputs.loss is not None    )





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       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				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.model_tester.prepare_config_and_inputs_for_common()

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





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

      if not self.model_tester.is_training:
         return

      __SCREAMING_SNAKE_CASE      							=       self.all_model_classes[1]
      __SCREAMING_SNAKE_CASE      							=       self.model_tester.prepare_config_and_inputs()

      __SCREAMING_SNAKE_CASE      							=       model_class(UpperCamelCase_    )
      model.to(UpperCamelCase_    )
      model.train()

      __SCREAMING_SNAKE_CASE      							=       model(UpperCamelCase_				,mask_labels=UpperCamelCase_				,class_labels=UpperCamelCase_    ).loss
      loss.backward()





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE      							=       model_class(UpperCamelCase_    ).to(UpperCamelCase_    )
      model.train()

      __SCREAMING_SNAKE_CASE      							=       model(UpperCamelCase_				,mask_labels=UpperCamelCase_				,class_labels=UpperCamelCase_    )

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

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

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

      __SCREAMING_SNAKE_CASE      							=       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    )





a        =						1E-4

def 						__magic_name__       (							)						->					List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
   return image







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





   @cached_property
   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

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





   @cached_property
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       MaskaFormerModel.from_pretrained(self.model_checkpoints    ).to(UpperCamelCase_    )
      __SCREAMING_SNAKE_CASE      							=       self.default_image_processor
      __SCREAMING_SNAKE_CASE      							=       prepare_img()
      __SCREAMING_SNAKE_CASE      							=       image_processor(UpperCamelCase_				,return_tensors="""pt"""    ).to(UpperCamelCase_    )
      __SCREAMING_SNAKE_CASE      							=       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():
         __SCREAMING_SNAKE_CASE      							=       model(**UpperCamelCase_    )

      __SCREAMING_SNAKE_CASE      							=       torch.tensor(
          [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]    ).to(UpperCamelCase_    )
      self.assertTrue(
          torch.allclose(
              outputs.encoder_last_hidden_state[0, 0, :3, :3]				,UpperCamelCase_				,atol=UpperCamelCase_    )    )

      __SCREAMING_SNAKE_CASE      							=       torch.tensor(
          [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]    ).to(UpperCamelCase_    )
      self.assertTrue(
          torch.allclose(
              outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]				,UpperCamelCase_				,atol=UpperCamelCase_    )    )

      __SCREAMING_SNAKE_CASE      							=       torch.tensor(
          [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]    ).to(UpperCamelCase_    )
      self.assertTrue(
          torch.allclose(
              outputs.transformer_decoder_last_hidden_state[0, :3, :3]				,UpperCamelCase_				,atol=UpperCamelCase_    )    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints    ).to(UpperCamelCase_    ).eval()
      __SCREAMING_SNAKE_CASE      							=       self.default_image_processor
      __SCREAMING_SNAKE_CASE      							=       prepare_img()
      __SCREAMING_SNAKE_CASE      							=       image_processor(UpperCamelCase_				,return_tensors="""pt"""    ).to(UpperCamelCase_    )
      __SCREAMING_SNAKE_CASE      							=       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():
         __SCREAMING_SNAKE_CASE      							=       model(**UpperCamelCase_    )
      # masks_queries_logits
      __SCREAMING_SNAKE_CASE      							=       outputs.masks_queries_logits
      self.assertEqual(
          masks_queries_logits.shape				,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)    )
      __SCREAMING_SNAKE_CASE      							=       [
          [-8.7_839, -9.0_056, -8.8_121],
          [-7.4_104, -7.0_313, -6.5_401],
          [-6.6_105, -6.3_427, -6.4_675],
      ]
      __SCREAMING_SNAKE_CASE      							=       torch.tensor(UpperCamelCase_    ).to(UpperCamelCase_    )
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]				,UpperCamelCase_				,atol=UpperCamelCase_    )    )
      # class_queries_logits
      __SCREAMING_SNAKE_CASE      							=       outputs.class_queries_logits
      self.assertEqual(class_queries_logits.shape				,(1, model.config.num_queries, model.config.num_labels + 1)    )
      __SCREAMING_SNAKE_CASE      							=       torch.tensor(
          [
              [1.8_324, -8.0_835, -4.1_922],
              [0.8_450, -9.0_050, -3.6_053],
              [0.3_045, -7.7_293, -3.0_275],
          ]    ).to(UpperCamelCase_    )
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]				,UpperCamelCase_				,atol=UpperCamelCase_    )    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints    ).to(UpperCamelCase_    ).eval()
      __SCREAMING_SNAKE_CASE      							=       self.default_image_processor

      __SCREAMING_SNAKE_CASE      							=       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"""				,)

      __SCREAMING_SNAKE_CASE      							=       inputs["pixel_values"].to(UpperCamelCase_    )
      __SCREAMING_SNAKE_CASE      							=       [el.to(UpperCamelCase_    ) for el in inputs["mask_labels"]]
      __SCREAMING_SNAKE_CASE      							=       [el.to(UpperCamelCase_    ) for el in inputs["class_labels"]]

      with torch.no_grad():
         __SCREAMING_SNAKE_CASE      							=       model(**UpperCamelCase_    )

      self.assertTrue(outputs.loss is not None    )












'''simple docstring'''

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

a        =						[
    "python",
    "tqdm",
    "regex",
    "requests",
    "packaging",
    "filelock",
    "numpy",
    "tokenizers",
    "huggingface-hub",
    "safetensors",
    "accelerate",
    "pyyaml",
]

for pkg in pkgs_to_check_at_runtime:
    if pkg in deps:
        if pkg == "tokenizers":
            # must be loaded here, or else tqdm check may fail
            from .utils import is_tokenizers_available

            if not is_tokenizers_available():
                continue  # not required, check version only if installed
        elif pkg == "accelerate":
            # must be loaded here, or else tqdm check may fail
            from .utils import is_accelerate_available

            # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
            # Transformers with PyTorch
            if not is_accelerate_available():
                continue  # not required, check version only if installed

        require_version_core(deps[pkg])
    else:
        raise ValueError(F'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=None      )						->					Optional[Any]:

   '''simple docstring'''







   require_version(deps[pkg]							,	__UpperCAmelCase      )





