





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     UpperCamelCase__					:






		'''simple docstring'''






		def __init__(							self,   snake_case__,   snake_case__=2,   snake_case__=True,   snake_case__=False,   snake_case__=10,   snake_case__=3,   snake_case__=32 * 8,   snake_case__=32 * 8,   snake_case__=4,   snake_case__=64,   )       -> Any:






									"""simple docstring"""
									lowercase_	:   str  				=     parent
									lowercase_	:   Any  				=     batch_size
									lowercase_	:   Dict  				=     is_training
									lowercase_	:   Optional[int]  				=     use_auxiliary_loss
									lowercase_	:   List[str]  				=     num_queries
									lowercase_	:   List[Any]  				=     num_channels
									lowercase_	:   List[str]  				=     min_size
									lowercase_	:   Dict  				=     max_size
									lowercase_	:   List[Any]  				=     num_labels
									lowercase_	:   Optional[int]  				=     hidden_dim
									lowercase_	:   Dict  				=     hidden_dim






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






									"""simple docstring"""
									lowercase_	:   Dict  				=     floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]  ).to(
									    snake_case__  )

									lowercase_	:   Union[str, Any]  				=     torch.ones([self.batch_size, self.min_size, self.max_size],   device=snake_case__  )

									lowercase_	:   Union[str, Any]  				=     (
									    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size],   device=snake_case__  ) > 0.5
									).float()
									lowercase_	:   List[Any]  				=     (torch.rand((self.batch_size, self.num_labels),   device=snake_case__  ) > 0.5).long()

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






		def 						snake_case__				(							self  )       -> Any:






									"""simple docstring"""
									lowercase_	:   Optional[int]  				=     MaskaFormerConfig(
									    hidden_size=self.hidden_dim,   )
									lowercase_	:   Union[str, Any]  				=     self.num_queries
									lowercase_	:   Optional[int]  				=     self.num_labels

									lowercase_	:   Optional[Any]  				=     [1, 1, 1, 1]
									lowercase_	:   List[str]  				=     self.num_channels

									lowercase_	:   Dict  				=     64
									lowercase_	:   Dict  				=     1_28
									lowercase_	:   Optional[int]  				=     self.hidden_dim
									lowercase_	:   List[Any]  				=     self.hidden_dim
									lowercase_	:   Dict  				=     self.hidden_dim
									return config






		def 						snake_case__				(							self  )       -> int:






									"""simple docstring"""
									lowercase_				,		lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   Optional[int]  				=     self.prepare_config_and_inputs()
									lowercase_	:   str  				=     {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
									return config, inputs_dict






		def 						snake_case__				(							self,   snake_case__,   snake_case__  )       -> Dict:






									"""simple docstring"""
									lowercase_	:   int  				=     output.encoder_hidden_states
									lowercase_	:   Tuple  				=     output.pixel_decoder_hidden_states
									lowercase_	:   Dict  				=     output.transformer_decoder_hidden_states

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






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__=False  )       -> str:






									"""simple docstring"""
									with torch.no_grad():
																lowercase_	:   str  				=     MaskaFormerModel(config=snake_case__  )
																model.to(snake_case__  )
																model.eval()

																lowercase_	:   Tuple  				=     model(pixel_values=snake_case__,   pixel_mask=snake_case__  )
																lowercase_	:   Any  				=     model(snake_case__,   output_hidden_states=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(snake_case__,   snake_case__  )






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






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     MaskaFormerForUniversalSegmentation(config=snake_case__  )
									model.to(snake_case__  )
									model.eval()

									def comm_check_on_output(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():
																lowercase_	:   Tuple  				=     model(pixel_values=snake_case__,   pixel_mask=snake_case__  )
																lowercase_	:   Dict  				=     model(snake_case__  )

																comm_check_on_output(snake_case__  )

																lowercase_	:   Any  				=     model(
																    pixel_values=snake_case__,   pixel_mask=snake_case__,   mask_labels=snake_case__,   class_labels=snake_case__  )

									comm_check_on_output(snake_case__  )

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




@require_torch
class     UpperCamelCase__					(     lowerCamelCase__				, lowerCamelCase__				, unittest.TestCase				):






		'''simple docstring'''






		__a       :       List[str]       =	(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
		__a       :       Dict       =	{"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

		__a       :       int       =	False
		__a       :       Dict       =	False
		__a       :       str       =	False
		__a       :       List[Any]       =	False






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






									"""simple docstring"""
									lowercase_	:   Optional[Any]  				=     MaskaFormerModelTester(self  )
									lowercase_	:   Optional[int]  				=     ConfigTester(self,   config_class=snake_case__,   has_text_modality=snake_case__  )






		def 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									self.config_tester.run_common_tests()






		def 						snake_case__				(							self  )       -> int:






									"""simple docstring"""
									lowercase_				,		lowercase_	:   Optional[Any]  				=     self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskaformer_model(snake_case__,   **snake_case__,   output_hidden_states=snake_case__  )






		def 						snake_case__				(							self  )       -> Optional[int]:






									"""simple docstring"""
									lowercase_	:   int  				=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*snake_case__  )






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






									"""simple docstring"""
									pass






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






									"""simple docstring"""
									pass






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






									"""simple docstring"""
									pass






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






									"""simple docstring"""
									pass






		def 						snake_case__				(							self  )       -> Any:






									"""simple docstring"""
									lowercase_				,		lowercase_	:   Tuple  				=     self.model_tester.prepare_config_and_inputs_for_common()

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

																lowercase_	:   Tuple  				=     ["""pixel_values"""]
																self.assertListEqual(arg_names[:1],   snake_case__  )






		@slow
		def 						snake_case__				(							self  )       -> Optional[int]:






									"""simple docstring"""
									for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																lowercase_	:   Any  				=     MaskaFormerModel.from_pretrained(snake_case__  )
																self.assertIsNotNone(snake_case__  )






		def 						snake_case__				(							self  )       -> str:






									"""simple docstring"""
									lowercase_	:   int  				=     (self.model_tester.min_size,) * 2
									lowercase_	:   str  				=     {
									    """pixel_values""": torch.randn((2, 3, *size),   device=snake_case__  ),
									    """mask_labels""": torch.randn((2, 10, *size),   device=snake_case__  ),
									    """class_labels""": torch.zeros(2,   10,   device=snake_case__  ).long(),
									}
									lowercase_	:   Optional[Any]  				=     self.model_tester.get_config()

									lowercase_	:   List[Any]  				=     MaskaFormerForUniversalSegmentation(snake_case__  ).to(snake_case__  )
									lowercase_	:   Any  				=     model(**snake_case__  )
									self.assertTrue(outputs.loss is not None  )






		def 						snake_case__				(							self  )       -> int:






									"""simple docstring"""
									lowercase_				,		lowercase_	:   List[str]  				=     self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskaformer_model(snake_case__,   **snake_case__,   output_hidden_states=snake_case__  )






		def 						snake_case__				(							self  )       -> Any:






									"""simple docstring"""
									lowercase_				,		lowercase_	:   List[str]  				=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																lowercase_	:   Optional[Any]  				=     model_class(snake_case__  ).to(snake_case__  )
																lowercase_	:   Optional[int]  				=     model(**snake_case__,   output_attentions=snake_case__  )
																self.assertTrue(outputs.attentions is not None  )






		def 						snake_case__				(							self  )       -> Optional[int]:






									"""simple docstring"""
									if not self.model_tester.is_training:
																return

									lowercase_	:   Union[str, Any]  				=     self.all_model_classes[1]
									lowercase_				,		lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   int  				=     self.model_tester.prepare_config_and_inputs()

									lowercase_	:   Dict  				=     model_class(snake_case__  )
									model.to(snake_case__  )
									model.train()

									lowercase_	:   Optional[Any]  				=     model(snake_case__,   mask_labels=snake_case__,   class_labels=snake_case__  ).loss
									loss.backward()






		def 						snake_case__				(							self  )       -> int:






									"""simple docstring"""
									lowercase_	:   List[Any]  				=     self.all_model_classes[1]
									lowercase_				,		lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   str  				=     self.model_tester.prepare_config_and_inputs()
									lowercase_	:   Tuple  				=     True
									lowercase_	:   int  				=     True

									lowercase_	:   str  				=     model_class(snake_case__  ).to(snake_case__  )
									model.train()

									lowercase_	:   Tuple  				=     model(snake_case__,   mask_labels=snake_case__,   class_labels=snake_case__  )

									lowercase_	:   List[str]  				=     outputs.encoder_hidden_states[0]
									encoder_hidden_states.retain_grad()

									lowercase_	:   Tuple  				=     outputs.pixel_decoder_hidden_states[0]
									pixel_decoder_hidden_states.retain_grad()

									lowercase_	:   Optional[int]  				=     outputs.transformer_decoder_hidden_states[0]
									transformer_decoder_hidden_states.retain_grad()

									lowercase_	:   List[str]  				=     outputs.attentions[0]
									attentions.retain_grad()

									outputs.loss.backward(retain_graph=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  )




UpperCAmelCase_							=				1e-4





def  __magic_name__  (				)				-> Tuple:

							"""simple docstring"""



							lowercase_	:   Optional[Any]  				=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
							return image




@require_vision
@slow
class     UpperCamelCase__					(     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  )       -> Union[str, Any]:






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






		def 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     MaskaFormerModel.from_pretrained(self.model_checkpoints  ).to(snake_case__  )
									lowercase_	:   Any  				=     self.default_image_processor
									lowercase_	:   int  				=     prepare_img()
									lowercase_	:   List[Any]  				=     image_processor(snake_case__,   return_tensors="""pt"""  ).to(snake_case__  )
									lowercase_	:   Tuple  				=     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(snake_case__,   (1, 3, 3_84, 3_84)  )

									with torch.no_grad():
																lowercase_	:   List[str]  				=     model(**snake_case__  )

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

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

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






		def 						snake_case__				(							self  )       -> Optional[int]:






									"""simple docstring"""
									lowercase_	:   List[Any]  				=     MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints  ).to(snake_case__  ).eval()
									lowercase_	:   List[str]  				=     self.default_image_processor
									lowercase_	:   List[Any]  				=     prepare_img()
									lowercase_	:   List[Any]  				=     image_processor(snake_case__,   return_tensors="""pt"""  ).to(snake_case__  )
									lowercase_	:   str  				=     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(snake_case__,   (1, 3, 3_84, 3_84)  )

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






		def 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									lowercase_	:   int  				=     MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints  ).to(snake_case__  ).eval()
									lowercase_	:   Any  				=     self.default_image_processor

									lowercase_	:   Optional[int]  				=     image_processor(
									    [np.zeros((3, 8_00, 13_33)  ), np.zeros((3, 8_00, 13_33)  )],   segmentation_maps=[np.zeros((3_84, 3_84)  ).astype(np.floataa  ), np.zeros((3_84, 3_84)  ).astype(np.floataa  )],   return_tensors="""pt""",   )

									lowercase_	:   Optional[int]  				=     inputs["""pixel_values"""].to(snake_case__  )
									lowercase_	:   Any  				=     [el.to(snake_case__  ) for el in inputs["""mask_labels"""]]
									lowercase_	:   Any  				=     [el.to(snake_case__  ) for el in inputs["""class_labels"""]]

									with torch.no_grad():
																lowercase_	:   List[str]  				=     model(**snake_case__  )

									self.assertTrue(outputs.loss is not None  )






UpperCAmelCase_							=				8.31_44_62  # Unit - J mol-1 K-1





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> float:

							"""simple docstring"""



							if moles < 0 or kelvin < 0 or volume < 0:
														raise ValueError("""Invalid inputs. Enter positive value."""				)
							return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> float:

							"""simple docstring"""



							if moles < 0 or kelvin < 0 or pressure < 0:
														raise ValueError("""Invalid inputs. Enter positive value."""				)
							return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
							from doctest import testmod

							testmod()