





"""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  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=2   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=10   ,      lowerCAmelCase__=3   ,      lowerCAmelCase__=32 * 8   ,      lowerCAmelCase__=32 * 8   ,      lowerCAmelCase__=4   ,      lowerCAmelCase__=64   ,      )			->				Tuple:
								a   :	List[str]			=       parent
								a   :	Optional[int]			=       batch_size
								a   :	str			=       is_training
								a   :	List[str]			=       use_auxiliary_loss
								a   :	Dict			=       num_queries
								a   :	int			=       num_channels
								a   :	int			=       min_size
								a   :	Optional[Any]			=       max_size
								a   :	str			=       num_labels
								a   :	List[str]			=       hidden_dim
								a   :	Optional[int]			=       hidden_dim






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	List[str]			=       floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]   ).to(
								    lowerCAmelCase__   )

								a   :	List[Any]			=       torch.ones([self.batch_size, self.min_size, self.max_size]   ,      device=lowerCAmelCase__   )

								a   :	Any			=       (
								    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]   ,      device=lowerCAmelCase__   ) > 0.5
								).float()
								a   :	Optional[Any]			=       (torch.rand((self.batch_size, self.num_labels)   ,      device=lowerCAmelCase__   ) > 0.5).long()

								a   :	Dict			=       self.get_config()
								return config, pixel_values, pixel_mask, mask_labels, class_labels






							def 					__a     (     self   )			->				Tuple:
								a   :	str			=       MaskaFormerConfig(
								    hidden_size=self.hidden_dim   ,      )
								a   :	Any			=       self.num_queries
								a   :	str			=       self.num_labels

								a   :	Union[str, Any]			=       [1, 1, 1, 1]
								a   :	str			=       self.num_channels

								a   :	List[Any]			=       64
								a   :	Any			=       128
								a   :	Optional[int]			=       self.hidden_dim
								a   :	List[Any]			=       self.hidden_dim
								a   :	Optional[Any]			=       self.hidden_dim
								return config






							def 					__a     (     self   )			->				Optional[int]:
								a, a, a, a, a   :	List[str]			=       self.prepare_config_and_inputs()
								a   :	int			=       {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
								return config, inputs_dict






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Tuple:
								a   :	Tuple			=       output.encoder_hidden_states
								a   :	Optional[int]			=       output.pixel_decoder_hidden_states
								a   :	Optional[Any]			=       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   )			->				Optional[Any]:
								with torch.no_grad():
									a   :	List[Any]			=       MaskaFormerModel(config=lowerCAmelCase__   )
									model.to(lowerCAmelCase__   )
									model.eval()

									a   :	Optional[int]			=       model(pixel_values=lowerCAmelCase__   ,      pixel_mask=lowerCAmelCase__   )
									a   :	Union[str, Any]			=       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:
								a   :	Any			=       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():
									a   :	List[str]			=       model(pixel_values=lowerCAmelCase__   ,      pixel_mask=lowerCAmelCase__   )
									a   :	Union[str, Any]			=       model(lowerCAmelCase__   )

									comm_check_on_output(lowerCAmelCase__   )

									a   :	Tuple			=       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  __UpperCamelCase       (		a__	, a__	, unittest.TestCase		):
							lowerCamelCase     :      Optional[Any]         =(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							lowerCamelCase     :      List[str]         ={"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

							lowerCamelCase     :      int         =False
							lowerCamelCase     :      Dict         =False
							lowerCamelCase     :      Any         =False
							lowerCamelCase     :      List[Any]         =False






							def 					__a     (     self   )			->				int:
								a   :	str			=       MaskaFormerModelTester(self   )
								a   :	Optional[Any]			=       ConfigTester(self   ,      config_class=lowerCAmelCase__   ,      has_text_modality=lowerCAmelCase__   )






							def 					__a     (     self   )			->				Optional[Any]:
								self.config_tester.run_common_tests()






							def 					__a     (     self   )			->				int:
								a, a   :	Union[str, Any]			=       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   )			->				Any:
								a   :	List[str]			=       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   )			->				Dict:
								pass






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






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






							@unittest.skip(reason="Mask2Former does not use token embeddings"   )
							def 					__a     (     self   )			->				List[Any]:
								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   )			->				List[str]:
								pass






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






							def 					__a     (     self   )			->				int:
								a, a   :	List[str]			=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									a   :	Optional[Any]			=       model_class(lowerCAmelCase__   )
									a   :	Union[str, Any]			=       inspect.signature(model.forward   )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									a   :	Dict			=       [*signature.parameters.keys()]

									a   :	Tuple			=       ["pixel_values"]
									self.assertListEqual(arg_names[:1]   ,      lowerCAmelCase__   )






							@slow
							def 					__a     (     self   )			->				List[Any]:
								for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
									a   :	Optional[Any]			=       MaskaFormerModel.from_pretrained(lowerCAmelCase__   )
									self.assertIsNotNone(lowerCAmelCase__   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Union[str, Any]			=       (self.model_tester.min_size,) * 2
								a   :	List[str]			=       {
								    "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(),
								}
								a   :	List[Any]			=       self.model_tester.get_config()

								a   :	Union[str, Any]			=       MaskaFormerForUniversalSegmentation(lowerCAmelCase__   ).to(lowerCAmelCase__   )
								a   :	List[Any]			=       model(**lowerCAmelCase__   )
								self.assertTrue(outputs.loss is not None   )






							def 					__a     (     self   )			->				Optional[int]:
								a, a   :	Dict			=       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   )			->				int:
								a, a   :	int			=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									a   :	Union[str, Any]			=       model_class(lowerCAmelCase__   ).to(lowerCAmelCase__   )
									a   :	Union[str, Any]			=       model(**lowerCAmelCase__   ,      output_attentions=lowerCAmelCase__   )
									self.assertTrue(outputs.attentions is not None   )






							def 					__a     (     self   )			->				int:
								if not self.model_tester.is_training:
									return

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

								a   :	List[str]			=       model_class(lowerCAmelCase__   )
								model.to(lowerCAmelCase__   )
								model.train()

								a   :	List[str]			=       model(lowerCAmelCase__   ,      mask_labels=lowerCAmelCase__   ,      class_labels=lowerCAmelCase__   ).loss
								loss.backward()






							def 					__a     (     self   )			->				List[str]:
								a   :	int			=       self.all_model_classes[1]
								a, a, a, a, a   :	int			=       self.model_tester.prepare_config_and_inputs()
								a   :	Tuple			=       True
								a   :	int			=       True

								a   :	Optional[int]			=       model_class(lowerCAmelCase__   ).to(lowerCAmelCase__   )
								model.train()

								a   :	int			=       model(lowerCAmelCase__   ,      mask_labels=lowerCAmelCase__   ,      class_labels=lowerCAmelCase__   )

								a   :	List[Any]			=       outputs.encoder_hidden_states[0]
								encoder_hidden_states.retain_grad()

								a   :	List[str]			=       outputs.pixel_decoder_hidden_states[0]
								pixel_decoder_hidden_states.retain_grad()

								a   :	Union[str, Any]			=       outputs.transformer_decoder_hidden_states[0]
								transformer_decoder_hidden_states.retain_grad()

								a   :	Tuple			=       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   )
a		:				Tuple  			=    1E-4






def    _SCREAMING_SNAKE_CASE	(			)				->str:






	'''simple docstring'''






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







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






							@cached_property
							def 					__a     (     self   )			->				Optional[int]:
								return "facebook/mask2former-swin-small-coco-instance"






							@cached_property
							def 					__a     (     self   )			->				Dict:
								return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints   ) if is_vision_available() else None






							def 					__a     (     self   )			->				str:
								a   :	str			=       MaskaFormerModel.from_pretrained(self.model_checkpoints   ).to(lowerCAmelCase__   )
								a   :	int			=       self.default_image_processor
								a   :	Tuple			=       prepare_img()
								a   :	Optional[int]			=       image_processor(lowerCAmelCase__   ,      return_tensors="pt"   ).to(lowerCAmelCase__   )
								a   :	Any			=       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():
									a   :	int			=       model(**lowerCAmelCase__   )

								a   :	Union[str, Any]			=       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(lowerCAmelCase__   )
								self.assertTrue(
								    torch.allclose(
								        outputs.encoder_last_hidden_state[0, 0, :3, :3]   ,      lowerCAmelCase__   ,      atol=lowerCAmelCase__   )   )

								a   :	Optional[int]			=       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(lowerCAmelCase__   )
								self.assertTrue(
								    torch.allclose(
								        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]   ,      lowerCAmelCase__   ,      atol=lowerCAmelCase__   )   )

								a   :	Dict			=       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(lowerCAmelCase__   )
								self.assertTrue(
								    torch.allclose(
								        outputs.transformer_decoder_last_hidden_state[0, :3, :3]   ,      lowerCAmelCase__   ,      atol=lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				int:
								a   :	Tuple			=       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(lowerCAmelCase__   ).eval()
								a   :	List[str]			=       self.default_image_processor
								a   :	Dict			=       prepare_img()
								a   :	Optional[int]			=       image_processor(lowerCAmelCase__   ,      return_tensors="pt"   ).to(lowerCAmelCase__   )
								a   :	int			=       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():
									a   :	Optional[Any]			=       model(**lowerCAmelCase__   )
								# masks_queries_logits
								a   :	Dict			=       outputs.masks_queries_logits
								self.assertEqual(
								    masks_queries_logits.shape   ,      (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)   )
								a   :	Any			=       [
								    [-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],
								]
								a   :	Any			=       torch.tensor(lowerCAmelCase__   ).to(lowerCAmelCase__   )
								self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]   ,      lowerCAmelCase__   ,      atol=lowerCAmelCase__   )   )
								# class_queries_logits
								a   :	Tuple			=       outputs.class_queries_logits
								self.assertEqual(class_queries_logits.shape   ,      (1, model.config.num_queries, model.config.num_labels + 1)   )
								a   :	Union[str, Any]			=       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(lowerCAmelCase__   )
								self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]   ,      lowerCAmelCase__   ,      atol=lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				Tuple:
								a   :	Optional[Any]			=       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(lowerCAmelCase__   ).eval()
								a   :	List[Any]			=       self.default_image_processor

								a   :	Any			=       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"   ,      )

								a   :	int			=       inputs["pixel_values"].to(lowerCAmelCase__   )
								a   :	Union[str, Any]			=       [el.to(lowerCAmelCase__   ) for el in inputs["mask_labels"]]
								a   :	str			=       [el.to(lowerCAmelCase__   ) for el in inputs["class_labels"]]

								with torch.no_grad():
									a   :	Dict			=       model(**lowerCAmelCase__   )

								self.assertTrue(outputs.loss is not None   )









"""simple docstring"""




from ..utils import DummyObject, requires_backends


class  __UpperCamelCase       (		metaclass=a__		):
							lowerCamelCase     :      Optional[Any]         =["""transformers""", """torch""", """note_seq"""]






							def __init__(     self   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Union[str, Any]:
								requires_backends(self   ,      ["transformers", "torch", "note_seq"]   )






							@classmethod
							def 					__a     (     cls   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Any:
								requires_backends(cls   ,      ["transformers", "torch", "note_seq"]   )






							@classmethod
							def 					__a     (     cls   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				int:
								requires_backends(cls   ,      ["transformers", "torch", "note_seq"]   )


