


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, 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 MaskFormerForInstanceSegmentation, MaskFormerModel

	if is_vision_available():
		from transformers import MaskFormerImageProcessor

if is_vision_available():
	from PIL import Image



class 							A   :
					'''simple docstring'''






					def __init__(self   :						Any				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						List[Any]=2				, _UpperCAmelCase   :						int=True				, _UpperCAmelCase   :						List[Any]=False				, _UpperCAmelCase   :						Optional[int]=10				, _UpperCAmelCase   :						List[str]=3				, _UpperCAmelCase   :						int=32 * 4				, _UpperCAmelCase   :						Dict=32 * 6				, _UpperCAmelCase   :						Union[str, Any]=4				, _UpperCAmelCase   :						int=32				, )						->       List[Any]:




							"""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__					      =		mask_feature_size

					def   lowerCamelCase__					(self   :						str		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
							    _SCREAMING_SNAKE_CASE		)

							lowercase__					      =		torch.ones([self.batch_size, self.min_size, self.max_size]				, device=_SCREAMING_SNAKE_CASE		)

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

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

					def   lowerCamelCase__					(self   :						Dict		)						->       Dict:




							"""simple docstring"""


							return MaskFormerConfig.from_backbone_and_decoder_configs(
							    backbone_config=SwinConfig(
							        depths=[1, 1, 1, 1]				, )				, decoder_config=DetrConfig(
							        decoder_ffn_dim=128				, num_queries=self.num_queries				, decoder_attention_heads=2				, d_model=self.mask_feature_size				, )				, mask_feature_size=self.mask_feature_size				, fpn_feature_size=self.mask_feature_size				, num_channels=self.num_channels				, num_labels=self.num_labels				, )

					def   lowerCamelCase__					(self   :						Tuple		)						->       str:




							"""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   lowerCamelCase__					(self   :						Union[str, Any]				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						List[str]		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		output.encoder_hidden_states
							lowercase__					      =		output.pixel_decoder_hidden_states
							lowercase__					      =		output.transformer_decoder_hidden_states

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

					def   lowerCamelCase__					(self   :						Union[str, Any]				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						int=False		)						->       List[Any]:




							"""simple docstring"""


							with torch.no_grad():
									lowercase__					      =		MaskFormerModel(config=_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()

									lowercase__					      =		model(pixel_values=_SCREAMING_SNAKE_CASE				, pixel_mask=_SCREAMING_SNAKE_CASE		)
									lowercase__					      =		model(_SCREAMING_SNAKE_CASE				, output_hidden_states=_SCREAMING_SNAKE_CASE		)
							# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
							# encoder and pixel decoder
							self.parent.assertEqual(
							    output.transformer_decoder_last_hidden_state.shape				, (self.batch_size, self.num_queries, self.mask_feature_size)				, )
							# 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(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE		)


					def   lowerCamelCase__					(self   :						Optional[int]				, _UpperCAmelCase   :						Any				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						Optional[int]		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		MaskFormerForInstanceSegmentation(config=_SCREAMING_SNAKE_CASE		)
							model.to(_SCREAMING_SNAKE_CASE		)
							model.eval()

							def comm_check_on_output(_UpperCAmelCase   :						Optional[int]		):
									# 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=_SCREAMING_SNAKE_CASE				, pixel_mask=_SCREAMING_SNAKE_CASE		)
									lowercase__					      =		model(_SCREAMING_SNAKE_CASE		)

									comm_check_on_output(_SCREAMING_SNAKE_CASE		)

									lowercase__					      =		model(
									    pixel_values=_SCREAMING_SNAKE_CASE				, pixel_mask=_SCREAMING_SNAKE_CASE				, mask_labels=_SCREAMING_SNAKE_CASE				, class_labels=_SCREAMING_SNAKE_CASE		)

							comm_check_on_output(_SCREAMING_SNAKE_CASE		)

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






@require_torch
class 							A   (	_UpperCamelCase			,  _UpperCamelCase			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
					A__      =			(
					    {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
					    if is_torch_available()
					    else {}
					)

					A__      =			False
					A__      =			False
					A__      =			False
					A__      =			False

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




							"""simple docstring"""


							lowercase__					      =		MaskFormerModelTester(self		)
							lowercase__					      =		ConfigTester(self				, config_class=_SCREAMING_SNAKE_CASE				, has_text_modality=_SCREAMING_SNAKE_CASE		)

					def   lowerCamelCase__					(self   :						Any		)						->       Optional[int]:




							"""simple docstring"""


							self.config_tester.run_common_tests()

					def   lowerCamelCase__					(self   :						int		)						->       Dict:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.create_and_check_maskformer_model(_SCREAMING_SNAKE_CASE				, **_SCREAMING_SNAKE_CASE				, output_hidden_states=_SCREAMING_SNAKE_CASE		)

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




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_SCREAMING_SNAKE_CASE		)

					@unittest.skip(reason="""MaskFormer does not use inputs_embeds"""		)
					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       Union[str, Any]:




							"""simple docstring"""


							pass

					@unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""		)
					def   lowerCamelCase__					(self   :						int		)						->       Optional[int]:




							"""simple docstring"""


							pass

					@unittest.skip(reason="""MaskFormer is not a generative model"""		)
					def   lowerCamelCase__					(self   :						List[str]		)						->       Dict:




							"""simple docstring"""


							pass

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




							"""simple docstring"""


							pass

					@require_torch_multi_gpu
					@unittest.skip(
					    reason="""MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`"""		)
					def   lowerCamelCase__					(self   :						Union[str, Any]		)						->       Union[str, Any]:




							"""simple docstring"""


							pass

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




							"""simple docstring"""


							pass

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       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(_SCREAMING_SNAKE_CASE		)
									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]				, _SCREAMING_SNAKE_CASE		)

					@slow
					def   lowerCamelCase__					(self   :						Union[str, Any]		)						->       Optional[Any]:




							"""simple docstring"""


							for model_name in ["facebook/maskformer-swin-small-coco"]:
									lowercase__					      =		MaskFormerModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
									self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)

					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       int:




							"""simple docstring"""


							lowercase__					      =		(self.model_tester.min_size,) * 2
							lowercase__					      =		{
							    """pixel_values""": torch.randn((2, 3, *size)				, device=_SCREAMING_SNAKE_CASE		),
							    """mask_labels""": torch.randn((2, 10, *size)				, device=_SCREAMING_SNAKE_CASE		),
							    """class_labels""": torch.zeros(2				, 10				, device=_SCREAMING_SNAKE_CASE		).long(),
							}

							lowercase__					      =		MaskFormerForInstanceSegmentation(MaskFormerConfig()		).to(_SCREAMING_SNAKE_CASE		)
							lowercase__					      =		model(**_SCREAMING_SNAKE_CASE		)
							self.assertTrue(outputs.loss is not None		)

					def   lowerCamelCase__					(self   :						List[str]		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.create_and_check_maskformer_model(_SCREAMING_SNAKE_CASE				, **_SCREAMING_SNAKE_CASE				, output_hidden_states=_SCREAMING_SNAKE_CASE		)

					def   lowerCamelCase__					(self   :						str		)						->       Dict:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

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

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




							"""simple docstring"""


							if not self.model_tester.is_training:
									return
							# only MaskFormerForInstanceSegmentation has the loss
							lowercase__					      =		self.all_model_classes[1]
							lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs()

							lowercase__					      =		model_class(_SCREAMING_SNAKE_CASE		)
							model.to(_SCREAMING_SNAKE_CASE		)
							model.train()

							lowercase__					      =		model(_SCREAMING_SNAKE_CASE				, mask_labels=_SCREAMING_SNAKE_CASE				, class_labels=_SCREAMING_SNAKE_CASE		).loss
							loss.backward()


					def   lowerCamelCase__					(self   :						Dict		)						->       int:




							"""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(_SCREAMING_SNAKE_CASE		)
							model.to(_SCREAMING_SNAKE_CASE		)
							model.train()

							lowercase__					      =		model(_SCREAMING_SNAKE_CASE				, mask_labels=_SCREAMING_SNAKE_CASE				, class_labels=_SCREAMING_SNAKE_CASE		)

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

							lowercase__					      =		outputs.pixel_decoder_hidden_states[0]
							pixel_decoder_hidden_states.retain_grad()
							# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
							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=_SCREAMING_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		)






A	:		Tuple    =       1e-4






def 			UpperCamelCase (     )       ->  List[Any]:
		"""simple docstring"""




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



@require_vision
@slow
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






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




							"""simple docstring"""


							return (
							    MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""		)
							    if is_vision_available()
							    else None
							)

					def   lowerCamelCase__					(self   :						str		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""		).to(_SCREAMING_SNAKE_CASE		)
							lowercase__					      =		self.default_image_processor
							lowercase__					      =		prepare_img()
							lowercase__					      =		image_processor(_SCREAMING_SNAKE_CASE				, return_tensors="""pt"""		).to(_SCREAMING_SNAKE_CASE		)
							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(_SCREAMING_SNAKE_CASE				, (1, 3, 800, 1088)		)

							with torch.no_grad():
									lowercase__					      =		model(**_SCREAMING_SNAKE_CASE		)

							lowercase__					      =		torch.tensor(
							    [[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]]		).to(_SCREAMING_SNAKE_CASE		)
							self.assertTrue(
							    torch.allclose(
							        outputs.encoder_last_hidden_state[0, 0, :3, :3]				, _SCREAMING_SNAKE_CASE				, atol=_SCREAMING_SNAKE_CASE		)		)

							lowercase__					      =		torch.tensor(
							    [[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]]		).to(_SCREAMING_SNAKE_CASE		)
							self.assertTrue(
							    torch.allclose(
							        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]				, _SCREAMING_SNAKE_CASE				, atol=_SCREAMING_SNAKE_CASE		)		)

							lowercase__					      =		torch.tensor(
							    [[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]]		).to(_SCREAMING_SNAKE_CASE		)
							self.assertTrue(
							    torch.allclose(
							        outputs.transformer_decoder_last_hidden_state[0, :3, :3]				, _SCREAMING_SNAKE_CASE				, atol=_SCREAMING_SNAKE_CASE		)		)

					def   lowerCamelCase__					(self   :						List[str]		)						->       int:




							"""simple docstring"""


							lowercase__					      =		(
							    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""		)
							    .to(_SCREAMING_SNAKE_CASE		)
							    .eval()
							)
							lowercase__					      =		self.default_image_processor
							lowercase__					      =		prepare_img()
							lowercase__					      =		image_processor(_SCREAMING_SNAKE_CASE				, return_tensors="""pt"""		).to(_SCREAMING_SNAKE_CASE		)
							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(_SCREAMING_SNAKE_CASE				, (1, 3, 800, 1088)		)

							with torch.no_grad():
									lowercase__					      =		model(**_SCREAMING_SNAKE_CASE		)
							# masks_queries_logits
							lowercase__					      =		outputs.masks_queries_logits
							self.assertEqual(
							    masks_queries_logits.shape				, (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)				, )
							lowercase__					      =		[
							    [-1.3_737_124, -1.7_724_937, -1.9_364_233],
							    [-1.5_977_281, -1.9_867_939, -2.1_523_695],
							    [-1.5_795_398, -1.9_269_832, -2.093_942],
							]
							lowercase__					      =		torch.tensor(_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)
							self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]				, _SCREAMING_SNAKE_CASE				, atol=_SCREAMING_SNAKE_CASE		)		)
							# class_queries_logits
							lowercase__					      =		outputs.class_queries_logits
							self.assertEqual(
							    class_queries_logits.shape				, (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)		)
							lowercase__					      =		torch.tensor(
							    [
							        [1.6_5_1_2E0_0, -5.2_5_7_2E0_0, -3.3_5_1_9E0_0],
							        [3.6_1_6_9E-0_2, -5.9_0_2_5E0_0, -2.9_3_1_3E0_0],
							        [1.0_7_6_6E-0_4, -7.7_6_3_0E0_0, -5.1_2_6_3E0_0],
							    ]		).to(_SCREAMING_SNAKE_CASE		)
							self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]				, _SCREAMING_SNAKE_CASE				, atol=_SCREAMING_SNAKE_CASE		)		)

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




							"""simple docstring"""


							lowercase__					      =		(
							    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""		)
							    .to(_SCREAMING_SNAKE_CASE		)
							    .eval()
							)
							lowercase__					      =		self.default_image_processor
							lowercase__					      =		prepare_img()
							lowercase__					      =		image_processor(_SCREAMING_SNAKE_CASE				, return_tensors="""pt"""		).to(_SCREAMING_SNAKE_CASE		)
							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(_SCREAMING_SNAKE_CASE				, (1, 3, 800, 1088)		)

							with torch.no_grad():
									lowercase__					      =		model(**_SCREAMING_SNAKE_CASE		)
							# masks_queries_logits
							lowercase__					      =		outputs.masks_queries_logits
							self.assertEqual(
							    masks_queries_logits.shape				, (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)				, )
							lowercase__					      =		[[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
							lowercase__					      =		torch.tensor(_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)
							self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]				, _SCREAMING_SNAKE_CASE				, atol=_SCREAMING_SNAKE_CASE		)		)
							# class_queries_logits
							lowercase__					      =		outputs.class_queries_logits
							self.assertEqual(
							    class_queries_logits.shape				, (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)		)
							lowercase__					      =		torch.tensor(
							    [[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]]		).to(_SCREAMING_SNAKE_CASE		)
							self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]				, _SCREAMING_SNAKE_CASE				, atol=_SCREAMING_SNAKE_CASE		)		)


					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		(
							    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""		)
							    .to(_SCREAMING_SNAKE_CASE		)
							    .eval()
							)
							lowercase__					      =		self.default_image_processor

							lowercase__					      =		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"""				, )

							lowercase__					      =		inputs["""pixel_values"""].to(_SCREAMING_SNAKE_CASE		)
							lowercase__					      =		[el.to(_SCREAMING_SNAKE_CASE		) for el in inputs["""mask_labels"""]]
							lowercase__					      =		[el.to(_SCREAMING_SNAKE_CASE		) for el in inputs["""class_labels"""]]

							with torch.no_grad():
									lowercase__					      =		model(**_SCREAMING_SNAKE_CASE		)

							self.assertTrue(outputs.loss is not None		)





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			42
					A__      =			42

					def __init__(self   :						Union[str, Any]				, _UpperCAmelCase   :						UNetaDModel				, _UpperCAmelCase   :						ScoreSdeVeScheduler		)						->       List[str]:




							"""simple docstring"""


							super().__init__()
							self.register_modules(unet=_UpperCAmelCase				, scheduler=_UpperCAmelCase		)


					@torch.no_grad()
					def __call__(self   :						Optional[Any]				, _UpperCAmelCase   :						int = 1				, _UpperCAmelCase   :						int = 2000				, _UpperCAmelCase   :						Optional[Union[torch.Generator, List[torch.Generator]]] = None				, _UpperCAmelCase   :						Optional[str] = "pil"				, _UpperCAmelCase   :						bool = True				, **_UpperCAmelCase   :						Any				, )						->       Union[ImagePipelineOutput, Tuple]:




							"""simple docstring"""


							lowercase__					      =		self.unet.config.sample_size
							lowercase__					      =		(batch_size, 3, img_size, img_size)

							lowercase__					      =		self.unet

							lowercase__					      =		randn_tensor(_UpperCAmelCase				, generator=_UpperCAmelCase		) * self.scheduler.init_noise_sigma
							lowercase__					      =		sample.to(self.device		)

							self.scheduler.set_timesteps(_UpperCAmelCase		)
							self.scheduler.set_sigmas(_UpperCAmelCase		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
									lowercase__					      =		self.scheduler.sigmas[i] * torch.ones(shape[0]				, device=self.device		)

									# correction step
									for _ in range(self.scheduler.config.correct_steps		):
											lowercase__					      =		self.unet(_UpperCAmelCase				, _UpperCAmelCase		).sample
											lowercase__					      =		self.scheduler.step_correct(_UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		).prev_sample

									# prediction step
									lowercase__					      =		model(_UpperCAmelCase				, _UpperCAmelCase		).sample
									lowercase__					      =		self.scheduler.step_pred(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		)

									lowercase__      ,     lowercase__					      =		output.prev_sample, output.prev_sample_mean

							lowercase__					      =		sample_mean.clamp(0				, 1		)
							lowercase__					      =		sample.cpu().permute(0				, 2				, 3				, 1		).numpy()
							if output_type == "pil":
									lowercase__					      =		self.numpy_to_pil(_UpperCAmelCase		)

							if not return_dict:
									return (sample,)

							return ImagePipelineOutput(images=_UpperCAmelCase		)

