import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

				if is_vision_available():
								from transformers import MaskaFormerImageProcessor

if is_vision_available():
				from PIL import Image



class        lowerCamelCase__				:





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





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase							=							floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
										    lowercase__						)

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

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

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										_lowerCAmelCase							=							MaskaFormerConfig(
										    hidden_size=self.hidden_dim  ,  )
										_lowerCAmelCase							=							self.num_queries
										_lowerCAmelCase							=							self.num_labels

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

										_lowerCAmelCase							=							64
										_lowerCAmelCase							=							1_28
										_lowerCAmelCase							=							self.hidden_dim
										_lowerCAmelCase							=							self.hidden_dim
										_lowerCAmelCase							=							self.hidden_dim
										return config





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase							=							self.prepare_config_and_inputs()
										_lowerCAmelCase							=							{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
										return config, inputs_dict





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict  ,  lowercase__	:	Dict  ,  lowercase__	:	int						):
										_lowerCAmelCase							=							output.encoder_hidden_states
										_lowerCAmelCase							=							output.pixel_decoder_hidden_states
										_lowerCAmelCase							=							output.transformer_decoder_hidden_states

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	str  ,  lowercase__	:	Union[str, Any]  ,  lowercase__	:	Dict  ,  lowercase__	:	Dict  ,  lowercase__	:	Any=False						):
										with torch.no_grad():
																	_lowerCAmelCase							=							MaskaFormerModel(config=lowercase__						)
																	model.to(lowercase__						)
																	model.eval()

																	_lowerCAmelCase							=							model(pixel_values=lowercase__  ,  pixel_mask=lowercase__						)
																	_lowerCAmelCase							=							model(lowercase__  ,  output_hidden_states=lowercase__						)

										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(lowercase__  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	str  ,  lowercase__	:	List[str]  ,  lowercase__	:	int  ,  lowercase__	:	str  ,  lowercase__	:	Any  ,  lowercase__	:	Any						):
										_lowerCAmelCase							=							MaskaFormerForUniversalSegmentation(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()

										def comm_check_on_output(lowercase__	:	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():
																	_lowerCAmelCase							=							model(pixel_values=lowercase__  ,  pixel_mask=lowercase__						)
																	_lowerCAmelCase							=							model(lowercase__						)

																	comm_check_on_output(lowercase__						)

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

										comm_check_on_output(lowercase__						)

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







@require_torch
class        lowerCamelCase__				(   UpperCAmelCase       ,UpperCAmelCase       ,unittest.TestCase				):
			UpperCamelCase__    							=(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
			UpperCamelCase__    							={"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

			UpperCamelCase__    							=False
			UpperCamelCase__    							=False
			UpperCamelCase__    							=False
			UpperCamelCase__    							=False





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										_lowerCAmelCase							=							MaskaFormerModelTester(self						)
										_lowerCAmelCase							=							ConfigTester(self  ,  config_class=lowercase__  ,  has_text_modality=lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										self.config_tester.run_common_tests()





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										_lowerCAmelCase			,		_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.create_and_check_maskaformer_model(lowercase__  ,  **lowercase__  ,  output_hidden_states=lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowercase__						)





			@unittest.skip(reason='Mask2Former does not use inputs_embeds'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										pass





			@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										pass





			@unittest.skip(reason='Mask2Former is not a generative model'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										pass





			@unittest.skip(reason='Mask2Former does not use token embeddings'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										pass





			@require_torch_multi_gpu
			@unittest.skip(
			    reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]						):
										pass





			@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]						):
										pass





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase			,		_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs_for_common()

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

																	_lowerCAmelCase							=							['pixel_values']
																	self.assertListEqual(arg_names[:1]  ,  lowercase__						)





			@slow
			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																	_lowerCAmelCase							=							MaskaFormerModel.from_pretrained(lowercase__						)
																	self.assertIsNotNone(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										_lowerCAmelCase							=							(self.model_tester.min_size,) * 2
										_lowerCAmelCase							=							{
										    'pixel_values': torch.randn((2, 3, *size)  ,  device=lowercase__						),
										    'mask_labels': torch.randn((2, 10, *size)  ,  device=lowercase__						),
										    'class_labels': torch.zeros(2  ,  10  ,  device=lowercase__						).long(),
										}
										_lowerCAmelCase							=							self.model_tester.get_config()

										_lowerCAmelCase							=							MaskaFormerForUniversalSegmentation(lowercase__						).to(lowercase__						)
										_lowerCAmelCase							=							model(**lowercase__						)
										self.assertTrue(outputs.loss is not None						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										_lowerCAmelCase			,		_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.create_and_check_maskaformer_model(lowercase__  ,  **lowercase__  ,  output_hidden_states=lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										_lowerCAmelCase			,		_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs_for_common()

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										if not self.model_tester.is_training:
																	return

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

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

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase							=							self.all_model_classes[1]
										_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										_lowerCAmelCase							=							True
										_lowerCAmelCase							=							True

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

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

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

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

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

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

										outputs.loss.backward(retain_graph=lowercase__						)

										self.assertIsNotNone(encoder_hidden_states.grad						)
										self.assertIsNotNone(pixel_decoder_hidden_states.grad						)
										self.assertIsNotNone(transformer_decoder_hidden_states.grad						)
										self.assertIsNotNone(attentions.grad						)







_lowercase:	Tuple       			=		1E-4






def _lowerCamelCase  (		):
							_lowerCAmelCase							=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
							return image



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





			@cached_property
			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										return "facebook/mask2former-swin-small-coco-instance"





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





			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase							=							MaskaFormerModel.from_pretrained(self.model_checkpoints						).to(lowercase__						)
										_lowerCAmelCase							=							self.default_image_processor
										_lowerCAmelCase							=							prepare_img()
										_lowerCAmelCase							=							image_processor(lowercase__  ,  return_tensors='pt'						).to(lowercase__						)
										_lowerCAmelCase							=							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(lowercase__  ,  (1, 3, 3_84, 3_84)						)

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

										_lowerCAmelCase							=							torch.tensor(
										    [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]						).to(lowercase__						)
										self.assertTrue(
										    torch.allclose(
										        outputs.encoder_last_hidden_state[0, 0, :3, :3]  ,  lowercase__  ,  atol=lowercase__						)						)

										_lowerCAmelCase							=							torch.tensor(
										    [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]						).to(lowercase__						)
										self.assertTrue(
										    torch.allclose(
										        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]  ,  lowercase__  ,  atol=lowercase__						)						)

										_lowerCAmelCase							=							torch.tensor(
										    [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]						).to(lowercase__						)
										self.assertTrue(
										    torch.allclose(
										        outputs.transformer_decoder_last_hidden_state[0, :3, :3]  ,  lowercase__  ,  atol=lowercase__						)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(lowercase__						).eval()
										_lowerCAmelCase							=							self.default_image_processor
										_lowerCAmelCase							=							prepare_img()
										_lowerCAmelCase							=							image_processor(lowercase__  ,  return_tensors='pt'						).to(lowercase__						)
										_lowerCAmelCase							=							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(lowercase__  ,  (1, 3, 3_84, 3_84)						)

										with torch.no_grad():
																	_lowerCAmelCase							=							model(**lowercase__						)
										# masks_queries_logits
										_lowerCAmelCase							=							outputs.masks_queries_logits
										self.assertEqual(
										    masks_queries_logits.shape  ,  (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						)
										_lowerCAmelCase							=							[
										    [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
										    [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
										    [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
										]
										_lowerCAmelCase							=							torch.tensor(lowercase__						).to(lowercase__						)
										self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,  lowercase__  ,  atol=lowercase__						)						)
										# class_queries_logits
										_lowerCAmelCase							=							outputs.class_queries_logits
										self.assertEqual(class_queries_logits.shape  ,  (1, model.config.num_queries, model.config.num_labels + 1)						)
										_lowerCAmelCase							=							torch.tensor(
										    [
										        [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
										        [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
										        [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
										    ]						).to(lowercase__						)
										self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,  lowercase__  ,  atol=lowercase__						)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase							=							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(lowercase__						).eval()
										_lowerCAmelCase							=							self.default_image_processor

										_lowerCAmelCase							=							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'  ,  )

										_lowerCAmelCase							=							inputs['pixel_values'].to(lowercase__						)
										_lowerCAmelCase							=							[el.to(lowercase__						) for el in inputs['mask_labels']]
										_lowerCAmelCase							=							[el.to(lowercase__						) for el in inputs['class_labels']]

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

										self.assertTrue(outputs.loss is not None						)







import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowercase:	List[str]       			=		logging.get_logger(__name__)  # pylint: disable=invalid-name






def _lowerCamelCase  (		snake_case  ):
							warnings.warn(
							    'The preprocess method is deprecated and will be removed in a future version. Please'
							    ' use VaeImageProcessor.preprocess instead'     , snake_case     , )
							if isinstance(snake_case     , torch.Tensor  ):
														return image
							elif isinstance(snake_case     , PIL.Image.Image  ):
														_lowerCAmelCase							=							[image]

							if isinstance(image[0]     , PIL.Image.Image  ):
														_lowerCAmelCase			,		_lowerCAmelCase							=							image[0].size
														_lowerCAmelCase			,		_lowerCAmelCase							=							(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

														_lowerCAmelCase							=							[np.array(i.resize((w, h)     , resample=PIL_INTERPOLATION['lanczos']  )  )[None, :] for i in image]
														_lowerCAmelCase							=							np.concatenate(snake_case     , axis=0  )
														_lowerCAmelCase							=							np.array(snake_case  ).astype(np.floataa  ) / 2_55.0
														_lowerCAmelCase							=							image.transpose(0     , 3     , 1     , 2  )
														_lowerCAmelCase							=							2.0 * image - 1.0
														_lowerCAmelCase							=							torch.from_numpy(snake_case  )
							elif isinstance(image[0]     , torch.Tensor  ):
														_lowerCAmelCase							=							torch.cat(snake_case     , dim=0  )
							return image






def _lowerCamelCase  (		snake_case  ):
							if isinstance(snake_case     , torch.Tensor  ):
														return mask
							elif isinstance(snake_case     , PIL.Image.Image  ):
														_lowerCAmelCase							=							[mask]

							if isinstance(mask[0]     , PIL.Image.Image  ):
														_lowerCAmelCase			,		_lowerCAmelCase							=							mask[0].size
														_lowerCAmelCase			,		_lowerCAmelCase							=							(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														_lowerCAmelCase							=							[np.array(m.convert('L'  ).resize((w, h)     , resample=PIL_INTERPOLATION['nearest']  )  )[None, :] for m in mask]
														_lowerCAmelCase							=							np.concatenate(snake_case     , axis=0  )
														_lowerCAmelCase							=							mask.astype(np.floataa  ) / 2_55.0
														_lowerCAmelCase							=							0
														_lowerCAmelCase							=							1
														_lowerCAmelCase							=							torch.from_numpy(snake_case  )
							elif isinstance(mask[0]     , torch.Tensor  ):
														_lowerCAmelCase							=							torch.cat(snake_case     , dim=0  )
							return mask



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=42
			UpperCamelCase__    							=42





			def __init__(       self	:	int  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Optional[Any]						):
										super().__init__()
										self.register_modules(unet=lowercase__  ,  scheduler=lowercase__						)





			@torch.no_grad()
			def __call__(       self	:	Any  ,  lowercase__	:	Union[torch.Tensor, PIL.Image.Image]  ,  lowercase__	:	Union[torch.Tensor, PIL.Image.Image]  ,  lowercase__	:	int = 2_50  ,  lowercase__	:	float = 0.0  ,  lowercase__	:	int = 10  ,  lowercase__	:	int = 10  ,  lowercase__	:	Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,  lowercase__	:	Optional[str] = "pil"  ,  lowercase__	:	bool = True  ,  ):
										_lowerCAmelCase							=							image

										_lowerCAmelCase							=							_preprocess_image(lowercase__						)
										_lowerCAmelCase							=							original_image.to(device=self.device  ,  dtype=self.unet.dtype						)
										_lowerCAmelCase							=							_preprocess_mask(lowercase__						)
										_lowerCAmelCase							=							mask_image.to(device=self.device  ,  dtype=self.unet.dtype						)

										_lowerCAmelCase							=							original_image.shape[0]

										# sample gaussian noise to begin the loop
										if isinstance(lowercase__  ,  lowercase__						) and len(lowercase__						) != batch_size:
																	raise ValueError(
																	    f'You have passed a list of generators of length {len(lowercase__						)}, but requested an effective batch'
																	    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'						)

										_lowerCAmelCase							=							original_image.shape
										_lowerCAmelCase							=							randn_tensor(lowercase__  ,  generator=lowercase__  ,  device=self.device  ,  dtype=self.unet.dtype						)

										# set step values
										self.scheduler.set_timesteps(lowercase__  ,  lowercase__  ,  lowercase__  ,  self.device						)
										_lowerCAmelCase							=							eta

										_lowerCAmelCase							=							self.scheduler.timesteps[0] + 1
										_lowerCAmelCase							=							generator[0] if isinstance(lowercase__  ,  lowercase__						) else generator
										for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
																	if t < t_last:
																								# predict the noise residual
																								_lowerCAmelCase							=							self.unet(lowercase__  ,  lowercase__						).sample
																								# compute previous image: x_t -> x_t-1
																								_lowerCAmelCase							=							self.scheduler.step(lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__						).prev_sample

																	else:
																								# compute the reverse: x_t-1 -> x_t
																								_lowerCAmelCase							=							self.scheduler.undo_step(lowercase__  ,  lowercase__  ,  lowercase__						)
																	_lowerCAmelCase							=							t

										_lowerCAmelCase							=							(image / 2 + 0.5).clamp(0  ,  1						)
										_lowerCAmelCase							=							image.cpu().permute(0  ,  2  ,  3  ,  1						).numpy()
										if output_type == "pil":
																	_lowerCAmelCase							=							self.numpy_to_pil(lowercase__						)

										if not return_dict:
																	return (image,)

										return ImagePipelineOutput(images=lowercase__						)






