



import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: List[str]		       =							None
	a__				: List[Any]		       =							None




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Tuple:
							return self.feat_extract_tester.prepare_feat_extract_dict()




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Tuple:
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_dict					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """feature_size"""					)					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """sampling_rate"""					)					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """padding_value"""					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			List[str]:
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_common()
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_dict					)
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}					)

							self.assertTrue(all(len(__UpperCAmelCase					) == len(__UpperCAmelCase					) for x, y in zip(__UpperCAmelCase						,  processed_features[input_name]					)					)					)

							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_common(equal_length=__UpperCAmelCase					)
							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}						,  tensor_type="""np"""					)

							UpperCAmelCase_=       processed_features[input_name]

							if len(batch_features_input.shape					) < 3:
													UpperCAmelCase_=       batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]					), self.feat_extract_tester.feature_size)					)




	@require_torch
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			int:
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_common(equal_length=__UpperCAmelCase					)
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_dict					)
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}						,  tensor_type="""pt"""					)

							UpperCAmelCase_=       processed_features[input_name]

							if len(batch_features_input.shape					) < 3:
													UpperCAmelCase_=       batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]					), self.feat_extract_tester.feature_size)					)




	@require_tf
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			List[str]:
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_common(equal_length=__UpperCAmelCase					)
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_dict					)
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}						,  tensor_type="""tf"""					)

							UpperCAmelCase_=       processed_features[input_name]

							if len(batch_features_input.shape					) < 3:
													UpperCAmelCase_=       batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]					), self.feat_extract_tester.feature_size)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict						,  __UpperCAmelCase     :						Union[str, Any]=False					)      ->			Union[str, Any]:
							def _inputs_have_equal_length(__UpperCAmelCase     :						str					):
													UpperCAmelCase_=       len(input[0]					)
													for input_slice in input[1:]:
																			if len(__UpperCAmelCase					) != length:
																									return False
													return True

							def _inputs_are_equal(__UpperCAmelCase     :						Dict						,  __UpperCAmelCase     :						List[Any]					):
													if len(__UpperCAmelCase					) != len(__UpperCAmelCase					):
																			return False

													for input_slice_a, input_slice_a in zip(__UpperCAmelCase						,  __UpperCAmelCase					):
																			if not np.allclose(np.asarray(__UpperCAmelCase					)						,  np.asarray(__UpperCAmelCase					)						,  atol=1E-3					):
																									return False
													return True

							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_dict					)
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_common(numpify=__UpperCAmelCase					)
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}					)

							UpperCAmelCase_=       self.feat_extract_tester.seq_length_diff
							UpperCAmelCase_=       self.feat_extract_tester.max_seq_length + pad_diff
							UpperCAmelCase_=       self.feat_extract_tester.min_seq_length
							UpperCAmelCase_=       self.feat_extract_tester.batch_size
							UpperCAmelCase_=       self.feat_extract_tester.feature_size

							# test padding for List[int] + numpy
							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding=__UpperCAmelCase					)
							UpperCAmelCase_=       input_a[input_name]

							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding="""longest"""					)
							UpperCAmelCase_=       input_a[input_name]

							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding="""max_length"""						,  max_length=len(speech_inputs[-1]					)					)
							UpperCAmelCase_=       input_a[input_name]

							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding="""longest"""						,  return_tensors="""np"""					)
							UpperCAmelCase_=       input_a[input_name]

							# max_length parameter has to be provided when setting `padding="max_length"`
							with self.assertRaises(__UpperCAmelCase					):
													feat_extract.pad(__UpperCAmelCase						,  padding="""max_length"""					)[input_name]

							UpperCAmelCase_=       feat_extract.pad(
							    __UpperCAmelCase						,  padding="""max_length"""						,  max_length=__UpperCAmelCase						,  return_tensors="""np"""					)
							UpperCAmelCase_=       input_a[input_name]

							self.assertFalse(_inputs_have_equal_length(__UpperCAmelCase					)					)
							self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase					)					)
							self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase					)					)
							self.assertTrue(_inputs_are_equal(__UpperCAmelCase						,  __UpperCAmelCase					)					)
							self.assertTrue(len(input_a[0]					) == pad_min_length					)
							self.assertTrue(len(input_a[1]					) == pad_min_length + pad_diff					)
							self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]					))					)
							self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)					)

							if feature_size > 1:
													self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size					)

							# test padding for `pad_to_multiple_of` for List[int] + numpy
							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  pad_to_multiple_of=10					)
							UpperCAmelCase_=       input_a[input_name]

							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding="""longest"""						,  pad_to_multiple_of=10					)
							UpperCAmelCase_=       input_a[input_name]

							UpperCAmelCase_=       feat_extract.pad(
							    __UpperCAmelCase						,  padding="""max_length"""						,  pad_to_multiple_of=10						,  max_length=__UpperCAmelCase					)
							UpperCAmelCase_=       input_a[input_name]

							UpperCAmelCase_=       feat_extract.pad(
							    __UpperCAmelCase						,  padding="""max_length"""						,  pad_to_multiple_of=10						,  max_length=__UpperCAmelCase						,  return_tensors="""np"""						,  )
							UpperCAmelCase_=       input_a[input_name]

							self.assertTrue(all(len(__UpperCAmelCase					) % 10 == 0 for x in input_a					)					)
							self.assertTrue(_inputs_are_equal(__UpperCAmelCase						,  __UpperCAmelCase					)					)

							UpperCAmelCase_=       pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
							self.assertTrue(all(len(__UpperCAmelCase					) == expected_mult_pad_length for x in input_a					)					)
							self.assertEqual(input_a.shape[:2]						,  (batch_size, expected_mult_pad_length)					)

							if feature_size > 1:
													self.assertTrue(input_a.shape[2] == feature_size					)

							# Check padding value is correct
							UpperCAmelCase_=       (np.ones(self.feat_extract_tester.feature_size					) * feat_extract.padding_value).sum()
							self.assertTrue(
							    abs(np.asarray(input_a[0]					)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)					)
							    < 1E-3					)
							self.assertTrue(
							    abs(
							        np.asarray(input_a[1]					)[pad_min_length + pad_diff :].sum()
							        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)					)
							    < 1E-3					)
							self.assertTrue(
							    abs(
							        np.asarray(input_a[2]					)[pad_min_length + 2 * pad_diff :].sum()
							        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)					)
							    < 1E-3					)
							self.assertTrue(
							    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)					) < 1E-3					)
							self.assertTrue(
							    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)					)
							    < 1E-3					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						List[Any]=False					)      ->			str:
							def _inputs_have_equal_length(__UpperCAmelCase     :						List[Any]					):
													UpperCAmelCase_=       len(input[0]					)
													for input_slice in input[1:]:
																			if len(__UpperCAmelCase					) != length:
																									return False
													return True

							def _inputs_are_equal(__UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						int					):
													if len(__UpperCAmelCase					) != len(__UpperCAmelCase					):
																			return False

													for input_slice_a, input_slice_a in zip(__UpperCAmelCase						,  __UpperCAmelCase					):
																			if not np.allclose(np.asarray(__UpperCAmelCase					)						,  np.asarray(__UpperCAmelCase					)						,  atol=1E-3					):
																									return False
													return True

							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_dict					)
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_common(numpify=__UpperCAmelCase					)
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}					)

							# truncate to smallest
							UpperCAmelCase_=       feat_extract.pad(
							    __UpperCAmelCase						,  padding="""max_length"""						,  max_length=len(speech_inputs[0]					)						,  truncation=__UpperCAmelCase					)
							UpperCAmelCase_=       input_a[input_name]

							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding="""max_length"""						,  max_length=len(speech_inputs[0]					)					)
							UpperCAmelCase_=       input_a[input_name]

							self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase					)					)
							self.assertFalse(_inputs_have_equal_length(__UpperCAmelCase					)					)

							# truncate to smallest with np
							UpperCAmelCase_=       feat_extract.pad(
							    __UpperCAmelCase						,  padding="""max_length"""						,  max_length=len(speech_inputs[0]					)						,  return_tensors="""np"""						,  truncation=__UpperCAmelCase						,  )
							UpperCAmelCase_=       input_a[input_name]

							UpperCAmelCase_=       feat_extract.pad(
							    __UpperCAmelCase						,  padding="""max_length"""						,  max_length=len(speech_inputs[0]					)						,  return_tensors="""np"""					)
							UpperCAmelCase_=       input_a[input_name]

							self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase					)					)
							self.assertTrue(input_a.shape[1] == len(speech_inputs[0]					)					)

							# since truncation forces padding to be smaller than longest input
							# function can't return `np.ndarray`, but has to return list
							self.assertFalse(_inputs_have_equal_length(__UpperCAmelCase					)					)

							# truncate to middle
							UpperCAmelCase_=       feat_extract.pad(
							    __UpperCAmelCase						,  padding="""max_length"""						,  max_length=len(speech_inputs[1]					)						,  truncation=__UpperCAmelCase						,  return_tensors="""np"""						,  )
							UpperCAmelCase_=       input_a[input_name]

							UpperCAmelCase_=       feat_extract.pad(
							    __UpperCAmelCase						,  padding="""max_length"""						,  max_length=len(speech_inputs[1]					)						,  truncation=__UpperCAmelCase					)
							UpperCAmelCase_=       input_a[input_name]

							UpperCAmelCase_=       feat_extract.pad(
							    __UpperCAmelCase						,  padding="""max_length"""						,  max_length=len(speech_inputs[1]					)						,  return_tensors="""np"""					)
							UpperCAmelCase_=       input_a[input_name]

							self.assertTrue(input_a.shape[1] == len(speech_inputs[1]					)					)
							self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase					)					)
							self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase					)					)
							self.assertTrue(_inputs_are_equal(__UpperCAmelCase						,  __UpperCAmelCase					)					)

							# since truncation forces padding to be smaller than longest input
							# function can't return `np.ndarray`, but has to return list
							self.assertFalse(_inputs_have_equal_length(__UpperCAmelCase					)					)
							self.assertTrue(len(input_a[-1]					) == len(speech_inputs[-1]					)					)

							# padding has to be max_length when setting `truncation=True`
							with self.assertRaises(__UpperCAmelCase					):
													feat_extract.pad(__UpperCAmelCase						,  truncation=__UpperCAmelCase					)[input_name]

							# padding has to be max_length when setting `truncation=True`
							with self.assertRaises(__UpperCAmelCase					):
													feat_extract.pad(__UpperCAmelCase						,  padding="""longest"""						,  truncation=__UpperCAmelCase					)[input_name]

							# padding has to be max_length when setting `truncation=True`
							with self.assertRaises(__UpperCAmelCase					):
													feat_extract.pad(__UpperCAmelCase						,  padding="""longest"""						,  truncation=__UpperCAmelCase					)[input_name]

							# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
							with self.assertRaises(__UpperCAmelCase					):
													feat_extract.pad(__UpperCAmelCase						,  padding="""max_length"""						,  truncation=__UpperCAmelCase					)[input_name]

							# test truncation for `pad_to_multiple_of` for List[int] + numpy
							UpperCAmelCase_=       12
							UpperCAmelCase_=       feat_extract.pad(
							    __UpperCAmelCase						,  padding="""max_length"""						,  max_length=len(speech_inputs[0]					)						,  pad_to_multiple_of=__UpperCAmelCase						,  truncation=__UpperCAmelCase						,  )
							UpperCAmelCase_=       input_a[input_name]

							UpperCAmelCase_=       feat_extract.pad(
							    __UpperCAmelCase						,  padding="""max_length"""						,  max_length=len(speech_inputs[0]					)						,  pad_to_multiple_of=__UpperCAmelCase						,  )
							UpperCAmelCase_=       input_a[input_name]

							# retrieve expected_length as multiple of pad_to_multiple_of
							UpperCAmelCase_=       len(speech_inputs[0]					)
							if expected_length % pad_to_multiple_of != 0:
													UpperCAmelCase_=       ((len(speech_inputs[0]					) // pad_to_multiple_of) + 1) * pad_to_multiple_of

							self.assertTrue(len(input_a[0]					) == expected_length					)
							self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase					)					)
							self.assertFalse(_inputs_have_equal_length(__UpperCAmelCase					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Any:
							self._check_padding(numpify=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Tuple:
							self._check_padding(numpify=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Union[str, Any]:
							self._check_truncation(numpify=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Optional[Any]:
							self._check_truncation(numpify=__UpperCAmelCase					)




	@require_torch
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Tuple:
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_dict					)
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_common()
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}					)

							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding="""longest"""						,  return_tensors="""np"""					)[input_name]
							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding="""longest"""						,  return_tensors="""pt"""					)[input_name]

							self.assertTrue(abs(input_np.astype(np.floataa					).sum() - input_pt.numpy().astype(np.floataa					).sum()					) < 1E-2					)




	@require_tf
	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Optional[int]:
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_dict					)
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_common()
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}					)

							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding="""longest"""						,  return_tensors="""np"""					)[input_name]
							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding="""longest"""						,  return_tensors="""tf"""					)[input_name]

							self.assertTrue(abs(input_np.astype(np.floataa					).sum() - input_tf.numpy().astype(np.floataa					).sum()					) < 1E-2					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			List[Any]:
							UpperCAmelCase_=       self.feat_extract_dict
							UpperCAmelCase_=       True
							UpperCAmelCase_=       self.feature_extraction_class(**__UpperCAmelCase					)
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_common()
							UpperCAmelCase_=       [len(__UpperCAmelCase					) for x in speech_inputs]
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}					)

							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding="""longest"""						,  return_tensors="""np"""					)
							self.assertIn("""attention_mask"""						,  __UpperCAmelCase					)
							self.assertListEqual(list(processed.attention_mask.shape					)						,  list(processed[input_name].shape[:2]					)					)
							self.assertListEqual(processed.attention_mask.sum(-1					).tolist()						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			int:
							UpperCAmelCase_=       self.feat_extract_dict
							UpperCAmelCase_=       True
							UpperCAmelCase_=       self.feature_extraction_class(**__UpperCAmelCase					)
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_common()
							UpperCAmelCase_=       [len(__UpperCAmelCase					) for x in speech_inputs]
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}					)
							UpperCAmelCase_=       min(__UpperCAmelCase					)

							UpperCAmelCase_=       feat_extract.pad(
							    __UpperCAmelCase						,  padding="""max_length"""						,  max_length=__UpperCAmelCase						,  truncation=__UpperCAmelCase						,  return_tensors="""np"""					)
							self.assertIn("""attention_mask"""						,  __UpperCAmelCase					)
							self.assertListEqual(
							    list(processed_pad.attention_mask.shape					)						,  [processed_pad[input_name].shape[0], max_length]					)
							self.assertListEqual(
							    processed_pad.attention_mask[:, :max_length].sum(-1					).tolist()						,  [max_length for x in speech_inputs]					)









from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						Dict						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						List[Any]					)      ->			List[str]:
							super().__init__()

							# make sure scheduler can always be converted to DDIM
							UpperCAmelCase_=       DDIMScheduler.from_config(scheduler.config					)

							self.register_modules(unet=__UpperCAmelCase						,  scheduler=__UpperCAmelCase					)




	@torch.no_grad()
	def __call__(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None						,  __UpperCAmelCase     :						float = 0.0						,  __UpperCAmelCase     :						int = 50						,  __UpperCAmelCase     :						Optional[bool] = None						,  __UpperCAmelCase     :						Optional[str] = "pil"						,  __UpperCAmelCase     :						bool = True						,  )      ->			Union[ImagePipelineOutput, Tuple]:

							# Sample gaussian noise to begin loop
							if isinstance(self.unet.config.sample_size						,  __UpperCAmelCase					):
													UpperCAmelCase_=       (
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
							else:
													UpperCAmelCase_=       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					) and len(__UpperCAmelCase					) != batch_size:
													raise ValueError(
													    F"""You have passed a list of generators of length {len(__UpperCAmelCase					)}, but requested an effective batch"""
													    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""					)

							UpperCAmelCase_=       randn_tensor(__UpperCAmelCase						,  generator=__UpperCAmelCase						,  device=self.device						,  dtype=self.unet.dtype					)

							# set step values
							self.scheduler.set_timesteps(__UpperCAmelCase					)

							for t in self.progress_bar(self.scheduler.timesteps					):
													# 1. predict noise model_output
													UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					).sample

													# 2. predict previous mean of image x_t-1 and add variance depending on eta
													# eta corresponds to η in paper and should be between [0, 1]
													# do x_t -> x_t-1
													UpperCAmelCase_=       self.scheduler.step(
													    __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  eta=__UpperCAmelCase						,  use_clipped_model_output=__UpperCAmelCase						,  generator=__UpperCAmelCase					).prev_sample

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

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=__UpperCAmelCase					)




