


from typing import List, Optional, Tuple, Union

import torch

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

class 	__a   (       UpperCAmelCase    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> str:




									"""simple docstring"""





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






		@torch.no_grad()
		def __call__(    self    ,					_SCREAMING_SNAKE_CASE = 1    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = 1000    ,					_SCREAMING_SNAKE_CASE = "pil"    ,					_SCREAMING_SNAKE_CASE = True    ,					)				-> Union[ImagePipelineOutput, Tuple]:




									"""simple docstring"""





									if isinstance(self.unet.config.sample_size    ,					_SCREAMING_SNAKE_CASE		):
																_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 self.device.type == "mps":
																# randn does not work reproducibly on mps
																_UpperCAmelCase					=    randn_tensor(_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    image.to(self.device		)
									else:
																_UpperCAmelCase					=    randn_tensor(_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE    ,					device=self.device		)

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

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

																# 2. compute previous image: x_t -> x_t-1
																_UpperCAmelCase					=    self.scheduler.step(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE		).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(_SCREAMING_SNAKE_CASE		)

									if not return_dict:
																return (image,)

									return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE		)




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 	__a   (       UpperCAmelCase    ):
		_a			:						int    			=			['image_processor', 'tokenizer']
		_a			:						Union[str, Any]    			=			'AutoImageProcessor'
		_a			:						Optional[int]    			=			'AutoTokenizer'
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					**_SCREAMING_SNAKE_CASE		)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    None
									if "feature_extractor" in kwargs:
																warnings.warn(
																    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																    ' instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
																_UpperCAmelCase					=    kwargs.pop('feature_extractor'		)

									_UpperCAmelCase					=    image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
																raise ValueError('You have to specify an image_processor.'		)
									if tokenizer is None:
																raise ValueError('You have to specify a tokenizer.'		)

									super().__init__(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    self.image_processor
		def __call__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					**_SCREAMING_SNAKE_CASE		)				-> Dict:




									"""simple docstring"""





									if text is None and images is None:
																raise ValueError('You have to specify either text or images. Both cannot be none.'		)

									if text is not None:
																_UpperCAmelCase					=    self.tokenizer(_SCREAMING_SNAKE_CASE    ,					return_tensors=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)

									if images is not None:
																_UpperCAmelCase					=    self.image_processor(_SCREAMING_SNAKE_CASE    ,					return_tensors=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)

									if text is not None and images is not None:
																_UpperCAmelCase					=    image_features.pixel_values
																return encoding
									elif text is not None:
																return encoding
									else:
																return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE		)    ,					tensor_type=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)				-> int:




									"""simple docstring"""





									return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)				-> Tuple:




									"""simple docstring"""





									return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
		@property
		def 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    self.tokenizer.model_input_names
									_UpperCAmelCase					=    self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)
		@property
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									warnings.warn(
									    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
									return self.image_processor_class






		@property
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									warnings.warn(
									    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
									return self.image_processor
