





from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
__lowercase							:List[Any]							=	TypeVar("T", bound="Enum")



@unique
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    80
							snake_case_							=    70
							snake_case_							=    60
							snake_case_							=    50
							snake_case_							=    40
							snake_case_							=    30
							snake_case_							=    20
							snake_case_							=    10







@unique
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    24
							snake_case_							=    21
							snake_case_							=    18
							snake_case_							=    15
							snake_case_							=    12
							snake_case_							=    9
							snake_case_							=    6
							snake_case_							=    3
							snake_case_							=    2
							snake_case_							=    1
							snake_case_							=    -1
							snake_case_							=    -2
							snake_case_							=    -3
							snake_case_							=    -6
							snake_case_							=    -9
							snake_case_							=    -12
							snake_case_							=    -15
							snake_case_							=    -18
							snake_case_							=    -21
							snake_case_							=    -24

							@classmethod
							def   A_	(   cls					:  type[T]     )						->dict:
													return {unit.name: unit.value for unit in cls if unit.value > 0}






							@classmethod
							def   A_	(   cls					:  type[T]     )						->dict:
													return {unit.name: unit.value for unit in cls if unit.value < 0}







def    UpperCAmelCase				(  _lowerCamelCase   :	float  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					str =       SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
						for name_prefix, value_prefix in prefixes.items():
												SCREAMING_SNAKE_CASE__  :					str =       value / (10**value_prefix)
												if numerical_part > 1:
																		return f"""{numerical_part!s} {name_prefix}"""
						return str(_lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	float  ):





						'''simple docstring'''


						for prefix in BinaryUnit:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       value / (2**prefix.value)
												if numerical_part > 1:
																		return f"""{numerical_part!s} {prefix.name}"""
						return str(_lowerCamelCase  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






from typing import List, Optional, Tuple, Union

import torch

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



class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  Dict				,						a					:  str				,						a					:  Optional[Any]     )						->Any:
													super().__init__()
													self.register_modules(unet=a				,						scheduler=a     )






							@torch.no_grad()
							def __call__(   self					:  int				,						a					:  int = 1				,						a					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None				,						a					:  int = 10_00				,						a					:  Optional[str] = "pil"				,						a					:  bool = True				,						)						->Union[ImagePipelineOutput, Tuple]:
													# Sample gaussian noise to begin loop
													if isinstance(self.unet.config.sample_size				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					int =       (
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			SCREAMING_SNAKE_CASE__  :					Dict =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if self.device.type == "mps":
																			# randn does not work reproducibly on mps
																			SCREAMING_SNAKE_CASE__  :					List[str] =       randn_tensor(a				,						generator=a     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       image.to(self.device     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       randn_tensor(a				,						generator=a				,						device=self.device     )

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

													for t in self.progress_bar(self.scheduler.timesteps     ):
																			# 1. predict noise model_output
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.unet(a				,						a     ).sample

																			# 2. compute previous image: x_t -> x_t-1
																			SCREAMING_SNAKE_CASE__  :					Tuple =       self.scheduler.step(a				,						a				,						a				,						generator=a     ).prev_sample

													SCREAMING_SNAKE_CASE__  :					List[str] =       (image / 2 + 0.5).clamp(0				,						1     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       image.cpu().permute(0				,						2				,						3				,						1     ).numpy()
													if output_type == "pil":
																			SCREAMING_SNAKE_CASE__  :					Dict =       self.numpy_to_pil(a     )

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=a     )