



'''simple docstring'''

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 	a__     (				_lowercase	):


	def __init__(self : int,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Any      )     ->			Dict:

					"""simple docstring"""

					super().__init__()

					# make sure scheduler can always be converted to DDIM
					SCREAMING_SNAKE_CASE  :						List[Any]      					= DDIMScheduler.from_config(scheduler.config      )

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






	@torch.no_grad()
	def __call__(self : int,					__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]:

					"""simple docstring"""

					if isinstance(self.unet.config.sample_size,					__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= (
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
					else:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= (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.'''      )

					SCREAMING_SNAKE_CASE  :						Any      					= 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
									SCREAMING_SNAKE_CASE  :						List[str]      					= 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
									SCREAMING_SNAKE_CASE  :						Dict      					= self.scheduler.step(
									    __UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					eta=__UpperCAmelCase,					use_clipped_model_output=__UpperCAmelCase,					generator=__UpperCAmelCase      ).prev_sample

					SCREAMING_SNAKE_CASE  :						Any      					= (image / 2 + 0.5).clamp(0,					1      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= image.cpu().permute(0,					2,					3,					1      ).numpy()
					if output_type == "pil":
									SCREAMING_SNAKE_CASE  :						Dict      					= self.numpy_to_pil(__UpperCAmelCase      )

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=__UpperCAmelCase      )









'''simple docstring'''

from ..utils import DummyObject, requires_backends


class 	a__     (				metaclass=_lowercase	):
	__magic_name__		:    Dict	    =     ["torch", "transformers", "onnx"]


	def __init__(self : List[str],					*__UpperCAmelCase : Dict,					**__UpperCAmelCase : List[Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

					requires_backends(self,					['''torch''', '''transformers''', '''onnx''']      )


	@classmethod
	def 				lowercase__  (cls : Optional[Any],					*__UpperCAmelCase : Tuple,					**__UpperCAmelCase : int      )     ->			int:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )






	@classmethod
	def 				lowercase__  (cls : Any,					*__UpperCAmelCase : Tuple,					**__UpperCAmelCase : Tuple      )     ->			Tuple:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )



class 	a__     (				metaclass=_lowercase	):
	__magic_name__		:    Any	    =     ["torch", "transformers", "onnx"]


	def __init__(self : Dict,					*__UpperCAmelCase : Union[str, Any],					**__UpperCAmelCase : Optional[Any]      )     ->			int:

					"""simple docstring"""

					requires_backends(self,					['''torch''', '''transformers''', '''onnx''']      )


	@classmethod
	def 				lowercase__  (cls : Any,					*__UpperCAmelCase : Tuple,					**__UpperCAmelCase : List[str]      )     ->			Optional[int]:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )






	@classmethod
	def 				lowercase__  (cls : int,					*__UpperCAmelCase : List[Any],					**__UpperCAmelCase : Any      )     ->			Optional[Any]:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )



class 	a__     (				metaclass=_lowercase	):
	__magic_name__		:    Optional[Any]	    =     ["torch", "transformers", "onnx"]


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

					"""simple docstring"""

					requires_backends(self,					['''torch''', '''transformers''', '''onnx''']      )


	@classmethod
	def 				lowercase__  (cls : Tuple,					*__UpperCAmelCase : Union[str, Any],					**__UpperCAmelCase : int      )     ->			Optional[Any]:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )






	@classmethod
	def 				lowercase__  (cls : Union[str, Any],					*__UpperCAmelCase : List[str],					**__UpperCAmelCase : str      )     ->			Optional[int]:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )



class 	a__     (				metaclass=_lowercase	):
	__magic_name__		:    Tuple	    =     ["torch", "transformers", "onnx"]


	def __init__(self : Union[str, Any],					*__UpperCAmelCase : Optional[int],					**__UpperCAmelCase : List[Any]      )     ->			Tuple:

					"""simple docstring"""

					requires_backends(self,					['''torch''', '''transformers''', '''onnx''']      )


	@classmethod
	def 				lowercase__  (cls : str,					*__UpperCAmelCase : Optional[int],					**__UpperCAmelCase : Tuple      )     ->			str:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )






	@classmethod
	def 				lowercase__  (cls : int,					*__UpperCAmelCase : List[Any],					**__UpperCAmelCase : List[str]      )     ->			Tuple:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )



class 	a__     (				metaclass=_lowercase	):
	__magic_name__		:    List[Any]	    =     ["torch", "transformers", "onnx"]


	def __init__(self : Dict,					*__UpperCAmelCase : Optional[Any],					**__UpperCAmelCase : int      )     ->			List[str]:

					"""simple docstring"""

					requires_backends(self,					['''torch''', '''transformers''', '''onnx''']      )


	@classmethod
	def 				lowercase__  (cls : List[str],					*__UpperCAmelCase : Union[str, Any],					**__UpperCAmelCase : Dict      )     ->			Union[str, Any]:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )






	@classmethod
	def 				lowercase__  (cls : Any,					*__UpperCAmelCase : List[str],					**__UpperCAmelCase : List[Any]      )     ->			Dict:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )



class 	a__     (				metaclass=_lowercase	):
	__magic_name__		:    Dict	    =     ["torch", "transformers", "onnx"]


	def __init__(self : Tuple,					*__UpperCAmelCase : Optional[Any],					**__UpperCAmelCase : List[str]      )     ->			Any:

					"""simple docstring"""

					requires_backends(self,					['''torch''', '''transformers''', '''onnx''']      )


	@classmethod
	def 				lowercase__  (cls : Tuple,					*__UpperCAmelCase : Any,					**__UpperCAmelCase : Union[str, Any]      )     ->			Optional[int]:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )






	@classmethod
	def 				lowercase__  (cls : Optional[Any],					*__UpperCAmelCase : Union[str, Any],					**__UpperCAmelCase : Optional[Any]      )     ->			int:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )




