


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  (						_UpperCAmelCase			):







     def __init__(						self :						Union[str, Any]   ,   _lowercase :						Tuple   ,   _lowercase :						Any      ):
           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=_lowercase   ,   scheduler=_lowercase      )







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

           # Sample gaussian noise to begin loop
           if isinstance(self.unet.config.sample_size   ,   _lowercase      ):
                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		(
                     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 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."""      )

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		randn_tensor(_lowercase   ,   generator=_lowercase   ,   device=self.device   ,   dtype=self.unet.dtype      )

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

           for t in self.progress_bar(self.scheduler.timesteps      ):
                 # 1. predict noise model_output
                 SCREAMING_SNAKE_CASE__       :  str						     =		self.unet(_lowercase   ,   _lowercase      ).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__       :  int						     =		self.scheduler.step(
                     _lowercase   ,   _lowercase   ,   _lowercase   ,   eta=_lowercase   ,   use_clipped_model_output=_lowercase   ,   generator=_lowercase      ).prev_sample

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

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=_lowercase      )











'''simple docstring'''





from __future__ import annotations




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    if b == 0:
        return (1, 0)
    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,a % b							)
    lowercase__									=    a // b
    return (y, x - k * y)




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,_snake_case							)
    lowercase__									=    na * na
    lowercase__									=    ra * x * na + ra * y * na
    return (n % m + m) % m




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,_snake_case							)
    if b < 0:
        lowercase__									=    (b % n + n) % n
    return b




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    lowercase__							,							lowercase__									=    invert_modulo(_snake_case  ,_snake_case							), invert_modulo(_snake_case  ,_snake_case							)
    lowercase__									=    na * na
    lowercase__									=    ra * x * na + ra * y * na
    return (n % m + m) % m


if __name__ == "__main__":
 from doctest import testmod

 testmod(name="chinese_remainder_theorem", verbose=True)
 testmod(name="chinese_remainder_theorem2", verbose=True)
 testmod(name="invert_modulo", verbose=True)
 testmod(name="extended_euclid", verbose=True)




