



"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

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







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:List[str]  ,    lowercase_					:List[str]  ,    lowercase_					:List[Any]   )							->     List[str]:
												super().__init__()
												self.register_modules(unet=lowercase_  ,    scheduler=lowercase_   )






						@torch.no_grad()
						def __call__(   self					:int  ,    lowercase_					:int = 1  ,    lowercase_					:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,    lowercase_					:int = 10_00  ,    lowercase_					:Optional[str] = "pil"  ,    lowercase_					:bool = True  ,    )							->     Union[ImagePipelineOutput, Tuple]:
												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size  ,    lowercase_   ):
																		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(lowercase_  ,    generator=lowercase_   )
																		UpperCAmelCase			 =					image.to(self.device   )
												else:
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_  ,    device=self.device   )

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

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

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

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowercase_   )













"""simple docstring"""




from __future__ import annotations






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				int  ,			UpperCamelCase_:				int       )							->				tuple[float, float]:







  '''simple docstring'''







  assert (
      c % greatest_common_divisor(UpperCamelCase_  ,			UpperCamelCase_       ) == 0
  )  # greatest_common_divisor(a,b) function implemented below
  ((_a)      ,						(_a)      ,						(_a))					      =						extended_gcd(UpperCamelCase_  ,			UpperCamelCase_       )  # extended_gcd(a,b) function implemented below
  _a					      =						c / d
  return (r * x, r * y)






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				int  ,			UpperCamelCase_:				int  ,			UpperCamelCase_:				int = 2       )							->				None:







  '''simple docstring'''







  ((_a)      ,						(_a))					      =						diophantine(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )  # Initial value
  _a					      =						greatest_common_divisor(UpperCamelCase_  ,			UpperCamelCase_       )
  _a					      =						a // d
  _a					      =						b // d

  for i in range(UpperCamelCase_       ):
    _a					      =						xa + i * q
    _a					      =						ya - i * p
    print(UpperCamelCase_  ,			UpperCamelCase_       )






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  if a < b:
    _a      ,						_a					      =						b, a

  while a % b != 0:
    _a      ,						_a					      =						b, a % b

  return b






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				int       )							->				tuple[int, int, int]:







  '''simple docstring'''







  assert a >= 0 and b >= 0

  if b == 0:
    _a      ,						_a      ,						_a					      =						a, 1, 0
  else:
    ((_a)      ,						(_a)      ,						(_a))					      =						extended_gcd(UpperCamelCase_  ,			a % b       )
    _a					      =						q
    _a					      =						p - q * (a // b)

  assert a % d == 0 and b % d == 0
  assert d == a * x + b * y

  return (d, x, y)


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

     testmod(name="""diophantine""", verbose=True)
     testmod(name="""diophantine_all_soln""", verbose=True)
     testmod(name="""extended_gcd""", verbose=True)
     testmod(name="""greatest_common_divisor""", verbose=True)


