import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_SCREAMING_SNAKE_CASE      						=	logging.get_logger(__name__)  # pylint: disable=invalid-name





def        lowercase(     UpperCamelCase_    )						->     Any:







 '''simple docstring'''
 warnings.warn(
     """The preprocess method is deprecated and will be removed in a future version. Please"""
     """ use VaeImageProcessor.preprocess instead"""				, lowerCamelCase_				, )
 if isinstance(lowerCamelCase_				, torch.Tensor    ):
  return image
 elif isinstance(lowerCamelCase_				, PIL.Image.Image    ):
  UpperCamelCase          =	[image]

 if isinstance(image[0]				, PIL.Image.Image    ):
  UpperCamelCase          =	image[0].size
  UpperCamelCase          =	(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

  UpperCamelCase          =	[np.array(i.resize((w, h)				, resample=PIL_INTERPOLATION["""lanczos"""]    )    )[None, :] for i in image]
  UpperCamelCase          =	np.concatenate(lowerCamelCase_				, axis=0    )
  UpperCamelCase          =	np.array(lowerCamelCase_    ).astype(np.floataa    ) / 255.0
  UpperCamelCase          =	image.transpose(0				, 3				, 1				, 2    )
  UpperCamelCase          =	2.0 * image - 1.0
  UpperCamelCase          =	torch.from_numpy(lowerCamelCase_    )
 elif isinstance(image[0]				, torch.Tensor    ):
  UpperCamelCase          =	torch.cat(lowerCamelCase_				, dim=0    )
 return image





def        lowercase(     UpperCamelCase_    )						->     str:







 '''simple docstring'''
 if isinstance(lowerCamelCase_				, torch.Tensor    ):
  return mask
 elif isinstance(lowerCamelCase_				, PIL.Image.Image    ):
  UpperCamelCase          =	[mask]

 if isinstance(mask[0]				, PIL.Image.Image    ):
  UpperCamelCase          =	mask[0].size
  UpperCamelCase          =	(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
  UpperCamelCase          =	[np.array(m.convert("""L"""    ).resize((w, h)				, resample=PIL_INTERPOLATION["""nearest"""]    )    )[None, :] for m in mask]
  UpperCamelCase          =	np.concatenate(lowerCamelCase_				, axis=0    )
  UpperCamelCase          =	mask.astype(np.floataa    ) / 255.0
  UpperCamelCase          =	0
  UpperCamelCase          =	1
  UpperCamelCase          =	torch.from_numpy(lowerCamelCase_    )
 elif isinstance(mask[0]				, torch.Tensor    ):
  UpperCamelCase          =	torch.cat(lowerCamelCase_				, dim=0    )
 return mask


class SCREAMING_SNAKE_CASE_						(	_a						):
   __lowerCAmelCase      =							42
   __lowerCAmelCase      =							42







   def __init__(			self		:		Any					,					lowerCamelCase_		:		Dict					,					lowerCamelCase_		:		Dict							):





    """simple docstring"""


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







   @torch.no_grad()
   def __call__(			self		:		Union[str, Any]					,					lowerCamelCase_		:		List[str]					,					lowerCamelCase_		:		Optional[int]					,					lowerCamelCase_		:		List[str] = 250					,					lowerCamelCase_		:		Any = 0.0					,					lowerCamelCase_		:		Union[str, Any] = 10					,					lowerCamelCase_		:		Optional[int] = 10					,					lowerCamelCase_		:		Dict = None					,					lowerCamelCase_		:		int = "pil"					,					lowerCamelCase_		:		Any = True					,					):





    """simple docstring"""


    UpperCamelCase          =	image

    UpperCamelCase          =	_preprocess_image(lowerCamelCase_							)
    UpperCamelCase          =	original_image.to(device=self.device					,					dtype=self.unet.dtype							)
    UpperCamelCase          =	_preprocess_mask(lowerCamelCase_							)
    UpperCamelCase          =	mask_image.to(device=self.device					,					dtype=self.unet.dtype							)

    UpperCamelCase          =	original_image.shape[0]

    # sample gaussian noise to begin the loop
    if isinstance(lowerCamelCase_					,					lowerCamelCase_							) and len(lowerCamelCase_							) != batch_size:
     raise ValueError(
         f"""You have passed a list of generators of length {len(lowerCamelCase_							)}, but requested an effective batch"""
         f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

    UpperCamelCase          =	original_image.shape
    UpperCamelCase          =	randn_tensor(lowerCamelCase_					,					generator=lowerCamelCase_					,					device=self.device					,					dtype=self.unet.dtype							)

    # set step values
    self.scheduler.set_timesteps(lowerCamelCase_					,					lowerCamelCase_					,					lowerCamelCase_					,					self.device							)
    UpperCamelCase          =	eta

    UpperCamelCase          =	self.scheduler.timesteps[0] + 1
    UpperCamelCase          =	generator[0] if isinstance(lowerCamelCase_					,					lowerCamelCase_							) else generator
    for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
     if t < t_last:
      # predict the noise residual
      UpperCamelCase          =	self.unet(lowerCamelCase_					,					lowerCamelCase_							).sample
      # compute previous image: x_t -> x_t-1
      UpperCamelCase          =	self.scheduler.step(lowerCamelCase_					,					lowerCamelCase_					,					lowerCamelCase_					,					lowerCamelCase_					,					lowerCamelCase_					,					lowerCamelCase_							).prev_sample

     else:
      # compute the reverse: x_t-1 -> x_t
      UpperCamelCase          =	self.scheduler.undo_step(lowerCamelCase_					,					lowerCamelCase_					,					lowerCamelCase_							)
     UpperCamelCase          =	t

    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(lowerCamelCase_							)

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=lowerCamelCase_							)




from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						bool:
    _lowercase       :							int      		=		int(number**0.5 )
    return number == sq * sq



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						tuple[int, int]:
    _lowercase       :							int      		=		x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
    _lowercase       :							int      		=		x_den * y_den * z_den
    _lowercase       :							int      		=		gcd(lowerCamelCase_							, lowerCamelCase_ )
    top //= hcf
    bottom //= hcf
    return top, bottom



def 				UpperCamelCase_(      lowerCamelCase_ = 35 )    ->						int:
    _lowercase       :							set      		=		set()
    _lowercase       :							int
    _lowercase       :							Fraction      		=		Fraction(0 )
    _lowercase       :							tuple[int, int]

    for x_num in range(1							, order + 1 ):
        for x_den in range(x_num + 1							, order + 1 ):
            for y_num in range(1							, order + 1 ):
                for y_den in range(y_num + 1							, order + 1 ):
                    # n=1
                    _lowercase       :							int      		=		x_num * y_den + x_den * y_num
                    _lowercase       :							int      		=		x_den * y_den
                    _lowercase       :							str      		=		gcd(lowerCamelCase_							, lowerCamelCase_ )
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        _lowercase       :							List[Any]      		=		add_three(
                            lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )
                        unique_s.add(lowerCamelCase_ )

                    # n=2
                    _lowercase       :							Dict      		=		(
                        x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                    )
                    _lowercase       :							List[Any]      		=		x_den * x_den * y_den * y_den
                    if is_sq(lowerCamelCase_ ) and is_sq(lowerCamelCase_ ):
                        _lowercase       :							Tuple      		=		int(sqrt(lowerCamelCase_ ) )
                        _lowercase       :							int      		=		int(sqrt(lowerCamelCase_ ) )
                        _lowercase       :							Any      		=		gcd(lowerCamelCase_							, lowerCamelCase_ )
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            _lowercase       :							Optional[int]      		=		add_three(
                                lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )
                            unique_s.add(lowerCamelCase_ )

                    # n=-1
                    _lowercase       :							Any      		=		x_num * y_num
                    _lowercase       :							str      		=		x_den * y_num + x_num * y_den
                    _lowercase       :							Any      		=		gcd(lowerCamelCase_							, lowerCamelCase_ )
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        _lowercase       :							int      		=		add_three(
                            lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )
                        unique_s.add(lowerCamelCase_ )

                    # n=2
                    _lowercase       :							str      		=		x_num * x_num * y_num * y_num
                    _lowercase       :							Optional[Any]      		=		(
                        x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                    )
                    if is_sq(lowerCamelCase_ ) and is_sq(lowerCamelCase_ ):
                        _lowercase       :							Tuple      		=		int(sqrt(lowerCamelCase_ ) )
                        _lowercase       :							List[str]      		=		int(sqrt(lowerCamelCase_ ) )
                        _lowercase       :							Union[str, Any]      		=		gcd(lowerCamelCase_							, lowerCamelCase_ )
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            _lowercase       :							Tuple      		=		add_three(
                                lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )
                            unique_s.add(lowerCamelCase_ )

    for num, den in unique_s:
        total += Fraction(lowerCamelCase_							, lowerCamelCase_ )

    return total.denominator + total.numerator


if __name__ == "__main__":
 print(F"{solution() = }")


