





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
class       A__     ( __snake_case    ):
      _UpperCAmelCase			:torch.FloatTensor
      _UpperCAmelCase			:torch.FloatTensor
      _UpperCAmelCase			:Optional[torch.FloatTensor]							=     None



class       A__     ( __snake_case ,     __snake_case    ):
      _UpperCAmelCase			:int							=     2





      @register_to_config
      def __init__(				self ,							A_ = 0.02 ,							A_ = 100 ,							A_ = 1.0_07 ,							A_ = 80 ,							A_ = 0.05 ,							A_ = 50 ,							):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     sigma_max

       # setable values
       UpperCamelCase			:      int 			=     None
       UpperCamelCase			:      np.IntTensor 			=     None
       UpperCamelCase			:      torch.FloatTensor 			=     None  # sigma(t_i)





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       return sample





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     num_inference_steps
       UpperCamelCase			:      List[str] 			=     np.arange(0 ,							self.num_inference_steps     )[::-1].copy()
       UpperCamelCase			:      List[Any] 			=     torch.from_numpy(A_     ).to(A_     )
       UpperCamelCase			:      Dict 			=     [
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       UpperCamelCase			:      int 			=     torch.tensor(A_ ,							dtype=torch.floataa ,							device=A_     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       if self.config.s_min <= sigma <= self.config.s_max:
        UpperCamelCase			:      Union[str, Any] 			=     min(self.config.s_churn / self.num_inference_steps ,							2**0.5 - 1     )
       else:
        UpperCamelCase			:      Dict 			=     0

       # sample eps ~ N(0, S_noise^2 * I)
       UpperCamelCase			:      Tuple 			=     self.config.s_noise * randn_tensor(sample.shape ,							generator=A_     ).to(sample.device     )
       UpperCamelCase			:      List[str] 			=     sigma + gamma * sigma
       UpperCamelCase			:      List[Any] 			=     sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_ ,							A_ = True ,							):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     sample_hat + sigma_hat * model_output
       UpperCamelCase			:      Dict 			=     (sample_hat - pred_original_sample) / sigma_hat
       UpperCamelCase			:      Optional[int] 			=     sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
        return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=A_ ,							derivative=A_ ,							pred_original_sample=A_     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_ ,							A_ ,							A_ ,							A_ = True ,							):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     sample_prev + sigma_prev * model_output
       UpperCamelCase			:      List[Any] 			=     (sample_prev - pred_original_sample) / sigma_prev
       UpperCamelCase			:      List[str] 			=     sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
        return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=A_ ,							derivative=A_ ,							pred_original_sample=A_     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       raise NotImplementedError()







from PIL import Image



def 	A_       (  _lowerCAmelCase  )		->				Image:
 UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     image.size
 UpperCamelCase			:      Union[str, Any] 			=     0
 UpperCamelCase			:      List[str] 			=     image.load()
 for i in range(_lowerCAmelCase  ):
  for j in range(_lowerCAmelCase  ):
   UpperCamelCase			:      List[Any] 			=     pixels[j, i]
   mean += pixel
 mean //= width * height

 for j in range(_lowerCAmelCase  ):
  for i in range(_lowerCAmelCase  ):
   UpperCamelCase			:      Union[str, Any] 			=     255 if pixels[i, j] > mean else 0
 return image


if __name__ == "__main__":
    __lowerCamelCase :  Union[str, Any]										=  mean_threshold(Image.open("""path_to_image""").convert("""L"""))
    image.save("""output_image_path""")
