"""simple docstring"""




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

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    torch.FloatTensor
     _UpperCamelCase							:    Optional[torch.FloatTensor]	      =	None







def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Dict=0.9_99    ,_lowerCamelCase		: List[str]="cosine"    ,) ->   List[Any]:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(_lowerCamelCase		: List[Any]			):
      return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2			) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(_lowerCamelCase		: Union[str, Any]			):
      return math.exp(t * -12.0			)

  else:
    raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"			)

  _lowerCAmelCase		: Optional[Any]						    =  []
  for i in range(_lowerCamelCase			):
    _lowerCAmelCase		: Union[str, Any]						    =  i / num_diffusion_timesteps
    _lowerCAmelCase		: int						    =  (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(_lowerCamelCase			) / alpha_bar_fn(_lowerCamelCase			)    ,_lowerCamelCase			)			)
  return torch.tensor(_lowerCamelCase    ,dtype=torch.floataa			)






class 		__A							(				SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    List[Any]	      =	1






     @register_to_config
     def __init__(	self       ,  a__ = 1000       ,  a__ = 0.0_0_0_1       ,  a__ = 0.0_2       ,  a__ = "linear"       ,  a__ = None       ,  a__ = True       ,  a__ = True       ,  a__ = 0       ,  a__ = "epsilon"       ,  a__ = 1.0       ,  **a__       ,  ):





       if kwargs.get("""set_alpha_to_one"""       ,  UpperCamelCase__						) is not None:
         _lowerCAmelCase		: Any						    =  (
             """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
         )
         deprecate("""set_alpha_to_one"""       ,  """1.0.0"""       ,  UpperCamelCase__       ,  standard_warn=UpperCamelCase__						)
         _lowerCAmelCase		: str						    =  kwargs["""set_alpha_to_one"""]
       if trained_betas is not None:
         _lowerCAmelCase		: Union[str, Any]						    =  torch.tensor(UpperCamelCase__       ,  dtype=torch.floataa						)
       elif beta_schedule == "linear":
         _lowerCAmelCase		: List[Any]						    =  torch.linspace(UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  dtype=torch.floataa						)
       elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         _lowerCAmelCase		: Dict						    =  (
             torch.linspace(beta_start**0.5       ,  beta_end**0.5       ,  UpperCamelCase__       ,  dtype=torch.floataa						) ** 2
         )
       elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         _lowerCAmelCase		: List[str]						    =  betas_for_alpha_bar(UpperCamelCase__						)
       else:
         raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"						)

       _lowerCAmelCase		: Dict						    =  1.0 - self.betas
       _lowerCAmelCase		: List[Any]						    =  torch.cumprod(self.alphas       ,  dim=0						)

       # At every step in inverted ddim, we are looking into the next alphas_cumprod
       # For the final step, there is no next alphas_cumprod, and the index is out of bounds
       # `set_alpha_to_zero` decides whether we set this parameter simply to zero
       # in this case, self.step() just output the predicted noise
       # or whether we use the final alpha of the "non-previous" one.
       _lowerCAmelCase		: int						    =  torch.tensor(0.0						) if set_alpha_to_zero else self.alphas_cumprod[-1]

       # standard deviation of the initial noise distribution
       _lowerCAmelCase		: Union[str, Any]						    =  1.0

       # setable values
       _lowerCAmelCase		: Optional[int]						    =  None
       _lowerCAmelCase		: int						    =  torch.from_numpy(np.arange(0       ,  UpperCamelCase__						).copy().astype(np.intaa						)						)






     def 					__A						(	self       ,  a__       ,  a__ = None						):
       return sample






     def 					__A						(	self       ,  a__       ,  a__ = None						):





       if num_inference_steps > self.config.num_train_timesteps:
         raise ValueError(
             F"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
             F" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
             F" maximal {self.config.num_train_timesteps} timesteps."						)

       _lowerCAmelCase		: List[Any]						    =  num_inference_steps
       _lowerCAmelCase		: List[Any]						    =  self.config.num_train_timesteps // self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       _lowerCAmelCase		: Optional[int]						    =  (np.arange(0       ,  UpperCamelCase__						) * step_ratio).round().copy().astype(np.intaa						)
       _lowerCAmelCase		: int						    =  torch.from_numpy(UpperCamelCase__						).to(UpperCamelCase__						)
       self.timesteps += self.config.steps_offset






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__ = 0.0       ,  a__ = False       ,  a__ = None       ,  a__ = True       ,  ):
       _lowerCAmelCase		: Optional[Any]						    =  timestep + self.config.num_train_timesteps // self.num_inference_steps

       # 2. compute alphas, betas
       # change original implementation to exactly match noise levels for analogous forward process
       _lowerCAmelCase		: int						    =  self.alphas_cumprod[timestep]
       _lowerCAmelCase		: Any						    =  (
           self.alphas_cumprod[prev_timestep]
           if prev_timestep < self.config.num_train_timesteps
           else self.final_alpha_cumprod
       )

       _lowerCAmelCase		: List[str]						    =  1 - alpha_prod_t

       # 3. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       if self.config.prediction_type == "epsilon":
         _lowerCAmelCase		: str						    =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         _lowerCAmelCase		: Optional[int]						    =  model_output
       elif self.config.prediction_type == "sample":
         _lowerCAmelCase		: List[Any]						    =  model_output
         _lowerCAmelCase		: Union[str, Any]						    =  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
       elif self.config.prediction_type == "v_prediction":
         _lowerCAmelCase		: Tuple						    =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         _lowerCAmelCase		: Tuple						    =  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
       else:
         raise ValueError(
             F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
             """ `v_prediction`"""						)

       # 4. Clip or threshold "predicted x_0"
       if self.config.clip_sample:
         _lowerCAmelCase		: Dict						    =  pred_original_sample.clamp(
             -self.config.clip_sample_range       ,  self.config.clip_sample_range						)

       # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       _lowerCAmelCase		: Dict						    =  (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

       # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       _lowerCAmelCase		: Tuple						    =  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

       if not return_dict:
         return (prev_sample, pred_original_sample)
       return DDIMSchedulerOutput(prev_sample=UpperCamelCase__       ,  pred_original_sample=UpperCamelCase__						)






     def __len__(	self						):
       return self.config.num_train_timesteps





"""simple docstring"""




from PIL import Image





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Image    ,_lowerCamelCase		: int			) ->   Image:
  _lowerCAmelCase		: Any						    =  (259 * (level + 255)) / (255 * (259 - level))

  def contrast(_lowerCamelCase		: int			) -> int:
    return int(128 + factor * (c - 128)			)

  return img.point(_lowerCamelCase			)


if __name__ == "__main__":
     # Load image
     with Image.open('image_data/lena.jpg') as img:
          # Change contrast to 170
          _a   :	str 					=     change_contrast(img, 170)
          cont_img.save('image_data/lena_high_contrast.png', format='png')




