
"""simple docstring"""

# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
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      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							torch.FloatTensor
      __SCREAMING_SNAKE_CASE   :							Optional[torch.FloatTensor]							      =	None




def     lowercase__						(					lowerCAmelCase				:					List[Any] ,				lowerCAmelCase				:					str=0.999 ,				lowerCAmelCase				:					Optional[int]="cosine" ,				) ->      Optional[Any]:






    """simple docstring"""




    if alpha_transform_type == "cosine":

        def alpha_bar_fn(lowerCAmelCase				:					Any ):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(lowerCAmelCase				:					Any ):
            return math.exp(t * -12.0 )

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

    UpperCAmelCase									=					[]
    for i in range(lowerCAmelCase ):
        UpperCAmelCase									=					i / num_diffusion_timesteps
        UpperCAmelCase									=					(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      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							Optional[Any]							      =	1




      @register_to_config
      def __init__(							self   ,       lowercase_ = 1_0_0_0   ,       lowercase_ = 0.0_0_0_1   ,       lowercase_ = 0.0_2   ,       lowercase_ = "linear"   ,       lowercase_ = None   ,       lowercase_ = True   ,       lowercase_ = True   ,       lowercase_ = 0   ,       lowercase_ = "epsilon"   ,       lowercase_ = 1.0   ,       **lowercase_   ,       )						->					str:
          if kwargs.get('set_alpha_to_one'   ,       lowercase_							) is not None:
              UpperCAmelCase									=					(
                  'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
              )
              deprecate('set_alpha_to_one'   ,       '1.0.0'   ,       lowercase_   ,       standard_warn=lowercase_							)
              UpperCAmelCase									=					kwargs['set_alpha_to_one']
          if trained_betas is not None:
              UpperCAmelCase									=					torch.tensor(lowercase_   ,       dtype=torch.floataa							)
          elif beta_schedule == "linear":
              UpperCAmelCase									=					torch.linspace(lowercase_   ,       lowercase_   ,       lowercase_   ,       dtype=torch.floataa							)
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              UpperCAmelCase									=					(
                  torch.linspace(beta_start**0.5   ,       beta_end**0.5   ,       lowercase_   ,       dtype=torch.floataa							) ** 2
              )
          elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              UpperCAmelCase									=					betas_for_alpha_bar(lowercase_							)
          else:
              raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"							)

          UpperCAmelCase									=					1.0 - self.betas
          UpperCAmelCase									=					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.
          UpperCAmelCase									=					torch.tensor(0.0							) if set_alpha_to_zero else self.alphas_cumprod[-1]

          # standard deviation of the initial noise distribution
          UpperCAmelCase									=					1.0

          # setable values
          UpperCAmelCase									=					None
          UpperCAmelCase									=					torch.from_numpy(np.arange(0   ,       lowercase_							).copy().astype(np.intaa							)							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_ = None							)						->					torch.FloatTensor:
          return sample




      def     a_      (							self   ,       lowercase_   ,       lowercase_ = None							)						->					Optional[int]:

          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."							)

          UpperCAmelCase									=					num_inference_steps
          UpperCAmelCase									=					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
          UpperCAmelCase									=					(np.arange(0   ,       lowercase_							) * step_ratio).round().copy().astype(np.intaa							)
          UpperCAmelCase									=					torch.from_numpy(lowercase_							).to(lowercase_							)
          self.timesteps += self.config.steps_offset




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ = 0.0   ,       lowercase_ = False   ,       lowercase_ = None   ,       lowercase_ = True   ,       )						->					Union[DDIMSchedulerOutput, Tuple]:
          # 1. get previous step value (=t+1)
          UpperCAmelCase									=					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
          UpperCAmelCase									=					self.alphas_cumprod[timestep]
          UpperCAmelCase									=					(
              self.alphas_cumprod[prev_timestep]
              if prev_timestep < self.config.num_train_timesteps
              else self.final_alpha_cumprod
          )

          UpperCAmelCase									=					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":
              UpperCAmelCase									=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
              UpperCAmelCase									=					model_output
          elif self.config.prediction_type == "sample":
              UpperCAmelCase									=					model_output
              UpperCAmelCase									=					(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
              UpperCAmelCase									=					(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
              UpperCAmelCase									=					(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:
              UpperCAmelCase									=					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
          UpperCAmelCase									=					(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
          UpperCAmelCase									=					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=lowercase_   ,       pred_original_sample=lowercase_							)




      def __len__(							self							)						->					Any:
          return self.config.num_train_timesteps





"""simple docstring"""

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






    """simple docstring"""




    return number | (1 << position)




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






    """simple docstring"""




    return number & ~(1 << position)




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






    """simple docstring"""




    return number ^ (1 << position)




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      bool:






    """simple docstring"""




    return ((number >> position) & 1) == 1




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






    """simple docstring"""




    return int((number & (1 << position)) != 0 )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



