





'''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	(     snake_case_  ):
      _lowercase:     torch.FloatTensor
      _lowercase:     Optional[torch.FloatTensor]			=  None




def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase=0.999				,      lowerCAmelCase="cosine"				,      ):





      """simple docstring"""


      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

      _lowerCAmelCase					=				[]
      for i in range(lowerCAmelCase    ):
            _lowerCAmelCase					=				i / num_diffusion_timesteps
            _lowerCAmelCase					=				(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	(     snake_case_				,						snake_case_  ):
      _lowercase:     Optional[Any]			=  1




      @register_to_config
      def __init__(     self     :					Optional[int]     ,     __snake_case     :					int = 10_00     ,     __snake_case     :					float = 0.00_01     ,     __snake_case     :					float = 0.02     ,     __snake_case     :					str = "linear"     ,     __snake_case     :					Optional[Union[np.ndarray, List[float]]] = None     ,     __snake_case     :					bool = True     ,     __snake_case     :					bool = True     ,     __snake_case     :					int = 0     ,     __snake_case     :					str = "epsilon"     ,     __snake_case     :					float = 1.0     ,     **__snake_case     :					Tuple     ,     )							-> Any:
            if kwargs.get("""set_alpha_to_one"""     ,     __snake_case    ) is not None:
                  _lowerCAmelCase					=				(
                      """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
                  )
                  deprecate("""set_alpha_to_one"""     ,     """1.0.0"""     ,     __snake_case     ,     standard_warn=__snake_case    )
                  _lowerCAmelCase					=				kwargs["""set_alpha_to_one"""]
            if trained_betas is not None:
                  _lowerCAmelCase					=				torch.tensor(__snake_case     ,     dtype=torch.floataa    )
            elif beta_schedule == "linear":
                  _lowerCAmelCase					=				torch.linspace(__snake_case     ,     __snake_case     ,     __snake_case     ,     dtype=torch.floataa    )
            elif beta_schedule == "scaled_linear":
                  # this schedule is very specific to the latent diffusion model.
                  _lowerCAmelCase					=				(
                      torch.linspace(beta_start**0.5     ,     beta_end**0.5     ,     __snake_case     ,     dtype=torch.floataa    ) ** 2
                  )
            elif beta_schedule == "squaredcos_cap_v2":
                  # Glide cosine schedule
                  _lowerCAmelCase					=				betas_for_alpha_bar(__snake_case    )
            else:
                  raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"    )

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

            # standard deviation of the initial noise distribution
            _lowerCAmelCase					=				1.0

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




      def 							lowercase__       (     self     :					List[str]     ,     __snake_case     :					torch.FloatTensor     ,     __snake_case     :					Optional[int] = None    )							-> torch.FloatTensor:
            return sample




      def 							lowercase__       (     self     :					Union[str, Any]     ,     __snake_case     :					int     ,     __snake_case     :					Union[str, torch.device] = None    )							-> Tuple:

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




      def 							lowercase__       (     self     :					str     ,     __snake_case     :					torch.FloatTensor     ,     __snake_case     :					int     ,     __snake_case     :					torch.FloatTensor     ,     __snake_case     :					float = 0.0     ,     __snake_case     :					bool = False     ,     __snake_case     :					Optional[torch.FloatTensor] = None     ,     __snake_case     :					bool = True     ,     )							-> Union[DDIMSchedulerOutput, Tuple]:
            # 1. get previous step value (=t+1)
            _lowerCAmelCase					=				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					=				self.alphas_cumprod[timestep]
            _lowerCAmelCase					=				(
                self.alphas_cumprod[prev_timestep]
                if prev_timestep < self.config.num_train_timesteps
                else self.final_alpha_cumprod
            )

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




      def __len__(     self     :					int    )							-> Optional[Any]:
            return self.config.num_train_timesteps













'''simple docstring'''






def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      if not isinstance(lowerCAmelCase				,      lowerCAmelCase    ):
            raise ValueError("""Input must be an integer"""    )
      if input_num <= 0:
            raise ValueError("""Input must be positive"""    )
      return sum(
          divisor for divisor in range(1				,      input_num // 2 + 1    ) if input_num % divisor == 0    )


if __name__ == "__main__":
      import doctest

      doctest.testmod()






