
# 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__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Tuple 				=    42
   __UpperCamelCase		:    Optional[Any] 				=    None






def   lowerCamelCase__							(					a		,		a=0.999		,		a="cosine"		,		)      ->   Union[str, Any]:



   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

   _A:  List[str]					=       []
   for i in range(__lowerCAmelCase      ):
      _A:  Union[str, Any]					=       i / num_diffusion_timesteps
      _A:  str					=       (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__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Dict 				=    1


   @register_to_config
   def __init__(					self     :							List[Any]			,			lowerCAmelCase_     :							Optional[int] = 1_0_0_0			,			lowerCAmelCase_     :							List[Any] = 0.0001			,			lowerCAmelCase_     :							Union[str, Any] = 0.02			,			lowerCAmelCase_     :							Optional[int] = "linear"			,			lowerCAmelCase_     :							List[str] = None			,			lowerCAmelCase_     :							Any = True			,			lowerCAmelCase_     :							Union[str, Any] = True			,			lowerCAmelCase_     :							str = 0			,			lowerCAmelCase_     :							List[str] = "epsilon"			,			lowerCAmelCase_     :							Any = 1.0			,			**lowerCAmelCase_     :							List[Any]			,			):

      """simple docstring"""







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

      _A:  Any					=       1.0 - self.betas
      _A:  Union[str, 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.
      _A:  Dict					=       torch.tensor(0.0     ) if set_alpha_to_zero else self.alphas_cumprod[-1]

      # standard deviation of the initial noise distribution
      _A:  List[str]					=       1.0

      # setable values
      _A:  int					=       None
      _A:  Any					=       torch.from_numpy(np.arange(0			,			lowerCAmelCase_     ).copy().astype(np.intaa     )     )


   def  __magic_name__				(					self     :							List[Any]			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							Tuple = None     ):

      """simple docstring"""
      return sample


   def  __magic_name__				(					self     :							Any			,			lowerCAmelCase_     :							Any			,			lowerCAmelCase_     :							Union[str, Any] = None     ):

      """simple docstring"""







      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."""     )

      _A:  Any					=       num_inference_steps
      _A:  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
      _A:  Union[str, Any]					=       (np.arange(0			,			lowerCAmelCase_     ) * step_ratio).round().copy().astype(np.intaa     )
      _A:  int					=       torch.from_numpy(lowerCAmelCase_     ).to(lowerCAmelCase_     )
      self.timesteps += self.config.steps_offset


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Any			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							Dict			,			lowerCAmelCase_     :							List[Any] = 0.0			,			lowerCAmelCase_     :							List[str] = False			,			lowerCAmelCase_     :							Union[str, Any] = None			,			lowerCAmelCase_     :							Dict = True			,			):

      """simple docstring"""
      _A:  List[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
      _A:  Union[str, Any]					=       self.alphas_cumprod[timestep]
      _A:  Tuple					=       (
          self.alphas_cumprod[prev_timestep]
          if prev_timestep < self.config.num_train_timesteps
          else self.final_alpha_cumprod
      )

      _A:  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":
         _A:  Any					=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         _A:  Tuple					=       model_output
      elif self.config.prediction_type == "sample":
         _A:  Any					=       model_output
         _A:  Dict					=       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
      elif self.config.prediction_type == "v_prediction":
         _A:  int					=       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         _A:  Optional[Any]					=       (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:
         _A:  Tuple					=       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
      _A:  int					=       (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
      _A:  Union[str, Any]					=       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=lowerCAmelCase_			,			pred_original_sample=lowerCAmelCase_     )


   def __len__(					self     :							Optional[int]     ):

      """simple docstring"""
      return self.config.num_train_timesteps











from __future__ import annotations


def __A    (      __lowerCAmelCase	)->  list[int]:





						"""simple docstring"""



						_UpperCAmelCase									=			2
						_UpperCAmelCase									=			[]
						while i * i <= n:
												if n % i:
																		i += 1
												else:
																		n //= i
																		factors.append(__lowerCAmelCase	)
						if n > 1:
												factors.append(__lowerCAmelCase	)
						return factors


if __name__ == "__main__":
		import doctest

		doctest.testmod()
