
"""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       lowerCAmelCase__				(						lowerCamelCase__	):
     __a						=					42
     __a						=					None






def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       Optional[int]=0.999 ,					__lowerCamelCase   :       int="cosine" ,					)     ->					str:
  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

  _snake_case					     =       []
  for i in range(_lowerCamelCase		):
    _snake_case					     =       i / num_diffusion_timesteps
    _snake_case					     =       (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       lowerCAmelCase__				(						lowerCamelCase__ ,	lowerCamelCase__	):
     __a						=					1







     @register_to_config
     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			int = 1000    ,      _lowerCamelCase			:			float = 0.0_0_0_1    ,      _lowerCamelCase			:			float = 0.0_2    ,      _lowerCamelCase			:			str = "linear"    ,      _lowerCamelCase			:			Optional[Union[np.ndarray, List[float]]] = None    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			int = 0    ,      _lowerCamelCase			:			str = "epsilon"    ,      _lowerCamelCase			:			float = 1.0    ,      **_lowerCamelCase			:			Optional[int]    ,      ):






       if kwargs.get('''set_alpha_to_one'''    ,      lowerCAmelCase__			) is not None:
         _snake_case					     =       (
             """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__			)
         _snake_case					     =       kwargs["""set_alpha_to_one"""]
       if trained_betas is not None:
         _snake_case					     =       torch.tensor(lowerCAmelCase__    ,      dtype=torch.floataa			)
       elif beta_schedule == "linear":
         _snake_case					     =       torch.linspace(lowerCAmelCase__    ,      lowerCAmelCase__    ,      lowerCAmelCase__    ,      dtype=torch.floataa			)
       elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         _snake_case					     =       (
             torch.linspace(beta_start**0.5    ,      beta_end**0.5    ,      lowerCAmelCase__    ,      dtype=torch.floataa			) ** 2
         )
       elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         _snake_case					     =       betas_for_alpha_bar(lowerCAmelCase__			)
       else:
         raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)

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

       # standard deviation of the initial noise distribution
       _snake_case					     =       1.0

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







     def      lowercase	(							self			:			int    ,      _lowerCamelCase			:			torch.FloatTensor    ,      _lowerCamelCase			:			Optional[int] = None			):
       return sample







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			Union[str, torch.device] = 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.'''			)

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







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			torch.FloatTensor    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			torch.FloatTensor    ,      _lowerCamelCase			:			float = 0.0    ,      _lowerCamelCase			:			bool = False    ,      _lowerCamelCase			:			Optional[torch.FloatTensor] = None    ,      _lowerCamelCase			:			bool = True    ,      ):
       _snake_case					     =       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
       _snake_case					     =       self.alphas_cumprod[timestep]
       _snake_case					     =       (
           self.alphas_cumprod[prev_timestep]
           if prev_timestep < self.config.num_train_timesteps
           else self.final_alpha_cumprod
       )

       _snake_case					     =       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":
         _snake_case					     =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         _snake_case					     =       model_output
       elif self.config.prediction_type == "sample":
         _snake_case					     =       model_output
         _snake_case					     =       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
       elif self.config.prediction_type == "v_prediction":
         _snake_case					     =       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         _snake_case					     =       (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:
         _snake_case					     =       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
       _snake_case					     =       (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
       _snake_case					     =       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			:			List[str]			):
       return self.config.num_train_timesteps








'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		assert (
		    isinstance(_lowerCamelCase       ,      _lowerCamelCase					) and number_of_steps > 0
		), F"number_of_steps needs to be positive integer, your input {number_of_steps}"
		if number_of_steps == 1:
				return 1
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Dict           =		1, 1
		for _ in range(number_of_steps - 1					):
				__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Tuple           =		current + previous, current
		return current


if __name__ == "__main__":
						import doctest

						doctest.testmod()