



"""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 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					42
       UpperCamelCase						=					None



def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__=0.999     ,      lowerCAmelCase__="cosine"     ,      ):
      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}"""				)

      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 				lowercase__       (   SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					1



       @register_to_config
       def __init__(							self      :  List[Any]		,    _UpperCAmelCase      :  int = 1000		,    _UpperCAmelCase      :  float = 0.0001		,    _UpperCAmelCase      :  float = 0.02		,    _UpperCAmelCase      :  str = "linear"		,    _UpperCAmelCase      :  Optional[Union[np.ndarray, List[float]]] = None		,    _UpperCAmelCase      :  bool = True		,    _UpperCAmelCase      :  bool = True		,    _UpperCAmelCase      :  int = 0		,    _UpperCAmelCase      :  str = "epsilon"		,    _UpperCAmelCase      :  float = 1.0		,    **_UpperCAmelCase      :  Any		,    )			->						List[Any]:


             '''simple docstring'''




             if kwargs.get("set_alpha_to_one"		,    _UpperCAmelCase   ) 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"		,    _UpperCAmelCase		,    standard_warn=_UpperCAmelCase   )
                   UpperCAmelCase_      			=							kwargs["set_alpha_to_one"]
             if trained_betas is not None:
                   UpperCAmelCase_      			=							torch.tensor(_UpperCAmelCase		,    dtype=torch.floataa   )
             elif beta_schedule == "linear":
                   UpperCAmelCase_      			=							torch.linspace(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase		,    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		,    _UpperCAmelCase		,    dtype=torch.floataa   ) ** 2
                   )
             elif beta_schedule == "squaredcos_cap_v2":
                   # Glide cosine schedule
                   UpperCAmelCase_      			=							betas_for_alpha_bar(_UpperCAmelCase   )
             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		,    _UpperCAmelCase   ).copy().astype(np.intaa   )   )



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


             '''simple docstring'''




             return sample



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


             '''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."""   )

             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		,    _UpperCAmelCase   ) * step_ratio).round().copy().astype(np.intaa   )
             UpperCAmelCase_      			=							torch.from_numpy(_UpperCAmelCase   ).to(_UpperCAmelCase   )
             self.timesteps += self.config.steps_offset



       def 					lowercase__    (							self      :  str		,    _UpperCAmelCase      :  torch.FloatTensor		,    _UpperCAmelCase      :  int		,    _UpperCAmelCase      :  torch.FloatTensor		,    _UpperCAmelCase      :  float = 0.0		,    _UpperCAmelCase      :  bool = False		,    _UpperCAmelCase      :  Optional[torch.FloatTensor] = None		,    _UpperCAmelCase      :  bool = True		,    )			->						Union[DDIMSchedulerOutput, Tuple]:


             '''simple docstring'''




             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=_UpperCAmelCase		,    pred_original_sample=_UpperCAmelCase   )







       def __len__(							self      :  List[Any]   )			->						List[str]:


             '''simple docstring'''




             return self.config.num_train_timesteps









"""simple docstring"""





def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      return [sentence[i : i + ngram_size] for i in range(len(lowerCAmelCase__				) - ngram_size + 1				)]


if __name__ == "__main__":
  from doctest import testmod

  testmod()




