





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	(   __SCREAMING_SNAKE_CASE):
      _lowercase    : torch.FloatTensor
      _lowercase    : Optional[torch.FloatTensor]            =      None
def 					_A	(		SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       Tuple=0.9_9_9	,	SCREAMING_SNAKE_CASE					:       Any="cosine"	,	):





  """simple docstring"""



  if alpha_transform_type == "cosine":

    def alpha_bar_fn(SCREAMING_SNAKE_CASE					:       Optional[Any]						):
      return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2						) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(SCREAMING_SNAKE_CASE					:       Dict						):
      return math.exp(t * -1_2.0						)

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

  a__     : Any  =[]
  for i in range(_SCREAMING_SNAKE_CASE						):
    a__     : Optional[Any]  =i / num_diffusion_timesteps
    a__     : List[Any]  =(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE						) / alpha_bar_fn(_SCREAMING_SNAKE_CASE						)	,	_SCREAMING_SNAKE_CASE						)						)
  return torch.tensor(_SCREAMING_SNAKE_CASE	,	dtype=torch.floataa						)







class     __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE						,    __SCREAMING_SNAKE_CASE):
      _lowercase    : List[Any]            =      1







      @register_to_config
      def __init__(   self		,				lowerCAmelCase__ = 1_0_0_0		,				lowerCAmelCase__ = 0.00_01		,				lowerCAmelCase__ = 0.02		,				lowerCAmelCase__ = "linear"		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = 0		,				lowerCAmelCase__ = "epsilon"		,				lowerCAmelCase__ = 1.0		,				**lowerCAmelCase__		,				)       ->						Optional[int]:



        '''simple docstring'''




        if kwargs.get("set_alpha_to_one"		,				_snake_case							) is not None:
          a__     : str  =(
              "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							)
          a__     : List[str]  =kwargs["set_alpha_to_one"]
        if trained_betas is not None:
          a__     : Union[str, Any]  =torch.tensor(_snake_case		,				dtype=torch.floataa							)
        elif beta_schedule == "linear":
          a__     : Union[str, Any]  =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.
          a__     : List[str]  =(
              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
          a__     : int  =betas_for_alpha_bar(_snake_case							)
        else:
          raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''							)

        a__     : Optional[int]  =1.0 - self.betas
        a__     : List[str]  =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__     : Optional[Any]  =torch.tensor(0.0							) if set_alpha_to_zero else self.alphas_cumprod[-1]

        # standard deviation of the initial noise distribution
        a__     : Tuple  =1.0

        # setable values
        a__     : Union[str, Any]  =None
        a__     : Union[str, Any]  =torch.from_numpy(np.arange(0		,				_snake_case							).copy().astype(np.intaa							)							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						List[Any]:



        '''simple docstring'''




        return sample







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						str:



        '''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__     : List[Any]  =num_inference_steps
        a__     : List[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__     : str  =(np.arange(0		,				_snake_case							) * step_ratio).round().copy().astype(np.intaa							)
        a__     : str  =torch.from_numpy(_snake_case							).to(_snake_case							)
        self.timesteps += self.config.steps_offset







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = 0.0		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = True		,				)       ->						Any:



        '''simple docstring'''




        a__     : Tuple  =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__     : List[Any]  =self.alphas_cumprod[timestep]
        a__     : List[str]  =(
            self.alphas_cumprod[prev_timestep]
            if prev_timestep < self.config.num_train_timesteps
            else self.final_alpha_cumprod
        )

        a__     : Tuple  =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__     : str  =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          a__     : Optional[Any]  =model_output
        elif self.config.prediction_type == "sample":
          a__     : Union[str, Any]  =model_output
          a__     : Union[str, Any]  =(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
        elif self.config.prediction_type == "v_prediction":
          a__     : Union[str, Any]  =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          a__     : List[str]  =(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__     : List[str]  =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__     : Union[str, Any]  =(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__     : str  =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							)       ->						str:



        '''simple docstring'''




        return self.config.num_train_timesteps







import random
from typing import Any
def 					_A	(		SCREAMING_SNAKE_CASE					:       list						):





  """simple docstring"""



  for _ in range(len(SCREAMING_SNAKE_CASE						)						):
    a__     : Tuple  =random.randint(0	,	len(SCREAMING_SNAKE_CASE						) - 1						)
    a__     : Any  =random.randint(0	,	len(SCREAMING_SNAKE_CASE						) - 1						)
    a__ ,							a__     : Tuple  =data[b], data[a]
  return data


if __name__ == "__main__":
     UpperCAmelCase     :     Dict							=	[0, 1, 2, 3, 4, 5, 6, 7]
     UpperCAmelCase     :     Optional[Any]							=	["""python""", """says""", """hello""", """!"""]
     print("""Fisher-Yates Shuffle:""")
     print("""List""", integers, strings)
     print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))






