
'''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__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				torch.FloatTensor
  __UpperCAmelCase				:				Optional[torch.FloatTensor]   =					None





def 					UpperCAmelCase_		(__a      :    Tuple ,	__a      :    Dict=0.999 ,	__a      :    List[Any]="cosine" ,	):






   """simple docstring"""

   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__a      :    List[str] ):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

   elif alpha_transform_type == "exp":

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

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

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







class UpperCAmelCase__	( lowercase__    ,				lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				Dict   =					1


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


     '''simple docstring'''






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

     _a				:	List[Any]			=   1.0 - self.betas
     _a				:	Dict			=   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				:	int			=   torch.tensor(0.0       ) if set_alpha_to_zero else self.alphas_cumprod[-1]

     # standard deviation of the initial noise distribution
     _a				:	Optional[int]			=   1.0

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


  def   __lowercase		(		self      :     List[Any]							,_a      :     torch.FloatTensor							,_a      :     Optional[int] = None       ):


     '''simple docstring'''






     return sample


  def   __lowercase		(		self      :     List[str]							,_a      :     int							,_a      :     Union[str, torch.device] = 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				:	str			=   num_inference_steps
     _a				:	Optional[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				:	Any			=   (np.arange(0							,_a       ) * step_ratio).round().copy().astype(np.intaa       )
     _a				:	List[Any]			=   torch.from_numpy(_a       ).to(_a       )
     self.timesteps += self.config.steps_offset


  def   __lowercase		(		self      :     List[str]							,_a      :     torch.FloatTensor							,_a      :     int							,_a      :     torch.FloatTensor							,_a      :     float = 0.0							,_a      :     bool = False							,_a      :     Optional[torch.FloatTensor] = None							,_a      :     bool = True							,):


     '''simple docstring'''






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


  def __len__(		self      :     List[str]       ):


     '''simple docstring'''






     return self.config.num_train_timesteps





'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCAmelCase         =    logging.get_logger(__name__)







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  def __init__(		self      :     int							,*_a      :     Optional[int]							,**_a      :     str       ):


     '''simple docstring'''






     warnings.warn(
         'The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
         ' use BeitImageProcessor instead.'							,_a							,)
     super().__init__(*_a							,**_a       )



