

'''simple docstring'''

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 	lowerCAmelCase  (   __snake_case ):
       lowerCAmelCase_												=			42
       lowerCAmelCase_												=			None






def 					__UpperCamelCase				(						lowercase__    :	str,       lowercase__    :	Any=0.999,       lowercase__    :	Any="cosine",       ):






     '''simple docstring'''





     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

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

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

     __lowercase      		=[]
     for i in range(_lowerCAmelCase			):
          __lowercase      		=i / num_diffusion_timesteps
          __lowercase      		=(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  (   __snake_case ,      __snake_case ):







       @register_to_config
       def __init__(				self      :			int    ,   __lowercase      :			Optional[Any] = 1000    ,   __lowercase      :			Optional[int] = "fixed_small_log"    ,   __lowercase      :			Any = True    ,   __lowercase      :			Dict = 1.0    ,   __lowercase      :			Tuple = "epsilon"    ,   __lowercase      :			int = "squaredcos_cap_v2"    ,   ):







            """simple docstring"""







            if beta_schedule != "squaredcos_cap_v2":
                 raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''   )

            __lowercase      		=betas_for_alpha_bar(A_   )

            __lowercase      		=1.0 - self.betas
            __lowercase      		=torch.cumprod(self.alphas    ,   dim=0   )
            __lowercase      		=torch.tensor(1.0   )

            # standard deviation of the initial noise distribution
            __lowercase      		=1.0

            # setable values
            __lowercase      		=None
            __lowercase      		=torch.from_numpy(np.arange(0    ,   A_   )[::-1].copy()   )

            __lowercase      		=variance_type







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			Any    ,   __lowercase      :			int = None   ):







            """simple docstring"""







            return sample







       def       snake_case (				self      :			int    ,   __lowercase      :			List[str]    ,   __lowercase      :			Tuple = None   ):







            """simple docstring"""







            __lowercase      		=num_inference_steps
            __lowercase      		=(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
            __lowercase      		=(np.arange(0    ,   A_   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
            __lowercase      		=torch.from_numpy(A_   ).to(A_   )







       def       snake_case (				self      :			Optional[int]    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			List[str]=None    ,   __lowercase      :			Optional[int]=None    ,   __lowercase      :			Union[str, Any]=None   ):







            """simple docstring"""







            if prev_timestep is None:
                 __lowercase      		=t - 1

            __lowercase      		=self.alphas_cumprod[t]
            __lowercase      		=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
            __lowercase      		=1 - alpha_prod_t
            __lowercase      		=1 - alpha_prod_t_prev

            if prev_timestep == t - 1:
                 __lowercase      		=self.betas[t]
            else:
                 __lowercase      		=1 - alpha_prod_t / alpha_prod_t_prev

            # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
            # and sample from it to get previous sample
            # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
            __lowercase      		=beta_prod_t_prev / beta_prod_t * beta

            if variance_type is None:
                 __lowercase      		=self.config.variance_type

            # hacks - were probably added for training stability
            if variance_type == "fixed_small_log":
                 __lowercase      		=torch.log(torch.clamp(A_    ,   min=1E-20   )   )
                 __lowercase      		=torch.exp(0.5 * variance   )
            elif variance_type == "learned_range":
                 # NOTE difference with DDPM scheduler
                 __lowercase      		=variance.log()
                 __lowercase      		=beta.log()

                 __lowercase      		=(predicted_variance + 1) / 2
                 __lowercase      		=frac * max_log + (1 - frac) * min_log

            return variance







       def       snake_case (				self      :			Tuple    ,   __lowercase      :			Optional[Any]    ,   __lowercase      :			Any    ,   __lowercase      :			List[str]    ,   __lowercase      :			int = None    ,   __lowercase      :			Tuple=None    ,   __lowercase      :			int = True    ,   ):







            """simple docstring"""







            __lowercase      		=timestep

            if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                 __lowercase      		=torch.split(A_    ,   sample.shape[1]    ,   dim=1   )
            else:
                 __lowercase      		=None

            # 1. compute alphas, betas
            if prev_timestep is None:
                 __lowercase      		=t - 1

            __lowercase      		=self.alphas_cumprod[t]
            __lowercase      		=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
            __lowercase      		=1 - alpha_prod_t
            __lowercase      		=1 - alpha_prod_t_prev

            if prev_timestep == t - 1:
                 __lowercase      		=self.betas[t]
                 __lowercase      		=self.alphas[t]
            else:
                 __lowercase      		=1 - alpha_prod_t / alpha_prod_t_prev
                 __lowercase      		=1 - beta

            # 2. compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
            if self.config.prediction_type == "epsilon":
                 __lowercase      		=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
            elif self.config.prediction_type == "sample":
                 __lowercase      		=model_output
            else:
                 raise ValueError(
                     f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
                     ' for the UnCLIPScheduler.'   )

            # 3. Clip "predicted x_0"
            if self.config.clip_sample:
                 __lowercase      		=torch.clamp(
                     A_    ,   -self.config.clip_sample_range    ,   self.config.clip_sample_range   )

            # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
            __lowercase      		=(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
            __lowercase      		=alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

            # 5. Compute predicted previous sample µ_t
            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
            __lowercase      		=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

            # 6. Add noise
            __lowercase      		=0
            if t > 0:
                 __lowercase      		=randn_tensor(
                     model_output.shape    ,   dtype=model_output.dtype    ,   generator=A_    ,   device=model_output.device   )

                 __lowercase      		=self._get_variance(
                     A_    ,   predicted_variance=A_    ,   prev_timestep=A_    ,   )

                 if self.variance_type == "fixed_small_log":
                      __lowercase      		=variance
                 elif self.variance_type == "learned_range":
                      __lowercase      		=(0.5 * variance).exp()
                 else:
                      raise ValueError(
                          f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
                          ' for the UnCLIPScheduler.'   )

                 __lowercase      		=variance * variance_noise

            __lowercase      		=pred_prev_sample + variance

            if not return_dict:
                 return (pred_prev_sample,)

            return UnCLIPSchedulerOutput(prev_sample=A_    ,   pred_original_sample=A_   )







       def       snake_case (				self      :			Dict    ,   __lowercase      :			List[Any]    ,   __lowercase      :			int    ,   __lowercase      :			List[str]    ,   ):







            """simple docstring"""







            __lowercase      		=self.alphas_cumprod.to(device=original_samples.device    ,   dtype=original_samples.dtype   )
            __lowercase      		=timesteps.to(original_samples.device   )

            __lowercase      		=alphas_cumprod[timesteps] ** 0.5
            __lowercase      		=sqrt_alpha_prod.flatten()
            while len(sqrt_alpha_prod.shape   ) < len(original_samples.shape   ):
                 __lowercase      		=sqrt_alpha_prod.unsqueeze(-1   )

            __lowercase      		=(1 - alphas_cumprod[timesteps]) ** 0.5
            __lowercase      		=sqrt_one_minus_alpha_prod.flatten()
            while len(sqrt_one_minus_alpha_prod.shape   ) < len(original_samples.shape   ):
                 __lowercase      		=sqrt_one_minus_alpha_prod.unsqueeze(-1   )

            __lowercase      		=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
            return noisy_samples









def 						A_							(	_lowerCAmelCase	)		->					bool:
 UpperCamelCase      :   List[Any]			      =  0
 for ch in input_str:
  UpperCamelCase      :   Optional[Any]			      =  ord(_lowerCAmelCase	)
  UpperCamelCase      :   Optional[Any]			      =  pow(2				,  _lowerCAmelCase	)

  # If we already turned on bit for current character's unicode
  if bitmap >> ch_unicode & 1 == 1:
   return False
  bitmap |= ch_bit_index_on
 return True


if __name__ == "__main__":
    import doctest

    doctest.testmod()
