


def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : float      ,					SCREAMING_SNAKE_CASE__      : int    ):
     if digit_amount > 0:
          return round(number - int(SCREAMING_SNAKE_CASE__    )      ,					SCREAMING_SNAKE_CASE__    )
     return number - int(SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
   print(decimal_isolate(1.53, 0))
   print(decimal_isolate(35.345, 1))
   print(decimal_isolate(35.345, 2))
   print(decimal_isolate(35.345, 3))
   print(decimal_isolate(-14.789, 3))
   print(decimal_isolate(0, 2))
   print(decimal_isolate(-14.123, 1))
   print(decimal_isolate(-14.123, 2))
   print(decimal_isolate(-14.123, 3))









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    UpperCAmelCase__	( A_		):



    """simple docstring"""






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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : int=0.999      ,					SCREAMING_SNAKE_CASE__      : str="cosine"      ,					):
     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
               return math.exp(t * -12.0    )

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

     __UpperCamelCase   							=[]
     for i in range(SCREAMING_SNAKE_CASE__    ):
          __UpperCamelCase   							=i / num_diffusion_timesteps
          __UpperCamelCase   							=(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    UpperCAmelCase__	( A_       ,  A_		):



    """simple docstring"""






    @register_to_config
    def __init__(      self ,       A_ = 1000 ,       A_ = "fixed_small_log" ,       A_ = True ,       A_ = 1.0 ,       A_ = "epsilon" ,       A_ = "squaredcos_cap_v2" ,       )  ->	Tuple:
         if beta_schedule != "squaredcos_cap_v2":
              raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''   )

         __UpperCamelCase   							=betas_for_alpha_bar(A_   )

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

         # standard deviation of the initial noise distribution
         __UpperCamelCase   							=1.0

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

         __UpperCamelCase   							=variance_type






    def _a (      self ,       A_ ,       A_ = None   )  ->	torch.FloatTensor:
         return sample






    def _a (      self ,       A_ ,       A_ = None   )  ->	Tuple:
         __UpperCamelCase   							=num_inference_steps
         __UpperCamelCase   							=(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         __UpperCamelCase   							=(np.arange(0 ,       A_   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
         __UpperCamelCase   							=torch.from_numpy(A_   ).to(A_   )






    def _a (      self ,       A_ ,       A_=None ,       A_=None ,       A_=None   )  ->	List[Any]:
         if prev_timestep is None:
              __UpperCamelCase   							=t - 1

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

         if prev_timestep == t - 1:
              __UpperCamelCase   							=self.betas[t]
         else:
              __UpperCamelCase   							=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
         __UpperCamelCase   							=beta_prod_t_prev / beta_prod_t * beta

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

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

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

         return variance






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ = None ,       A_=None ,       A_ = True ,       )  ->	Union[UnCLIPSchedulerOutput, Tuple]:
         __UpperCamelCase   							=timestep

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

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

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

         if prev_timestep == t - 1:
              __UpperCamelCase   							=self.betas[t]
              __UpperCamelCase   							=self.alphas[t]
         else:
              __UpperCamelCase   							=1 - alpha_prod_t / alpha_prod_t_prev
              __UpperCamelCase   							=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":
              __UpperCamelCase   							=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
              __UpperCamelCase   							=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:
              __UpperCamelCase   							=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
         __UpperCamelCase   							=(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         __UpperCamelCase   							=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
         __UpperCamelCase   							=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

              if self.variance_type == "fixed_small_log":
                   __UpperCamelCase   							=variance
              elif self.variance_type == "learned_range":
                   __UpperCamelCase   							=(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.'   )

              __UpperCamelCase   							=variance * variance_noise

         __UpperCamelCase   							=pred_prev_sample + variance

         if not return_dict:
              return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=A_ ,       pred_original_sample=A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ ,       )  ->	torch.FloatTensor:
         # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
         __UpperCamelCase   							=self.alphas_cumprod.to(device=original_samples.device ,       dtype=original_samples.dtype   )
         __UpperCamelCase   							=timesteps.to(original_samples.device   )

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

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

         __UpperCamelCase   							=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples





