


'''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        a   (   _a				):
 _lowerCAmelCase           =		42
 _lowerCAmelCase           =		None






def 		_A (lowerCAmelCase__     :List[Any] ,  lowerCAmelCase__     :Optional[int]=0.9_9_9 ,  lowerCAmelCase__     :List[str]="cosine" ,  )      ->     Optional[int]:




     '''simple docstring'''



     if alpha_transform_type == "cosine":

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

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

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






class        a   (   _a     , _a				):
 @register_to_config
 def __init__(       self					, __magic_name__ = 10_00					, __magic_name__ = "fixed_small_log"					, __magic_name__ = True					, __magic_name__ = 1.0					, __magic_name__ = "epsilon"					, __magic_name__ = "squaredcos_cap_v2"					, )    ->							str:



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

      _a						=							betas_for_alpha_bar(__magic_name__  )

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

      # standard deviation of the initial noise distribution
      _a						=							1.0

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

      _a						=							variance_type
 def 							__UpperCAmelCase						(       self					, __magic_name__					, __magic_name__ = None  )    ->							torch.FloatTensor:
      return sample
 def 							__UpperCAmelCase						(       self					, __magic_name__					, __magic_name__ = None  )    ->							str:
      _a						=							num_inference_steps
      _a						=							(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
      _a						=							(np.arange(0					, __magic_name__  ) * step_ratio).round()[::-1].copy().astype(np.intaa  )
      _a						=							torch.from_numpy(__magic_name__  ).to(__magic_name__  )
 def 							__UpperCAmelCase						(       self					, __magic_name__					, __magic_name__=None					, __magic_name__=None					, __magic_name__=None  )    ->							Optional[Any]:



      if prev_timestep is None:
           _a						=							t - 1

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

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

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

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

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

      return variance
 def 							__UpperCAmelCase						(       self					, __magic_name__					, __magic_name__					, __magic_name__					, __magic_name__ = None					, __magic_name__=None					, __magic_name__ = True					, )    ->							Union[UnCLIPSchedulerOutput, Tuple]:
      _a						=							timestep

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

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

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

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

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

           _a						=							self._get_variance(
               __magic_name__					, predicted_variance=__magic_name__					, prev_timestep=__magic_name__					, )

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

           _a						=							variance * variance_noise

      _a						=							pred_prev_sample + variance

      if not return_dict:
           return (pred_prev_sample,)

      return UnCLIPSchedulerOutput(prev_sample=__magic_name__					, pred_original_sample=__magic_name__  )




 def 							__UpperCAmelCase						(       self					, __magic_name__					, __magic_name__					, __magic_name__					, )    ->							torch.FloatTensor:
      _a						=							self.alphas_cumprod.to(device=original_samples.device					, dtype=original_samples.dtype  )
      _a						=							timesteps.to(original_samples.device  )

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

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

      _a						=							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples








from __future__ import annotations






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						tuple[float, list[float]]:






				"""simple docstring"""


				snake_case_						:    Dict				=  list(range(len(_UpperCamelCase ) ) )
				snake_case_						:    Dict				=  [v / w for v, w in zip(_UpperCamelCase ,				_UpperCamelCase )]
				index.sort(key=lambda _UpperCamelCase : ratio[i] ,				reverse=_UpperCamelCase )

				snake_case_						:    float				=  0
				snake_case_						:    list[float]				=  [0] * len(_UpperCamelCase )
				for i in index:
								if weight[i] <= capacity:
												snake_case_						:    Dict				=  1
												max_value += value[i]
												capacity -= weight[i]
								else:
												snake_case_						:    Union[str, Any]				=  capacity / weight[i]
												max_value += value[i] * capacity / weight[i]
												break

				return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()
