




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__	(lowerCAmelCase__     ):

    '''simple docstring'''



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




def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=0.999 ,_SCREAMING_SNAKE_CASE="cosine" ,)	-> List[str]:
       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

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

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

       lowerCamelCase   :      str			 =					[]
       for i in range(_SCREAMING_SNAKE_CASE    ):
              lowerCamelCase   :      Optional[int]			 =					i / num_diffusion_timesteps
              lowerCamelCase   :      int			 =					(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__	(lowerCAmelCase__    ,				lowerCAmelCase__     ):

    '''simple docstring'''



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

           lowerCamelCase   :      Dict			 =					betas_for_alpha_bar(UpperCamelCase__     )

           lowerCamelCase   :      List[Any]			 =					1.0 - self.betas
           lowerCamelCase   :      List[Any]			 =					torch.cumprod(self.alphas     ,  dim=0     )
           lowerCamelCase   :      int			 =					torch.tensor(1.0     )

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

           # setable values
           lowerCamelCase   :      int			 =					None
           lowerCamelCase   :      Dict			 =					torch.from_numpy(np.arange(0     ,  UpperCamelCase__     )[::-1].copy()     )

           lowerCamelCase   :      Optional[Any]			 =					variance_type







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      torch.FloatTensor:
           return sample







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      List[Any]:
           lowerCamelCase   :      Dict			 =					num_inference_steps
           lowerCamelCase   :      Tuple			 =					(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
           lowerCamelCase   :      int			 =					(np.arange(0     ,  UpperCamelCase__     ) * step_ratio).round()[::-1].copy().astype(np.intaa     )
           lowerCamelCase   :      Optional[int]			 =					torch.from_numpy(UpperCamelCase__     ).to(UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     )     ->      Union[str, Any]:
           if prev_timestep is None:
                  lowerCamelCase   :      List[str]			 =					t - 1

           lowerCamelCase   :      Optional[int]			 =					self.alphas_cumprod[t]
           lowerCamelCase   :      Optional[Any]			 =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
           lowerCamelCase   :      Tuple			 =					1 - alpha_prod_t
           lowerCamelCase   :      int			 =					1 - alpha_prod_t_prev

           if prev_timestep == t - 1:
                  lowerCamelCase   :      Optional[int]			 =					self.betas[t]
           else:
                  lowerCamelCase   :      List[Any]			 =					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
           lowerCamelCase   :      Tuple			 =					beta_prod_t_prev / beta_prod_t * beta

           if variance_type is None:
                  lowerCamelCase   :      Optional[Any]			 =					self.config.variance_type

           # hacks - were probably added for training stability
           if variance_type == "fixed_small_log":
                  lowerCamelCase   :      List[str]			 =					torch.log(torch.clamp(UpperCamelCase__     ,  min=1e-20     )     )
                  lowerCamelCase   :      Optional[Any]			 =					torch.exp(0.5 * variance     )
           elif variance_type == "learned_range":
                  # NOTE difference with DDPM scheduler
                  lowerCamelCase   :      Optional[int]			 =					variance.log()
                  lowerCamelCase   :      Optional[int]			 =					beta.log()

                  lowerCamelCase   :      List[Any]			 =					(predicted_variance + 1) / 2
                  lowerCamelCase   :      Optional[Any]			 =					frac * max_log + (1 - frac) * min_log

           return variance







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     ,  UpperCamelCase__=None     ,  UpperCamelCase__ = True     ,  )     ->      Union[UnCLIPSchedulerOutput, Tuple]:
           lowerCamelCase   :      str			 =					timestep

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

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

           lowerCamelCase   :      Optional[int]			 =					self.alphas_cumprod[t]
           lowerCamelCase   :      Dict			 =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
           lowerCamelCase   :      Union[str, Any]			 =					1 - alpha_prod_t
           lowerCamelCase   :      Optional[int]			 =					1 - alpha_prod_t_prev

           if prev_timestep == t - 1:
                  lowerCamelCase   :      Union[str, Any]			 =					self.betas[t]
                  lowerCamelCase   :      List[Any]			 =					self.alphas[t]
           else:
                  lowerCamelCase   :      Any			 =					1 - alpha_prod_t / alpha_prod_t_prev
                  lowerCamelCase   :      Any			 =					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":
                  lowerCamelCase   :      int			 =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           elif self.config.prediction_type == "sample":
                  lowerCamelCase   :      Union[str, Any]			 =					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:
                  lowerCamelCase   :      Optional[Any]			 =					torch.clamp(
                      UpperCamelCase__     ,  -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
           lowerCamelCase   :      int			 =					(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
           lowerCamelCase   :      Dict			 =					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
           lowerCamelCase   :      str			 =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

           # 6. Add noise
           lowerCamelCase   :      Optional[int]			 =					0
           if t > 0:
                  lowerCamelCase   :      Optional[Any]			 =					randn_tensor(
                      model_output.shape     ,  dtype=model_output.dtype     ,  generator=UpperCamelCase__     ,  device=model_output.device     )

                  lowerCamelCase   :      Dict			 =					self._get_variance(
                      UpperCamelCase__     ,  predicted_variance=UpperCamelCase__     ,  prev_timestep=UpperCamelCase__     ,  )

                  if self.variance_type == "fixed_small_log":
                         lowerCamelCase   :      Optional[int]			 =					variance
                  elif self.variance_type == "learned_range":
                         lowerCamelCase   :      Dict			 =					(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."     )

                  lowerCamelCase   :      str			 =					variance * variance_noise

           lowerCamelCase   :      Tuple			 =					pred_prev_sample + variance

           if not return_dict:
                  return (pred_prev_sample,)

           return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase__     ,  pred_original_sample=UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  )     ->      torch.FloatTensor:
           # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
           lowerCamelCase   :      List[str]			 =					self.alphas_cumprod.to(device=original_samples.device     ,  dtype=original_samples.dtype     )
           lowerCamelCase   :      str			 =					timesteps.to(original_samples.device     )

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

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

           lowerCamelCase   :      Optional[int]			 =					sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
           return noisy_samples












import math

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> float:
       if (
           not isinstance(_SCREAMING_SNAKE_CASE ,(int, float)    )
           or power_factor < -1
           or power_factor > 1
       ):
              raise ValueError("power_factor must be a valid float value between -1 and 1."    )
       return apparent_power * power_factor

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> float:
       if (
           not isinstance(_SCREAMING_SNAKE_CASE ,(int, float)    )
           or power_factor < -1
           or power_factor > 1
       ):
              raise ValueError("power_factor must be a valid float value between -1 and 1."    )
       return apparent_power * math.sqrt(1 - power_factor**2    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






