'''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_	):
    a__       :						torch.FloatTensor
    a__       :						Optional[torch.FloatTensor]          =  None





def 	lowerCAmelCase_				(					snake_case_       : Tuple					,    snake_case_       : Tuple=0.999					,    snake_case_       : Optional[Any]="cosine"					,    )    ->      Any:


     '''simple docstring'''




     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

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

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

     UpperCAmelCase_           =   []
     for i in range(__lowerCamelCase			):
          UpperCAmelCase_           =   i / num_diffusion_timesteps
          UpperCAmelCase_           =   (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        __A      ( A_		,	A_	):



    @register_to_config
    def __init__(self    :    str   ,			__a    :    int = 1000   ,			__a    :    str = "fixed_small_log"   ,			__a    :    bool = True   ,			__a    :    Optional[float] = 1.0   ,			__a    :    str = "epsilon"   ,			__a    :    str = "squaredcos_cap_v2"   ,			):


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

         UpperCAmelCase_           =   betas_for_alpha_bar(snake_case__ )

         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   ,			snake_case__ )[::-1].copy() )

         UpperCAmelCase_           =   variance_type



    def 					_lowercase						(self    :    int   ,			__a    :    torch.FloatTensor   ,			__a    :    Optional[int] = None ):
         return sample



    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    int   ,			__a    :    Union[str, torch.device] = None ):
         UpperCAmelCase_           =   num_inference_steps
         UpperCAmelCase_           =   (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         UpperCAmelCase_           =   (np.arange(0   ,			snake_case__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
         UpperCAmelCase_           =   torch.from_numpy(snake_case__ ).to(snake_case__ )



    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    Dict   ,			__a    :    Union[str, Any]=None   ,			__a    :    Optional[Any]=None   ,			__a    :    Tuple=None ):


         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(snake_case__   ,			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 					_lowercase						(self    :    List[str]   ,			__a    :    torch.FloatTensor   ,			__a    :    int   ,			__a    :    torch.FloatTensor   ,			__a    :    Optional[int] = None   ,			__a    :    int=None   ,			__a    :    bool = True   ,			):
         UpperCAmelCase_           =   timestep

         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
              UpperCAmelCase_           =   torch.split(snake_case__   ,			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(
                  snake_case__   ,			-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=snake_case__   ,			device=model_output.device )

              UpperCAmelCase_           =   self._get_variance(
                  snake_case__   ,			predicted_variance=snake_case__   ,			prev_timestep=snake_case__   ,			)

              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=snake_case__   ,			pred_original_sample=snake_case__ )






    def 					_lowercase						(self    :    Dict   ,			__a    :    torch.FloatTensor   ,			__a    :    torch.FloatTensor   ,			__a    :    torch.IntTensor   ,			):
         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






__lowerCamelCase							=       {
    "joule": 1.0,
    "kilojoule": 10_00,
    "megajoule": 1_00_00_00,
    "gigajoule": 10_00_00_00_00,
    "wattsecond": 1.0,
    "watthour": 36_00,
    "kilowatthour": 3_60_00_00,
    "newtonmeter": 1.0,
    "calorie_nutr": 41_86.8,
    "kilocalorie_nutr": 4_18_68_00.00,
    "electronvolt": 1.602_176_634e-19,
    "britishthermalunit_it": 10_55.0_55_85,
    "footpound": 1.35_5818,
}


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : str				,					__lowerCamelCase     : float    ):
       if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
              snake_case       :   List[Any]							=      (
                  f"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
                  f"""Valid values are: {', '.join(__lowerCamelCase    )}"""
              )
              raise ValueError(__lowerCamelCase    )
       return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
   import doctest

   doctest.testmod()





