"""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 			UpperCamelCase__(	__snake_case       ):
						lowerCAmelCase__     :  torch.FloatTensor
						lowerCAmelCase__     :  Optional[torch.FloatTensor]  							=						None






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__=0.9_9_9				,			UpperCamelCase__="cosine"				,			):






							"""simple docstring"""





							if alpha_transform_type == "cosine":

														def alpha_bar_fn(UpperCamelCase__ ):
																					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(UpperCamelCase__ ):
																					return math.exp(t * -1_2.0 )

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

							A__		   =							[]
							for i in range(SCREAMING_SNAKE_CASE_ ):
														A__		   =							i / num_diffusion_timesteps
														A__		   =							(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__(	__snake_case						,  __snake_case       ):





						@register_to_config
						def __init__(				self						,__UpperCAmelCase = 10_00						,__UpperCAmelCase = "fixed_small_log"						,__UpperCAmelCase = True						,__UpperCAmelCase = 1.0						,__UpperCAmelCase = "epsilon"						,__UpperCAmelCase = "squaredcos_cap_v2"						,)    -> List[Any]:
													if beta_schedule != "squaredcos_cap_v2":
																				raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''   )

													A__		   =							betas_for_alpha_bar(__UpperCAmelCase   )

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

													A__		   =							variance_type





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None   )    -> List[Any]:
													return sample





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None   )    -> str:
													A__		   =							num_inference_steps
													A__		   =							(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
													A__		   =							(np.arange(0						,__UpperCAmelCase   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
													A__		   =							torch.from_numpy(__UpperCAmelCase   ).to(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=None						,__UpperCAmelCase=None						,__UpperCAmelCase=None   )    -> Tuple:
													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(__UpperCAmelCase						,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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase=None						,__UpperCAmelCase = True						,)    -> Tuple:
													A__		   =							timestep

													if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																				A__		   =							torch.split(__UpperCAmelCase						,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(
																				    __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
													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=__UpperCAmelCase						,device=model_output.device   )

																				A__		   =							self._get_variance(
																				    __UpperCAmelCase						,predicted_variance=__UpperCAmelCase						,prev_timestep=__UpperCAmelCase						,)

																				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=__UpperCAmelCase						,pred_original_sample=__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,)    -> List[str]:
													# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
													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








def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     if not all(char in '01' for char in bin_string			):
          raise ValueError('Non-binary value was passed to the function'			)
     if not bin_string:
          raise ValueError('Empty string was passed to the function'			)
     lowercase__    :   Union[str, Any]      =       ''
     while len(SCREAMING_SNAKE_CASE_			) % 3 != 0:
          lowercase__    :   List[str]      =       '0' + bin_string
     lowercase__    :   Any      =       [
         bin_string[index : index + 3]
         for index in range(len(SCREAMING_SNAKE_CASE_			)			)
         if index % 3 == 0
     ]
     for bin_group in bin_string_in_3_list:
          lowercase__    :   str      =       0
          for index, val in enumerate(SCREAMING_SNAKE_CASE_			):
               oct_val += int(2 ** (2 - index) * int(SCREAMING_SNAKE_CASE_			)			)
          oct_string += str(SCREAMING_SNAKE_CASE_			)
     return oct_string


if __name__ == "__main__":
       from doctest import testmod

       testmod()

