





"""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  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					torch.FloatTensor
			lowerCamelCase_					:					Optional[torch.FloatTensor]	      =				None






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=0.999			,    _UpperCamelCase="cosine"			,    )		->						Union[str, Any]:






				"""simple docstring"""


				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

				snake_case_						:   Union[str, Any]           =  []
				for i in range(_UpperCamelCase			):
								snake_case_						:   Any           =  i / num_diffusion_timesteps
								snake_case_						:   List[str]           =  (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  __lowerCAmelCase	(      _a,					_a					):






			@register_to_config
			def __init__(self ,							__magic_name__ = 1000 ,							__magic_name__ = "fixed_small_log" ,							__magic_name__ = True ,							__magic_name__ = 1.0 ,							__magic_name__ = "epsilon" ,							__magic_name__ = "squaredcos_cap_v2" ,							)					->       int:


							'''simple docstring'''



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

							snake_case_						:   Optional[int]           =  betas_for_alpha_bar(__magic_name__   )

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

							# standard deviation of the initial noise distribution
							snake_case_						:   Union[str, Any]           =  1.0

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

							snake_case_						:   str           =  variance_type






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None   )					->       torch.FloatTensor:


							'''simple docstring'''



							return sample






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Any           =  num_inference_steps
							snake_case_						:   List[str]           =  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
							snake_case_						:   Dict           =  (np.arange(0 ,							__magic_name__   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
							snake_case_						:   int           =  torch.from_numpy(__magic_name__   ).to(__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=None   )					->       Dict:


							'''simple docstring'''



							if prev_timestep is None:
											snake_case_						:   List[str]           =  t - 1

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

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

							if variance_type is None:
											snake_case_						:   Tuple           =  self.config.variance_type

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

											snake_case_						:   Optional[int]           =  (predicted_variance + 1) / 2
											snake_case_						:   Optional[int]           =  frac * max_log + (1 - frac) * min_log

							return variance






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							__magic_name__=None ,							__magic_name__ = True ,							)					->       Union[UnCLIPSchedulerOutput, Tuple]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  timestep

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

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

							snake_case_						:   Optional[int]           =  self.alphas_cumprod[t]
							snake_case_						:   int           =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
							snake_case_						:   str           =  1 - alpha_prod_t
							snake_case_						:   List[str]           =  1 - alpha_prod_t_prev

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

							# 6. Add noise
							snake_case_						:   Optional[Any]           =  0
							if t > 0:
											snake_case_						:   Union[str, Any]           =  randn_tensor(
											    model_output.shape ,							dtype=model_output.dtype ,							generator=__magic_name__ ,							device=model_output.device   )

											snake_case_						:   Union[str, Any]           =  self._get_variance(
											    __magic_name__ ,							predicted_variance=__magic_name__ ,							prev_timestep=__magic_name__ ,							)

											if self.variance_type == "fixed_small_log":
															snake_case_						:   Dict           =  variance
											elif self.variance_type == "learned_range":
															snake_case_						:   Any           =  (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.'''   )

											snake_case_						:   Dict           =  variance * variance_noise

							snake_case_						:   str           =  pred_prev_sample + variance

							if not return_dict:
											return (pred_prev_sample,)

							return UnCLIPSchedulerOutput(prev_sample=__magic_name__ ,							pred_original_sample=__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       torch.FloatTensor:


							'''simple docstring'''



							snake_case_						:   Any           =  self.alphas_cumprod.to(device=original_samples.device ,							dtype=original_samples.dtype   )
							snake_case_						:   List[str]           =  timesteps.to(original_samples.device   )

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

							snake_case_						:   Any           =  (1 - alphas_cumprod[timesteps]) ** 0.5
							snake_case_						:   Any           =  sqrt_one_minus_alpha_prod.flatten()
							while len(sqrt_one_minus_alpha_prod.shape   ) < len(original_samples.shape   ):
											snake_case_						:   Union[str, Any]           =  sqrt_one_minus_alpha_prod.unsqueeze(-1   )

							snake_case_						:   Union[str, Any]           =  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples







def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase			,    _UpperCamelCase			):
								raise ValueError('''multiplicative_persistence() only accepts integral values'''			)
				if num < 0:
								raise ValueError('''multiplicative_persistence() does not accept negative values'''			)

				snake_case_						:   List[Any]           =  0
				snake_case_						:   Tuple           =  str(_UpperCamelCase			)

				while len(_UpperCamelCase			) != 1:
								snake_case_						:   Tuple           =  [int(_UpperCamelCase			) for i in num_string]

								snake_case_						:   Dict           =  1
								for i in range(0			,    len(_UpperCamelCase			)			):
												total *= numbers[i]

								snake_case_						:   str           =  str(_UpperCamelCase			)

								steps += 1
				return steps






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase			,    _UpperCamelCase			):
								raise ValueError('''additive_persistence() only accepts integral values'''			)
				if num < 0:
								raise ValueError('''additive_persistence() does not accept negative values'''			)

				snake_case_						:   Any           =  0
				snake_case_						:   Tuple           =  str(_UpperCamelCase			)

				while len(_UpperCamelCase			) != 1:
								snake_case_						:   List[str]           =  [int(_UpperCamelCase			) for i in num_string]

								snake_case_						:   Optional[int]           =  0
								for i in range(0			,    len(_UpperCamelCase			)			):
												total += numbers[i]

								snake_case_						:   Tuple           =  str(_UpperCamelCase			)

								steps += 1
				return steps


if __name__ == "__main__":
	import doctest

	doctest.testmod()
