
"""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       ):



							'''simple docstring'''







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





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

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

			elif alpha_transform_type == "exp":

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

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

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







class 					__a     ( __a					,   __a       ):



							'''simple docstring'''







							@register_to_config
							def __init__(     self  ,    _lowerCamelCase = 1_000  ,    _lowerCamelCase = "fixed_small_log"  ,    _lowerCamelCase = True  ,    _lowerCamelCase = 1.0  ,    _lowerCamelCase = "epsilon"  ,    _lowerCamelCase = "squaredcos_cap_v2"  ,    )			->							Any:




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

										__lowercase       =       betas_for_alpha_bar(_lowerCamelCase       )

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

										# standard deviation of the initial noise distribution
										__lowercase       =       1.0

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

										__lowercase       =       variance_type
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							torch.FloatTensor:




										'''simple docstring'''
										return sample
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							str:




										'''simple docstring'''
										__lowercase       =       num_inference_steps
										__lowercase       =       (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
										__lowercase       =       (np.arange(0  ,    _lowerCamelCase       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
										__lowercase       =       torch.from_numpy(_lowerCamelCase       ).to(_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase=None  ,    _lowerCamelCase=None  ,    _lowerCamelCase=None       )			->							Dict:




										'''simple docstring'''
										if prev_timestep is None:
													__lowercase       =       t - 1

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

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

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

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

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

										return variance
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    _lowerCamelCase=None  ,    _lowerCamelCase = True  ,    )			->							Union[UnCLIPSchedulerOutput, Tuple]:




										'''simple docstring'''
										__lowercase       =       timestep

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

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

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

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

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

													__lowercase       =       self._get_variance(
													    _lowerCamelCase  ,    predicted_variance=_lowerCamelCase  ,    prev_timestep=_lowerCamelCase  ,    )

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

													__lowercase       =       variance * variance_noise

										__lowercase       =       pred_prev_sample + variance

										if not return_dict:
													return (pred_prev_sample,)

										return UnCLIPSchedulerOutput(prev_sample=_lowerCamelCase  ,    pred_original_sample=_lowerCamelCase       )





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    )			->							torch.FloatTensor:




										'''simple docstring'''
										# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
										__lowercase       =       self.alphas_cumprod.to(device=original_samples.device  ,    dtype=original_samples.dtype       )
										__lowercase       =       timesteps.to(original_samples.device       )

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

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

										__lowercase       =       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
										return noisy_samples







"""simple docstring"""






def      lowerCAmelCase__			(		__magic_name__      )						->int:

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

			__lowercase       =       0
			__lowercase       =       str(__magic_name__      )

			while len(__magic_name__      ) != 1:
						__lowercase       =       [int(__magic_name__      ) for i in num_string]

						__lowercase       =       1
						for i in range(0			,   len(__magic_name__      )      ):
									total *= numbers[i]

						__lowercase       =       str(__magic_name__      )

						steps += 1
			return steps



def      lowerCAmelCase__			(		__magic_name__      )						->int:

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

			__lowercase       =       0
			__lowercase       =       str(__magic_name__      )

			while len(__magic_name__      ) != 1:
						__lowercase       =       [int(__magic_name__      ) for i in num_string]

						__lowercase       =       0
						for i in range(0			,   len(__magic_name__      )      ):
									total += numbers[i]

						__lowercase       =       str(__magic_name__      )

						steps += 1
			return steps


if __name__ == "__main__":
					import doctest

					doctest.testmod()





