



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		):
			UpperCamelCase           =  4_2
			UpperCamelCase           =  None





def 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Any=0.999							,     _UpperCAmelCase       :     int="cosine"							,     )       ->				str:







			'''simple docstring'''
			if alpha_transform_type == "cosine":

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

			elif alpha_transform_type == "exp":

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

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

			_UpperCAmelCase	 =     []
			for i in range(_UpperCAmelCase					):
						_UpperCAmelCase	 =     i / num_diffusion_timesteps
						_UpperCAmelCase	 =     (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     :						int				,	A     :						int = 10_00				,	A     :						str = "fixed_small_log"				,	A     :						bool = True				,	A     :						Optional[float] = 1.0				,	A     :						str = "epsilon"				,	A     :						str = "squaredcos_cap_v2"				,	)     ->					Any:



						"""simple docstring"""






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

						_UpperCAmelCase	 =     betas_for_alpha_bar(A)

						_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				,	A)[::-1].copy())

						_UpperCAmelCase	 =     variance_type



			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						torch.FloatTensor				,	A     :						Optional[int] = None)     ->					torch.FloatTensor:



						"""simple docstring"""






						return sample



			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						int				,	A     :						Union[str, torch.device] = None)     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     num_inference_steps
						_UpperCAmelCase	 =     (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
						_UpperCAmelCase	 =     (np.arange(0				,	A) * step_ratio).round()[::-1].copy().astype(np.intaa)
						_UpperCAmelCase	 =     torch.from_numpy(A).to(A)



			def 					_lowerCamelCase (		self     :						Tuple				,	A     :						List[str]				,	A     :						Dict=None				,	A     :						Optional[Any]=None				,	A     :						Union[str, Any]=None)     ->					List[str]:



						"""simple docstring"""






						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(A				,	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 					_lowerCamelCase (		self     :						int				,	A     :						torch.FloatTensor				,	A     :						int				,	A     :						torch.FloatTensor				,	A     :						Optional[int] = None				,	A     :						Any=None				,	A     :						bool = True				,	)     ->					Union[UnCLIPSchedulerOutput, Tuple]:



						"""simple docstring"""






						_UpperCAmelCase	 =     timestep

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

									_UpperCAmelCase	 =     self._get_variance(
									    A				,	predicted_variance=A				,	prev_timestep=A				,	)

									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=A				,	pred_original_sample=A)



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						torch.FloatTensor				,	A     :						torch.FloatTensor				,	A     :						torch.IntTensor				,	)     ->					torch.FloatTensor:



						"""simple docstring"""






						_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





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     int					)       ->				str:







			'''simple docstring'''
			if a < 0 or b < 0:
						raise ValueError('the value of both inputs must be positive'					)

			_UpperCAmelCase	 =     str(bin(_UpperCAmelCase					)					)[2:]  # remove the leading "0b"
			_UpperCAmelCase	 =     str(bin(_UpperCAmelCase					)					)[2:]  # remove the leading "0b"

			_UpperCAmelCase	 =     max(len(_UpperCAmelCase					)							,     len(_UpperCAmelCase					)					)

			return "0b" + "".join(
			    str(int(char_a != char_b					)					)
			    for char_a, char_b in zip(a_binary.zfill(_UpperCAmelCase					)							,     b_binary.zfill(_UpperCAmelCase					)					)					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
