



"""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    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       42
		_lowerCamelCase			  =       None




def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			Dict=0.999					, __UpperCamelCase      :			Optional[Any]="cosine"					, ):


						'''simple docstring'''






						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(__UpperCamelCase      :			Union[str, Any]    ):
																		return math.exp(t * -12.0    )

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

						snake_case_   : List[str]						=			[]
						for i in range(__UpperCamelCase    ):
												snake_case_   : Optional[Any]						=			i / num_diffusion_timesteps
												snake_case_   : 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    (				SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		@register_to_config
		def __init__(					self							,    _lowercase = 1_0_0_0							,    _lowercase = "fixed_small_log"							,    _lowercase = True							,    _lowercase = 1.0							,    _lowercase = "epsilon"							,    _lowercase = "squaredcos_cap_v2"							,    ) ->						Tuple:







								'''simple docstring'''

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

								snake_case_   : List[str]						=			betas_for_alpha_bar(_lowercase   )

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

								# standard deviation of the initial noise distribution
								snake_case_   : str						=			1.0

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

								snake_case_   : Dict						=			variance_type





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase = None   ) ->						torch.FloatTensor:







								'''simple docstring'''

								return sample





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase = None   ) ->						Union[str, Any]:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase=None							,    _lowercase=None							,    _lowercase=None   ) ->						Any:







								'''simple docstring'''

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

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

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

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

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

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

								return variance





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







								'''simple docstring'''

								snake_case_   : int						=			timestep

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

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

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

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

								# 6. Add noise
								snake_case_   : List[str]						=			0
								if t > 0:
														snake_case_   : Dict						=			randn_tensor(
														    model_output.shape							,    dtype=model_output.dtype							,    generator=_lowercase							,    device=model_output.device   )

														snake_case_   : str						=			self._get_variance(
														    _lowercase							,    predicted_variance=_lowercase							,    prev_timestep=_lowercase							,    )

														if self.variance_type == "fixed_small_log":
																				snake_case_   : List[str]						=			variance
														elif self.variance_type == "learned_range":
																				snake_case_   : List[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_   : Optional[Any]						=			variance * variance_noise

								snake_case_   : List[Any]						=			pred_prev_sample + variance

								if not return_dict:
														return (pred_prev_sample,)

								return UnCLIPSchedulerOutput(prev_sample=_lowercase							,    pred_original_sample=_lowercase   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    ) ->						torch.FloatTensor:







								'''simple docstring'''

								snake_case_   : Any						=			self.alphas_cumprod.to(device=original_samples.device							,    dtype=original_samples.dtype   )
								snake_case_   : Tuple						=			timesteps.to(original_samples.device   )

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

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

								snake_case_   : List[Any]						=			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
								return noisy_samples






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






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

						snake_case_   : List[str]						=			str(bin(__UpperCamelCase    )    )[2:]  # remove the leading "0b"
						snake_case_   : Union[str, Any]						=			str(bin(__UpperCamelCase    )    )[2:]  # remove the leading "0b"

						snake_case_   : int						=			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()

