




def 		lowerCAmelCase_ (						lowerCamelCase ):
				return "".join([hex(lowerCamelCase )[2:].zfill(2 ).upper() for byte in list(lowerCamelCase )] )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				# Check data validity, following RFC3548
				# https://www.ietf.org/rfc/rfc3548.txt
				if (len(lowerCamelCase ) % 2) != 0:
								raise ValueError(
								    """Base16 encoded data is invalid:
Data does not have an even number of hex digits.""" )
				# Check the character set - the standard base16 alphabet
				# is uppercase according to RFC3548 section 6
				if not set(lowerCamelCase ) <= set("""0123456789ABCDEF""" ):
								raise ValueError(
								    """Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.""" )
				# For every two hexadecimal digits (= a byte), turn it into an integer.
				# Then, string the result together into bytes, and return it.
				return bytes(int(data[i] + data[i + 1]						,  16 ) for i in range(0						,  len(lowerCamelCase )						,  2 ) )


if __name__ == "__main__":
							import doctest

							doctest.testmod()










# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       1

						@register_to_config
						def __init__(		self :Any     ,			__snake_case :Tuple=20_00     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Any=20     ,			__snake_case :Optional[int]=1E-3			):
										'''simple docstring'''

										__magic_name__						:						Dict										=None
										__magic_name__						:						List[str]										=None
										__magic_name__						:						str										=None

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :Union[str, torch.device] = None			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=torch.linspace(1     ,			self.config.sampling_eps     ,			__snake_case     ,			device=__snake_case			)

						def      A__    (		self :List[str]     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

										if self.timesteps is None:
														raise ValueError(
														    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""			)

										# TODO(Patrick) better comments + non-PyTorch
										# postprocess model score
										__magic_name__						:						int										=(
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										__magic_name__						:						Optional[int]										=torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
										__magic_name__						:						str										=std.flatten()
										while len(std.shape			) < len(score.shape			):
														__magic_name__						:						List[str]										=std.unsqueeze(-1			)
										__magic_name__						:						Union[str, Any]										=-score / std

										# compute
										__magic_name__						:						Tuple										=-1.0 / len(self.timesteps			)

										__magic_name__						:						int										=self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
										__magic_name__						:						Dict										=beta_t.flatten()
										while len(beta_t.shape			) < len(x.shape			):
														__magic_name__						:						Any										=beta_t.unsqueeze(-1			)
										__magic_name__						:						Dict										=-0.5 * beta_t * x

										__magic_name__						:						Optional[int]										=torch.sqrt(__snake_case			)
										__magic_name__						:						int										=drift - diffusion**2 * score
										__magic_name__						:						List[str]										=x + drift * dt

										# add noise
										__magic_name__						:						Optional[int]										=randn_tensor(x.shape     ,			layout=x.layout     ,			generator=__snake_case     ,			device=x.device     ,			dtype=x.dtype			)
										__magic_name__						:						Optional[Any]										=x_mean + diffusion * math.sqrt(-dt			) * noise

										return x, x_mean


						def __len__(		self :List[Any]			):
										'''simple docstring'''

										return self.config.num_train_timesteps




