






'''simple docstring'''





# 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 						lowerCamelCase_				(      lowerCamelCase			,				lowerCamelCase ):
				a__					       =  1







				@register_to_config
				def __init__(						self       ,					__lowerCAmelCase=2_0_0_0       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=2_0       ,					__lowerCAmelCase=1E-3     ):
								"""simple docstring"""
								__magic_name__		:str        =      None
								__magic_name__		:Optional[Any]        =      None
								__magic_name__		:int        =      None







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								__magic_name__		:int        =      torch.linspace(1       ,					self.config.sampling_eps       ,					__lowerCAmelCase       ,					device=__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase=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__		:Union[str, Any]        =      (
								    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
								)
								__magic_name__		:Optional[Any]        =      torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff     )     )
								__magic_name__		:int        =      std.flatten()
								while len(std.shape     ) < len(score.shape     ):
												__magic_name__		:Tuple        =      std.unsqueeze(-1     )
								__magic_name__		:Tuple        =      -score / std

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

								__magic_name__		:Tuple        =      self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
								__magic_name__		:Optional[Any]        =      beta_t.flatten()
								while len(beta_t.shape     ) < len(x.shape     ):
												__magic_name__		:str        =      beta_t.unsqueeze(-1     )
								__magic_name__		:Optional[Any]        =      -0.5 * beta_t * x

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

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

								return x, x_mean







				def __len__(						self     ):
								"""simple docstring"""
								return self.config.num_train_timesteps














from typing import List

from .keymap import KEYMAP, get_character

def      __lowercase					( snake_case ):







				"""simple docstring"""






				def decorator(snake_case ):
								__magic_name__		:int        =      getattr(snake_case,  '''handle_key''',  [] )
								handle += [key]
								setattr(snake_case,  '''handle_key''',  snake_case )
								return func

				return decorator

def      __lowercase					( *snake_case ):







				"""simple docstring"""






				def decorator(snake_case ):
								__magic_name__		:Union[str, Any]        =      getattr(snake_case,  '''handle_key''',  [] )
								handle += keys
								setattr(snake_case,  '''handle_key''',  snake_case )
								return func

				return decorator
class 						lowerCamelCase_				(      lowerCamelCase ):







				def __new__(						cls       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      super().__new__(cls       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )
								if not hasattr(__lowerCAmelCase       ,					'''key_handler'''     ):
												setattr(__lowerCAmelCase       ,					'''key_handler'''       ,					{}     )
								setattr(__lowerCAmelCase       ,					'''handle_input'''       ,					KeyHandler.handle_input     )

								for value in attrs.values():
												__magic_name__		:Dict        =      getattr(__lowerCAmelCase       ,					'''handle_key'''       ,					[]     )
												for key in handled_keys:
																__magic_name__		:List[Any]        =      value
								return new_cls







				@staticmethod
				def 			A	(						cls     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      get_character()
								if char != KEYMAP["undefined"]:
												__magic_name__		:Dict        =      ord(__lowerCAmelCase     )
								__magic_name__		:str        =      cls.key_handler.get(__lowerCAmelCase     )
								if handler:
												__magic_name__		:Optional[Any]        =      char
												return handler(cls     )
								else:
												return None







def      __lowercase					( cls ):







				"""simple docstring"""






				return KeyHandler(cls.__name__,  cls.__bases__,  cls.__dict__.copy() )






