"""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 				snake_case       ( __UpperCAmelCase      ,    __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = 1







					@register_to_config
					def __init__(				self   :	str    ,lowerCamelCase__   :	int=2_000    ,lowerCamelCase__   :	Optional[Any]=0.1    ,lowerCamelCase__   :	Optional[int]=20    ,lowerCamelCase__   :	Union[str, Any]=1e-3			):
									UpperCAmelCase__             =							None
									UpperCAmelCase__             =							None
									UpperCAmelCase__             =							None







					def 		__lowerCAmelCase						(				self   :	List[str]    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	Union[str, torch.device] = None			):
									UpperCAmelCase__             =							torch.linspace(1    ,self.config.sampling_eps    ,lowerCamelCase__    ,device=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	Dict    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	int=None			):
									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
									UpperCAmelCase__             =							(
									    -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
									)
									UpperCAmelCase__             =							torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
									UpperCAmelCase__             =							std.flatten()
									while len(std.shape			) < len(score.shape			):
													UpperCAmelCase__             =							std.unsqueeze(-1			)
									UpperCAmelCase__             =							-score / std

									# compute
									UpperCAmelCase__             =							-1.0 / len(self.timesteps			)

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

									UpperCAmelCase__             =							torch.sqrt(lowerCamelCase__			)
									UpperCAmelCase__             =							drift - diffusion**2 * score
									UpperCAmelCase__             =							x + drift * dt

									# add noise
									UpperCAmelCase__             =							randn_tensor(x.shape    ,layout=x.layout    ,generator=lowerCamelCase__    ,device=x.device    ,dtype=x.dtype			)
									UpperCAmelCase__             =							x_mean + diffusion * math.sqrt(-dt			) * noise

									return x, x_mean







					def __len__(				self   :	Optional[Any]			):
									return self.config.num_train_timesteps








from collections import deque

from .hash_table import HashTable




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  Optional[Any], *_snake_case  :  Optional[Any], **_snake_case  :  List[Any]					)   ->Optional[int]:
            super().__init__(*_snake_case, **_snake_case					)






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  Tuple, _snake_case  :  Dict					)   ->Dict:
            snake_case__							:	int   	=	deque([]					) if self.values[key] is None else self.values[key]
            self.values[key].appendleft(_snake_case					)
            snake_case__							:	Dict   	=	self.values[key]






     def    lowercase_   (       self  :  Any					)   ->Optional[Any]:
            return (
                sum(self.charge_factor - len(_snake_case					) for slot in self.values					)
                / self.size_table
                * self.charge_factor
            )






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str, _snake_case  :  Optional[int]=None					)   ->Optional[Any]:
            if not (
                len(self.values[key]					) == self.charge_factor and self.values.count(_snake_case					) == 0
            ):
                   return key
            return super()._collision_resolution(_snake_case, _snake_case					)






