





"""simple docstring"""






import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput




class 			__lowercase     (     _UpperCamelCase				, _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					1







					@register_to_config
					def __init__(  self		,     _UpperCAmelCase = 1000		,     _UpperCAmelCase = None       ):
									# set `betas`, `alphas`, `timesteps`
									self.set_timesteps(_UpperCAmelCase       )

									# standard deviation of the initial noise distribution
									__a	:  Dict         =					1.0

									# For now we only support F-PNDM, i.e. the runge-kutta method
									# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
									# mainly at formula (9), (12), (13) and the Algorithm 2.
									__a	:  Union[str, Any]         =					4

									# running values
									__a	:  Optional[Any]         =					[]







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = None       ):
									__a	:  Union[str, Any]         =					num_inference_steps
									__a	:  str         =					torch.linspace(1		,     0		,     num_inference_steps + 1       )[:-1]
									__a	:  Union[str, Any]         =					torch.cat([steps, torch.tensor([0.0]       )]       )

									if self.config.trained_betas is not None:
													__a	:  Tuple         =					torch.tensor(self.config.trained_betas		,     dtype=torch.floataa       )
									else:
													__a	:  Dict         =					torch.sin(steps * math.pi / 2       ) ** 2

									__a	:  Optional[int]         =					(1.0 - self.betas**2) ** 0.5

									__a	:  Dict         =					(torch.atana(self.betas		,     self.alphas       ) / math.pi * 2)[:-1]
									__a	:  Optional[Any]         =					timesteps.to(_UpperCAmelCase       )

									__a	:  Any         =					[]







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase = True		,     ):
									if self.num_inference_steps is None:
													raise ValueError(
													    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''       )

									__a	:  Optional[int]         =					(self.timesteps == timestep).nonzero().item()
									__a	:  Tuple         =					timestep_index + 1

									__a	:  Optional[Any]         =					sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
									self.ets.append(_UpperCAmelCase       )

									if len(self.ets       ) == 1:
													__a	:  Dict         =					self.ets[-1]
									elif len(self.ets       ) == 2:
													__a	:  str         =					(3 * self.ets[-1] - self.ets[-2]) / 2
									elif len(self.ets       ) == 3:
													__a	:  Optional[int]         =					(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
									else:
													__a	:  Optional[Any]         =					(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

									__a	:  List[str]         =					self._get_prev_sample(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       )

									if not return_dict:
													return (prev_sample,)

									return SchedulerOutput(prev_sample=_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									return sample







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  Optional[Any]         =					self.alphas[timestep_index]
									__a	:  Optional[int]         =					self.betas[timestep_index]

									__a	:  int         =					self.alphas[prev_timestep_index]
									__a	:  Optional[Any]         =					self.betas[prev_timestep_index]

									__a	:  List[str]         =					(sample - sigma * ets) / max(_UpperCAmelCase		,     1e-8       )
									__a	:  Any         =					next_alpha * pred + ets * next_sigma

									return prev_sample







					def __len__(  self       ):
									return self.config.num_train_timesteps






"""simple docstring"""






from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

A	     =					TypeVar('''T''')




class 			__lowercase     (     Generic[T]							):







					'''simple docstring'''




					__lowerCAmelCase					=					42  # Cache store of keys
					__lowerCAmelCase					=					42  # References of the keys in cache
					__lowerCAmelCase					=					10  # Maximum capacity of cache







					def __init__(  self		,     _UpperCAmelCase       ):
									__a	:  Optional[int]         =					deque()
									__a	:  Dict         =					set()
									if not n:
													__a	:  List[Any]         =					sys.maxsize
									elif n < 0:
													raise ValueError('''n should be an integer greater than 0.'''       )
									else:
													__a	:  str         =					n







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									if x not in self.key_reference:
													if len(self.dq_store       ) == LRUCache._MAX_CAPACITY:
																	__a	:  int         =					self.dq_store.pop()
																	self.key_reference.remove(_UpperCAmelCase       )
									else:
													self.dq_store.remove(_UpperCAmelCase       )

									self.dq_store.appendleft(_UpperCAmelCase       )
									self.key_reference.add(_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									for k in self.dq_store:
													print(_UpperCAmelCase       )







					def __repr__(  self       ):
									return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store       )}"""


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		A	     =					LRUCache(4)
		lru_cache.refer('''A''')
		lru_cache.refer(2)
		lru_cache.refer(3)
		lru_cache.refer('''A''')
		lru_cache.refer(4)
		lru_cache.refer(5)
		lru_cache.display()

		print(lru_cache)
		assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"