
'''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     __snake_case		(   a__    ,   a__):
					_lowerCAmelCase			     =	1
					@register_to_config
					def __init__(     self,    A = 1000,    A = None      ):

									"""simple docstring"""


									self.set_timesteps(A      )

									# standard deviation of the initial noise distribution
									lowerCamelCase					:			Optional[Any]									= 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.
									lowerCamelCase					:			Dict									= 4

									# running values
									lowerCamelCase					:			Optional[int]									= []
					def     UpperCAmelCase_							(     self,    A,    A = None      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= num_inference_steps
									lowerCamelCase					:			List[Any]									= torch.linspace(1,    0,    num_inference_steps + 1      )[:-1]
									lowerCamelCase					:			Tuple									= torch.cat([steps, torch.tensor([0.0]      )]      )

									if self.config.trained_betas is not None:
													lowerCamelCase					:			Union[str, Any]									= torch.tensor(self.config.trained_betas,    dtype=torch.floataa      )
									else:
													lowerCamelCase					:			Union[str, Any]									= torch.sin(steps * math.pi / 2      ) ** 2

									lowerCamelCase					:			List[Any]									= (1.0 - self.betas**2) ** 0.5

									lowerCamelCase					:			Union[str, Any]									= (torch.atana(self.betas,    self.alphas      ) / math.pi * 2)[:-1]
									lowerCamelCase					:			Any									= timesteps.to(A      )

									lowerCamelCase					:			List[Any]									= []
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A = True,    ):

									"""simple docstring"""


									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'      )

									lowerCamelCase					:			int									= (self.timesteps == timestep).nonzero().item()
									lowerCamelCase					:			Union[str, Any]									= timestep_index + 1

									lowerCamelCase					:			Optional[int]									= sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
									self.ets.append(A      )

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

									lowerCamelCase					:			Tuple									= self._get_prev_sample(A,    A,    A,    A      )

									if not return_dict:
													return (prev_sample,)

									return SchedulerOutput(prev_sample=A      )
					def     UpperCAmelCase_							(     self,    A,    *A,    **A      ):

									"""simple docstring"""


									return sample
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= self.alphas[timestep_index]
									lowerCamelCase					:			List[str]									= self.betas[timestep_index]

									lowerCamelCase					:			Optional[int]									= self.alphas[prev_timestep_index]
									lowerCamelCase					:			Optional[int]									= self.betas[prev_timestep_index]

									lowerCamelCase					:			List[Any]									= (sample - sigma * ets) / max(A,    1e-8      )
									lowerCamelCase					:			int									= next_alpha * pred + ets * next_sigma

									return prev_sample




					def __len__(     self      ):

									"""simple docstring"""


									return self.config.num_train_timesteps


'''simple docstring'''






from __future__ import annotations

A			      =      '#'




class     __snake_case		:
					def __init__(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			dict									= {}
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= self._trie
									for char in text:
													if char not in trie:
																	lowerCamelCase					:			Dict									= {}
													lowerCamelCase					:			Optional[int]									= trie[char]
									lowerCamelCase					:			Optional[Any]									= True
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= self._trie
									for char in prefix:
													if char in trie:
																	lowerCamelCase					:			int									= trie[char]
													else:
																	return []
									return self._elements(A      )




					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= []
									for c, v in d.items():
													lowerCamelCase					:			Optional[Any]									= [' '] if c == END else [(c + s) for s in self._elements(A      )]
													result.extend(A      )
									return tuple(A      )





A			      =      Trie()
A			      =      ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
					trie.insert_word(word)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Any									= trie.find_word(UpperCAmelCase__)
				return tuple(string + word for word in suffixes)





def    UpperCAmelCase							(  ):
				print(autocomplete_using_trie('de'))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
