




import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def __init__(      self				:     Tuple					,      __lowerCamelCase				:     str = "▁"					,      __lowerCamelCase				:     bool = True					,      __lowerCamelCase				:     Union[str, AddedToken] = "<unk>"					,      __lowerCamelCase				:     Union[str, AddedToken] = "</s>"					,      __lowerCamelCase				:     Union[str, AddedToken] = "<pad>"					,      )					->     int:
							a    =			{
							    "pad": {"id": 0, "token": pad_token},
							    "eos": {"id": 1, "token": eos_token},
							    "unk": {"id": 2, "token": unk_token},
							}

							a    =			[None] * len(self.special_tokens							)
							for token_dict in self.special_tokens.values():
										a    =			token_dict["token"]

							a    =			Tokenizer(Unigram()							)

							a    =			normalizers.Sequence(
							    [
							        normalizers.Nmt(),
							        normalizers.NFKC(),
							        normalizers.Replace(Regex(" {2,}"							)					,      " "							),
							        normalizers.Lowercase(),
							    ]							)
							a    =			pre_tokenizers.Sequence(
							    [
							        pre_tokenizers.Metaspace(replacement=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase							),
							        pre_tokenizers.Digits(individual_digits=__lowerCamelCase							),
							        pre_tokenizers.Punctuation(),
							    ]							)
							a    =			decoders.Metaspace(replacement=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase							)

							a    =			TemplateProcessing(
							    single=f"""$A {self.special_tokens["eos"]["token"]}"""					,      special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])]					,      )

							a    =			{
							    "model": "SentencePieceUnigram",
							    "replacement": replacement,
							    "add_prefix_space": add_prefix_space,
							}

							super().__init__(__lowerCamelCase					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     Union[str, List[str]]					,      __lowerCamelCase				:     int = 80_00					,      __lowerCamelCase				:     bool = True					,      )					->     Optional[Any]:
							a    =			trainers.UnigramTrainer(
							    vocab_size=__lowerCamelCase					,      special_tokens=self.special_tokens_list					,      show_progress=__lowerCamelCase					,      )

							if isinstance(__lowerCamelCase					,      __lowerCamelCase							):
										a    =			[files]
							self._tokenizer.train(__lowerCamelCase					,      trainer=__lowerCamelCase							)

							self.add_unk_id()





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     Union[Iterator[str], Iterator[Iterator[str]]]					,      __lowerCamelCase				:     int = 80_00					,      __lowerCamelCase				:     bool = True					,      )					->     Optional[int]:
							a    =			trainers.UnigramTrainer(
							    vocab_size=__lowerCamelCase					,      special_tokens=self.special_tokens_list					,      show_progress=__lowerCamelCase					,      )

							self._tokenizer.train_from_iterator(__lowerCamelCase					,      trainer=__lowerCamelCase							)

							self.add_unk_id()





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Union[str, Any]:
							a    =			json.loads(self._tokenizer.to_str()							)

							a    =			self.special_tokens["unk"]["id"]

							a    =			Tokenizer.from_str(json.dumps(__lowerCamelCase							)							)












import numpy as np




def 			__magic_name__				( A		:					np.array   ):






			'''simple docstring'''







			return 1 / (1 + np.exp(-vector   ))




def 			__magic_name__				( A		:					np.array   ):






			'''simple docstring'''







			return vector * sigmoid(1.7_02 * vector   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






