




def 			_a   (		_lowerCAmelCase							:       float  ):

					if edge <= 0 or not isinstance(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  ):
										raise ValueError("Length must be a positive."  )
					return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)







def 			_a   (		_lowerCAmelCase							:       float  ):

					if edge <= 0 or not isinstance(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  ):
										raise ValueError("Length must be a positive."  )
					return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
					import doctest

					doctest.testmod()










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     a__					(      snake_case__   ):






						def __init__(     self	,    _A = "▁"	,    _A = True	,    _A = "<unk>"	,    _A = "</s>"	,    _A = "<pad>"	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = {
											    "pad": {"id": 0, "token": pad_token},
											    "eos": {"id": 1, "token": eos_token},
											    "unk": {"id": 2, "token": unk_token},
											}

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

											__lowerCAmelCase            = Tokenizer(Unigram()    )

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

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

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

											super().__init__(_A	,    _A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A = 8_0_0_0	,    _A = True	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = trainers.UnigramTrainer(
											    vocab_size=_A	,    special_tokens=self.special_tokens_list	,    show_progress=_A	,    )

											if isinstance(_A	,    _A    ):
																__lowerCAmelCase            = [files]
											self._tokenizer.train(_A	,    trainer=_A    )

											self.add_unk_id()






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A = 8_0_0_0	,    _A = True	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = trainers.UnigramTrainer(
											    vocab_size=_A	,    special_tokens=self.special_tokens_list	,    show_progress=_A	,    )

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

											self.add_unk_id()






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = json.loads(self._tokenizer.to_str()    )

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

											__lowerCAmelCase            = Tokenizer.from_str(json.dumps(_A    )    )




