






'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A     :      List[Any]   					=logging.get_logger(__name__)

_A     :      Optional[Any]   					={'''vocab_file''': '''spm_char.model'''}

_A     :      Optional[Any]   					={
    '''vocab_file''': {
        '''microsoft/speecht5_asr''': '''https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model''',
        '''microsoft/speecht5_tts''': '''https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model''',
        '''microsoft/speecht5_vc''': '''https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model''',
    }
}

_A     :      str   					={
    '''microsoft/speecht5_asr''': 1_024,
    '''microsoft/speecht5_tts''': 1_024,
    '''microsoft/speecht5_vc''': 1_024,
}

class _lowercase     (    _lowercase		):
							a    				=	VOCAB_FILES_NAMES
							a    				=	PRETRAINED_VOCAB_FILES_MAP
							a    				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a    				=	["""input_ids""", """attention_mask"""]




							def __init__(							self:    Dict      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Any="<s>"      ,							UpperCamelCase__:    Tuple="</s>"      ,							UpperCamelCase__:    str="<unk>"      ,							UpperCamelCase__:    Optional[int]="<pad>"      ,							UpperCamelCase__:    Optional[Dict[str, Any]] = None      ,							**UpperCamelCase__:    List[str]      ,							):
														lowerCamelCase__ :     int	  =	{} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    bos_token=UpperCamelCase__      ,							eos_token=UpperCamelCase__      ,							unk_token=UpperCamelCase__      ,							pad_token=UpperCamelCase__      ,							sp_model_kwargs=self.sp_model_kwargs      ,							**UpperCamelCase__      ,							)

														lowerCamelCase__ :     List[Any]	  =	vocab_file

														lowerCamelCase__ :     Tuple	  =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
														self.sp_model.Load(UpperCamelCase__					)




							@property
							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														return self.sp_model.get_piece_size()




							def 		lowerCamelCase_    (							self:    Any					):
														lowerCamelCase__ :     Dict	  =	{self.convert_ids_to_tokens(UpperCamelCase__					): i for i in range(self.vocab_size					)}
														vocab.update(self.added_tokens_encoder					)
														return vocab




							def __getstate__(							self:    Any					):
														lowerCamelCase__ :     Dict	  =	self.__dict__.copy()
														lowerCamelCase__ :     str	  =	None
														return state




							def __setstate__(							self:    int      ,							UpperCamelCase__:    Optional[Any]					):
														lowerCamelCase__ :     Union[str, Any]	  =	d

														# for backward compatibility
														if not hasattr(self      ,							"""sp_model_kwargs"""					):
																					lowerCamelCase__ :     Optional[Any]	  =	{}

														lowerCamelCase__ :     Union[str, Any]	  =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
														self.sp_model.Load(self.vocab_file					)




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    str					):
														return self.sp_model.encode(UpperCamelCase__      ,							out_type=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    Any					):
														return self.sp_model.piece_to_id(UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    Dict					):
														lowerCamelCase__ :     List[Any]	  =	self.sp_model.IdToPiece(UpperCamelCase__					)
														return token




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    List[str]					):
														lowerCamelCase__ :     List[Any]	  =	[]
														lowerCamelCase__ :     Optional[Any]	  =	""""""
														for token in tokens:
																					# make sure that special tokens are not decoded using sentencepiece model
																					if token in self.all_special_tokens:
																												out_string += self.sp_model.decode(UpperCamelCase__					) + token
																												lowerCamelCase__ :     Dict	  =	[]
																					else:
																												current_sub_tokens.append(UpperCamelCase__					)
														out_string += self.sp_model.decode(UpperCamelCase__					)
														return out_string.strip()




							def 		lowerCamelCase_    (							self:    Tuple      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Dict=None					):
														if token_ids_a is None:
																					return token_ids_a + [self.eos_token_id]
														# We don't expect to process pairs, but leave the pair logic for API consistency
														return token_ids_a + token_ids_a + [self.eos_token_id]




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    List[int]      ,							UpperCamelCase__:    Optional[List[int]] = None      ,							UpperCamelCase__:    bool = False					):
														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=UpperCamelCase__      ,							token_ids_a=UpperCamelCase__      ,							already_has_special_tokens=UpperCamelCase__					)

														lowerCamelCase__ :     Dict	  =	[1]
														if token_ids_a is None:
																					return ([0] * len(UpperCamelCase__					)) + suffix_ones
														return ([0] * len(UpperCamelCase__					)) + ([0] * len(UpperCamelCase__					)) + suffix_ones




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Optional[str] = None					):
														if not os.path.isdir(UpperCamelCase__					):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
																					return
														lowerCamelCase__ :     List[str]	  =	os.path.join(
														    UpperCamelCase__      ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

														if os.path.abspath(self.vocab_file					) != os.path.abspath(UpperCamelCase__					) and os.path.isfile(self.vocab_file					):
																					copyfile(self.vocab_file      ,							UpperCamelCase__					)
														elif not os.path.isfile(self.vocab_file					):
																					with open(UpperCamelCase__      ,							"""wb"""					) as fi:
																												lowerCamelCase__ :     Union[str, Any]	  =	self.sp_model.serialized_model_proto()
																												fi.write(UpperCamelCase__					)

														return (out_vocab_file,)









'''simple docstring'''







from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_A     :      Optional[Any]   					=typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_A     :      Optional[int]   					=typing.Union[np.floataa, int, float]  # noqa: UP007





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	VectorOut:
							return np.sqrt(np.sum((np.asarray(UpperCamelCase				) - np.asarray(UpperCamelCase				)) ** 2				)				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	VectorOut:
							return sum((va - va) ** 2 for va, va in zip(UpperCamelCase    ,      UpperCamelCase				)				) ** (1 / 2)


if __name__ == "__main__":





			def 			SCREAMING_SNAKE_CASE_	()				->	None:
										from timeit import timeit

										print("""Without Numpy"""				)
										print(
										    timeit(
										        """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""    ,      number=10000    ,      globals=globals()    ,      )				)
										print("""With Numpy"""				)
										print(
										    timeit(
										        """euclidean_distance([1, 2, 3], [4, 5, 6])"""    ,      number=10000    ,      globals=globals()    ,      )				)



			benchmark()

