



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

import sentencepiece as spm

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


__A   			=logging.get_logger(__name__)

__A   			={"""vocab_file""": """sentencepiece.model"""}

__A   			={
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

__A   			={
    """google/rembert""": 2_5_6,
}

class 		UpperCAmelCase__			(			UpperCAmelCase_   ):




			'''simple docstring'''






			UpperCamelCase									=       VOCAB_FILES_NAMES
			UpperCamelCase									=       PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase									=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




			def __init__(   self							: List[Any]       ,       a_							: Any       ,       a_							: Optional[int]=False       ,       a_							: Union[str, Any]=True       ,       a_							: Any=True       ,       a_							: List[str]="[CLS]"       ,       a_							: Tuple="[SEP]"       ,       a_							: List[str]="[UNK]"       ,       a_							: Union[str, Any]="[SEP]"       ,       a_							: List[Any]="[PAD]"       ,       a_							: int="[CLS]"       ,       a_							: Optional[Any]="[MASK]"       ,       **a_							: Tuple       ,       ):
					'''simple docstring'''





					super().__init__(
					    do_lower_case=lowerCAmelCase__       ,       remove_space=lowerCAmelCase__       ,       keep_accents=lowerCAmelCase__       ,       bos_token=lowerCAmelCase__       ,       eos_token=lowerCAmelCase__       ,       unk_token=lowerCAmelCase__       ,       sep_token=lowerCAmelCase__       ,       pad_token=lowerCAmelCase__       ,       cls_token=lowerCAmelCase__       ,       mask_token=lowerCAmelCase__       ,       **lowerCAmelCase__       ,       )

					__UpperCAmelCase    :     Optional[int]													=	do_lower_case
					__UpperCAmelCase    :     Any													=	remove_space
					__UpperCAmelCase    :     Tuple													=	keep_accents
					__UpperCAmelCase    :     int													=	vocab_file

					__UpperCAmelCase    :     List[Any]													=	spm.SentencePieceProcessor()
					self.sp_model.Load(lowerCAmelCase__ )




			@property
			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					return len(self.sp_model )




			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	{self.convert_ids_to_tokens(lowerCAmelCase__ ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab




			def __getstate__(   self							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	self.__dict__.copy()
					__UpperCAmelCase    :     Any													=	None
					return state




			def __setstate__(   self							: str       ,       a_							: Any ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	d
					__UpperCAmelCase    :     Dict													=	spm.SentencePieceProcessor()
					self.sp_model.Load(self.vocab_file )




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: Optional[Any]       ,       a_							: List[str]=False ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	self.sp_model.EncodeAsPieces(lowerCAmelCase__ )
					return pieces




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: Any ):
					'''simple docstring'''





					return self.sp_model.PieceToId(lowerCAmelCase__ )




			def  snake_case__     (   self							: Optional[int]       ,       a_							: Tuple ):
					'''simple docstring'''





					return self.sp_model.IdToPiece(lowerCAmelCase__ )




			def  snake_case__     (   self							: Dict       ,       a_							: Optional[int] ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	self.sp_model.decode_pieces(lowerCAmelCase__ )
					return out_string




			def  snake_case__     (   self							: List[str]       ,       a_							: List[int]       ,       a_							: Optional[List[int]] = None ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	[self.sep_token_id]
					__UpperCAmelCase    :     str													=	[self.cls_token_id]
					if token_ids_a is None:
							return cls + token_ids_a + sep
					return cls + token_ids_a + sep + token_ids_a + sep




			def  snake_case__     (   self							: Dict       ,       a_							: List[int]       ,       a_							: Optional[List[int]] = None       ,       a_							: bool = False ):
					'''simple docstring'''





					if already_has_special_tokens:
							if token_ids_a is not None:
									raise ValueError(
									    '''You should not supply a second sequence if the provided sequence of '''
									    '''ids is already formatted with special tokens for the model.''' )
							return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

					if token_ids_a is not None:
							return [1] + ([0] * len(lowerCAmelCase__ )) + [1] + ([0] * len(lowerCAmelCase__ )) + [1]
					return [1] + ([0] * len(lowerCAmelCase__ )) + [1]




			def  snake_case__     (   self							: List[Any]       ,       a_							: List[int]       ,       a_							: Optional[List[int]] = None ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	[self.sep_token_id]
					__UpperCAmelCase    :     str													=	[self.cls_token_id]

					if token_ids_a is None:
							return len(cls + token_ids_a + sep ) * [0]
					return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]




			def  snake_case__     (   self							: Any       ,       a_							: str       ,       a_							: Optional[str] = None ):
					'''simple docstring'''





					if not os.path.isdir(lowerCAmelCase__ ):
							logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCAmelCase__ ) )
							return
					__UpperCAmelCase    :     Any													=	os.path.join(
					    lowerCAmelCase__       ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

					if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase__ ):
							copyfile(self.vocab_file       ,       lowerCAmelCase__ )

					return (out_vocab_file,)








import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate



@dataclass(frozen=UpperCAmelCase_	)
class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							str										=     field(default='''automatic-speech-recognition'''		,       metadata={'''include_in_asdict_even_if_is_default''': True}	)
			_UpperCAmelCase   :							ClassVar[Features]										=     Features({'''audio''': Audio()}	)
			_UpperCAmelCase   :							ClassVar[Features]										=     Features({'''transcription''': Value('''string'''	)}	)
			_UpperCAmelCase   :							str										=     "audio"
			_UpperCAmelCase   :							str										=     "transcription"



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     int):
									if self.audio_column not in features:
															raise ValueError(F"Column {self.audio_column} is not present in features.")
									if not isinstance(features[self.audio_column]   ,						lowerCAmelCase__):
															raise ValueError(F"Column {self.audio_column} is not an Audio type.")
									SCREAMING_SNAKE_CASE_: Tuple  		=       copy.deepcopy(self)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.input_schema.copy()
									SCREAMING_SNAKE_CASE_: Dict  		=       features[self.audio_column]
									SCREAMING_SNAKE_CASE_: int  		=       input_schema
									return task_template




			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									return {self.audio_column: "audio", self.transcription_column: "transcription"}






