




"""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_				:    Any										=     logging.get_logger(__name__)

a_				:    Optional[int]										=     {'''vocab_file''': '''spiece.model'''}

a_				:    Optional[int]										=     {
    '''vocab_file''': {
        '''bert_for_seq_generation''': (
            '''https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'''
        ),
    }
}

a_				:    Any										=     {'''bert_for_seq_generation''': 5_12}

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						int     		= VOCAB_FILES_NAMES
					__a						:						Dict     		= PRETRAINED_VOCAB_FILES_MAP
					__a						:						List[str]     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a						:						List[int]     		= []
					__a						:						Any     		= ['input_ids', 'attention_mask']
					def __init__(    self						,	__a						,	__a="<s>"						,	__a="</s>"						,	__a="<unk>"						,	__a="<pad>"						,	__a="<::::>"						,	__a = None						,	**__a						,	):
								__lowerCamelCase					:	Union[str, Any]	       =				{} if sp_model_kwargs is None else sp_model_kwargs

								# Add extra_ids to the special token list
								super().__init__(
								    bos_token=__a						,	eos_token=__a						,	unk_token=__a						,	pad_token=__a						,	sep_token=__a						,	sp_model_kwargs=self.sp_model_kwargs						,	**__a						,	)

								__lowerCamelCase					:	List[str]	       =				vocab_file

								__lowerCamelCase					:	Optional[Any]	       =				spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(__a     )
					@property
					def     snake_case_				(    self     ):
								return self.sp_model.get_piece_size()
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				{self.convert_ids_to_tokens(__a     ): i for i in range(self.vocab_size     )}
								vocab.update(self.added_tokens_encoder     )
								return vocab
					def __getstate__(    self     ):
								__lowerCamelCase					:	Optional[int]	       =				self.__dict__.copy()
								__lowerCamelCase					:	Dict	       =				None
								return state
					def __setstate__(    self						,	__a     ):
								__lowerCamelCase					:	Dict	       =				d

								# for backward compatibility
								if not hasattr(self						,	'sp_model_kwargs'     ):
											__lowerCamelCase					:	str	       =				{}

								__lowerCamelCase					:	str	       =				spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(self.vocab_file     )
					def     snake_case_				(    self						,	__a     ):
								return self.sp_model.encode(__a						,	out_type=__a     )
					def     snake_case_				(    self						,	__a     ):
								return self.sp_model.piece_to_id(__a     )
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	int	       =				self.sp_model.IdToPiece(__a     )
								return token
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	Any	       =				[]
								__lowerCamelCase					:	Optional[int]	       =				''
								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(__a     ) + token
														__lowerCamelCase					:	Dict	       =				[]
											else:
														current_sub_tokens.append(__a     )
								out_string += self.sp_model.decode(__a     )
								return out_string.strip()





					def     snake_case_				(    self						,	__a						,	__a = None     ):
								if not os.path.isdir(__a     ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
											return
								__lowerCamelCase					:	Tuple	       =				os.path.join(
								    __a						,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

								if os.path.abspath(self.vocab_file     ) != os.path.abspath(__a     ) and os.path.isfile(self.vocab_file     ):
											copyfile(self.vocab_file						,	__a     )
								elif not os.path.isfile(self.vocab_file     ):
											with open(__a						,	'wb'     ) as fi:
														__lowerCamelCase					:	Optional[int]	       =				self.sp_model.serialized_model_proto()
														fi.write(__a     )

								return (out_vocab_file,)









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a_				:    List[Any]										=     {
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a_				:    int										=     [
					    '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''EncodecModel''',
					    '''EncodecPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_encodec import (
					    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    EncodecConfig,
					)
					from .feature_extraction_encodec import EncodecFeatureExtractor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_encodec import (
										    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
										    EncodecModel,
										    EncodecPreTrainedModel,
										)

else:
					import sys

					a_				:    Tuple										=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



