

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


lowerCamelCase      				=     logging.get_logger(__name__)

lowerCamelCase      				=     {'''vocab_file''': '''sentencepiece.model'''}

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

lowerCamelCase      				=     {
    '''google/rembert''': 2_5_6,
}





class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       =VOCAB_FILES_NAMES
							__UpperCAmelCase       =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




							def __init__( self					,						_A					,						_A=False					,						_A=True					,						_A=True					,						_A="[CLS]"					,						_A="[SEP]"					,						_A="[UNK]"					,						_A="[SEP]"					,						_A="[PAD]"					,						_A="[CLS]"					,						_A="[MASK]"					,						**_A					,						):
												super().__init__(
												    do_lower_case=_A					,						remove_space=_A					,						keep_accents=_A					,						bos_token=_A					,						eos_token=_A					,						unk_token=_A					,						sep_token=_A					,						pad_token=_A					,						cls_token=_A					,						mask_token=_A					,						**_A					,						)

												__lowerCAmelCase					=							do_lower_case
												__lowerCAmelCase					=							remove_space
												__lowerCAmelCase					=							keep_accents
												__lowerCAmelCase					=							vocab_file

												__lowerCAmelCase					=							spm.SentencePieceProcessor()
												self.sp_model.Load(_A							)




							@property
							def        A__  ( self							):
												return len(self.sp_model							)




							def        A__  ( self							):
												__lowerCAmelCase					=							{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					=							self.__dict__.copy()
												__lowerCAmelCase					=							None
												return state




							def __setstate__( self					,						_A							):
												__lowerCAmelCase					=							d
												__lowerCAmelCase					=							spm.SentencePieceProcessor()
												self.sp_model.Load(self.vocab_file							)




							def        A__  ( self					,						_A					,						_A=False							):
												__lowerCAmelCase					=							self.sp_model.EncodeAsPieces(_A							)
												return pieces




							def        A__  ( self					,						_A							):
												return self.sp_model.PieceToId(_A							)




							def        A__  ( self					,						_A							):
												return self.sp_model.IdToPiece(_A							)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							self.sp_model.decode_pieces(_A							)
												return out_string




							def        A__  ( self					,						_A					,						_A = None							):
												__lowerCAmelCase					=							[self.sep_token_id]
												__lowerCAmelCase					=							[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        A__  ( self					,						_A					,						_A = None					,						_A = False							):

												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(_A							)) + [1] + ([0] * len(_A							)) + [1]
												return [1] + ([0] * len(_A							)) + [1]




							def        A__  ( self					,						_A					,						_A = None							):
												__lowerCAmelCase					=							[self.sep_token_id]
												__lowerCAmelCase					=							[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        A__  ( self					,						_A					,						_A = None							):
												if not os.path.isdir(_A							):
																	logger.error('Vocabulary path ({}) should be a directory'.format(_A							)							)
																	return
												__lowerCAmelCase					=							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							):
																	copyfile(self.vocab_file					,						_A							)

												return (out_vocab_file,)









from typing import TYPE_CHECKING

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


lowerCamelCase      				=     {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''UniSpeechForCTC''',
		    '''UniSpeechForPreTraining''',
		    '''UniSpeechForSequenceClassification''',
		    '''UniSpeechModel''',
		    '''UniSpeechPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech import (
				    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechForCTC,
				    UniSpeechForPreTraining,
				    UniSpeechForSequenceClassification,
				    UniSpeechModel,
				    UniSpeechPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase      				=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






