






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


SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				     =			{'''vocab_file''': '''spiece.model'''}

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

SCREAMING_SNAKE_CASE__				     =			{'''bert_for_seq_generation''': 512}






class 			_UpperCamelCase(       __lowerCamelCase		):
		__SCREAMING_SNAKE_CASE  :       int							=							VOCAB_FILES_NAMES
		__SCREAMING_SNAKE_CASE  :       List[str]							=							PRETRAINED_VOCAB_FILES_MAP
		__SCREAMING_SNAKE_CASE  :       Any							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__SCREAMING_SNAKE_CASE  :       List[int]							=							[]
		__SCREAMING_SNAKE_CASE  :       int							=							['''input_ids''', '''attention_mask''']

		def __init__(					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    List[Any]="<s>"    ,       SCREAMING_SNAKE_CASE__		:    Tuple="</s>"    ,       SCREAMING_SNAKE_CASE__		:    Any="<unk>"    ,       SCREAMING_SNAKE_CASE__		:    int="<pad>"    ,       SCREAMING_SNAKE_CASE__		:    List[str]="<::::>"    ,       SCREAMING_SNAKE_CASE__		:    Optional[Dict[str, Any]] = None    ,       **SCREAMING_SNAKE_CASE__		:    Tuple    ,       ):



					'''simple docstring'''



					__a						:  Tuple									=     {} if sp_model_kwargs is None else sp_model_kwargs

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

					__a						:  int									=     vocab_file

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

		@property
		def    __lowerCAmelCase  (					self		:    Optional[int]    ):



					'''simple docstring'''



					return self.sp_model.get_piece_size()

		def    __lowerCAmelCase  (					self		:    int    ):



					'''simple docstring'''



					__a						:  Dict									=     {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__    ): i for i in range(self.vocab_size    )}
					vocab.update(self.added_tokens_encoder    )
					return vocab

		def __getstate__(					self		:    Optional[Any]    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     self.__dict__.copy()
					__a						:  Any									=     None
					return state

		def __setstate__(					self		:    int    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ):



					'''simple docstring'''



					__a						:  str									=     d

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

					__a						:  List[Any]									=     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(self.vocab_file    )

		def    __lowerCAmelCase  (					self		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					return self.sp_model.encode(SCREAMING_SNAKE_CASE__    ,       out_type=SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ):



					'''simple docstring'''



					return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    int    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					__a						:  int									=     self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__    )
					return token

		def    __lowerCAmelCase  (					self		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ):



					'''simple docstring'''



					__a						:  Optional[Any]									=     []
					__a						:  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(SCREAMING_SNAKE_CASE__    ) + token
											__a						:  Dict									=     []
								else:
											current_sub_tokens.append(SCREAMING_SNAKE_CASE__    )
					out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__    )
					return out_string.strip()



		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    Optional[str] = None    ):



					'''simple docstring'''



					if not os.path.isdir(SCREAMING_SNAKE_CASE__    ):
								logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
								return
					__a						:  Tuple									=     os.path.join(
					    SCREAMING_SNAKE_CASE__    ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

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

					return (out_vocab_file,)









from typing import TYPE_CHECKING

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


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

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case   							=						[
        '''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

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


