





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 		=						"""▁"""

_SCREAMING_SNAKE_CASE 		=						{"""vocab_file""": """spiece.model"""}

_SCREAMING_SNAKE_CASE 		=						{
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

_SCREAMING_SNAKE_CASE 		=						{
    """google/reformer-crime-and-punishment""": 52_42_88,
}
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     Dict	 =    VOCAB_FILES_NAMES
						__magic_name__:     List[Any]	 =    PRETRAINED_VOCAB_FILES_MAP
						__magic_name__:     int	 =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__magic_name__:     Optional[Any]	 =    ["input_ids", "attention_mask"]






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







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=_A						,				unk_token=_A						,				additional_special_tokens=_A						,				sp_model_kwargs=self.sp_model_kwargs						,				**_A						,				)

							snake_case_						:     List[str]     =		vocab_file
							snake_case_						:     Dict     =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
							self.sp_model.Load(_A		)






						@property
						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Optional[int]:







							"""simple docstring"""


							return self.sp_model.get_piece_size()






						def UpperCAmelCase_			( self : Tuple		)							->      Dict[str, int]:







							"""simple docstring"""


							snake_case_						:     int     =		{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 : int		)							->      str:







							"""simple docstring"""


							snake_case_						:     Tuple     =		self.__dict__.copy()
							snake_case_						:     List[str]     =		None
							return state






						def __setstate__( self : Dict						,				_A : List[Any]		)							->      Any:







							"""simple docstring"""


							snake_case_						:     Any     =		d

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

							snake_case_						:     str     =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
							self.sp_model.Load(self.vocab_file		)






						def UpperCAmelCase_			( self : Union[str, Any]						,				_A : str		)							->      List[str]:







							"""simple docstring"""


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






						def UpperCAmelCase_			( self : Union[str, Any]						,				_A : Union[str, Any]		)							->      Dict:







							"""simple docstring"""


							return self.sp_model.piece_to_id(_A		)






						def UpperCAmelCase_			( self : Dict						,				_A : Tuple		)							->      int:







							"""simple docstring"""


							if index < self.sp_model.get_piece_size():
								snake_case_						:     Optional[Any]     =		self.sp_model.IdToPiece(_A		)
							return token






						def UpperCAmelCase_			( self : List[Any]						,				_A : Optional[int]		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     int     =		[]
							snake_case_						:     List[str]     =		''
							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
									snake_case_						:     Union[str, Any]     =		[]
								else:
									current_sub_tokens.append(_A		)
							out_string += self.sp_model.decode(_A		)
							return out_string.strip()






						def UpperCAmelCase_			( self : str						,				_A : str						,				_A : Optional[str] = None		)							->      Tuple[str]:







							"""simple docstring"""


							if not os.path.isdir(_A		):
								logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
								return
							snake_case_						:     Optional[int]     =		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:
									snake_case_						:     Tuple     =		self.sp_model.serialized_model_proto()
									fi.write(_A		)

							return (out_vocab_file,)








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MCTCTForCTC""",
						    """MCTCTModel""",
						    """MCTCTPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
						from .feature_extraction_mctct import MCTCTFeatureExtractor
						from .processing_mctct import MCTCTProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
						import sys

						_SCREAMING_SNAKE_CASE 		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

