





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


UpperCAmelCase_							=				logging.get_logger(__name__)


UpperCAmelCase_							=				"""▁"""

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

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

UpperCAmelCase_							=				{
    """google/reformer-crime-and-punishment""": 524_288,
}
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       List[Any]       =	VOCAB_FILES_NAMES
		__a       :       List[str]       =	PRETRAINED_VOCAB_FILES_MAP
		__a       :       Optional[int]       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__a       :       str       =	["""input_ids""", """attention_mask"""]






		def __init__(							self,   snake_case__,   snake_case__="</s>",   snake_case__="<unk>",   snake_case__=[],   snake_case__ = None,   **snake_case__,   )       -> None:






									"""simple docstring"""
									lowercase_	:   int  				=     {} if sp_model_kwargs is None else sp_model_kwargs

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

									lowercase_	:   List[Any]  				=     vocab_file
									lowercase_	:   Union[str, Any]  				=     spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.Load(snake_case__  )






		@property
		def 						snake_case__				(							self  )       -> List[str]:






									"""simple docstring"""
									return self.sp_model.get_piece_size()






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






									"""simple docstring"""
									lowercase_	:   Tuple  				=     {self.convert_ids_to_tokens(snake_case__  ): i for i in range(self.vocab_size  )}
									vocab.update(self.added_tokens_encoder  )
									return vocab






		def __getstate__(							self  )       -> Tuple:






									"""simple docstring"""
									lowercase_	:   Optional[int]  				=     self.__dict__.copy()
									lowercase_	:   Union[str, Any]  				=     None
									return state






		def __setstate__(							self,   snake_case__  )       -> Dict:






									"""simple docstring"""
									lowercase_	:   int  				=     d

									# for backward compatibility
									if not hasattr(self,   """sp_model_kwargs"""  ):
																lowercase_	:   Any  				=     {}

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






		def 						snake_case__				(							self,   snake_case__  )       -> List[str]:






									"""simple docstring"""
									return self.sp_model.encode(snake_case__,   out_type=snake_case__  )






		def 						snake_case__				(							self,   snake_case__  )       -> List[Any]:






									"""simple docstring"""
									return self.sp_model.piece_to_id(snake_case__  )






		def 						snake_case__				(							self,   snake_case__  )       -> int:






									"""simple docstring"""
									if index < self.sp_model.get_piece_size():
																lowercase_	:   Union[str, Any]  				=     self.sp_model.IdToPiece(snake_case__  )
									return token






		def 						snake_case__				(							self,   snake_case__  )       -> Dict:






									"""simple docstring"""
									lowercase_	:   Optional[int]  				=     []
									lowercase_	:   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(snake_case__  ) + token
																							lowercase_	:   int  				=     []
																else:
																							current_sub_tokens.append(snake_case__  )
									out_string += self.sp_model.decode(snake_case__  )
									return out_string.strip()






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None  )       -> Tuple[str]:






									"""simple docstring"""
									if not os.path.isdir(snake_case__  ):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
																return
									lowercase_	:   int  				=     os.path.join(
									    snake_case__,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

									if os.path.abspath(self.vocab_file  ) != os.path.abspath(snake_case__  ) and os.path.isfile(self.vocab_file  ):
																copyfile(self.vocab_file,   snake_case__  )
									elif not os.path.isfile(self.vocab_file  ):
																with open(snake_case__,   """wb"""  ) as fi:
																							lowercase_	:   Dict  				=     self.sp_model.serialized_model_proto()
																							fi.write(snake_case__  )

									return (out_vocab_file,)






from typing import TYPE_CHECKING

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


UpperCAmelCase_							=				{
    """configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
    """tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				[
							    """M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """M2M100ForConditionalGeneration""",
							    """M2M100Model""",
							    """M2M100PreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
							from .tokenization_mam_aaa import MaMaaaTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mam_aaa import (
														    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaMaaaForConditionalGeneration,
														    MaMaaaModel,
														    MaMaaaPreTrainedModel,
														)


else:
							import sys

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