





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


_lowerCAmelCase 				=     logging.get_logger(__name__)


_lowerCAmelCase 				=     """▁"""

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

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

_lowerCAmelCase 				=     {
    """google/reformer-crime-and-punishment""": 524_288,
}

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							VOCAB_FILES_NAMES
      a						       =							PRETRAINED_VOCAB_FILES_MAP
      a						       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a						       =							['''input_ids''', '''attention_mask''']
      def __init__(		self      ,       a__      ,       a__="</s>"      ,       a__="<unk>"      ,       a__=[]      ,       a__ = None      ,       **a__      ,       ):
         A_     :				List[str]			      =	{} 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__      ,       )

         A_     :				str			      =	vocab_file
         A_     :				Tuple			      =	spm.SentencePieceProcessor(**self.sp_model_kwargs       )
         self.sp_model.Load(a__       )
      @property
      def 		_lowerCamelCase      (		self       ):
         return self.sp_model.get_piece_size()
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	{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       ):
         A_     :				str			      =	self.__dict__.copy()
         A_     :				List[Any]			      =	None
         return state
      def __setstate__(		self      ,       a__       ):
         A_     :				Any			      =	d

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

         A_     :				Optional[Any]			      =	spm.SentencePieceProcessor(**self.sp_model_kwargs       )
         self.sp_model.Load(self.vocab_file       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         return self.sp_model.encode(a__      ,       out_type=a__       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         return self.sp_model.piece_to_id(a__       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         if index < self.sp_model.get_piece_size():
            A_     :				int			      =	self.sp_model.IdToPiece(a__       )
         return token
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				str			      =	[]
         A_     :				Tuple			      =	""""""
         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
               A_     :				Union[str, Any]			      =	[]
            else:
               current_sub_tokens.append(a__       )
         out_string += self.sp_model.decode(a__       )
         return out_string.strip()






      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
         if not os.path.isdir(a__       ):
            logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
            return
         A_     :				Union[str, Any]			      =	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:
               A_     :				List[Any]			      =	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


_lowerCAmelCase 				=     {
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     [
          """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """NllbMoeForConditionalGeneration""",
          """NllbMoeModel""",
          """NllbMoePreTrainedModel""",
          """NllbMoeTop2Router""",
          """NllbMoeSparseMLP""",
      ]


if TYPE_CHECKING:
      from .configuration_nllb_moe import (
          NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
          NllbMoeConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nllb_moe import (
                NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
                NllbMoeForConditionalGeneration,
                NllbMoeModel,
                NllbMoePreTrainedModel,
                NllbMoeSparseMLP,
                NllbMoeTopaRouter,
            )


else:
      import sys

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






