



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_:Optional[Any]					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:str					=						{"""vocab_file""": """sentencepiece.bpe.model"""}

SCREAMING_SNAKE_CASE_:Tuple					=						{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

SCREAMING_SNAKE_CASE_:Optional[Any]					=						{
    """camembert-base""": 512,
}

SCREAMING_SNAKE_CASE_:List[Any]					=						"""▁"""






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






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






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__="<s>",		lowerCamelCase__="</s>",		lowerCamelCase__="</s>",		lowerCamelCase__="<s>",		lowerCamelCase__="<unk>",		lowerCamelCase__="<pad>",		lowerCamelCase__="<mask>",		lowerCamelCase__=["<s>NOTUSED", "</s>NOTUSED"],		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        # Mask token behave like a normal word, i.e. include the space before it
        A			:	str										=       AddedToken(lowerCamelCase__,		lstrip=lowerCamelCase__,		rstrip=lowerCamelCase__		) if isinstance(lowerCamelCase__,		lowerCamelCase__		) else mask_token

        A			:	Dict										=       {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=lowerCamelCase__,		eos_token=lowerCamelCase__,		unk_token=lowerCamelCase__,		sep_token=lowerCamelCase__,		cls_token=lowerCamelCase__,		pad_token=lowerCamelCase__,		mask_token=lowerCamelCase__,		additional_special_tokens=lowerCamelCase__,		sp_model_kwargs=self.sp_model_kwargs,		**lowerCamelCase__,		)
        A			:	Tuple										=       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(str(lowerCamelCase__		)		)
        A			:	Any										=       vocab_file
        # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
        # sentencepiece vocabulary (this is the case for <s> and </s>
        A			:	int										=       {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
        A			:	List[str]										=       len(self.fairseq_tokens_to_ids		)
        A			:	Tuple										=       len(self.sp_model		) + len(self.fairseq_tokens_to_ids		)
        A			:	Dict										=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None		):

        if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        A			:	str										=       [self.cls_token_id]
        A			:	Union[str, Any]										=       [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = False		):
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=lowerCamelCase__,		token_ids_a=lowerCamelCase__,		already_has_special_tokens=lowerCamelCase__		)

        if token_ids_a is None:
            return [1] + ([0] * len(lowerCamelCase__		)) + [1]
        return [1] + ([0] * len(lowerCamelCase__		)) + [1, 1] + ([0] * len(lowerCamelCase__		)) + [1]






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        A			:	Union[str, Any]										=       [self.sep_token_id]
        A			:	List[str]										=       [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 + sep + token_ids_a + sep		) * [0]






    @property
    def       _lowerCAmelCase (	self		):
        return len(self.fairseq_tokens_to_ids		) + len(self.sp_model		)






    def       _lowerCAmelCase (	self		):
        A			:	Optional[int]										=       {self.convert_ids_to_tokens(lowerCamelCase__		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        return self.sp_model.encode(lowerCamelCase__,		out_type=lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
        elif self.sp_model.PieceToId(lowerCamelCase__		) == 0:
            # Convert sentence piece unk token to fairseq unk token index
            return self.unk_token_id
        return self.fairseq_offset + self.sp_model.PieceToId(lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	Optional[Any]										=       []
        A			:	str										=       """"""
        A			:	int										=       False
        for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                if not prev_is_special:
                    out_string += " "
                out_string += self.sp_model.decode(lowerCamelCase__		) + token
                A			:	Tuple										=       True
                A			:	Optional[Any]										=       []
            else:
                current_sub_tokens.append(lowerCamelCase__		)
                A			:	List[str]										=       False
        out_string += self.sp_model.decode(lowerCamelCase__		)
        return out_string.strip()






    def __getstate__(	self		):
        A			:	Any										=       self.__dict__.copy()
        A			:	Optional[Any]										=       None
        return state






    def __setstate__(	self,		lowerCamelCase__		):
        A			:	Union[str, Any]										=       d

        # for backward compatibility
        if not hasattr(self,		"""sp_model_kwargs"""		):
            A			:	List[Any]										=       {}

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






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        if not os.path.isdir(lowerCamelCase__		):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
            return
        A			:	Optional[Any]										=       os.path.join(
            lowerCamelCase__,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

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

        return (out_vocab_file,)







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_:Optional[int]					=						{
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[int]					=						[
         """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TapasForMaskedLM""",
         """TapasForQuestionAnswering""",
         """TapasForSequenceClassification""",
         """TapasModel""",
         """TapasPreTrainedModel""",
         """load_tf_weights_in_tapas""",
     ]
try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[Any]					=						[
         """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFTapasForMaskedLM""",
         """TFTapasForQuestionAnswering""",
         """TFTapasForSequenceClassification""",
         """TFTapasModel""",
         """TFTapasPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
     from .tokenization_tapas import TapasTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tapas import (
              TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TapasForMaskedLM,
              TapasForQuestionAnswering,
              TapasForSequenceClassification,
              TapasModel,
              TapasPreTrainedModel,
              load_tf_weights_in_tapas,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_tapas import (
              TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTapasForMaskedLM,
              TFTapasForQuestionAnswering,
              TFTapasForSequenceClassification,
              TFTapasModel,
              TFTapasPreTrainedModel,
          )


else:
     import sys

     SCREAMING_SNAKE_CASE_:str					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


