

"""simple docstring"""


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


a					:Optional[int]    = logging.get_logger(__name__)

a					:Optional[Any]    = {"vocab_file": "sentencepiece.bpe.model"}

a					:Union[str, Any]    = {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

a					:Any    = {
    "camembert-base": 512,
}

a					:Any    = "▁"

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Dict       = VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE						:Optional[int]       = PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE						:Tuple       = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE						:str       = ["""input_ids""", """attention_mask"""]

     def __init__(						self	,    _a	,    _a="<s>"	,    _a="</s>"	,    _a="</s>"	,    _a="<s>"	,    _a="<unk>"	,    _a="<pad>"	,    _a="<mask>"	,    _a=["<s>NOTUSED", "</s>NOTUSED"]	,    _a = None	,    **_a	,    )  ->      None:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	AddedToken(_a	,    lstrip=_a	,    rstrip=_a  ) if isinstance(_a	,    _a  ) else mask_token

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

           super().__init__(
               bos_token=_a	,    eos_token=_a	,    unk_token=_a	,    sep_token=_a	,    cls_token=_a	,    pad_token=_a	,    mask_token=_a	,    additional_special_tokens=_a	,    sp_model_kwargs=self.sp_model_kwargs	,    **_a	,    )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
           self.sp_model.Load(str(_a  )  )
           SCREAMING_SNAKE_CASE__   :      int      		=	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>
           SCREAMING_SNAKE_CASE__   :      Any      		=	{"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	len(self.fairseq_tokens_to_ids  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	len(self.sp_model  ) + len(self.fairseq_tokens_to_ids  )
           SCREAMING_SNAKE_CASE__   :      str      		=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}

     def 					_a			(						self	,    _a	,    _a = None  )  ->      List[int]:


           """simple docstring"""


           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[self.cls_token_id]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	[self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep

     def 					_a			(						self	,    _a	,    _a = None	,    _a = False  )  ->      List[int]:


           """simple docstring"""


           if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=_a	,    token_ids_a=_a	,    already_has_special_tokens=_a  )

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

     def 					_a			(						self	,    _a	,    _a = None  )  ->      List[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	[self.sep_token_id]
           SCREAMING_SNAKE_CASE__   :      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 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           return len(self.fairseq_tokens_to_ids  ) + len(self.sp_model  )

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	{self.convert_ids_to_tokens(_a  ): i for i in range(self.vocab_size  )}
           vocab.update(self.added_tokens_encoder  )
           return vocab

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


           """simple docstring"""


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

     def 					_a			(						self	,    _a  )  ->      Dict:


           """simple docstring"""


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

     def 					_a			(						self	,    _a  )  ->      Optional[Any]:


           """simple docstring"""


           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 					_a			(						self	,    _a  )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	""""""
           SCREAMING_SNAKE_CASE__   :      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(_a  ) + token
                       SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	True
                       SCREAMING_SNAKE_CASE__   :      str      		=	[]
                 else:
                       current_sub_tokens.append(_a  )
                       SCREAMING_SNAKE_CASE__   :      List[Any]      		=	False
           out_string += self.sp_model.decode(_a  )
           return out_string.strip()

     def __getstate__(						self  )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.__dict__.copy()
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	None
           return state

     def __setstate__(						self	,    _a  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	d

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

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
           self.sp_model.Load(self.vocab_file  )





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


           """simple docstring"""


           if not os.path.isdir(_a  ):
                 logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
                 return
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	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:
                       SCREAMING_SNAKE_CASE__   :      str      		=	self.sp_model.serialized_model_proto()
                       fi.write(_a  )

           return (out_vocab_file,)







"""simple docstring"""


from typing import TYPE_CHECKING

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


a					:Any    = {
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:Union[str, Any]    = [
      "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RobertaPreLayerNormForCausalLM",
      "RobertaPreLayerNormForMaskedLM",
      "RobertaPreLayerNormForMultipleChoice",
      "RobertaPreLayerNormForQuestionAnswering",
      "RobertaPreLayerNormForSequenceClassification",
      "RobertaPreLayerNormForTokenClassification",
      "RobertaPreLayerNormModel",
      "RobertaPreLayerNormPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:Optional[Any]    = [
      "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFRobertaPreLayerNormForCausalLM",
      "TFRobertaPreLayerNormForMaskedLM",
      "TFRobertaPreLayerNormForMultipleChoice",
      "TFRobertaPreLayerNormForQuestionAnswering",
      "TFRobertaPreLayerNormForSequenceClassification",
      "TFRobertaPreLayerNormForTokenClassification",
      "TFRobertaPreLayerNormMainLayer",
      "TFRobertaPreLayerNormModel",
      "TFRobertaPreLayerNormPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:List[Any]    = [
      "FlaxRobertaPreLayerNormForCausalLM",
      "FlaxRobertaPreLayerNormForMaskedLM",
      "FlaxRobertaPreLayerNormForMultipleChoice",
      "FlaxRobertaPreLayerNormForQuestionAnswering",
      "FlaxRobertaPreLayerNormForSequenceClassification",
      "FlaxRobertaPreLayerNormForTokenClassification",
      "FlaxRobertaPreLayerNormModel",
      "FlaxRobertaPreLayerNormPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_roberta_prelayernorm import (
      ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
      RobertaPreLayerNormConfig,
      RobertaPreLayerNormOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        RobertaPreLayerNormForCausalLM,
        RobertaPreLayerNormForMaskedLM,
        RobertaPreLayerNormForMultipleChoice,
        RobertaPreLayerNormForQuestionAnswering,
        RobertaPreLayerNormForSequenceClassification,
        RobertaPreLayerNormForTokenClassification,
        RobertaPreLayerNormModel,
        RobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roberta_prelayernorm import (
        TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRobertaPreLayerNormForCausalLM,
        TFRobertaPreLayerNormForMaskedLM,
        TFRobertaPreLayerNormForMultipleChoice,
        TFRobertaPreLayerNormForQuestionAnswering,
        TFRobertaPreLayerNormForSequenceClassification,
        TFRobertaPreLayerNormForTokenClassification,
        TFRobertaPreLayerNormMainLayer,
        TFRobertaPreLayerNormModel,
        TFRobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roberta_prelayernorm import (
        FlaxRobertaPreLayerNormForCausalLM,
        FlaxRobertaPreLayerNormForMaskedLM,
        FlaxRobertaPreLayerNormForMultipleChoice,
        FlaxRobertaPreLayerNormForQuestionAnswering,
        FlaxRobertaPreLayerNormForSequenceClassification,
        FlaxRobertaPreLayerNormForTokenClassification,
        FlaxRobertaPreLayerNormModel,
        FlaxRobertaPreLayerNormPreTrainedModel,
    )

else:
  import sys

  a					:Optional[int]    = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




