


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


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

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

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

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

__UpperCamelCase    :						Any     =							"▁"



class 							_UpperCamelCase (						UpperCamelCase_						):

   '''simple docstring'''







   a_				:							Dict    = VOCAB_FILES_NAMES
   a_				:							Optional[int]    = PRETRAINED_VOCAB_FILES_MAP
   a_				:							Tuple    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a_				:							str    = ["""input_ids""", """attention_mask"""]

   def __init__(  self  : Optional[int]       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : Optional[int]="<s>"       ,  _lowerCamelCase  : Any="</s>"       ,  _lowerCamelCase  : Optional[int]="</s>"       ,  _lowerCamelCase  : Any="<s>"       ,  _lowerCamelCase  : Any="<unk>"       ,  _lowerCamelCase  : int="<pad>"       ,  _lowerCamelCase  : List[str]="<mask>"       ,  _lowerCamelCase  : Dict=["<s>NOTUSED", "</s>NOTUSED"]       ,  _lowerCamelCase  : List[str] = None       ,  **_lowerCamelCase  : str       ,  ):






         '''simple docstring'''



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

         __lowerCamelCase     :      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       ,  )
         __lowerCamelCase     :      List[str]       						=						spm.SentencePieceProcessor(**self.sp_model_kwargs							)
         self.sp_model.Load(str(_a							)							)
         __lowerCamelCase     :      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>
         __lowerCamelCase     :      Any       						=						{"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
         __lowerCamelCase     :      List[str]       						=						len(self.fairseq_tokens_to_ids							)
         __lowerCamelCase     :      List[str]       						=						len(self.sp_model							) + len(self.fairseq_tokens_to_ids							)
         __lowerCamelCase     :      str       						=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}

   def   _snake_case    (  self  : Union[str, Any]       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : Union[str, Any] = None							):






         '''simple docstring'''



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

   def   _snake_case    (  self  : Tuple       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Optional[int] = None       ,  _lowerCamelCase  : Dict = False							):






         '''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   _snake_case    (  self  : Optional[int]       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : List[str] = None							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						[self.sep_token_id]
         __lowerCamelCase     :      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   _snake_case    (  self  : Any							):






         '''simple docstring'''



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

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      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   _snake_case    (  self  : List[Any]       ,  _lowerCamelCase  : List[Any]							):






         '''simple docstring'''



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

   def   _snake_case    (  self  : int       ,  _lowerCamelCase  : Optional[int]							):






         '''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   _snake_case    (  self  : int       ,  _lowerCamelCase  : 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   _snake_case    (  self  : int       ,  _lowerCamelCase  : Any							):






         '''simple docstring'''



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

   def __getstate__(  self  : Optional[int]							):






         '''simple docstring'''



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

   def __setstate__(  self  : int       ,  _lowerCamelCase  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						d

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

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


   def   _snake_case    (  self  : Optional[Any]       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : Union[str, Any] = None							):






         '''simple docstring'''



         if not os.path.isdir(_a							):
               logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""							)
               return
         __lowerCamelCase     :      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:
                     __lowerCamelCase     :      str       						=						self.sp_model.serialized_model_proto()
                     fi.write(_a							)

         return (out_vocab_file,)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase    :						Union[str, Any]     =							{
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						List[str]     =							['LayoutLMv2TokenizerFast']

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						List[str]     =							['LayoutLMv2FeatureExtractor']
    __UpperCamelCase    :						Union[str, Any]     =							['LayoutLMv2ImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						Dict     =							[
        'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'LayoutLMv2ForQuestionAnswering',
        'LayoutLMv2ForSequenceClassification',
        'LayoutLMv2ForTokenClassification',
        'LayoutLMv2Layer',
        'LayoutLMv2Model',
        'LayoutLMv2PreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
    from .processing_layoutlmva import LayoutLMvaProcessor
    from .tokenization_layoutlmva import LayoutLMvaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlmva import (
            LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMvaForQuestionAnswering,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaLayer,
            LayoutLMvaModel,
            LayoutLMvaPreTrainedModel,
        )
else:
    import sys

    __UpperCamelCase    :						Optional[Any]     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



