





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


lowercase				:	str      						=  logging.get_logger(__name__)

lowercase				:	int      						=  {"""vocab_file""": """spiece.model"""}

lowercase				:	Optional[int]      						=  {
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

lowercase				:	Union[str, Any]      						=  {"""bert_for_seq_generation""": 5_1_2}





class 			A__   (							UpperCamelCase__     ):




   """simple docstring"""



   __A       :    List[str]	  =  VOCAB_FILES_NAMES
   __A       :    Dict	  =  PRETRAINED_VOCAB_FILES_MAP
   __A       :    List[str]	  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A       :    List[int]	  =  []
   __A       :    List[Any]	  =  ['''input_ids''', '''attention_mask''']

   def __init__( self ,  lowercase ,  lowercase="<s>" ,  lowercase="</s>" ,  lowercase="<unk>" ,  lowercase="<pad>" ,  lowercase="<::::>" ,  lowercase = None ,  **lowercase ,  )      ->						None:







          '''simple docstring'''

          a__						:     Any			=    {} if sp_model_kwargs is None else sp_model_kwargs

          # Add extra_ids to the special token list
          super().__init__(
              bos_token=UpperCamelCase_ ,  eos_token=UpperCamelCase_ ,  unk_token=UpperCamelCase_ ,  pad_token=UpperCamelCase_ ,  sep_token=UpperCamelCase_ ,  sp_model_kwargs=self.sp_model_kwargs ,  **UpperCamelCase_ ,  )

          a__						:     str			=    vocab_file

          a__						:     List[str]			=    spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(UpperCamelCase_)

   @property
   def 							__lowercase		( self)      ->						Union[str, Any]:







          '''simple docstring'''

          return self.sp_model.get_piece_size()

   def 							__lowercase		( self)      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     Dict			=    {self.convert_ids_to_tokens(UpperCamelCase_): i for i in range(self.vocab_size)}
          vocab.update(self.added_tokens_encoder)
          return vocab

   def __getstate__( self)      ->						Dict:







          '''simple docstring'''

          a__						:     Any			=    self.__dict__.copy()
          a__						:     Tuple			=    None
          return state

   def __setstate__( self ,  lowercase)      ->						str:







          '''simple docstring'''

          a__						:     str			=    d

          # for backward compatibility
          if not hasattr(self ,  'sp_model_kwargs'):
                 a__						:     Union[str, Any]			=    {}

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

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







          '''simple docstring'''

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

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







          '''simple docstring'''

          return self.sp_model.piece_to_id(UpperCamelCase_)

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







          '''simple docstring'''

          a__						:     Dict			=    self.sp_model.IdToPiece(UpperCamelCase_)
          return token

   def 							__lowercase		( self ,  lowercase)      ->						int:







          '''simple docstring'''

          a__						:     List[Any]			=    []
          a__						:     Union[str, Any]			=    ''
          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(UpperCamelCase_) + token
                        a__						:     Optional[int]			=    []
                 else:
                        current_sub_tokens.append(UpperCamelCase_)
          out_string += self.sp_model.decode(UpperCamelCase_)
          return out_string.strip()

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







          '''simple docstring'''

          if not os.path.isdir(UpperCamelCase_):
                 logger.error(F'Vocabulary path ({save_directory}) should be a directory')
                 return
          a__						:     Dict			=    os.path.join(
              UpperCamelCase_ ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

          if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_) and os.path.isfile(self.vocab_file):
                 copyfile(self.vocab_file ,  UpperCamelCase_)
          elif not os.path.isfile(self.vocab_file):
                 with open(UpperCamelCase_ ,  'wb') as fi:
                        a__						:     Optional[Any]			=    self.sp_model.serialized_model_proto()
                        fi.write(UpperCamelCase_)

          return (out_vocab_file,)









from typing import TYPE_CHECKING

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


lowercase				:	str      						=  {
    """configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderOnnxConfig"""]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Optional[Any]      						=  ["""VisionEncoderDecoderModel"""]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Optional[int]      						=  ["""TFVisionEncoderDecoderModel"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	List[str]      						=  ["""FlaxVisionEncoderDecoderModel"""]

if TYPE_CHECKING:
    from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
    import sys

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


