




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


_lowerCamelCase	:		List[str]							       =   """▁"""

_lowerCamelCase	:		List[Any]							       =   {"""vocab_file""": """spiece.model"""}

_lowerCamelCase	:		List[str]							       =   {
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

_lowerCamelCase	:		Optional[int]							       =   {
    """google/reformer-crime-and-punishment""": 524288,
}


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      VOCAB_FILES_NAMES
    UpperCAmelCase__							  =      PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =      ['''input_ids''', '''attention_mask''']


    def __init__(			self      :	Union[str, Any]					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	List[str]="</s>"					, UpperCAmelCase__      :	Optional[Any]="<unk>"					, UpperCAmelCase__      :	List[str]=[]					, UpperCAmelCase__      :	Optional[Dict[str, Any]] = None					, **UpperCAmelCase__      :	Dict					, )						->None:





           '''simple docstring'''

           A__       =    {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=UpperCAmelCase__					, unk_token=UpperCAmelCase__					, additional_special_tokens=UpperCAmelCase__					, sp_model_kwargs=self.sp_model_kwargs					, **UpperCAmelCase__					, )

           A__       =    vocab_file
           A__       =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(UpperCAmelCase__)


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->List[str]:





           '''simple docstring'''

           return self.sp_model.get_piece_size()


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Dict[str, int]:





           '''simple docstring'''

           A__       =    {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      :	Any)						->Dict:





           '''simple docstring'''

           A__       =    self.__dict__.copy()
           A__       =    None
           return state


    def __setstate__(			self      :	int					, UpperCAmelCase__      :	List[str])						->Dict:





           '''simple docstring'''

           A__       =    d

           # for backward compatibility
           if not hasattr(self					, '''sp_model_kwargs'''):
                  A__       =    {}

           A__       =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(self.vocab_file)


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





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	Any)						->int:





           '''simple docstring'''

           return self.sp_model.piece_to_id(UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	List[str])						->Tuple:





           '''simple docstring'''

           if index < self.sp_model.get_piece_size():
                  A__       =    self.sp_model.IdToPiece(UpperCAmelCase__)
           return token


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	Dict)						->Tuple:





           '''simple docstring'''

           A__       =    []
           A__       =    ''''''
           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__       =    []
                  else:
                         current_sub_tokens.append(UpperCAmelCase__)
           out_string += self.sp_model.decode(UpperCAmelCase__)
           return out_string.strip()





    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	Optional[str] = None)						->Tuple[str]:





           '''simple docstring'''

           if not os.path.isdir(UpperCAmelCase__):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
                  return
           A__       =    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__       =    self.sp_model.serialized_model_proto()
                         fi.write(UpperCAmelCase__)

           return (out_vocab_file,)








from typing import TYPE_CHECKING

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


_lowerCamelCase	:		int							       =   {
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		Tuple							       =   ["""BlipImageProcessor"""]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		List[Any]							       =   [
      """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BlipModel""",
      """BlipPreTrainedModel""",
      """BlipForConditionalGeneration""",
      """BlipForQuestionAnswering""",
      """BlipVisionModel""",
      """BlipTextModel""",
      """BlipForImageTextRetrieval""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		Optional[Any]							       =   [
      """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFBlipModel""",
      """TFBlipPreTrainedModel""",
      """TFBlipForConditionalGeneration""",
      """TFBlipForQuestionAnswering""",
      """TFBlipVisionModel""",
      """TFBlipTextModel""",
      """TFBlipForImageTextRetrieval""",
  ]

if TYPE_CHECKING:
  from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
  from .processing_blip import BlipProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_blip import BlipImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blip import (
        BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlipForConditionalGeneration,
        BlipForImageTextRetrieval,
        BlipForQuestionAnswering,
        BlipModel,
        BlipPreTrainedModel,
        BlipTextModel,
        BlipVisionModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_blip import (
        TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFBlipForConditionalGeneration,
        TFBlipForImageTextRetrieval,
        TFBlipForQuestionAnswering,
        TFBlipModel,
        TFBlipPreTrainedModel,
        TFBlipTextModel,
        TFBlipVisionModel,
    )

else:
  import sys

  _lowerCamelCase	:		List[Any]							       =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


