





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


_snake_case  :    int						    =  """▁"""

_snake_case  :    Any						    =  {"""vocab_file""": """spiece.model"""}

_snake_case  :    Dict						    =  {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

_snake_case  :    Union[str, Any]						    =  {
    """google/pegasus-xsum""": 512,
}


_snake_case  :    Union[str, Any]						    =  logging.get_logger(__name__)



class 				_UpperCAmelCase    (    _UpperCAmelCase    ):

  """simple docstring"""


  a_        = VOCAB_FILES_NAMES

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






  def __init__(				self :		str					,  lowerCAmelCase_ :		Tuple					,  lowerCAmelCase_ :		str="<pad>"					,  lowerCAmelCase_ :		Dict="</s>"					,  lowerCAmelCase_ :		str="<unk>"					,  lowerCAmelCase_ :		Optional[Any]="<mask_2>"					,  lowerCAmelCase_ :		Any="<mask_1>"					,  lowerCAmelCase_ :		Tuple=None					,  lowerCAmelCase_ :		Optional[int]=1_0_3					,  lowerCAmelCase_ :		int = None					,  **lowerCAmelCase_ :		Optional[Any]					,  )							->						None:
     __lowerCAmelCase								=	offset
     if additional_special_tokens is not None:
        if not isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_     ):
           raise TypeError(
               f"""additional_special_tokens should be of type {type(SCREAMING_SNAKE_CASE_     )}, but is"""
               f""" {type(SCREAMING_SNAKE_CASE_     )}"""     )

        __lowerCAmelCase								=	(
            ([mask_token_sent] + additional_special_tokens)
            if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
            else additional_special_tokens
        )
        # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
        additional_special_tokens_extended += [
            f"""<unk_{i}>""" for i in range(len(SCREAMING_SNAKE_CASE_     )					,  self.offset - 1     )
        ]

        if len(set(SCREAMING_SNAKE_CASE_     )     ) != len(SCREAMING_SNAKE_CASE_     ):
           raise ValueError(
               'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
               f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""     )
        __lowerCAmelCase								=	additional_special_tokens_extended
     else:
        __lowerCAmelCase								=	[mask_token_sent] if mask_token_sent is not None else []
        additional_special_tokens += [f"""<unk_{i}>""" for i in range(2					,  self.offset     )]

     __lowerCAmelCase								=	{} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         eos_token=SCREAMING_SNAKE_CASE_					,  unk_token=SCREAMING_SNAKE_CASE_					,  mask_token=SCREAMING_SNAKE_CASE_					,  pad_token=SCREAMING_SNAKE_CASE_					,  mask_token_sent=SCREAMING_SNAKE_CASE_					,  offset=SCREAMING_SNAKE_CASE_					,  additional_special_tokens=SCREAMING_SNAKE_CASE_					,  sp_model_kwargs=self.sp_model_kwargs					,  **SCREAMING_SNAKE_CASE_					,  )
     __lowerCAmelCase								=	mask_token_sent
     __lowerCAmelCase								=	vocab_file
     __lowerCAmelCase								=	spm.SentencePieceProcessor(**self.sp_model_kwargs     )
     self.sp_model.Load(SCREAMING_SNAKE_CASE_     )

     # add special tokens to encoder dict
     __lowerCAmelCase								=	{
         0: self.pad_token,
         1: self.eos_token,
     }

     if self.mask_token_sent is not None:
        self.encoder.update(
            {
                2: self.mask_token_sent,
                3: self.mask_token,
            }     )

     if self.offset > 0:
        # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
        # mask_token_sent is already added to list -> so start at 1
        self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1					,  self.offset - 1     )}     )

     __lowerCAmelCase								=	{v: k for k, v in self.encoder.items()}






  @property
  def 				lowercase		(				self :		List[Any]     )							->						int:
     return len(self.sp_model     ) + self.offset






  def 				lowercase		(				self :		List[str]     )							->						Dict[str, int]:
     __lowerCAmelCase								=	{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_     ): i for i in range(self.vocab_size     )}
     vocab.update(self.added_tokens_encoder     )
     return vocab






  def __getstate__(				self :		Optional[int]     )							->						List[Any]:
     __lowerCAmelCase								=	self.__dict__.copy()
     __lowerCAmelCase								=	None
     return state






  def __setstate__(				self :		Any					,  lowerCAmelCase_ :		Any     )							->						Any:
     __lowerCAmelCase								=	d

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

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






  def 				lowercase		(				self :		Union[str, Any]					,  lowerCAmelCase_ :		List[Any]     )							->						List[str]:
     return self.sp_model.encode(SCREAMING_SNAKE_CASE_					,  out_type=SCREAMING_SNAKE_CASE_     )






  def 				lowercase		(				self :		List[str]					,  lowerCAmelCase_ :		Union[str, Any]     )							->						int:
     if token in self.decoder:
        return self.decoder[token]
     elif token in self.added_tokens_decoder:
        return self.added_tokens_decoder[token]
     __lowerCAmelCase								=	self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE_     )
     return sp_id + self.offset






  def 				lowercase		(				self :		Tuple					,  lowerCAmelCase_ :		Dict     )							->						str:
     if index in self.encoder:
        return self.encoder[index]
     elif index in self.added_tokens_encoder:
        return self.added_tokens_encoder[index]
     else:
        __lowerCAmelCase								=	self.sp_model.IdToPiece(index - self.offset     )
     return token






  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		Tuple     )							->						Optional[Any]:
     __lowerCAmelCase								=	[]
     __lowerCAmelCase								=	''
     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(SCREAMING_SNAKE_CASE_     ) + token
           __lowerCAmelCase								=	[]
        else:
           current_sub_tokens.append(SCREAMING_SNAKE_CASE_     )
     out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_     )
     return out_string.strip()






  def 				lowercase		(				self :		Any					,  lowerCAmelCase_ :		Tuple=False     )							->						str:
     return 1






  def 				lowercase		(				self :		Dict					,  lowerCAmelCase_ :		Optional[Any]     )							->						int:
     __lowerCAmelCase								=	set(self.all_special_ids     )  # call it once instead of inside list comp
     all_special_ids.remove(self.unk_token_id     )  # <unk> is only sometimes special

     return [1 if x in all_special_ids else 0 for x in seq]






  def 				lowercase		(				self :		Dict					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		str = None					,  lowerCAmelCase_ :		Any = False     )							->						List[int]:
     if already_has_special_tokens:
        return self._special_token_mask(SCREAMING_SNAKE_CASE_     )
     elif token_ids_a is None:
        return self._special_token_mask(SCREAMING_SNAKE_CASE_     ) + [1]
     else:
        return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]






  def 				lowercase		(				self :		List[Any]					,  lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		int=None     )							->						List[int]:
     if token_ids_a is None:
        return token_ids_a + [self.eos_token_id]
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return token_ids_a + token_ids_a + [self.eos_token_id]






  def 				lowercase		(				self :		Union[str, Any]					,  lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		str = None     )							->						Tuple[str]:
     if not os.path.isdir(SCREAMING_SNAKE_CASE_     ):
        logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""     )
        return
     __lowerCAmelCase								=	os.path.join(
         SCREAMING_SNAKE_CASE_					,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

     if os.path.abspath(self.vocab_file     ) != os.path.abspath(SCREAMING_SNAKE_CASE_     ) and os.path.isfile(self.vocab_file     ):
        copyfile(self.vocab_file					,  SCREAMING_SNAKE_CASE_     )
     elif not os.path.isfile(self.vocab_file     ):
        with open(SCREAMING_SNAKE_CASE_					,  'wb'     ) as fi:
           __lowerCAmelCase								=	self.sp_model.serialized_model_proto()
           fi.write(SCREAMING_SNAKE_CASE_     )

     return (out_vocab_file,)





'''simple docstring'''



from typing import TYPE_CHECKING

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


A__		:					List[str]												=  {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					str												=  ["""BeitFeatureExtractor"""]
      A__		:					List[str]												=  ["""BeitImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Optional[int]												=  [
          """BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BeitForImageClassification""",
          """BeitForMaskedImageModeling""",
          """BeitForSemanticSegmentation""",
          """BeitModel""",
          """BeitPreTrainedModel""",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Any												=  [
          """FlaxBeitForImageClassification""",
          """FlaxBeitForMaskedImageModeling""",
          """FlaxBeitModel""",
          """FlaxBeitPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_beit import BeitFeatureExtractor
            from .image_processing_beit import BeitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_beit import (
                BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BeitForImageClassification,
                BeitForMaskedImageModeling,
                BeitForSemanticSegmentation,
                BeitModel,
                BeitPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_beit import (
                FlaxBeitForImageClassification,
                FlaxBeitForMaskedImageModeling,
                FlaxBeitModel,
                FlaxBeitPreTrainedModel,
            )


else:
      import sys

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






