


'''simple docstring'''







import os
import unicodedata
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 SPIECE_UNDERLINE, logging


A				:					str    						=			logging.get_logger(__name__)

A				:					Any    						=			{'''vocab_file''': '''spiece.model'''}

A				:					List[str]    						=			{
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    }
}

A				:					Dict    						=			{
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

# Segments (not really needed)
A				:					List[Any]    						=			0
A				:					str    						=			1
A				:					Any    						=			2
A				:					List[Any]    						=			3
A				:					int    						=			4


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   VOCAB_FILES_NAMES
    a													=   PRETRAINED_VOCAB_FILES_MAP
    a													=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a													=   "left"


    def __init__(  self      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							str=False				,							SCREAMING_SNAKE_CASE      :							Any=True				,							SCREAMING_SNAKE_CASE      :							Dict=False				,							SCREAMING_SNAKE_CASE      :							int="<s>"				,							SCREAMING_SNAKE_CASE      :							Any="</s>"				,							SCREAMING_SNAKE_CASE      :							Tuple="<unk>"				,							SCREAMING_SNAKE_CASE      :							Tuple="<sep>"				,							SCREAMING_SNAKE_CASE      :							Optional[int]="<pad>"				,							SCREAMING_SNAKE_CASE      :							int="<cls>"				,							SCREAMING_SNAKE_CASE      :							Any="<mask>"				,							SCREAMING_SNAKE_CASE      :							str=["<eop>", "<eod>"]				,							SCREAMING_SNAKE_CASE      :							Optional[Dict[str, Any]] = None				,							**SCREAMING_SNAKE_CASE      :							List[Any]				,							):
           # Mask token behave like a normal word, i.e. include the space before it
           _A					:     Optional[Any]     = AddedToken(SCREAMING_SNAKE_CASE				,							lstrip=SCREAMING_SNAKE_CASE				,							rstrip=SCREAMING_SNAKE_CASE) if isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE) else mask_token

           _A					:     Union[str, Any]     = {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=SCREAMING_SNAKE_CASE				,							remove_space=SCREAMING_SNAKE_CASE				,							keep_accents=SCREAMING_SNAKE_CASE				,							bos_token=SCREAMING_SNAKE_CASE				,							eos_token=SCREAMING_SNAKE_CASE				,							unk_token=SCREAMING_SNAKE_CASE				,							sep_token=SCREAMING_SNAKE_CASE				,							pad_token=SCREAMING_SNAKE_CASE				,							cls_token=SCREAMING_SNAKE_CASE				,							mask_token=SCREAMING_SNAKE_CASE				,							additional_special_tokens=SCREAMING_SNAKE_CASE				,							sp_model_kwargs=self.sp_model_kwargs				,							**SCREAMING_SNAKE_CASE				,							)

           _A					:     Optional[Any]     = 3

           _A					:     List[str]     = do_lower_case
           _A					:     int     = remove_space
           _A					:     Optional[int]     = keep_accents
           _A					:     Optional[Any]     = vocab_file

           _A					:     str     = spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(SCREAMING_SNAKE_CASE)


    @property
    def        A   (  self      :							Dict):
           return len(self.sp_model)


    def        A   (  self      :							Dict):
           _A					:     int     = {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      :							int):
           _A					:     List[Any]     = self.__dict__.copy()
           _A					:     Dict     = None
           return state


    def __setstate__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Tuple):
           _A					:     List[str]     = d

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

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


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]):
           if self.remove_space:
                  _A					:     List[Any]     = ' '.join(inputs.strip().split())
           else:
                  _A					:     int     = inputs
           _A					:     int     = outputs.replace('``'				,							'"').replace('\'\''				,							'"')

           if not self.keep_accents:
                  _A					:     Tuple     = unicodedata.normalize('NFKD'				,							SCREAMING_SNAKE_CASE)
                  _A					:     Dict     = ''.join([c for c in outputs if not unicodedata.combining(SCREAMING_SNAKE_CASE)])
           if self.do_lower_case:
                  _A					:     Optional[int]     = outputs.lower()

           return outputs


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     List[Any]     = self.preprocess_text(SCREAMING_SNAKE_CASE)
           _A					:     List[str]     = self.sp_model.encode(SCREAMING_SNAKE_CASE				,							out_type=SCREAMING_SNAKE_CASE)
           _A					:     Union[str, Any]     = []
           for piece in pieces:
                  if len(SCREAMING_SNAKE_CASE) > 1 and piece[-1] == str(',') and piece[-2].isdigit():
                         _A					:     Optional[int]     = self.sp_model.EncodeAsPieces(piece[:-1].replace(SCREAMING_SNAKE_CASE				,							''))
                         if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                if len(cur_pieces[0]) == 1:
                                       _A					:     Any     = cur_pieces[1:]
                                else:
                                       _A					:     Optional[int]     = cur_pieces[0][1:]
                         cur_pieces.append(piece[-1])
                         new_pieces.extend(SCREAMING_SNAKE_CASE)
                  else:
                         new_pieces.append(SCREAMING_SNAKE_CASE)

           return new_pieces


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[Any]):
           return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							List[Any]):
           return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     int     = ''.join(SCREAMING_SNAKE_CASE).replace(SCREAMING_SNAKE_CASE				,							' ').strip()
           return out_string


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							List[int]				,							SCREAMING_SNAKE_CASE      :							bool = False				,							SCREAMING_SNAKE_CASE      :							bool = None				,							SCREAMING_SNAKE_CASE      :							bool = True				,							**SCREAMING_SNAKE_CASE      :							List[str]				,							):
           _A					:     Dict     = kwargs.pop('use_source_tokenizer'				,							SCREAMING_SNAKE_CASE)

           _A					:     Tuple     = self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE				,							skip_special_tokens=SCREAMING_SNAKE_CASE)

           # To avoid mixing byte-level and unicode for byte-level BPT
           # we need to build string separately for added tokens and byte-level tokens
           # cf. https://github.com/huggingface/transformers/issues/1133
           _A					:     Optional[Any]     = []
           _A					:     Union[str, Any]     = []
           for token in filtered_tokens:
                  if skip_special_tokens and token in self.all_special_ids:
                         continue
                  if token in self.added_tokens_encoder:
                         if current_sub_text:
                                sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE))
                                _A					:     Tuple     = []
                         sub_texts.append(SCREAMING_SNAKE_CASE)
                  else:
                         current_sub_text.append(SCREAMING_SNAKE_CASE)
           if current_sub_text:
                  sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE))

           # Mimic the behavior of the Rust tokenizer:
           # By default, there are no spaces between special tokens
           _A					:     int     = ''.join(SCREAMING_SNAKE_CASE)

           _A					:     List[Any]     = (
               clean_up_tokenization_spaces
               if clean_up_tokenization_spaces is not None
               else self.clean_up_tokenization_spaces
           )
           if clean_up_tokenization_spaces:
                  _A					:     Dict     = self.clean_up_tokenization(SCREAMING_SNAKE_CASE)
                  return clean_text
           else:
                  return text


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							List[int]				,							SCREAMING_SNAKE_CASE      :							Optional[List[int]] = None):
           _A					:     Optional[int]     = [self.sep_token_id]
           _A					:     str     = [self.cls_token_id]
           if token_ids_a is None:
                  return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[int]				,							SCREAMING_SNAKE_CASE      :							Optional[List[int]] = None				,							SCREAMING_SNAKE_CASE      :							bool = False):

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

           if token_ids_a is not None:
                  return ([0] * len(SCREAMING_SNAKE_CASE)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE)) + [1, 1]
           return ([0] * len(SCREAMING_SNAKE_CASE)) + [1, 1]


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							List[int]				,							SCREAMING_SNAKE_CASE      :							Optional[List[int]] = None):
           _A					:     Optional[int]     = [self.sep_token_id]
           _A					:     Dict     = [2]

           if token_ids_a is None:
                  return len(token_ids_a + sep) * [0] + cls_segment_id
           return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Optional[str] = None):
           if not os.path.isdir(SCREAMING_SNAKE_CASE):
                  logger.error(F'Vocabulary path ({save_directory}) should be a directory')
                  return
           _A					:     str     = 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:
                         _A					:     Union[str, Any]     = 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_tf_available,
    is_torch_available,
    is_vision_available,
)


A				:					str    						=			{
    '''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:
   A				:					Optional[Any]    						=			['''BlipImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					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:
   A				:					Union[str, 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

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






