





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


lowercase				:	Optional[Any]      						=  {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase				:	Optional[int]      						=  {
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

lowercase				:	str      						=  {
    """unc-nlp/lxmert-base-uncased""": 5_1_2,
}

lowercase				:	List[str]      						=  {
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    Dict	  =  VOCAB_FILES_NAMES
   __A       :    str	  =  PRETRAINED_VOCAB_FILES_MAP
   __A       :    List[Any]	  =  PRETRAINED_INIT_CONFIGURATION
   __A       :    Union[str, Any]	  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A       :    Union[str, Any]	  =  LxmertTokenizer

   def __init__( self ,  lowercase=None ,  lowercase=None ,  lowercase=True ,  lowercase="[UNK]" ,  lowercase="[SEP]" ,  lowercase="[PAD]" ,  lowercase="[CLS]" ,  lowercase="[MASK]" ,  lowercase=True ,  lowercase=None ,  **lowercase ,  )      ->						Optional[int]:







          '''simple docstring'''

          super().__init__(
              lowercase ,  tokenizer_file=lowercase ,  do_lower_case=lowercase ,  unk_token=lowercase ,  sep_token=lowercase ,  pad_token=lowercase ,  cls_token=lowercase ,  mask_token=lowercase ,  tokenize_chinese_chars=lowercase ,  strip_accents=lowercase ,  **lowercase ,  )

          a__						:     List[str]			=    json.loads(self.backend_tokenizer.normalizer.__getstate__())
          if (
              normalizer_state.get('lowercase' ,  lowercase) != do_lower_case
              or normalizer_state.get('strip_accents' ,  lowercase) != strip_accents
              or normalizer_state.get('handle_chinese_chars' ,  lowercase) != tokenize_chinese_chars
          ):
                 a__						:     Optional[Any]			=    getattr(lowercase ,  normalizer_state.pop('type'))
                 a__						:     List[str]			=    do_lower_case
                 a__						:     int			=    strip_accents
                 a__						:     int			=    tokenize_chinese_chars
                 a__						:     Dict			=    normalizer_class(**lowercase)

          a__						:     Any			=    do_lower_case

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







          '''simple docstring'''

          a__						:     int			=    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
                 output += token_ids_a + [self.sep_token_id]

          return output

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						List[int]:







          '''simple docstring'''

          a__						:     List[str]			=    [self.sep_token_id]
          a__						:     Optional[int]			=    [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) * [0] + len(token_ids_a + sep) * [1]

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







          '''simple docstring'''

          a__						:     Optional[Any]			=    self._tokenizer.model.save(lowercase ,  name=lowercase)
          return tuple(lowercase)









import re

from filelock import FileLock


try:
    import nltk

    lowercase				:	List[str]      						=  True
except (ImportError, ModuleNotFoundError):
    lowercase				:	Tuple      						=  False

if NLTK_AVAILABLE:
    with FileLock(""".lock""") as lock:
        nltk.download("""punkt""", quiet=True)



def A_     (		A__    )  ->		str:
       re.sub('<n>'		,				''		,				A__    )  # remove pegasus newline char
       assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
       return "\n".join(nltk.sent_tokenize(A__    )    )


