






"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_xglm import XGLMTokenizer
else:
   _UpperCamelCase   :						Any     						= None


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

_UpperCamelCase   :						List[Any]     						= {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase   :						List[Any]     						= {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json""",
    },
}

_UpperCamelCase   :						Union[str, Any]     						= {
    """facebook/xglm-564M""": 20_48,
}






class __a   (   lowerCamelCase__				):
  __lowercase    :    int						=					VOCAB_FILES_NAMES
  __lowercase    :    List[str]						=					PRETRAINED_VOCAB_FILES_MAP
  __lowercase    :    Dict						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase    :    Any						=					['input_ids', 'attention_mask']
  __lowercase    :    Any						=					XGLMTokenizer



  def __init__(self  :		Optional[Any]				,			lowercase_  :		List[str]=None				,			lowercase_  :		Optional[Any]=None				,			lowercase_  :		Tuple="<s>"				,			lowercase_  :		Dict="</s>"				,			lowercase_  :		Dict="</s>"				,			lowercase_  :		Optional[int]="<s>"				,			lowercase_  :		List[str]="<unk>"				,			lowercase_  :		List[Any]="<pad>"				,			**lowercase_  :		Union[str, Any]				,			)		->							Any:
   __A	:       Tuple       =	7
   __A	:       int       =	[f"<madeupword{i}>" for i in range(self.num_madeup_words   )]

   __A	:       str       =	kwargs.get('additional_special_tokens'				,			[]   )
   kwargs["additional_special_tokens"] += [
       word for word in madeup_words if word not in kwargs["additional_special_tokens"]
   ]

   super().__init__(
       lowercase_				,			tokenizer_file=lowercase_				,			bos_token=lowercase_				,			eos_token=lowercase_				,			sep_token=lowercase_				,			cls_token=lowercase_				,			unk_token=lowercase_				,			pad_token=lowercase_				,			**lowercase_				,			)

   __A	:       Optional[int]       =	vocab_file
   __A	:       Optional[Any]       =	False if not self.vocab_file else True



  def 						A_				(self  :		Dict				,			lowercase_  :		Any				,			lowercase_  :		Tuple = None   )		->							List[int]:






   if token_ids_a is None:
    return [self.sep_token_id] + token_ids_a
   __A	:       Optional[Any]       =	[self.sep_token_id]
   return sep + token_ids_a + sep + sep + token_ids_a



  def 						A_				(self  :		Any				,			lowercase_  :		Dict				,			lowercase_  :		str = None   )		->							List[int]:
   __A	:       Tuple       =	[self.sep_token_id]

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



  def 						A_				(self  :		Optional[Any]				,			lowercase_  :		Any				,			lowercase_  :		str = None   )		->							Tuple[str]:






   if not self.can_save_slow_tokenizer:
    raise ValueError(
        'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
        'tokenizer.'   )

   if not os.path.isdir(lowercase_   ):
    logger.error(f"Vocabulary path ({save_directory}) should be a directory."   )
    return
   __A	:       Optional[int]       =	os.path.join(
       lowercase_				,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

   if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowercase_   ):
    copyfile(self.vocab_file				,			lowercase_   )

   return (out_vocab_file,)













"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


_UpperCamelCase   :						str     						= logging.get_logger(__name__)






class __a   (   snake_case_				):



  def __init__(self  :		Union[str, Any]				,			*lowercase_  :		Any				,			**lowercase_  :		Optional[Any]   )		->							None:
   warnings.warn(
       'The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
       ' Please use LayoutLMv3ImageProcessor instead.'				,			lowercase_				,			)
   super().__init__(*lowercase_				,			**lowercase_   )





