


"""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:
       lowerCamelCase_  :  int     		=    None


lowerCamelCase_  :  Tuple     		=    logging.get_logger(__name__)

lowerCamelCase_  :  Tuple     		=    {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_  :  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""",
    },
}

lowerCamelCase_  :  Any     		=    {
    """facebook/xglm-564M""": 2_0_4_8,
}






class  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 __lowerCAmelCase					   =    VOCAB_FILES_NAMES
 __lowerCAmelCase					   =    PRETRAINED_VOCAB_FILES_MAP
 __lowerCAmelCase					   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCAmelCase					   =    ["input_ids", "attention_mask"]
 __lowerCAmelCase					   =    XGLMTokenizer


 def __init__(						self					,       __A=None					,       __A=None					,       __A="<s>"					,       __A="</s>"					,       __A="</s>"					,       __A="<s>"					,       __A="<unk>"					,       __A="<pad>"					,       **__A					,       )   ->	Optional[int]:
     # Compatibility with the original tokenizer
     a											=7
     a											=[f'''<madeupword{i}>''' for i in range(self.num_madeup_words		)]

     a											=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__(
         __A					,       tokenizer_file=__A					,       bos_token=__A					,       eos_token=__A					,       sep_token=__A					,       cls_token=__A					,       unk_token=__A					,       pad_token=__A					,       **__A					,       )

     a											=vocab_file
     a											=False if not self.vocab_file else True


 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A = None		)   ->	List[int]:

     if token_ids_a is None:
         return [self.sep_token_id] + token_ids_a
     a											=[self.sep_token_id]
     return sep + token_ids_a + sep + sep + token_ids_a


 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A = None		)   ->	List[int]:
     a											=[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  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A = 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(__A		):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''		)
         return
     a											=os.path.join(
         __A					,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

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

     return (out_vocab_file,)



"""simple docstring"""







def _A		(		lowercase						):







    """simple docstring"""
    a											=[]
    a											=1

    for i in range(len(lowercase						)						):
        if i + 1 < len(lowercase						) and text[i] == text[i + 1]:
            count += 1
        else:
            encoded.append((text[i], count)						)
            a											=1

    return encoded
def _A		(		lowercase						):







    """simple docstring"""
    return "".join(char * length for char, length in encoded						)


if __name__ == "__main__":
       from doctest import testmod

       testmod(name="""run_length_encode""", verbose=True)
       testmod(name="""run_length_decode""", verbose=True)