





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

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


if is_sentencepiece_available():
  from .tokenization_big_bird import BigBirdTokenizer
else:
  __a      :       str				  =   None

__a      :       str				  =   logging.get_logger(__name__)
__a      :       List[str]				  =   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__a      :       Optional[int]				  =   {
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    },
    "tokenizer_file": {
        "google/bigbird-roberta-base": (
            "https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
        ),
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
        ),
    },
}

__a      :       Dict				  =   {
    "google/bigbird-roberta-base": 4096,
    "google/bigbird-roberta-large": 4096,
    "google/bigbird-base-trivia-itc": 4096,
}


__a      :       Dict				  =   "▁"


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE   						=				PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE   						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE   						=				BigBirdTokenizer
     SCREAMING_SNAKE_CASE   						=				["input_ids", "attention_mask"]
     SCREAMING_SNAKE_CASE   						=				[]




     def __init__(				self       :			List[str] ,	UpperCamelCase_       :			int=None ,	UpperCamelCase_       :			Tuple=None ,	UpperCamelCase_       :			Any="<unk>" ,	UpperCamelCase_       :			int="<s>" ,	UpperCamelCase_       :			Optional[Any]="</s>" ,	UpperCamelCase_       :			Any="<pad>" ,	UpperCamelCase_       :			Optional[int]="[SEP]" ,	UpperCamelCase_       :			int="[MASK]" ,	UpperCamelCase_       :			List[str]="[CLS]" ,	**UpperCamelCase_       :			Tuple ,	):


         """simple docstring"""



         __A         =	AddedToken(UpperCamelCase_ ,	lstrip=UpperCamelCase_ ,	rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_ ,	UpperCamelCase_				) else bos_token
         __A         =	AddedToken(UpperCamelCase_ ,	lstrip=UpperCamelCase_ ,	rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_ ,	UpperCamelCase_				) else eos_token
         __A         =	AddedToken(UpperCamelCase_ ,	lstrip=UpperCamelCase_ ,	rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_ ,	UpperCamelCase_				) else unk_token
         __A         =	AddedToken(UpperCamelCase_ ,	lstrip=UpperCamelCase_ ,	rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_ ,	UpperCamelCase_				) else pad_token
         __A         =	AddedToken(UpperCamelCase_ ,	lstrip=UpperCamelCase_ ,	rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_ ,	UpperCamelCase_				) else cls_token
         __A         =	AddedToken(UpperCamelCase_ ,	lstrip=UpperCamelCase_ ,	rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_ ,	UpperCamelCase_				) else sep_token

         # Mask token behave like a normal word, i.e. include the space before it
         __A         =	AddedToken(UpperCamelCase_ ,	lstrip=UpperCamelCase_ ,	rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_ ,	UpperCamelCase_				) else mask_token

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

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




     def 		lowerCAmelCase_     (				self       :			str ,	UpperCamelCase_       :			List[int] ,	UpperCamelCase_       :			Optional[List[int]] = None				):


         """simple docstring"""



         __A         =	[self.sep_token_id]
         __A         =	[self.cls_token_id]
         if token_ids_a is None:
             return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep




     def 		lowerCAmelCase_     (				self       :			str ,	UpperCamelCase_       :			List[int] ,	UpperCamelCase_       :			Optional[List[int]] = None ,	UpperCamelCase_       :			bool = False				):


         """simple docstring"""



         if already_has_special_tokens:
             if token_ids_a is not None:
                 raise ValueError(
                     """You should not supply a second sequence if the provided sequence of """
                     """ids is already formatted with special tokens for the model."""				)
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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




     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			List[int] ,	UpperCamelCase_       :			Optional[List[int]] = None				):


         """simple docstring"""



         __A         =	[self.sep_token_id]
         __A         =	[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 		lowerCAmelCase_     (				self       :			Tuple ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Optional[str] = None				):


         """simple docstring"""



         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(UpperCamelCase_				):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"				)
             return
         __A         =	os.path.join(
             UpperCamelCase_ ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

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

         return (out_vocab_file,)












from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def 			_SCREAMING_SNAKE_CASE						( )						->      Tuple:





    """simple docstring"""

    __A         =	HfArgumentParser(__lowercase    )
    __A         =	parser.parse_args_into_dataclasses()[0]
    __A         =	TensorFlowBenchmark(args=__lowercase    )
    try:
        __A         =	parser.parse_args_into_dataclasses()[0]
    except ValueError as e:
        __A         =	"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
        __A         =	""" """.join(str(__lowercase    ).split(""" """    )[:-1]    )
        __A         =	""""""
        __A         =	eval(str(__lowercase    ).split(""" """    )[-1]    )
        __A         =	[]
        for arg in depreciated_args:
            # arg[2:] removes '--'
            if arg[2:] in TensorFlowBenchmark.deprecated_args:
                # arg[5:] removes '--no_'
                full_error_msg += arg_error_msg.format(arg[5:]    )
            else:
                wrong_args.append(__lowercase    )
        if len(__lowercase    ) > 0:
            __A         =	full_error_msg + begin_error_msg + str(__lowercase    )
        raise ValueError(__lowercase    )
    benchmark.run()


if __name__ == "__main__":
  main()




