


"""simple docstring"""
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_albert import AlbertTokenizer
else:
    _a      :			str					=							None

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

_a      :			Tuple					=							{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
    },
}

_a      :			Optional[Any]					=							{
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

_a      :			Dict					=							"""▁"""






class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: Dict  =					VOCAB_FILES_NAMES
    __lowercase							: int  =					PRETRAINED_VOCAB_FILES_MAP
    __lowercase							: List[Any]  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowercase							: Tuple  =					AlbertTokenizer
    def __init__(    self						,    snake_case_=None						,    snake_case_=None						,    snake_case_=True						,    snake_case_=True						,    snake_case_=False						,    snake_case_="[CLS]"						,    snake_case_="[SEP]"						,    snake_case_="<unk>"						,    snake_case_="[SEP]"						,    snake_case_="<pad>"						,    snake_case_="[CLS]"						,    snake_case_="[MASK]"						,    **snake_case_						,    ):
      # Mask token behave like a normal word, i.e. include the space before it and
      # is included in the raw text, there should be a match in a non-normalized sentence.
      _snake_case     :						str						=   (
          AddedToken(snake_case_						,    lstrip=snake_case_						,    rstrip=snake_case_						,    normalized=snake_case_     )
          if isinstance(snake_case_						,    snake_case_     )
          else mask_token
      )

      super().__init__(
          snake_case_						,    tokenizer_file=snake_case_						,    do_lower_case=snake_case_						,    remove_space=snake_case_						,    keep_accents=snake_case_						,    bos_token=snake_case_						,    eos_token=snake_case_						,    unk_token=snake_case_						,    sep_token=snake_case_						,    pad_token=snake_case_						,    cls_token=snake_case_						,    mask_token=snake_case_						,    **snake_case_						,    )

      _snake_case     :						Dict						=   do_lower_case
      _snake_case     :						Tuple						=   remove_space
      _snake_case     :						Optional[int]						=   keep_accents
      _snake_case     :						Optional[int]						=   vocab_file
      _snake_case     :						Dict						=   False if not self.vocab_file else True
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_ = None     ):
      _snake_case     :						Any						=   [self.sep_token_id]
      _snake_case     :						Tuple						=   [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						,    snake_case_						,    snake_case_ = None     ):
      _snake_case     :						Union[str, Any]						=   [self.sep_token_id]
      _snake_case     :						Any						=   [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						,    snake_case_						,    snake_case_ = None     ):
      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(snake_case_     ):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'     )
        return
      _snake_case     :						List[str]						=   os.path.join(
          snake_case_						,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

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

      return (out_vocab_file,)



"""simple docstring"""
import argparse
import json
import subprocess
def 				a__   (   a   :				Optional[Any]       ,     a   :				Optional[int]		):

  """simple docstring"""







  _snake_case     :						str						=   []

  _snake_case     :						Optional[Any]						=   (
      f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
      " https://api.github.com/repos/huggingface/transformers/actions/runners"
  )
  _snake_case     :						Dict						=   subprocess.run(a       ,     shell=a       ,     stdout=subprocess.PIPE		)
  _snake_case     :						Tuple						=   output.stdout.decode("utf-8"		)
  _snake_case     :						List[str]						=   json.loads(a		)

  _snake_case     :						Any						=   status["runners"]
  for runner in runners:
    if runner["name"] in target_runners:
      if runner["status"] == "offline":
        offline_runners.append(a		)

    # save the result so we can report them on Slack
  with open("offline_runners.txt"       ,     "w"		) as fp:
    fp.write(json.dumps(a		)		)

  if len(a		) > 0:
    _snake_case     :						Any						=   "\n".join([x["name"] for x in offline_runners]		)
    raise ValueError(f'The following runners are offline:\n{failed}'		)


if __name__ == "__main__":
    def 				a__   (   a   :				Optional[int]		):

      """simple docstring"""







      return values.split(","		)

    _a      :			Optional[int]					=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--target_runners""",
        default=None,
        type=list_str,
        required=True,
        help="""Comma-separated list of runners to check status.""",
    )

    parser.add_argument(
        """--token""", default=None, type=str, required=True, help="""A token that has actions:read permission."""
    )
    _a      :			List[str]					=							parser.parse_args()



    get_runner_status(args.target_runners, args.token)

