





'''simple docstring'''
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


a__							:							int =logging.get_logger(__name__)

@dataclass
class      snake_case     :




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					str					=field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()       )}       )
 SCREAMING_SNAKE_CASE_	:					str					=field(
     metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}       )
 SCREAMING_SNAKE_CASE_	:					int					=field(
     default=128			,       metadata={
         "help": (
             "The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded."
         )
     }			,       )
 SCREAMING_SNAKE_CASE_	:					bool					=field(
     default=__lowerCamelCase			,       metadata={"help": "Overwrite the cached training and evaluation sets"}       )






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  self.task_name.lower()






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[Any]					="train"
 SCREAMING_SNAKE_CASE_	:					Dict					="dev"
 SCREAMING_SNAKE_CASE_	:					Dict					="test"






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					GlueDataTrainingArguments
 SCREAMING_SNAKE_CASE_	:					str
 SCREAMING_SNAKE_CASE_	:					List[InputFeatures]






 def __init__(       self       :							Optional[Any]							,	__A       :							GlueDataTrainingArguments							,	__A       :							PreTrainedTokenizerBase							,	__A       :							Optional[int] = None							,	__A       :							Union[str, Split] = Split.train							,	__A       :							Optional[str] = None							,	):
        warnings.warn(
            'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
            'library. You can have a look at this example script for pointers: '
            'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'							,	__A							,	)
        __UpperCamelCase       =  args
        __UpperCamelCase       =  glue_processors[args.task_name]()
        __UpperCamelCase       =  glue_output_modes[args.task_name]
        if isinstance(__A							,	__A	):
               try:
                      __UpperCamelCase       =  Split[mode]
               except KeyError:
                      raise KeyError('mode is not a valid split name'	)
        # Load data features from cache or dataset file
        __UpperCamelCase       =  os.path.join(
            cache_dir if cache_dir is not None else args.data_dir							,	f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''							,	)
        __UpperCamelCase       =  self.processor.get_labels()
        if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
            "RobertaTokenizer",
            "RobertaTokenizerFast",
            "XLMRobertaTokenizer",
            "BartTokenizer",
            "BartTokenizerFast",
        ):
               # HACK(label indices are swapped in RoBERTa pretrained model)
               __UpperCamelCase       ,						__UpperCamelCase       =  label_list[2], label_list[1]
        __UpperCamelCase       =  label_list

        # Make sure only the first process in distributed training processes the dataset,
        # and the others will use the cache.
        __UpperCamelCase       =  cached_features_file + '.lock'
        with FileLock(__A	):
               if os.path.exists(__A	) and not args.overwrite_cache:
                      __UpperCamelCase       =  time.time()
                      __UpperCamelCase       =  torch.load(__A	)
                      logger.info(
                          f'''Loading features from cached file {cached_features_file} [took %.3f s]'''							,	time.time() - start	)
               else:
                      logger.info(f'''Creating features from dataset file at {args.data_dir}'''	)

                      if mode == Split.dev:
                             __UpperCamelCase       =  self.processor.get_dev_examples(args.data_dir	)
                      elif mode == Split.test:
                             __UpperCamelCase       =  self.processor.get_test_examples(args.data_dir	)
                      else:
                             __UpperCamelCase       =  self.processor.get_train_examples(args.data_dir	)
                      if limit_length is not None:
                             __UpperCamelCase       =  examples[:limit_length]
                      __UpperCamelCase       =  glue_convert_examples_to_features(
                          __A							,	__A							,	max_length=args.max_seq_length							,	label_list=__A							,	output_mode=self.output_mode							,	)
                      __UpperCamelCase       =  time.time()
                      torch.save(self.features							,	__A	)
                      # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                      logger.info(
                          f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''	)






 def __len__(       self       :							Any	):
        return len(self.features	)






 def __getitem__(       self       :							Dict							,	__A       :							Union[str, Any]	):
        return self.features[i]






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        return self.label_list

"""simple docstring"""






import argparse
import json
import subprocess


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Tuple					,		lowerCAmelCase__     :List[Any]		) ->    Optional[Any]:
      '''simple docstring'''


      lowercase														=	[]

      lowercase														=	(
          f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
          """ https://api.github.com/repos/huggingface/transformers/actions/runners"""
      )
      lowercase														=	subprocess.run(lowerCAmelCase__					,		shell=lowerCAmelCase__					,		stdout=subprocess.PIPE		)
      lowercase														=	output.stdout.decode("""utf-8"""		)
      lowercase														=	json.loads(lowerCAmelCase__		)

      lowercase														=	status["""runners"""]
      for runner in runners:
            if runner["name"] in target_runners:
                  if runner["status"] == "offline":
                        offline_runners.append(lowerCAmelCase__		)

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

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


if __name__ == "__main__":


       def 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[int]		) ->    Tuple:
             '''simple docstring'''


             return values.split(""","""		)

       __lowerCAmelCase    :       str					     =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."""
       )
       __lowerCAmelCase    :       str					     =parser.parse_args()






       get_runner_status(args.target_runners, args.token)



