





import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm
      import torch_xla.debug.metrics as met
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):


   def __init__(					self    :       Union[str, Any]    ,  *a    :       Optional[Any]    ,  a    :       int=None    ,  a    :       Union[str, Any]=None    ,  **a    :       List[str] )->     int:





     """simple docstring"""







     super().__init__(*UpperCamelCase__    ,  **UpperCamelCase__ )
     lowercase__			    =						eval_examples
     lowercase__			    =						post_process_function


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple    ,  a    :       Union[str, Any]=None    ,  a    :       Optional[Any]=None    ,  a    :       Any=None    ,  a    :       Dict = "eval" )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						self.eval_dataset if eval_dataset is None else eval_dataset
     lowercase__			    =						self.get_eval_dataloader(UpperCamelCase__ )
     lowercase__			    =						self.eval_examples if eval_examples is None else eval_examples

     # Temporarily disable metric computation, we will do it in the loop here.
     lowercase__			    =						self.compute_metrics
     lowercase__			    =						None
     lowercase__			    =						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     lowercase__			    =						time.time()
     try:
       lowercase__			    =						eval_loop(
           UpperCamelCase__    ,  description='Evaluation'    ,  prediction_loss_only=True if compute_metrics is None else None    ,  ignore_keys=UpperCamelCase__    ,  metric_key_prefix=UpperCamelCase__    ,  )
     finally:
       lowercase__			    =						compute_metrics
     lowercase__			    =						self.args.eval_batch_size * self.args.world_size
     if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
       start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
     output.metrics.update(
         speed_metrics(
             UpperCamelCase__    ,  UpperCamelCase__    ,  num_samples=output.num_samples    ,  num_steps=math.ceil(output.num_samples / total_batch_size )    ,  ) )
     if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
       # Only the main node write the results by default
       lowercase__			    =						self.post_process_function(UpperCamelCase__    ,  UpperCamelCase__    ,  output.predictions )
       lowercase__			    =						self.compute_metrics(UpperCamelCase__ )

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys() ):
         if not key.startswith(f"""{metric_key_prefix}_""" ):
           lowercase__			    =						metrics.pop(UpperCamelCase__ )
       metrics.update(output.metrics )
     else:
       lowercase__			    =						output.metrics

     if self.args.should_log:
       # Only the main node log the results by default
       self.log(UpperCamelCase__ )

     if self.args.tpu_metrics_debug or self.args.debug:
       # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
       xm.master_print(met.metrics_report() )

     lowercase__			    =						self.callback_handler.on_evaluate(self.args    ,  self.state    ,  self.control    ,  UpperCamelCase__ )
     return metrics


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  a    :       Union[str, Any]    ,  a    :       Any    ,  a    :       List[Any]=None    ,  a    :       Union[str, Any] = "test" )->     List[str]:





     """simple docstring"""







     lowercase__			    =						self.get_test_dataloader(UpperCamelCase__ )

     # Temporarily disable metric computation, we will do it in the loop here.
     lowercase__			    =						self.compute_metrics
     lowercase__			    =						None
     lowercase__			    =						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     lowercase__			    =						time.time()
     try:
       lowercase__			    =						eval_loop(
           UpperCamelCase__    ,  description='Prediction'    ,  prediction_loss_only=True if compute_metrics is None else None    ,  ignore_keys=UpperCamelCase__    ,  metric_key_prefix=UpperCamelCase__    ,  )
     finally:
       lowercase__			    =						compute_metrics
     lowercase__			    =						self.args.eval_batch_size * self.args.world_size
     if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
       start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
     output.metrics.update(
         speed_metrics(
             UpperCamelCase__    ,  UpperCamelCase__    ,  num_samples=output.num_samples    ,  num_steps=math.ceil(output.num_samples / total_batch_size )    ,  ) )

     if self.post_process_function is None or self.compute_metrics is None:
       return output

     lowercase__			    =						self.post_process_function(UpperCamelCase__    ,  UpperCamelCase__    ,  output.predictions    ,  'predict' )
     lowercase__			    =						self.compute_metrics(UpperCamelCase__ )

     # Prefix all keys with metric_key_prefix + '_'
     for key in list(metrics.keys() ):
       if not key.startswith(f"""{metric_key_prefix}_""" ):
         lowercase__			    =						metrics.pop(UpperCamelCase__ )
     metrics.update(output.metrics )
     return PredictionOutput(predictions=predictions.predictions    ,  label_ids=predictions.label_ids    ,  metrics=UpperCamelCase__ )











import argparse
import json
import subprocess




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Optional[int]:
  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(_SCREAMING_SNAKE_CASE	,		shell=_SCREAMING_SNAKE_CASE	,		stdout=subprocess.PIPE	)
  lowercase__			    =						output.stdout.decode('utf-8'	)
  lowercase__			    =						json.loads(_SCREAMING_SNAKE_CASE	)

  lowercase__			    =						status['runners']
  for runner in runners:
    if runner["name"] in target_runners:
      if runner["status"] == "offline":
        offline_runners.append(_SCREAMING_SNAKE_CASE	)

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

  if len(_SCREAMING_SNAKE_CASE	) > 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						(_SCREAMING_SNAKE_CASE	)       ->     str:
        return values.split(','	)

      lowercase_					     =	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."""
      )
      lowercase_					     =	parser.parse_args()


      get_runner_status(args.target_runners, args.token)


