
'''simple docstring'''
import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
A__   :		Optional[int] 		=logging.getLogger(__name__)







def        A_     (						__SCREAMING_SNAKE_CASE						:  str )				->       Dict:





  """simple docstring"""

  __A		:     List[Any]												=		git.Repo(search_parent_directories=__SCREAMING_SNAKE_CASE )
  __A		:     int												=		{
      """repo_id""": str(__SCREAMING_SNAKE_CASE ),
      """repo_sha""": str(repo.head.object.hexsha ),
      """repo_branch""": str(repo.active_branch ),
  }

  with open(os.path.join(__SCREAMING_SNAKE_CASE			,     """git_log.json""" )			,     """w""" ) as f:
    json.dump(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     indent=4 )







def        A_     (						__SCREAMING_SNAKE_CASE						:  Tuple )				->       List[str]:





  """simple docstring"""

  if params.n_gpu <= 0:
    __A		:     List[Any]												=		0
    __A		:     List[str]												=		-1
    __A		:     int												=		True
    __A		:     List[Any]												=		False
    return

  assert torch.cuda.is_available()

  logger.info("""Initializing GPUs""" )
  if params.n_gpu > 1:
    assert params.local_rank != -1

    __A		:     List[Any]												=		int(os.environ["""WORLD_SIZE"""] )
    __A		:     Tuple												=		int(os.environ["""N_GPU_NODE"""] )
    __A		:     Any												=		int(os.environ["""RANK"""] )

    # number of nodes / node ID
    __A		:     Optional[Any]												=		params.world_size // params.n_gpu_per_node
    __A		:     Dict												=		params.global_rank // params.n_gpu_per_node
    __A		:     Optional[Any]												=		True

    assert params.n_nodes == int(os.environ["""N_NODES"""] )
    assert params.node_id == int(os.environ["""NODE_RANK"""] )

  # local job (single GPU)
  else:
    assert params.local_rank == -1

    __A		:     Dict												=		1
    __A		:     List[Any]												=		0
    __A		:     List[Any]												=		0
    __A		:     Any												=		0
    __A		:     Union[str, Any]												=		1
    __A		:     List[str]												=		1
    __A		:     Tuple												=		False

  # sanity checks
  assert params.n_nodes >= 1
  assert 0 <= params.node_id < params.n_nodes
  assert 0 <= params.local_rank <= params.global_rank < params.world_size
  assert params.world_size == params.n_nodes * params.n_gpu_per_node

  # define whether this is the master process / if we are in multi-node distributed mode
  __A		:     Any												=		params.node_id == 0 and params.local_rank == 0
  __A		:     Dict												=		params.n_nodes > 1

  # summary
  __A		:     Dict												=		F"--- Global rank: {params.global_rank} - "
  logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes )
  logger.info(PREFIX + """Node ID        : %i""" % params.node_id )
  logger.info(PREFIX + """Local rank     : %i""" % params.local_rank )
  logger.info(PREFIX + """World size     : %i""" % params.world_size )
  logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node )
  logger.info(PREFIX + """Master         : %s""" % str(params.is_master ) )
  logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node ) )
  logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu ) )
  logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname() )

  # set GPU device
  torch.cuda.set_device(params.local_rank )

  # initialize multi-GPU
  if params.multi_gpu:
    logger.info("""Initializing PyTorch distributed""" )
    torch.distributed.init_process_group(
        init_method="""env://"""			,     backend="""nccl"""			,     )







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[int] )				->       Dict:





  """simple docstring"""

  np.random.seed(args.seed )
  torch.manual_seed(args.seed )
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed )





'''simple docstring'''
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Tuple:





  """simple docstring"""

  __A		:     Dict												=		{}

  __A		:     Optional[Any]												=		job["""started_at"""]
  __A		:     Tuple												=		job["""completed_at"""]

  __A		:     Optional[Any]												=		date_parser.parse(__SCREAMING_SNAKE_CASE )
  __A		:     List[Any]												=		date_parser.parse(__SCREAMING_SNAKE_CASE )

  __A		:     Union[str, Any]												=		round((end_datetime - start_datetime).total_seconds() / 60.0 )

  __A		:     List[Any]												=		start
  __A		:     Tuple												=		end
  __A		:     int												=		duration_in_min

  return job_info







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  int=None )				->       Dict:





  """simple docstring"""

  __A		:     List[Any]												=		None
  if token is not None:
    __A		:     Dict												=		{"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

  __A		:     Tuple												=		F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
  __A		:     List[str]												=		requests.get(__SCREAMING_SNAKE_CASE			,     headers=__SCREAMING_SNAKE_CASE ).json()
  __A		:     List[str]												=		{}

  try:
    job_time.update({job["""name"""]: extract_time_from_single_job(__SCREAMING_SNAKE_CASE ) for job in result["""jobs"""]} )
    __A		:     Dict												=		math.ceil((result["""total_count"""] - 100) / 100 )

    for i in range(__SCREAMING_SNAKE_CASE ):
      __A		:     int												=		requests.get(url + F"&page={i + 2}"			,     headers=__SCREAMING_SNAKE_CASE ).json()
      job_time.update({job["""name"""]: extract_time_from_single_job(__SCREAMING_SNAKE_CASE ) for job in result["""jobs"""]} )

    return job_time
  except Exception:
    print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}" )

  return {}


if __name__ == "__main__":
     A__   :		Any 		=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
     A__   :		Any 		=parser.parse_args()

     A__   :		Any 		=get_job_time(args.workflow_run_id)
     A__   :		Dict 		=dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

     for k, v in job_time.items():
          print(F"{k}: {v['duration']}")



