import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests





def      lowerCAmelCase_       ( __lowerCAmelCase  )->     str:






      '''simple docstring'''





      UpperCAmelCase			:       Union[str, Any]			     ={}

      UpperCAmelCase			:       Union[str, Any]			     =job['''started_at''']
      UpperCAmelCase			:       Union[str, Any]			     =job['''completed_at''']

      UpperCAmelCase			:       List[str]			     =date_parser.parse(A__  )
      UpperCAmelCase			:       Optional[Any]			     =date_parser.parse(A__  )

      UpperCAmelCase			:       Optional[Any]			     =round((end_datetime - start_datetime).total_seconds() / 60.0  )

      UpperCAmelCase			:       Optional[int]			     =start
      UpperCAmelCase			:       Optional[Any]			     =end
      UpperCAmelCase			:       Optional[int]			     =duration_in_min

      return job_info





def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase=None  )->     Tuple:






      '''simple docstring'''





      UpperCAmelCase			:       str			     =None
      if token is not None:
            UpperCAmelCase			:       List[Any]			     ={'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'''Bearer {token}'''}

      UpperCAmelCase			:       Tuple			     =f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
      UpperCAmelCase			:       List[str]			     =requests.get(A__						,    headers=A__  ).json()
      UpperCAmelCase			:       Optional[int]			     ={}

      try:
            job_time.update({job['''name''']: extract_time_from_single_job(A__  ) for job in result['''jobs''']}  )
            UpperCAmelCase			:       List[str]			     =math.ceil((result['''total_count'''] - 1_00) / 1_00  )

            for i in range(A__  ):
                  UpperCAmelCase			:       int			     =requests.get(url + f'''&page={i + 2}'''						,    headers=A__  ).json()
                  job_time.update({job['''name''']: extract_time_from_single_job(A__  ) 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__":
     __snake_case						=			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
     __snake_case						=			parser.parse_args()

     __snake_case						=			get_job_time(args.workflow_run_id)
     __snake_case						=			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"]}')



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowercase_									=   'Create a default config file for Accelerate with only a few flags set.'
def      a      (			A__	:   Optional[Any]="no"      ,   A__	:   str = default_json_config_file      ,   A__	:   bool = False     )    ->      Optional[int]:





      """simple docstring"""



      _lowercase	       =Path(A__     )
      path.parent.mkdir(parents=A__      ,   exist_ok=A__     )
      if path.exists():
            print(
                F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''     )
            return False
      _lowercase	       =mixed_precision.lower()
      if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
            raise ValueError(
                F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''     )
      _lowercase	       ={
          'compute_environment': 'LOCAL_MACHINE',
          'mixed_precision': mixed_precision,
      }
      if torch.cuda.is_available():
            _lowercase	       =torch.cuda.device_count()
            _lowercase	       =num_gpus
            _lowercase	       =False
            if num_gpus > 1:
                  _lowercase	       ='MULTI_GPU'
            else:
                  _lowercase	       ='NO'
      elif is_xpu_available() and use_xpu:
            _lowercase	       =torch.xpu.device_count()
            _lowercase	       =num_xpus
            _lowercase	       =False
            if num_xpus > 1:
                  _lowercase	       ='MULTI_XPU'
            else:
                  _lowercase	       ='NO'
      elif is_npu_available():
            _lowercase	       =torch.npu.device_count()
            _lowercase	       =num_npus
            _lowercase	       =False
            if num_npus > 1:
                  _lowercase	       ='MULTI_NPU'
            else:
                  _lowercase	       ='NO'
      else:
            _lowercase	       =0
            _lowercase	       =True
            _lowercase	       =1
            _lowercase	       ='NO'
      _lowercase	       =ClusterConfig(**A__     )
      config.to_json_file(A__     )
      return path
def      a      (			A__	:   Dict      ,   A__	:   Optional[Any]     )    ->      List[Any]:





      """simple docstring"""



      _lowercase	       =parser.add_parser('default'      ,   parents=A__      ,   help=A__      ,   formatter_class=A__     )
      parser.add_argument(
          '--config_file'      ,   default=A__      ,   help=(
              'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
              'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
              'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
              'with \'huggingface\'.'
          )      ,   dest='save_location'      ,   )

      parser.add_argument(
          '--mixed_precision'      ,   choices=['no', 'fp16', 'bf16']      ,   type=A__      ,   help='Whether or not to use mixed precision training. '
          'Choose between FP16 and BF16 (bfloat16) training. '
          'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'      ,   default='no'      ,   )
      parser.set_defaults(func=A__     )
      return parser







def      a      (			A__	:   List[str]     )    ->      Any:





      """simple docstring"""



      _lowercase	       =write_basic_config(args.mixed_precision      ,   args.save_location     )
      if config_file:
            print(F'''accelerate configuration saved at {config_file}'''     )


