
"""simple docstring"""




import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests



def 							a__ (				lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						{}

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

     UpperCAmelCase__							: str 							=						date_parser.parse(lowerCAmelCase		)
     UpperCAmelCase__							: List[Any] 							=						date_parser.parse(lowerCAmelCase		)

     UpperCAmelCase__							: Optional[int] 							=						round((end_datetime - start_datetime).total_seconds() / 60.0		)

     UpperCAmelCase__							: Union[str, Any] 							=						start
     UpperCAmelCase__							: Tuple 							=						end
     UpperCAmelCase__							: Optional[int] 							=						duration_in_min

     return job_info



def 							a__ (				lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       Any=None		):






     '''simple docstring'''
     UpperCAmelCase__							: int 							=						None
     if token is not None:
          UpperCAmelCase__							: Union[str, Any] 							=						{"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

     UpperCAmelCase__							: Dict 							=						F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
     UpperCAmelCase__							: Optional[Any] 							=						requests.get(lowerCAmelCase       ,    headers=lowerCAmelCase		).json()
     UpperCAmelCase__							: Optional[Any] 							=						{}

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

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

    A__      :	Optional[Any]   		=    get_job_time(args.workflow_run_id)
    A__      :	str   		=    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"]}""")





"""simple docstring"""




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD



def 							a__ (				lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     def wrapper(*lowerCAmelCase	:       Any       ,    **lowerCAmelCase	:       Tuple		):
          UpperCAmelCase__							: Optional[int] 							=						timeit.default_timer()
          UpperCAmelCase__							: int 							=						func(*lowerCAmelCase       ,    **lowerCAmelCase		)
          UpperCAmelCase__							: List[Any] 							=						timeit.default_timer() - starttime
          return delta

     UpperCAmelCase__							: int 							=						func.__name__

     return wrapper



def 							a__ (				lowerCAmelCase	:       dict       ,    lowerCAmelCase	:       Optional[int]=100       ,    lowerCAmelCase	:       List[str]=None		):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						[]
     UpperCAmelCase__							: Optional[Any] 							=						seq_shapes or {}
     for i in range(lowerCAmelCase		):
          UpperCAmelCase__							: int 							=						{}
          for col_id, (k, v) in enumerate(features.items()		):
               if isinstance(lowerCAmelCase       ,    _ArrayXD		):
                    UpperCAmelCase__							: List[str] 							=						np.random.rand(*v.shape		).astype(v.dtype		)
               elif isinstance(lowerCAmelCase       ,    datasets.Value		):
                    if v.dtype == "string":
                         UpperCAmelCase__							: Dict 							=						"The small grey turtle was surprisingly fast when challenged."
                    else:
                         UpperCAmelCase__							: str 							=						np.random.randint(10       ,    size=1		).astype(v.dtype		).item()
               elif isinstance(lowerCAmelCase       ,    datasets.Sequence		):
                    while isinstance(lowerCAmelCase       ,    datasets.Sequence		):
                         UpperCAmelCase__							: List[str] 							=						v.feature
                    UpperCAmelCase__							: Optional[int] 							=						seq_shapes[k]
                    UpperCAmelCase__							: Optional[int] 							=						np.random.rand(*lowerCAmelCase		).astype(v.dtype		)
               UpperCAmelCase__							: Union[str, Any] 							=						data

          dummy_data.append((i, example)		)

     return dummy_data



def 							a__ (				lowerCAmelCase	:       List[str]       ,    lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       List[str]=100       ,    lowerCAmelCase	:       Optional[int]=None		):






     '''simple docstring'''
     UpperCAmelCase__							: int 							=						generate_examples(lowerCAmelCase       ,    num_examples=lowerCAmelCase       ,    seq_shapes=lowerCAmelCase		)

     with ArrowWriter(features=lowerCAmelCase       ,    path=lowerCAmelCase		) as writer:
          for key, record in dummy_data:
               UpperCAmelCase__							: List[Any] 							=						features.encode_example(lowerCAmelCase		)
               writer.write(lowerCAmelCase		)

          UpperCAmelCase__							,	UpperCAmelCase__							: Optional[Any] 							=						writer.finalize()

     if not num_final_examples == num_examples:
          raise ValueError(
              F"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."		)

     UpperCAmelCase__							: Optional[int] 							=						datasets.Dataset.from_file(filename=lowerCAmelCase       ,    info=datasets.DatasetInfo(features=lowerCAmelCase		)		)

     return dataset



