"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import torch




class        __snake_case	(					TensorFormatter[Mapping, """torch.Tensor""", Mapping]    ):



      def __init__(						self   ,      lowercase=None   ,      **lowercase)     ->      Optional[int]:





        '''simple docstring'''





        super().__init__(features=lowercase)
        a__:					List[Any]      							=     torch_tensor_kwargs
        import torch  # noqa import torch at initialization



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      List[str]:





        '''simple docstring'''





        import torch

        if isinstance(lowercase   ,      lowercase) and column:
          if all(
              isinstance(lowercase   ,      torch.Tensor) and x.shape == column[0].shape and x.dtype == column[0].dtype
              for x in column):
            return torch.stack(lowercase)
        return column



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      int:





        '''simple docstring'''





        import torch

        if isinstance(lowercase   ,      (str, bytes, type(lowercase))):
          return value
        elif isinstance(lowercase   ,      (np.character, np.ndarray)) and np.issubdtype(value.dtype   ,      np.character):
          return value.tolist()

        a__:					List[Any]      							=     {}

        if isinstance(lowercase   ,      (np.number, np.ndarray)) and np.issubdtype(value.dtype   ,      np.integer):
          a__:					Any      							=     {'dtype': torch.intaa}
        elif isinstance(lowercase   ,      (np.number, np.ndarray)) and np.issubdtype(value.dtype   ,      np.floating):
          a__:					Optional[Any]      							=     {'dtype': torch.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
          import PIL.Image

          if isinstance(lowercase   ,      PIL.Image.Image):
            a__:					Any      							=     np.asarray(lowercase)
        return torch.tensor(lowercase   ,      **{**default_dtype, **self.torch_tensor_kwargs})



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Optional[Any]:





        '''simple docstring'''





        import torch

        # support for torch, tf, jax etc.
        if hasattr(lowercase   ,      '__array__') and not isinstance(lowercase   ,      torch.Tensor):
          a__:					Optional[Any]      							=     data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(lowercase   ,      np.ndarray):
          if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
            return self._consolidate([self.recursive_tensorize(lowercase) for substruct in data_struct])
        elif isinstance(lowercase   ,      (list, tuple)):
          return self._consolidate([self.recursive_tensorize(lowercase) for substruct in data_struct])
        return self._tensorize(lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Dict:





        '''simple docstring'''





        return map_nested(self._recursive_tensorize   ,      lowercase   ,      map_list=lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Mapping:





        '''simple docstring'''





        a__:					Dict      							=     self.numpy_arrow_extractor().extract_row(lowercase)
        a__:					Optional[Any]      							=     self.python_features_decoder.decode_row(lowercase)
        return self.recursive_tensorize(lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      "torch.Tensor":





        '''simple docstring'''





        a__:					Optional[Any]      							=     self.numpy_arrow_extractor().extract_column(lowercase)
        a__:					List[str]      							=     self.python_features_decoder.decode_column(lowercase   ,      pa_table.column_names[0])
        a__:					Optional[int]      							=     self.recursive_tensorize(lowercase)
        a__:					str      							=     self._consolidate(lowercase)
        return column



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Mapping:





        '''simple docstring'''





        a__:					Optional[Any]      							=     self.numpy_arrow_extractor().extract_batch(lowercase)
        a__:					Dict      							=     self.python_features_decoder.decode_batch(lowercase)
        a__:					Union[str, Any]      							=     self.recursive_tensorize(lowercase)
        for column_name in batch:
          a__:					Optional[int]      							=     self._consolidate(batch[column_name])
        return batch



"""simple docstring"""
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Tuple:
  a__:					Tuple      							=     {}

  a__:					Tuple      							=     job['started_at']
  a__:					int      							=     job['completed_at']

  a__:					Any      							=     date_parser.parse(_SCREAMING_SNAKE_CASE       )
  a__:					Tuple      							=     date_parser.parse(_SCREAMING_SNAKE_CASE       )

  a__:					str      							=     round((end_datetime - start_datetime).total_seconds() / 60.0       )

  a__:					Any      							=     start
  a__:					Dict      							=     end
  a__:					Optional[int]      							=     duration_in_min

  return job_info







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=None       )     ->Optional[int]:
  a__:					Tuple      							=     None
  if token is not None:
    a__:					List[str]      							=     {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

  a__:					int      							=     F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
  a__:					Union[str, Any]      							=     requests.get(_SCREAMING_SNAKE_CASE		,					headers=_SCREAMING_SNAKE_CASE       ).json()
  a__:					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__:					str      							=     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__":
 lowercase__ 	=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
 lowercase__ 	=     parser.parse_args()

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


