





import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests




def      a__			(       lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           ={}

   UpperCAmelCase_           =job["started_at"]
   UpperCAmelCase_           =job["completed_at"]

   UpperCAmelCase_           =date_parser.parse(lowercase__	)
   UpperCAmelCase_           =date_parser.parse(lowercase__	)

   UpperCAmelCase_           =round((end_datetime - start_datetime).total_seconds() / 60.0	)

   UpperCAmelCase_           =start
   UpperCAmelCase_           =end
   UpperCAmelCase_           =duration_in_min

   return job_info




def      a__			(       lowercase__	,     lowercase__=None	):






   '''simple docstring'''






   UpperCAmelCase_           =None
   if token is not None:
      UpperCAmelCase_           ={"Accept": "application/vnd.github+json", "Authorization": F'Bearer {token}'}

   UpperCAmelCase_           =F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
   UpperCAmelCase_           =requests.get(lowercase__	,     headers=lowercase__	).json()
   UpperCAmelCase_           ={}

   try:
      job_time.update({job["name"]: extract_time_from_single_job(lowercase__	) for job in result["jobs"]}	)
      UpperCAmelCase_           =math.ceil((result["total_count"] - 1_0_0) / 1_0_0	)

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

     __lowercase	: Optional[int]       =get_job_time(args.workflow_run_id)
     __lowercase	: Optional[Any]       =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 numpy as np






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ = 1E-12 , snake_case__ = 100 , ):
	assert np.shape(snake_case__       )[0] == np.shape(snake_case__       )[1]
	# Ensure proper dimensionality.
	assert np.shape(snake_case__       )[0] == np.shape(snake_case__       )[0]
	# Ensure inputs are either both complex or both real
	assert np.iscomplexobj(snake_case__       ) == np.iscomplexobj(snake_case__       )
	A_		:    Union[str, Any]						     = np.iscomplexobj(snake_case__       )
	if is_complex:
		# Ensure complex input_matrix is Hermitian
		assert np.array_equal(snake_case__ , input_matrix.conj().T       )

	# Set convergence to False. Will define convergence when we exceed max_iterations
	# or when we have small changes from one iteration to next.

	A_		:    List[str]						     = False
	A_		:    int						     = 0
	A_		:    List[Any]						     = 0
	A_		:    Tuple						     = 1E12

	while not convergence:
		# Multiple matrix by the vector.
		A_		:    Dict						     = np.dot(snake_case__ , snake_case__       )
		# Normalize the resulting output vector.
		A_		:    str						     = w / np.linalg.norm(snake_case__       )
		# Find rayleigh quotient
		# (faster than usual b/c we know vector is normalized already)
		A_		:    str						     = vector.conj().T if is_complex else vector.T
		A_		:    Optional[Any]						     = np.dot(snake_case__ , np.dot(snake_case__ , snake_case__       )       )

		# Check convergence.
		A_		:    Any						     = np.abs(lambda_ - lambda_previous       ) / lambda_
		iterations += 1

		if error <= error_tol or iterations >= max_iterations:
			A_		:    Optional[Any]						     = True

		A_		:    Union[str, Any]						     = lambda_

	if is_complex:
		A_		:    str						     = np.real(lambda_       )

	return lambda_, vector






def       __UpperCamelCase						(		):
	A_		:    Optional[int]						     = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]       )
	A_		:    Tuple						     = np.array([41, 4, 20]       )
	A_		:    Tuple						     = real_input_matrix.astype(np.complexaaa       )
	A_		:    List[Any]						     = np.triu(1J * complex_input_matrix , 1       )
	complex_input_matrix += imag_matrix
	complex_input_matrix += -1 * imag_matrix.T
	A_		:    Any						     = np.array([41, 4, 20]       ).astype(np.complexaaa       )

	for problem_type in ["real", "complex"]:
		if problem_type == "real":
			A_		:    Optional[int]						     = real_input_matrix
			A_		:    List[str]						     = real_vector
		elif problem_type == "complex":
			A_		:    List[Any]						     = complex_input_matrix
			A_		:    str						     = complex_vector

		# Our implementation.
		A_     ,  A_		:    Dict						     = power_iteration(snake_case__ , snake_case__       )

		# Numpy implementation.

		# Get eigenvalues and eigenvectors using built-in numpy
		# eigh (eigh used for symmetric or hermetian matrices).
		A_     ,  A_		:    Optional[Any]						     = np.linalg.eigh(snake_case__       )
		# Last eigenvalue is the maximum one.
		A_		:    List[Any]						     = eigen_values[-1]
		# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
		A_		:    Optional[Any]						     = eigen_vectors[:, -1]

		# Check our implementation and numpy gives close answers.
		assert np.abs(eigen_value - eigen_value_max       ) <= 1E-6
		# Take absolute values element wise of each eigenvector.
		# as they are only unique to a minus sign.
		assert np.linalg.norm(np.abs(snake_case__       ) - np.abs(snake_case__       )       ) <= 1E-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()

