





"""simple docstring"""




import numpy

# List of input, output pairs
A   : Optional[int]			=				(
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
A   : int			=				(((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A   : List[Any]			=				[2, 4, 1, 5]
A   : Optional[int]			=				len(train_data)
A   : str			=				0.009







def        __lowerCamelCase (							__a       :List[Any]   ,					__a       :List[str]="train"   )		->    int:






		"""simple docstring"""

		return calculate_hypothesis_value(UpperCAmelCase_   ,					UpperCAmelCase_   ) - output(
		    UpperCAmelCase_   ,					UpperCAmelCase_   )







def        __lowerCamelCase (							__a       :Tuple   )		->    Dict:






		"""simple docstring"""

		A__  				=       0
		for i in range(len(UpperCAmelCase_   ) - 1   ):
				hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
		hyp_val += parameter_vector[0]
		return hyp_val







def        __lowerCamelCase (							__a       :str   ,					__a       :List[Any]   )		->    List[Any]:






		"""simple docstring"""

		if data_set == "train":
				return train_data[example_no][1]
		elif data_set == "test":
				return test_data[example_no][1]
		return None







def        __lowerCamelCase (							__a       :Optional[Any]   ,					__a       :Optional[int]   )		->    Any:






		"""simple docstring"""

		if data_set == "train":
				return _hypothesis_value(train_data[example_no][0]   )
		elif data_set == "test":
				return _hypothesis_value(test_data[example_no][0]   )
		return None







def        __lowerCamelCase (							__a       :Union[str, Any]   ,					__a       :Any=m   )		->    Union[str, Any]:






		"""simple docstring"""

		A__  				=       0
		for i in range(UpperCAmelCase_   ):
				if index == -1:
						summation_value += _error(UpperCAmelCase_   )
				else:
						summation_value += _error(UpperCAmelCase_   ) * train_data[i][0][index]
		return summation_value







def        __lowerCamelCase (							__a       :int   )		->    str:






		"""simple docstring"""

		A__  				=       summation_of_cost_derivative(UpperCAmelCase_   ,					UpperCAmelCase_   ) / m
		return cost_derivative_value







def        __lowerCamelCase (							)		->    List[str]:






		"""simple docstring"""

		global parameter_vector
		# Tune these values to set a tolerance value for predicted output
		A__  				=       0.000002
		A__  				=       0
		A__  				=       0
		while True:
				j += 1
				A__  				=       [0, 0, 0, 0]
				for i in range(0   ,					len(UpperCAmelCase_   )   ):
						A__  				=       get_cost_derivative(i - 1   )
						A__  				=       (
						    parameter_vector[i] - LEARNING_RATE * cost_derivative
						)
				if numpy.allclose(
				    UpperCAmelCase_   ,					UpperCAmelCase_   ,					atol=UpperCAmelCase_   ,					rtol=UpperCAmelCase_   ,					):
						break
				A__  				=       temp_parameter_vector
		print(("""Number of iterations:""", j)   )







def        __lowerCamelCase (							)		->    int:






		"""simple docstring"""

		for i in range(len(UpperCAmelCase_   )   ):
				print(("""Actual output value:""", output(UpperCAmelCase_   ,					"""test"""   ))   )
				print(("""Hypothesis output:""", calculate_hypothesis_value(UpperCAmelCase_   ,					"""test"""   ))   )


if __name__ == "__main__":
					run_gradient_descent()
					print('''\nTesting gradient descent for a linear hypothesis function.\n''')
					test_gradient_descent()










import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests







def        __lowerCamelCase (							__a       :str   )		->    Optional[int]:






		"""simple docstring"""

		A__  				=       {}

		A__  				=       job["""started_at"""]
		A__  				=       job["""completed_at"""]

		A__  				=       date_parser.parse(__a   )
		A__  				=       date_parser.parse(__a   )

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

		A__  				=       start
		A__  				=       end
		A__  				=       duration_in_min

		return job_info







def        __lowerCamelCase (							__a       :Optional[Any]   ,					__a       :List[str]=None   )		->    List[Any]:






		"""simple docstring"""

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

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

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

				for i in range(__a   ):
						A__  				=       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__":
					A   : Optional[Any]			=				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   : List[Any]			=				get_job_time(args.workflow_run_id)
					A   : int			=				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"]}''')



