




'''simple docstring'''


import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad

class       A   (		unittest.TestCase   ):







							def        __lowerCAmelCase      (  self	:  Any )      ->							Tuple:


														"""simple docstring"""


														_a     					=    10







							def        __lowerCAmelCase      (  self	:  List[str] )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    [1, 2, 3, 4]
														_a     					=    [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
														self.assertEqual(truncate_or_pad(lowerCAmelCase_							,      self.block_size							,      0 )							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							List[str]:


														"""simple docstring"""


														_a     					=    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
														_a     					=    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
														self.assertEqual(truncate_or_pad(lowerCAmelCase_							,      self.block_size							,      0 )							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							str:


														"""simple docstring"""


														_a     					=    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
														_a     					=    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
														self.assertEqual(truncate_or_pad(lowerCAmelCase_							,      self.block_size							,      0 )							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  int )      ->							Optional[int]:


														"""simple docstring"""


														_a     					=    '''It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this.'''
														_a					,							_a     					=    process_story(lowerCAmelCase_ )
														self.assertEqual(lowerCAmelCase_							,      [] )







							def        __lowerCAmelCase      (  self	:  Any )      ->							int:


														"""simple docstring"""


														_a     					=    ''''''
														_a					,							_a     					=    process_story(lowerCAmelCase_ )
														self.assertEqual(lowerCAmelCase_							,      [] )
														self.assertEqual(lowerCAmelCase_							,      [] )







							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							List[str]:


														"""simple docstring"""


														_a     					=    (
														    '''It was the year of Our Lord one thousand seven hundred and '''
														    '''seventy-five\n\nSpiritual revelations were conceded to England '''
														    '''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
														)
														_a					,							_a     					=    process_story(lowerCAmelCase_ )

														_a     					=    [
														    '''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
														    '''Spiritual revelations were conceded to England at that favoured period, as at this.''',
														]
														self.assertEqual(lowerCAmelCase_							,      lowerCAmelCase_ )

														_a     					=    ['''It was the best of times.''']
														self.assertEqual(lowerCAmelCase_							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    torch.tensor([1, 2, 3, 4] )
														_a     					=    torch.tensor([1, 1, 1, 1] )
														np.testing.assert_array_equal(build_mask(lowerCAmelCase_							,      0 ).numpy()							,      expected.numpy() )







							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							int:


														"""simple docstring"""


														_a     					=    torch.tensor([1, 2, 3, 4, 23, 23, 23] )
														_a     					=    torch.tensor([1, 1, 1, 1, 0, 0, 0] )
														np.testing.assert_array_equal(build_mask(lowerCAmelCase_							,      23 ).numpy()							,      expected.numpy() )







							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							Any:


														"""simple docstring"""


														_a     					=    torch.tensor([8, 2, 3, 4, 1, 1, 1] )
														_a     					=    torch.tensor([1, 1, 1, 1, 0, 0, 0] )
														np.testing.assert_array_equal(build_mask(lowerCAmelCase_							,      1 ).numpy()							,      expected.numpy() )







							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							List[str]:


														"""simple docstring"""


														_a     					=    1_01
														_a     					=    torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_01, 5, 6], [1, 1_01, 3, 4, 1_01, 6]] )
														_a     					=    torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )

														_a     					=    compute_token_type_ids(lowerCAmelCase_							,      lowerCAmelCase_ )
														np.testing.assert_array_equal(lowerCAmelCase_							,      lowerCAmelCase_ )







'''simple docstring'''


import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests
def 		snake_case_   (UpperCamelCase	:  Dict				):





							'''simple docstring'''


							_a     					=    {}

							_a     					=    job['''started_at''']
							_a     					=    job['''completed_at''']

							_a     					=    date_parser.parse(UpperCamelCase				)
							_a     					=    date_parser.parse(UpperCamelCase				)

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

							_a     					=    start
							_a     					=    end
							_a     					=    duration_in_min

							return job_info
def 		snake_case_   (UpperCamelCase	:  int			,						UpperCamelCase	:  int=None				):





							'''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(UpperCamelCase			,						headers=UpperCamelCase				).json()
							_a     					=    {}

							try:
														job_time.update({job['''name''']: extract_time_from_single_job(UpperCamelCase				) for job in result['''jobs''']}				)
														_a     					=    math.ceil((result['''total_count'''] - 100) / 100				)

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

				_snake_case				:				int       							=		get_job_time(args.workflow_run_id)
				_snake_case				:				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']}''')

