




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case				:				List[Any]       							=		{
    'configuration_longformer': [
        'LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LongformerConfig',
        'LongformerOnnxConfig',
    ],
    'tokenization_longformer': ['LongformerTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				int       							=		['LongformerTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				Optional[int]       							=		[
				    'LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'LongformerForMaskedLM',
				    'LongformerForMultipleChoice',
				    'LongformerForQuestionAnswering',
				    'LongformerForSequenceClassification',
				    'LongformerForTokenClassification',
				    'LongformerModel',
				    'LongformerPreTrainedModel',
				    'LongformerSelfAttention',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				str       							=		[
				    'TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFLongformerForMaskedLM',
				    'TFLongformerForMultipleChoice',
				    'TFLongformerForQuestionAnswering',
				    'TFLongformerForSequenceClassification',
				    'TFLongformerForTokenClassification',
				    'TFLongformerModel',
				    'TFLongformerPreTrainedModel',
				    'TFLongformerSelfAttention',
				]


if TYPE_CHECKING:
				from .configuration_longformer import (
				    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    LongformerConfig,
				    LongformerOnnxConfig,
				)
				from .tokenization_longformer import LongformerTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_longformer_fast import LongformerTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_longformer import (
								    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LongformerForMaskedLM,
								    LongformerForMultipleChoice,
								    LongformerForQuestionAnswering,
								    LongformerForSequenceClassification,
								    LongformerForTokenClassification,
								    LongformerModel,
								    LongformerPreTrainedModel,
								    LongformerSelfAttention,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_longformer import (
								    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFLongformerForMaskedLM,
								    TFLongformerForMultipleChoice,
								    TFLongformerForQuestionAnswering,
								    TFLongformerForSequenceClassification,
								    TFLongformerForTokenClassification,
								    TFLongformerModel,
								    TFLongformerPreTrainedModel,
								    TFLongformerSelfAttention,
								)

else:
				import sys

				_snake_case				:				Tuple       							=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''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']}''')

