
"""simple docstring"""

import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   {}

		lowerCamelCase					:      Union[str, Any]          =   job['started_at']
		lowerCamelCase					:      Any          =   job['completed_at']

		lowerCamelCase					:      Union[str, Any]          =   date_parser.parse(a_      )
		lowerCamelCase					:      Tuple          =   date_parser.parse(a_      )

		lowerCamelCase					:      Optional[int]          =   round((end_datetime - start_datetime).total_seconds() / 6_0.0      )

		lowerCamelCase					:      List[Any]          =   start
		lowerCamelCase					:      List[str]          =   end
		lowerCamelCase					:      Union[str, Any]          =   duration_in_min

		return job_info
def 		UpperCAmelCase (		a_,						a_=None      ):





		'''simple docstring'''


		lowerCamelCase					:      Union[str, Any]          =   None
		if token is not None:
				lowerCamelCase					:      Tuple          =   {'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

		lowerCamelCase					:      int          =   F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
		lowerCamelCase					:      Dict          =   requests.get(a_,						headers=a_      ).json()
		lowerCamelCase					:      List[Any]          =   {}

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

				for i in range(a_      ):
						lowerCamelCase					:      Dict          =   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						=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
					_A						=			parser.parse_args()

					_A						=			get_job_time(args.workflow_run_id)
					_A						=			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 warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     ['image_processor', 'tokenizer']
	lowercase_				=     'ChineseCLIPImageProcessor'
	lowercase_				=     ('BertTokenizer', 'BertTokenizerFast')




	def __init__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							List[str]:
			lowerCamelCase					:      Tuple          =   None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
					    ' instead.'		,			UpperCAmelCase_		,			)
					lowerCamelCase					:      int          =   kwargs.pop('feature_extractor' )

			lowerCamelCase					:      Any          =   image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
					raise ValueError('You need to specify an `image_processor`.' )
			if tokenizer is None:
					raise ValueError('You need to specify a `tokenizer`.' )

			super().__init__(UpperCAmelCase_		,			UpperCAmelCase_ )
			lowerCamelCase					:      Tuple          =   self.image_processor




	def __call__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							str:

			if text is None and images is None:
					raise ValueError('You have to specify either text or images. Both cannot be none.' )

			if text is not None:
					lowerCamelCase					:      Any          =   self.tokenizer(UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			**UpperCAmelCase_ )

			if images is not None:
					lowerCamelCase					:      Dict          =   self.image_processor(UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			**UpperCAmelCase_ )

			if text is not None and images is not None:
					lowerCamelCase					:      Tuple          =   image_features.pixel_values
					return encoding
			elif text is not None:
					return encoding
			else:
					return BatchEncoding(data=dict(**UpperCAmelCase_ )		,			tensor_type=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							List[str]:
			return self.tokenizer.batch_decode(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							Optional[int]:
			return self.tokenizer.decode(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      List[Any]          =   self.tokenizer.model_input_names
			lowerCamelCase					:      Union[str, Any]          =   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )




	@property
	def       _UpperCamelCase   (			self )		->							List[str]:
			warnings.warn(
			    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,			UpperCAmelCase_		,			)
			return self.image_processor_class






