



'''simple docstring'''





import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests



def   UpperCAmelCase			(  a_						)					->    str:



   """simple docstring"""






   A_   :						int		= {}

   A_   :						int		= job['''started_at''']
   A_   :						Dict		= job['''completed_at''']

   A_   :						Optional[Any]		= date_parser.parse(A_						)
   A_   :						str		= date_parser.parse(A_						)

   A_   :						int		= round((end_datetime - start_datetime).total_seconds() / 60.0						)

   A_   :						Optional[Any]		= start
   A_   :						Dict		= end
   A_   :						Optional[int]		= duration_in_min

   return job_info



def   UpperCAmelCase			(  a_	,						a_=None						)					->    Tuple:



   """simple docstring"""






   A_   :						Optional[int]		= None
   if token is not None:
      A_   :						str		= {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"Bearer {token}"}

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

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

      for i in range(A_						):
         A_   :						List[str]		= 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__":
  UpperCamelCase__  :			Union[str, Any]          =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
  UpperCamelCase__  :			List[str]          =					parser.parse_args()

  UpperCamelCase__  :			Optional[int]          =					get_job_time(args.workflow_run_id)
  UpperCamelCase__  :			str          =					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"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__UpperCamelCase  :      Optional[Any]							      =		logging.get_logger(__name__)


if is_vision_available():
							import PIL







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       ["pixel_values"]







					def __init__(       self			:				str   ,lowercase_			:				bool = True   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				PILImageResampling = PILImageResampling.BICUBIC   ,lowercase_			:				bool = True   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				bool = True   ,lowercase_			:				Union[int, float] = 1 / 2_5_5   ,lowercase_			:				bool = True   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				bool = True   ,**lowercase_			:				Optional[Any]   ,):
						super().__init__(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	size if size is not None else {'''shortest_edge''': 2_2_4}
						lowerCAmelCase__	:						Tuple  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
						lowerCAmelCase__	:						Optional[Any]  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_   ,param_name='''crop_size'''		)

						lowerCAmelCase__	:						Dict  				=	do_resize
						lowerCAmelCase__	:						Optional[int]  				=	size
						lowerCAmelCase__	:						Dict  				=	resample
						lowerCAmelCase__	:						Optional[Any]  				=	do_center_crop
						lowerCAmelCase__	:						Dict  				=	crop_size
						lowerCAmelCase__	:						Tuple  				=	do_rescale
						lowerCAmelCase__	:						str  				=	rescale_factor
						lowerCAmelCase__	:						List[str]  				=	do_normalize
						lowerCAmelCase__	:						Tuple  				=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						lowerCAmelCase__	:						Optional[Any]  				=	image_std if image_std is not None else OPENAI_CLIP_STD
						lowerCAmelCase__	:						Optional[Any]  				=	do_convert_rgb







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				np.ndarray   ,lowercase_			:				Dict[str, int]   ,lowercase_			:				PILImageResampling = PILImageResampling.BICUBIC   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Optional[Any]   ,):
						lowerCAmelCase__	:						Dict  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_		)
						if "shortest_edge" not in size:
							raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'		)
						lowerCAmelCase__	:						Optional[Any]  				=	get_resize_output_image_size(lowercase_   ,size=size['''shortest_edge''']   ,default_to_square=lowercase_		)
						return resize(lowercase_   ,size=lowercase_   ,resample=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				np.ndarray   ,lowercase_			:				Dict[str, int]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				str   ,):
						lowerCAmelCase__	:						List[str]  				=	get_size_dict(lowercase_		)
						if "height" not in size or "width" not in size:
							raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'		)
						return center_crop(lowercase_   ,size=(size['''height'''], size['''width'''])   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				np.ndarray   ,lowercase_			:				Union[int, float]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Union[str, Any]   ,):
						return rescale(lowercase_   ,scale=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				np.ndarray   ,lowercase_			:				Union[float, List[float]]   ,lowercase_			:				Union[float, List[float]]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				int   ,):
						return normalize(lowercase_   ,mean=lowercase_   ,std=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				ImageInput   ,lowercase_			:				bool = None   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				PILImageResampling = None   ,lowercase_			:				bool = None   ,lowercase_			:				int = None   ,lowercase_			:				bool = None   ,lowercase_			:				float = None   ,lowercase_			:				bool = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				bool = None   ,lowercase_			:				Optional[Union[str, TensorType]] = None   ,lowercase_			:				Optional[ChannelDimension] = ChannelDimension.FIRST   ,**lowercase_			:				List[Any]   ,):
						lowerCAmelCase__	:						Optional[int]  				=	do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__	:						Optional[int]  				=	size if size is not None else self.size
						lowerCAmelCase__	:						Union[str, Any]  				=	get_size_dict(lowercase_   ,param_name='''size'''   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	resample if resample is not None else self.resample
						lowerCAmelCase__	:						Optional[Any]  				=	do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCAmelCase__	:						Optional[int]  				=	crop_size if crop_size is not None else self.crop_size
						lowerCAmelCase__	:						Dict  				=	get_size_dict(lowercase_   ,param_name='''crop_size'''   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						int  				=	do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase__	:						List[str]  				=	rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase__	:						Optional[int]  				=	do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__	:						str  				=	image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase__	:						str  				=	image_std if image_std is not None else self.image_std
						lowerCAmelCase__	:						int  				=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						lowerCAmelCase__	:						Any  				=	make_list_of_images(lowercase_		)

						if not valid_images(lowercase_		):
							raise ValueError(
							    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
							    '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

						if do_resize and size is None:
							raise ValueError('''Size must be specified if do_resize is True.'''		)

						if do_center_crop and crop_size is None:
							raise ValueError('''Crop size must be specified if do_center_crop is True.'''		)

						if do_rescale and rescale_factor is None:
							raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

						if do_normalize and (image_mean is None or image_std is None):
							raise ValueError('''Image mean and std must be specified if do_normalize is True.'''		)

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
							lowerCAmelCase__	:						Tuple  				=	[convert_to_rgb(lowercase_		) for image in images]

						# All transformations expect numpy arrays.
						lowerCAmelCase__	:						Optional[int]  				=	[to_numpy_array(lowercase_		) for image in images]

						if do_resize:
							lowerCAmelCase__	:						Optional[int]  				=	[self.resize(image=lowercase_   ,size=lowercase_   ,resample=lowercase_		) for image in images]

						if do_center_crop:
							lowerCAmelCase__	:						Tuple  				=	[self.center_crop(image=lowercase_   ,size=lowercase_		) for image in images]

						if do_rescale:
							lowerCAmelCase__	:						Tuple  				=	[self.rescale(image=lowercase_   ,scale=lowercase_		) for image in images]

						if do_normalize:
							lowerCAmelCase__	:						Union[str, Any]  				=	[self.normalize(image=lowercase_   ,mean=lowercase_   ,std=lowercase_		) for image in images]

						lowerCAmelCase__	:						Optional[Any]  				=	[to_channel_dimension_format(lowercase_   ,lowercase_		) for image in images]

						lowerCAmelCase__	:						List[Any]  				=	{'''pixel_values''': images}
						return BatchFeature(data=lowercase_   ,tensor_type=lowercase_		)




