"""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


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)


if is_vision_available():
					import PIL



class 		lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Tuple     					=  ["""pixel_values"""]

				def __init__(			self						,      lowerCAmelCase = True						,      lowerCAmelCase = None						,      lowerCAmelCase = PILImageResampling.BICUBIC						,      lowerCAmelCase = True						,      lowerCAmelCase = None						,      lowerCAmelCase = True						,      lowerCAmelCase = 1 / 2_55						,      lowerCAmelCase = True						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = True						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase      )
										snake_case        =     size if size is not None else {'shortest_edge': 2_24}
										snake_case        =     get_size_dict(lowerCAmelCase						,      default_to_square=lowerCAmelCase      )
										snake_case        =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
										snake_case        =     get_size_dict(lowerCAmelCase						,      default_to_square=lowerCAmelCase						,      param_name='crop_size'      )

										snake_case        =     do_resize
										snake_case        =     size
										snake_case        =     resample
										snake_case        =     do_center_crop
										snake_case        =     crop_size
										snake_case        =     do_rescale
										snake_case        =     rescale_factor
										snake_case        =     do_normalize
										snake_case        =     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										snake_case        =     image_std if image_std is not None else OPENAI_CLIP_STD
										snake_case        =     do_convert_rgb

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = PILImageResampling.BICUBIC						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     get_size_dict(lowerCAmelCase						,      default_to_square=lowerCAmelCase      )
										if "shortest_edge" not in size:
																raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""      )
										snake_case        =     get_resize_output_image_size(lowerCAmelCase						,      size=size['shortest_edge']						,      default_to_square=lowerCAmelCase      )
										return resize(lowerCAmelCase						,      size=lowerCAmelCase						,      resample=lowerCAmelCase						,      data_format=lowerCAmelCase						,      **lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     get_size_dict(lowerCAmelCase      )
										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(lowerCAmelCase						,      size=(size['height'], size['width'])						,      data_format=lowerCAmelCase						,      **lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										return rescale(lowerCAmelCase						,      scale=lowerCAmelCase						,      data_format=lowerCAmelCase						,      **lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										return normalize(lowerCAmelCase						,      mean=lowerCAmelCase						,      std=lowerCAmelCase						,      data_format=lowerCAmelCase						,      **lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = ChannelDimension.FIRST						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     do_resize if do_resize is not None else self.do_resize
										snake_case        =     size if size is not None else self.size
										snake_case        =     get_size_dict(lowerCAmelCase						,      param_name='size'						,      default_to_square=lowerCAmelCase      )
										snake_case        =     resample if resample is not None else self.resample
										snake_case        =     do_center_crop if do_center_crop is not None else self.do_center_crop
										snake_case        =     crop_size if crop_size is not None else self.crop_size
										snake_case        =     get_size_dict(lowerCAmelCase						,      param_name='crop_size'						,      default_to_square=lowerCAmelCase      )
										snake_case        =     do_rescale if do_rescale is not None else self.do_rescale
										snake_case        =     rescale_factor if rescale_factor is not None else self.rescale_factor
										snake_case        =     do_normalize if do_normalize is not None else self.do_normalize
										snake_case        =     image_mean if image_mean is not None else self.image_mean
										snake_case        =     image_std if image_std is not None else self.image_std
										snake_case        =     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										snake_case        =     make_list_of_images(lowerCAmelCase      )

										if not valid_images(lowerCAmelCase      ):
																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:
																snake_case        =     [convert_to_rgb(lowerCAmelCase      ) for image in images]

										# All transformations expect numpy arrays.
										snake_case        =     [to_numpy_array(lowerCAmelCase      ) for image in images]

										if do_resize:
																snake_case        =     [self.resize(image=lowerCAmelCase						,      size=lowerCAmelCase						,      resample=lowerCAmelCase      ) for image in images]

										if do_center_crop:
																snake_case        =     [self.center_crop(image=lowerCAmelCase						,      size=lowerCAmelCase      ) for image in images]

										if do_rescale:
																snake_case        =     [self.rescale(image=lowerCAmelCase						,      scale=lowerCAmelCase      ) for image in images]

										if do_normalize:
																snake_case        =     [self.normalize(image=lowerCAmelCase						,      mean=lowerCAmelCase						,      std=lowerCAmelCase      ) for image in images]

										snake_case        =     [to_channel_dimension_format(lowerCAmelCase						,      lowerCAmelCase      ) for image in images]

										snake_case        =     {'pixel_values': images}
										return BatchFeature(data=lowerCAmelCase						,      tensor_type=lowerCAmelCase      )



"""simple docstring"""
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Any   )   ->			int:





						"""simple docstring"""






						snake_case        =     {}

						snake_case        =     job['started_at']
						snake_case        =     job['completed_at']

						snake_case        =     date_parser.parse(_UpperCamelCase   )
						snake_case        =     date_parser.parse(_UpperCamelCase   )

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

						snake_case        =     start
						snake_case        =     end
						snake_case        =     duration_in_min

						return job_info





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			Any=None   )   ->			Union[str, Any]:





						"""simple docstring"""






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

						snake_case        =     f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
						snake_case        =     requests.get(_UpperCamelCase	,     headers=_UpperCamelCase   ).json()
						snake_case        =     {}

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

												for i in range(_UpperCamelCase   ):
																		snake_case        =     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__":
					SCREAMING_SNAKE_CASE__   =					argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
					SCREAMING_SNAKE_CASE__   =					parser.parse_args()

					SCREAMING_SNAKE_CASE__   =					get_job_time(args.workflow_run_id)
					SCREAMING_SNAKE_CASE__   =					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']}""")


