






'''simple docstring'''







from typing import Dict, Optional

import numpy as np

import datasets


A									="\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"

A									="\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n"

A									="\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"





def 		snake_case_							(_a			:							int					,     _a			:							Optional[int]					,     _a			:							Tuple					,     _a			:							Tuple					,     _a			:							List[str] = None					,     _a			:							Optional[int] = False					,     ):


    if label_map is not None:
        for old_id, new_id in label_map.items():
            UpperCAmelCase     =	new_id

        # turn into Numpy arrays
    UpperCAmelCase     =	np.array(__lowerCAmelCase  )
    UpperCAmelCase     =	np.array(__lowerCAmelCase  )

    if reduce_labels:
        UpperCAmelCase     =	2_5_5
        UpperCAmelCase     =	label - 1
        UpperCAmelCase     =	2_5_5

    UpperCAmelCase     =	label != ignore_index
    UpperCAmelCase     =	np.not_equal(__lowerCAmelCase					,     __lowerCAmelCase  )
    UpperCAmelCase     =	pred_label[mask]
    UpperCAmelCase     =	np.array(__lowerCAmelCase  )[mask]

    UpperCAmelCase     =	pred_label[pred_label == label]

    UpperCAmelCase     =	np.histogram(__lowerCAmelCase					,     bins=__lowerCAmelCase					,     range=(0, num_labels - 1)  )[0]
    UpperCAmelCase     =	np.histogram(__lowerCAmelCase					,     bins=__lowerCAmelCase					,     range=(0, num_labels - 1)  )[0]
    UpperCAmelCase     =	np.histogram(__lowerCAmelCase					,     bins=__lowerCAmelCase					,     range=(0, num_labels - 1)  )[0]

    UpperCAmelCase     =	area_pred_label + area_label - area_intersect

    return area_intersect, area_union, area_pred_label, area_label





def 		snake_case_							(_a			:							Dict					,     _a			:							Optional[int]					,     _a			:							Tuple					,     _a			:							int					,     _a			:							str = None					,     _a			:							Union[str, Any] = False					,     ):
    UpperCAmelCase     =	np.zeros((num_labels,)					,     dtype=np.floataa  )
    UpperCAmelCase     =	np.zeros((num_labels,)					,     dtype=np.floataa  )
    UpperCAmelCase     =	np.zeros((num_labels,)					,     dtype=np.floataa  )
    UpperCAmelCase     =	np.zeros((num_labels,)					,     dtype=np.floataa  )
    for result, gt_seg_map in zip(__lowerCAmelCase					,     __lowerCAmelCase  ):
        UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	intersect_and_union(
            __lowerCAmelCase					,     __lowerCAmelCase					,     __lowerCAmelCase					,     __lowerCAmelCase					,     __lowerCAmelCase					,     __lowerCAmelCase  )
        total_area_intersect += area_intersect
        total_area_union += area_union
        total_area_pred_label += area_pred_label
        total_area_label += area_label
    return total_area_intersect, total_area_union, total_area_pred_label, total_area_label





def 		snake_case_							(_a			:							int					,     _a			:							List[str]					,     _a			:							str					,     _a			:							Tuple					,     _a			:							int = None					,     _a			:							Tuple = None					,     _a			:							Optional[Any] = False					,     ):
    UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	total_intersect_and_union(
        __lowerCAmelCase					,     __lowerCAmelCase					,     __lowerCAmelCase					,     __lowerCAmelCase					,     __lowerCAmelCase					,     __lowerCAmelCase  )

    # compute metrics
    UpperCAmelCase     =	{}

    UpperCAmelCase     =	total_area_intersect.sum() / total_area_label.sum()
    UpperCAmelCase     =	total_area_intersect / total_area_union
    UpperCAmelCase     =	total_area_intersect / total_area_label

    UpperCAmelCase     =	np.nanmean(__lowerCAmelCase  )
    UpperCAmelCase     =	np.nanmean(__lowerCAmelCase  )
    UpperCAmelCase     =	all_acc
    UpperCAmelCase     =	iou
    UpperCAmelCase     =	acc

    if nan_to_num is not None:
        UpperCAmelCase     =	{metric: np.nan_to_num(__lowerCAmelCase					,     nan=__lowerCAmelCase  ) for metric, metric_value in metrics.items()}

    return metrics







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,							_KWARGS_DESCRIPTION				)
class     _a						(			datasets.Metric				):
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           return datasets.MetricInfo(
               description=_DESCRIPTION     ,       citation=_CITATION     ,       inputs_description=_KWARGS_DESCRIPTION     ,       features=datasets.Features(
                   # 1st Seq - height dim, 2nd - width dim
                   {
                       '''predictions''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''       )       )       ),
                       '''references''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''       )       )       ),
                   }       )     ,       reference_urls=[
                   '''https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'''
               ]     ,       )







       def 					A   (			self		:			List[str]     ,       lowercase		:			Optional[int]     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			int     ,       lowercase		:			bool     ,       lowercase		:			Optional[int] = None     ,       lowercase		:			Optional[Dict[int, int]] = None     ,       lowercase		:			bool = False     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	mean_iou(
               results=lowerCAmelCase_     ,       gt_seg_maps=lowerCAmelCase_     ,       num_labels=lowerCAmelCase_     ,       ignore_index=lowerCAmelCase_     ,       nan_to_num=lowerCAmelCase_     ,       label_map=lowerCAmelCase_     ,       reduce_labels=lowerCAmelCase_     ,       )
           return iou_result










'''simple docstring'''







import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests





def 		snake_case_							(_a			:							List[str]  ):
    UpperCAmelCase     =	{}

    UpperCAmelCase     =	job['''started_at''']
    UpperCAmelCase     =	job['''completed_at''']

    UpperCAmelCase     =	date_parser.parse(_a  )
    UpperCAmelCase     =	date_parser.parse(_a  )

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

    UpperCAmelCase     =	start
    UpperCAmelCase     =	end
    UpperCAmelCase     =	duration_in_min

    return job_info





def 		snake_case_							(_a			:							str					,     _a			:							List[str]=None  ):
    UpperCAmelCase     =	None
    if token is not None:
        UpperCAmelCase     =	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"Bearer {token}"}

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

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

        for i in range(_a  ):
            UpperCAmelCase     =	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"]}""")




