"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
 import PIL

if is_torch_available():
 import torch


lowercase__ 	=     logging.get_logger(__name__)




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		["""pixel_values"""]



      def __init__(						self   ,      lowercase = True   ,      lowercase = None   ,      lowercase = PILImageResampling.BILINEAR   ,      lowercase = True   ,      lowercase = 1 / 2_55   ,      lowercase = True   ,      lowercase = None   ,      lowercase = True   ,      **lowercase   ,      )     ->      None:





        '''simple docstring'''





        super().__init__(**lowercase)
        a__:					Dict      							=     size if size is not None else {'shortest_edge': 2_24}
        a__:					str      							=     get_size_dict(lowercase   ,      default_to_square=lowercase)
        a__:					str      							=     crop_size if crop_size is not None else {'height': 2_56, 'width': 2_56}
        a__:					List[Any]      							=     get_size_dict(lowercase   ,      param_name='crop_size')

        a__:					Optional[Any]      							=     do_resize
        a__:					Any      							=     size
        a__:					List[Any]      							=     resample
        a__:					Optional[int]      							=     do_rescale
        a__:					List[str]      							=     rescale_factor
        a__:					Any      							=     do_center_crop
        a__:					Tuple      							=     crop_size
        a__:					Tuple      							=     do_flip_channel_order



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = PIL.Image.BILINEAR   ,      lowercase = None   ,      **lowercase   ,      )     ->      np.ndarray:





        '''simple docstring'''





        a__:					Dict      							=     get_size_dict(lowercase   ,      default_to_square=lowercase)
        if "shortest_edge" not in size:
          raise ValueError(f'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}')
        a__:					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   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      **lowercase   ,      )     ->      np.ndarray:





        '''simple docstring'''





        a__:					List[str]      							=     get_size_dict(lowercase)
        if "height" not in size or "width" not in size:
          raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}')
        return center_crop(lowercase   ,      size=(size['height'], size['width'])   ,      data_format=lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      **lowercase   ,      )     ->      str:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      np.ndarray:





        '''simple docstring'''





        return flip_channel_order(lowercase   ,      data_format=lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = ChannelDimension.FIRST   ,      **lowercase   ,      )     ->      PIL.Image.Image:





        '''simple docstring'''





        a__:					List[Any]      							=     do_resize if do_resize is not None else self.do_resize
        a__:					Dict      							=     resample if resample is not None else self.resample
        a__:					Tuple      							=     do_rescale if do_rescale is not None else self.do_rescale
        a__:					Dict      							=     rescale_factor if rescale_factor is not None else self.rescale_factor
        a__:					Union[str, Any]      							=     do_center_crop if do_center_crop is not None else self.do_center_crop
        a__:					List[Any]      							=     (
            do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
        )

        a__:					Optional[Any]      							=     size if size is not None else self.size
        a__:					List[str]      							=     get_size_dict(lowercase   ,      default_to_square=lowercase)
        a__:					Any      							=     crop_size if crop_size is not None else self.crop_size
        a__:					Union[str, Any]      							=     get_size_dict(lowercase   ,      param_name='crop_size')

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

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

        # All transformations expect numpy arrays.
        a__:					Optional[Any]      							=     [to_numpy_array(lowercase) for image in images]

        if do_resize:
          a__:					Union[str, Any]      							=     [self.resize(image=lowercase   ,      size=lowercase   ,      resample=lowercase) for image in images]

        if do_center_crop:
          a__:					int      							=     [self.center_crop(image=lowercase   ,      size=lowercase) for image in images]

        if do_rescale:
          a__:					List[Any]      							=     [self.rescale(image=lowercase   ,      scale=lowercase) for image in images]

        # the pretrained checkpoints assume images are BGR, not RGB
        if do_flip_channel_order:
          a__:					str      							=     [self.flip_channel_order(image=lowercase) for image in images]

        a__:					Tuple      							=     [to_channel_dimension_format(lowercase   ,      lowercase) for image in images]

        a__:					Any      							=     {'pixel_values': images}
        return BatchFeature(data=lowercase   ,      tensor_type=lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     outputs.logits

        # Resize logits and compute semantic segmentation maps
        if target_sizes is not None:
          if len(lowercase) != len(lowercase):
            raise ValueError(
                'Make sure that you pass in as many target sizes as the batch dimension of the logits')

          if is_torch_tensor(lowercase):
            a__:					Any      							=     target_sizes.numpy()

          a__:					int      							=     []

          for idx in range(len(lowercase)):
            a__:					Dict      							=     torch.nn.functional.interpolate(
                logits[idx].unsqueeze(dim=0)   ,      size=target_sizes[idx]   ,      mode='bilinear'   ,      align_corners=lowercase)
            a__:					Optional[Any]      							=     resized_logits[0].argmax(dim=0)
            semantic_segmentation.append(lowercase)
        else:
          a__:					Optional[int]      							=     logits.argmax(dim=1)
          a__:					Tuple      							=     [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

        return semantic_segmentation



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

import dateutil.parser as date_parser
import requests







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Tuple:
  a__:					Tuple      							=     {}

  a__:					Tuple      							=     job['started_at']
  a__:					int      							=     job['completed_at']

  a__:					Any      							=     date_parser.parse(_SCREAMING_SNAKE_CASE       )
  a__:					Tuple      							=     date_parser.parse(_SCREAMING_SNAKE_CASE       )

  a__:					str      							=     round((end_datetime - start_datetime).total_seconds() / 60.0       )

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

  return job_info







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=None       )     ->Optional[int]:
  a__:					Tuple      							=     None
  if token is not None:
    a__:					List[str]      							=     {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

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

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

    for i in range(_SCREAMING_SNAKE_CASE       ):
      a__:					str      							=     requests.get(url + F'&page={i + 2}'		,					headers=_SCREAMING_SNAKE_CASE       ).json()
      job_time.update({job['name']: extract_time_from_single_job(_SCREAMING_SNAKE_CASE       ) 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__":
 lowercase__ 	=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
 lowercase__ 	=     parser.parse_args()

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


