


"""simple docstring"""







from statistics import mean

import numpy as np
def 					a__			(				SCREAMING_SNAKE_CASE    :    list				,      SCREAMING_SNAKE_CASE    :    list				,      SCREAMING_SNAKE_CASE    :    list				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  lowerCAmelCase     :     List[str]								=							0
  # Number of processes finished
  lowerCAmelCase     :     Dict								=							0
  # Displays the finished process.
  # If it is 0, the performance is completed if it is 1, before the performance.
  lowerCAmelCase     :     int								=							[0] * no_of_process
  # List to include calculation results
  lowerCAmelCase     :     str								=							[0] * no_of_process

  # Sort by arrival time.
  lowerCAmelCase     :     str								=							[burst_time[i] for i in np.argsort(SCREAMING_SNAKE_CASE				)]
  lowerCAmelCase     :     Tuple								=							[process_name[i] for i in np.argsort(SCREAMING_SNAKE_CASE				)]
  arrival_time.sort()

  while no_of_process > finished_process_count:
    lowerCAmelCase     :     Union[str, Any]								=							0
    while finished_process[i] == 1:
      i += 1
    if current_time < arrival_time[i]:
      lowerCAmelCase     :     Tuple								=							arrival_time[i]

    lowerCAmelCase     :     Optional[Any]								=							0
    # Index showing the location of the process being performed
    lowerCAmelCase     :     Dict								=							0
    # Saves the current response ratio.
    lowerCAmelCase     :     Union[str, Any]								=							0
    for i in range(0				,      SCREAMING_SNAKE_CASE				):
      if finished_process[i] == 0 and arrival_time[i] <= current_time:
        lowerCAmelCase     :     List[str]								=							(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
            i
        ]
      if response_ratio < temp:
        lowerCAmelCase     :     List[str]								=							temp
        lowerCAmelCase     :     Any								=							i

        # Calculate the turn around time
    lowerCAmelCase     :     Union[str, Any]								=							current_time + burst_time[loc] - arrival_time[loc]
    current_time += burst_time[loc]
    # Indicates that the process has been performed.
    lowerCAmelCase     :     Optional[int]								=							1
    # Increase finished_process_count by 1
    finished_process_count += 1

  return turn_around_time
def 					a__			(				SCREAMING_SNAKE_CASE    :    list				,      SCREAMING_SNAKE_CASE    :    list				,      SCREAMING_SNAKE_CASE    :    list				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[int]								=							[0] * no_of_process
  for i in range(0				,      SCREAMING_SNAKE_CASE				):
    lowerCAmelCase     :     List[str]								=							turn_around_time[i] - burst_time[i]
  return waiting_time


if __name__ == "__main__":
     lowerCAmelCase__      				=   5
     lowerCAmelCase__      				=   ['''A''', '''B''', '''C''', '''D''', '''E''']
     lowerCAmelCase__      				=   [1, 2, 3, 4, 5]
     lowerCAmelCase__      				=   [1, 2, 3, 4, 5]

     lowerCAmelCase__      				=   calculate_turn_around_time(
         process_name, arrival_time, burst_time, no_of_process
     )
     lowerCAmelCase__      				=   calculate_waiting_time(
         process_name, turn_around_time, burst_time, no_of_process
     )

     print('''Process name \tArrival time \tBurst time \tTurn around time \tWaiting time''')
     for i in range(0, no_of_process):
          print(
              F"{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t"
              F"{turn_around_time[i]}\t\t\t{waiting_time[i]}"
          )

     print(F"average waiting time : {mean(waiting_time):.5f}")
     print(F"average turn around time : {mean(turn_around_time):.5f}")









"""simple docstring"""







import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY


@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       a  :				int   =MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							hf_hub_download(
             repo_id="nateraw/video-demo"		,     filename="archery.mp4"		,     repo_type="dataset"						)
         lowerCAmelCase     :     Dict								=							VideoClassificationPipeline(model=snake_case__		,     image_processor=snake_case__		,     top_k=2						)
         lowerCAmelCase     :     Any								=							[
             example_video_filepath,
             "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
         ]
         return video_classifier, examples

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         for example in examples:
           lowerCAmelCase     :     str								=							video_classifier(snake_case__						)

           self.assertEqual(
               snake_case__		,     [
                   {"score": ANY(snake_case__						), "label": ANY(snake_case__						)},
                   {"score": ANY(snake_case__						), "label": ANY(snake_case__						)},
               ]		,     )

       @require_torch
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
         lowerCAmelCase     :     str								=							VideoMAEFeatureExtractor(
             size={"shortest_edge": 10}		,     crop_size={"height": 10, "width": 10}						)
         lowerCAmelCase     :     int								=							pipeline(
             "video-classification"		,     model=snake_case__		,     feature_extractor=snake_case__		,     frame_sampling_rate=4						)

         lowerCAmelCase     :     Optional[int]								=							hf_hub_download(repo_id="nateraw/video-demo"		,     filename="archery.mp4"		,     repo_type="dataset"						)
         lowerCAmelCase     :     Union[str, Any]								=							video_classifier(snake_case__		,     top_k=2						)
         self.assertEqual(
             nested_simplify(snake_case__		,     decimals=4						)		,     [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}]		,     )

         lowerCAmelCase     :     Tuple								=							video_classifier(
             [
                 video_file_path,
                 video_file_path,
             ]		,     top_k=2		,     )
         self.assertEqual(
             nested_simplify(snake_case__		,     decimals=4						)		,     [
                 [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
                 [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
             ]		,     )







       @require_tf
       def        lowercase__							(						self						):






         """simple docstring"""





         pass





