





from __future__ import annotations

import pandas as pd





def      _UpperCamelCase  (a__ :list[int] ,      a__ :list[int] ,      a__ :int							):






							"""simple docstring"""





							UpperCamelCase__					  =      [0] * no_of_processes
							UpperCamelCase__					  =      [0] * no_of_processes
							# Copy the burst time into remaining_time[]
							for i in range(a__							):
														UpperCamelCase__					  =      burst_time[i]

							UpperCamelCase__					  =      0
							UpperCamelCase__					  =      0
							UpperCamelCase__					  =      9_9999_9999
							UpperCamelCase__					  =      0
							UpperCamelCase__					  =      False

							# Process until all processes are completed
							while complete != no_of_processes:
														for j in range(a__							):
																					if arrival_time[j] <= increment_time and remaining_time[j] > 0:
																												if remaining_time[j] < minm:
																																			UpperCamelCase__					  =      remaining_time[j]
																																			UpperCamelCase__					  =      j
																																			UpperCamelCase__					  =      True

														if not check:
																					increment_time += 1
																					continue
														remaining_time[short] -= 1

														UpperCamelCase__					  =      remaining_time[short]
														if minm == 0:
																					UpperCamelCase__					  =      9_9999_9999

														if remaining_time[short] == 0:
																					complete += 1
																					UpperCamelCase__					  =      False

																					# Find finish time of current process
																					UpperCamelCase__					  =      increment_time + 1

																					# Calculate waiting time
																					UpperCamelCase__					  =      finish_time - arrival_time[short]
																					UpperCamelCase__					  =      finar - burst_time[short]

																					if waiting_time[short] < 0:
																												UpperCamelCase__					  =      0

        # Increment time
														increment_time += 1
							return waiting_time





def      _UpperCamelCase  (a__ :list[int] ,      a__ :int ,      a__ :list[int]							):






							"""simple docstring"""





							UpperCamelCase__					  =      [0] * no_of_processes
							for i in range(a__							):
														UpperCamelCase__					  =      burst_time[i] + waiting_time[i]
							return turn_around_time





def      _UpperCamelCase  (a__ :list[int] ,      a__ :list[int] ,      a__ :int							):






							"""simple docstring"""





							UpperCamelCase__					  =      0
							UpperCamelCase__					  =      0
							for i in range(a__							):
														UpperCamelCase__					  =      total_waiting_time + waiting_time[i]
														UpperCamelCase__					  =      total_turn_around_time + turn_around_time[i]
							print(f"""Average waiting time = {total_waiting_time / no_of_processes:.5f}"""							)
							print("""Average turn around time =""" ,      total_turn_around_time / no_of_processes							)


if __name__ == "__main__":
					print("Enter how many process you want to analyze")
					UpperCamelCase__           =			int(input())
					UpperCamelCase__           =			[0] * no_of_processes
					UpperCamelCase__           =			[0] * no_of_processes
					UpperCamelCase__           =			list(range(1, no_of_processes + 1))

					for i in range(no_of_processes):
										print("Enter the arrival time and burst time for process:--" + str(i + 1))
										UpperCamelCase__       ,			UpperCamelCase__           =			map(int, input().split())

					UpperCamelCase__           =			calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					UpperCamelCase__           =			burst_time
					UpperCamelCase__           =			no_of_processes
					UpperCamelCase__           =			waiting_time
					UpperCamelCase__           =			calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					UpperCamelCase__           =			pd.DataFrame(
					    list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
					    columns=[
					        "Process",
					        "BurstTime",
					        "ArrivalTime",
					        "WaitingTime",
					        "TurnAroundTime",
					    ],
					)

					# Printing the dataFrame
					pd.set_option("display.max_rows", fcfs.shape[0] + 1)
					print(fcfs)











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							):
							snake_case  :   List[str]      =							MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      hf_hub_download(
														    repo_id="""nateraw/video-demo"""	,			filename="""archery.mp4"""	,			repo_type="""dataset"""	)
														UpperCamelCase__					  =      VideoClassificationPipeline(model=__lowerCAmelCase	,			image_processor=__lowerCAmelCase	,			top_k=2	)
														UpperCamelCase__					  =      [
														    example_video_filepath,
														    """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
														]
														return video_classifier, examples






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														for example in examples:
																					UpperCamelCase__					  =      video_classifier(__lowerCAmelCase	)

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






							@require_torch
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
														UpperCamelCase__					  =      VideoMAEFeatureExtractor(
														    size={"""shortest_edge""": 10}	,			crop_size={"""height""": 10, """width""": 10}	)
														UpperCamelCase__					  =      pipeline(
														    """video-classification"""	,			model=__lowerCAmelCase	,			feature_extractor=__lowerCAmelCase	,			frame_sampling_rate=4	)

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

														UpperCamelCase__					  =      video_classifier(
														    [
														        video_file_path,
														        video_file_path,
														    ]	,			top_k=2	,			)
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase	,			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 					_lowerCamelCase		(    self	):
														pass




