






from statistics import mean

import numpy as np




def 				A		(					lowercase__  :  list ,		lowercase__  :  list ,		lowercase__  :  list ,		lowercase__  :  int  )		-> list:
	UpperCamelCase__ :Optional[Any]						=      0
	# Number of processes finished
	UpperCamelCase__ :List[str]						=      0
	# Displays the finished process.
	# If it is 0, the performance is completed if it is 1, before the performance.
	UpperCamelCase__ :Optional[Any]						=      [0] * no_of_process
	# List to include calculation results
	UpperCamelCase__ :Tuple						=      [0] * no_of_process

	# Sort by arrival time.
	UpperCamelCase__ :Dict						=      [burst_time[i] for i in np.argsort(lowercase__  )]
	UpperCamelCase__ :Tuple						=      [process_name[i] for i in np.argsort(lowercase__  )]
	arrival_time.sort()

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

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

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

	return turn_around_time




def 				A		(					lowercase__  :  list ,		lowercase__  :  list ,		lowercase__  :  list ,		lowercase__  :  int  )		-> list:
	UpperCamelCase__ :List[str]						=      [0] * no_of_process
	for i in range(0 ,		lowercase__  ):
		UpperCamelCase__ :List[str]						=      turn_around_time[i] - burst_time[i]
	return waiting_time


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

							UpperCamelCase        =				calculate_turn_around_time(
							    process_name, arrival_time, burst_time, no_of_process
							)
							UpperCamelCase        =				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'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
 from PIL import Image

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		int     				=    '''dandelin/vilt-b32-finetuned-vqa'''
 __lowercase					:		int     				=    (
     '''This is a tool that answers a question about an image. It takes an input named `image` which should be the '''
     '''image containing the information, as well as a `question` which should be the question in English. It '''
     '''returns a text that is the answer to the question.'''
 )
 __lowercase					:		Union[str, Any]     				=    '''image_qa'''
 __lowercase					:		Optional[Any]     				=    AutoProcessor
 __lowercase					:		Tuple     				=    AutoModelForVisualQuestionAnswering

 __lowercase					:		Tuple     				=    ['''image''', '''text''']
 __lowercase					:		List[str]     				=    ['''text''']


 def __init__(   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  requires_backends(self   ,   ['vision']    )
  super().__init__(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  return self.pre_processor(__lowercase   ,   __lowercase   ,   return_tensors='pt'    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  with torch.no_grad():
   return self.model(**__lowercase    ).logits







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      outputs.argmax(-1    ).item()
  return self.model.config.idalabel[idx]






