

# 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 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Dict									=				"dandelin/vilt-b32-finetuned-vqa"
			SCREAMING_SNAKE_CASE     :   str									=				(
			    "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."
			)
			SCREAMING_SNAKE_CASE     :   Any									=				"image_qa"
			SCREAMING_SNAKE_CASE     :   str									=				AutoProcessor
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				AutoModelForVisualQuestionAnswering

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["image", "text"]
			SCREAMING_SNAKE_CASE     :   List[Any]									=				["text"]







			def __init__(     self :				Optional[Any]							,   *_UpperCamelCase :				Optional[int]							,   **_UpperCamelCase :				Union[str, Any]							)      ->List[Any]:
									requires_backends(self							,   ['''vision''']							)
									super().__init__(*_UpperCamelCase							,   **_UpperCamelCase							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				"Image"							,   _UpperCamelCase :				str							)      ->Union[str, Any]:
									return self.pre_processor(_UpperCamelCase							,   _UpperCamelCase							,   return_tensors='''pt'''							)







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				Dict							)      ->int:
									with torch.no_grad():
															return self.model(**_UpperCamelCase							).logits







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Tuple							)      ->Tuple:
									snake_case_   =	outputs.argmax(-1							).item()
									return self.model.config.idalabel[idx]






from statistics import mean

import numpy as np







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    list:
  __snake_case:						List[Any]   =						0
  # Number of processes finished
  __snake_case:						Union[str, Any]   =						0
  # Displays the finished process.
  # If it is 0, the performance is completed if it is 1, before the performance.
  __snake_case:						Dict   =						[0] * no_of_process
  # List to include calculation results
  __snake_case:						Tuple   =						[0] * no_of_process

  # Sort by arrival time.
  __snake_case:						int   =						[burst_time[i] for i in np.argsort(SCREAMING_SNAKE_CASE__)]
  __snake_case:						Any   =						[process_name[i] for i in np.argsort(SCREAMING_SNAKE_CASE__)]
  arrival_time.sort()

  while no_of_process > finished_process_count:
    __snake_case:						Tuple   =						0
    while finished_process[i] == 1:
      i += 1
    if current_time < arrival_time[i]:
      __snake_case:						Any   =						arrival_time[i]

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

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

  return turn_around_time







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    list:
  __snake_case:						Union[str, Any]   =						[0] * no_of_process
  for i in range(0					,					SCREAMING_SNAKE_CASE__):
    __snake_case:						Optional[int]   =						turn_around_time[i] - burst_time[i]
  return waiting_time


if __name__ == "__main__":
      __UpperCAmelCase       :   Optional[Any]				       =							5
      __UpperCAmelCase       :   Tuple				       =							["A", "B", "C", "D", "E"]
      __UpperCAmelCase       :   str				       =							[1, 2, 3, 4, 5]
      __UpperCAmelCase       :   Dict				       =							[1, 2, 3, 4, 5]

      __UpperCAmelCase       :   List[str]				       =							calculate_turn_around_time(
          process_name, arrival_time, burst_time, no_of_process
      )
      __UpperCAmelCase       :   List[str]				       =							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}')






