from __future__ import annotations

import pandas as pd







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->list[int]:
  a__:					int      							=     [0] * no_of_processes
  a__:					Any      							=     [0] * no_of_processes
  # Copy the burst time into remaining_time[]
  for i in range(_SCREAMING_SNAKE_CASE       ):
    a__:					Any      							=     burst_time[i]

  a__:					Union[str, Any]      							=     0
  a__:					List[Any]      							=     0
  a__:					List[Any]      							=     999999999
  a__:					Optional[int]      							=     0
  a__:					Optional[int]      							=     False

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

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

    a__:					int      							=     remaining_time[short]
    if minm == 0:
      a__:					str      							=     999999999

    if remaining_time[short] == 0:
      complete += 1
      a__:					Optional[Any]      							=     False

      # Find finish time of current process
      a__:					List[Any]      							=     increment_time + 1

      # Calculate waiting time
      a__:					Tuple      							=     finish_time - arrival_time[short]
      a__:					Dict      							=     finar - burst_time[short]

      if waiting_time[short] < 0:
        a__:					List[str]      							=     0

        # Increment time
    increment_time += 1
  return waiting_time







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->list[int]:
  a__:					Union[str, Any]      							=     [0] * no_of_processes
  for i in range(_SCREAMING_SNAKE_CASE       ):
    a__:					Any      							=     burst_time[i] + waiting_time[i]
  return turn_around_time







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->None:
  a__:					Tuple      							=     0
  a__:					int      							=     0
  for i in range(_SCREAMING_SNAKE_CASE       ):
    a__:					Optional[Any]      							=     total_waiting_time + waiting_time[i]
    a__:					int      							=     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')
 lowercase__ 	=     int(input())
 lowercase__ 	=     [0] * no_of_processes
 lowercase__ 	=     [0] * no_of_processes
 lowercase__ 	=     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))
  lowercase__	, lowercase__ 	=     map(int, input().split())

 lowercase__ 	=     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

 calculate_average_times(waiting_time, turn_around_time, no_of_processes)

 lowercase__ 	=     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)



"""simple docstring"""
from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from ..image_utils import load_image


if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowercase__ 	=     logging.get_logger(__name__)


lowercase__ 	=     Dict[str, Any]
lowercase__ 	=     List[Prediction]




@add_end_docstrings(__lowerCAmelCase    )
class        __snake_case	(					__lowerCAmelCase    ):



      def __init__(						self   ,      *lowercase   ,      **lowercase)     ->      Dict:





        '''simple docstring'''





        super().__init__(*lowercase   ,      **lowercase)

        if self.framework == "tf":
          raise ValueError(f'The {self.__class__} is only available in PyTorch.')

        requires_backends(self   ,      'vision')
        self.check_model_type(
            dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      int:





        '''simple docstring'''





        a__:					Optional[Any]      							=     {}
        if "threshold" in kwargs:
          a__:					Dict      							=     kwargs['threshold']
        return {}, {}, postprocess_kwargs



      def __call__(						self   ,      *lowercase   ,      **lowercase)     ->      Union[Predictions, List[Prediction]]:





        '''simple docstring'''





        return super().__call__(*lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      List[Any]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     load_image(lowercase)
        a__:					List[Any]      							=     torch.IntTensor([[image.height, image.width]])
        a__:					Any      							=     self.image_processor(images=[image]   ,      return_tensors='pt')
        if self.tokenizer is not None:
          a__:					Any      							=     self.tokenizer(text=inputs['words']   ,      boxes=inputs['boxes']   ,      return_tensors='pt')
        a__:					List[str]      							=     target_size
        return inputs



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      int:





        '''simple docstring'''





        a__:					Any      							=     model_inputs.pop('target_size')
        a__:					Union[str, Any]      							=     self.model(**lowercase)
        a__:					List[str]      							=     outputs.__class__({'target_size': target_size, **outputs})
        if self.tokenizer is not None:
          a__:					Union[str, Any]      							=     model_inputs['bbox']
        return model_outputs



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase=0.9)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     model_outputs['target_size']
        if self.tokenizer is not None:
          # This is a LayoutLMForTokenClassification variant.
          # The OCR got the boxes and the model classified the words.
          a__							,   a__:					str      							=     target_size[0].tolist()

          def unnormalize(lowercase):
            return self._get_bounding_box(
                torch.Tensor(
                    [
                        (width * bbox[0] / 10_00),
                        (height * bbox[1] / 10_00),
                        (width * bbox[2] / 10_00),
                        (height * bbox[3] / 10_00),
                    ]))

          a__							,   a__:					Optional[Any]      							=     model_outputs['logits'].squeeze(0).softmax(dim=-1).max(dim=-1)
          a__:					str      							=     [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
          a__:					Union[str, Any]      							=     [unnormalize(lowercase) for bbox in model_outputs['bbox'].squeeze(0)]
          a__:					Dict      							=     ['score', 'label', 'box']
          a__:					Any      							=     [dict(zip(lowercase   ,      lowercase)) for vals in zip(scores.tolist()   ,      lowercase   ,      lowercase) if vals[0] > threshold]
        else:
          # This is a regular ForObjectDetectionModel
          a__:					List[str]      							=     self.image_processor.post_process_object_detection(lowercase   ,      lowercase   ,      lowercase)
          a__:					Tuple      							=     raw_annotations[0]
          a__:					List[str]      							=     raw_annotation['scores']
          a__:					int      							=     raw_annotation['labels']
          a__:					int      							=     raw_annotation['boxes']

          a__:					List[Any]      							=     scores.tolist()
          a__:					Any      							=     [self.model.config.idalabel[label.item()] for label in labels]
          a__:					Dict      							=     [self._get_bounding_box(lowercase) for box in boxes]

          # {"scores": [...], ...} --> [{"score":x, ...}, ...]
          a__:					Optional[Any]      							=     ['score', 'label', 'box']
          a__:					List[Any]      							=     [
              dict(zip(lowercase   ,      lowercase))
              for vals in zip(raw_annotation['scores']   ,      raw_annotation['labels']   ,      raw_annotation['boxes'])
          ]

        return annotation



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Dict[str, int]:





        '''simple docstring'''





        if self.framework != "pt":
          raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.')
        a__							,   a__							,   a__							,   a__:					List[Any]      							=     box.int().tolist()
        a__:					Optional[int]      							=     {
            'xmin': xmin,
            'ymin': ymin,
            'xmax': xmax,
            'ymax': ymax,
        }
        return bbox


