

'''simple docstring'''
from __future__ import annotations

from statistics import mean


def        _lowerCAmelCase					(						_UpperCamelCase						:     list[int]							, _UpperCamelCase						:     list[int]							, _UpperCamelCase						:     int				)      ->			list[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[0] * no_of_processes
     _SCREAMING_SNAKE_CASE							    =[0] * no_of_processes

     # Initialize remaining_time to waiting_time.

     for i in range(_UpperCamelCase				):
          _SCREAMING_SNAKE_CASE							    =burst_time[i]
     _SCREAMING_SNAKE_CASE							    =[]

     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0

     # When processes are not completed,
     # A process whose arrival time has passed \
     # and has remaining execution time is put into the ready_process.
     # The shortest process in the ready_process, target_process is executed.

     while completed != no_of_processes:
          _SCREAMING_SNAKE_CASE							    =[]
          _SCREAMING_SNAKE_CASE							    =-1

          for i in range(_UpperCamelCase				):
               if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
                    ready_process.append(_UpperCamelCase				)

          if len(_UpperCamelCase				) > 0:
               _SCREAMING_SNAKE_CASE							    =ready_process[0]
               for i in ready_process:
                    if remaining_time[i] < remaining_time[target_process]:
                         _SCREAMING_SNAKE_CASE							    =i
               total_time += burst_time[target_process]
               completed += 1
               _SCREAMING_SNAKE_CASE							    =0
               _SCREAMING_SNAKE_CASE							    =(
                   total_time - arrival_time[target_process] - burst_time[target_process]
               )
          else:
               total_time += 1

     return waiting_time


def        _lowerCAmelCase					(						_UpperCamelCase						:     list[int]							, _UpperCamelCase						:     int							, _UpperCamelCase						:     list[int]				)      ->			list[int]:
     """simple docstring"""




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


if __name__ == "__main__":
     print("[TEST CASE 01]")

     lowerCamelCase :      Optional[Any]					 =     4
     lowerCamelCase :      List[str]					 =     [2, 5, 3, 7]
     lowerCamelCase :      int					 =     [0, 0, 0, 0]
     lowerCamelCase :      Optional[int]					 =     calculate_waitingtime(arrival_time, burst_time, no_of_processes)
     lowerCamelCase :      int					 =     calculate_turnaroundtime(
         burst_time, no_of_processes, waiting_time
     )

     # Printing the Result
     print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
     for i, process_id in enumerate(list(range(1, 5))):
          print(
              f'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
              f'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
          )
     print(f'''\nAverage waiting time = {mean(waiting_time):.5f}''')
     print(f'''Average turnaround time = {mean(turn_around_time):.5f}''')



'''simple docstring'''
import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

     from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
     lowerCamelCase :      int					 =     False

if is_vision_available():
     from PIL import Image

     from transformers import PixaStructImageProcessor







class 							A__    (       unittest.TestCase  ):





  def __init__(		self      :	List[str]							,					_a      :	List[Any]							,					_a      :	List[str]=7							,					_a      :	List[str]=3							,					_a      :	Tuple=18							,					_a      :	Tuple=30							,					_a      :	str=400							,					_a      :	Tuple=None							,					_a      :	Union[str, Any]=True							,					_a      :	List[str]=True							,					_a      :	Optional[int]=None							,					)	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =size if size is not None else {'height': 20, 'width': 20}
       _SCREAMING_SNAKE_CASE							    =parent
       _SCREAMING_SNAKE_CASE							    =batch_size
       _SCREAMING_SNAKE_CASE							    =num_channels
       _SCREAMING_SNAKE_CASE							    =image_size
       _SCREAMING_SNAKE_CASE							    =min_resolution
       _SCREAMING_SNAKE_CASE							    =max_resolution
       _SCREAMING_SNAKE_CASE							    =size
       _SCREAMING_SNAKE_CASE							    =do_normalize
       _SCREAMING_SNAKE_CASE							    =do_convert_rgb
       _SCREAMING_SNAKE_CASE							    =[512, 1024, 2048, 4096]
       _SCREAMING_SNAKE_CASE							    =patch_size if patch_size is not None else {'height': 16, 'width': 16}





  def 						A  (		self      :	Any )	->    List[str]:





       '''simple docstring'''






       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





  def 						A  (		self      :	int )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    ='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
       _SCREAMING_SNAKE_CASE							    =Image.open(requests.get(_a							,					stream=_a ).raw ).convert('RGB' )
       return raw_image







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,   reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'     ,   )
@require_torch
@require_vision
class 							A__    (       A__     ,   unittest.TestCase  ):
  A__		 =				PixaStructImageProcessor if is_vision_available() else None





  def 						A  (		self      :	Dict )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =PixaStructImageProcessingTester(self )





  @property
  def 						A  (		self      :	Optional[Any] )	->    int:





       '''simple docstring'''






       return self.image_processor_tester.prepare_image_processor_dict()





  def 						A  (		self      :	Any )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(_a							,					'do_normalize' ) )
       self.assertTrue(hasattr(_a							,					'do_convert_rgb' ) )





  def 						A  (		self      :	Any )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processor_tester.prepare_dummy_image()

       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       _SCREAMING_SNAKE_CASE							    =2048

       _SCREAMING_SNAKE_CASE							    =image_processor(_a							,					return_tensors='pt'							,					max_patches=_a )
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,					torch.tensor(0.06_06 )							,					atol=1e-3							,					rtol=1e-3 ) )





  def 						A  (		self      :	Any )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       _SCREAMING_SNAKE_CASE							    =prepare_image_inputs(self.image_processor_tester							,					equal_resolution=_a )
       for image in image_inputs:
            self.assertIsInstance(_a							,					Image.Image )

       # Test not batched input
       _SCREAMING_SNAKE_CASE							    =(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            _SCREAMING_SNAKE_CASE							    =image_processor(
                image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(1, max_patch, expected_hidden_dim)							,					)

            # Test batched
            _SCREAMING_SNAKE_CASE							    =image_processor(
                _a							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,					)





  def 						A  (		self      :	List[str] )	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       _SCREAMING_SNAKE_CASE							    =prepare_image_inputs(self.image_processor_tester							,					equal_resolution=_a )
       for image in image_inputs:
            self.assertIsInstance(_a							,					Image.Image )

       # Test not batched input
       _SCREAMING_SNAKE_CASE							    =(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       _SCREAMING_SNAKE_CASE							    =True

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            with self.assertRaises(_a ):
                 _SCREAMING_SNAKE_CASE							    =image_processor(
                     image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches

            _SCREAMING_SNAKE_CASE							    ='Hello'

            _SCREAMING_SNAKE_CASE							    =image_processor(
                image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a							,					header_text=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(1, max_patch, expected_hidden_dim)							,					)

            # Test batched
            _SCREAMING_SNAKE_CASE							    =image_processor(
                _a							,					return_tensors='pt'							,					max_patches=_a							,					header_text=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,					)





  def 						A  (		self      :	List[Any] )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       _SCREAMING_SNAKE_CASE							    =prepare_image_inputs(self.image_processor_tester							,					equal_resolution=_a							,					numpify=_a )
       for image in image_inputs:
            self.assertIsInstance(_a							,					np.ndarray )

       _SCREAMING_SNAKE_CASE							    =(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            _SCREAMING_SNAKE_CASE							    =image_processor(
                image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(1, max_patch, expected_hidden_dim)							,					)

            # Test batched
            _SCREAMING_SNAKE_CASE							    =image_processor(
                _a							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,					)





  def 						A  (		self      :	Union[str, Any] )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       _SCREAMING_SNAKE_CASE							    =prepare_image_inputs(self.image_processor_tester							,					equal_resolution=_a							,					torchify=_a )
       for image in image_inputs:
            self.assertIsInstance(_a							,					torch.Tensor )

       # Test not batched input
       _SCREAMING_SNAKE_CASE							    =(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            _SCREAMING_SNAKE_CASE							    =image_processor(
                image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(1, max_patch, expected_hidden_dim)							,					)

            # Test batched
            _SCREAMING_SNAKE_CASE							    =image_processor(
                _a							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,					)







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,   reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'     ,   )
@require_torch
@require_vision
class 							A__    (       A__     ,   unittest.TestCase  ):
  A__		 =				PixaStructImageProcessor if is_vision_available() else None





  def 						A  (		self      :	str )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =PixaStructImageProcessingTester(self							,					num_channels=4 )
       _SCREAMING_SNAKE_CASE							    =3





  @property
  def 						A  (		self      :	List[str] )	->    Optional[Any]:





       '''simple docstring'''






       return self.image_processor_tester.prepare_image_processor_dict()





  def 						A  (		self      :	List[str] )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(_a							,					'do_normalize' ) )
       self.assertTrue(hasattr(_a							,					'do_convert_rgb' ) )





  def 						A  (		self      :	Dict )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       _SCREAMING_SNAKE_CASE							    =prepare_image_inputs(self.image_processor_tester							,					equal_resolution=_a )
       for image in image_inputs:
            self.assertIsInstance(_a							,					Image.Image )

       # Test not batched input
       _SCREAMING_SNAKE_CASE							    =(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * (self.image_processor_tester.num_channels - 1)
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            _SCREAMING_SNAKE_CASE							    =image_processor(
                image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(1, max_patch, expected_hidden_dim)							,					)

            # Test batched
            _SCREAMING_SNAKE_CASE							    =image_processor(
                _a							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,					)
