



'''simple docstring'''





from __future__ import annotations

import pandas as pd
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= [0] * no_of_processes
    lowerCAmelCase__			:  Any											= [0] * no_of_processes
    # Copy the burst time into remaining_time[]
    for i in range(UpperCamelCase      ):
        lowerCAmelCase__			:  Dict											= burst_time[i]

    lowerCAmelCase__			:  Optional[int]											= 0
    lowerCAmelCase__			:  Dict											= 0
    lowerCAmelCase__			:  Optional[int]											= 999999999
    lowerCAmelCase__			:  Any											= 0
    lowerCAmelCase__			:  Optional[int]											= False

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

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

        lowerCAmelCase__			:  int											= remaining_time[short]
        if minm == 0:
            lowerCAmelCase__			:  Union[str, Any]											= 999999999

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

            # Find finish time of current process
            lowerCAmelCase__			:  Dict											= increment_time + 1

            # Calculate waiting time
            lowerCAmelCase__			:  Tuple											= finish_time - arrival_time[short]
            lowerCAmelCase__			:  List[str]											= finar - burst_time[short]

            if waiting_time[short] < 0:
                lowerCAmelCase__			:  Union[str, Any]											= 0

        # Increment time
        increment_time += 1
    return waiting_time
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Optional[Any]											= [0] * no_of_processes
    for i in range(UpperCamelCase      ):
        lowerCAmelCase__			:  Union[str, Any]											= burst_time[i] + waiting_time[i]
    return turn_around_time
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  List[str]											= 0
    lowerCAmelCase__			:  Optional[Any]											= 0
    for i in range(UpperCamelCase      ):
        lowerCAmelCase__			:  Dict											= total_waiting_time + waiting_time[i]
        lowerCAmelCase__			:  Optional[Any]											= 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''')
 _lowerCAmelCase										=     int(input())
 _lowerCAmelCase										=     [0] * no_of_processes
 _lowerCAmelCase										=     [0] * no_of_processes
 _lowerCAmelCase										=     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))
  _lowerCAmelCase						,      _lowerCAmelCase										=     map(int, input().split())

 _lowerCAmelCase										=     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

 calculate_average_times(waiting_time, turn_around_time, no_of_processes)

 _lowerCAmelCase										=     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'''





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										=     False

if is_vision_available():
 from PIL import Image

 from transformers import PixaStructImageProcessor






class 	lowerCAmelCase_(    unittest.TestCase					):
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase=7  ,__UpperCAmelCase=3  ,__UpperCAmelCase=18  ,__UpperCAmelCase=30  ,__UpperCAmelCase=400  ,__UpperCAmelCase=None  ,__UpperCAmelCase=True  ,__UpperCAmelCase=True  ,__UpperCAmelCase=None  ,)   ->		Optional[Any]:
      lowerCAmelCase__			:  Optional[Any]											= size if size is not None else {"""height""": 20, """width""": 20}
      lowerCAmelCase__			:  List[Any]											= parent
      lowerCAmelCase__			:  List[str]											= batch_size
      lowerCAmelCase__			:  Tuple											= num_channels
      lowerCAmelCase__			:  Optional[int]											= image_size
      lowerCAmelCase__			:  Optional[Any]											= min_resolution
      lowerCAmelCase__			:  Tuple											= max_resolution
      lowerCAmelCase__			:  List[Any]											= size
      lowerCAmelCase__			:  List[str]											= do_normalize
      lowerCAmelCase__			:  Optional[int]											= do_convert_rgb
      lowerCAmelCase__			:  str											= [512, 1024, 2048, 4096]
      lowerCAmelCase__			:  int											= patch_size if patch_size is not None else {"""height""": 16, """width""": 16}




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__			:  Optional[Any]											= """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
      lowerCAmelCase__			:  Union[str, Any]											= Image.open(requests.get(__UpperCAmelCase  ,stream=__UpperCAmelCase				).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 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				Union[str, Any]           =				PixaStructImageProcessor if is_vision_available() else None




  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__			:  Union[str, Any]											= PixaStructImageProcessingTester(self				)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      return self.image_processor_tester.prepare_image_processor_dict()




  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      lowerCAmelCase__			:  Dict											= self.image_processing_class(**self.image_processor_dict				)
      self.assertTrue(hasattr(__UpperCAmelCase  ,"""do_normalize"""				)				)
      self.assertTrue(hasattr(__UpperCAmelCase  ,"""do_convert_rgb"""				)				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      lowerCAmelCase__			:  List[Any]											= self.image_processor_tester.prepare_dummy_image()

      lowerCAmelCase__			:  List[Any]											= self.image_processing_class(**self.image_processor_dict				)
      lowerCAmelCase__			:  str											= 2048

      lowerCAmelCase__			:  Tuple											= image_processor(__UpperCAmelCase  ,return_tensors="""pt"""  ,max_patches=__UpperCAmelCase				)
      self.assertTrue(torch.allclose(inputs.flattened_patches.mean()  ,torch.tensor(0.0_6_0_6				)  ,atol=1E-3  ,rtol=1E-3				)				)




  def 							UpperCAmelCase_     (   self				)   ->		str:
      # Initialize image_processor
      lowerCAmelCase__			:  int											= self.image_processing_class(**self.image_processor_dict				)
      # create random PIL images
      lowerCAmelCase__			:  int											= prepare_image_inputs(self.image_processor_tester  ,equal_resolution=__UpperCAmelCase				)
      for image in image_inputs:
          self.assertIsInstance(__UpperCAmelCase  ,Image.Image				)

      # Test not batched input
      lowerCAmelCase__			:  List[str]											= (
          (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
          lowerCAmelCase__			:  str											= image_processor(
              image_inputs[0]  ,return_tensors="""pt"""  ,max_patches=__UpperCAmelCase				).flattened_patches
          self.assertEqual(
              encoded_images.shape  ,(1, max_patch, expected_hidden_dim)  ,)

          # Test batched
          lowerCAmelCase__			:  Any											= image_processor(
              __UpperCAmelCase  ,return_tensors="""pt"""  ,max_patches=__UpperCAmelCase				).flattened_patches
          self.assertEqual(
              encoded_images.shape  ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,)




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      # Initialize image_processor
      lowerCAmelCase__			:  Optional[int]											= self.image_processing_class(**self.image_processor_dict				)
      # create random PIL images
      lowerCAmelCase__			:  Optional[int]											= prepare_image_inputs(self.image_processor_tester  ,equal_resolution=__UpperCAmelCase				)
      for image in image_inputs:
          self.assertIsInstance(__UpperCAmelCase  ,Image.Image				)

      # Test not batched input
      lowerCAmelCase__			:  Tuple											= (
          (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
          * self.image_processor_tester.num_channels
      ) + 2

      lowerCAmelCase__			:  Optional[int]											= True

      for max_patch in self.image_processor_tester.max_patches:
          # Test not batched input
          with self.assertRaises(__UpperCAmelCase				):
              lowerCAmelCase__			:  Any											= image_processor(
                  image_inputs[0]  ,return_tensors="""pt"""  ,max_patches=__UpperCAmelCase				).flattened_patches

          lowerCAmelCase__			:  Optional[Any]											= """Hello"""

          lowerCAmelCase__			:  List[str]											= image_processor(
              image_inputs[0]  ,return_tensors="""pt"""  ,max_patches=__UpperCAmelCase  ,header_text=__UpperCAmelCase				).flattened_patches
          self.assertEqual(
              encoded_images.shape  ,(1, max_patch, expected_hidden_dim)  ,)

          # Test batched
          lowerCAmelCase__			:  str											= image_processor(
              __UpperCAmelCase  ,return_tensors="""pt"""  ,max_patches=__UpperCAmelCase  ,header_text=__UpperCAmelCase				).flattened_patches
          self.assertEqual(
              encoded_images.shape  ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,)




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      # Initialize image_processor
      lowerCAmelCase__			:  Optional[Any]											= self.image_processing_class(**self.image_processor_dict				)
      # create random numpy tensors
      lowerCAmelCase__			:  List[str]											= prepare_image_inputs(self.image_processor_tester  ,equal_resolution=__UpperCAmelCase  ,numpify=__UpperCAmelCase				)
      for image in image_inputs:
          self.assertIsInstance(__UpperCAmelCase  ,np.ndarray				)

      lowerCAmelCase__			:  Tuple											= (
          (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
          lowerCAmelCase__			:  Any											= image_processor(
              image_inputs[0]  ,return_tensors="""pt"""  ,max_patches=__UpperCAmelCase				).flattened_patches
          self.assertEqual(
              encoded_images.shape  ,(1, max_patch, expected_hidden_dim)  ,)

          # Test batched
          lowerCAmelCase__			:  int											= image_processor(
              __UpperCAmelCase  ,return_tensors="""pt"""  ,max_patches=__UpperCAmelCase				).flattened_patches
          self.assertEqual(
              encoded_images.shape  ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,)






  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      # Initialize image_processor
      lowerCAmelCase__			:  Tuple											= self.image_processing_class(**self.image_processor_dict				)
      # create random PyTorch tensors
      lowerCAmelCase__			:  int											= prepare_image_inputs(self.image_processor_tester  ,equal_resolution=__UpperCAmelCase  ,torchify=__UpperCAmelCase				)
      for image in image_inputs:
          self.assertIsInstance(__UpperCAmelCase  ,torch.Tensor				)

      # Test not batched input
      lowerCAmelCase__			:  Optional[int]											= (
          (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
          lowerCAmelCase__			:  Dict											= image_processor(
              image_inputs[0]  ,return_tensors="""pt"""  ,max_patches=__UpperCAmelCase				).flattened_patches
          self.assertEqual(
              encoded_images.shape  ,(1, max_patch, expected_hidden_dim)  ,)

          # Test batched
          lowerCAmelCase__			:  Union[str, Any]											= image_processor(
              __UpperCAmelCase  ,return_tensors="""pt"""  ,max_patches=__UpperCAmelCase				).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 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				Optional[int]           =				PixaStructImageProcessor if is_vision_available() else None




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= PixaStructImageProcessingTester(self  ,num_channels=4				)
      lowerCAmelCase__			:  str											= 3




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      return self.image_processor_tester.prepare_image_processor_dict()




  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__			:  Union[str, Any]											= self.image_processing_class(**self.image_processor_dict				)
      self.assertTrue(hasattr(__UpperCAmelCase  ,"""do_normalize"""				)				)
      self.assertTrue(hasattr(__UpperCAmelCase  ,"""do_convert_rgb"""				)				)






  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      # Initialize image_processor
      lowerCAmelCase__			:  Optional[Any]											= self.image_processing_class(**self.image_processor_dict				)
      # create random PIL images
      lowerCAmelCase__			:  Dict											= prepare_image_inputs(self.image_processor_tester  ,equal_resolution=__UpperCAmelCase				)
      for image in image_inputs:
          self.assertIsInstance(__UpperCAmelCase  ,Image.Image				)

      # Test not batched input
      lowerCAmelCase__			:  Dict											= (
          (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
          lowerCAmelCase__			:  int											= image_processor(
              image_inputs[0]  ,return_tensors="""pt"""  ,max_patches=__UpperCAmelCase				).flattened_patches
          self.assertEqual(
              encoded_images.shape  ,(1, max_patch, expected_hidden_dim)  ,)

          # Test batched
          lowerCAmelCase__			:  Dict											= image_processor(
              __UpperCAmelCase  ,return_tensors="""pt"""  ,max_patches=__UpperCAmelCase				).flattened_patches
          self.assertEqual(
              encoded_images.shape  ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,)


