



import unittest

import numpy as np

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

if is_vision_available():
    from PIL import Image

    from transformers import ViltImageProcessor



class 						_snake_case (     unittest.TestCase			):





 """simple docstring"""



 def __init__(  self		,      a		,      a=7		,      a=3		,      a=1_8		,      a=3_0		,      a=4_0_0		,      a=True		,      a=None		,      a=2		,      a=True		,      a=[0.5, 0.5, 0.5]		,      a=[0.5, 0.5, 0.5]		,      )		->							List[Any]:




   """simple docstring"""







   _A          =      size if size is not None else {'''shortest_edge''': 3_0}
   _A          =      parent
   _A          =      batch_size
   _A          =      num_channels
   _A          =      image_size
   _A          =      min_resolution
   _A          =      max_resolution
   _A          =      do_resize
   _A          =      size
   _A          =      size_divisor
   _A          =      do_normalize
   _A          =      image_mean
   _A          =      image_std
 def lowercase_						(  self   )		->							Dict:




   """simple docstring"""







   return {
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_normalize": self.do_normalize,
       "do_resize": self.do_resize,
       "size": self.size,
       "size_divisor": self.size_divisor,
   }



 def lowercase_						(  self		,      a		,      a=False   )		->							Optional[int]:




   """simple docstring"""







   if not batched:
     _A          =      self.size['''shortest_edge''']
     _A          =      image_inputs[0]
     if isinstance(a		,      Image.Image   ):
       _A      ,				_A          =      image.size
     else:
       _A      ,				_A          =      image.shape[1], image.shape[2]
     _A          =      size / min(a		,      a   )
     if h < w:
       _A      ,				_A          =      size, scale * w
     else:
       _A      ,				_A          =      scale * h, size

     _A          =      int((1_3_3_3 / 8_0_0) * size   )
     if max(a		,      a   ) > max_size:
       _A          =      max_size / max(a		,      a   )
       _A          =      newh * scale
       _A          =      neww * scale

     _A      ,				_A          =      int(newh + 0.5   ), int(neww + 0.5   )
     _A      ,				_A          =      (
         newh // self.size_divisor * self.size_divisor,
         neww // self.size_divisor * self.size_divisor,
     )

   else:
     _A          =      []
     for image in image_inputs:
       _A      ,				_A          =      self.get_expected_values([image]   )
       expected_values.append((expected_height, expected_width)   )
     _A          =      max(a		,      key=lambda a   : item[0]   )[0]
     _A          =      max(a		,      key=lambda a   : item[1]   )[1]

   return expected_height, expected_width







@require_torch
@require_vision
class 						_snake_case (     lowerCamelCase   ,unittest.TestCase			):





 """simple docstring"""



 lowerCamelCase_       =     ViltImageProcessor if is_vision_available() else None
 def lowercase_						(  self   )		->							List[str]:




   """simple docstring"""







   _A          =      ViltImageProcessingTester(self   )
 @property
 def lowercase_						(  self   )		->							Optional[Any]:




   """simple docstring"""







   return self.image_processor_tester.prepare_image_processor_dict()
 def lowercase_						(  self   )		->							str:




   """simple docstring"""







   _A          =      self.image_processing_class(**self.image_processor_dict   )
   self.assertTrue(hasattr(a		,      '''image_mean'''   )   )
   self.assertTrue(hasattr(a		,      '''image_std'''   )   )
   self.assertTrue(hasattr(a		,      '''do_normalize'''   )   )
   self.assertTrue(hasattr(a		,      '''do_resize'''   )   )
   self.assertTrue(hasattr(a		,      '''size'''   )   )
   self.assertTrue(hasattr(a		,      '''size_divisor'''   )   )
 def lowercase_						(  self   )		->							Any:




   """simple docstring"""







   _A          =      self.image_processing_class.from_dict(self.image_processor_dict   )
   self.assertEqual(image_processor.size		,      {'''shortest_edge''': 3_0}   )

   _A          =      self.image_processing_class.from_dict(self.image_processor_dict		,      size=4_2   )
   self.assertEqual(image_processor.size		,      {'''shortest_edge''': 4_2}   )
 def lowercase_						(  self   )		->							List[str]:




   """simple docstring"""







   pass
 def lowercase_						(  self   )		->							int:




   """simple docstring"""







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

   # Test not batched input
   _A          =      image_processing(image_inputs[0]		,      return_tensors='''pt'''   ).pixel_values

   _A      ,				_A          =      self.image_processor_tester.get_expected_values(a   )
   self.assertEqual(
       encoded_images.shape		,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,      )

   # Test batched
   _A          =      image_processing(a		,      return_tensors='''pt'''   ).pixel_values

   _A      ,				_A          =      self.image_processor_tester.get_expected_values(a		,      batched=a   )
   self.assertEqual(
       encoded_images.shape		,      (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )		,      )
 def lowercase_						(  self   )		->							int:




   """simple docstring"""







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

   # Test not batched input
   _A          =      image_processing(image_inputs[0]		,      return_tensors='''pt'''   ).pixel_values

   _A      ,				_A          =      self.image_processor_tester.get_expected_values(a   )
   self.assertEqual(
       encoded_images.shape		,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,      )

   # Test batched
   _A          =      image_processing(a		,      return_tensors='''pt'''   ).pixel_values

   _A      ,				_A          =      self.image_processor_tester.get_expected_values(a		,      batched=a   )
   self.assertEqual(
       encoded_images.shape		,      (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )		,      )



 def lowercase_						(  self   )		->							Optional[Any]:




   """simple docstring"""







   _A          =      self.image_processing_class(**self.image_processor_dict   )
   # create random PyTorch tensors
   _A          =      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
   _A          =      image_processing(image_inputs[0]		,      return_tensors='''pt'''   ).pixel_values

   _A      ,				_A          =      self.image_processor_tester.get_expected_values(a   )
   self.assertEqual(
       encoded_images.shape		,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,      )

   # Test batched
   _A          =      image_processing(a		,      return_tensors='''pt'''   ).pixel_values

   _A      ,				_A          =      self.image_processor_tester.get_expected_values(a		,      batched=a   )
   self.assertEqual(
       encoded_images.shape		,      (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )		,      )




from __future__ import annotations




def       UpperCAmelCase__	(							__snake_case							)				->	list[int]:
  _A          =      [0] * len(__snake_case							)
  for i in range(1  ,			len(__snake_case							)							):
    _A          =      duration_times[i - 1] + waiting_times[i - 1]
  return waiting_times




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case							)				->	list[int]:
  return [
      duration_time + waiting_times[i]
      for i, duration_time in enumerate(__snake_case							)
  ]




def       UpperCAmelCase__	(							__snake_case							)				->	float:
  return sum(__snake_case							) / len(__snake_case							)




def       UpperCAmelCase__	(							__snake_case							)				->	float:
  return sum(__snake_case							) / len(__snake_case							)


if __name__ == "__main__":
    # process id's
    __lowerCamelCase								=   [1, 2, 3]

    # ensure that we actually have processes
    if len(processes) == 0:
        print("""Zero amount of processes""")
        raise SystemExit(0)

    # duration time of all processes
    __lowerCamelCase								=   [1_9, 8, 9]

    # ensure we can match each id to a duration time
    if len(duration_times) != len(processes):
        print("""Unable to match all id's with their duration time""")
        raise SystemExit(0)

    # get the waiting times and the turnaround times
    __lowerCamelCase								=   calculate_waiting_times(duration_times)
    __lowerCamelCase								=   calculate_turnaround_times(duration_times, waiting_times)

    # get the average times
    __lowerCamelCase								=   calculate_average_waiting_time(waiting_times)
    __lowerCamelCase								=   calculate_average_turnaround_time(turnaround_times)

    # print all the results
    print("""Process ID\tDuration Time\tWaiting Time\tTurnaround Time""")
    for i, process in enumerate(processes):
        print(
            f'''{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'''
            f'''{turnaround_times[i]}'''
        )
    print(f'''Average waiting time = {average_waiting_time}''')
    print(f'''Average turn around time = {average_turnaround_time}''')