code: 
  """simple docstring"""
  
  
  
  
  
  import json
  import os
  import tempfile
  
  from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  
  if is_torch_available():
      import numpy as np
      import torch
  
  if is_vision_available():
      from PIL import Image
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase=False,	__lowerCamelCase=False,	__lowerCamelCase=False						):
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
     UpperCAmelCase_      :      List[Any]      =					[]
     for i in range(image_processor_tester.batch_size						):
        if equal_resolution:
           UpperCAmelCase_      :      Tuple      =					image_processor_tester.max_resolution
        else:
           # To avoid getting image width/height 0
           UpperCAmelCase_      :      str      =					image_processor_tester.min_resolution
           if getattr(__lowerCamelCase,	"size_divisor",	__lowerCamelCase						):
              # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
              UpperCAmelCase_      :      int      =					max(image_processor_tester.size_divisor,	__lowerCamelCase						)
           UpperCAmelCase_      :      Optional[Any]      =					np.random.choice(np.arange(__lowerCamelCase,	image_processor_tester.max_resolution						),	2						)
        image_inputs.append(
            np.random.randint(255,	size=(image_processor_tester.num_channels, width, height),	dtype=np.uinta						)						)
  
     if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        UpperCAmelCase_      :      Tuple      =					[Image.fromarray(np.moveaxis(__lowerCamelCase,	0,	-1						)						) for image in image_inputs]
  
     if torchify:
        UpperCAmelCase_      :      List[str]      =					[torch.from_numpy(__lowerCamelCase						) for image in image_inputs]
  
     return image_inputs
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase=10,	__lowerCamelCase=10,	__lowerCamelCase=False,	__lowerCamelCase=False						):
     UpperCAmelCase_      :      Tuple      =					[]
     for i in range(image_processor_tester.num_frames						):
        video.append(np.random.randint(255,	size=(image_processor_tester.num_channels, width, height),	dtype=np.uinta						)						)
  
     if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        UpperCAmelCase_      :      Tuple      =					[Image.fromarray(np.moveaxis(__lowerCamelCase,	0,	-1						)						) for frame in video]
  
     if torchify:
        UpperCAmelCase_      :      List[str]      =					[torch.from_numpy(__lowerCamelCase						) for frame in video]
  
     return video
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase=False,	__lowerCamelCase=False,	__lowerCamelCase=False						):
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
     UpperCAmelCase_      :      Optional[Any]      =					[]
     for i in range(image_processor_tester.batch_size						):
        if equal_resolution:
           UpperCAmelCase_      :      List[str]      =					image_processor_tester.max_resolution
        else:
           UpperCAmelCase_      :      List[Any]      =					np.random.choice(
               np.arange(image_processor_tester.min_resolution,	image_processor_tester.max_resolution						),	2						)
           UpperCAmelCase_      :      str      =					prepare_video(
               image_processor_tester=__lowerCamelCase,	width=__lowerCamelCase,	height=__lowerCamelCase,	numpify=__lowerCamelCase,	torchify=__lowerCamelCase,	)
        video_inputs.append(__lowerCamelCase						)
  
     return video_inputs
  
  
  class A_			:
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		List[str]						  =	None
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					self.image_processing_class(**self.image_processor_dict						)
       UpperCAmelCase_      :      Optional[int]      =					json.loads(image_processor.to_json_string()						)
       for key, value in self.image_processor_dict.items():
          self.assertEqual(obj[key]       ,     lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      int      =					self.image_processing_class(**self.image_processor_dict						)
  
       with tempfile.TemporaryDirectory() as tmpdirname:
          UpperCAmelCase_      :      List[str]      =					os.path.join(lowercase_       ,     "image_processor.json"						)
          image_processor_first.to_json_file(lowercase_						)
          UpperCAmelCase_      :      str      =					self.image_processing_class.from_json_file(lowercase_						)
  
       self.assertEqual(image_processor_second.to_dict()       ,     image_processor_first.to_dict()						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					self.image_processing_class(**self.image_processor_dict						)
  
       with tempfile.TemporaryDirectory() as tmpdirname:
          UpperCAmelCase_      :      Union[str, Any]      =					image_processor_first.save_pretrained(lowercase_						)[0]
          check_json_file_has_correct_format(lowercase_						)
          UpperCAmelCase_      :      Any      =					self.image_processing_class.from_pretrained(lowercase_						)
  
       self.assertEqual(image_processor_second.to_dict()       ,     image_processor_first.to_dict()						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					self.image_processing_class()
       self.assertIsNotNone(lowercase_						)
  
  
  
  
  
  
  
    @require_torch
    @require_vision
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       if self.test_cast_dtype is not None:
          # Initialize image_processor
          UpperCAmelCase_      :      List[str]      =					self.image_processing_class(**self.image_processor_dict						)
  
          # create random PyTorch tensors
          UpperCAmelCase_      :      List[Any]      =					prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=lowercase_       ,     torchify=lowercase_						)
  
          UpperCAmelCase_      :      Any      =					image_processor(lowercase_       ,     return_tensors="pt"						)
          # for layoutLM compatiblity
          self.assertEqual(encoding.pixel_values.device       ,     torch.device("cpu"						)						)
          self.assertEqual(encoding.pixel_values.dtype       ,     torch.floataa						)
  
          UpperCAmelCase_      :      Dict      =					image_processor(lowercase_       ,     return_tensors="pt"						).to(torch.floataa						)
          self.assertEqual(encoding.pixel_values.device       ,     torch.device("cpu"						)						)
          self.assertEqual(encoding.pixel_values.dtype       ,     torch.floataa						)
  
          UpperCAmelCase_      :      str      =					image_processor(lowercase_       ,     return_tensors="pt"						).to("cpu"       ,     torch.bfloataa						)
          self.assertEqual(encoding.pixel_values.device       ,     torch.device("cpu"						)						)
          self.assertEqual(encoding.pixel_values.dtype       ,     torch.bfloataa						)
  
          with self.assertRaises(lowercase_						):
             UpperCAmelCase_      :      Dict      =					image_processor(lowercase_       ,     return_tensors="pt"						).to(torch.bfloataa       ,     "cpu"						)
  
          # Try with text + image feature
          UpperCAmelCase_      :      Optional[int]      =					image_processor(lowercase_       ,     return_tensors="pt"						)
          encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]						)}						)
          UpperCAmelCase_      :      Dict      =					encoding.to(torch.floataa						)
  
          self.assertEqual(encoding.pixel_values.device       ,     torch.device("cpu"						)						)
          self.assertEqual(encoding.pixel_values.dtype       ,     torch.floataa						)
          self.assertEqual(encoding.input_ids.dtype       ,     torch.long						)
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from __future__ import annotations
  
  from statistics import mean
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase						):
     UpperCAmelCase_      :      Dict      =					2
     UpperCAmelCase_      :      Any      =					list(__lowerCamelCase						)
     UpperCAmelCase_      :      List[Any]      =					[0] * len(__lowerCamelCase						)
     UpperCAmelCase_      :      str      =					0
     while True:
        UpperCAmelCase_      :      int      =					True
        for i, burst_time in enumerate(__lowerCamelCase						):
           if rem_burst_times[i] > 0:
              UpperCAmelCase_      :      List[Any]      =					False
              if rem_burst_times[i] > quantum:
                 t += quantum
                 rem_burst_times[i] -= quantum
              else:
                 t += rem_burst_times[i]
                 UpperCAmelCase_      :      Optional[int]      =					t - burst_time
                 UpperCAmelCase_      :      Union[str, Any]      =					0
        if done is True:
           return waiting_times
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase						):
     return [burst + waiting for burst, waiting in zip(__lowerCamelCase,	__lowerCamelCase						)]
  
  
  if __name__ == "__main__":
      _a	 =							[3, 5, 7]
      _a	 =							calculate_waiting_times(burst_times)
      _a	 =							calculate_turn_around_times(burst_times, waiting_times)
      print('Process ID \tBurst Time \tWaiting Time \tTurnaround Time')
      for i, burst_time in enumerate(burst_times):
          print(
              f"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
              f"""{turn_around_times[i]}"""
          )
      print(f"""\nAverage waiting time = {mean(waiting_times):.5f}""")
      print(f"""Average turn around time = {mean(turn_around_times):.5f}""")
  
  
  