code: 
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  if is_vision_available():
  						import PIL
  
  
  __lowerCamelCase       :       Any				   =			logging.get_logger(__name__)
  
  
  
  class 			a      (      UpperCamelCase_   ):
  		__lowercase													=		["""pixel_values"""]
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase = True	,     __UpperCamelCase = None	,     __UpperCamelCase = PIL.Image.NEAREST	,     __UpperCamelCase = False	,     __UpperCamelCase = None	,     __UpperCamelCase = 1 / 2_55	,     __UpperCamelCase = False	,     __UpperCamelCase = True	,     __UpperCamelCase = True	,     __UpperCamelCase = None	,     __UpperCamelCase = None	,     __UpperCamelCase = True	,     **__UpperCamelCase	,     )->			None:
  						'''simple docstring'''
  
  
  						super().__init__(**__UpperCamelCase							)
  						A__     :							str   				=size if size is not None else {'''height''': 3_46, '''width''': 3_46}
  						A__     :							List[Any]   				=get_size_dict(__UpperCamelCase							)
  						A__     :							Dict   				=crop_size if crop_size is not None else {'''height''': 2_89, '''width''': 2_89}
  						A__     :							Optional[int]   				=get_size_dict(__UpperCamelCase	,     param_name='''crop_size'''							)
  
  						A__     :							Union[str, Any]   				=do_resize
  						A__     :							Tuple   				=size
  						A__     :							Dict   				=resample
  						A__     :							Optional[int]   				=do_center_crop
  						A__     :							Optional[int]   				=crop_size
  						A__     :							Optional[Any]   				=do_rescale
  						A__     :							Dict   				=rescale_factor
  						A__     :							List[str]   				=rescale_offset
  						A__     :							str   				=do_normalize
  						A__     :							Optional[int]   				=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
  						A__     :							Dict   				=image_std if image_std is not None else IMAGENET_STANDARD_STD
  						A__     :							Union[str, Any]   				=include_top
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase = PIL.Image.NEAREST	,     __UpperCamelCase = None	,     **__UpperCamelCase	,     )->			np.ndarray:
  						'''simple docstring'''
  
  
  						A__     :							Optional[int]   				=get_size_dict(__UpperCamelCase							)
  						if "height" not in size or "width" not in size:
  										raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'							)
  						return resize(
  						    __UpperCamelCase	,     size=(size['''height'''], size['''width'''])	,     resample=__UpperCamelCase	,     data_format=__UpperCamelCase	,     **__UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase = None	,     **__UpperCamelCase	,     )->			np.ndarray:
  						'''simple docstring'''
  
  
  						A__     :							Tuple   				=get_size_dict(__UpperCamelCase							)
  						if "height" not in size or "width" not in size:
  										raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'							)
  						return center_crop(__UpperCamelCase	,     size=(size['''height'''], size['''width'''])	,     data_format=__UpperCamelCase	,     **__UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase = True	,     __UpperCamelCase = None	,     **__UpperCamelCase	,     )->			List[str]:
  						'''simple docstring'''
  
  
  						if offset:
  										A__     :							str   				=(image - 127.5) * scale
  										if data_format is not None:
  														A__     :							Optional[Any]   				=to_channel_dimension_format(__UpperCamelCase	,     __UpperCamelCase							)
  										A__     :							Any   				=rescaled_image.astype(np.floataa							)
  						else:
  										A__     :							List[Any]   				=rescale(__UpperCamelCase	,     scale=__UpperCamelCase	,     data_format=__UpperCamelCase	,     **__UpperCamelCase							)
  						return rescaled_image
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase = None	,     **__UpperCamelCase	,     )->			np.ndarray:
  						'''simple docstring'''
  
  
  						return normalize(__UpperCamelCase	,     mean=__UpperCamelCase	,     std=__UpperCamelCase	,     data_format=__UpperCamelCase	,     **__UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase = None	,     __UpperCamelCase = None	,     __UpperCamelCase=None	,     __UpperCamelCase = None	,     __UpperCamelCase = None	,     __UpperCamelCase = None	,     __UpperCamelCase = None	,     __UpperCamelCase = None	,     __UpperCamelCase = None	,     __UpperCamelCase = None	,     __UpperCamelCase = None	,     __UpperCamelCase = None	,     __UpperCamelCase = None	,     __UpperCamelCase = ChannelDimension.FIRST	,     **__UpperCamelCase	,     )->			PIL.Image.Image:
  						'''simple docstring'''
  
  
  						A__     :							List[str]   				=do_resize if do_resize is not None else self.do_resize
  						A__     :							List[Any]   				=resample if resample is not None else self.resample
  						A__     :							int   				=do_center_crop if do_center_crop is not None else self.do_center_crop
  						A__     :							int   				=do_rescale if do_rescale is not None else self.do_rescale
  						A__     :							Optional[Any]   				=rescale_factor if rescale_factor is not None else self.rescale_factor
  						A__     :							Optional[Any]   				=rescale_offset if rescale_offset is not None else self.rescale_offset
  						A__     :							Optional[int]   				=do_normalize if do_normalize is not None else self.do_normalize
  						A__     :							int   				=image_mean if image_mean is not None else self.image_mean
  						A__     :							Optional[Any]   				=image_std if image_std is not None else self.image_std
  						A__     :							Dict   				=include_top if include_top is not None else self.include_top
  
  						A__     :							List[Any]   				=size if size is not None else self.size
  						A__     :							Union[str, Any]   				=get_size_dict(__UpperCamelCase							)
  						A__     :							Optional[Any]   				=crop_size if crop_size is not None else self.crop_size
  						A__     :							Union[str, Any]   				=get_size_dict(__UpperCamelCase	,     param_name='''crop_size'''							)
  
  						A__     :							Tuple   				=make_list_of_images(__UpperCamelCase							)
  
  						if not valid_images(__UpperCamelCase							):
  										raise ValueError(
  										    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
  										    '''torch.Tensor, tf.Tensor or jax.ndarray.'''							)
  
  						if do_resize and size is None or resample is None:
  										raise ValueError('''Size and resample must be specified if do_resize is True.'''							)
  
  						if do_center_crop and crop_size is None:
  										raise ValueError('''Crop size must be specified if do_center_crop is True.'''							)
  
  						if do_rescale and rescale_factor is None:
  										raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)
  
  						if do_normalize and (image_mean is None or image_std is None):
  										raise ValueError('''Image mean and std must be specified if do_normalize is True.'''							)
  
  						# All transformations expect numpy arrays.
  						A__     :							int   				=[to_numpy_array(__UpperCamelCase							) for image in images]
  
  						if do_resize:
  										A__     :							Dict   				=[self.resize(image=__UpperCamelCase	,     size=__UpperCamelCase	,     resample=__UpperCamelCase							) for image in images]
  
  						if do_center_crop:
  										A__     :							int   				=[self.center_crop(image=__UpperCamelCase	,     size=__UpperCamelCase							) for image in images]
  
  						if do_rescale:
  										A__     :							Union[str, Any]   				=[self.rescale(image=__UpperCamelCase	,     scale=__UpperCamelCase	,     offset=__UpperCamelCase							) for image in images]
  
  						if do_normalize:
  										A__     :							Union[str, Any]   				=[self.normalize(image=__UpperCamelCase	,     mean=__UpperCamelCase	,     std=__UpperCamelCase							) for image in images]
  
  						if include_top:
  										A__     :							Tuple   				=[self.normalize(image=__UpperCamelCase	,     mean=[0, 0, 0]	,     std=__UpperCamelCase							) for image in images]
  
  						A__     :							Optional[int]   				=[to_channel_dimension_format(__UpperCamelCase	,     __UpperCamelCase							) for image in images]
  
  						A__     :							Optional[int]   				={'''pixel_values''': images}
  						return BatchFeature(data=__UpperCamelCase	,     tensor_type=__UpperCamelCase							)
  
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							list[int]:
  				A__     :							Optional[int]   				=[0] * len(snake_case_	)
  				for i in range(1,	len(snake_case_	)	):
  								A__     :							List[Any]   				=duration_times[i - 1] + waiting_times[i - 1]
  				return waiting_times
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							list[int]:
  				return [
  				    duration_time + waiting_times[i]
  				    for i, duration_time in enumerate(snake_case_	)
  				]
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							float:
  				return sum(snake_case_	) / len(snake_case_	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							float:
  				return sum(snake_case_	) / len(snake_case_	)
  
  
  if __name__ == "__main__":
  						# process id's
  						__lowerCamelCase       :       List[str]				   =			[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       :       Dict				   =			[19, 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       :       int				   =			calculate_waiting_times(duration_times)
  						__lowerCamelCase       :       Optional[int]				   =			calculate_turnaround_times(duration_times, waiting_times)
  
  						# get the average times
  						__lowerCamelCase       :       List[str]				   =			calculate_average_waiting_time(waiting_times)
  						__lowerCamelCase       :       Dict				   =			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}")
  
  
  
  