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

from statistics import mean






def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	a_      				=						[0] * no_of_processes
	a_      				=						[0] * no_of_processes

	# Initialize remaining_time to waiting_time.

	for i in range(_lowercase						):
		a_      				=						burst_time[i]
	a_      				=						[]

	a_      				=						0
	a_      				=						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:
		a_      				=						[]
		a_      				=						-1

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

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

	return waiting_time






def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	a_      				=						[0] * no_of_processes
	for i in range(_lowercase						):
		a_      				=						burst_time[i] + waiting_time[i]
	return turn_around_time


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

		lowercase__											=4
		lowercase__											=[2, 5, 3, 7]
		lowercase__											=[0, 0, 0, 0]
		lowercase__											=calculate_waitingtime(arrival_time, burst_time, no_of_processes)
		lowercase__											=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"""




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


a		:					int	      =     logging.get_logger(__name__)


class 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   Optional[int]    =["""pixel_values"""]






							def __init__(  self ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = PIL.Image.BICUBIC ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = 1 / 255 ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					None:
								super().__init__(**lowerCAmelCase__ )
								a :						Optional[int]      =							size if size is not None else {"height": 256, "width": 256}
								a :						Tuple      =							get_size_dict(lowerCAmelCase__ )
								a :						Any      =							crop_size if crop_size is not None else {"height": 224, "width": 224}
								a :						Dict      =							get_size_dict(lowerCAmelCase__ ,   param_name="crop_size" )

								a :						Tuple      =							do_resize
								a :						int      =							size
								a :						List[str]      =							resample
								a :						List[Any]      =							do_center_crop
								a :						List[Any]      =							crop_size
								a :						Union[str, Any]      =							do_rescale
								a :						List[Any]      =							rescale_factor
								a :						Union[str, Any]      =							do_normalize
								a :						Union[str, Any]      =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								a :						List[str]      =							image_std if image_std is not None else IMAGENET_STANDARD_STD






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = PIL.Image.BICUBIC ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								a :						Optional[int]      =							get_size_dict(lowerCAmelCase__ )
								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(
								    lowerCAmelCase__ ,   size=(size["height"], size["width"]) ,   resample=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								a :						List[str]      =							get_size_dict(lowerCAmelCase__ )
								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(lowerCAmelCase__ ,   size=(size["height"], size["width"]) ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					Union[str, Any]:
								return rescale(lowerCAmelCase__ ,   scale=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								return normalize(lowerCAmelCase__ ,   mean=lowerCAmelCase__ ,   std=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__=None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = ChannelDimension.FIRST ,   **lowerCAmelCase__ ,   )      ->					PIL.Image.Image:
								a :						List[Any]      =							do_resize if do_resize is not None else self.do_resize
								a :						str      =							resample if resample is not None else self.resample
								a :						Dict      =							do_center_crop if do_center_crop is not None else self.do_center_crop
								a :						Union[str, Any]      =							do_rescale if do_rescale is not None else self.do_rescale
								a :						str      =							rescale_factor if rescale_factor is not None else self.rescale_factor
								a :						Dict      =							do_normalize if do_normalize is not None else self.do_normalize
								a :						List[str]      =							image_mean if image_mean is not None else self.image_mean
								a :						Union[str, Any]      =							image_std if image_std is not None else self.image_std

								a :						str      =							size if size is not None else self.size
								a :						Any      =							get_size_dict(lowerCAmelCase__ )
								a :						Dict      =							crop_size if crop_size is not None else self.crop_size
								a :						List[str]      =							get_size_dict(lowerCAmelCase__ ,   param_name="crop_size" )

								a :						str      =							make_list_of_images(lowerCAmelCase__ )

								if not valid_images(lowerCAmelCase__ ):
									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 :						Optional[Any]      =							[to_numpy_array(lowerCAmelCase__ ) for image in images]

								if do_resize:
									a :						Optional[int]      =							[self.resize(image=lowerCAmelCase__ ,   size=lowerCAmelCase__ ,   resample=lowerCAmelCase__ ) for image in images]

								if do_center_crop:
									a :						Dict      =							[self.center_crop(image=lowerCAmelCase__ ,   size=lowerCAmelCase__ ) for image in images]

								if do_rescale:
									a :						Any      =							[self.rescale(image=lowerCAmelCase__ ,   scale=lowerCAmelCase__ ) for image in images]

								if do_normalize:
									a :						Optional[Any]      =							[self.normalize(image=lowerCAmelCase__ ,   mean=lowerCAmelCase__ ,   std=lowerCAmelCase__ ) for image in images]

								a :						Optional[Any]      =							[to_channel_dimension_format(lowerCAmelCase__ ,   lowerCAmelCase__ ) for image in images]

								a :						Tuple      =							{"pixel_values": images}
								return BatchFeature(data=lowerCAmelCase__ ,   tensor_type=lowerCAmelCase__ )


