



"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase__              =							logging.get_logger(__name__)






class 			__snake_case			(      _lowercase):
  snake_case__							:  Optional[int]         =		["pixel_values"]




  def __init__(					self	:		str		,   __lowerCAmelCase	:		bool = True		,   __lowerCAmelCase	:		Optional[Dict[str, int]] = None		,   __lowerCAmelCase	:		PILImageResampling = PILImageResampling.BICUBIC		,   __lowerCAmelCase	:		bool = True		,   __lowerCAmelCase	:		bool = True		,   __lowerCAmelCase	:		Union[int, float] = 1 / 2_5_5		,   __lowerCAmelCase	:		Dict[str, int] = None		,   __lowerCAmelCase	:		bool = True		,   __lowerCAmelCase	:		Optional[Union[float, List[float]]] = None		,   __lowerCAmelCase	:		Optional[Union[float, List[float]]] = None		,   **__lowerCAmelCase	:		Union[str, Any]		,   ):






         """simple docstring"""






         super().__init__(**__lowerCAmelCase						)
         _lowerCamelCase		:							Optional[int]            =					size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
         _lowerCamelCase		:							Optional[Any]            =					get_size_dict(__lowerCAmelCase						)
         _lowerCamelCase		:							Optional[int]            =					crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
         _lowerCamelCase		:							Union[str, Any]            =					get_size_dict(__lowerCAmelCase		,   default_to_square=__lowerCAmelCase		,   param_name='''crop_size'''						)

         _lowerCamelCase		:							Optional[Any]            =					do_resize
         _lowerCamelCase		:							int            =					do_rescale
         _lowerCamelCase		:							List[Any]            =					do_normalize
         _lowerCamelCase		:							List[Any]            =					do_center_crop
         _lowerCamelCase		:							Any            =					crop_size
         _lowerCamelCase		:							Tuple            =					size
         _lowerCamelCase		:							str            =					resample
         _lowerCamelCase		:							List[Any]            =					rescale_factor
         _lowerCamelCase		:							int            =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         _lowerCamelCase		:							Dict            =					image_std if image_std is not None else IMAGENET_DEFAULT_STD




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple		,   __lowerCAmelCase	:		np.ndarray		,   __lowerCAmelCase	:		Dict[str, int]		,   __lowerCAmelCase	:		PILImageResampling = PILImageResampling.BILINEAR		,   __lowerCAmelCase	:		Optional[Union[str, ChannelDimension]] = None		,   **__lowerCAmelCase	:		Dict		,   ):






         """simple docstring"""






         _lowerCamelCase		:							List[str]            =					get_size_dict(__lowerCAmelCase						)

         if "shortest_edge" in size:
                _lowerCamelCase		:							Any            =					get_resize_output_image_size(__lowerCAmelCase		,   size=size['''shortest_edge''']		,   default_to_square=__lowerCAmelCase						)
                # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
                _lowerCamelCase		:							int            =					(size['''height'''], size['''width'''])
         else:
                raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''						)
         return resize(__lowerCAmelCase		,   size=__lowerCAmelCase		,   resample=__lowerCAmelCase		,   data_format=__lowerCAmelCase		,   **__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]		,   __lowerCAmelCase	:		np.ndarray		,   __lowerCAmelCase	:		Dict[str, int]		,   __lowerCAmelCase	:		Optional[Union[str, ChannelDimension]] = None		,   **__lowerCAmelCase	:		Optional[int]		,   ):






         """simple docstring"""






         _lowerCamelCase		:							Tuple            =					get_size_dict(__lowerCAmelCase						)
         if "height" not in size or "width" not in size:
                raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''						)
         return center_crop(__lowerCAmelCase		,   size=(size['''height'''], size['''width'''])		,   data_format=__lowerCAmelCase		,   **__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]		,   __lowerCAmelCase	:		np.ndarray		,   __lowerCAmelCase	:		float		,   __lowerCAmelCase	:		Optional[Union[str, ChannelDimension]] = None		,   **__lowerCAmelCase	:		Tuple						):






         """simple docstring"""






         return rescale(__lowerCAmelCase		,   scale=__lowerCAmelCase		,   data_format=__lowerCAmelCase		,   **__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		int		,   __lowerCAmelCase	:		np.ndarray		,   __lowerCAmelCase	:		Union[float, List[float]]		,   __lowerCAmelCase	:		Union[float, List[float]]		,   __lowerCAmelCase	:		Optional[Union[str, ChannelDimension]] = None		,   **__lowerCAmelCase	:		Tuple		,   ):






         """simple docstring"""






         return normalize(__lowerCAmelCase		,   mean=__lowerCAmelCase		,   std=__lowerCAmelCase		,   data_format=__lowerCAmelCase		,   **__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]		,   __lowerCAmelCase	:		ImageInput		,   __lowerCAmelCase	:		Optional[bool] = None		,   __lowerCAmelCase	:		Dict[str, int] = None		,   __lowerCAmelCase	:		PILImageResampling = None		,   __lowerCAmelCase	:		bool = None		,   __lowerCAmelCase	:		int = None		,   __lowerCAmelCase	:		Optional[bool] = None		,   __lowerCAmelCase	:		Optional[float] = None		,   __lowerCAmelCase	:		Optional[bool] = None		,   __lowerCAmelCase	:		Optional[Union[float, List[float]]] = None		,   __lowerCAmelCase	:		Optional[Union[float, List[float]]] = None		,   __lowerCAmelCase	:		Optional[Union[str, TensorType]] = None		,   __lowerCAmelCase	:		Union[str, ChannelDimension] = ChannelDimension.FIRST		,   **__lowerCAmelCase	:		Any		,   ):






         """simple docstring"""






         _lowerCamelCase		:							Dict            =					do_resize if do_resize is not None else self.do_resize
         _lowerCamelCase		:							str            =					do_rescale if do_rescale is not None else self.do_rescale
         _lowerCamelCase		:							Optional[Any]            =					do_normalize if do_normalize is not None else self.do_normalize
         _lowerCamelCase		:							str            =					do_center_crop if do_center_crop is not None else self.do_center_crop
         _lowerCamelCase		:							Dict            =					crop_size if crop_size is not None else self.crop_size
         _lowerCamelCase		:							List[str]            =					get_size_dict(__lowerCAmelCase		,   param_name='''crop_size'''		,   default_to_square=__lowerCAmelCase						)
         _lowerCamelCase		:							int            =					resample if resample is not None else self.resample
         _lowerCamelCase		:							Optional[int]            =					rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowerCamelCase		:							Tuple            =					image_mean if image_mean is not None else self.image_mean
         _lowerCamelCase		:							Any            =					image_std if image_std is not None else self.image_std

         _lowerCamelCase		:							Optional[int]            =					size if size is not None else self.size
         _lowerCamelCase		:							int            =					get_size_dict(__lowerCAmelCase						)

         if not is_batched(__lowerCAmelCase						):
                _lowerCamelCase		:							str            =					[images]

         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:
                raise ValueError('''Size 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.'''						)

         # All transformations expect numpy arrays.
         _lowerCamelCase		:							Dict            =					[to_numpy_array(__lowerCAmelCase						) for image in images]

         if do_resize:
                _lowerCamelCase		:							Any            =					[self.resize(image=__lowerCAmelCase		,   size=__lowerCAmelCase		,   resample=__lowerCAmelCase						) for image in images]

         if do_center_crop:
                _lowerCamelCase		:							List[Any]            =					[self.center_crop(image=__lowerCAmelCase		,   size=__lowerCAmelCase						) for image in images]

         if do_rescale:
                _lowerCamelCase		:							str            =					[self.rescale(image=__lowerCAmelCase		,   scale=__lowerCAmelCase						) for image in images]

         if do_normalize:
                _lowerCamelCase		:							Optional[int]            =					[self.normalize(image=__lowerCAmelCase		,   mean=__lowerCAmelCase		,   std=__lowerCAmelCase						) for image in images]

         _lowerCamelCase		:							str            =					[to_channel_dimension_format(__lowerCAmelCase		,   __lowerCAmelCase						) for image in images]

         _lowerCamelCase		:							Tuple            =					{'''pixel_values''': images}
         return BatchFeature(data=__lowerCAmelCase		,   tensor_type=__lowerCAmelCase						)









'''simple docstring'''




from __future__ import annotations

from statistics import mean



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				List[Any]		=			[0] * no_of_processes
		SCREAMING_SNAKE_CASE :				int		=			[0] * no_of_processes

		# Initialize remaining_time to waiting_time.

		for i in range(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Optional[int]		=			burst_time[i]
		SCREAMING_SNAKE_CASE :				list[int]		=			[]

		SCREAMING_SNAKE_CASE :				List[str]		=			0
		SCREAMING_SNAKE_CASE :				List[Any]		=			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:
				SCREAMING_SNAKE_CASE :				Optional[int]		=			[]
				SCREAMING_SNAKE_CASE :				Any		=			-1

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

				if len(lowerCamelCase_	) > 0:
						SCREAMING_SNAKE_CASE :				Union[str, Any]		=			ready_process[0]
						for i in ready_process:
								if remaining_time[i] < remaining_time[target_process]:
										SCREAMING_SNAKE_CASE :				Any		=			i
						total_time += burst_time[target_process]
						completed += 1
						SCREAMING_SNAKE_CASE :				Any		=			0
						SCREAMING_SNAKE_CASE :				Optional[int]		=			(
						    total_time - arrival_time[target_process] - burst_time[target_process]
						)
				else:
						total_time += 1

		return waiting_time



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Any		=			[0] * no_of_processes
		for i in range(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			burst_time[i] + waiting_time[i]
		return turn_around_time


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

							__UpperCAmelCase            =       4
							__UpperCAmelCase            =       [2, 5, 3, 7]
							__UpperCAmelCase            =       [0, 0, 0, 0]
							__UpperCAmelCase            =       calculate_waitingtime(arrival_time, burst_time, no_of_processes)
							__UpperCAmelCase            =       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}''')



