
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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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


lowercase__		:Optional[Any]			  =   logging.get_logger(__name__)







class     lowercase		( __SCREAMING_SNAKE_CASE		):
							lowercase_	:	str	 =["pixel_values"]




							def __init__(    self       ,A__ = True       ,A__ = None       ,A__ = PILImageResampling.BICUBIC       ,A__ = True       ,A__ = 1 / 2_5_5       ,A__ = True       ,A__ = None       ,A__ = None       ,A__ = True       ,**A__       ,):
												super().__init__(**__UpperCAmelCase)
												lowercase						     =						size if size is not None else {"""height""": 3_8_4, """width""": 3_8_4}
												lowercase						     =						get_size_dict(__UpperCAmelCase       ,default_to_square=__UpperCAmelCase)

												lowercase						     =						do_resize
												lowercase						     =						size
												lowercase						     =						resample
												lowercase						     =						do_rescale
												lowercase						     =						rescale_factor
												lowercase						     =						do_normalize
												lowercase						     =						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
												lowercase						     =						image_std if image_std is not None else OPENAI_CLIP_STD
												lowercase						     =						do_convert_rgb




							def 			A__				(    self       ,A__       ,A__       ,A__ = PILImageResampling.BICUBIC       ,A__ = None       ,**A__       ,):
												lowercase						     =						get_size_dict(__UpperCAmelCase       ,default_to_square=__UpperCAmelCase)
												if "height" not in size or "width" not in size:
																	raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}')
												lowercase						     =						(size["""height"""], size["""width"""])
												return resize(__UpperCAmelCase       ,size=__UpperCAmelCase       ,resample=__UpperCAmelCase       ,data_format=__UpperCAmelCase       ,**__UpperCAmelCase)




							def 			A__				(    self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
												return rescale(__UpperCAmelCase       ,scale=__UpperCAmelCase       ,data_format=__UpperCAmelCase       ,**__UpperCAmelCase)




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__ = None       ,**A__       ,):
												return normalize(__UpperCAmelCase       ,mean=__UpperCAmelCase       ,std=__UpperCAmelCase       ,data_format=__UpperCAmelCase       ,**__UpperCAmelCase)





							def 			A__				(    self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,**A__       ,):
												lowercase						     =						do_resize if do_resize is not None else self.do_resize
												lowercase						     =						resample if resample is not None else self.resample
												lowercase						     =						do_rescale if do_rescale is not None else self.do_rescale
												lowercase						     =						rescale_factor if rescale_factor is not None else self.rescale_factor
												lowercase						     =						do_normalize if do_normalize is not None else self.do_normalize
												lowercase						     =						image_mean if image_mean is not None else self.image_mean
												lowercase						     =						image_std if image_std is not None else self.image_std
												lowercase						     =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

												lowercase						     =						size if size is not None else self.size
												lowercase						     =						get_size_dict(__UpperCAmelCase       ,default_to_square=__UpperCAmelCase)

												lowercase						     =						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_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.''')

												# PIL RGBA images are converted to RGB
												if do_convert_rgb:
																	lowercase						     =						[convert_to_rgb(__UpperCAmelCase) for image in images]

												# All transformations expect numpy arrays.
												lowercase						     =						[to_numpy_array(__UpperCAmelCase) for image in images]

												if do_resize:
																	lowercase						     =						[self.resize(image=__UpperCAmelCase       ,size=__UpperCAmelCase       ,resample=__UpperCAmelCase) for image in images]

												if do_rescale:
																	lowercase						     =						[self.rescale(image=__UpperCAmelCase       ,scale=__UpperCAmelCase) for image in images]

												if do_normalize:
																	lowercase						     =						[self.normalize(image=__UpperCAmelCase       ,mean=__UpperCAmelCase       ,std=__UpperCAmelCase) for image in images]

												lowercase						     =						[to_channel_dimension_format(__UpperCAmelCase       ,__UpperCAmelCase) for image in images]

												lowercase						     =						BatchFeature(data={'''pixel_values''': images}       ,tensor_type=__UpperCAmelCase)

												return encoded_outputs




from statistics import mean

import numpy as np



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						0
					# Number of processes finished
					lowercase						     =						0
					# Displays the finished process.
					# If it is 0, the performance is completed if it is 1, before the performance.
					lowercase						     =						[0] * no_of_process
					# List to include calculation results
					lowercase						     =						[0] * no_of_process

					# Sort by arrival time.
					lowercase						     =						[burst_time[i] for i in np.argsort(lowerCAmelCase__		)]
					lowercase						     =						[process_name[i] for i in np.argsort(lowerCAmelCase__		)]
					arrival_time.sort()

					while no_of_process > finished_process_count:
										lowercase						     =						0
										while finished_process[i] == 1:
															i += 1
										if current_time < arrival_time[i]:
															lowercase						     =						arrival_time[i]

										lowercase						     =						0
										# Index showing the location of the process being performed
										lowercase						     =						0
										# Saves the current response ratio.
										lowercase						     =						0
										for i in range(0			,      lowerCAmelCase__		):
															if finished_process[i] == 0 and arrival_time[i] <= current_time:
																				lowercase						     =						(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
																				    i
																				]
															if response_ratio < temp:
																				lowercase						     =						temp
																				lowercase						     =						i

        # Calculate the turn around time
										lowercase						     =						current_time + burst_time[loc] - arrival_time[loc]
										current_time += burst_time[loc]
										# Indicates that the process has been performed.
										lowercase						     =						1
										# Increase finished_process_count by 1
										finished_process_count += 1

					return turn_around_time



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						[0] * no_of_process
					for i in range(0			,      lowerCAmelCase__		):
										lowercase						     =						turn_around_time[i] - burst_time[i]
					return waiting_time


if __name__ == "__main__":
							lowercase__		:Dict			  =   5
							lowercase__		:str			  =   ["A", "B", "C", "D", "E"]
							lowercase__		:Optional[int]			  =   [1, 2, 3, 4, 5]
							lowercase__		:List[Any]			  =   [1, 2, 3, 4, 5]

							lowercase__		:List[str]			  =   calculate_turn_around_time(
							    process_name, arrival_time, burst_time, no_of_process
							)
							lowercase__		:List[Any]			  =   calculate_waiting_time(
							    process_name, turn_around_time, burst_time, no_of_process
							)

							print("Process name \tArrival time \tBurst time \tTurn around time \tWaiting time")
							for i in range(0, no_of_process):
														print(
														    F'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
														    F'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
														)

							print(F'average waiting time : {mean(waiting_time):.5f}')
							print(F'average turn around time : {mean(turn_around_time):.5f}')




