






"""simple docstring"""







from statistics import mean

import numpy as np





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  0
					# Number of processes finished
					a__          =  0
					# Displays the finished process.
					# If it is 0, the performance is completed if it is 1, before the performance.
					a__          =  [0] * no_of_process
					# List to include calculation results
					a__          =  [0] * no_of_process

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

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

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

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

					return turn_around_time





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  [0] * no_of_process
					for i in range(0 ,     __snake_case):
										a__          =  turn_around_time[i] - burst_time[i]
					return waiting_time


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

	__a       :	str             = calculate_turn_around_time(
	    process_name, arrival_time, burst_time, no_of_process
	)
	__a       :	Tuple             = 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}''')










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a       :	Any             = logging.get_logger(__name__)

__a       :	Union[str, Any]             = {
    'caidas/swin2sr-classicalsr-x2-64': (
        'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
    ),
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='swin2sr'

							_SCREAMING_SNAKE_CASE    ={
							    'hidden_size': 'embed_dim',
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}




							def __init__(   self:     Union[str, Any]				,		__A:     List[Any]=64				,		__A:     int=1				,		__A:     Dict=3				,		__A:     List[Any]=180				,		__A:     int=[6, 6, 6, 6, 6, 6]				,		__A:     Tuple=[6, 6, 6, 6, 6, 6]				,		__A:     int=8				,		__A:     Optional[int]=2.0				,		__A:     Optional[int]=True				,		__A:     int=0.0				,		__A:     Any=0.0				,		__A:     Optional[Any]=0.1				,		__A:     Optional[Any]="gelu"				,		__A:     Dict=False				,		__A:     List[Any]=0.0_2				,		__A:     List[Any]=1e-5				,		__A:     List[str]=2				,		__A:     int=1.0				,		__A:     Dict="1conv"				,		__A:     Optional[Any]="pixelshuffle"				,		**__A:     Dict				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  image_size
												a__          =  patch_size
												a__          =  num_channels
												a__          =  embed_dim
												a__          =  depths
												a__          =  len(__A    )
												a__          =  num_heads
												a__          =  window_size
												a__          =  mlp_ratio
												a__          =  qkv_bias
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  drop_path_rate
												a__          =  hidden_act
												a__          =  use_absolute_embeddings
												a__          =  layer_norm_eps
												a__          =  initializer_range
												a__          =  upscale
												a__          =  img_range
												a__          =  resi_connection
												a__          =  upsampler


