




'''simple docstring'''


from statistics import mean

import numpy as np
def 		snake_case_   (UpperCamelCase	:  list			,						UpperCamelCase	:  list			,						UpperCamelCase	:  list			,						UpperCamelCase	:  int				):





							'''simple docstring'''


							_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(UpperCamelCase				)]
							_a     					=    [process_name[i] for i in np.argsort(UpperCamelCase				)]
							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			,						UpperCamelCase				):
																					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 		snake_case_   (UpperCamelCase	:  list			,						UpperCamelCase	:  list			,						UpperCamelCase	:  list			,						UpperCamelCase	:  int				):





							'''simple docstring'''


							_a     					=    [0] * no_of_process
							for i in range(0			,						UpperCamelCase				):
														_a     					=    turn_around_time[i] - burst_time[i]
							return waiting_time


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

				_snake_case				:				Union[str, Any]       							=		calculate_turn_around_time(
				    process_name, arrival_time, burst_time, no_of_process
				)
				_snake_case				:				int       							=		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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case				:				Dict       							=		logging.get_logger(__name__)

_snake_case				:				Optional[Any]       							=		{
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}

class       A   (		_a					,_a   ):
							lowercase_          =    'nat'

							lowercase_          =    {
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  str=4							,      lowerCAmelCase_	:  Any=3							,      lowerCAmelCase_	:  List[Any]=64							,      lowerCAmelCase_	:  Dict=[3, 4, 6, 5]							,      lowerCAmelCase_	:  Dict=[2, 4, 8, 16]							,      lowerCAmelCase_	:  str=7							,      lowerCAmelCase_	:  Dict=3.0							,      lowerCAmelCase_	:  int=True							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=0.0							,      lowerCAmelCase_	:  List[str]=0.1							,      lowerCAmelCase_	:  int="gelu"							,      lowerCAmelCase_	:  List[str]=0.0_2							,      lowerCAmelCase_	:  str=1e-5							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=None							,      lowerCAmelCase_	:  int=None							,      **lowerCAmelCase_	:  List[Any]							,      )      ->							Any:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_ )

														_a     					=    patch_size
														_a     					=    num_channels
														_a     					=    embed_dim
														_a     					=    depths
														_a     					=    len(lowerCAmelCase_ )
														_a     					=    num_heads
														_a     					=    kernel_size
														_a     					=    mlp_ratio
														_a     					=    qkv_bias
														_a     					=    hidden_dropout_prob
														_a     					=    attention_probs_dropout_prob
														_a     					=    drop_path_rate
														_a     					=    hidden_act
														_a     					=    layer_norm_eps
														_a     					=    initializer_range
														# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														_a     					=    int(embed_dim * 2 ** (len(lowerCAmelCase_ ) - 1) )
														_a     					=    layer_scale_init_value
														_a     					=    ['''stem'''] + [F'stage{idx}' for idx in range(1							,      len(lowerCAmelCase_ ) + 1 )]
														_a					,							_a     					=    get_aligned_output_features_output_indices(
														    out_features=lowerCAmelCase_							,      out_indices=lowerCAmelCase_							,      stage_names=self.stage_names )

