



'''simple docstring'''



from __future__ import annotations

from statistics import mean
def 					UpperCAmelCase   (						A  :				list[int]					,		A  :				list[int]					,		A  :				int				):
       SCREAMING_SNAKE_CASE					:      int     					=			[0] * no_of_processes
       SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[0] * no_of_processes

       # Initialize remaining_time to waiting_time.

       for i in range(A				):
              SCREAMING_SNAKE_CASE					:      List[Any]     					=			burst_time[i]
       SCREAMING_SNAKE_CASE					:      list[int]     					=			[]

       SCREAMING_SNAKE_CASE					:      Tuple     					=			0
       SCREAMING_SNAKE_CASE					:      int     					=			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[Any]     					=			[]
              SCREAMING_SNAKE_CASE					:      List[Any]     					=			-1

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

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

       return waiting_time
def 					UpperCAmelCase   (						A  :				list[int]					,		A  :				int					,		A  :				list[int]				):
       SCREAMING_SNAKE_CASE					:      Any     					=			[0] * no_of_processes
       for i in range(A				):
              SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			burst_time[i] + waiting_time[i]
       return turn_around_time


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

       lowerCAmelCase_     :       Dict								=						4
       lowerCAmelCase_     :       List[Any]								=						[2, 5, 3, 7]
       lowerCAmelCase_     :       int								=						[0, 0, 0, 0]
       lowerCAmelCase_     :       Tuple								=						calculate_waitingtime(arrival_time, burst_time, no_of_processes)
       lowerCAmelCase_     :       Any								=						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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_     :       Optional[int]								=						logging.get_logger(__name__)

lowerCAmelCase_     :       Optional[Any]								=						{
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}
class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   str	     =				'swinv2'

						_lowerCAmelCase     :   Optional[int]	     =				{
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}







						def __init__(  self   :      Optional[int]				,    lowerCAmelCase__   :      Any=2_24				,    lowerCAmelCase__   :      Optional[Any]=4				,    lowerCAmelCase__   :      Tuple=3				,    lowerCAmelCase__   :      Tuple=96				,    lowerCAmelCase__   :      Optional[int]=[2, 2, 6, 2]				,    lowerCAmelCase__   :      Tuple=[3, 6, 12, 24]				,    lowerCAmelCase__   :      List[str]=7				,    lowerCAmelCase__   :      Tuple=4.0				,    lowerCAmelCase__   :      Dict=True				,    lowerCAmelCase__   :      Any=0.0				,    lowerCAmelCase__   :      Optional[int]=0.0				,    lowerCAmelCase__   :      Optional[Any]=0.1				,    lowerCAmelCase__   :      int="gelu"				,    lowerCAmelCase__   :      Tuple=False				,    lowerCAmelCase__   :      Tuple=0.02				,    lowerCAmelCase__   :      Dict=1e-5				,    lowerCAmelCase__   :      List[str]=32				,    **lowerCAmelCase__   :      Union[str, Any]				,    ):

													"""simple docstring"""
													super().__init__(**lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			image_size
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			patch_size
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			num_channels
													SCREAMING_SNAKE_CASE					:      int     					=			embed_dim
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			depths
													SCREAMING_SNAKE_CASE					:      str     					=			len(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			num_heads
													SCREAMING_SNAKE_CASE					:      Tuple     					=			window_size
													SCREAMING_SNAKE_CASE					:      Any     					=			mlp_ratio
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			qkv_bias
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			hidden_dropout_prob
													SCREAMING_SNAKE_CASE					:      int     					=			attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE					:      List[str]     					=			drop_path_rate
													SCREAMING_SNAKE_CASE					:      Any     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			use_absolute_embeddings
													SCREAMING_SNAKE_CASE					:      List[str]     					=			layer_norm_eps
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			initializer_range
													SCREAMING_SNAKE_CASE					:      Dict     					=			encoder_stride
													# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
													# this indicates the channel dimension after the last stage of the model
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			int(embed_dim * 2 ** (len(lowerCAmelCase__				) - 1)				)
													SCREAMING_SNAKE_CASE					:      int     					=			(0, 0, 0, 0)




