

from __future__ import annotations

from statistics import mean




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Tuple:



    """simple docstring"""






    UpperCamelCase_			  =						[0] * no_of_processes
    UpperCamelCase_			  =						[0] * no_of_processes

    # Initialize remaining_time to waiting_time.

    for i in range(lowerCamelCase_			):
        UpperCamelCase_			  =						burst_time[i]
    UpperCamelCase_			  =						[]

    UpperCamelCase_			  =						0
    UpperCamelCase_			  =						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:
        UpperCamelCase_			  =						[]
        UpperCamelCase_			  =						-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:
            UpperCamelCase_			  =						ready_process[0]
            for i in ready_process:
                if remaining_time[i] < remaining_time[target_process]:
                    UpperCamelCase_			  =						i
            total_time += burst_time[target_process]
            completed += 1
            UpperCamelCase_			  =						0
            UpperCamelCase_			  =						(
                total_time - arrival_time[target_process] - burst_time[target_process]
            )
        else:
            total_time += 1

    return waiting_time




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Tuple:



    """simple docstring"""






    UpperCamelCase_			  =						[0] * no_of_processes
    for i in range(lowerCamelCase_			):
        UpperCamelCase_			  =						burst_time[i] + waiting_time[i]
    return turn_around_time


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

 SCREAMING_SNAKE_CASE		:Dict						=     4
 SCREAMING_SNAKE_CASE		:List[Any]						=     [2, 5, 3, 7]
 SCREAMING_SNAKE_CASE		:int						=     [0, 0, 0, 0]
 SCREAMING_SNAKE_CASE		:Any						=     calculate_waitingtime(arrival_time, burst_time, no_of_processes)
 SCREAMING_SNAKE_CASE		:Dict						=     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


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''donut-swin'''

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


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

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)

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



