


"""simple docstring"""







from __future__ import annotations

from statistics import mean
def  a__      (    SCREAMING_SNAKE_CASE   :   list[int]    ,				SCREAMING_SNAKE_CASE   :   list[int]    ,				SCREAMING_SNAKE_CASE   :   int    ):


		'''simple docstring'''







		lowerCAmelCase					:					Optional[int]								=							[0] * no_of_processes
		lowerCAmelCase					:					Union[str, Any]								=							[0] * no_of_processes

		# Initialize remaining_time to waiting_time.

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

		lowerCAmelCase					:					Optional[int]								=							0
		lowerCAmelCase					:					Dict								=							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:
				lowerCAmelCase					:					Optional[Any]								=							[]
				lowerCAmelCase					:					List[Any]								=							-1

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

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

		return waiting_time
def  a__      (    SCREAMING_SNAKE_CASE   :   list[int]    ,				SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   list[int]    ):


		'''simple docstring'''







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


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

					lowerCAmelCase__				    =       4
					lowerCAmelCase__				    =       [2, 5, 3, 7]
					lowerCAmelCase__				    =       [0, 0, 0, 0]
					lowerCAmelCase__				    =       calculate_waitingtime(arrival_time, burst_time, no_of_processes)
					lowerCAmelCase__				    =       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 typing import List, Optional

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


lowerCAmelCase__				    =       logging.get_logger(__name__)

lowerCAmelCase__				    =       {
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Dict       ="autoformer"
       a  :    Dict       ={
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
           "num_hidden_layers": "encoder_layers",
       }

       def __init__(						self		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = "student_t"		,					snake_case__ = "nll"		,					snake_case__ = 1		,					snake_case__ = [1, 2, 3, 4, 5, 6, 7]		,					snake_case__ = True		,					snake_case__ = 0		,					snake_case__ = 0		,					snake_case__ = 0		,					snake_case__ = 0		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = 64		,					snake_case__ = 2		,					snake_case__ = 2		,					snake_case__ = 2		,					snake_case__ = 2		,					snake_case__ = 32		,					snake_case__ = 32		,					snake_case__ = "gelu"		,					snake_case__ = 0.1		,					snake_case__ = 0.1		,					snake_case__ = 0.1		,					snake_case__ = 0.1		,					snake_case__ = 0.1		,					snake_case__ = 100		,					snake_case__ = 0.02		,					snake_case__ = True		,					snake_case__=True		,					snake_case__ = 10		,					snake_case__ = 25		,					snake_case__ = 3		,					**snake_case__		,					):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							prediction_length
         lowerCAmelCase					:					Dict								=							context_length if context_length is not None else prediction_length
         lowerCAmelCase					:					Tuple								=							distribution_output
         lowerCAmelCase					:					List[Any]								=							loss
         lowerCAmelCase					:					int								=							input_size
         lowerCAmelCase					:					str								=							num_time_features
         lowerCAmelCase					:					str								=							lags_sequence
         lowerCAmelCase					:					List[str]								=							scaling
         lowerCAmelCase					:					List[Any]								=							num_dynamic_real_features
         lowerCAmelCase					:					Tuple								=							num_static_real_features
         lowerCAmelCase					:					Dict								=							num_static_categorical_features
         if cardinality is not None and num_static_categorical_features > 0:
           if len(snake_case__						) != num_static_categorical_features:
             raise ValueError(
                 "The cardinality should be a list of the same length as `num_static_categorical_features`"						)
           lowerCAmelCase					:					Any								=							cardinality
         else:
           lowerCAmelCase					:					Union[str, Any]								=							[0]
         if embedding_dimension is not None and num_static_categorical_features > 0:
           if len(snake_case__						) != num_static_categorical_features:
             raise ValueError(
                 "The embedding dimension should be a list of the same length as `num_static_categorical_features`"						)
           lowerCAmelCase					:					Tuple								=							embedding_dimension
         else:
           lowerCAmelCase					:					Any								=							[min(50		,					(cat + 1) // 2						) for cat in self.cardinality]
         lowerCAmelCase					:					Any								=							num_parallel_samples

         # Transformer architecture configuration
         lowerCAmelCase					:					str								=							input_size * len(self.lags_sequence						) + self._number_of_features
         lowerCAmelCase					:					Any								=							d_model
         lowerCAmelCase					:					List[str]								=							encoder_attention_heads
         lowerCAmelCase					:					Union[str, Any]								=							decoder_attention_heads
         lowerCAmelCase					:					Optional[int]								=							encoder_ffn_dim
         lowerCAmelCase					:					Optional[Any]								=							decoder_ffn_dim
         lowerCAmelCase					:					int								=							encoder_layers
         lowerCAmelCase					:					int								=							decoder_layers

         lowerCAmelCase					:					List[Any]								=							dropout
         lowerCAmelCase					:					Optional[int]								=							attention_dropout
         lowerCAmelCase					:					Union[str, Any]								=							activation_dropout
         lowerCAmelCase					:					Optional[int]								=							encoder_layerdrop
         lowerCAmelCase					:					Dict								=							decoder_layerdrop

         lowerCAmelCase					:					Tuple								=							activation_function
         lowerCAmelCase					:					Optional[Any]								=							init_std

         lowerCAmelCase					:					List[Any]								=							use_cache

         # Autoformer
         lowerCAmelCase					:					Any								=							label_length
         lowerCAmelCase					:					Any								=							moving_average
         lowerCAmelCase					:					Optional[Any]								=							autocorrelation_factor

         super().__init__(is_encoder_decoder=snake_case__		,					**snake_case__						)







       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return (
             sum(self.embedding_dimension						)
             + self.num_dynamic_real_features
             + self.num_time_features
             + self.num_static_real_features
             + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
         )





