




"""simple docstring"""
import functools
import operator

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


a_				:    Dict										=     logging.get_logger(__name__)

a_				:    Optional[Any]										=     {
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						Optional[Any]     		= 'sew'
					def __init__(    self						,	__a=32						,	__a=768						,	__a=12						,	__a=12						,	__a=3072						,	__a=2						,	__a="gelu"						,	__a=0.1						,	__a=0.1						,	__a=0.1						,	__a=0.0						,	__a=0.1						,	__a=0.1						,	__a=0.02						,	__a=1E-5						,	__a="group"						,	__a="gelu"						,	__a=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)						,	__a=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)						,	__a=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)						,	__a=False						,	__a=128						,	__a=16						,	__a=True						,	__a=0.05						,	__a=10						,	__a=2						,	__a=0.0						,	__a=10						,	__a=0						,	__a="mean"						,	__a=False						,	__a=False						,	__a=256						,	__a=0						,	__a=1						,	__a=2						,	**__a						,	):
								super().__init__(**__a						,	pad_token_id=__a						,	bos_token_id=__a						,	eos_token_id=__a     )
								__lowerCamelCase					:	int	       =				hidden_size
								__lowerCamelCase					:	int	       =				feat_extract_norm
								__lowerCamelCase					:	Dict	       =				feat_extract_activation
								__lowerCamelCase					:	Dict	       =				list(__a     )
								__lowerCamelCase					:	Optional[int]	       =				list(__a     )
								__lowerCamelCase					:	List[Any]	       =				list(__a     )
								__lowerCamelCase					:	List[Any]	       =				conv_bias
								__lowerCamelCase					:	List[str]	       =				num_conv_pos_embeddings
								__lowerCamelCase					:	str	       =				num_conv_pos_embedding_groups
								__lowerCamelCase					:	Tuple	       =				len(self.conv_dim     )
								__lowerCamelCase					:	Tuple	       =				num_hidden_layers
								__lowerCamelCase					:	Union[str, Any]	       =				intermediate_size
								__lowerCamelCase					:	List[Any]	       =				squeeze_factor
								__lowerCamelCase					:	Dict	       =				hidden_act
								__lowerCamelCase					:	List[str]	       =				num_attention_heads
								__lowerCamelCase					:	List[str]	       =				hidden_dropout
								__lowerCamelCase					:	str	       =				attention_dropout
								__lowerCamelCase					:	Optional[int]	       =				activation_dropout
								__lowerCamelCase					:	Optional[Any]	       =				feat_proj_dropout
								__lowerCamelCase					:	Any	       =				final_dropout
								__lowerCamelCase					:	Optional[Any]	       =				layerdrop
								__lowerCamelCase					:	int	       =				layer_norm_eps
								__lowerCamelCase					:	Any	       =				initializer_range
								__lowerCamelCase					:	Any	       =				vocab_size

								if (
								    (len(self.conv_stride     ) != self.num_feat_extract_layers)
								    or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
								    or (len(self.conv_dim     ) != self.num_feat_extract_layers)
								):
											raise ValueError(
											    'Configuration for convolutional layers is incorrect.'
											    'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
											    f'''but is `len(config.conv_dim) = {len(self.conv_dim     )}`, `len(config.conv_stride)'''
											    f'''= {len(self.conv_stride     )}`, `len(config.conv_kernel) = {len(self.conv_kernel     )}`.'''     )

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								__lowerCamelCase					:	List[Any]	       =				apply_spec_augment
								__lowerCamelCase					:	Dict	       =				mask_time_prob
								__lowerCamelCase					:	str	       =				mask_time_length
								__lowerCamelCase					:	Any	       =				mask_time_min_masks
								__lowerCamelCase					:	Dict	       =				mask_feature_prob
								__lowerCamelCase					:	int	       =				mask_feature_length
								__lowerCamelCase					:	Optional[int]	       =				mask_feature_min_masks

								# ctc loss
								__lowerCamelCase					:	Dict	       =				ctc_loss_reduction
								__lowerCamelCase					:	Optional[Any]	       =				ctc_zero_infinity

								# sequence classification
								__lowerCamelCase					:	List[Any]	       =				use_weighted_layer_sum
								__lowerCamelCase					:	str	       =				classifier_proj_size





					@property
					def     snake_case_				(    self     ):
								return functools.reduce(operator.mul						,	self.conv_stride						,	1     )









"""simple docstring"""
from __future__ import annotations

import pandas as pd





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    list[int]							,				A__:    int     )  ->     list[int]:
			__lowerCamelCase					:	List[Any]	       =				[0] * no_of_processes
			__lowerCamelCase					:	Any	       =				[0] * no_of_processes
			# Copy the burst time into remaining_time[]
			for i in range(A__     ):
						__lowerCamelCase					:	Dict	       =				burst_time[i]

			__lowerCamelCase					:	Any	       =				0
			__lowerCamelCase					:	Tuple	       =				0
			__lowerCamelCase					:	Union[str, Any]	       =				999999999
			__lowerCamelCase					:	str	       =				0
			__lowerCamelCase					:	Optional[int]	       =				False

			# Process until all processes are completed
			while complete != no_of_processes:
						for j in range(A__     ):
									if arrival_time[j] <= increment_time and remaining_time[j] > 0:
												if remaining_time[j] < minm:
															__lowerCamelCase					:	List[Any]	       =				remaining_time[j]
															__lowerCamelCase					:	List[str]	       =				j
															__lowerCamelCase					:	Optional[int]	       =				True

						if not check:
									increment_time += 1
									continue
						remaining_time[short] -= 1

						__lowerCamelCase					:	Optional[Any]	       =				remaining_time[short]
						if minm == 0:
									__lowerCamelCase					:	Optional[int]	       =				999999999

						if remaining_time[short] == 0:
									complete += 1
									__lowerCamelCase					:	List[str]	       =				False

									# Find finish time of current process
									__lowerCamelCase					:	Dict	       =				increment_time + 1

									# Calculate waiting time
									__lowerCamelCase					:	Any	       =				finish_time - arrival_time[short]
									__lowerCamelCase					:	Dict	       =				finar - burst_time[short]

									if waiting_time[short] < 0:
												__lowerCamelCase					:	Optional[Any]	       =				0

        # Increment time
						increment_time += 1
			return waiting_time





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    int							,				A__:    list[int]     )  ->     list[int]:
			__lowerCamelCase					:	List[Any]	       =				[0] * no_of_processes
			for i in range(A__     ):
						__lowerCamelCase					:	Tuple	       =				burst_time[i] + waiting_time[i]
			return turn_around_time





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    list[int]							,				A__:    int     )  ->     None:
			__lowerCamelCase					:	int	       =				0
			__lowerCamelCase					:	Dict	       =				0
			for i in range(A__     ):
						__lowerCamelCase					:	str	       =				total_waiting_time + waiting_time[i]
						__lowerCamelCase					:	Union[str, Any]	       =				total_turn_around_time + turn_around_time[i]
			print(f'''Average waiting time = {total_waiting_time / no_of_processes:.5f}'''     )
			print('Average turn around time ='							,				total_turn_around_time / no_of_processes     )


if __name__ == "__main__":
					print('''Enter how many process you want to analyze''')
					a_				:    int										=     int(input())
					a_				:    List[str]										=     [0] * no_of_processes
					a_				:    int										=     [0] * no_of_processes
					a_				:    int										=     list(range(1, no_of_processes + 1))

					for i in range(no_of_processes):
										print('''Enter the arrival time and burst time for process:--''' + str(i + 1))
										a_						,							a_				:    Union[str, Any]										=     map(int, input().split())

					a_				:    Any										=     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					a_				:    List[str]										=     burst_time
					a_				:    List[Any]										=     no_of_processes
					a_				:    Tuple										=     waiting_time
					a_				:    Optional[Any]										=     calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					a_				:    List[Any]										=     pd.DataFrame(
					    list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
					    columns=[
					        '''Process''',
					        '''BurstTime''',
					        '''ArrivalTime''',
					        '''WaitingTime''',
					        '''TurnAroundTime''',
					    ],
					)

					# Printing the dataFrame
					pd.set_option('''display.max_rows''', fcfs.shape[0] + 1)
					print(fcfs)



