





'''simple docstring'''




import functools
import operator

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


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """facebook/wav2vec2-base-960h""": """https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json""",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''wav2vec2'''


					def __init__(						self				:			Any      ,						lowerCamelCase_				:			Optional[Any]=32      ,						lowerCamelCase_				:			Optional[Any]=7_68      ,						lowerCamelCase_				:			Dict=12      ,						lowerCamelCase_				:			Optional[int]=12      ,						lowerCamelCase_				:			List[str]=30_72      ,						lowerCamelCase_				:			str="gelu"      ,						lowerCamelCase_				:			Optional[int]=0.1      ,						lowerCamelCase_				:			Union[str, Any]=0.1      ,						lowerCamelCase_				:			Optional[Any]=0.1      ,						lowerCamelCase_				:			Optional[Any]=0.0      ,						lowerCamelCase_				:			Any=0.0      ,						lowerCamelCase_				:			Dict=0.1      ,						lowerCamelCase_				:			List[str]=0.1      ,						lowerCamelCase_				:			Tuple=0.02      ,						lowerCamelCase_				:			Optional[int]=1e-5      ,						lowerCamelCase_				:			List[Any]="group"      ,						lowerCamelCase_				:			List[str]="gelu"      ,						lowerCamelCase_				:			int=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)      ,						lowerCamelCase_				:			Dict=(5, 2, 2, 2, 2, 2, 2)      ,						lowerCamelCase_				:			Dict=(10, 3, 3, 3, 3, 2, 2)      ,						lowerCamelCase_				:			Union[str, Any]=False      ,						lowerCamelCase_				:			List[str]=1_28      ,						lowerCamelCase_				:			int=16      ,						lowerCamelCase_				:			Optional[Any]=False      ,						lowerCamelCase_				:			int=True      ,						lowerCamelCase_				:			int=0.05      ,						lowerCamelCase_				:			Union[str, Any]=10      ,						lowerCamelCase_				:			Any=2      ,						lowerCamelCase_				:			int=0.0      ,						lowerCamelCase_				:			Optional[Any]=10      ,						lowerCamelCase_				:			Union[str, Any]=0      ,						lowerCamelCase_				:			str=3_20      ,						lowerCamelCase_				:			List[str]=2      ,						lowerCamelCase_				:			Any=0.1      ,						lowerCamelCase_				:			Optional[int]=1_00      ,						lowerCamelCase_				:			List[str]=2_56      ,						lowerCamelCase_				:			str=2_56      ,						lowerCamelCase_				:			str=0.1      ,						lowerCamelCase_				:			Any="sum"      ,						lowerCamelCase_				:			Tuple=False      ,						lowerCamelCase_				:			int=False      ,						lowerCamelCase_				:			Any=2_56      ,						lowerCamelCase_				:			List[Any]=(5_12, 5_12, 5_12, 5_12, 15_00)      ,						lowerCamelCase_				:			List[Any]=(5, 3, 3, 1, 1)      ,						lowerCamelCase_				:			str=(1, 2, 3, 1, 1)      ,						lowerCamelCase_				:			Optional[Any]=5_12      ,						lowerCamelCase_				:			Dict=0      ,						lowerCamelCase_				:			str=1      ,						lowerCamelCase_				:			Optional[Any]=2      ,						lowerCamelCase_				:			Optional[int]=False      ,						lowerCamelCase_				:			Any=3      ,						lowerCamelCase_				:			Optional[Any]=2      ,						lowerCamelCase_				:			Dict=3      ,						lowerCamelCase_				:			List[Any]=None      ,						lowerCamelCase_				:			Any=None      ,						**lowerCamelCase_				:			List[Any]      ,						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_      ,						pad_token_id=lowerCamelCase_      ,						bos_token_id=lowerCamelCase_      ,						eos_token_id=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			hidden_size
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			feat_extract_norm
							SCREAMING_SNAKE_CASE :				Tuple		=			feat_extract_activation
							SCREAMING_SNAKE_CASE :				Tuple		=			list(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			list(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			list(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			conv_bias
							SCREAMING_SNAKE_CASE :				Any		=			num_conv_pos_embeddings
							SCREAMING_SNAKE_CASE :				str		=			num_conv_pos_embedding_groups
							SCREAMING_SNAKE_CASE :				str		=			len(self.conv_dim						)
							SCREAMING_SNAKE_CASE :				Tuple		=			num_hidden_layers
							SCREAMING_SNAKE_CASE :				Any		=			intermediate_size
							SCREAMING_SNAKE_CASE :				str		=			hidden_act
							SCREAMING_SNAKE_CASE :				Optional[int]		=			num_attention_heads
							SCREAMING_SNAKE_CASE :				Dict		=			hidden_dropout
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			attention_dropout
							SCREAMING_SNAKE_CASE :				Optional[int]		=			activation_dropout
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			feat_proj_dropout
							SCREAMING_SNAKE_CASE :				Dict		=			final_dropout
							SCREAMING_SNAKE_CASE :				str		=			layerdrop
							SCREAMING_SNAKE_CASE :				Any		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				Tuple		=			initializer_range
							SCREAMING_SNAKE_CASE :				int		=			vocab_size
							SCREAMING_SNAKE_CASE :				Dict		=			do_stable_layer_norm
							SCREAMING_SNAKE_CASE :				Dict		=			use_weighted_layer_sum

							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)`, but is `len(config.conv_dim) ="""
									    f''' {len(self.conv_dim						)}`, `len(config.conv_stride) = {len(self.conv_stride						)}`,'''
									    f''' `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'''						)

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

							# parameters for pretraining with codevector quantized representations
							SCREAMING_SNAKE_CASE :				List[str]		=			num_codevectors_per_group
							SCREAMING_SNAKE_CASE :				Optional[int]		=			num_codevector_groups
							SCREAMING_SNAKE_CASE :				Dict		=			contrastive_logits_temperature
							SCREAMING_SNAKE_CASE :				Optional[int]		=			feat_quantizer_dropout
							SCREAMING_SNAKE_CASE :				List[str]		=			num_negatives
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			codevector_dim
							SCREAMING_SNAKE_CASE :				Tuple		=			proj_codevector_dim
							SCREAMING_SNAKE_CASE :				Optional[int]		=			diversity_loss_weight

							# ctc loss
							SCREAMING_SNAKE_CASE :				Any		=			ctc_loss_reduction
							SCREAMING_SNAKE_CASE :				List[str]		=			ctc_zero_infinity

							# adapter
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			add_adapter
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			adapter_kernel_size
							SCREAMING_SNAKE_CASE :				List[str]		=			adapter_stride
							SCREAMING_SNAKE_CASE :				List[str]		=			num_adapter_layers
							SCREAMING_SNAKE_CASE :				Optional[int]		=			output_hidden_size or hidden_size
							SCREAMING_SNAKE_CASE :				int		=			adapter_attn_dim

							# SequenceClassification-specific parameter. Feel free to ignore for other classes.
							SCREAMING_SNAKE_CASE :				Optional[int]		=			classifier_proj_size

							# XVector-specific parameters. Feel free to ignore for other classes.
							SCREAMING_SNAKE_CASE :				int		=			list(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			list(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			list(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			xvector_output_dim



					@property
					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							return functools.reduce(operator.mul      ,						self.conv_stride      ,						1						)










'''simple docstring'''




from __future__ import annotations

from statistics import mean



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				List[Any]		=			[0] * no_of_processes
		SCREAMING_SNAKE_CASE :				int		=			[0] * no_of_processes

		# Initialize remaining_time to waiting_time.

		for i in range(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Optional[int]		=			burst_time[i]
		SCREAMING_SNAKE_CASE :				list[int]		=			[]

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

		return waiting_time



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



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


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

							__UpperCAmelCase            =       4
							__UpperCAmelCase            =       [2, 5, 3, 7]
							__UpperCAmelCase            =       [0, 0, 0, 0]
							__UpperCAmelCase            =       calculate_waitingtime(arrival_time, burst_time, no_of_processes)
							__UpperCAmelCase            =       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}''')



