
from statistics import mean

import numpy as np






def 		_UpperCAmelCase   (	UpperCamelCase:     Tuple			,							UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     Optional[Any]  ):






 """simple docstring"""


 __lowerCAmelCase         =							0
 # Number of processes finished
 __lowerCAmelCase         =							0
 # Displays the finished process.
 # If it is 0, the performance is completed if it is 1, before the performance.
 __lowerCAmelCase         =							[0] * no_of_process
 # List to include calculation results
 __lowerCAmelCase         =							[0] * no_of_process

 # Sort by arrival time.
 __lowerCAmelCase         =							[burst_time[i] for i in np.argsort(UpperCamelCase  )]
 __lowerCAmelCase         =							[process_name[i] for i in np.argsort(UpperCamelCase  )]
 arrival_time.sort()

 while no_of_process > finished_process_count:
  __lowerCAmelCase         =							0
  while finished_process[i] == 1:
   i += 1
  if current_time < arrival_time[i]:
   __lowerCAmelCase         =							arrival_time[i]

  __lowerCAmelCase         =							0
  # Index showing the location of the process being performed
  __lowerCAmelCase         =							0
  # Saves the current response ratio.
  __lowerCAmelCase         =							0
  for i in range(0			,							UpperCamelCase  ):
   if finished_process[i] == 0 and arrival_time[i] <= current_time:
    __lowerCAmelCase         =							(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
        i
    ]
   if response_ratio < temp:
    __lowerCAmelCase         =							temp
    __lowerCAmelCase         =							i

        # Calculate the turn around time
  __lowerCAmelCase         =							current_time + burst_time[loc] - arrival_time[loc]
  current_time += burst_time[loc]
  # Indicates that the process has been performed.
  __lowerCAmelCase         =							1
  # Increase finished_process_count by 1
  finished_process_count += 1

 return turn_around_time






def 		_UpperCAmelCase   (	UpperCamelCase:     int			,							UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     Dict			,							UpperCamelCase:     Dict  ):






 """simple docstring"""


 __lowerCAmelCase         =							[0] * no_of_process
 for i in range(0			,							UpperCamelCase  ):
  __lowerCAmelCase         =							turn_around_time[i] - burst_time[i]
 return waiting_time


if __name__ == "__main__":
     UpperCamelCase_   	=	5
     UpperCamelCase_   	=	["""A""", """B""", """C""", """D""", """E"""]
     UpperCamelCase_   	=	[1, 2, 3, 4, 5]
     UpperCamelCase_   	=	[1, 2, 3, 4, 5]

     UpperCamelCase_   	=	calculate_turn_around_time(
         process_name, arrival_time, burst_time, no_of_process
     )
     UpperCamelCase_   	=	calculate_waiting_time(
         process_name, turn_around_time, burst_time, no_of_process
     )

     print("Process name \tArrival time \tBurst time \tTurn around time \tWaiting time")
     for i in range(0, no_of_process):
          print(
              f'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
              f'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
          )

     print(f'''average waiting time : {mean(waiting_time):.5f}''')
     print(f'''average turn around time : {mean(turn_around_time):.5f}''')








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


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Union[str, Any]   							=  'mctct'







							def __init__(						self  :  Dict      ,				snake_case__  :  Any=8_065      ,				snake_case__  :  Tuple=1_536      ,				snake_case__  :  Union[str, Any]=36      ,				snake_case__  :  List[Any]=6_144      ,				snake_case__  :  Tuple=4      ,				snake_case__  :  List[str]=384      ,				snake_case__  :  List[Any]=920      ,				snake_case__  :  Any=1E-5      ,				snake_case__  :  List[Any]=0.3      ,				snake_case__  :  List[str]="relu"      ,				snake_case__  :  List[Any]=0.0_2      ,				snake_case__  :  Optional[Any]=0.3      ,				snake_case__  :  List[str]=0.3      ,				snake_case__  :  List[Any]=1      ,				snake_case__  :  Optional[int]=0      ,				snake_case__  :  Any=2      ,				snake_case__  :  Any=1      ,				snake_case__  :  List[str]=0.3      ,				snake_case__  :  Tuple=1      ,				snake_case__  :  Union[str, Any]=(7,)      ,				snake_case__  :  List[Any]=(3,)      ,				snake_case__  :  List[Any]=80      ,				snake_case__  :  Optional[int]=1      ,				snake_case__  :  List[str]=None      ,				snake_case__  :  int="sum"      ,				snake_case__  :  Dict=False      ,				**snake_case__  :  List[str]      ,				):
								"""simple docstring"""







								super().__init__(**snake_case__      ,				pad_token_id=snake_case__      ,				bos_token_id=snake_case__      ,				eos_token_id=snake_case__ )
								__lowerCAmelCase         =							vocab_size
								__lowerCAmelCase         =							hidden_size
								__lowerCAmelCase         =							num_hidden_layers
								__lowerCAmelCase         =							intermediate_size
								__lowerCAmelCase         =							num_attention_heads
								__lowerCAmelCase         =							attention_head_dim
								__lowerCAmelCase         =							max_position_embeddings
								__lowerCAmelCase         =							layer_norm_eps
								__lowerCAmelCase         =							layerdrop
								__lowerCAmelCase         =							hidden_act
								__lowerCAmelCase         =							initializer_range
								__lowerCAmelCase         =							hidden_dropout_prob
								__lowerCAmelCase         =							attention_probs_dropout_prob
								__lowerCAmelCase         =							pad_token_id
								__lowerCAmelCase         =							bos_token_id
								__lowerCAmelCase         =							eos_token_id
								__lowerCAmelCase         =							conv_glu_dim
								__lowerCAmelCase         =							conv_dropout
								__lowerCAmelCase         =							num_conv_layers
								__lowerCAmelCase         =							input_feat_per_channel
								__lowerCAmelCase         =							input_channels
								__lowerCAmelCase         =							conv_channels
								__lowerCAmelCase         =							ctc_loss_reduction
								__lowerCAmelCase         =							ctc_zero_infinity

								# prevents config testing fail with exporting to json
								__lowerCAmelCase         =							list(snake_case__ )
								__lowerCAmelCase         =							list(snake_case__ )

								if len(self.conv_kernel ) != self.num_conv_layers:
									raise ValueError(
									    "Configuration for convolutional module is incorrect. "
									    "It is required that `len(config.conv_kernel)` == `config.num_conv_layers` "
									    F"but is `len(config.conv_kernel) = {len(self.conv_kernel )}`, "
									    F"`config.num_conv_layers = {self.num_conv_layers}`." )






