

from statistics import mean

import numpy as np




def 			UpperCAmelCase__				(			_A      : Union[str, Any]	,      _A      : Union[str, Any]	,      _A      : Optional[Any]	,      _A      : Dict							):





  '''simple docstring'''







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

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

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

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

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

  return turn_around_time




def 			UpperCAmelCase__				(			_A      : Dict	,      _A      : Union[str, Any]	,      _A      : Any	,      _A      : Dict							):





  '''simple docstring'''







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


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

       lowerCamelCase           =					calculate_turn_around_time(
           process_name, arrival_time, burst_time, no_of_process
       )
       lowerCamelCase           =					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}""")




'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case							      =     logging.get_logger(__name__)

__snake_case							      =     {
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				'camembert'




		def __init__(		self    ,   UpperCamelCase_=30522    ,   UpperCamelCase_=768    ,   UpperCamelCase_=12    ,   UpperCamelCase_=12    ,   UpperCamelCase_=3072    ,   UpperCamelCase_="gelu"    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=512    ,   UpperCamelCase_=2    ,   UpperCamelCase_=0.02    ,   UpperCamelCase_=1e-12    ,   UpperCamelCase_=1    ,   UpperCamelCase_=0    ,   UpperCamelCase_=2    ,   UpperCamelCase_="absolute"    ,   UpperCamelCase_=True    ,   UpperCamelCase_=None    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__(pad_token_id=UpperCamelCase_    ,   bos_token_id=UpperCamelCase_    ,   eos_token_id=UpperCamelCase_    ,   **UpperCamelCase_							)

							UpperCamelCase__   :int												= vocab_size
							UpperCamelCase__   :Optional[int]												= hidden_size
							UpperCamelCase__   :Optional[int]												= num_hidden_layers
							UpperCamelCase__   :List[Any]												= num_attention_heads
							UpperCamelCase__   :Union[str, Any]												= hidden_act
							UpperCamelCase__   :List[Any]												= intermediate_size
							UpperCamelCase__   :int												= hidden_dropout_prob
							UpperCamelCase__   :Tuple												= attention_probs_dropout_prob
							UpperCamelCase__   :Union[str, Any]												= max_position_embeddings
							UpperCamelCase__   :Tuple												= type_vocab_size
							UpperCamelCase__   :int												= initializer_range
							UpperCamelCase__   :List[str]												= layer_norm_eps
							UpperCamelCase__   :int												= position_embedding_type
							UpperCamelCase__   :Any												= use_cache
							UpperCamelCase__   :Any												= classifier_dropout



class    lowercase	(							A__					):

		"""simple docstring"""

		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if self.task == "multiple-choice":
												UpperCamelCase__   :List[str]												= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
												UpperCamelCase__   :Tuple												= {0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ]							)