



"""simple docstring"""







from statistics import mean

import numpy as np



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      list:
		lowercase__   :    Union[str, Any]				=			0
		# Number of processes finished
		lowercase__   :    Optional[Any]				=			0
		# Displays the finished process.
		# If it is 0, the performance is completed if it is 1, before the performance.
		lowercase__   :    Union[str, Any]				=			[0] * no_of_process
		# List to include calculation results
		lowercase__   :    List[Any]				=			[0] * no_of_process

		# Sort by arrival time.
		lowercase__   :    int				=			[burst_time[i] for i in np.argsort(__lowerCamelCase	)]
		lowercase__   :    str				=			[process_name[i] for i in np.argsort(__lowerCamelCase	)]
		arrival_time.sort()

		while no_of_process > finished_process_count:
				lowercase__   :    List[Any]				=			0
				while finished_process[i] == 1:
						i += 1
				if current_time < arrival_time[i]:
						lowercase__   :    str				=			arrival_time[i]

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

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

		return turn_around_time



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      list:
		lowercase__   :    str				=			[0] * no_of_process
		for i in range(0						,     __lowerCamelCase	):
				lowercase__   :    str				=			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}''')




import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class 	UpperCAmelCase_					(					UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




   __A :      List[Any]     		=		BioGptTokenizer
   __A :      Optional[int]     		=		False






   def _snake_case    (		self ):






     """simple docstring"""





     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     lowerCamelCase :					Union[str, Any]    =				[
         "l",
         "o",
         "w",
         "e",
         "r",
         "s",
         "t",
         "i",
         "d",
         "n",
         "w</w>",
         "r</w>",
         "t</w>",
         "lo",
         "low",
         "er</w>",
         "low</w>",
         "lowest</w>",
         "newer</w>",
         "wider</w>",
         "<unk>",
     ]
     lowerCamelCase :					str    =				dict(zip(__A						,				range(len(__A ) ) ) )
     lowerCamelCase :					Dict    =				["l o 123", "lo w 1456", "e r</w> 1789", ""]

     lowerCamelCase :					Dict    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["vocab_file"] )
     lowerCamelCase :					int    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["merges_file"] )
     with open(self.vocab_file						,				"w" ) as fp:
       fp.write(json.dumps(__A ) )
     with open(self.merges_file						,				"w" ) as fp:
       fp.write("\n".join(__A ) )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				"lower newer"
     lowerCamelCase :					Union[str, Any]    =				"lower newer"
     return input_text, output_text






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				BioGptTokenizer(self.vocab_file						,				self.merges_file )

     lowerCamelCase :					Optional[int]    =				"lower"
     lowerCamelCase :					Any    =				["low", "er</w>"]
     lowerCamelCase :					List[str]    =				tokenizer.tokenize(__A )
     self.assertListEqual(__A						,				__A )

     lowerCamelCase :					Union[str, Any]    =				tokens + ["<unk>"]
     lowerCamelCase :					List[str]    =				[14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A )						,				__A )






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				BioGptTokenizer.from_pretrained("microsoft/biogpt" )

     lowerCamelCase :					Optional[int]    =				tokenizer.encode("sequence builders"						,				add_special_tokens=__A )
     lowerCamelCase :					Tuple    =				tokenizer.encode("multi-sequence build"						,				add_special_tokens=__A )

     lowerCamelCase :					Tuple    =				tokenizer.build_inputs_with_special_tokens(__A )
     lowerCamelCase :					List[str]    =				tokenizer.build_inputs_with_special_tokens(__A						,				__A )

     self.assertTrue(encoded_sentence == [2] + text )
     self.assertTrue(encoded_pair == [2] + text + [2] + text_a )



