


"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


a__				:							int						=     logging.get_logger(__name__)

a__				:							Optional[int]						=     {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a__				:							List[Any]						=     {
    '''vocab_file''': {'''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'''},
    '''tokenizer_file''': {
        '''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'''
    },
}

a__				:							Optional[Any]						=     {'''mobilebert-uncased''': 5_1_2}


a__				:							Any						=     {}




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  Tuple    					=			VOCAB_FILES_NAMES
	snake_case__				:  str    					=			PRETRAINED_VOCAB_FILES_MAP
	snake_case__				:  Optional[Any]    					=			PRETRAINED_INIT_CONFIGURATION
	snake_case__				:  Optional[Any]    					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__				:  Dict    					=			MobileBertTokenizer




	def __init__( self			:				Tuple			,			UpperCAmelCase__			:				int=None			,			UpperCAmelCase__			:				int=None			,			UpperCAmelCase__			:				Optional[Any]=True			,			UpperCAmelCase__			:				List[Any]="[UNK]"			,			UpperCAmelCase__			:				List[Any]="[SEP]"			,			UpperCAmelCase__			:				Optional[Any]="[PAD]"			,			UpperCAmelCase__			:				Tuple="[CLS]"			,			UpperCAmelCase__			:				Tuple="[MASK]"			,			UpperCAmelCase__			:				Tuple=True			,			UpperCAmelCase__			:				Dict=None			,			**UpperCAmelCase__			:				List[str]			,			)       ->	Tuple:
						super().__init__(
						    UpperCAmelCase__			,			tokenizer_file=UpperCAmelCase__			,			do_lower_case=UpperCAmelCase__			,			unk_token=UpperCAmelCase__			,			sep_token=UpperCAmelCase__			,			pad_token=UpperCAmelCase__			,			cls_token=UpperCAmelCase__			,			mask_token=UpperCAmelCase__			,			tokenize_chinese_chars=UpperCAmelCase__			,			strip_accents=UpperCAmelCase__			,			**UpperCAmelCase__			,			)

						__SCREAMING_SNAKE_CASE						   =				json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
						if (
						    normalizer_state.get("lowercase"			,			UpperCAmelCase__   ) != do_lower_case
						    or normalizer_state.get("strip_accents"			,			UpperCAmelCase__   ) != strip_accents
						    or normalizer_state.get("handle_chinese_chars"			,			UpperCAmelCase__   ) != tokenize_chinese_chars
						):
											__SCREAMING_SNAKE_CASE						   =				getattr(UpperCAmelCase__			,			normalizer_state.pop("type"   )   )
											__SCREAMING_SNAKE_CASE						   =				do_lower_case
											__SCREAMING_SNAKE_CASE						   =				strip_accents
											__SCREAMING_SNAKE_CASE						   =				tokenize_chinese_chars
											__SCREAMING_SNAKE_CASE						   =				normalizer_class(**UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				do_lower_case




	def   UpperCAmelCase_      ( self			:				Union[str, Any]			,			UpperCAmelCase__			:				Tuple			,			UpperCAmelCase__			:				Tuple=None   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
											output += token_ids_a + [self.sep_token_id]

						return output




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				List[int]			,			UpperCAmelCase__			:				Optional[List[int]] = None   )       ->	List[int]:
						__SCREAMING_SNAKE_CASE						   =				[self.sep_token_id]
						__SCREAMING_SNAKE_CASE						   =				[self.cls_token_id]
						if token_ids_a is None:
											return len(cls + token_ids_a + sep   ) * [0]
						return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]




	def   UpperCAmelCase_      ( self			:				Tuple			,			UpperCAmelCase__			:				str			,			UpperCAmelCase__			:				Optional[str] = None   )       ->	Tuple[str]:
						__SCREAMING_SNAKE_CASE						   =				self._tokenizer.model.save(UpperCAmelCase__			,			name=UpperCAmelCase__   )
						return tuple(UpperCAmelCase__   )




"""simple docstring"""







from __future__ import annotations

import pandas as pd




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				[0] * no_of_processes
					__SCREAMING_SNAKE_CASE						   =				[0] * no_of_processes
					# Copy the burst time into remaining_time[]
					for i in range(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				burst_time[i]

					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				9_9999_9999
					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				False

					# Process until all processes are completed
					while complete != no_of_processes:
										for j in range(lowerCAmelCase_   ):
															if arrival_time[j] <= increment_time and remaining_time[j] > 0:
																				if remaining_time[j] < minm:
																									__SCREAMING_SNAKE_CASE						   =				remaining_time[j]
																									__SCREAMING_SNAKE_CASE						   =				j
																									__SCREAMING_SNAKE_CASE						   =				True

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

										__SCREAMING_SNAKE_CASE						   =				remaining_time[short]
										if minm == 0:
															__SCREAMING_SNAKE_CASE						   =				9_9999_9999

										if remaining_time[short] == 0:
															complete += 1
															__SCREAMING_SNAKE_CASE						   =				False

															# Find finish time of current process
															__SCREAMING_SNAKE_CASE						   =				increment_time + 1

															# Calculate waiting time
															__SCREAMING_SNAKE_CASE						   =				finish_time - arrival_time[short]
															__SCREAMING_SNAKE_CASE						   =				finar - burst_time[short]

															if waiting_time[short] < 0:
																				__SCREAMING_SNAKE_CASE						   =				0

        # Increment time
										increment_time += 1
					return waiting_time




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				[0] * no_of_processes
					for i in range(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				burst_time[i] + waiting_time[i]
					return turn_around_time




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				0
					for i in range(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				total_waiting_time + waiting_time[i]
										__SCREAMING_SNAKE_CASE						   =				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__				:							Optional[Any]						=     int(input())
		a__				:							Optional[int]						=     [0] * no_of_processes
		a__				:							int						=     [0] * no_of_processes
		a__				:							List[Any]						=     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__				:							Tuple						=     map(int, input().split())

		a__				:							int						=     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

		a__				:							Dict						=     burst_time
		a__				:							Any						=     no_of_processes
		a__				:							Optional[int]						=     waiting_time
		a__				:							Union[str, Any]						=     calculate_turnaroundtime(bt, n, wt)

		calculate_average_times(waiting_time, turn_around_time, no_of_processes)

		a__				:							str						=     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)
