




"""simple docstring"""
from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a_				:    Union[str, Any]										=     logging.get_logger(__name__)


a_				:    List[str]										=     {
    '''nielsr/canine-s''': 20_48,
}

# Unicode defines 1,114,112 total “codepoints”
a_				:    Optional[int]										=     1_11_41_12

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
a_				:    Union[str, Any]										=     0

a_				:    Optional[Any]										=     0xE000
a_				:    Any										=     0xE001
a_				:    List[Any]										=     0xE002
a_				:    int										=     0xE003
a_				:    Optional[int]										=     0xE004

# Maps special codepoints to human-readable names.
a_				:    Dict[int, str]										=     {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
a_				:    Dict[str, int]										=     {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						Optional[Any]     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					def __init__(    self						,	__a=chr(__a     )						,	__a=chr(__a     )						,	__a=chr(__a     )						,	__a=chr(__a     )						,	__a=chr(__a     )						,	__a=chr(__a     )						,	__a=False						,	__a=2048						,	**__a						,	):
								__lowerCamelCase					:	List[Any]	       =				AddedToken(__a						,	lstrip=__a						,	rstrip=__a     ) if isinstance(__a						,	__a     ) else bos_token
								__lowerCamelCase					:	Union[str, Any]	       =				AddedToken(__a						,	lstrip=__a						,	rstrip=__a     ) if isinstance(__a						,	__a     ) else eos_token
								__lowerCamelCase					:	Dict	       =				AddedToken(__a						,	lstrip=__a						,	rstrip=__a     ) if isinstance(__a						,	__a     ) else sep_token
								__lowerCamelCase					:	str	       =				AddedToken(__a						,	lstrip=__a						,	rstrip=__a     ) if isinstance(__a						,	__a     ) else cls_token
								__lowerCamelCase					:	Tuple	       =				AddedToken(__a						,	lstrip=__a						,	rstrip=__a     ) if isinstance(__a						,	__a     ) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								__lowerCamelCase					:	Union[str, Any]	       =				AddedToken(__a						,	lstrip=__a						,	rstrip=__a     ) if isinstance(__a						,	__a     ) else mask_token

								super().__init__(
								    bos_token=__a						,	eos_token=__a						,	sep_token=__a						,	cls_token=__a						,	pad_token=__a						,	mask_token=__a						,	add_prefix_space=__a						,	model_max_length=__a						,	**__a						,	)

								# Creates a mapping for looking up the IDs of special symbols.
								__lowerCamelCase					:	Dict[str, int]	       =				{}
								for codepoint, name in SPECIAL_CODEPOINTS.items():
											__lowerCamelCase					:	Tuple	       =				codepoint

								# Creates a mapping for looking up the string forms of special symbol IDs.
								__lowerCamelCase					:	Dict[int, str]	       =				{
								    codepoint: name for name, codepoint in self._special_codepoints.items()
								}

								__lowerCamelCase					:	Optional[Any]	       =				UNICODE_VOCAB_SIZE
								__lowerCamelCase					:	int	       =				len(self._special_codepoints     )
					@property
					def     snake_case_				(    self     ):
								return self._unicode_vocab_size
					def     snake_case_				(    self						,	__a     ):
								return list(__a     )
					def     snake_case_				(    self						,	__a     ):
								try:
											return ord(__a     )
								except TypeError:
											raise ValueError(f'''invalid token: \'{token}\''''     )
					def     snake_case_				(    self						,	__a     ):
								try:
											if index in SPECIAL_CODEPOINTS:
														return SPECIAL_CODEPOINTS[index]
											return chr(__a     )
								except TypeError:
											raise ValueError(f'''invalid id: {index}'''     )
					def     snake_case_				(    self						,	__a     ):
								return "".join(__a     )
					def     snake_case_				(    self						,	__a						,	__a = None     ):
								__lowerCamelCase					:	str	       =				[self.sep_token_id]
								__lowerCamelCase					:	List[str]	       =				[self.cls_token_id]

								__lowerCamelCase					:	Optional[Any]	       =				cls + token_ids_a + sep
								if token_ids_a is not None:
											result += token_ids_a + sep
								return result
					def     snake_case_				(    self						,	__a						,	__a = None						,	__a = False     ):
								if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=__a						,	token_ids_a=__a						,	already_has_special_tokens=__a     )

								__lowerCamelCase					:	List[Any]	       =				[1] + ([0] * len(__a     )) + [1]
								if token_ids_a is not None:
											result += ([0] * len(__a     )) + [1]
								return result
					def     snake_case_				(    self						,	__a						,	__a = None     ):
								__lowerCamelCase					:	Tuple	       =				[self.sep_token_id]
								__lowerCamelCase					:	str	       =				[self.cls_token_id]

								__lowerCamelCase					:	List[str]	       =				len(cls + token_ids_a + sep     ) * [0]
								if token_ids_a is not None:
											result += len(token_ids_a + sep     ) * [1]
								return result





					def     snake_case_				(    self						,	__a						,	__a = None     ):
								return ()









"""simple docstring"""
from __future__ import annotations

import pandas as pd





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    list[int]							,				A__:    int     )  ->     list[int]:
			__lowerCamelCase					:	List[Any]	       =				[0] * no_of_processes
			__lowerCamelCase					:	Any	       =				[0] * no_of_processes
			# Copy the burst time into remaining_time[]
			for i in range(A__     ):
						__lowerCamelCase					:	Dict	       =				burst_time[i]

			__lowerCamelCase					:	Any	       =				0
			__lowerCamelCase					:	Tuple	       =				0
			__lowerCamelCase					:	Union[str, Any]	       =				999999999
			__lowerCamelCase					:	str	       =				0
			__lowerCamelCase					:	Optional[int]	       =				False

			# Process until all processes are completed
			while complete != no_of_processes:
						for j in range(A__     ):
									if arrival_time[j] <= increment_time and remaining_time[j] > 0:
												if remaining_time[j] < minm:
															__lowerCamelCase					:	List[Any]	       =				remaining_time[j]
															__lowerCamelCase					:	List[str]	       =				j
															__lowerCamelCase					:	Optional[int]	       =				True

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

						__lowerCamelCase					:	Optional[Any]	       =				remaining_time[short]
						if minm == 0:
									__lowerCamelCase					:	Optional[int]	       =				999999999

						if remaining_time[short] == 0:
									complete += 1
									__lowerCamelCase					:	List[str]	       =				False

									# Find finish time of current process
									__lowerCamelCase					:	Dict	       =				increment_time + 1

									# Calculate waiting time
									__lowerCamelCase					:	Any	       =				finish_time - arrival_time[short]
									__lowerCamelCase					:	Dict	       =				finar - burst_time[short]

									if waiting_time[short] < 0:
												__lowerCamelCase					:	Optional[Any]	       =				0

        # Increment time
						increment_time += 1
			return waiting_time





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    int							,				A__:    list[int]     )  ->     list[int]:
			__lowerCamelCase					:	List[Any]	       =				[0] * no_of_processes
			for i in range(A__     ):
						__lowerCamelCase					:	Tuple	       =				burst_time[i] + waiting_time[i]
			return turn_around_time





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    list[int]							,				A__:    int     )  ->     None:
			__lowerCamelCase					:	int	       =				0
			__lowerCamelCase					:	Dict	       =				0
			for i in range(A__     ):
						__lowerCamelCase					:	str	       =				total_waiting_time + waiting_time[i]
						__lowerCamelCase					:	Union[str, Any]	       =				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_				:    int										=     int(input())
					a_				:    List[str]										=     [0] * no_of_processes
					a_				:    int										=     [0] * no_of_processes
					a_				:    int										=     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_				:    Union[str, Any]										=     map(int, input().split())

					a_				:    Any										=     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					a_				:    List[str]										=     burst_time
					a_				:    List[Any]										=     no_of_processes
					a_				:    Tuple										=     waiting_time
					a_				:    Optional[Any]										=     calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					a_				:    List[Any]										=     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)



