

import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {'vocab_file': 'vocab.txt', 'emoji_file': 'emoji.json'}

SCREAMING_SNAKE_CASE_        =      {
    'vocab_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt',
    },
    'emoji_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json',
    },
}

SCREAMING_SNAKE_CASE_        =      {
    'abeja/gpt-neox-japanese-2.7b': 2048,
}

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  List[Any] )   ->       List[Any]:
				with open(lowerCAmelCase  ,      "r"  ,      encoding="utf-8" ) as f:
								_UpperCAmelCase       :		List[Any] 		=    json.loads(f.read() )

				_UpperCAmelCase       :		int 		=    collections.OrderedDict()
				_UpperCAmelCase       :		Optional[int] 		=    collections.OrderedDict()
				_UpperCAmelCase       :		str 		=    collections.OrderedDict()
				with open(lowerCAmelCase  ,      "r"  ,      encoding="utf-8" ) as f:
								_UpperCAmelCase       :		Union[str, Any] 		=    f.readlines()
				_UpperCAmelCase       :		List[Any] 		=    [[t.rstrip("\n" )] if (t == "," or "," not in t) else t.rstrip("\n" ).split("," ) for t in token]
				for idx, b in enumerate(lowerCAmelCase ):
								_UpperCAmelCase       :		Union[str, Any] 		=    b
								_UpperCAmelCase       :		Tuple 		=    idx
								for wd in b:
												_UpperCAmelCase       :		List[str] 		=    idx

				return vocab, raw_vocab, ids_to_tokens, emoji

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			VOCAB_FILES_NAMES
						_lowercase    						=			PRETRAINED_VOCAB_FILES_MAP
						_lowercase    						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase    						=			["input_ids", "attention_mask"]


						def __init__(       self						,					A_						,					A_						,					A_="<|endoftext|>"						,					A_="<|endoftext|>"						,					A_="<|startoftext|>"						,					A_="<|endoftext|>"						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										super().__init__(
										    unk_token=A_						,					pad_token=A_						,					bos_token=A_						,					eos_token=A_						,					do_clean_text=A_						,					**A_						,					)
										if not os.path.isfile(A_	):
														raise ValueError(
														    f'Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'
														    " model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"	)
										if not os.path.isfile(A_	):
														raise ValueError(
														    f'Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'
														    " pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"	)
										_UpperCAmelCase       :		Dict 		=    do_clean_text
										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    load_vocab_and_emoji(A_						,					A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    SubWordJapaneseTokenizer(
										    vocab=self.vocab						,					ids_to_tokens=self.ids_to_tokens						,					emoji=self.emoji	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return len(self.raw_vocab	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return dict(self.raw_vocab						,					**self.added_tokens_encoder	)


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										return self.subword_tokenizer.tokenize(A_						,					clean=self.do_clean_text	)


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										return self.vocab.get(A_						,					self.vocab.get(self.unk_token	)	)


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										return self.subword_tokenizer.convert_id_to_token(A_	)


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    "".join(A_	).strip()
										return out_string


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    []
										for is_user, text in conversation.iter_texts():
														input_ids.extend(self.encode(A_						,					add_special_tokens=A_	) + [self.eos_token_id]	)

										if len(A_	) > self.model_max_length:
														_UpperCAmelCase       :		List[Any] 		=    input_ids[-self.model_max_length :]
										return input_ids





						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    0
										if os.path.isdir(A_	):
														_UpperCAmelCase       :		Union[str, Any] 		=    os.path.join(
														    A_						,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
														_UpperCAmelCase       :		int 		=    os.path.join(
														    A_						,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]	)
										else:
														_UpperCAmelCase       :		Optional[int] 		=    (
														    (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
														)
														_UpperCAmelCase       :		Dict 		=    (
														    (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
														)
										with open(A_						,					"w"						,					encoding="utf-8"	) as writer:
														for token_index, token in self.ids_to_tokens.items():
																		if index != token_index:
																						logger.warning(
																						    f'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
																						    " Please check that the vocabulary is not corrupted!"	)
																						_UpperCAmelCase       :		Union[str, Any] 		=    token_index
																		writer.write(",".join(A_	) + "\n"	)
																		index += 1
										with open(A_						,					"w"						,					encoding="utf-8"	) as writer:
														json.dump(self.emoji						,					A_	)
										return vocab_file, emoji_file







class 				a  (	UpperCAmelCase				):


						def __init__(       self						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    vocab  # same as swe
										_UpperCAmelCase       :		Tuple 		=    ids_to_tokens  # same as bpe
										_UpperCAmelCase       :		int 		=    emoji
										_UpperCAmelCase       :		Optional[Any] 		=    np.max([len(A_	) for w in self.vocab.keys()]	)
										_UpperCAmelCase       :		Optional[int] 		=    re.compile(R"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"	)
										_UpperCAmelCase       :		Optional[int] 		=    re.compile(R"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"	)
										_UpperCAmelCase       :		str 		=    re.compile(R"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"	)
										_UpperCAmelCase       :		Any 		=    re.compile(
										    R"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"	)
										_UpperCAmelCase       :		Any 		=    re.compile(
										    R"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"	)
										_UpperCAmelCase       :		Union[str, Any] 		=    re.compile(
										    R"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"	)
										_UpperCAmelCase       :		List[Any] 		=    "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
										_UpperCAmelCase       :		List[Any] 		=    "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
										_UpperCAmelCase       :		Dict 		=    str.maketrans({k: "<BLOCK>" for k in keisen + blocks}	)


						def __len__(       self	):







										'''simple docstring'''


										return len(self.ids_to_tokens	)


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.content_repattera.sub("<URL>"						,					A_	)
										_UpperCAmelCase       :		str 		=    self.content_repattera.sub("<EMAIL>"						,					A_	)
										_UpperCAmelCase       :		Optional[int] 		=    self.content_repattera.sub("<TEL>"						,					A_	)
										_UpperCAmelCase       :		List[Any] 		=    self.content_repattera.sub("<DATE>"						,					A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    self.content_repattera.sub("<DATE>"						,					A_	)
										_UpperCAmelCase       :		Union[str, Any] 		=    self.content_repattera.sub("<PRICE>"						,					A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    content.translate(self.content_transa	)
										while "<BLOCK><BLOCK>" in content:
														_UpperCAmelCase       :		Optional[int] 		=    content.replace("<BLOCK><BLOCK>"						,					"<BLOCK>"	)
										return content


						def        _UpperCAmelCase				(       self						,					A_						,					A_=False	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    text.replace(" "						,					"<SP>"	)
										_UpperCAmelCase       :		int 		=    text.replace("　"						,					"<SP>"	)
										_UpperCAmelCase       :		List[Any] 		=    text.replace("\r\n"						,					"<BR>"	)
										_UpperCAmelCase       :		Optional[Any] 		=    text.replace("\n"						,					"<BR>"	)
										_UpperCAmelCase       :		Tuple 		=    text.replace("\r"						,					"<BR>"	)
										_UpperCAmelCase       :		Optional[Any] 		=    text.replace("\t"						,					"<TAB>"	)
										_UpperCAmelCase       :		Any 		=    text.replace("—"						,					"ー"	)
										_UpperCAmelCase       :		Optional[Any] 		=    text.replace("−"						,					"ー"	)
										for k, v in self.emoji["emoji"].items():
														if k in text:
																		_UpperCAmelCase       :		str 		=    text.replace(A_						,					A_	)
										if clean:
														_UpperCAmelCase       :		Dict 		=    self.clean_text(A_	)

										def check_simbol(A_	):
														_UpperCAmelCase       :		Any 		=    x.encode()
														if len(A_	) == 1 and len(A_	) == 2:
																		_UpperCAmelCase       :		Dict 		=    (int(e[0]	) << 8) + int(e[1]	)
																		if (
																		    (c >= 0xc_2a1 and c <= 0xc_2bf)
																		    or (c >= 0xc_780 and c <= 0xc_783)
																		    or (c >= 0xc_ab9 and c <= 0xc_bbf)
																		    or (c >= 0xc_c80 and c <= 0xc_da2)
																		):
																						return True
														return False

										def checkuae(A_	):
														_UpperCAmelCase       :		Union[str, Any] 		=    x.encode()
														if len(A_	) == 1 and len(A_	) == 3:
																		_UpperCAmelCase       :		str 		=    (int(e[0]	) << 16) + (int(e[1]	) << 8) + int(e[2]	)
																		if c >= 0xe28_080 and c <= 0xe2b_07f:
																						return True
														return False

										_UpperCAmelCase       :		List[str] 		=    0
										_UpperCAmelCase       :		Union[str, Any] 		=    []
										while pos < len(A_	):
														_UpperCAmelCase       :		Optional[Any] 		=    min(len(A_	)						,					pos + self.maxlen + 1	) if text[pos] == "<" else pos + 3
														_UpperCAmelCase       :		Tuple 		=    []  # (token_id, token, pos)
														for e in range(A_						,					A_						,					-1	):
																		_UpperCAmelCase       :		Any 		=    text[pos:e]
																		if wd in self.vocab:
																						if wd[0] == "<" and len(A_	) > 2:
																										_UpperCAmelCase       :		Optional[Any] 		=    [(self.vocab[wd], wd, e)]
																										break
																						else:
																										candidates.append((self.vocab[wd], wd, e)	)
														if len(A_	) > 0:
																		# the smallest token_id is adopted
																		_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    sorted(A_						,					key=lambda A_	: x[0]	)[0]
																		result.append(A_	)
																		_UpperCAmelCase       :		Optional[int] 		=    e
														else:
																		_UpperCAmelCase       :		Any 		=    pos + 1
																		_UpperCAmelCase       :		Dict 		=    text[pos:end]
																		if check_simbol(A_	):
																						result.append("<KIGOU>"	)
																		elif checkuae(A_	):
																						result.append("<U2000U2BFF>"	)
																		else:
																						for i in wd.encode("utf-8"	):
																										result.append("<|byte%d|>" % i	)
																		_UpperCAmelCase       :		Optional[int] 		=    end
										return result





						def        _UpperCAmelCase				(       self						,					A_						,					A_="\n"	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    []
										_UpperCAmelCase       :		Union[str, Any] 		=    []
										_UpperCAmelCase       :		List[str] 		=    self.ids_to_tokens[index][0]
										if word[:6] == "<|byte" and word[-2:] == "|>":
														byte_tokens.append(int(word[6:-2]	)	)
										else:
														if len(A_	) > 0:
																		words.append(bytearray(A_	).decode("utf-8"						,					errors="replace"	)	)
																		_UpperCAmelCase       :		Optional[Any] 		=    []
														if word[:7] == "<|emoji" and word[-2:] == "|>":
																		words.append(self.emoji["emoji_inv"][word]	)
														elif word == "<SP>":
																		words.append(" "	)
														elif word == "<BR>":
																		words.append(A_	)
														elif word == "<TAB>":
																		words.append("\t"	)
														elif word == "<BLOCK>":
																		words.append("▀"	)
														elif word == "<KIGOU>":
																		words.append("ǀ"	)
														elif word == "<U2000U2BFF>":
																		words.append("‖"	)
														else:
																		words.append(A_	)
										if len(A_	) > 0:
														words.append(bytearray(A_	).decode("utf-8"						,					errors="replace"	)	)
										_UpperCAmelCase       :		Any 		=    "".join(A_	)
										return text





from __future__ import annotations

import pandas as pd

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

				_UpperCAmelCase       :		Any 		=    0
				_UpperCAmelCase       :		Union[str, Any] 		=    0
				_UpperCAmelCase       :		str 		=    9_9999_9999
				_UpperCAmelCase       :		List[Any] 		=    0
				_UpperCAmelCase       :		Any 		=    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:
																				_UpperCAmelCase       :		Optional[int] 		=    remaining_time[j]
																				_UpperCAmelCase       :		int 		=    j
																				_UpperCAmelCase       :		Tuple 		=    True

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

								_UpperCAmelCase       :		List[Any] 		=    remaining_time[short]
								if minm == 0:
												_UpperCAmelCase       :		List[Any] 		=    9_9999_9999

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

												# Find finish time of current process
												_UpperCAmelCase       :		Optional[int] 		=    increment_time + 1

												# Calculate waiting time
												_UpperCAmelCase       :		str 		=    finish_time - arrival_time[short]
												_UpperCAmelCase       :		Union[str, Any] 		=    finar - burst_time[short]

												if waiting_time[short] < 0:
																_UpperCAmelCase       :		Optional[int] 		=    0

        # Increment time
								increment_time += 1
				return waiting_time

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int  ,      lowerCAmelCase:  list[int] )   ->       list[int]:
				_UpperCAmelCase       :		str 		=    [0] * no_of_processes
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		str 		=    burst_time[i] + waiting_time[i]
				return turn_around_time

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int )   ->       None:
				_UpperCAmelCase       :		Dict 		=    0
				_UpperCAmelCase       :		List[str] 		=    0
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		Union[str, Any] 		=    total_waiting_time + waiting_time[i]
								_UpperCAmelCase       :		int 		=    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')
					SCREAMING_SNAKE_CASE_        =      int(input())
					SCREAMING_SNAKE_CASE_        =      [0] * no_of_processes
					SCREAMING_SNAKE_CASE_        =      [0] * no_of_processes
					SCREAMING_SNAKE_CASE_        =      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))
										SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_        =      map(int, input().split())

					SCREAMING_SNAKE_CASE_        =      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					SCREAMING_SNAKE_CASE_        =      burst_time
					SCREAMING_SNAKE_CASE_        =      no_of_processes
					SCREAMING_SNAKE_CASE_        =      waiting_time
					SCREAMING_SNAKE_CASE_        =      calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					SCREAMING_SNAKE_CASE_        =      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)


