

import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

SCREAMING_SNAKE_CASE_        =      {
    'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
    'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}

SCREAMING_SNAKE_CASE_        =      {
    'ctrl': 256,
}

SCREAMING_SNAKE_CASE_        =      {
    'Pregnancy': 168629,
    'Christianity': 7675,
    'Explain': 106423,
    'Fitness': 63440,
    'Saving': 63163,
    'Ask': 27171,
    'Ass': 95985,
    'Joke': 163509,
    'Questions': 45622,
    'Thoughts': 49605,
    'Retail': 52342,
    'Feminism': 164338,
    'Writing': 11992,
    'Atheism': 192263,
    'Netflix': 48616,
    'Computing': 39639,
    'Opinion': 43213,
    'Alone': 44967,
    'Funny': 58917,
    'Gaming': 40358,
    'Human': 4088,
    'India': 1331,
    'Joker': 77138,
    'Diet': 36206,
    'Legal': 11859,
    'Norman': 4939,
    'Tip': 72689,
    'Weight': 52343,
    'Movies': 46273,
    'Running': 23425,
    'Science': 2090,
    'Horror': 37793,
    'Confession': 60572,
    'Finance': 12250,
    'Politics': 16360,
    'Scary': 191985,
    'Support': 12654,
    'Technologies': 32516,
    'Teenage': 66160,
    'Event': 32769,
    'Learned': 67460,
    'Notion': 182770,
    'Wikipedia': 37583,
    'Books': 6665,
    'Extract': 76050,
    'Confessions': 102701,
    'Conspiracy': 75932,
    'Links': 63674,
    'Narcissus': 150425,
    'Relationship': 54766,
    'Relationships': 134796,
    'Reviews': 41671,
    'News': 4256,
    'Translation': 26820,
    'multilingual': 128406,
}

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int] )   ->       Optional[Any]:
				_UpperCAmelCase       :		List[Any] 		=    set()
				_UpperCAmelCase       :		Optional[Any] 		=    word[0]
				for char in word[1:]:
								pairs.add((prev_char, char) )
								_UpperCAmelCase       :		List[str] 		=    char

				_UpperCAmelCase       :		List[Any] 		=    set(lowerCAmelCase )
				return pairs





class 				a  (	UpperCAmelCase				):
						_lowercase    						=			VOCAB_FILES_NAMES
						_lowercase    						=			PRETRAINED_VOCAB_FILES_MAP
						_lowercase    						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase    						=			CONTROL_CODES


						def __init__(       self						,					A_						,					A_						,					A_="<unk>"						,					**A_	):







										'''simple docstring'''


										super().__init__(unk_token=A_						,					**A_	)

										with open(A_						,					encoding="utf-8"	) as vocab_handle:
														_UpperCAmelCase       :		List[str] 		=    json.load(A_	)
										_UpperCAmelCase       :		Union[str, Any] 		=    {v: k for k, v in self.encoder.items()}
										with open(A_						,					encoding="utf-8"	) as merges_handle:
														_UpperCAmelCase       :		Optional[Any] 		=    merges_handle.read().split("\n"	)[1:-1]
										_UpperCAmelCase       :		List[Any] 		=    [tuple(merge.split()	) for merge in merges]
										_UpperCAmelCase       :		Tuple 		=    dict(zip(A_						,					range(len(A_	)	)	)	)
										_UpperCAmelCase       :		List[Any] 		=    {}


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return len(self.encoder	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										if token in self.cache:
														return self.cache[token]
										_UpperCAmelCase       :		str 		=    tuple(A_	)
										_UpperCAmelCase       :		List[Any] 		=    tuple(list(word[:-1]	) + [word[-1] + "</w>"]	)
										_UpperCAmelCase       :		Union[str, Any] 		=    get_pairs(A_	)

										if not pairs:
														return token

										while True:
														_UpperCAmelCase       :		Tuple 		=    min(A_						,					key=lambda A_	: self.bpe_ranks.get(A_						,					float("inf"	)	)	)
														if bigram not in self.bpe_ranks:
																		break
														_UpperCAmelCase		,							_UpperCAmelCase       :		Union[str, Any] 		=    bigram
														_UpperCAmelCase       :		str 		=    []
														_UpperCAmelCase       :		Tuple 		=    0
														while i < len(A_	):
																		try:
																						_UpperCAmelCase       :		int 		=    word.index(A_						,					A_	)
																		except ValueError:
																						new_word.extend(word[i:]	)
																						break
																		else:
																						new_word.extend(word[i:j]	)
																						_UpperCAmelCase       :		int 		=    j

																		if word[i] == first and i < len(A_	) - 1 and word[i + 1] == second:
																						new_word.append(first + second	)
																						i += 2
																		else:
																						new_word.append(word[i]	)
																						i += 1
														_UpperCAmelCase       :		List[Any] 		=    tuple(A_	)
														_UpperCAmelCase       :		str 		=    new_word
														if len(A_	) == 1:
																		break
														else:
																		_UpperCAmelCase       :		List[str] 		=    get_pairs(A_	)
										_UpperCAmelCase       :		Optional[int] 		=    "@@ ".join(A_	)
										_UpperCAmelCase       :		Tuple 		=    word[:-4]
										_UpperCAmelCase       :		List[Any] 		=    word
										return word


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    []

										_UpperCAmelCase       :		Optional[int] 		=    re.findall(R"\S+\n?"						,					A_	)

										for token in words:
														split_tokens.extend(list(self.bpe(A_	).split(" "	)	)	)
										return split_tokens


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    " ".join(A_	).replace("@@ "						,					""	).strip()
										return out_string





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







										'''simple docstring'''


										if not os.path.isdir(A_	):
														logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
														return
										_UpperCAmelCase       :		Optional[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["merges_file"]	)

										with open(A_						,					"w"						,					encoding="utf-8"	) as f:
														f.write(json.dumps(self.encoder						,					indent=2						,					sort_keys=A_						,					ensure_ascii=A_	) + "\n"	)

										_UpperCAmelCase       :		List[Any] 		=    0
										with open(A_						,					"w"						,					encoding="utf-8"	) as writer:
														writer.write("#version: 0.2\n"	)
														for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,					key=lambda A_	: kv[1]	):
																		if index != token_index:
																						logger.warning(
																						    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																						    " Please check that the tokenizer is not corrupted!"	)
																						_UpperCAmelCase       :		Tuple 		=    token_index
																		writer.write(" ".join(A_	) + "\n"	)
																		index += 1

										return vocab_file, merge_file

						# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
						#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
						#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
						#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
						#     return ''.join(tokens_generated_so_far)





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)


