

import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  Dict  ,      lowerCAmelCase:  Tuple  ,      lowerCAmelCase:  Optional[Any]  ,      lowerCAmelCase:  Optional[Any]=True  ,      lowerCAmelCase:  Optional[Any]="pt" )   ->       int:
				_UpperCAmelCase       :		Optional[Any] 		=    {"add_prefix_space": True} if isinstance(lowerCAmelCase  ,      lowerCAmelCase ) and not line.startswith(" " ) else {}
				_UpperCAmelCase       :		str 		=    padding_side
				return tokenizer(
				    [line]  ,      max_length=lowerCAmelCase  ,      padding="max_length" if pad_to_max_length else None  ,      truncation=lowerCAmelCase  ,      return_tensors=lowerCAmelCase  ,      add_special_tokens=lowerCAmelCase  ,      **lowerCAmelCase  ,      )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict  ,      lowerCAmelCase:  Optional[Any]  ,      lowerCAmelCase:  List[str]=None  ,      )   ->       List[Any]:
				_UpperCAmelCase       :		Any 		=    input_ids.ne(lowerCAmelCase ).any(dim=0 )
				if attention_mask is None:
								return input_ids[:, keep_column_mask]
				else:
								return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])

class 				a  (	UpperCAmelCase				):


						def __init__(       self						,					A_						,					A_						,					A_						,					A_						,					A_="train"						,					A_=None						,					A_=None						,					A_=None						,					A_=""						,					):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		Dict 		=    Path(A_	).joinpath(type_path + ".source"	)
										_UpperCAmelCase       :		Dict 		=    Path(A_	).joinpath(type_path + ".target"	)
										_UpperCAmelCase       :		Optional[Any] 		=    self.get_char_lens(self.src_file	)
										_UpperCAmelCase       :		Tuple 		=    max_source_length
										_UpperCAmelCase       :		int 		=    max_target_length
										assert min(self.src_lens	) > 0, f'found empty line in {self.src_file}'
										_UpperCAmelCase       :		Tuple 		=    tokenizer
										_UpperCAmelCase       :		Optional[Any] 		=    prefix
										if n_obs is not None:
														_UpperCAmelCase       :		str 		=    self.src_lens[:n_obs]
										_UpperCAmelCase       :		int 		=    src_lang
										_UpperCAmelCase       :		Optional[Any] 		=    tgt_lang


						def __len__(       self	):







										'''simple docstring'''


										return len(self.src_lens	)


						def __getitem__(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    index + 1  # linecache starts at 1
										_UpperCAmelCase       :		List[str] 		=    self.prefix + linecache.getline(str(self.src_file	)						,					A_	).rstrip("\n"	)
										_UpperCAmelCase       :		Union[str, Any] 		=    linecache.getline(str(self.tgt_file	)						,					A_	).rstrip("\n"	)
										assert source_line, f'empty source line for index {index}'
										assert tgt_line, f'empty tgt line for index {index}'

										# Need to add eos token manually for T5
										if isinstance(self.tokenizer						,					A_	):
														source_line += self.tokenizer.eos_token
														tgt_line += self.tokenizer.eos_token

										# Pad source and target to the right
										_UpperCAmelCase       :		Union[str, Any] 		=    (
										    self.tokenizer.question_encoder if isinstance(self.tokenizer						,					A_	) else self.tokenizer
										)
										_UpperCAmelCase       :		Any 		=    self.tokenizer.generator if isinstance(self.tokenizer						,					A_	) else self.tokenizer

										_UpperCAmelCase       :		Tuple 		=    encode_line(A_						,					A_						,					self.max_source_length						,					"right"	)
										_UpperCAmelCase       :		Any 		=    encode_line(A_						,					A_						,					self.max_target_length						,					"right"	)

										_UpperCAmelCase       :		List[str] 		=    source_inputs["input_ids"].squeeze()
										_UpperCAmelCase       :		Optional[int] 		=    target_inputs["input_ids"].squeeze()
										_UpperCAmelCase       :		str 		=    source_inputs["attention_mask"].squeeze()
										return {
										    "input_ids": source_ids,
										    "attention_mask": src_mask,
										    "decoder_input_ids": target_ids,
										}


						@staticmethod
						def        _UpperCAmelCase				(       A_	):







										'''simple docstring'''


										return [len(A_	) for x in Path(A_	).open().readlines()]





						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    torch.stack([x["input_ids"] for x in batch]	)
										_UpperCAmelCase       :		str 		=    torch.stack([x["attention_mask"] for x in batch]	)
										_UpperCAmelCase       :		str 		=    torch.stack([x["decoder_input_ids"] for x in batch]	)
										_UpperCAmelCase       :		int 		=    (
										    self.tokenizer.generator.pad_token_id
										    if isinstance(self.tokenizer						,					A_	)
										    else self.tokenizer.pad_token_id
										)
										_UpperCAmelCase       :		Any 		=    (
										    self.tokenizer.question_encoder.pad_token_id
										    if isinstance(self.tokenizer						,					A_	)
										    else self.tokenizer.pad_token_id
										)
										_UpperCAmelCase       :		Optional[Any] 		=    trim_batch(A_						,					A_	)
										_UpperCAmelCase		,							_UpperCAmelCase       :		List[Any] 		=    trim_batch(A_						,					A_						,					attention_mask=A_	)
										_UpperCAmelCase       :		Any 		=    {
										    "input_ids": source_ids,
										    "attention_mask": source_mask,
										    "decoder_input_ids": y,
										}
										return batch







SCREAMING_SNAKE_CASE_        =      getLogger(__name__)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[List] )   ->       Optional[int]:
				return list(itertools.chain.from_iterable(lowerCAmelCase ) )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str )   ->       None:
				_UpperCAmelCase       :		str 		=    get_git_info()
				save_json(lowerCAmelCase  ,      os.path.join(lowerCAmelCase  ,      "git_log.json" ) )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict  ,      lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  List[str]=4  ,      **lowerCAmelCase:  Union[str, Any] )   ->       Dict:
				with open(lowerCAmelCase  ,      "w" ) as f:
								json.dump(lowerCAmelCase  ,      lowerCAmelCase  ,      indent=lowerCAmelCase  ,      **lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str] )   ->       Dict:
				with open(lowerCAmelCase ) as f:
								return json.load(lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				)   ->       int:
				_UpperCAmelCase       :		Optional[int] 		=    git.Repo(search_parent_directories=lowerCAmelCase )
				_UpperCAmelCase       :		str 		=    {
				    "repo_id": str(lowerCAmelCase ),
				    "repo_sha": str(repo.head.object.hexsha ),
				    "repo_branch": str(repo.active_branch ),
				    "hostname": str(socket.gethostname() ),
				}
				return repo_infos

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Callable  ,      lowerCAmelCase:  Iterable )   ->       List:
				return list(map(lowerCAmelCase  ,      lowerCAmelCase ) )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  List[str] )   ->       Union[str, Any]:
				with open(lowerCAmelCase  ,      "wb" ) as f:
								return pickle.dump(lowerCAmelCase  ,      lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int] )   ->       int:

				def remove_articles(lowerCAmelCase:  Optional[int] ):
								return re.sub(R"\b(a|an|the)\b"  ,      " "  ,      lowerCAmelCase )

				def white_space_fix(lowerCAmelCase:  Optional[Any] ):
								return " ".join(text.split() )

				def remove_punc(lowerCAmelCase:  Any ):
								_UpperCAmelCase       :		Any 		=    set(string.punctuation )
								return "".join(ch for ch in text if ch not in exclude )

				def lower(lowerCAmelCase:  List[Any] ):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase ) ) ) )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  Dict )   ->       str:
				_UpperCAmelCase       :		int 		=    normalize_answer(lowerCAmelCase ).split()
				_UpperCAmelCase       :		Union[str, Any] 		=    normalize_answer(lowerCAmelCase ).split()
				_UpperCAmelCase       :		str 		=    Counter(lowerCAmelCase ) & Counter(lowerCAmelCase )
				_UpperCAmelCase       :		int 		=    sum(common.values() )
				if num_same == 0:
								return 0
				_UpperCAmelCase       :		List[Any] 		=    1.0 * num_same / len(lowerCAmelCase )
				_UpperCAmelCase       :		List[str] 		=    1.0 * num_same / len(lowerCAmelCase )
				_UpperCAmelCase       :		Optional[Any] 		=    (2 * precision * recall) / (precision + recall)
				return fa

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  int )   ->       Dict:
				return normalize_answer(lowerCAmelCase ) == normalize_answer(lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  List[str] )   ->       Dict:
				assert len(lowerCAmelCase ) == len(lowerCAmelCase )
				_UpperCAmelCase       :		Any 		=    0
				for hypo, pred in zip(lowerCAmelCase  ,      lowerCAmelCase ):
								em += exact_match_score(lowerCAmelCase  ,      lowerCAmelCase )
				if len(lowerCAmelCase ) > 0:
								em /= len(lowerCAmelCase )
				return {"em": em}

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str )   ->       str:
				return model_prefix.startswith("rag" )





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  Dict )   ->       int:
				_UpperCAmelCase       :		List[Any] 		=    {p: p for p in extra_params}
				# T5 models don't have `dropout` param, they have `dropout_rate` instead
				_UpperCAmelCase       :		Optional[Any] 		=    "dropout_rate"
				for p in extra_params:
								if getattr(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase ):
												if not hasattr(lowerCAmelCase  ,      lowerCAmelCase ) and not hasattr(lowerCAmelCase  ,      equivalent_param[p] ):
																logger.info("config doesn't have a `{}` attribute".format(lowerCAmelCase ) )
																delattr(lowerCAmelCase  ,      lowerCAmelCase )
																continue
												_UpperCAmelCase       :		Optional[int] 		=    p if hasattr(lowerCAmelCase  ,      lowerCAmelCase ) else equivalent_param[p]
												setattr(lowerCAmelCase  ,      lowerCAmelCase  ,      getattr(lowerCAmelCase  ,      lowerCAmelCase ) )
												delattr(lowerCAmelCase  ,      lowerCAmelCase )
				return hparams, config





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)


