



'''simple docstring'''

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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]=None							,		)      ->						int:
	lowerCAmelCase   =     input_ids.ne(_SCREAMING_SNAKE_CASE							).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 __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



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


						def __len__( self				)     ->     Tuple:
							return len(self.src_lens				)


						def __getitem__( self     ,	A_				)     ->     Dict[str, torch.Tensor]:
							lowerCAmelCase   =     index + 1  # linecache starts at 1
							lowerCAmelCase   =     self.prefix + linecache.getline(str(self.src_file				)     ,	A_				).rstrip("""\n"""				)
							lowerCAmelCase   =     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
							lowerCAmelCase   =     (
							    self.tokenizer.question_encoder if isinstance(self.tokenizer     ,	A_				) else self.tokenizer
							)
							lowerCAmelCase   =     self.tokenizer.generator if isinstance(self.tokenizer     ,	A_				) else self.tokenizer

							lowerCAmelCase   =     encode_line(A_     ,	A_     ,	self.max_source_length     ,	"""right"""				)
							lowerCAmelCase   =     encode_line(A_     ,	A_     ,	self.max_target_length     ,	"""right"""				)

							lowerCAmelCase   =     source_inputs["""input_ids"""].squeeze()
							lowerCAmelCase   =     target_inputs["""input_ids"""].squeeze()
							lowerCAmelCase   =     source_inputs["""attention_mask"""].squeeze()
							return {
							    "input_ids": source_ids,
							    "attention_mask": src_mask,
							    "decoder_input_ids": target_ids,
							}


						@staticmethod
						def     __snake_case   ( A_				)     ->     Optional[int]:
							return [len(A_				) for x in Path(A_				).open().readlines()]




						def     __snake_case   ( self     ,	A_				)     ->     Dict[str, torch.Tensor]:
							lowerCAmelCase   =     torch.stack([x["""input_ids"""] for x in batch]				)
							lowerCAmelCase   =     torch.stack([x["""attention_mask"""] for x in batch]				)
							lowerCAmelCase   =     torch.stack([x["""decoder_input_ids"""] for x in batch]				)
							lowerCAmelCase   =     (
							    self.tokenizer.generator.pad_token_id
							    if isinstance(self.tokenizer     ,	A_				)
							    else self.tokenizer.pad_token_id
							)
							lowerCAmelCase   =     (
							    self.tokenizer.question_encoder.pad_token_id
							    if isinstance(self.tokenizer     ,	A_				)
							    else self.tokenizer.pad_token_id
							)
							lowerCAmelCase   =     trim_batch(A_     ,	A_				)
							lowerCAmelCase,       lowerCAmelCase   =     trim_batch(A_     ,	A_     ,	attention_mask=A_				)
							lowerCAmelCase   =     {
							    """input_ids""": source_ids,
							    """attention_mask""": source_mask,
							    """decoder_input_ids""": y,
							}
							return batch







UpperCAmelCase     		=   getLogger(__name__)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[List]							)      ->						List[Any]:
	return list(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE							)							)






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






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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							)      ->						List[Any]:
	with open(_SCREAMING_SNAKE_CASE							) as f:
		return json.load(_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( )      ->						Dict:
	lowerCAmelCase   =     git.Repo(search_parent_directories=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     {
	    """repo_id""": str(_SCREAMING_SNAKE_CASE							),
	    """repo_sha""": str(repo.head.object.hexsha							),
	    """repo_branch""": str(repo.active_branch							),
	    """hostname""": str(socket.gethostname()							),
	}
	return repo_infos






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Callable							,		_SCREAMING_SNAKE_CASE					:						Iterable							)      ->						List:
	return list(map(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]							)      ->						List[Any]:
	with open(_SCREAMING_SNAKE_CASE							,		"""wb"""							) as f:
		return pickle.dump(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							)      ->						List[Any]:

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

	def white_space_fix(_SCREAMING_SNAKE_CASE					:						List[str]							):
		return " ".join(text.split()							)

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

	def lower(_SCREAMING_SNAKE_CASE					:						List[str]							):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE							)							)							)							)






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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						int:
	return normalize_answer(_SCREAMING_SNAKE_CASE							) == normalize_answer(_SCREAMING_SNAKE_CASE							)






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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[int]							)      ->						Optional[Any]:
	return model_prefix.startswith("""rag"""							)






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




'''simple docstring'''

from __future__ import annotations

import pandas as pd






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						list[int]:







	"""simple docstring"""
	lowerCAmelCase   =     [0] * no_of_processes
	lowerCAmelCase   =     [0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     burst_time[i]

	lowerCAmelCase   =     0
	lowerCAmelCase   =     0
	lowerCAmelCase   =     999_999_999
	lowerCAmelCase   =     0
	lowerCAmelCase   =     False

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

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

		lowerCAmelCase   =     remaining_time[short]
		if minm == 0:
			lowerCAmelCase   =     999_999_999

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

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

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

			if waiting_time[short] < 0:
				lowerCAmelCase   =     0

        # Increment time
		increment_time += 1
	return waiting_time






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						list[int]							)      ->						list[int]:







	"""simple docstring"""
	lowerCAmelCase   =     [0] * no_of_processes
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     burst_time[i] + waiting_time[i]
	return turn_around_time






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						None:







	"""simple docstring"""
	lowerCAmelCase   =     0
	lowerCAmelCase   =     0
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     total_waiting_time + waiting_time[i]
		lowerCAmelCase   =     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')
				UpperCAmelCase     		=   int(input())
				UpperCAmelCase     		=   [0] * no_of_processes
				UpperCAmelCase     		=   [0] * no_of_processes
				UpperCAmelCase     		=   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))
								UpperCAmelCase		,       UpperCAmelCase     		=   map(int, input().split())

				UpperCAmelCase     		=   calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

				calculate_average_times(waiting_time, turn_around_time, no_of_processes)

				UpperCAmelCase     		=   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)