

from __future__ import annotations

import pandas as pd

def 						UpperCamelCase (lowercase_:			list[int]   ,				lowercase_:			list[int]   ,				lowercase_:			int )					->				list[int]:
	A__							:   Optional[int]					       =		[0] * no_of_processes
	A__							:   Dict					       =		[0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(lowercase_ ):
		A__							:   str					       =		burst_time[i]

	A__							:   Dict					       =		0
	A__							:   Union[str, Any]					       =		0
	A__							:   Union[str, Any]					       =		999999999
	A__							:   str					       =		0
	A__							:   Tuple					       =		False

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

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

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

		if remaining_time[short] == 0:
			complete += 1
			A__							:   Optional[Any]					       =		False

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

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

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

        # Increment time
		increment_time += 1
	return waiting_time

def 						UpperCamelCase (lowercase_:			list[int]   ,				lowercase_:			int   ,				lowercase_:			list[int] )					->				list[int]:
	A__							:   Dict					       =		[0] * no_of_processes
	for i in range(lowercase_ ):
		A__							:   str					       =		burst_time[i] + waiting_time[i]
	return turn_around_time

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

	A_	:			Dict  	=      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

	A_	:			int  	=      burst_time
	A_	:			List[str]  	=      no_of_processes
	A_	:			int  	=      waiting_time
	A_	:			Tuple  	=      calculate_turnaroundtime(bt, n, wt)

	calculate_average_times(waiting_time, turn_around_time, no_of_processes)

	A_	:			List[str]  	=      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)






import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
	import onnxruntime as ort


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

A_	:			str  	=      {
    'tensor(bool)': np.bool_,
    'tensor(int8)': np.inta,
    'tensor(uint8)': np.uinta,
    'tensor(int16)': np.intaa,
    'tensor(uint16)': np.uintaa,
    'tensor(int32)': np.intaa,
    'tensor(uint32)': np.uintaa,
    'tensor(int64)': np.intaa,
    'tensor(uint64)': np.uintaa,
    'tensor(float16)': np.floataa,
    'tensor(float)': np.floataa,
    'tensor(double)': np.floataa,
}



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__=None   ,	**A__      ):
						logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""      )
						A__							:   Dict					       =		model
						A__							:   Any					       =		kwargs.get("""model_save_dir"""   ,	A__      )
						A__							:   Optional[int]					       =		kwargs.get("""latest_model_name"""   ,	A__      )






					def __call__(			self   ,	**A__      ):
						A__							:   int					       =		{k: np.array(A__      ) for k, v in kwargs.items()}
						return self.model.run(A__   ,	A__      )






					@staticmethod
					def   __A      (			A__   ,	A__=None   ,	A__=None      ):
						if provider is None:
							logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""      )
							A__							:   List[Any]					       =		"""CPUExecutionProvider"""

						return ort.InferenceSession(A__   ,	providers=[provider]   ,	sess_options=A__      )






					def   __A      (			self   ,	A__   ,	A__ = None   ,	**A__      ):
						A__							:   List[Any]					       =		file_name if file_name is not None else ONNX_WEIGHTS_NAME

						A__							:   List[Any]					       =		self.model_save_dir.joinpath(self.latest_model_name      )
						A__							:   Optional[int]					       =		Path(A__      ).joinpath(A__      )
						try:
							shutil.copyfile(A__   ,	A__      )
						except shutil.SameFileError:
							pass

						# copy external weights (for models >2GB)
						A__							:   str					       =		self.model_save_dir.joinpath(A__      )
						if src_path.exists():
							A__							:   List[str]					       =		Path(A__      ).joinpath(A__      )
							try:
								shutil.copyfile(A__   ,	A__      )
							except shutil.SameFileError:
								pass






					def   __A      (			self   ,	A__   ,	**A__   ,	):
						if os.path.isfile(A__      ):
							logger.error(F"""Provided path ({save_directory}) should be a directory, not a file"""      )
							return

						os.makedirs(A__   ,	exist_ok=A__      )

						# saving model weights/files
						self._save_pretrained(A__   ,	**A__      )






					@classmethod
					def   __A      (			cls   ,	A__   ,	A__ = None   ,	A__ = None   ,	A__ = False   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	**A__   ,	):
						A__							:   str					       =		file_name if file_name is not None else ONNX_WEIGHTS_NAME
						# load model from local directory
						if os.path.isdir(A__      ):
							A__							:   Dict					       =		OnnxRuntimeModel.load_model(
							    os.path.join(A__   ,	A__      )   ,	provider=A__   ,	sess_options=A__      )
							A__							:   Optional[Any]					       =		Path(A__      )
						# load model from hub
						else:
							# download model
							A__							:   Union[str, Any]					       =		hf_hub_download(
							    repo_id=A__   ,	filename=A__   ,	use_auth_token=A__   ,	revision=A__   ,	cache_dir=A__   ,	force_download=A__   ,	)
							A__							:   List[str]					       =		Path(A__      ).parent
							A__							:   str					       =		Path(A__      ).name
							A__							:   Optional[int]					       =		OnnxRuntimeModel.load_model(A__   ,	provider=A__   ,	sess_options=A__      )
						return cls(model=A__   ,	**A__      )






					@classmethod
					def   __A      (			cls   ,	A__   ,	A__ = True   ,	A__ = None   ,	A__ = None   ,	**A__   ,	):
						A__							:   Optional[Any]					       =		None
						if len(str(A__      ).split("""@"""      )      ) == 2:
							A__       ,       A__							:   Union[str, Any]					       =		model_id.split("""@"""      )

						return cls._from_pretrained(
						    model_id=A__   ,	revision=A__   ,	cache_dir=A__   ,	force_download=A__   ,	use_auth_token=A__   ,	**A__   ,	)



