from __future__ import annotations

from statistics import mean


def SCREAMING_SNAKE_CASE					(  lowercase_:						list[int]					):
       _UpperCamelCase  :							str	    =	2
       _UpperCamelCase  :							Optional[int]	    =	list(lowercase_					)
       _UpperCamelCase  :							Union[str, Any]	    =	[0] * len(lowercase_					)
       _UpperCamelCase  :							List[str]	    =	0
       while True:
              _UpperCamelCase  :							List[str]	    =	True
              for i, burst_time in enumerate(lowercase_					):
                     if rem_burst_times[i] > 0:
                            _UpperCamelCase  :							List[str]	    =	False
                            if rem_burst_times[i] > quantum:
                                   t += quantum
                                   rem_burst_times[i] -= quantum
                            else:
                                   t += rem_burst_times[i]
                                   _UpperCamelCase  :							str	    =	t - burst_time
                                   _UpperCamelCase  :							Union[str, Any]	    =	0
              if done is True:
                     return waiting_times


def SCREAMING_SNAKE_CASE					(  lowercase_:						list[int]		,    lowercase_:						list[int]					):
       return [burst + waiting for burst, waiting in zip(lowercase_		,    lowercase_					)]


if __name__ == "__main__":
 UpperCAmelCase      		=					[3, 5, 7]
 UpperCAmelCase      		=					calculate_waiting_times(burst_times)
 UpperCAmelCase      		=					calculate_turn_around_times(burst_times, waiting_times)
 print('''Process ID \tBurst Time \tWaiting Time \tTurnaround Time''')
 for i, burst_time in enumerate(burst_times):
  print(
      f'''  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  '''
      f'''{turn_around_times[i]}'''
  )
 print(f'''\nAverage waiting time = {mean(waiting_times):.5f}''')
 print(f'''Average turn around time = {mean(turn_around_times):.5f}''')




import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput




@dataclass
class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Union[List[PIL.Image.Image], np.ndarray]







class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		int	)			->				Tuple:
        _UpperCamelCase  :							str	    =	CustomOutput(images=np.random.rand(1							,      3							,      4							,      4	)	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      np.ndarray	)
        assert outputs.images.shape == (1, 3, 4, 4)
        assert isinstance(outputs['images']							,      np.ndarray	)
        assert outputs["images"].shape == (1, 3, 4, 4)
        assert isinstance(outputs[0]							,      np.ndarray	)
        assert outputs[0].shape == (1, 3, 4, 4)

        # test with a non-tensor attribute
        _UpperCamelCase  :							str	    =	CustomOutput(images=[PIL.Image.new('RGB'							,      (4, 4)	)]	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      lowerCamelCase	)
        assert isinstance(outputs.images[0]							,      PIL.Image.Image	)
        assert isinstance(outputs['images']							,      lowerCamelCase	)
        assert isinstance(outputs['images'][0]							,      PIL.Image.Image	)
        assert isinstance(outputs[0]							,      lowerCamelCase	)
        assert isinstance(outputs[0][0]							,      PIL.Image.Image	)

 def        __snake_case       (      self							:		Any	)			->				Dict:
        # test output reinitialization with a `dict` for compatibility with `accelerate`
        _UpperCamelCase  :							Tuple	    =	CustomOutput({'images': np.random.rand(1							,      3							,      4							,      4	)}	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      np.ndarray	)
        assert outputs.images.shape == (1, 3, 4, 4)
        assert isinstance(outputs['images']							,      np.ndarray	)
        assert outputs["images"].shape == (1, 3, 4, 4)
        assert isinstance(outputs[0]							,      np.ndarray	)
        assert outputs[0].shape == (1, 3, 4, 4)

        # test with a non-tensor attribute
        _UpperCamelCase  :							int	    =	CustomOutput({'images': [PIL.Image.new('RGB'							,      (4, 4)	)]}	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      lowerCamelCase	)
        assert isinstance(outputs.images[0]							,      PIL.Image.Image	)
        assert isinstance(outputs['images']							,      lowerCamelCase	)
        assert isinstance(outputs['images'][0]							,      PIL.Image.Image	)
        assert isinstance(outputs[0]							,      lowerCamelCase	)
        assert isinstance(outputs[0][0]							,      PIL.Image.Image	)



