






import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor





class        a__      (	unittest.TestCase		):






		@property
		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							1
				__A    =							3
				__A    =							8
				__A    =							8

				__A    =							torch.rand((batch_size, num_channels, height, width)						)

				return sample






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				Any:
				if isinstance(image[0]							, PIL.Image.Image						):
						return np.stack([np.array(lowercase__						) for i in image]							, axis=0						)
				elif isinstance(lowercase__							, torch.Tensor						):
						return image.cpu().numpy().transpose(0							, 2							, 3							, 1						)
				return image






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)

				__A    =							self.dummy_sample
				__A    =							self.to_np(lowercase__						)

				for output_type in ["pt", "np", "pil"]:
						__A    =							image_processor.postprocess(
						    image_processor.preprocess(lowercase__						)							, output_type=lowercase__							, )
						__A    =							self.to_np(lowercase__						)
						__A    =							(input_np * 255).round() if output_type == "pil" else input_np
						assert (
						    np.abs(in_np - out_np						).max() < 1e-6
						), F"""decoded output does not match input for output_type {output_type}"""






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)
				__A    =							self.dummy_sample.cpu().numpy().transpose(0							, 2							, 3							, 1						)

				for output_type in ["pt", "np", "pil"]:
						__A    =							image_processor.postprocess(image_processor.preprocess(lowercase__						)							, output_type=lowercase__						)

						__A    =							self.to_np(lowercase__						)
						__A    =							(input_np * 255).round() if output_type == "pil" else input_np
						assert (
						    np.abs(in_np - out_np						).max() < 1e-6
						), F"""decoded output does not match input for output_type {output_type}"""






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)

				__A    =							self.dummy_sample.cpu().numpy().transpose(0							, 2							, 3							, 1						)
				__A    =							image_processor.numpy_to_pil(lowercase__						)

				for output_type in ["pt", "np", "pil"]:
						__A    =							image_processor.postprocess(image_processor.preprocess(lowercase__						)							, output_type=lowercase__						)
						for i, o in zip(lowercase__							, lowercase__						):
								__A    =							np.array(lowercase__						)
								__A    =							self.to_np(lowercase__						) if output_type == "pil" else (self.to_np(lowercase__						) * 255).round()
								assert (
								    np.abs(in_np - out_np						).max() < 1e-6
								), F"""decoded output does not match input for output_type {output_type}"""






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)

				__A    =							self.dummy_sample
				__A    =							input_pt_ad.squeeze(0						)

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )
				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				__A    =							self.to_np(self.dummy_sample						)
				__A    =							input_np_ad.squeeze(0						)

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )
				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				assert np.abs(out_pt_ad - out_pt_ad						).max() < 1e-6
				assert np.abs(out_np_ad - out_np_ad						).max() < 1e-6






		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)

				__A    =							self.dummy_sample
				__A    =							list(lowercase__						)

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				__A    =							self.to_np(self.dummy_sample						)
				list(lowercase__						)

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				assert np.abs(out_pt_ad - out_pt_list						).max() < 1e-6
				assert np.abs(out_np_ad - out_np_list						).max() < 1e-6


from __future__ import annotations

from statistics import mean
def 					__lowercase						(    lowerCamelCase__       )					->list[int]:

  '''simple docstring'''




  a__  		=  2
  a__  		=  list(lowerCamelCase__       )
  a__  		=  [0] * len(lowerCamelCase__       )
  a__  		=  0
  while True:
    a__  		=  True
    for i, burst_time in enumerate(lowerCamelCase__       ):
      if rem_burst_times[i] > 0:
        a__  		=  False
        if rem_burst_times[i] > quantum:
          t += quantum
          rem_burst_times[i] -= quantum
        else:
          t += rem_burst_times[i]
          a__  		=  t - burst_time
          a__  		=  0
    if done is True:
      return waiting_times
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->list[int]:

  '''simple docstring'''




  return [burst + waiting for burst, waiting in zip(lowerCamelCase__			,					lowerCamelCase__       )]


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     [3, 5, 7]
     _SCREAMING_SNAKE_CASE     =     calculate_waiting_times(burst_times)
     _SCREAMING_SNAKE_CASE     =     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}""")

