



"""simple docstring"""
from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      tuple[int | None, int | None, float]:
		if not arr:
				return None, None, 0
		if low == high:
				return low, high, arr[low]

		a_     :      Dict 						=     (low + high) // 2
		a_						,				a_						,				a_     :      int 						=     max_subarray(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		a_						,				a_						,				a_     :      Tuple 						=     max_subarray(SCREAMING_SNAKE_CASE__, mid + 1, SCREAMING_SNAKE_CASE__	)
		a_						,				a_						,				a_     :      List[str] 						=     max_cross_sum(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		if left_sum >= right_sum and left_sum >= cross_sum:
				return left_low, left_high, left_sum
		elif right_sum >= left_sum and right_sum >= cross_sum:
				return right_low, right_high, right_sum
		return cross_left, cross_right, cross_sum



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      tuple[int, int, float]:
		a_						,				a_     :      Dict 						=     float("-inf"	), -1
		a_						,				a_     :      Optional[Any] 						=     float("-inf"	), -1

		a_     :      int | float 						=     0
		for i in range(SCREAMING_SNAKE_CASE__, low - 1, -1	):
				summ += arr[i]
				if summ > left_sum:
						a_     :      List[Any] 						=     summ
						a_     :      List[Any] 						=     i

		a_     :      Any 						=     0
		for i in range(mid + 1, high + 1	):
				summ += arr[i]
				if summ > right_sum:
						a_     :      Union[str, Any] 						=     summ
						a_     :      str 						=     i

		return max_left, max_right, (left_sum + right_sum)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      float:
		a_     :      Dict 						=     [randint(1, SCREAMING_SNAKE_CASE__	) for _ in range(SCREAMING_SNAKE_CASE__	)]
		a_     :      Optional[Any] 						=     time.time()
		max_subarray(SCREAMING_SNAKE_CASE__, 0, input_size - 1	)
		a_     :      int 						=     time.time()
		return end - start



def        lowerCAmelCase_						(							)			->      None:
		a_     :      Tuple 						=     [10, 100, 1_000, 10_000, 50_000, 100_000, 200_000, 300_000, 400_000, 500_000]
		a_     :      Dict 						=     [time_max_subarray(SCREAMING_SNAKE_CASE__	) for input_size in input_sizes]
		print("No of Inputs\t\tTime Taken"	)
		for input_size, runtime in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
				print(SCREAMING_SNAKE_CASE__, "\t\t", SCREAMING_SNAKE_CASE__	)
		plt.plot(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		plt.xlabel("Number of Inputs"	)
		plt.ylabel("Time taken in seconds"	)
		plt.show()


if __name__ == "__main__":
					from doctest import testmod

					testmod()




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 			snake_case_						(						unittest.TestCase				):






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      Any 						=     UNetaDModel(
									    block_out_channels=(3_2, 6_4)			,     layers_per_block=2			,     sample_size=3_2			,     in_channels=3			,     out_channels=3			,     down_block_types=("DownBlock2D", "AttnDownBlock2D")			,     up_block_types=("AttnUpBlock2D", "UpBlock2D")			,     )
									return model






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.dummy_uncond_unet
									a_     :      Any 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )

									a_     :      Tuple 						=     torch.manual_seed(0      )
									a_     :      Union[str, Any] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"      ).images

									a_     :      int 						=     torch.manual_seed(0      )
									a_     :      Optional[int] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"			,     return_dict=a_      )[0]

									a_     :      List[Any] 						=     image[0, -3:, -3:, -1]
									a_     :      Union[str, Any] 						=     image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      List[Any] 						=     np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




@slow
@require_torch
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     "google/ddpm-cifar10-32"

									a_     :      Optional[int] 						=     UNetaDModel.from_pretrained(a_      )
									a_     :      Union[str, Any] 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )
									a_     :      Optional[int] 						=     torch.manual_seed(0      )
									a_     :      List[Any] 						=     pndm(generator=a_			,     output_type="numpy"      ).images

									a_     :      str 						=     image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      Optional[Any] 						=     np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2