





'''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 			lowerCamelCase					(						unittest.TestCase  ):


					@property
					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											torch.manual_seed(0       )
											_snake_case:   Optional[Any]  					=		UNetaDModel(
											    block_out_channels=(32, 64)       ,     layers_per_block=2       ,     sample_size=32       ,     in_channels=3       ,     out_channels=3       ,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')       ,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')       ,     )
											return model




					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											_snake_case:   str  					=		self.dummy_uncond_unet
											_snake_case:   Optional[Any]  					=		PNDMScheduler()

											_snake_case:   str  					=		PNDMPipeline(unet=__snake_case       ,     scheduler=__snake_case       )
											pndm.to(__snake_case       )
											pndm.set_progress_bar_config(disable=__snake_case       )

											_snake_case:   List[Any]  					=		torch.manual_seed(0       )
											_snake_case:   Tuple  					=		pndm(generator=__snake_case       ,     num_inference_steps=20       ,     output_type='numpy'       ).images

											_snake_case:   int  					=		torch.manual_seed(0       )
											_snake_case:   Tuple  					=		pndm(generator=__snake_case       ,     num_inference_steps=20       ,     output_type='numpy'       ,     return_dict=__snake_case       )[0]

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

											assert image.shape == (1, 32, 32, 3)
											_snake_case:   Optional[int]  					=		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 			lowerCamelCase					(						unittest.TestCase  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Tuple  					=		'google/ddpm-cifar10-32'

											_snake_case:   int  					=		UNetaDModel.from_pretrained(__snake_case       )
											_snake_case:   Union[str, Any]  					=		PNDMScheduler()

											_snake_case:   List[str]  					=		PNDMPipeline(unet=__snake_case       ,     scheduler=__snake_case       )
											pndm.to(__snake_case       )
											pndm.set_progress_bar_config(disable=__snake_case       )
											_snake_case:   Optional[Any]  					=		torch.manual_seed(0       )
											_snake_case:   List[Any]  					=		pndm(generator=__snake_case       ,     output_type='numpy'       ).images

											_snake_case:   Union[str, Any]  					=		image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											_snake_case:   int  					=		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












'''simple docstring'''






from __future__ import annotations

A   : Union[str, Any]             =			[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
A   : Optional[int]             =			[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]





def       lowercase_      (		lowercase__						)   ->list[float]:
						_snake_case:   Tuple  					=		[]
						_snake_case:   List[Any]  					=		len(lowercase__						)

						for i in range(lowercase__						):
												_snake_case:   float  					=		-1
												for j in range(i + 1				,  lowercase__						):
																		if arr[i] < arr[j]:
																								_snake_case:   List[str]  					=		arr[j]
																								break
												result.append(lowercase__						)
						return result





def       lowercase_      (		lowercase__						)   ->list[float]:
						_snake_case:   Tuple  					=		[]
						for i, outer in enumerate(lowercase__						):
												_snake_case:   float  					=		-1
												for inner in arr[i + 1 :]:
																		if outer < inner:
																								_snake_case:   List[Any]  					=		inner
																								break
												result.append(lowercase__						)
						return result





def       lowercase_      (		lowercase__						)   ->list[float]:
						_snake_case:   int  					=		len(lowercase__						)
						_snake_case:   list[float]  					=		[]
						_snake_case:   list[float]  					=		[-1] * arr_size

						for index in reversed(range(lowercase__						)						):
												if stack:
																		while stack[-1] <= arr[index]:
																								stack.pop()
																								if not stack:
																														break
												if stack:
																		_snake_case:   Dict  					=		stack[-1]
												stack.append(arr[index]						)
						return result


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

		testmod()
		print(next_greatest_element_slow(arr))
		print(next_greatest_element_fast(arr))
		print(next_greatest_element(arr))

		A   : Union[str, Any]             =			(
		    'from __main__ import arr, next_greatest_element_slow, '
		    'next_greatest_element_fast, next_greatest_element'
		)
		print(
		    'next_greatest_element_slow():',
		    timeit('next_greatest_element_slow(arr)', setup=setup),
		)
		print(
		    'next_greatest_element_fast():',
		    timeit('next_greatest_element_fast(arr)', setup=setup),
		)
		print(
		    '     next_greatest_element():',
		    timeit('next_greatest_element(arr)', setup=setup),
		)





