



"""simple docstring"""
def        lowerCAmelCase_						(							)			->      list[list[int]]:
		return [list(range(1_000 - i, -1_000 - i, -1	)	) for i in range(1_000	)]


SCREAMING_SNAKE_CASE_				= generate_large_matrix()
SCREAMING_SNAKE_CASE_				= (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      None:
		assert all(row == sorted(SCREAMING_SNAKE_CASE__, reverse=SCREAMING_SNAKE_CASE__	) for row in grid	)
		assert all(list(SCREAMING_SNAKE_CASE__	) == sorted(SCREAMING_SNAKE_CASE__, reverse=SCREAMING_SNAKE_CASE__	) for col in zip(*SCREAMING_SNAKE_CASE__	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      int:
		a_     :      Union[str, Any] 						=     0
		a_     :      Any 						=     len(SCREAMING_SNAKE_CASE__	) - 1

		# Edge cases such as no values or all numbers are negative.
		if not array or array[0] < 0:
				return 0

		while right + 1 > left:
				a_     :      str 						=     (left + right) // 2
				a_     :      List[Any] 						=     array[mid]

				# Num must be negative and the index must be greater than or equal to 0.
				if num < 0 and array[mid - 1] >= 0:
						return mid

				if num >= 0:
						a_     :      int 						=     mid + 1
				else:
						a_     :      Union[str, Any] 						=     mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
		return len(SCREAMING_SNAKE_CASE__	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      int:
		a_     :      Union[str, Any] 						=     0
		a_     :      str 						=     len(grid[0]	)

		for i in range(len(SCREAMING_SNAKE_CASE__	)	):
				a_     :      List[str] 						=     find_negative_index(grid[i][:bound]	)
				total += bound
		return (len(SCREAMING_SNAKE_CASE__	) * len(grid[0]	)) - total



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      int:
		return len([number for row in grid for number in row if number < 0]	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      int:
		a_     :      Tuple 						=     0
		for row in grid:
				for i, number in enumerate(SCREAMING_SNAKE_CASE__	):
						if number < 0:
								total += len(SCREAMING_SNAKE_CASE__	) - i
								break
		return total



def        lowerCAmelCase_						(							)			->      None:
		from timeit import timeit

		print("Running benchmarks"	)
		a_     :      Dict 						=     (
		    "from __main__ import count_negatives_binary_search, "
		    "count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
		)
		for func in (
		    "count_negatives_binary_search",  # took 0.7727 seconds
		    "count_negatives_brute_force_with_break",  # took 4.6505 seconds
		    "count_negatives_brute_force",  # took 12.8160 seconds
		):
				a_     :      Union[str, Any] 						=     timeit(F"""{func}(grid=grid)""", setup=SCREAMING_SNAKE_CASE__, number=500	)
				print(F"""{func}() took {time:0.4f} seconds"""	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


SCREAMING_SNAKE_CASE_				= False




class 			snake_case_						(						unittest.TestCase				):
							pass




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






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"      )
									pipe.to(a_      )
									pipe.set_progress_bar_config(disable=a_      )

									a_     :      Optional[Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"      )
									a_     :      Optional[Any] 						=     torch.manual_seed(0      )
									a_     :      Dict 						=     pipe(
									    image=a_			,     generator=a_			,     guidance_scale=7.5			,     num_inference_steps=5_0			,     output_type="numpy"			,     ).images

									a_     :      List[Any] 						=     image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									a_     :      Any 						=     np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]      )

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