




'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      ):
  return [list(range(1000 - i				,					-1000 - i				,					-1			)			) for i in range(1000			)]


UpperCAmelCase_ 		=      generate_large_matrix()
UpperCAmelCase_ 		=      (
    [[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 						SCREAMING_SNAKE_CASE       (      a_	:							list[list[int]]			):
  assert all(row == sorted(a_				,					reverse=a_			) for row in grid			)
  assert all(list(a_			) == sorted(a_				,					reverse=a_			) for col in zip(*a_			)			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							list[int]			):
  __a			=					0
  __a			=					len(a_			) - 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			=					(left + right) // 2
    __a			=					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			=					mid + 1
    else:
      __a			=					mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
  return len(a_			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							list[list[int]]			):
  __a			=					0
  __a			=					len(grid[0]			)

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


def 						SCREAMING_SNAKE_CASE       (      a_	:							list[list[int]]			):
  return len([number for row in grid for number in row if number < 0]			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							list[list[int]]			):
  __a			=					0
  for row in grid:
    for i, number in enumerate(a_			):
      if number < 0:
        total += len(a_			) - i
        break
  return total


def 						SCREAMING_SNAKE_CASE       (      ):
  from timeit import timeit

  print('Running benchmarks'			)
  __a			=					(
      '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			=					timeit(f"{func}(grid=grid)"				,					setup=a_				,					number=500			)
    print(f"{func}() took {time:0.4f} seconds"			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  benchmark()






'''simple docstring'''


import torch

from diffusers import StableDiffusionPipeline


UpperCAmelCase_ 		=      "path-to-your-trained-model"
UpperCAmelCase_ 		=      StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

UpperCAmelCase_ 		=      "A photo of sks dog in a bucket"
UpperCAmelCase_ 		=      pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")



