'''simple docstring'''

def __a (	__lowerCamelCase			:						int ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						list[list[int]]				)	->							int:
      '''simple docstring'''


      def update_area_of_max_square(__lowerCamelCase			:						int ,		__lowerCamelCase			:						int				) -> int:
            # BASE CASE
            if row >= rows or col >= cols:
                  return 0

            lowercase_     =      update_area_of_max_square(__lowerCamelCase ,		col + 1				)
            lowercase_     =      update_area_of_max_square(row + 1 ,		col + 1				)
            lowercase_     =      update_area_of_max_square(row + 1 ,		__lowerCamelCase				)

            if mat[row][col]:
                  lowercase_     =      1 + min([right, diagonal, down]				)
                  lowercase_     =      max(largest_square_area[0] ,		__lowerCamelCase				)
                  return sub_problem_sol
            else:
                  return 0

      lowercase_     =      [0]
      update_area_of_max_square(0 ,		0				)
      return largest_square_area[0]


def __a (	__lowerCamelCase			:						int ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						list[list[int]]				)	->							int:
      '''simple docstring'''


      def update_area_of_max_square_using_dp_array(
          __lowerCamelCase			:						int ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						list[list[int]]				) -> int:
            if row >= rows or col >= cols:
                  return 0
            if dp_array[row][col] != -1:
                  return dp_array[row][col]

            lowercase_     =      update_area_of_max_square_using_dp_array(__lowerCamelCase ,		col + 1 ,		__lowerCamelCase				)
            lowercase_     =      update_area_of_max_square_using_dp_array(row + 1 ,		col + 1 ,		__lowerCamelCase				)
            lowercase_     =      update_area_of_max_square_using_dp_array(row + 1 ,		__lowerCamelCase ,		__lowerCamelCase				)

            if mat[row][col]:
                  lowercase_     =      1 + min([right, diagonal, down]				)
                  lowercase_     =      max(largest_square_area[0] ,		__lowerCamelCase				)
                  lowercase_     =      sub_problem_sol
                  return sub_problem_sol
            else:
                  return 0

      lowercase_     =      [0]
      lowercase_     =      [[-1] * cols for _ in range(__lowerCamelCase				)]
      update_area_of_max_square_using_dp_array(0 ,		0 ,		__lowerCamelCase				)

      return largest_square_area[0]


def __a (	__lowerCamelCase			:						int ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						list[list[int]]				)	->							int:
      '''simple docstring'''


      lowercase_     =      [[0] * (cols + 1) for _ in range(rows + 1				)]
      lowercase_     =      0
      for row in range(rows - 1 ,		-1 ,		-1				):
            for col in range(cols - 1 ,		-1 ,		-1				):
                  lowercase_     =      dp_array[row][col + 1]
                  lowercase_     =      dp_array[row + 1][col + 1]
                  lowercase_     =      dp_array[row + 1][col]

                  if mat[row][col] == 1:
                        lowercase_     =      1 + min(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase				)
                        lowercase_     =      max(dp_array[row][col] ,		__lowerCamelCase				)
                  else:
                        lowercase_     =      0

      return largest_square_area


def __a (	__lowerCamelCase			:						int ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						list[list[int]]				)	->							int:
      '''simple docstring'''


      lowercase_     =      [0] * (cols + 1)
      lowercase_     =      [0] * (cols + 1)
      lowercase_     =      0
      for row in range(rows - 1 ,		-1 ,		-1				):
            for col in range(cols - 1 ,		-1 ,		-1				):
                  lowercase_     =      current_row[col + 1]
                  lowercase_     =      next_row[col + 1]
                  lowercase_     =      next_row[col]

                  if mat[row][col] == 1:
                        lowercase_     =      1 + min(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase				)
                        lowercase_     =      max(current_row[col] ,		__lowerCamelCase				)
                  else:
                        lowercase_     =      0
            lowercase_     =      current_row

      return largest_square_area


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))







'''simple docstring'''

import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard





@slow
@require_flax
class 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	int)      ->					List[str]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()


   def __UpperCAmelCase       (   self				:	Dict)      ->					Any:
         lowercase_ ,							lowercase_     =      FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-canny"   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_ ,							lowercase_     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,		controlnet=__lowerCAmelCase   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_     =      controlnet_params

         lowercase_     =      "bird"
         lowercase_     =      jax.device_count()
         lowercase_     =      pipe.prepare_text_inputs([prompts] * num_samples)

         lowercase_     =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png")
         lowercase_     =      pipe.prepare_image_inputs([canny_image] * num_samples)

         lowercase_     =      jax.random.PRNGKey(0)
         lowercase_     =      jax.random.split(__lowerCAmelCase   ,		jax.device_count())

         lowercase_     =      replicate(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)

         lowercase_     =      pipe(
             prompt_ids=__lowerCAmelCase   ,		image=__lowerCAmelCase   ,		params=__lowerCAmelCase   ,		prng_seed=__lowerCAmelCase   ,		num_inference_steps=50   ,		jit=__lowerCAmelCase   ,		).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowercase_     =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
         lowercase_     =      images[0, 253:256, 253:256, -1]

         lowercase_     =      jnp.asarray(jax.device_get(image_slice.flatten()))
         lowercase_     =      jnp.array(
             [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078])
         print(F'output_slice: {output_slice}')
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2


   def __UpperCAmelCase       (   self				:	Tuple)      ->					List[str]:
         lowercase_ ,							lowercase_     =      FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-openpose"   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_ ,							lowercase_     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,		controlnet=__lowerCAmelCase   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_     =      controlnet_params

         lowercase_     =      "Chef in the kitchen"
         lowercase_     =      jax.device_count()
         lowercase_     =      pipe.prepare_text_inputs([prompts] * num_samples)

         lowercase_     =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png")
         lowercase_     =      pipe.prepare_image_inputs([pose_image] * num_samples)

         lowercase_     =      jax.random.PRNGKey(0)
         lowercase_     =      jax.random.split(__lowerCAmelCase   ,		jax.device_count())

         lowercase_     =      replicate(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)

         lowercase_     =      pipe(
             prompt_ids=__lowerCAmelCase   ,		image=__lowerCAmelCase   ,		params=__lowerCAmelCase   ,		prng_seed=__lowerCAmelCase   ,		num_inference_steps=50   ,		jit=__lowerCAmelCase   ,		).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowercase_     =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
         lowercase_     =      images[0, 253:256, 253:256, -1]

         lowercase_     =      jnp.asarray(jax.device_get(image_slice.flatten()))
         lowercase_     =      jnp.array(
             [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]])
         print(F'output_slice: {output_slice}')
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2






