



import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
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     __lowerCAmelCase					(    unittest.TestCase		):



			def 					_lowerCamelCase (		self     :						Tuple)     ->					Tuple:



						"""simple docstring"""






						super().tearDown()
						gc.collect()



			def 					_lowerCamelCase (		self     :						Tuple)     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png')
						_UpperCAmelCase	 =     load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png')

						_UpperCAmelCase	 =     'xvjiarui/stable-diffusion-2-inpainting'
						_UpperCAmelCase ,  _UpperCAmelCase	 =     FlaxStableDiffusionInpaintPipeline.from_pretrained(A				,	safety_checker=A)

						_UpperCAmelCase	 =     'Face of a yellow cat, high resolution, sitting on a park bench'

						_UpperCAmelCase	 =     jax.random.PRNGKey(0)
						_UpperCAmelCase	 =     50

						_UpperCAmelCase	 =     jax.device_count()
						_UpperCAmelCase	 =     num_samples * [prompt]
						_UpperCAmelCase	 =     num_samples * [init_image]
						_UpperCAmelCase	 =     num_samples * [mask_image]
						_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     pipeline.prepare_inputs(A				,	A				,	A)

						# shard inputs and rng
						_UpperCAmelCase	 =     replicate(A)
						_UpperCAmelCase	 =     jax.random.split(A				,	jax.device_count())
						_UpperCAmelCase	 =     shard(A)
						_UpperCAmelCase	 =     shard(A)
						_UpperCAmelCase	 =     shard(A)

						_UpperCAmelCase	 =     pipeline(
						    A				,	A				,	A				,	A				,	A				,	A				,	jit=A)

						_UpperCAmelCase	 =     output.images.reshape(A				,	5_12				,	5_12				,	3)

						_UpperCAmelCase	 =     images[0, 2_53:2_56, 2_53:2_56, -1]

						_UpperCAmelCase	 =     jnp.asarray(jax.device_get(image_slice.flatten()))
						_UpperCAmelCase	 =     jnp.array(
						    [0.3_6_1_1_3_0_7, 0.3_7_6_4_9_7_3_6, 0.3_7_5_7_4_0_8, 0.3_8_2_1_3_9_5_3, 0.3_9_2_9_5_1_6_7, 0.3_8_4_1_6_3_1, 0.4_1_5_5_4_9_7_8, 0.4_1_3_7_4_7_5, 0.4_2_1_7_0_8_4])
						print(F"output_slice: {output_slice}")
						assert jnp.abs(output_slice - expected_slice).max() < 1E-2





import os

# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase__     = [int(0.5 * n * (n + 1)) for n in range(1, 101)]





def 							A			(  )       ->				List[str]:







			'''simple docstring'''
			_UpperCAmelCase	 =     os.path.dirname(os.path.realpath(_UpperCAmelCase					)					)
			_UpperCAmelCase	 =     os.path.join(_UpperCAmelCase							,     'words.txt'					)

			_UpperCAmelCase	 =     ''
			with open(_UpperCAmelCase					) as f:
						_UpperCAmelCase	 =     f.readline()

			_UpperCAmelCase	 =     [word.strip('"'					) for word in words.strip('\r\n'					).split(','					)]
			_UpperCAmelCase	 =     [
			    word
			    for word in [sum(ord(_UpperCAmelCase					) - 64 for x in word					) for word in words]
			    if word in TRIANGULAR_NUMBERS
			]
			return len(_UpperCAmelCase					)


if __name__ == "__main__":
							print(solution())
