






'''simple docstring'''





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a_		:						List[Any]             =    False

class 		_snake_case  (  unittest.TestCase					):
			pass



@nightly
@require_torch_gpu
class 		_snake_case  (  unittest.TestCase					):






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion')
						# remove text_unet
						pipe.remove_unused_weights()
						pipe.to(a)
						pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE						= 'A painting of a squirrel eating a burger '
						SCREAMING_SNAKE_CASE						= torch.manual_seed(0)
						SCREAMING_SNAKE_CASE						= pipe(
						    prompt=a		,					generator=a		,					guidance_scale=7.5		,					num_inference_steps=2		,					output_type='numpy').images

						with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(a)
									SCREAMING_SNAKE_CASE						= VersatileDiffusionTextToImagePipeline.from_pretrained(a)
						pipe.to(a)
						pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE						= generator.manual_seed(0)
						SCREAMING_SNAKE_CASE						= pipe(
						    prompt=a		,					generator=a		,					guidance_scale=7.5		,					num_inference_steps=2		,					output_type='numpy').images

						assert np.abs(image - new_image).sum() < 1E-5, "Models don't have the same forward pass"







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= VersatileDiffusionTextToImagePipeline.from_pretrained(
						    'shi-labs/versatile-diffusion'		,					torch_dtype=torch.floataa)
						pipe.to(a)
						pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE						= 'A painting of a squirrel eating a burger '
						SCREAMING_SNAKE_CASE						= torch.manual_seed(0)
						SCREAMING_SNAKE_CASE						= pipe(
						    prompt=a		,					generator=a		,					guidance_scale=7.5		,					num_inference_steps=50		,					output_type='numpy').images

						SCREAMING_SNAKE_CASE						= image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						SCREAMING_SNAKE_CASE						= np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78])

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












import os

# Precomputes a list of the 100 first triangular numbers
a_		:						str             =    [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= os.path.dirname(os.path.realpath(_UpperCAmelCase))
			SCREAMING_SNAKE_CASE						= os.path.join(_UpperCAmelCase							,       'words.txt')

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

			SCREAMING_SNAKE_CASE						= [word.strip('"') for word in words.strip('\r\n').split(',')]
			SCREAMING_SNAKE_CASE						= [
			    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())




