

"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCAmelCase							:  List[Any]            =						False



class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			pass







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




			"""simple docstring"""







			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_:			str  		=	VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"       ,						torch_dtype=torch.floataa)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Dict  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg")

									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			str  		=	pipe.dual_guided(
									    prompt="first prompt"       ,						image=lowerCAmelCase__       ,						text_to_image_strength=0.75       ,						generator=lowerCAmelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=2       ,						output_type="numpy"       ,						).images

									with tempfile.TemporaryDirectory() as tmpdirname:
															pipe.save_pretrained(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	VersatileDiffusionPipeline.from_pretrained(lowerCAmelCase__       ,						torch_dtype=torch.floataa)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	generator.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	pipe.dual_guided(
									    prompt="first prompt"       ,						image=lowerCAmelCase__       ,						text_to_image_strength=0.75       ,						generator=lowerCAmelCase__       ,						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  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			int  		=	VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"       ,						torch_dtype=torch.floataa)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			int  		=	"cyberpunk 2077"
									SCREAMING_SNAKE_CASE_:			Dict  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg")
									SCREAMING_SNAKE_CASE_:			Dict  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Dict  		=	pipe.dual_guided(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						text_to_image_strength=0.75       ,						generator=lowerCAmelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=50       ,						output_type="numpy"       ,						).images

									SCREAMING_SNAKE_CASE_:			Tuple  		=	image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE_:			Dict  		=	np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001])

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

									SCREAMING_SNAKE_CASE_:			List[str]  		=	"A painting of a squirrel eating a burger "
									SCREAMING_SNAKE_CASE_:			str  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	pipe.text_to_image(
									    prompt=lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=50       ,						output_type="numpy").images

									SCREAMING_SNAKE_CASE_:			Any  		=	image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778])

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

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	pipe.image_variation(lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						output_type="numpy").images

									SCREAMING_SNAKE_CASE_:			Any  		=	image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE_:			int  		=	np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456])

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









def       A_				(   _UpperCAmelCase = 10**9   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	1
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	2

						SCREAMING_SNAKE_CASE_:			int  		=	0
						SCREAMING_SNAKE_CASE_:			Dict  		=	0
						SCREAMING_SNAKE_CASE_:			List[str]  		=	0
						while perimeter <= max_perimeter:
												perimeters_sum += perimeter

												prev_value += 2 * value
												value += prev_value

												SCREAMING_SNAKE_CASE_:			Any  		=	2 * value + 2 if i % 2 == 0 else 2 * value - 2
												i += 1

						return perimeters_sum


if __name__ == "__main__":
						print(f'''{solution() = }''')






