


from __future__ import annotations

import typing
from collections import Counter


def      a	(							SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	typing.Counter[int]   	=		Counter()
       for base in range(1							,			max_perimeter + 1       ):
              for perpendicular in range(SCREAMING_SNAKE_CASE_							,			max_perimeter + 1       ):
                     UpperCamelCase    :	int   	=		(base * base + perpendicular * perpendicular) ** 0.5
                     if hypotenuse == int(SCREAMING_SNAKE_CASE_       ):
                            UpperCamelCase    :	List[str]   	=		int(base + perpendicular + hypotenuse       )
                            if perimeter > max_perimeter:
                                   continue
                            triplets[perimeter] += 1
       return triplets


def      a	(							SCREAMING_SNAKE_CASE_      :		int = 1_0_0_0       ):






       """simple docstring"""





       UpperCamelCase    :	Any   	=		pythagorean_triple(SCREAMING_SNAKE_CASE_       )
       return triplets.most_common(1       )[0][0]


if __name__ == "__main__":
       print(f'''Perimeter {solution()} has maximum solutions''')





import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__UpperCAmelCase : Dict 			=				False



class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''
       pass




@slow
@require_torch_gpu
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''						)
              pipe.to(__SCREAMING_SNAKE_CASE						)
              pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''						)
              UpperCamelCase    :	str   	=		torch.manual_seed(0						)
              UpperCamelCase    :	Union[str, Any]   	=		pipe(
                  image=__SCREAMING_SNAKE_CASE ,					generator=__SCREAMING_SNAKE_CASE ,					guidance_scale=7.5 ,					num_inference_steps=50 ,					output_type='''numpy''' ,					).images

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

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase    :	Dict   	=		np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]						)

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

