






"""simple docstring"""






def 						_UpperCamelCase			(  ):

   for a in range(300			):
      for b in range(a + 1    ,       400			):
         for c in range(b + 1    ,       500			):
            if (a + b + c) == 1_000 and (a**2) + (b**2) == (c**2):
               return a * b * c

   return -1




def 						_UpperCamelCase			(  ):

   for a in range(300			):
      for b in range(400			):
         UpperCamelCase_     =1_000 - a - b
         if a < b < c and (a**2) + (b**2) == (c**2):
            return a * b * c

   return -1




def 						_UpperCamelCase			(  ):
   import timeit

   print(
       timeit.timeit("solution()"    ,       setup="from __main__ import solution"    ,       number=1_000			)			)
   print(
       timeit.timeit(
           "solution_fast()"    ,       setup="from __main__ import solution_fast"    ,       number=1_000			)			)


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








"""simple docstring"""






import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput
@dataclass
class 						__lowerCAmelCase   (			UpperCAmelCase       ):







  '''simple docstring'''


  __lowerCamelCase       :    Union[List[PIL.Image.Image], np.ndarray]




class 						__lowerCAmelCase   (			unittest.TestCase       ):







  '''simple docstring'''


  def 					UpperCamelCase__   (      self:		Tuple					):
     UpperCamelCase_     =CustomOutput(images=np.random.rand(1				,			3				,			4				,			4					)					)

     # check every way of getting the attribute
     assert isinstance(outputs.images				,			np.ndarray					)
     assert outputs.images.shape == (1, 3, 4, 4)
     assert isinstance(outputs["images"]				,			np.ndarray					)
     assert outputs["images"].shape == (1, 3, 4, 4)
     assert isinstance(outputs[0]				,			np.ndarray					)
     assert outputs[0].shape == (1, 3, 4, 4)

     # test with a non-tensor attribute
     UpperCamelCase_     =CustomOutput(images=[PIL.Image.new("RGB"				,			(4, 4)					)]					)

     # check every way of getting the attribute
     assert isinstance(outputs.images				,			UpperCamelCase_					)
     assert isinstance(outputs.images[0]				,			PIL.Image.Image					)
     assert isinstance(outputs["images"]				,			UpperCamelCase_					)
     assert isinstance(outputs["images"][0]				,			PIL.Image.Image					)
     assert isinstance(outputs[0]				,			UpperCamelCase_					)
     assert isinstance(outputs[0][0]				,			PIL.Image.Image					)





  def 					UpperCamelCase__   (      self:		Optional[int]					):
     # test output reinitialization with a `dict` for compatibility with `accelerate`
     UpperCamelCase_     =CustomOutput({"images": np.random.rand(1				,			3				,			4				,			4					)}					)

     # check every way of getting the attribute
     assert isinstance(outputs.images				,			np.ndarray					)
     assert outputs.images.shape == (1, 3, 4, 4)
     assert isinstance(outputs["images"]				,			np.ndarray					)
     assert outputs["images"].shape == (1, 3, 4, 4)
     assert isinstance(outputs[0]				,			np.ndarray					)
     assert outputs[0].shape == (1, 3, 4, 4)

     # test with a non-tensor attribute
     UpperCamelCase_     =CustomOutput({"images": [PIL.Image.new("RGB"				,			(4, 4)					)]}					)

     # check every way of getting the attribute
     assert isinstance(outputs.images				,			UpperCamelCase_					)
     assert isinstance(outputs.images[0]				,			PIL.Image.Image					)
     assert isinstance(outputs["images"]				,			UpperCamelCase_					)
     assert isinstance(outputs["images"][0]				,			PIL.Image.Image					)
     assert isinstance(outputs[0]				,			UpperCamelCase_					)
     assert isinstance(outputs[0][0]				,			PIL.Image.Image					)
