
from collections import defaultdict
from math import gcd




def 	A_ (							snake_case							:   int = 1500000 )      ->    int:



						'''simple docstring'''

						__UpperCamelCase       =			defaultdict(snake_case )
						__UpperCamelCase       =			2
						while 2 * euclid_m * (euclid_m + 1) <= limit:
												for euclid_n in range((euclid_m % 2) + 1 ,      snake_case ,      2 ):
																		if gcd(snake_case ,      snake_case ) > 1:
																								continue
																		__UpperCamelCase       =			2 * euclid_m * (euclid_m + euclid_n)
																		for perimeter in range(snake_case ,      limit + 1 ,      snake_case ):
																								frequencies[perimeter] += 1
												euclid_m += 1
						return sum(1 for frequency in frequencies.values() if frequency == 1 )


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






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
				import jax






@require_flax
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  FlaxAutoencoderKL



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			4
													__UpperCamelCase       =			3
													__UpperCamelCase       =			(32, 32)

													__UpperCamelCase       =			jax.random.PRNGKey(0			)
													__UpperCamelCase       =			jax.random.uniform(SCREAMING_SNAKE_CASE_     ,	((batch_size, num_channels) + sizes)			)

													return {"sample": image, "prng_key": prng_key}



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			{
													    '''block_out_channels''': [32, 64],
													    '''in_channels''': 3,
													    '''out_channels''': 3,
													    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
													    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
													    '''latent_channels''': 4,
													}
													__UpperCamelCase       =			self.dummy_input
													return init_dict, inputs_dict




