






"""simple docstring"""

import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
			import jax
			import jax.numpy as jnp

@slow
@require_flax
class 						A_							(      unittest.TestCase  ):







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						List[Any]	)				->					str:
										return f'''gaussian_noise_s={seed}_shape={"_".join([str(__lowerCamelCase	) for s in shape]	)}.npy'''







				def     _snake_case					(							self							:						Any	)				->					Optional[int]:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						Union[str, Any]=0						,    __lowerCamelCase							:						List[str]=(4, 4, 6_4, 6_4)						,    __lowerCamelCase							:						Any=False	)				->					Any:
										__magic_name__            =     jnp.bfloataa if fpaa else jnp.floataa
										__magic_name__            =     jnp.array(load_hf_numpy(self.get_file_format(__lowerCamelCase						,    __lowerCamelCase	)	)						,    dtype=__lowerCamelCase	)
										return image







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						Tuple=False						,    __lowerCamelCase							:						Union[str, Any]="CompVis/stable-diffusion-v1-4"	)				->					List[str]:
										__magic_name__            =     jnp.bfloataa if fpaa else jnp.floataa
										__magic_name__            =     "bf16" if fpaa else None

										__magic_name__      ,    __magic_name__            =     FlaxUNetaDConditionModel.from_pretrained(
										    __lowerCamelCase						,    subfolder="unet"						,    dtype=__lowerCamelCase						,    revision=__lowerCamelCase	)
										return model, params







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Tuple=0						,    __lowerCamelCase							:						Optional[Any]=(4, 7_7, 7_6_8)						,    __lowerCamelCase							:						int=False	)				->					List[Any]:
										__magic_name__            =     jnp.bfloataa if fpaa else jnp.floataa
										__magic_name__            =     jnp.array(load_hf_numpy(self.get_file_format(__lowerCamelCase						,    __lowerCamelCase	)	)						,    dtype=__lowerCamelCase	)
										return hidden_states







				@parameterized.expand(
				    [
				        # fmt: off
				        [8_3, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
				        [1_7, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
				        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
				        [3, 1_0_0_0, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
				        # fmt: on
				    ]	)
				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						int	)				->					Any:
										__magic_name__      ,    __magic_name__            =     self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"						,    fpaa=__lowerCamelCase	)
										__magic_name__            =     self.get_latents(__lowerCamelCase						,    fpaa=__lowerCamelCase	)
										__magic_name__            =     self.get_encoder_hidden_states(__lowerCamelCase						,    fpaa=__lowerCamelCase	)

										__magic_name__            =     model.apply(
										    {"params": params}						,    __lowerCamelCase						,    jnp.array(__lowerCamelCase						,    dtype=jnp.intaa	)						,    encoder_hidden_states=__lowerCamelCase						,    ).sample

										assert sample.shape == latents.shape

										__magic_name__            =     jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)						,    dtype=jnp.floataa	)
										__magic_name__            =     jnp.array(__lowerCamelCase						,    dtype=jnp.floataa	)

										# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
										assert jnp.allclose(__lowerCamelCase						,    __lowerCamelCase						,    atol=1e-2	)







				@parameterized.expand(
				    [
				        # fmt: off
				        [8_3, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
				        [1_7, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
				        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
				        [3, 1_0_0_0, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
				        # fmt: on
				    ]	)
				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						Optional[int]	)				->					Dict:
										__magic_name__      ,    __magic_name__            =     self.get_unet_model(model_id="stabilityai/stable-diffusion-2"						,    fpaa=__lowerCamelCase	)
										__magic_name__            =     self.get_latents(__lowerCamelCase						,    shape=(4, 4, 9_6, 9_6)						,    fpaa=__lowerCamelCase	)
										__magic_name__            =     self.get_encoder_hidden_states(__lowerCamelCase						,    shape=(4, 7_7, 1_0_2_4)						,    fpaa=__lowerCamelCase	)

										__magic_name__            =     model.apply(
										    {"params": params}						,    __lowerCamelCase						,    jnp.array(__lowerCamelCase						,    dtype=jnp.intaa	)						,    encoder_hidden_states=__lowerCamelCase						,    ).sample

										assert sample.shape == latents.shape

										__magic_name__            =     jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)						,    dtype=jnp.floataa	)
										__magic_name__            =     jnp.array(__lowerCamelCase						,    dtype=jnp.floataa	)

										# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
										assert jnp.allclose(__lowerCamelCase						,    __lowerCamelCase						,    atol=1e-2	)











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:int = 2_0_0_0_0_0_0   ):

						'''simple docstring'''






						__magic_name__            =     [0 for i in range(n + 1   )]
						__magic_name__            =     1
						__magic_name__            =     1

						for i in range(2 ,						int(n**0.5   ) + 1   ):
												if primality_list[i] == 0:
																		for j in range(i * i ,						n + 1 ,						__lowerCamelCase   ):
																								__magic_name__            =     1
						__magic_name__            =     0
						for i in range(__lowerCamelCase   ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


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



