
'''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     __snake_case		(   unittest.TestCase):
					def     UpperCAmelCase_							(     self,    A,    A      ):

									"""simple docstring"""


									return F'''gaussian_noise_s={seed}_shape={'_'.join([str(A      ) for s in shape]      )}.npy'''
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									super().tearDown()
									gc.collect()
					def     UpperCAmelCase_							(     self,    A=0,    A=(4, 4, 64, 64),    A=False      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= jnp.bfloataa if fpaa else jnp.floataa
									lowerCamelCase					:			List[Any]									= jnp.array(load_hf_numpy(self.get_file_format(A,    A      )      ),    dtype=A      )
									return image
					def     UpperCAmelCase_							(     self,    A=False,    A="CompVis/stable-diffusion-v1-4"      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= jnp.bfloataa if fpaa else jnp.floataa
									lowerCamelCase					:			Any									= 'bf16' if fpaa else None

									lowerCamelCase			, lowerCamelCase					:			List[str]									= FlaxUNetaDConditionModel.from_pretrained(
									    A,    subfolder='unet',    dtype=A,    revision=A      )
									return model, params
					def     UpperCAmelCase_							(     self,    A=0,    A=(4, 77, 768),    A=False      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= jnp.bfloataa if fpaa else jnp.floataa
									lowerCamelCase					:			int									= jnp.array(load_hf_numpy(self.get_file_format(A,    A      )      ),    dtype=A      )
									return hidden_states
					@parameterized.expand(
					    [
					        # fmt: off
					        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
					        [17, 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, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
					        # fmt: on
					    ]      )
					def     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase			, lowerCamelCase					:			Union[str, Any]									= self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4',    fpaa=A      )
									lowerCamelCase					:			Optional[int]									= self.get_latents(A,    fpaa=A      )
									lowerCamelCase					:			Optional[int]									= self.get_encoder_hidden_states(A,    fpaa=A      )

									lowerCamelCase					:			List[str]									= model.apply(
									    {'params': params},    A,    jnp.array(A,    dtype=jnp.intaa      ),    encoder_hidden_states=A,    ).sample

									assert sample.shape == latents.shape

									lowerCamelCase					:			Union[str, Any]									= jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())      ),    dtype=jnp.floataa      )
									lowerCamelCase					:			Tuple									= jnp.array(A,    dtype=jnp.floataa      )

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




					@parameterized.expand(
					    [
					        # fmt: off
					        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
					        [17, 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, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
					        # fmt: on
					    ]      )
					def     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase			, lowerCamelCase					:			Optional[int]									= self.get_unet_model(model_id='stabilityai/stable-diffusion-2',    fpaa=A      )
									lowerCamelCase					:			List[Any]									= self.get_latents(A,    shape=(4, 4, 96, 96),    fpaa=A      )
									lowerCamelCase					:			int									= self.get_encoder_hidden_states(A,    shape=(4, 77, 1024),    fpaa=A      )

									lowerCamelCase					:			Optional[Any]									= model.apply(
									    {'params': params},    A,    jnp.array(A,    dtype=jnp.intaa      ),    encoder_hidden_states=A,    ).sample

									assert sample.shape == latents.shape

									lowerCamelCase					:			Union[str, Any]									= jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())      ),    dtype=jnp.floataa      )
									lowerCamelCase					:			List[Any]									= jnp.array(A,    dtype=jnp.floataa      )

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


'''simple docstring'''






def    UpperCAmelCase							(  UpperCAmelCase__							:							int = 10_00):
				lowerCamelCase					:			Optional[Any]									= 2**power
				lowerCamelCase					:			str									= str(UpperCAmelCase__)
				lowerCamelCase					:			Union[str, Any]									= list(UpperCAmelCase__)
				lowerCamelCase					:			Optional[Any]									= 0

				for i in list_num:
								sum_of_num += int(UpperCAmelCase__)

				return sum_of_num


if __name__ == "__main__":
					A			      =      int(input('Enter the power of 2: ').strip())
					print('2 ^ ', power, ' = ', 2**power)
					A			      =      solution(power)
					print('Sum of the digits is: ', result)
