



"""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 snake_case_  (   self			,     a_			,     a_      ):
								return F"""gaussian_noise_s={seed}_shape={'_'.join([str(UpperCamelCase__      ) for s in shape]      )}.npy"""






							def snake_case_  (   self      ):
								super().tearDown()
								gc.collect()






							def snake_case_  (   self			,     a_=0			,     a_=(4, 4, 6_4, 6_4)			,     a_=False      ):
								a_     :      int 						=     jnp.bfloataa if fpaa else jnp.floataa
								a_     :      Any 						=     jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase__			,     UpperCamelCase__      )      )			,     dtype=UpperCamelCase__      )
								return image






							def snake_case_  (   self			,     a_=False			,     a_="CompVis/stable-diffusion-v1-4"      ):
								a_     :      int 						=     jnp.bfloataa if fpaa else jnp.floataa
								a_     :      Any 						=     '''bf16''' if fpaa else None

								a_     :      List[Any] 						=     FlaxUNetaDConditionModel.from_pretrained(
								    UpperCamelCase__			,     subfolder="unet"			,     dtype=UpperCamelCase__			,     revision=UpperCamelCase__      )
								return model, params






							def snake_case_  (   self			,     a_=0			,     a_=(4, 7_7, 7_6_8)			,     a_=False      ):
								a_     :      Tuple 						=     jnp.bfloataa if fpaa else jnp.floataa
								a_     :      List[Any] 						=     jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase__			,     UpperCamelCase__      )      )			,     dtype=UpperCamelCase__      )
								return hidden_states






							@parameterized.expand(
							    [
							        # fmt: off
							        [8_3, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
							        [1_7, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
							        [8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
							        [3, 1_0_0_0, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
							        # fmt: on
							    ]      )
							def snake_case_  (   self			,     a_			,     a_			,     a_      ):
								a_     :      str 						=     self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"			,     fpaa=UpperCamelCase__      )
								a_     :      Tuple 						=     self.get_latents(UpperCamelCase__			,     fpaa=UpperCamelCase__      )
								a_     :      List[Any] 						=     self.get_encoder_hidden_states(UpperCamelCase__			,     fpaa=UpperCamelCase__      )

								a_     :      Optional[int] 						=     model.apply(
								    {"params": params}			,     UpperCamelCase__			,     jnp.array(UpperCamelCase__			,     dtype=jnp.intaa      )			,     encoder_hidden_states=UpperCamelCase__			,     ).sample

								assert sample.shape == latents.shape

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

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






							@parameterized.expand(
							    [
							        # fmt: off
							        [8_3, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
							        [1_7, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
							        [8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
							        [3, 1_0_0_0, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
							        # fmt: on
							    ]      )
							def snake_case_  (   self			,     a_			,     a_			,     a_      ):
								a_     :      Optional[Any] 						=     self.get_unet_model(model_id="stabilityai/stable-diffusion-2"			,     fpaa=UpperCamelCase__      )
								a_     :      List[Any] 						=     self.get_latents(UpperCamelCase__			,     shape=(4, 4, 9_6, 9_6)			,     fpaa=UpperCamelCase__      )
								a_     :      Optional[Any] 						=     self.get_encoder_hidden_states(UpperCamelCase__			,     shape=(4, 7_7, 1_0_2_4)			,     fpaa=UpperCamelCase__      )

								a_     :      Union[str, Any] 						=     model.apply(
								    {"params": params}			,     UpperCamelCase__			,     jnp.array(UpperCamelCase__			,     dtype=jnp.intaa      )			,     encoder_hidden_states=UpperCamelCase__			,     ).sample

								assert sample.shape == latents.shape

								a_     :      Optional[Any] 						=     jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())      )			,     dtype=jnp.floataa      )
								a_     :      Optional[int] 						=     jnp.array(UpperCamelCase__			,     dtype=jnp.floataa      )

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




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(a - b	) for a, b in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      None:
		if point:
				if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
						for item in point:
								if not isinstance(SCREAMING_SNAKE_CASE__, (int, float)	):
										a_     :      int 						=     (
										    "Expected a list of numbers as input, found "
										    F"""{type(SCREAMING_SNAKE_CASE__	).__name__}"""
										)
										raise TypeError(SCREAMING_SNAKE_CASE__	)
				else:
						a_     :      Dict 						=     F"""Expected a list of numbers as input, found {type(SCREAMING_SNAKE_CASE__	).__name__}"""
						raise TypeError(SCREAMING_SNAKE_CASE__	)
		else:
				raise ValueError("Missing an input"	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(x - y	) for x, y in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()