






"""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        lowercase__      (							unittest.TestCase       ):


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  str       ,	snake_case__					:  List[str] ):
										return F"""gaussian_noise_s={seed}_shape={'_'.join([str(snake_case__ ) for s in shape] )}.npy"""


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


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  List[str]=0       ,	snake_case__					:  Union[str, Any]=(4, 4, 64, 64)       ,	snake_case__					:  Union[str, Any]=False ):
										lowerCamelCase_	:							List[Any]									=jnp.bfloataa if fpaa else jnp.floataa
										lowerCamelCase_	:							Any									=jnp.array(load_hf_numpy(self.get_file_format(snake_case__       ,	snake_case__ ) )       ,	dtype=snake_case__ )
										return image


			def      UpperCAmelCase__						(				self					:  Any       ,	snake_case__					:  int=False       ,	snake_case__					:  List[Any]="CompVis/stable-diffusion-v1-4" ):
										lowerCamelCase_	:							str									=jnp.bfloataa if fpaa else jnp.floataa
										lowerCamelCase_	:							Union[str, Any]									="bf16" if fpaa else None

										lowerCamelCase_							,						lowerCamelCase_	:							Any									=FlaxUNetaDConditionModel.from_pretrained(
										    snake_case__       ,	subfolder="unet"       ,	dtype=snake_case__       ,	revision=snake_case__ )
										return model, params


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  Optional[int]=0       ,	snake_case__					:  List[Any]=(4, 77, 768)       ,	snake_case__					:  Tuple=False ):
										lowerCamelCase_	:							Any									=jnp.bfloataa if fpaa else jnp.floataa
										lowerCamelCase_	:							Optional[Any]									=jnp.array(load_hf_numpy(self.get_file_format(snake_case__       ,	snake_case__ ) )       ,	dtype=snake_case__ )
										return hidden_states


			@parameterized.expand(
			    [
			        # fmt: off
			        [83, 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]],
			        [17, 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, 1000, [-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      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  int       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  List[Any] ):
										lowerCamelCase_							,						lowerCamelCase_	:							List[str]									=self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"       ,	fpaa=snake_case__ )
										lowerCamelCase_	:							List[str]									=self.get_latents(snake_case__       ,	fpaa=snake_case__ )
										lowerCamelCase_	:							int									=self.get_encoder_hidden_states(snake_case__       ,	fpaa=snake_case__ )

										lowerCamelCase_	:							Union[str, Any]									=model.apply(
										    {"params": params}       ,	snake_case__       ,	jnp.array(snake_case__       ,	dtype=jnp.intaa )       ,	encoder_hidden_states=snake_case__       ,	).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_	:							Any									=jnp.array(snake_case__       ,	dtype=jnp.floataa )

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


			@parameterized.expand(
			    [
			        # fmt: off
			        [83, 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]],
			        [17, 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, 1000, [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      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  Any       ,	snake_case__					:  Any       ,	snake_case__					:  Dict ):
										lowerCamelCase_							,						lowerCamelCase_	:							Union[str, Any]									=self.get_unet_model(model_id="stabilityai/stable-diffusion-2"       ,	fpaa=snake_case__ )
										lowerCamelCase_	:							Tuple									=self.get_latents(snake_case__       ,	shape=(4, 4, 96, 96)       ,	fpaa=snake_case__ )
										lowerCamelCase_	:							Union[str, Any]									=self.get_encoder_hidden_states(snake_case__       ,	shape=(4, 77, 1024)       ,	fpaa=snake_case__ )

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

										assert sample.shape == latents.shape

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

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











"""simple docstring"""



from __future__ import annotations

from collections import namedtuple

def 			_snake_case    (					lowerCamelCase__     :   float    ,						lowerCamelCase__     :   float    ,						lowerCamelCase__     :   float  )    ->     tuple:
							lowerCamelCase_	:							Optional[Any]									=namedtuple("result"    ,						"name value"  )
							if (voltage, current, power).count(0  ) != 1:
														raise ValueError("Only one argument must be 0"  )
							elif power < 0:
														raise ValueError(
														    "Power cannot be negative in any electrical/electronics system"  )
							elif voltage == 0:
														return result("voltage"    ,						power / current  )
							elif current == 0:
														return result("current"    ,						power / voltage  )
							elif power == 0:
														return result("power"    ,						float(round(abs(voltage * current  )    ,						2  )  )  )
							else:
														raise ValueError("Exactly one argument must be 0"  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



