





"""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        _lowerCamelCase      (       unittest.TestCase ):


						def     snake_case_   (self   ,							__a   ,							__a		) ->   Optional[int]:
								return F"gaussian_noise_s={seed}_shape={'_'.join([str(lowerCamelCase_		) for s in shape]		)}.npy"


						def     snake_case_   (self		) ->   Optional[Any]:
								super().tearDown()
								gc.collect()


						def     snake_case_   (self   ,							__a=0   ,							__a=(4, 4, 64, 64)   ,							__a=False		) ->   List[Any]:
								UpperCamelCase   =	jnp.bfloataa if fpaa else jnp.floataa
								UpperCamelCase   =	jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase_   ,							lowerCamelCase_		)		)   ,							dtype=lowerCamelCase_		)
								return image


						def     snake_case_   (self   ,							__a=False   ,							__a="CompVis/stable-diffusion-v1-4"		) ->   Optional[Any]:
								UpperCamelCase   =	jnp.bfloataa if fpaa else jnp.floataa
								UpperCamelCase   =	"""bf16""" if fpaa else None

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


						def     snake_case_   (self   ,							__a=0   ,							__a=(4, 77, 7_68)   ,							__a=False		) ->   str:
								UpperCamelCase   =	jnp.bfloataa if fpaa else jnp.floataa
								UpperCamelCase   =	jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase_   ,							lowerCamelCase_		)		)   ,							dtype=lowerCamelCase_		)
								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, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
						        # fmt: on
						    ]		)
						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   Optional[int]:
								UpperCamelCase   =	self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"   ,							fpaa=lowerCamelCase_		)
								UpperCamelCase   =	self.get_latents(lowerCamelCase_   ,							fpaa=lowerCamelCase_		)
								UpperCamelCase   =	self.get_encoder_hidden_states(lowerCamelCase_   ,							fpaa=lowerCamelCase_		)

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

								assert sample.shape == latents.shape

								UpperCamelCase   =	jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)   ,							dtype=jnp.floataa		)
								UpperCamelCase   =	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
						        [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, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
						        # fmt: on
						    ]		)
						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   List[Any]:
								UpperCamelCase   =	self.get_unet_model(model_id="stabilityai/stable-diffusion-2"   ,							fpaa=lowerCamelCase_		)
								UpperCamelCase   =	self.get_latents(lowerCamelCase_   ,							shape=(4, 4, 96, 96)   ,							fpaa=lowerCamelCase_		)
								UpperCamelCase   =	self.get_encoder_hidden_states(lowerCamelCase_   ,							shape=(4, 77, 10_24)   ,							fpaa=lowerCamelCase_		)

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

								assert sample.shape == latents.shape

								UpperCamelCase   =	jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)   ,							dtype=jnp.floataa		)
								UpperCamelCase   =	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"""





from diffusers.utils.testing_utils import require_onnxruntime





@require_onnxruntime
class        _lowerCamelCase      :
						pass






