


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 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     int							)      ->       Union[str, Any]:





									"""simple docstring"""







									return f'gaussian_noise_s={seed}_shape={"_".join([str(UpperCamelCase_							) for s in shape]							)}.npy'

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       Tuple:





									"""simple docstring"""







									super().tearDown()
									gc.collect()

							def 				lowerCamelCase_			(							self:     str   ,						UpperCamelCase_:     str=0   ,						UpperCamelCase_:     Tuple=(4, 4, 64, 64)   ,						UpperCamelCase_:     Optional[int]=False							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				jnp.bfloataa if fpaa else jnp.floataa
									lowercase__						=				jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase_   ,						UpperCamelCase_							)							)   ,						dtype=UpperCamelCase_							)
									return image

							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     List[Any]=False   ,						UpperCamelCase_:     Dict="CompVis/stable-diffusion-v1-4"							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				jnp.bfloataa if fpaa else jnp.floataa
									lowercase__						=				'''bf16''' if fpaa else None

									lowercase__					,		lowercase__						=				FlaxUNetaDConditionModel.from_pretrained(
									    UpperCamelCase_   ,						subfolder='''unet'''   ,						dtype=UpperCamelCase_   ,						revision=UpperCamelCase_							)
									return model, params

							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     Dict=0   ,						UpperCamelCase_:     Tuple=(4, 77, 768)   ,						UpperCamelCase_:     Tuple=False							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				jnp.bfloataa if fpaa else jnp.floataa
									lowercase__						=				jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase_   ,						UpperCamelCase_							)							)   ,						dtype=UpperCamelCase_							)
									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, 1_000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
							        # fmt: on
							    ]							)
							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     Any   ,						UpperCamelCase_:     Optional[int]							)      ->       Tuple:





									"""simple docstring"""







									lowercase__					,		lowercase__						=				self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''   ,						fpaa=UpperCamelCase_							)
									lowercase__						=				self.get_latents(UpperCamelCase_   ,						fpaa=UpperCamelCase_							)
									lowercase__						=				self.get_encoder_hidden_states(UpperCamelCase_   ,						fpaa=UpperCamelCase_							)

									lowercase__						=				model.apply(
									    {'''params''': params}   ,						UpperCamelCase_   ,						jnp.array(UpperCamelCase_   ,						dtype=jnp.intaa							)   ,						encoder_hidden_states=UpperCamelCase_   ,						).sample

									assert sample.shape == latents.shape

									lowercase__						=				jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)   ,						dtype=jnp.floataa							)
									lowercase__						=				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
							        [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, 1_000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
							        # fmt: on
							    ]							)
							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Optional[Any]							)      ->       str:





									"""simple docstring"""







									lowercase__					,		lowercase__						=				self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''   ,						fpaa=UpperCamelCase_							)
									lowercase__						=				self.get_latents(UpperCamelCase_   ,						shape=(4, 4, 96, 96)   ,						fpaa=UpperCamelCase_							)
									lowercase__						=				self.get_encoder_hidden_states(UpperCamelCase_   ,						shape=(4, 77, 1_024)   ,						fpaa=UpperCamelCase_							)

									lowercase__						=				model.apply(
									    {'''params''': params}   ,						UpperCamelCase_   ,						jnp.array(UpperCamelCase_   ,						dtype=jnp.intaa							)   ,						encoder_hidden_states=UpperCamelCase_   ,						).sample

									assert sample.shape == latents.shape

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






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
			import torch

			from transformers.activations import gelu_new, gelu_python, get_activation


@require_torch
class 	_a							(      unittest.TestCase      ):

							def 				lowerCamelCase_			(							self:     Dict							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]							)
									lowercase__						=				get_activation('''gelu'''							)
									self.assertTrue(torch.allclose(gelu_python(UpperCamelCase_							)   ,						torch_builtin(UpperCamelCase_							)							)							)
									self.assertFalse(torch.allclose(gelu_python(UpperCamelCase_							)   ,						gelu_new(UpperCamelCase_							)							)							)

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       str:





									"""simple docstring"""







									lowercase__						=				torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]							)
									lowercase__						=				get_activation('''gelu'''							)
									lowercase__						=				get_activation('''gelu_10'''							)

									lowercase__						=				torch_builtin(UpperCamelCase_							)
									lowercase__						=				geluaa(UpperCamelCase_							)

									lowercase__						=				torch.where(y_gelu_aa < 10.0   ,						1   ,						0							)

									self.assertTrue(torch.max(UpperCamelCase_							).item() == 10.0							)
									self.assertTrue(torch.allclose(y_gelu * clipped_mask   ,						y_gelu_aa * clipped_mask							)							)

							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       Tuple:





									"""simple docstring"""







									get_activation('''gelu'''							)
									get_activation('''gelu_10'''							)
									get_activation('''gelu_fast'''							)
									get_activation('''gelu_new'''							)
									get_activation('''gelu_python'''							)
									get_activation('''gelu_pytorch_tanh'''							)
									get_activation('''linear'''							)
									get_activation('''mish'''							)
									get_activation('''quick_gelu'''							)
									get_activation('''relu'''							)
									get_activation('''sigmoid'''							)
									get_activation('''silu'''							)
									get_activation('''swish'''							)
									get_activation('''tanh'''							)
									with self.assertRaises(UpperCamelCase_							):
											get_activation('''bogus'''							)
									with self.assertRaises(UpperCamelCase_							):
											get_activation(UpperCamelCase_							)


							def 				lowerCamelCase_			(							self:     Tuple							)      ->       int:





									"""simple docstring"""







									lowercase__						=				get_activation('''gelu'''							)
									lowercase__						=				1
									lowercase__						=				get_activation('''gelu'''							)
									self.assertEqual(acta.a   ,						1							)
									with self.assertRaises(UpperCamelCase_							):
											lowercase__						=				acta.a


