




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     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  Any							)      ->  Union[str, Any]:
							return f"""gaussian_noise_s={seed}_shape={"_".join([str(__lowerCamelCase							) for s in shape]							)}.npy"""





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





				def 							__UpperCAmelCase							(   self       :  Any      ,   __lowerCamelCase       :  List[str]=0      ,   __lowerCamelCase       :  Dict=(4, 4, 64, 64)      ,   __lowerCamelCase       :  Union[str, Any]=False							)      ->  Union[str, Any]:
							a					=    jnp.bfloataa if fpaa else jnp.floataa
							a					=    jnp.array(load_hf_numpy(self.get_file_format(__lowerCamelCase      ,   __lowerCamelCase							)							)      ,   dtype=__lowerCamelCase							)
							return image





				def 							__UpperCAmelCase							(   self       :  List[Any]      ,   __lowerCamelCase       :  List[Any]=False      ,   __lowerCamelCase       :  Any="CompVis/stable-diffusion-v1-4"							)      ->  List[str]:
							a					=    jnp.bfloataa if fpaa else jnp.floataa
							a					=    "bf16" if fpaa else None

							a					,	a					=    FlaxUNetaDConditionModel.from_pretrained(
							    __lowerCamelCase      ,   subfolder="unet"      ,   dtype=__lowerCamelCase      ,   revision=__lowerCamelCase							)
							return model, params





				def 							__UpperCAmelCase							(   self       :  Tuple      ,   __lowerCamelCase       :  int=0      ,   __lowerCamelCase       :  Dict=(4, 77, 7_68)      ,   __lowerCamelCase       :  Dict=False							)      ->  Union[str, Any]:
							a					=    jnp.bfloataa if fpaa else jnp.floataa
							a					=    jnp.array(load_hf_numpy(self.get_file_format(__lowerCamelCase      ,   __lowerCamelCase							)							)      ,   dtype=__lowerCamelCase							)
							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, 10_00, [-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       :  List[str]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Union[str, Any]							)      ->  List[Any]:
							a					,	a					=    self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"      ,   fpaa=__lowerCamelCase							)
							a					=    self.get_latents(__lowerCamelCase      ,   fpaa=__lowerCamelCase							)
							a					=    self.get_encoder_hidden_states(__lowerCamelCase      ,   fpaa=__lowerCamelCase							)

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

							assert sample.shape == latents.shape

							a					=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)      ,   dtype=jnp.floataa							)
							a					=    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, [0.1_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, 10_00, [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       :  int      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  str							)      ->  List[Any]:
							a					,	a					=    self.get_unet_model(model_id="stabilityai/stable-diffusion-2"      ,   fpaa=__lowerCamelCase							)
							a					=    self.get_latents(__lowerCamelCase      ,   shape=(4, 4, 96, 96)      ,   fpaa=__lowerCamelCase							)
							a					=    self.get_encoder_hidden_states(__lowerCamelCase      ,   shape=(4, 77, 10_24)      ,   fpaa=__lowerCamelCase							)

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

							assert sample.shape == latents.shape

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












import math

import flax.linen as nn
import jax.numpy as jnp




def     __magic_name__       (				A	:      jnp.ndarray,						A	:      int,						A	:      float = 1,						A	:      float = 1,						A	:      float = 1.0E4,						A	:      bool = False,						A	:      float = 1.0,						):






			'''simple docstring'''







			assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
			assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
			a					=    float(embedding_dim // 2     )
			a					=    math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
			a					=    min_timescale * jnp.exp(jnp.arange(A,						dtype=jnp.floataa     ) * -log_timescale_increment     )
			a					=    jnp.expand_dims(A,						1     ) * jnp.expand_dims(A,						0     )

			# scale embeddings
			a					=    scale * emb

			if flip_sin_to_cos:
						a					=    jnp.concatenate([jnp.cos(A     ), jnp.sin(A     )],						axis=1     )
			else:
						a					=    jnp.concatenate([jnp.sin(A     ), jnp.cos(A     )],						axis=1     )
			a					=    jnp.reshape(A,						[jnp.shape(A     )[0], embedding_dim]     )
			return signal




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      32
				SCREAMING_SNAKE_CASE_ :							jnp.dtype											=      jnp.floataa





				@nn.compact
				def __call__(   self       :  Tuple      ,   __lowerCamelCase       :  Optional[Any]							)      ->  List[Any]:
							a					=    nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_1"							)(__lowerCamelCase							)
							a					=    nn.silu(__lowerCamelCase							)
							a					=    nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_2"							)(__lowerCamelCase							)
							return temb




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      32
				SCREAMING_SNAKE_CASE_ :							bool											=      False
				SCREAMING_SNAKE_CASE_ :							float											=      1





				@nn.compact
				def __call__(   self       :  Tuple      ,   __lowerCamelCase       :  int							)      ->  Union[str, Any]:
							return get_sinusoidal_embeddings(
							    __lowerCamelCase      ,   embedding_dim=self.dim      ,   flip_sin_to_cos=self.flip_sin_to_cos      ,   freq_shift=self.freq_shift							)






