






"""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 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Any:
										return F"""gaussian_noise_s={seed}_shape={"_".join([str(lowerCAmelCase_							) for s in shape]							)}.npy"""







						def 			A      (    self							)		->							int:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()







						def 			A      (    self   ,				lowerCAmelCase_=0   ,				lowerCAmelCase_=(4, 4, 6_4, 6_4)   ,				lowerCAmelCase_=False							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jnp.bfloataa if fpaa else jnp.floataa
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_   ,				lowerCAmelCase_							)							)   ,				dtype=lowerCAmelCase_							)
										return image







						def 			A      (    self   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_="CompVis/stable-diffusion-v1-4"							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    jnp.bfloataa if fpaa else jnp.floataa
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    'bf16' if fpaa else None

										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    FlaxUNetaDConditionModel.from_pretrained(
										    lowerCAmelCase_   ,				subfolder='unet'   ,				dtype=lowerCAmelCase_   ,				revision=lowerCAmelCase_							)
										return model, params







						def 			A      (    self   ,				lowerCAmelCase_=0   ,				lowerCAmelCase_=(4, 7_7, 7_6_8)   ,				lowerCAmelCase_=False							)		->							str:
										_SCREAMING_SNAKE_CASE   :		str       		=    jnp.bfloataa if fpaa else jnp.floataa
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_   ,				lowerCAmelCase_							)							)   ,				dtype=lowerCAmelCase_							)
										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 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'   ,				fpaa=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.get_latents(lowerCAmelCase_   ,				fpaa=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    self.get_encoder_hidden_states(lowerCAmelCase_   ,				fpaa=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    model.apply(
										    {'params': params}   ,				lowerCAmelCase_   ,				jnp.array(lowerCAmelCase_   ,				dtype=jnp.intaa							)   ,				encoder_hidden_states=lowerCAmelCase_   ,				).sample

										assert sample.shape == latents.shape

										_SCREAMING_SNAKE_CASE   :		int       		=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)   ,				dtype=jnp.floataa							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    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
						        [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 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Tuple:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.get_unet_model(model_id='stabilityai/stable-diffusion-2'   ,				fpaa=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    self.get_latents(lowerCAmelCase_   ,				shape=(4, 4, 9_6, 9_6)   ,				fpaa=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    self.get_encoder_hidden_states(lowerCAmelCase_   ,				shape=(4, 7_7, 1_0_2_4)   ,				fpaa=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    model.apply(
										    {'params': params}   ,				lowerCAmelCase_   ,				jnp.array(lowerCAmelCase_   ,				dtype=jnp.intaa							)   ,				encoder_hidden_states=lowerCAmelCase_   ,				).sample

										assert sample.shape == latents.shape

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)   ,				dtype=jnp.floataa							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    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"""
import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
			import jax
			import jax.numpy as jnp
			from flax.jax_utils import replicate
			from flax.training.common_utils import shard


@slow
@require_flax
class _lowerCAmelCase						(    unittest.TestCase       ):







						def 			A      (    self							)		->							Any:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    'stabilityai/stable-diffusion-2'   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    sd_pipe(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				num_inference_steps=2_5   ,				jit=lowerCAmelCase_							)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										_SCREAMING_SNAKE_CASE   :		int       		=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]							)
										print(F"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		str       		=    'stabilityai/stable-diffusion-2'
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		int       		=    FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCAmelCase_   ,				subfolder='scheduler'							)
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[str]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)
										_SCREAMING_SNAKE_CASE   :		Any       		=    scheduler_params

										_SCREAMING_SNAKE_CASE   :		int       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		int       		=    sd_pipe(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				num_inference_steps=2_5   ,				jit=lowerCAmelCase_							)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										_SCREAMING_SNAKE_CASE   :		str       		=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										_SCREAMING_SNAKE_CASE   :		str       		=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										_SCREAMING_SNAKE_CASE   :		int       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]							)
										print(F"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2

