






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			):

					'''simple docstring'''

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


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	List[Any]							)			->  Any:


												"""simple docstring"""

												__lowercase      :							Any				=     FlaxStableDiffusionPipeline.from_pretrained(
												    """stabilityai/stable-diffusion-2"""		,     revision="""bf16"""		,     dtype=jnp.bfloataa		,     )

												__lowercase      :							List[str]				=     """A painting of a squirrel eating a burger"""
												__lowercase      :							Any				=     jax.device_count()
												__lowercase      :							List[str]				=     num_samples * [prompt]
												__lowercase      :							Dict				=     sd_pipe.prepare_inputs(__a							)

												__lowercase      :							List[str]				=     replicate(__a							)
												__lowercase      :							Tuple				=     shard(__a							)

												__lowercase      :							Union[str, Any]				=     jax.random.PRNGKey(0							)
												__lowercase      :							int				=     jax.random.split(__a		,     jax.device_count()							)

												__lowercase      :							List[Any]				=     sd_pipe(__a		,     __a		,     __a		,     num_inference_steps=25		,     jit=__a							)[0]
												assert images.shape == (jax.device_count(), 1, 768, 768, 3)

												__lowercase      :							Dict				=     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
												__lowercase      :							Any				=     images[0, 253:256, 253:256, -1]

												__lowercase      :							Optional[int]				=     jnp.asarray(jax.device_get(image_slice.flatten()							)							)
												__lowercase      :							Optional[int]				=     jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]							)
												print(F"output_slice: {output_slice}"							)
												assert jnp.abs(output_slice - expected_slice							).max() < 1E-2



					def       lowerCAmelCase				(  self				:	Any							)			->  str:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     """stabilityai/stable-diffusion-2"""
												__lowercase      :							List[str]				=     FlaxDPMSolverMultistepScheduler.from_pretrained(__a		,     subfolder="""scheduler"""							)
												__lowercase      :							Any				=     FlaxStableDiffusionPipeline.from_pretrained(
												    __a		,     scheduler=__a		,     revision="""bf16"""		,     dtype=jnp.bfloataa		,     )
												__lowercase      :							Optional[int]				=     scheduler_params

												__lowercase      :							Optional[int]				=     """A painting of a squirrel eating a burger"""
												__lowercase      :							Optional[Any]				=     jax.device_count()
												__lowercase      :							str				=     num_samples * [prompt]
												__lowercase      :							Any				=     sd_pipe.prepare_inputs(__a							)

												__lowercase      :							int				=     replicate(__a							)
												__lowercase      :							Tuple				=     shard(__a							)

												__lowercase      :							str				=     jax.random.PRNGKey(0							)
												__lowercase      :							Optional[int]				=     jax.random.split(__a		,     jax.device_count()							)

												__lowercase      :							str				=     sd_pipe(__a		,     __a		,     __a		,     num_inference_steps=25		,     jit=__a							)[0]
												assert images.shape == (jax.device_count(), 1, 768, 768, 3)

												__lowercase      :							str				=     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
												__lowercase      :							Optional[Any]				=     images[0, 253:256, 253:256, -1]

												__lowercase      :							Optional[int]				=     jnp.asarray(jax.device_get(image_slice.flatten()							)							)
												__lowercase      :							List[str]				=     jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]							)
												print(F"output_slice: {output_slice}"							)
												assert jnp.abs(output_slice - expected_slice							).max() < 1E-2













from torch import nn

class 		lowerCAmelCase     ( nn.Module			):

					'''simple docstring'''

					def __init__(  self				:	Tuple		,     __a				:	int		,     __a				:	Optional[int]							)			->  Union[str, Any]:


												"""simple docstring"""

												super().__init__()
												__lowercase      :							int				=     class_size
												__lowercase      :							int				=     embed_size
												# self.mlp1 = nn.Linear(embed_size, embed_size)
												# self.mlp2 = (nn.Linear(embed_size, class_size))
												__lowercase      :							str				=     nn.Linear(__a		,     __a							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	int							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							str				=     self.mlp(__a							)
												return logits