



"""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  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





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



					def 	snake_case ( self							:						List[Any]  )->      Optional[int]:
								lowerCamelCase__			,     lowerCamelCase__	:	List[str]				=FlaxStableDiffusionPipeline.from_pretrained(
								    '''stabilityai/stable-diffusion-2''',						revision='''bf16''',						dtype=jnp.bfloataa,						)

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

								lowerCamelCase__	:	Optional[Any]				=replicate(lowerCamelCase  )
								lowerCamelCase__	:	Optional[int]				=shard(lowerCamelCase  )

								lowerCamelCase__	:	Optional[int]				=jax.random.PRNGKey(0  )
								lowerCamelCase__	:	Tuple				=jax.random.split(lowerCamelCase,						jax.device_count()  )

								lowerCamelCase__	:	Tuple				=sd_pipe(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						num_inference_steps=25,						jit=lowerCamelCase  )[0]
								assert images.shape == (jax.device_count(), 1, 768, 768, 3)

								lowerCamelCase__	:	int				=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
								lowerCamelCase__	:	int				=images[0, 253:256, 253:256, -1]

								lowerCamelCase__	:	Optional[Any]				=jnp.asarray(jax.device_get(image_slice.flatten()  )  )
								lowerCamelCase__	:	int				=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 	snake_case ( self							:						int  )->      Tuple:
								lowerCamelCase__	:	List[Any]				='''stabilityai/stable-diffusion-2'''
								lowerCamelCase__			,     lowerCamelCase__	:	Any				=FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCamelCase,						subfolder='''scheduler'''  )
								lowerCamelCase__			,     lowerCamelCase__	:	Tuple				=FlaxStableDiffusionPipeline.from_pretrained(
								    lowerCamelCase,						scheduler=lowerCamelCase,						revision='''bf16''',						dtype=jnp.bfloataa,						)
								lowerCamelCase__	:	List[Any]				=scheduler_params

								lowerCamelCase__	:	int				='''A painting of a squirrel eating a burger'''
								lowerCamelCase__	:	str				=jax.device_count()
								lowerCamelCase__	:	List[str]				=num_samples * [prompt]
								lowerCamelCase__	:	Optional[int]				=sd_pipe.prepare_inputs(lowerCamelCase  )

								lowerCamelCase__	:	str				=replicate(lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=shard(lowerCamelCase  )

								lowerCamelCase__	:	Union[str, Any]				=jax.random.PRNGKey(0  )
								lowerCamelCase__	:	Optional[int]				=jax.random.split(lowerCamelCase,						jax.device_count()  )

								lowerCamelCase__	:	Optional[int]				=sd_pipe(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						num_inference_steps=25,						jit=lowerCamelCase  )[0]
								assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

								lowerCamelCase__	:	str				=jnp.asarray(jax.device_get(image_slice.flatten()  )  )
								lowerCamelCase__	:	Tuple				=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









"""simple docstring"""






from ..utils import DummyObject, requires_backends

class  __SCREAMING_SNAKE_CASE			(   metaclass=lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							['onnx']



					def __init__( self							:						List[str],						*lowerCamelCase							:						Union[str, Any],						**lowerCamelCase							:						str  )->      Optional[int]:
								requires_backends(self,						['''onnx''']  )



					@classmethod
					def 	snake_case ( cls							:						List[str],						*lowerCamelCase							:						Any,						**lowerCamelCase							:						Union[str, Any]  )->      Optional[int]:
								requires_backends(cls,						['''onnx''']  )




					@classmethod
					def 	snake_case ( cls							:						Union[str, Any],						*lowerCamelCase							:						Tuple,						**lowerCamelCase							:						Tuple  )->      Optional[int]:
								requires_backends(cls,						['''onnx''']  )




