
"""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 			A__	(     unittest.TestCase):

		def 							__lowerCamelCase							(				self			):
				super().tearDown()
				gc.collect()

		def 							__lowerCamelCase							(				self			):
				__lowerCAmelCase      :						int						      =				FlaxStableDiffusionPipeline.from_pretrained(
				    'stabilityai/stable-diffusion-2'	,		revision='bf16'	,		dtype=jnp.bfloataa	,		)

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

				__lowerCAmelCase      :						List[str]						      =				replicate(_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						Any						      =				shard(_SCREAMING_SNAKE_CASE			)

				__lowerCAmelCase      :						int						      =				jax.random.PRNGKey(0			)
				__lowerCAmelCase      :						Tuple						      =				jax.random.split(_SCREAMING_SNAKE_CASE	,		jax.device_count()			)

				__lowerCAmelCase      :						Dict						      =				sd_pipe(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		num_inference_steps=25	,		jit=_SCREAMING_SNAKE_CASE			)[0]
				assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)

				__lowerCAmelCase      :						Union[str, Any]						      =				images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
				__lowerCAmelCase      :						int						      =				images[0, 2_53:2_56, 2_53:2_56, -1]

				__lowerCAmelCase      :						Any						      =				jnp.asarray(jax.device_get(image_slice.flatten()			)			)
				__lowerCAmelCase      :						str						      =				jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.4_5508, 0.4512]			)
				print(f"output_slice: {output_slice}"			)
				assert jnp.abs(output_slice - expected_slice			).max() < 1E-2







		def 							__lowerCamelCase							(				self			):
				__lowerCAmelCase      :						Optional[Any]						      =				"""stabilityai/stable-diffusion-2"""
				__lowerCAmelCase      :						Dict						      =				FlaxDPMSolverMultistepScheduler.from_pretrained(_SCREAMING_SNAKE_CASE	,		subfolder='scheduler'			)
				__lowerCAmelCase      :						Optional[Any]						      =				FlaxStableDiffusionPipeline.from_pretrained(
				    _SCREAMING_SNAKE_CASE	,		scheduler=_SCREAMING_SNAKE_CASE	,		revision='bf16'	,		dtype=jnp.bfloataa	,		)
				__lowerCAmelCase      :						Optional[Any]						      =				scheduler_params

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

				__lowerCAmelCase      :						Optional[int]						      =				replicate(_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						Dict						      =				shard(_SCREAMING_SNAKE_CASE			)

				__lowerCAmelCase      :						int						      =				jax.random.PRNGKey(0			)
				__lowerCAmelCase      :						Tuple						      =				jax.random.split(_SCREAMING_SNAKE_CASE	,		jax.device_count()			)

				__lowerCAmelCase      :						List[str]						      =				sd_pipe(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		num_inference_steps=25	,		jit=_SCREAMING_SNAKE_CASE			)[0]
				assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)

				__lowerCAmelCase      :						Dict						      =				images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
				__lowerCAmelCase      :						Optional[Any]						      =				images[0, 2_53:2_56, 2_53:2_56, -1]

				__lowerCAmelCase      :						Any						      =				jnp.asarray(jax.device_get(image_slice.flatten()			)			)
				__lowerCAmelCase      :						List[Any]						      =				jnp.array([0.4336, 0.4_2969, 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

"""simple docstring"""







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)