



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      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


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



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								A__  ,			A__	  =     FlaxStableDiffusionPipeline.from_pretrained(
								    'stabilityai/stable-diffusion-2'					,       revision='bf16'					,       dtype=jnp.bfloataa					,       )

								A__	  =     'A painting of a squirrel eating a burger'
								A__	  =     jax.device_count()
								A__	  =     num_samples * [prompt]
								A__	  =     sd_pipe.prepare_inputs(_snake_case       )

								A__	  =     replicate(_snake_case       )
								A__	  =     shard(_snake_case       )

								A__	  =     jax.random.PRNGKey(0       )
								A__	  =     jax.random.split(_snake_case					,       jax.device_count()       )

								A__	  =     sd_pipe(_snake_case					,       _snake_case					,       _snake_case					,       num_inference_steps=25					,       jit=_snake_case       )[0]
								assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)

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

								A__	  =     jnp.asarray(jax.device_get(image_slice.flatten()       )       )
								A__	  =     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      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								A__	  =     'stabilityai/stable-diffusion-2'
								A__  ,			A__	  =     FlaxDPMSolverMultistepScheduler.from_pretrained(_snake_case					,       subfolder='scheduler'       )
								A__  ,			A__	  =     FlaxStableDiffusionPipeline.from_pretrained(
								    _snake_case					,       scheduler=_snake_case					,       revision='bf16'					,       dtype=jnp.bfloataa					,       )
								A__	  =     scheduler_params

								A__	  =     'A painting of a squirrel eating a burger'
								A__	  =     jax.device_count()
								A__	  =     num_samples * [prompt]
								A__	  =     sd_pipe.prepare_inputs(_snake_case       )

								A__	  =     replicate(_snake_case       )
								A__	  =     shard(_snake_case       )

								A__	  =     jax.random.PRNGKey(0       )
								A__	  =     jax.random.split(_snake_case					,       jax.device_count()       )

								A__	  =     sd_pipe(_snake_case					,       _snake_case					,       _snake_case					,       num_inference_steps=25					,       jit=_snake_case       )[0]
								assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)

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

								A__	  =     jnp.asarray(jax.device_get(image_slice.flatten()       )       )
								A__	  =     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









import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					def __init__(  self       :							Dict					,       *_snake_case       :							int					,       **_snake_case       :							Optional[int]       ):






								"""simple docstring"""


								warnings.warn(
								    'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
								    ' use CLIPImageProcessor instead.'					,       _snake_case					,       )
								super().__init__(*_snake_case					,       **_snake_case       )




