





import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, 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   ):
			'''simple docstring'''




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







					"""simple docstring"""






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






			def        a_	(      self       :   Optional[int]       )     ->     Optional[Any]:







					"""simple docstring"""






					A__				,      A__  				=       FlaxControlNetModel.from_pretrained(
					    """lllyasviel/sd-controlnet-canny"""							,     from_pt=__lowerCAmelCase							,     dtype=jnp.bfloataa       )
					A__				,      A__  				=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    """runwayml/stable-diffusion-v1-5"""							,     controlnet=__lowerCAmelCase							,     from_pt=__lowerCAmelCase							,     dtype=jnp.bfloataa       )
					A__  				=       controlnet_params

					A__  				=       """bird"""
					A__  				=       jax.device_count()
					A__  				=       pipe.prepare_text_inputs([prompts] * num_samples       )

					A__  				=       load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""       )
					A__  				=       pipe.prepare_image_inputs([canny_image] * num_samples       )

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

					A__  				=       replicate(__lowerCAmelCase       )
					A__  				=       shard(__lowerCAmelCase       )
					A__  				=       shard(__lowerCAmelCase       )

					A__  				=       pipe(
					    prompt_ids=__lowerCAmelCase							,     image=__lowerCAmelCase							,     params=__lowerCAmelCase							,     prng_seed=__lowerCAmelCase							,     num_inference_steps=50							,     jit=__lowerCAmelCase							,     ).images
					assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 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(
					    [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]       )
					print(f'output_slice: {output_slice}'       )
					assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2







			def        a_	(      self       :   int       )     ->     List[Any]:







					"""simple docstring"""






					A__				,      A__  				=       FlaxControlNetModel.from_pretrained(
					    """lllyasviel/sd-controlnet-openpose"""							,     from_pt=__lowerCAmelCase							,     dtype=jnp.bfloataa       )
					A__				,      A__  				=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    """runwayml/stable-diffusion-v1-5"""							,     controlnet=__lowerCAmelCase							,     from_pt=__lowerCAmelCase							,     dtype=jnp.bfloataa       )
					A__  				=       controlnet_params

					A__  				=       """Chef in the kitchen"""
					A__  				=       jax.device_count()
					A__  				=       pipe.prepare_text_inputs([prompts] * num_samples       )

					A__  				=       load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"""       )
					A__  				=       pipe.prepare_image_inputs([pose_image] * num_samples       )

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

					A__  				=       replicate(__lowerCAmelCase       )
					A__  				=       shard(__lowerCAmelCase       )
					A__  				=       shard(__lowerCAmelCase       )

					A__  				=       pipe(
					    prompt_ids=__lowerCAmelCase							,     image=__lowerCAmelCase							,     params=__lowerCAmelCase							,     prng_seed=__lowerCAmelCase							,     num_inference_steps=50							,     jit=__lowerCAmelCase							,     ).images
					assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 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(
					    [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]       )
					print(f'output_slice: {output_slice}'       )
					assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2










from typing import TYPE_CHECKING

from ...utils import _LazyModule


A   : Optional[Any]			=				{'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}


if TYPE_CHECKING:
					from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
					import sys

					A   : List[str]			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



