
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			):




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a							,     __a										=    FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-canny'''		,     from_pt=_snake_case		,     dtype=jnp.bfloataa				)
								__a							,     __a										=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5'''		,     controlnet=_snake_case		,     from_pt=_snake_case		,     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(_snake_case		,     jax.device_count()				)

								__a										=    replicate(_snake_case				)
								__a										=    shard(_snake_case				)
								__a										=    shard(_snake_case				)

								__a										=    pipe(
								    prompt_ids=_snake_case		,     image=_snake_case		,     params=_snake_case		,     prng_seed=_snake_case		,     num_inference_steps=50		,     jit=_snake_case		,     ).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

								__a										=    jnp.asarray(jax.device_get(image_slice.flatten()				)				)
								__a										=    jnp.array(
								    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]				)
								print(F"""output_slice: {output_slice}"""				)
								assert jnp.abs(output_slice - expected_slice				).max() < 1E-2




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a							,     __a										=    FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-openpose'''		,     from_pt=_snake_case		,     dtype=jnp.bfloataa				)
								__a							,     __a										=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5'''		,     controlnet=_snake_case		,     from_pt=_snake_case		,     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(_snake_case		,     jax.device_count()				)

								__a										=    replicate(_snake_case				)
								__a										=    shard(_snake_case				)
								__a										=    shard(_snake_case				)

								__a										=    pipe(
								    prompt_ids=_snake_case		,     image=_snake_case		,     params=_snake_case		,     prng_seed=_snake_case		,     num_inference_steps=50		,     jit=_snake_case		,     ).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

								__a										=    jnp.asarray(jax.device_get(image_slice.flatten()				)				)
								__a										=    jnp.array(
								    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]				)
								print(F"""output_slice: {output_slice}"""				)
								assert jnp.abs(output_slice - expected_slice				).max() < 1E-2



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
						from .pipeline_unclip import UnCLIPPipeline
						from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
						from .text_proj import UnCLIPTextProjModel