


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 					lowerCAmelCase_ (       unittest.TestCase):




						def  _snake_case					(	self						:   Union[str, Any]						)      ->Optional[int]:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   str						)      ->Dict:






									"""simple docstring"""






									a__  ,  a__ :Any						  =    FlaxControlNetModel.from_pretrained(
									    "lllyasviel/sd-controlnet-canny"   ,     from_pt=__A   ,     dtype=jnp.bfloataa						)
									a__  ,  a__ :Tuple						  =    FlaxStableDiffusionControlNetPipeline.from_pretrained(
									    "runwayml/stable-diffusion-v1-5"   ,     controlnet=__A   ,     from_pt=__A   ,     dtype=jnp.bfloataa						)
									a__ :Any						  =    controlnet_params

									a__ :List[str]						  =    "bird"
									a__ :List[str]						  =    jax.device_count()
									a__ :Union[str, Any]						  =    pipe.prepare_text_inputs([prompts] * num_samples						)

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

									a__ :Optional[int]						  =    jax.random.PRNGKey(0						)
									a__ :Dict						  =    jax.random.split(__A   ,     jax.device_count()						)

									a__ :Optional[int]						  =    replicate(__A						)
									a__ :List[str]						  =    shard(__A						)
									a__ :List[Any]						  =    shard(__A						)

									a__ :Dict						  =    pipe(
									    prompt_ids=__A   ,     image=__A   ,     params=__A   ,     prng_seed=__A   ,     num_inference_steps=50   ,     jit=__A   ,     ).images
									assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

									a__ :List[str]						  =    jnp.asarray(jax.device_get(image_slice.flatten()						)						)
									a__ :Optional[Any]						  =    jnp.array(
									    [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]						)
									print(F'''output_slice: {output_slice}'''						)
									assert jnp.abs(output_slice - expected_slice						).max() < 1E-2






						def  _snake_case					(	self						:   str						)      ->List[str]:






									"""simple docstring"""






									a__  ,  a__ :str						  =    FlaxControlNetModel.from_pretrained(
									    "lllyasviel/sd-controlnet-openpose"   ,     from_pt=__A   ,     dtype=jnp.bfloataa						)
									a__  ,  a__ :List[Any]						  =    FlaxStableDiffusionControlNetPipeline.from_pretrained(
									    "runwayml/stable-diffusion-v1-5"   ,     controlnet=__A   ,     from_pt=__A   ,     dtype=jnp.bfloataa						)
									a__ :Dict						  =    controlnet_params

									a__ :List[Any]						  =    "Chef in the kitchen"
									a__ :List[Any]						  =    jax.device_count()
									a__ :str						  =    pipe.prepare_text_inputs([prompts] * num_samples						)

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

									a__ :Tuple						  =    jax.random.PRNGKey(0						)
									a__ :Tuple						  =    jax.random.split(__A   ,     jax.device_count()						)

									a__ :Any						  =    replicate(__A						)
									a__ :Optional[int]						  =    shard(__A						)
									a__ :Dict						  =    shard(__A						)

									a__ :Optional[int]						  =    pipe(
									    prompt_ids=__A   ,     image=__A   ,     params=__A   ,     prng_seed=__A   ,     num_inference_steps=50   ,     jit=__A   ,     ).images
									assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

									a__ :str						  =    jnp.asarray(jax.device_get(image_slice.flatten()						)						)
									a__ :List[str]						  =    jnp.array(
									    [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]						)
									print(F'''output_slice: {output_slice}'''						)
									assert jnp.abs(output_slice - expected_slice						).max() < 1E-2






def 				lowerCamelCase__	(      a	:   int = 100				)    ->	int:





			"""simple docstring"""
			a__ :Tuple						  =    n * (n + 1) * (2 * n + 1) / 6
			a__ :Tuple						  =    (n * (n + 1) / 2) ** 2
			return int(square_of_sum - sum_of_squares				)


if __name__ == "__main__":
		print(f'''{solution() = }''')


