






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 					_snake_case						(			unittest.TestCase ):






	def    lowerCAmelCase_							(		self       )						->    int:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :Tuple         =   FlaxControlNetModel.from_pretrained(
					    "lllyasviel/sd-controlnet-canny"					,from_pt=UpperCamelCase					,dtype=jnp.bfloataa       )
					snake_case__      :Any         =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"					,controlnet=UpperCamelCase					,from_pt=UpperCamelCase					,dtype=jnp.bfloataa       )
					snake_case__      :List[str]         =   controlnet_params

					snake_case__      :Union[str, Any]         =   "bird"
					snake_case__      :Optional[int]         =   jax.device_count()
					snake_case__      :Tuple         =   pipe.prepare_text_inputs([prompts] * num_samples       )

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

					snake_case__      :List[str]         =   jax.random.PRNGKey(0       )
					snake_case__      :str         =   jax.random.split(UpperCamelCase					,jax.device_count()       )

					snake_case__      :int         =   replicate(UpperCamelCase       )
					snake_case__      :Any         =   shard(UpperCamelCase       )
					snake_case__      :Any         =   shard(UpperCamelCase       )

					snake_case__      :str         =   pipe(
					    prompt_ids=UpperCamelCase					,image=UpperCamelCase					,params=UpperCamelCase					,prng_seed=UpperCamelCase					,num_inference_steps=50					,jit=UpperCamelCase					,).images
					assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

					snake_case__      :Union[str, Any]         =   jnp.asarray(jax.device_get(image_slice.flatten()       )       )
					snake_case__      :List[Any]         =   jnp.array(
					    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]       )
					print(f'output_slice: {output_slice}'       )
					assert jnp.abs(output_slice - expected_slice       ).max() < 1E-2






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :List[str]         =   FlaxControlNetModel.from_pretrained(
					    "lllyasviel/sd-controlnet-openpose"					,from_pt=UpperCamelCase					,dtype=jnp.bfloataa       )
					snake_case__      :Optional[Any]         =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"					,controlnet=UpperCamelCase					,from_pt=UpperCamelCase					,dtype=jnp.bfloataa       )
					snake_case__      :str         =   controlnet_params

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

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

					snake_case__      :List[str]         =   jax.random.PRNGKey(0       )
					snake_case__      :Any         =   jax.random.split(UpperCamelCase					,jax.device_count()       )

					snake_case__      :Dict         =   replicate(UpperCamelCase       )
					snake_case__      :Tuple         =   shard(UpperCamelCase       )
					snake_case__      :Optional[int]         =   shard(UpperCamelCase       )

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

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

					snake_case__      :Tuple         =   jnp.asarray(jax.device_get(image_slice.flatten()       )       )
					snake_case__      :List[str]         =   jnp.array(
					    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]       )
					print(f'output_slice: {output_slice}'       )
					assert jnp.abs(output_slice - expected_slice       ).max() < 1E-2












def 					lowercase_ (    __snake_case     :							str      )     -> list:





				'''simple docstring'''


				return [
				    txt[:a] + txt[a].upper() + txt[a + 1 :]
				    for a in range(len(__snake_case      )      )
				    if txt[a].isalpha()
				]


if __name__ == "__main__":
	__import__("doctest").testmod()