




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      UpperCAmelCase_  (						unittest.TestCase							):




    def 							__magic_name__							(   self						:	List[Any]				)				->							str:




     '''simple docstring'''

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




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











"""simple docstring"""



from argparse import ArgumentParser

from .env import EnvironmentCommand







def   _SCREAMING_SNAKE_CASE       (							)						->		List[Any]:
 A__	=    ArgumentParser("Diffusers CLI tool"      ,			usage="diffusers-cli <command> [<args>]"						)
 A__	=    parser.add_subparsers(help="diffusers-cli command helpers"						)

 # Register commands
 EnvironmentCommand.register_subcommand(lowercase_						)

 # Let's go
 A__	=    parser.parse_args()

 if not hasattr(lowercase_      ,			"func"						):
  parser.print_help()
  exit(1						)

 # Run
 A__	=    args.func(lowercase_						)
 service.run()


if __name__ == "__main__":
      main()





