


"""simple docstring"""


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      _UpperCamelCase       ( self							:					Dict				):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__           =		FlaxControlNetModel.from_pretrained(
              'lllyasviel/sd-controlnet-canny'							,					from_pt=SCREAMING_SNAKE_CASE__							,					dtype=jnp.bfloataa				)
          lowerCamelCase__           =		FlaxStableDiffusionControlNetPipeline.from_pretrained(
              'runwayml/stable-diffusion-v1-5'							,					controlnet=SCREAMING_SNAKE_CASE__							,					from_pt=SCREAMING_SNAKE_CASE__							,					dtype=jnp.bfloataa				)
          lowerCamelCase__           =		controlnet_params

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

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

          lowerCamelCase__           =		jax.random.PRNGKey(0				)
          lowerCamelCase__           =		jax.random.split(SCREAMING_SNAKE_CASE__							,					jax.device_count()				)

          lowerCamelCase__           =		replicate(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		shard(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		shard(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		pipe(
              prompt_ids=SCREAMING_SNAKE_CASE__							,					image=SCREAMING_SNAKE_CASE__							,					params=SCREAMING_SNAKE_CASE__							,					prng_seed=SCREAMING_SNAKE_CASE__							,					num_inference_steps=50							,					jit=SCREAMING_SNAKE_CASE__							,					).images
          assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

          lowerCamelCase__           =		images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
          lowerCamelCase__           =		images[0, 2_53:2_56, 2_53:2_56, -1]

          lowerCamelCase__           =		jnp.asarray(jax.device_get(image_slice.flatten()				)				)
          lowerCamelCase__           =		jnp.array(
              [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]				)
          print(F'output_slice: {output_slice}'				)
          assert jnp.abs(output_slice - expected_slice				).max() < 1e-2


    def      _UpperCamelCase       ( self							:					List[Any]				):
          lowerCamelCase__           =		FlaxControlNetModel.from_pretrained(
              'lllyasviel/sd-controlnet-openpose'							,					from_pt=SCREAMING_SNAKE_CASE__							,					dtype=jnp.bfloataa				)
          lowerCamelCase__           =		FlaxStableDiffusionControlNetPipeline.from_pretrained(
              'runwayml/stable-diffusion-v1-5'							,					controlnet=SCREAMING_SNAKE_CASE__							,					from_pt=SCREAMING_SNAKE_CASE__							,					dtype=jnp.bfloataa				)
          lowerCamelCase__           =		controlnet_params

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

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

          lowerCamelCase__           =		jax.random.PRNGKey(0				)
          lowerCamelCase__           =		jax.random.split(SCREAMING_SNAKE_CASE__							,					jax.device_count()				)

          lowerCamelCase__           =		replicate(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		shard(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		shard(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		pipe(
              prompt_ids=SCREAMING_SNAKE_CASE__							,					image=SCREAMING_SNAKE_CASE__							,					params=SCREAMING_SNAKE_CASE__							,					prng_seed=SCREAMING_SNAKE_CASE__							,					num_inference_steps=50							,					jit=SCREAMING_SNAKE_CASE__							,					).images
          assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

          lowerCamelCase__           =		images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
          lowerCamelCase__           =		images[0, 2_53:2_56, 2_53:2_56, -1]

          lowerCamelCase__           =		jnp.asarray(jax.device_get(image_slice.flatten()				)				)
          lowerCamelCase__           =		jnp.array(
              [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]				)
          print(F'output_slice: {output_slice}'				)
          assert jnp.abs(output_slice - expected_slice				).max() < 1e-2







"""simple docstring"""


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


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
   from .camera import create_pan_cameras
   from .pipeline_shap_e import ShapEPipeline
   from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
   from .renderer import (
       BoundingBoxVolume,
       ImportanceRaySampler,
       MLPNeRFModelOutput,
       MLPNeRSTFModel,
       ShapEParamsProjModel,
       ShapERenderer,
       StratifiedRaySampler,
       VoidNeRFModel,
   )



