





'''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    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Tuple:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Tuple:
     A__,	A__				=			FlaxControlNetModel.from_pretrained(
         "lllyasviel/sd-controlnet-canny"						,       from_pt=lowercase_						,       dtype=jnp.bfloataa							)
     A__,	A__				=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
         "runwayml/stable-diffusion-v1-5"						,       controlnet=lowercase_						,       from_pt=lowercase_						,       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(lowercase_						,       jax.device_count()							)

     A__				=			replicate(lowercase_							)
     A__				=			shard(lowercase_							)
     A__				=			shard(lowercase_							)

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

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

     A__				=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
     A__				=			jnp.array(
         [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]							)
     print(F"output_slice: {output_slice}"							)
     assert jnp.abs(output_slice - expected_slice							).max() < 1E-2



 def      UpperCAmelCase_							(				self      :Tuple							)->							Tuple:
     A__,	A__				=			FlaxControlNetModel.from_pretrained(
         "lllyasviel/sd-controlnet-openpose"						,       from_pt=lowercase_						,       dtype=jnp.bfloataa							)
     A__,	A__				=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
         "runwayml/stable-diffusion-v1-5"						,       controlnet=lowercase_						,       from_pt=lowercase_						,       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(lowercase_						,       jax.device_count()							)

     A__				=			replicate(lowercase_							)
     A__				=			shard(lowercase_							)
     A__				=			shard(lowercase_							)

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

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

     A__				=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
     A__				=			jnp.array(
         [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]							)
     print(F"output_slice: {output_slice}"							)
     assert jnp.abs(output_slice - expected_slice							).max() < 1E-2










'''simple docstring'''

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    "stable diffusion controlnet",
    "0.22.0",
    "Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.",
    standard_warn=False,
    stacklevel=3,
)



