

'''simple docstring'''



import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()



class 				_snake_case					(   a_     , a_     , unittest.TestCase			):
  SCREAMING_SNAKE_CASE			:       List[str]				=     AutoencoderKL
  SCREAMING_SNAKE_CASE			:       Dict				=     '''sample'''
  SCREAMING_SNAKE_CASE			:       List[str]				=     1e-2

  @property
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							4
      lowerCAmelCase 						=							3
      lowerCAmelCase 						=							(32, 32)

      lowerCAmelCase 						=							floats_tensor((batch_size, num_channels) + sizes	).to(_SCREAMING_SNAKE_CASE	)

      return {"sample": image}

  @property
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return (3, 32, 32)

  @property
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return (3, 32, 32)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							{
          'block_out_channels': [32, 64],
          'in_channels': 3,
          'out_channels': 3,
          'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
          'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
          'latent_channels': 4,
      }
      lowerCAmelCase 						=							self.dummy_input
      return init_dict, inputs_dict

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      pass

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      pass

  @unittest.skipIf(torch_device == 'mps'      , 'Gradient checkpointing skipped on MPS'	)
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							self.prepare_init_args_and_inputs_for_common()
      lowerCAmelCase 						=							self.model_class(**_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)

      assert not model.is_gradient_checkpointing and model.training

      lowerCAmelCase 						=							model(**_SCREAMING_SNAKE_CASE	).sample
      # run the backwards pass on the model. For backwards pass, for simplicity purpose,
      # we won't calculate the loss and rather backprop on out.sum()
      model.zero_grad()

      lowerCAmelCase 						=							torch.randn_like(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							(out - labels).mean()
      loss.backward()

      # re-instantiate the model now enabling gradient checkpointing
      lowerCAmelCase 						=							self.model_class(**_SCREAMING_SNAKE_CASE	)
      # clone model
      model_a.load_state_dict(model.state_dict()	)
      model_a.to(_SCREAMING_SNAKE_CASE	)
      model_a.enable_gradient_checkpointing()

      assert model_a.is_gradient_checkpointing and model_a.training

      lowerCAmelCase 						=							model_a(**_SCREAMING_SNAKE_CASE	).sample
      # run the backwards pass on the model. For backwards pass, for simplicity purpose,
      # we won't calculate the loss and rather backprop on out.sum()
      model_a.zero_grad()
      lowerCAmelCase 						=							(out_a - labels).mean()
      loss_a.backward()

      # compare the output and parameters gradients
      self.assertTrue((loss - loss_a).abs() < 1e-5	)
      lowerCAmelCase 						=							dict(model.named_parameters()	)
      lowerCAmelCase 						=							dict(model_a.named_parameters()	)
      for name, param in named_params.items():
          self.assertTrue(torch_all_close(param.grad.data      , named_params_a[name].grad.data      , atol=5e-5	)	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'      , output_loading_info=_SCREAMING_SNAKE_CASE	)
      self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)
      self.assertEqual(len(loading_info['missing_keys']	)      , 0	)

      model.to(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							model(**self.dummy_input	)

      assert image is not None, "Make sure output is not None"


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'	)
      lowerCAmelCase 						=							model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()

      if torch_device == "mps":
          lowerCAmelCase 						=							torch.manual_seed(0	)
      else:
          lowerCAmelCase 						=							torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)

      lowerCAmelCase 						=							torch.randn(
          1      , model.config.in_channels      , model.config.sample_size      , model.config.sample_size      , generator=torch.manual_seed(0	)      , )
      lowerCAmelCase 						=							image.to(_SCREAMING_SNAKE_CASE	)
      with torch.no_grad():
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , sample_posterior=_SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE	).sample

      lowerCAmelCase 						=							output[0, -1, -3:, -3:].flatten().cpu()

      # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
      # the expected output slices are not the same for CPU and GPU.
      if torch_device == "mps":
          lowerCAmelCase 						=							torch.tensor(
              [
                  -4.0078e-01,
                  -3.8323e-04,
                  -1.2681e-01,
                  -1.1462e-01,
                  2.0095e-01,
                  1.0893e-01,
                  -8.8247e-02,
                  -3.0361e-01,
                  -9.8644e-03,
              ]	)
      elif torch_device == "cpu":
          lowerCAmelCase 						=							torch.tensor(
              [-0.1_352, 0.0_878, 0.0_419, -0.0_818, -0.1_069, 0.0_688, -0.1_458, -0.4_446, -0.0_026]	)
      else:
          lowerCAmelCase 						=							torch.tensor(
              [-0.2_421, 0.4_642, 0.2_507, -0.0_438, 0.0_682, 0.3_160, -0.2_018, -0.0_727, 0.2_485]	)

      self.assertTrue(torch_all_close(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , rtol=1e-2	)	)



@slow
class 				_snake_case					(   unittest.TestCase			):

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      return F'gaussian_noise_s={seed}_shape={"_".join([str(_SCREAMING_SNAKE_CASE	) for s in shape]	)}.npy'

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE=0      , _SCREAMING_SNAKE_CASE=(4, 3, 5_12, 5_12)      , _SCREAMING_SNAKE_CASE=False	):





      '''simple docstring'''

      lowerCAmelCase 						=							torch.floataa if fpaa else torch.floataa
      lowerCAmelCase 						=							torch.from_numpy(load_hf_numpy(self.get_file_format(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)	)	).to(_SCREAMING_SNAKE_CASE	).to(_SCREAMING_SNAKE_CASE	)
      return image

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE="CompVis/stable-diffusion-v1-4"      , _SCREAMING_SNAKE_CASE=False	):





      '''simple docstring'''

      lowerCAmelCase 						=							'fp16' if fpaa else None
      lowerCAmelCase 						=							torch.floataa if fpaa else torch.floataa

      lowerCAmelCase 						=							AutoencoderKL.from_pretrained(
          _SCREAMING_SNAKE_CASE      , subfolder='vae'      , torch_dtype=_SCREAMING_SNAKE_CASE      , revision=_SCREAMING_SNAKE_CASE      , )
      model.to(_SCREAMING_SNAKE_CASE	).eval()

      return model

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE=0	):





      '''simple docstring'''

      if torch_device == "mps":
          return torch.manual_seed(_SCREAMING_SNAKE_CASE	)
      return torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(_SCREAMING_SNAKE_CASE	)

  @parameterized.expand(
      [
          # fmt: off
          [33, [-0.1_603, 0.9_878, -0.0_495, -0.0_790, -0.2_709, 0.8_375, -0.2_060, -0.0_824], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
          [47, [-0.2_376, 0.1_168, 0.1_332, -0.4_840, -0.2_508, -0.0_791, -0.0_493, -0.4_089], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
          # fmt: on
      ]	)
  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.get_sd_vae_model()
      lowerCAmelCase 						=							self.get_sd_image(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.get_generator(_SCREAMING_SNAKE_CASE	)

      with torch.no_grad():
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE      , sample_posterior=_SCREAMING_SNAKE_CASE	).sample

      assert sample.shape == image.shape

      lowerCAmelCase 						=							sample[-1, -2:, -2:, :2].flatten().float().cpu()
      lowerCAmelCase 						=							torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice	)

      assert torch_all_close(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , atol=3e-3	)

  @parameterized.expand(
      [
          # fmt: off
          [33, [-0.0_513, 0.0_289, 1.3_799, 0.2_166, -0.2_573, -0.0_871, 0.5_103, -0.0_999]],
          [47, [-0.4_128, -0.1_320, -0.3_704, 0.1_965, -0.4_116, -0.2_332, -0.3_340, 0.2_247]],
          # fmt: on
      ]	)
  @require_torch_gpu
  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.get_sd_vae_model(fpaa=_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.get_sd_image(_SCREAMING_SNAKE_CASE      , fpaa=_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.get_generator(_SCREAMING_SNAKE_CASE	)

      with torch.no_grad():
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE      , sample_posterior=_SCREAMING_SNAKE_CASE	).sample

      assert sample.shape == image.shape

      lowerCAmelCase 						=							sample[-1, -2:, :2, -2:].flatten().float().cpu()
      lowerCAmelCase 						=							torch.tensor(_SCREAMING_SNAKE_CASE	)

      assert torch_all_close(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , atol=1e-2	)

  @parameterized.expand(
      [
          # fmt: off
          [33, [-0.1_609, 0.9_866, -0.0_487, -0.0_777, -0.2_716, 0.8_368, -0.2_055, -0.0_814], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
          [47, [-0.2_377, 0.1_147, 0.1_333, -0.4_841, -0.2_506, -0.0_805, -0.0_491, -0.4_085], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
          # fmt: on
      ]	)
  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.get_sd_vae_model()
      lowerCAmelCase 						=							self.get_sd_image(_SCREAMING_SNAKE_CASE	)

      with torch.no_grad():
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE	).sample

      assert sample.shape == image.shape

      lowerCAmelCase 						=							sample[-1, -2:, -2:, :2].flatten().float().cpu()
      lowerCAmelCase 						=							torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice	)

      assert torch_all_close(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , atol=3e-3	)

  @parameterized.expand(
      [
          # fmt: off
          [13, [-0.2_051, -0.1_803, -0.2_311, -0.2_114, -0.3_292, -0.3_574, -0.2_953, -0.3_323]],
          [37, [-0.2_632, -0.2_625, -0.2_199, -0.2_741, -0.4_539, -0.4_990, -0.3_720, -0.4_925]],
          # fmt: on
      ]	)
  @require_torch_gpu
  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.get_sd_vae_model()
      lowerCAmelCase 						=							self.get_sd_image(_SCREAMING_SNAKE_CASE      , shape=(3, 4, 64, 64)	)

      with torch.no_grad():
          lowerCAmelCase 						=							model.decode(_SCREAMING_SNAKE_CASE	).sample

      assert list(sample.shape	) == [3, 3, 5_12, 5_12]

      lowerCAmelCase 						=							sample[-1, -2:, :2, -2:].flatten().cpu()
      lowerCAmelCase 						=							torch.tensor(_SCREAMING_SNAKE_CASE	)

      assert torch_all_close(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , atol=1e-3	)

  @parameterized.expand(
      [
          # fmt: off
          [27, [-0.0_369, 0.0_207, -0.0_776, -0.0_682, -0.1_747, -0.1_930, -0.1_465, -0.2_039]],
          [16, [-0.1_628, -0.2_134, -0.2_747, -0.2_642, -0.3_774, -0.4_404, -0.3_687, -0.4_277]],
          # fmt: on
      ]	)
  @require_torch_gpu
  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.get_sd_vae_model(fpaa=_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.get_sd_image(_SCREAMING_SNAKE_CASE      , shape=(3, 4, 64, 64)      , fpaa=_SCREAMING_SNAKE_CASE	)

      with torch.no_grad():
          lowerCAmelCase 						=							model.decode(_SCREAMING_SNAKE_CASE	).sample

      assert list(sample.shape	) == [3, 3, 5_12, 5_12]

      lowerCAmelCase 						=							sample[-1, -2:, :2, -2:].flatten().float().cpu()
      lowerCAmelCase 						=							torch.tensor(_SCREAMING_SNAKE_CASE	)

      assert torch_all_close(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , atol=5e-3	)

  @parameterized.expand([(13,), (16,), (27,)]	)
  @require_torch_gpu
  @unittest.skipIf(not is_xformers_available()      , reason='xformers is not required when using PyTorch 2.0.'	)
  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.get_sd_vae_model(fpaa=_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.get_sd_image(_SCREAMING_SNAKE_CASE      , shape=(3, 4, 64, 64)      , fpaa=_SCREAMING_SNAKE_CASE	)

      with torch.no_grad():
          lowerCAmelCase 						=							model.decode(_SCREAMING_SNAKE_CASE	).sample

      model.enable_xformers_memory_efficient_attention()
      with torch.no_grad():
          lowerCAmelCase 						=							model.decode(_SCREAMING_SNAKE_CASE	).sample

      assert list(sample.shape	) == [3, 3, 5_12, 5_12]

      assert torch_all_close(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , atol=1e-1	)

  @parameterized.expand([(13,), (16,), (37,)]	)
  @require_torch_gpu
  @unittest.skipIf(not is_xformers_available()      , reason='xformers is not required when using PyTorch 2.0.'	)
  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.get_sd_vae_model()
      lowerCAmelCase 						=							self.get_sd_image(_SCREAMING_SNAKE_CASE      , shape=(3, 4, 64, 64)	)

      with torch.no_grad():
          lowerCAmelCase 						=							model.decode(_SCREAMING_SNAKE_CASE	).sample

      model.enable_xformers_memory_efficient_attention()
      with torch.no_grad():
          lowerCAmelCase 						=							model.decode(_SCREAMING_SNAKE_CASE	).sample

      assert list(sample.shape	) == [3, 3, 5_12, 5_12]

      assert torch_all_close(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , atol=1e-2	)


  @parameterized.expand(
      [
          # fmt: off
          [33, [-0.3_001, 0.0_918, -2.6_984, -3.9_720, -3.2_099, -5.0_353, 1.7_338, -0.2_065, 3.4_267]],
          [47, [-1.5_030, -4.3_871, -6.0_355, -9.1_157, -1.6_661, -2.7_853, 2.1_607, -5.0_823, 2.5_633]],
          # fmt: on
      ]	)
  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.get_sd_vae_model()
      lowerCAmelCase 						=							self.get_sd_image(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.get_generator(_SCREAMING_SNAKE_CASE	)

      with torch.no_grad():
          lowerCAmelCase 						=							model.encode(_SCREAMING_SNAKE_CASE	).latent_dist
          lowerCAmelCase 						=							dist.sample(generator=_SCREAMING_SNAKE_CASE	)

      assert list(sample.shape	) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

      lowerCAmelCase 						=							sample[0, -1, -3:, -3:].flatten().cpu()
      lowerCAmelCase 						=							torch.tensor(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							3e-3 if torch_device != 'mps' else 1e-2
      assert torch_all_close(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , atol=_SCREAMING_SNAKE_CASE	)








'''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 				_snake_case					(   unittest.TestCase			):

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-canny'      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase						,   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(
          [0.167_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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-openpose'      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase						,   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(
          [[0.271_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





