

"""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        _lowercase					(     unittest.TestCase     ):







     def 	lowerCAmelCase__    (      self       ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__   ,						__magic_name__     =      FlaxControlNetModel.from_pretrained(
          '''lllyasviel/sd-controlnet-canny''' ,						from_pt=UpperCamelCase_ ,						dtype=jnp.bfloataa       )
      __magic_name__   ,						__magic_name__     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
          '''runwayml/stable-diffusion-v1-5''' ,						controlnet=UpperCamelCase_ ,						from_pt=UpperCamelCase_ ,						dtype=jnp.bfloataa       )
      __magic_name__     =      controlnet_params

      __magic_name__     =      '''bird'''
      __magic_name__     =      jax.device_count()
      __magic_name__     =      pipe.prepare_text_inputs([prompts] * num_samples       )

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

      __magic_name__     =      jax.random.PRNGKey(0       )
      __magic_name__     =      jax.random.split(UpperCamelCase_ ,						jax.device_count()       )

      __magic_name__     =      replicate(UpperCamelCase_       )
      __magic_name__     =      shard(UpperCamelCase_       )
      __magic_name__     =      shard(UpperCamelCase_       )

      __magic_name__     =      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)

      __magic_name__     =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
      __magic_name__     =      images[0, 253:256, 253:256, -1]

      __magic_name__     =      jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      __magic_name__     =      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 	lowerCAmelCase__    (      self       ):
      __magic_name__   ,						__magic_name__     =      FlaxControlNetModel.from_pretrained(
          '''lllyasviel/sd-controlnet-openpose''' ,						from_pt=UpperCamelCase_ ,						dtype=jnp.bfloataa       )
      __magic_name__   ,						__magic_name__     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
          '''runwayml/stable-diffusion-v1-5''' ,						controlnet=UpperCamelCase_ ,						from_pt=UpperCamelCase_ ,						dtype=jnp.bfloataa       )
      __magic_name__     =      controlnet_params

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

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

      __magic_name__     =      jax.random.PRNGKey(0       )
      __magic_name__     =      jax.random.split(UpperCamelCase_ ,						jax.device_count()       )

      __magic_name__     =      replicate(UpperCamelCase_       )
      __magic_name__     =      shard(UpperCamelCase_       )
      __magic_name__     =      shard(UpperCamelCase_       )

      __magic_name__     =      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)

      __magic_name__     =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
      __magic_name__     =      images[0, 253:256, 253:256, -1]

      __magic_name__     =      jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      __magic_name__     =      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"""






import pytest

import datasets


# Import fixture modules as plugins
__lowerCamelCase										=		["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      str:
 # Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
 for item in items:
  if any(marker in item.keywords for marker in ['''integration''', '''unit''']						):
   continue
  item.add_marker(pytest.mark.unit						)





def 	lowercase      (   __UpperCamelCase						) ->      Union[str, Any]:
 config.addinivalue_line('''markers'''      ,   '''torchaudio_latest: mark test to run with torchaudio>=0.12'''						)





@pytest.fixture(autouse=__UpperCamelCase						)
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Dict:
 # test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
 __magic_name__     =      tmp_path_factory.getbasetemp() / '''cache'''
 __magic_name__     =      test_hf_cache_home / '''datasets'''
 __magic_name__     =      test_hf_cache_home / '''metrics'''
 __magic_name__     =      test_hf_cache_home / '''modules'''
 monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE'''      ,   str(__UpperCamelCase						)						)
 monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE'''      ,   str(__UpperCamelCase						)						)
 monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE'''      ,   str(__UpperCamelCase						)						)
 __magic_name__     =      test_hf_datasets_cache / '''downloads'''
 monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH'''      ,   str(__UpperCamelCase						)						)
 __magic_name__     =      test_hf_datasets_cache / '''downloads''' / '''extracted'''
 monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''      ,   str(__UpperCamelCase						)						)





@pytest.fixture(autouse=__UpperCamelCase      ,   scope='''session'''						)
def 	lowercase      (   ) ->      Any:
 datasets.disable_progress_bar()





@pytest.fixture(autouse=__UpperCamelCase						)
def 	lowercase      (   __UpperCamelCase						) ->      str:
 # don't take tests into account when counting downloads
 monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'''      ,   __UpperCamelCase						)





@pytest.fixture
def 	lowercase      (   __UpperCamelCase						) ->      int:
 # Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
 # To be removed once SQLAlchemy 2.0 supported
 monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'''      ,   __UpperCamelCase						)

