





'''simple docstring'''






import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
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):


  """simple docstring"""







  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						List[str]:
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()




  def   UpperCamelCase__      (				self    :					int  )     ->						Any:
    _UpperCamelCase       			=load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-inpaint/init_image.png'''  )
    _UpperCamelCase       			=load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''  )

    _UpperCamelCase       			='''xvjiarui/stable-diffusion-2-inpainting'''
    _UpperCamelCase			,     _UpperCamelCase       			=FlaxStableDiffusionInpaintPipeline.from_pretrained(UpperCamelCase__							,	safety_checker=UpperCamelCase__  )

    _UpperCamelCase       			='''Face of a yellow cat, high resolution, sitting on a park bench'''

    _UpperCamelCase       			=jax.random.PRNGKey(0  )
    _UpperCamelCase       			=50

    _UpperCamelCase       			=jax.device_count()
    _UpperCamelCase       			=num_samples * [prompt]
    _UpperCamelCase       			=num_samples * [init_image]
    _UpperCamelCase       			=num_samples * [mask_image]
    _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase       			=pipeline.prepare_inputs(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  )

    # shard inputs and rng
    _UpperCamelCase       			=replicate(UpperCamelCase__  )
    _UpperCamelCase       			=jax.random.split(UpperCamelCase__							,	jax.device_count()  )
    _UpperCamelCase       			=shard(UpperCamelCase__  )
    _UpperCamelCase       			=shard(UpperCamelCase__  )
    _UpperCamelCase       			=shard(UpperCamelCase__  )

    _UpperCamelCase       			=pipeline(
        UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	jit=UpperCamelCase__  )

    _UpperCamelCase       			=output.images.reshape(UpperCamelCase__							,	512							,	512							,	3  )

    _UpperCamelCase       			=images[0, 253:256, 253:256, -1]

    _UpperCamelCase       			=jnp.asarray(jax.device_get(image_slice.flatten()  )  )
    _UpperCamelCase       			=jnp.array(
        [0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084]  )
    print(F'''output_slice: {output_slice}'''  )
    assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2













'''simple docstring'''






import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


__lowerCamelCase						: Union[str, Any]		=   logging.get_logger(__name__)







class 		UpperCAmelCase   :


  """simple docstring"""







  lowerCAmelCase_					=				None




@experimental
def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  if ParallelBackendConfig.backend_name is None:
    return _map_with_multiprocessing_pool(
        __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

  return _map_with_joblib(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=num_proc if num_proc <= len(__SCREAMING_SNAKE_CASE   ) else len(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=[]  # We organize the splits ourselve (contiguous splits)
  for index in range(__SCREAMING_SNAKE_CASE   ):
    _UpperCamelCase       			=len(__SCREAMING_SNAKE_CASE   ) // num_proc
    _UpperCamelCase       			=len(__SCREAMING_SNAKE_CASE   ) % num_proc
    _UpperCamelCase       			=div * index + min(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=start + div + (1 if index < mod else 0)
    split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)   )

  if len(__SCREAMING_SNAKE_CASE   ) != sum(len(i[1]   ) for i in split_kwds   ):
    raise ValueError(
        f'''Error dividing inputs iterable among processes. '''
        f'''Total number of objects {len(__SCREAMING_SNAKE_CASE   )}, '''
        f'''length: {sum(len(i[1]   ) for i in split_kwds   )}'''   )

  logger.info(
      f'''Spawning {num_proc} processes for {len(__SCREAMING_SNAKE_CASE   )} objects in slices of {[len(i[1]   ) for i in split_kwds]}'''   )
  _UpperCamelCase			,     _UpperCamelCase       			=None, None
  if not disable_tqdm:
    _UpperCamelCase			,     _UpperCamelCase       			=(RLock(),), tqdm.set_lock
  with Pool(__SCREAMING_SNAKE_CASE  ,      initargs=__SCREAMING_SNAKE_CASE  ,      initializer=__SCREAMING_SNAKE_CASE   ) as pool:
    _UpperCamelCase       			=pool.map(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  logger.info(f'''Finished {num_proc} processes'''   )
  _UpperCamelCase       			=[obj for proc_res in mapped for obj in proc_res]
  logger.info(f'''Unpacked {len(__SCREAMING_SNAKE_CASE   )} objects'''   )

  return mapped


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  import joblib

  with joblib.parallel_backend(ParallelBackendConfig.backend_name  ,      n_jobs=__SCREAMING_SNAKE_CASE   ):
    return joblib.Parallel()(
        joblib.delayed(__SCREAMING_SNAKE_CASE   )((function, obj, types, None, True, None)   ) for obj in iterable   )


@experimental
@contextlib.contextmanager
def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=backend_name

  if backend_name == "spark":
    from joblibspark import register_spark

    register_spark()

    # TODO: call create_cache_and_write_probe if "download" in steps
    # TODO: raise NotImplementedError when Dataset.map etc is called

  try:
    yield
  finally:
    _UpperCamelCase       			=None






