



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):







							def    snake_case__      (   self):

														'''simple docstring'''

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/sd2-inpaint/init_image.png")
														_lowerCAmelCase     :							Optional[int]    =    load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")

														_lowerCAmelCase     :							Any    =    "xvjiarui/stable-diffusion-2-inpainting"
														_lowerCAmelCase   ,    _lowerCAmelCase     :							str    =    FlaxStableDiffusionInpaintPipeline.from_pretrained(__a,			safety_checker=__a)

														_lowerCAmelCase     :							List[Any]    =    "Face of a yellow cat, high resolution, sitting on a park bench"

														_lowerCAmelCase     :							List[Any]    =    jax.random.PRNGKey(0)
														_lowerCAmelCase     :							int    =    50

														_lowerCAmelCase     :							Optional[Any]    =    jax.device_count()
														_lowerCAmelCase     :							Dict    =    num_samples * [prompt]
														_lowerCAmelCase     :							Dict    =    num_samples * [init_image]
														_lowerCAmelCase     :							Any    =    num_samples * [mask_image]
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    pipeline.prepare_inputs(__a,			__a,			__a)

														# shard inputs and rng
														_lowerCAmelCase     :							Any    =    replicate(__a)
														_lowerCAmelCase     :							List[Any]    =    jax.random.split(__a,			jax.device_count())
														_lowerCAmelCase     :							Optional[int]    =    shard(__a)
														_lowerCAmelCase     :							List[str]    =    shard(__a)
														_lowerCAmelCase     :							Optional[int]    =    shard(__a)

														_lowerCAmelCase     :							int    =    pipeline(
														    __a,			__a,			__a,			__a,			__a,			__a,			jit=__a)

														_lowerCAmelCase     :							Dict    =    output.images.reshape(__a,			512,			512,			3)

														_lowerCAmelCase     :							Optional[Any]    =    images[0, 253:256, 253:256, -1]

														_lowerCAmelCase     :							List[str]    =    jnp.asarray(jax.device_get(image_slice.flatten()))
														_lowerCAmelCase     :							Dict    =    jnp.array(
														    [0.3_611_307, 0.37_649_736, 0.3_757_408, 0.38_213_953, 0.39_295_167, 0.3_841_631, 0.41_554_978, 0.4_137_475, 0.4_217_084])
														print(f"output_slice: {output_slice}")
														assert jnp.abs(output_slice - expected_slice).max() < 1E-2





import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs






@pytest.mark.parametrize(
    "kwargs, expected"				, [
        ({"num_shards": 0, "max_num_jobs": 1}, []),
        ({"num_shards": 10, "max_num_jobs": 1}, [range(10  )]),
        ({"num_shards": 10, "max_num_jobs": 10}, [range(_lowerCamelCase				, i + 1  ) for i in range(10  )]),
        ({"num_shards": 1, "max_num_jobs": 10}, [range(1  )]),
        ({"num_shards": 10, "max_num_jobs": 3}, [range(0				, 4  ), range(4				, 7  ), range(7				, 10  )]),
        ({"num_shards": 3, "max_num_jobs": 10}, [range(0				, 1  ), range(1				, 2  ), range(2				, 3  )]),
    ]				, )
def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[Any]    =    _distribute_shards(**_lowerCamelCase  )
							assert out == expected






@pytest.mark.parametrize(
    "gen_kwargs, max_num_jobs, expected"				, [
        ({"foo": 0}, 10, [{"foo": 0}]),
        ({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
        ({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
        ({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
        ({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
    ]				, )
def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[int]    =    _split_gen_kwargs(_lowerCamelCase				, _lowerCamelCase  )
							assert out == expected






@pytest.mark.parametrize(
    "gen_kwargs, expected"				, [
        ({"foo": 0}, 1),
        ({"shards": [0]}, 1),
        ({"shards": [0, 1, 2, 3]}, 4),
        ({"shards": [0, 1, 2, 3], "foo": 0}, 4),
        ({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
        ({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
    ]				, )
def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if expected is RuntimeError:
														with pytest.raises(_lowerCamelCase  ):
																					_number_of_shards_in_gen_kwargs(_lowerCamelCase  )
							else:
														_lowerCAmelCase     :							Optional[int]    =    _number_of_shards_in_gen_kwargs(_lowerCamelCase  )
														assert out == expected
