






"""simple docstring"""





import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
							import jax
							import jax.numpy as jnp
							from flax.jax_utils import replicate
							from flax.training.common_utils import shard

							from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline



@require_flax
class       __A		(   unittest.TestCase			):

					def   lowerCamelCase__   (			self  :      List[str]					)			->							Dict:
											with tempfile.TemporaryDirectory() as tmpdirname:
																	# pipeline has Flax weights
																	__magic_name__:	List[Any]							=       FlaxDiffusionPipeline.from_pretrained(
																	    """hf-internal-testing/tiny-stable-diffusion-pipe"""     ,		safety_checker=__snake_case     ,		cache_dir=__snake_case					)

																	__magic_name__:	List[str]							=       [t[-1] for t in os.walk(os.path.join(__snake_case     ,		os.listdir(__snake_case					)[0]     ,		"""snapshots"""					)					)]
																	__magic_name__:	Tuple							=       [item for sublist in all_root_files for item in sublist]

																	# None of the downloaded files should be a PyTorch file even if we have some here:
																	# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
																	assert not any(f.endswith(""".bin"""					) for f in files					)



@slow
@require_flax
class       __A		(   unittest.TestCase			):

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Tuple:
											__magic_name__,			__magic_name__:	Optional[Any]							=       FlaxStableDiffusionPipeline.from_pretrained(
											    """hf-internal-testing/tiny-stable-diffusion-pipe"""     ,		safety_checker=__snake_case					)

											__magic_name__:	List[str]							=       (
											    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
											    """ field, close up, split lighting, cinematic"""
											)

											__magic_name__:	str							=       jax.random.PRNGKey(0					)
											__magic_name__:	Optional[int]							=       4

											__magic_name__:	List[str]							=       jax.device_count()
											__magic_name__:	Dict							=       num_samples * [prompt]
											__magic_name__:	List[str]							=       pipeline.prepare_inputs(__snake_case					)

											# shard inputs and rng
											__magic_name__:	Tuple							=       replicate(__snake_case					)
											__magic_name__:	List[str]							=       jax.random.split(__snake_case     ,		__snake_case					)
											__magic_name__:	List[Any]							=       shard(__snake_case					)

											__magic_name__:	Dict							=       pipeline(__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		jit=__snake_case					).images

											assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
											if jax.device_count() == 8:
																	assert np.abs(np.abs(images[0, 0, :2, :2, -2:]     ,		dtype=np.floataa					).sum() - 4.1514745					) < 1E-3
																	assert np.abs(np.abs(__snake_case     ,		dtype=np.floataa					).sum() - 49947.875					) < 5E-1

											__magic_name__:	str							=       pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]					)					)					)
											assert len(__snake_case					) == num_samples

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							Any:
											__magic_name__,			__magic_name__:	Optional[int]							=       FlaxStableDiffusionPipeline.from_pretrained(
											    """CompVis/stable-diffusion-v1-4"""     ,		revision="""flax"""     ,		safety_checker=__snake_case					)

											__magic_name__:	List[Any]							=       (
											    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
											    """ field, close up, split lighting, cinematic"""
											)

											__magic_name__:	Dict							=       jax.random.PRNGKey(0					)
											__magic_name__:	int							=       5_0

											__magic_name__:	int							=       jax.device_count()
											__magic_name__:	str							=       num_samples * [prompt]
											__magic_name__:	str							=       pipeline.prepare_inputs(__snake_case					)

											# shard inputs and rng
											__magic_name__:	List[Any]							=       replicate(__snake_case					)
											__magic_name__:	int							=       jax.random.split(__snake_case     ,		__snake_case					)
											__magic_name__:	Tuple							=       shard(__snake_case					)

											__magic_name__:	Tuple							=       pipeline(__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		jit=__snake_case					).images

											assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
											if jax.device_count() == 8:
																	assert np.abs((np.abs(images[0, 0, :2, :2, -2:]     ,		dtype=np.floataa					).sum() - 0.05652401)					) < 1E-3
																	assert np.abs((np.abs(__snake_case     ,		dtype=np.floataa					).sum() - 2383808.2)					) < 5E-1

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Any:
											__magic_name__,			__magic_name__:	Optional[int]							=       FlaxStableDiffusionPipeline.from_pretrained(
											    """CompVis/stable-diffusion-v1-4"""     ,		revision="""bf16"""     ,		dtype=jnp.bfloataa     ,		safety_checker=__snake_case					)

											__magic_name__:	str							=       (
											    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
											    """ field, close up, split lighting, cinematic"""
											)

											__magic_name__:	List[str]							=       jax.random.PRNGKey(0					)
											__magic_name__:	Optional[int]							=       5_0

											__magic_name__:	Tuple							=       jax.device_count()
											__magic_name__:	Dict							=       num_samples * [prompt]
											__magic_name__:	Dict							=       pipeline.prepare_inputs(__snake_case					)

											# shard inputs and rng
											__magic_name__:	List[str]							=       replicate(__snake_case					)
											__magic_name__:	Tuple							=       jax.random.split(__snake_case     ,		__snake_case					)
											__magic_name__:	Optional[Any]							=       shard(__snake_case					)

											__magic_name__:	List[Any]							=       pipeline(__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		jit=__snake_case					).images

											assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
											if jax.device_count() == 8:
																	assert np.abs((np.abs(images[0, 0, :2, :2, -2:]     ,		dtype=np.floataa					).sum() - 0.04003906)					) < 1E-3
																	assert np.abs((np.abs(__snake_case     ,		dtype=np.floataa					).sum() - 2373516.75)					) < 5E-1

					def   lowerCamelCase__   (			self  :      Any					)			->							Union[str, Any]:
											__magic_name__,			__magic_name__:	Optional[int]							=       FlaxStableDiffusionPipeline.from_pretrained(
											    """CompVis/stable-diffusion-v1-4"""     ,		revision="""bf16"""     ,		dtype=jnp.bfloataa					)

											__magic_name__:	str							=       (
											    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
											    """ field, close up, split lighting, cinematic"""
											)

											__magic_name__:	Dict							=       jax.random.PRNGKey(0					)
											__magic_name__:	Union[str, Any]							=       5_0

											__magic_name__:	Optional[Any]							=       jax.device_count()
											__magic_name__:	int							=       num_samples * [prompt]
											__magic_name__:	Union[str, Any]							=       pipeline.prepare_inputs(__snake_case					)

											# shard inputs and rng
											__magic_name__:	Optional[Any]							=       replicate(__snake_case					)
											__magic_name__:	Any							=       jax.random.split(__snake_case     ,		__snake_case					)
											__magic_name__:	Optional[int]							=       shard(__snake_case					)

											__magic_name__:	Optional[int]							=       pipeline(__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		jit=__snake_case					).images

											assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
											if jax.device_count() == 8:
																	assert np.abs((np.abs(images[0, 0, :2, :2, -2:]     ,		dtype=np.floataa					).sum() - 0.04003906)					) < 1E-3
																	assert np.abs((np.abs(__snake_case     ,		dtype=np.floataa					).sum() - 2373516.75)					) < 5E-1

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Dict:
											__magic_name__:	Union[str, Any]							=       FlaxDDIMScheduler(
											    beta_start=0.00085     ,		beta_end=0.012     ,		beta_schedule="""scaled_linear"""     ,		set_alpha_to_one=__snake_case     ,		steps_offset=1     ,		)

											__magic_name__,			__magic_name__:	str							=       FlaxStableDiffusionPipeline.from_pretrained(
											    """CompVis/stable-diffusion-v1-4"""     ,		revision="""bf16"""     ,		dtype=jnp.bfloataa     ,		scheduler=__snake_case     ,		safety_checker=__snake_case     ,		)
											__magic_name__:	Dict							=       scheduler.create_state()

											__magic_name__:	List[str]							=       scheduler_state

											__magic_name__:	Optional[Any]							=       (
											    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
											    """ field, close up, split lighting, cinematic"""
											)

											__magic_name__:	Optional[Any]							=       jax.random.PRNGKey(0					)
											__magic_name__:	List[Any]							=       5_0

											__magic_name__:	Tuple							=       jax.device_count()
											__magic_name__:	Union[str, Any]							=       num_samples * [prompt]
											__magic_name__:	Any							=       pipeline.prepare_inputs(__snake_case					)

											# shard inputs and rng
											__magic_name__:	List[str]							=       replicate(__snake_case					)
											__magic_name__:	List[str]							=       jax.random.split(__snake_case     ,		__snake_case					)
											__magic_name__:	Dict							=       shard(__snake_case					)

											__magic_name__:	Optional[Any]							=       pipeline(__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		jit=__snake_case					).images

											assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
											if jax.device_count() == 8:
																	assert np.abs((np.abs(images[0, 0, :2, :2, -2:]     ,		dtype=np.floataa					).sum() - 0.045043945)					) < 1E-3
																	assert np.abs((np.abs(__snake_case     ,		dtype=np.floataa					).sum() - 2347693.5)					) < 5E-1






					def   lowerCamelCase__   (			self  :      Tuple					)			->							List[str]:
											__magic_name__:	List[Any]							=       (
											    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
											    """ field, close up, split lighting, cinematic"""
											)

											__magic_name__:	Union[str, Any]							=       jax.device_count()
											__magic_name__:	List[str]							=       num_samples * [prompt]
											__magic_name__:	List[str]							=       jax.random.split(jax.random.PRNGKey(0					)     ,		__snake_case					)

											__magic_name__,			__magic_name__:	int							=       FlaxStableDiffusionPipeline.from_pretrained(
											    """CompVis/stable-diffusion-v1-4"""     ,		revision="""bf16"""     ,		dtype=jnp.bfloataa     ,		safety_checker=__snake_case     ,		)

											__magic_name__:	str							=       replicate(__snake_case					)
											__magic_name__:	List[Any]							=       pipeline.prepare_inputs(__snake_case					)
											__magic_name__:	Optional[Any]							=       shard(__snake_case					)
											__magic_name__:	Any							=       pipeline(__snake_case     ,		__snake_case     ,		__snake_case     ,		jit=__snake_case					).images
											assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
											__magic_name__:	Optional[int]							=       images[2, 0, 2_5_6, 1_0:1_7, 1]

											# With memory efficient attention
											__magic_name__,			__magic_name__:	Optional[int]							=       FlaxStableDiffusionPipeline.from_pretrained(
											    """CompVis/stable-diffusion-v1-4"""     ,		revision="""bf16"""     ,		dtype=jnp.bfloataa     ,		safety_checker=__snake_case     ,		use_memory_efficient_attention=__snake_case     ,		)

											__magic_name__:	Union[str, Any]							=       replicate(__snake_case					)
											__magic_name__:	List[str]							=       pipeline.prepare_inputs(__snake_case					)
											__magic_name__:	Dict							=       shard(__snake_case					)
											__magic_name__:	List[Any]							=       pipeline(__snake_case     ,		__snake_case     ,		__snake_case     ,		jit=__snake_case					).images
											assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
											__magic_name__:	Tuple							=       images[2, 0, 2_5_6, 1_0:1_7, 1]

											# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
											# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
											assert abs(slice_eff - slice					).max() < 1E-2










import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)





