






def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  int:





						"""simple docstring"""



						while a != 0:
												_UpperCAmelCase					, _UpperCAmelCase									=			b % a, a
						return b


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  int:





						"""simple docstring"""



						if gcd(__lowerCAmelCase ,		__lowerCAmelCase	) != 1:
												_UpperCAmelCase									=			F"""mod inverse of {a!r} and {m!r} does not exist"""
												raise ValueError(__lowerCAmelCase	)
						_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			1, 0, a
						_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			0, 1, m
						while va != 0:
												_UpperCAmelCase									=			ua // va
												_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
						return ua % m








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 __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							with tempfile.TemporaryDirectory() as tmpdirname:
													# pipeline has Flax weights
													_UpperCAmelCase									=			FlaxDiffusionPipeline.from_pretrained(
													    'hf-internal-testing/tiny-stable-diffusion-pipe'       ,    safety_checker=UpperCAmelCase       ,    cache_dir=UpperCAmelCase    )

													_UpperCAmelCase									=			[t[-1] for t in os.walk(os.path.join(UpperCAmelCase       ,    os.listdir(UpperCAmelCase    )[0]       ,    'snapshots'    )    )]
													_UpperCAmelCase									=			[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 __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			FlaxStableDiffusionPipeline.from_pretrained(
							    'hf-internal-testing/tiny-stable-diffusion-pipe'       ,    safety_checker=UpperCAmelCase    )

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

							_UpperCAmelCase									=			jax.random.PRNGKey(0    )
							_UpperCAmelCase									=			4

							_UpperCAmelCase									=			jax.device_count()
							_UpperCAmelCase									=			num_samples * [prompt]
							_UpperCAmelCase									=			pipeline.prepare_inputs(UpperCAmelCase    )

							# shard inputs and rng
							_UpperCAmelCase									=			replicate(UpperCAmelCase    )
							_UpperCAmelCase									=			jax.random.split(UpperCAmelCase       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			shard(UpperCAmelCase    )

							_UpperCAmelCase									=			pipeline(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    jit=UpperCAmelCase    ).images

							assert images.shape == (num_samples, 1, 64, 64, 3)
							if jax.device_count() == 8:
													assert np.abs(np.abs(images[0, 0, :2, :2, -2:]       ,    dtype=np.floataa    ).sum() - 4.1_51_47_45    ) < 1e-3
													assert np.abs(np.abs(UpperCAmelCase       ,    dtype=np.floataa    ).sum() - 4_99_47.8_75    ) < 5e-1

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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			FlaxStableDiffusionPipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'       ,    revision='flax'       ,    safety_checker=UpperCAmelCase    )

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

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

							_UpperCAmelCase									=			jax.device_count()
							_UpperCAmelCase									=			num_samples * [prompt]
							_UpperCAmelCase									=			pipeline.prepare_inputs(UpperCAmelCase    )

							# shard inputs and rng
							_UpperCAmelCase									=			replicate(UpperCAmelCase    )
							_UpperCAmelCase									=			jax.random.split(UpperCAmelCase       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			shard(UpperCAmelCase    )

							_UpperCAmelCase									=			pipeline(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    jit=UpperCAmelCase    ).images

							assert images.shape == (num_samples, 1, 512, 512, 3)
							if jax.device_count() == 8:
													assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,    dtype=np.floataa    ).sum() - 0.05_65_24_01)    ) < 1e-3
													assert np.abs((np.abs(UpperCAmelCase       ,    dtype=np.floataa    ).sum() - 2_38_38_08.2)    ) < 5e-1

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			FlaxStableDiffusionPipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'       ,    revision='bf16'       ,    dtype=jnp.bfloataa       ,    safety_checker=UpperCAmelCase    )

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

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

							_UpperCAmelCase									=			jax.device_count()
							_UpperCAmelCase									=			num_samples * [prompt]
							_UpperCAmelCase									=			pipeline.prepare_inputs(UpperCAmelCase    )

							# shard inputs and rng
							_UpperCAmelCase									=			replicate(UpperCAmelCase    )
							_UpperCAmelCase									=			jax.random.split(UpperCAmelCase       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			shard(UpperCAmelCase    )

							_UpperCAmelCase									=			pipeline(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    jit=UpperCAmelCase    ).images

							assert images.shape == (num_samples, 1, 512, 512, 3)
							if jax.device_count() == 8:
													assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,    dtype=np.floataa    ).sum() - 0.04_00_39_06)    ) < 1e-3
													assert np.abs((np.abs(UpperCAmelCase       ,    dtype=np.floataa    ).sum() - 2_37_35_16.75)    ) < 5e-1

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			FlaxStableDiffusionPipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'       ,    revision='bf16'       ,    dtype=jnp.bfloataa    )

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

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

							_UpperCAmelCase									=			jax.device_count()
							_UpperCAmelCase									=			num_samples * [prompt]
							_UpperCAmelCase									=			pipeline.prepare_inputs(UpperCAmelCase    )

							# shard inputs and rng
							_UpperCAmelCase									=			replicate(UpperCAmelCase    )
							_UpperCAmelCase									=			jax.random.split(UpperCAmelCase       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			shard(UpperCAmelCase    )

							_UpperCAmelCase									=			pipeline(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    jit=UpperCAmelCase    ).images

							assert images.shape == (num_samples, 1, 512, 512, 3)
							if jax.device_count() == 8:
													assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,    dtype=np.floataa    ).sum() - 0.04_00_39_06)    ) < 1e-3
													assert np.abs((np.abs(UpperCAmelCase       ,    dtype=np.floataa    ).sum() - 2_37_35_16.75)    ) < 5e-1

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			FlaxDDIMScheduler(
							    beta_start=0.0_00_85       ,    beta_end=0.0_12       ,    beta_schedule='scaled_linear'       ,    set_alpha_to_one=UpperCAmelCase       ,    steps_offset=1       ,    )

							_UpperCAmelCase					, _UpperCAmelCase									=			FlaxStableDiffusionPipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'       ,    revision='bf16'       ,    dtype=jnp.bfloataa       ,    scheduler=UpperCAmelCase       ,    safety_checker=UpperCAmelCase       ,    )
							_UpperCAmelCase									=			scheduler.create_state()

							_UpperCAmelCase									=			scheduler_state

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

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

							_UpperCAmelCase									=			jax.device_count()
							_UpperCAmelCase									=			num_samples * [prompt]
							_UpperCAmelCase									=			pipeline.prepare_inputs(UpperCAmelCase    )

							# shard inputs and rng
							_UpperCAmelCase									=			replicate(UpperCAmelCase    )
							_UpperCAmelCase									=			jax.random.split(UpperCAmelCase       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			shard(UpperCAmelCase    )

							_UpperCAmelCase									=			pipeline(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    jit=UpperCAmelCase    ).images

							assert images.shape == (num_samples, 1, 512, 512, 3)
							if jax.device_count() == 8:
													assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,    dtype=np.floataa    ).sum() - 0.0_45_04_39_45)    ) < 1e-3
													assert np.abs((np.abs(UpperCAmelCase       ,    dtype=np.floataa    ).sum() - 2_34_76_93.5)    ) < 5e-1

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

							_UpperCAmelCase									=			jax.device_count()
							_UpperCAmelCase									=			num_samples * [prompt]
							_UpperCAmelCase									=			jax.random.split(jax.random.PRNGKey(0    )       ,    UpperCAmelCase    )

							_UpperCAmelCase					, _UpperCAmelCase									=			FlaxStableDiffusionPipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'       ,    revision='bf16'       ,    dtype=jnp.bfloataa       ,    safety_checker=UpperCAmelCase       ,    )

							_UpperCAmelCase									=			replicate(UpperCAmelCase    )
							_UpperCAmelCase									=			pipeline.prepare_inputs(UpperCAmelCase    )
							_UpperCAmelCase									=			shard(UpperCAmelCase    )
							_UpperCAmelCase									=			pipeline(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    jit=UpperCAmelCase    ).images
							assert images.shape == (num_samples, 1, 512, 512, 3)
							_UpperCAmelCase									=			images[2, 0, 256, 10:17, 1]

							# With memory efficient attention
							_UpperCAmelCase					, _UpperCAmelCase									=			FlaxStableDiffusionPipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'       ,    revision='bf16'       ,    dtype=jnp.bfloataa       ,    safety_checker=UpperCAmelCase       ,    use_memory_efficient_attention=UpperCAmelCase       ,    )

							_UpperCAmelCase									=			replicate(UpperCAmelCase    )
							_UpperCAmelCase									=			pipeline.prepare_inputs(UpperCAmelCase    )
							_UpperCAmelCase									=			shard(UpperCAmelCase    )
							_UpperCAmelCase									=			pipeline(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    jit=UpperCAmelCase    ).images
							assert images_eff.shape == (num_samples, 1, 512, 512, 3)
							_UpperCAmelCase									=			images[2, 0, 256, 10:17, 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
