






"""simple docstring"""
import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


class _lowerCAmelCase						(    unittest.TestCase       ):







						def 			A      (    self							)		->							Dict:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







						@property
						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Any       		=    1
										_SCREAMING_SNAKE_CASE   :		str       		=    3
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    (3_2, 3_2)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    floats_tensor((batch_size, num_channels) + sizes   ,				rng=random.Random(0							)							).to(lowerCAmelCase_							)
										return image







						@property
						def 			A      (    self							)		->							Optional[int]:
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4)   ,				layers_per_block=2   ,				sample_size=3_2   ,				in_channels=4   ,				out_channels=4   ,				down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,				up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,				cross_attention_dim=3_2   ,				)
										return model







						@property
						def 			A      (    self							)		->							List[Any]:
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    AutoencoderKL(
										    block_out_channels=[3_2, 6_4]   ,				in_channels=3   ,				out_channels=3   ,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,				latent_channels=4   ,				)
										return model







						@property
						def 			A      (    self							)		->							Any:
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		str       		=    CLIPTextConfig(
										    bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=3_2   ,				intermediate_size=3_7   ,				layer_norm_eps=1e-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1_0_0_0   ,				)
										return CLIPTextModel(lowerCAmelCase_							)







						@property
						def 			A      (    self							)		->							Any:
										def extract(*lowerCAmelCase_   ,				**lowerCAmelCase_							):


														class _lowerCAmelCase						:







																				def __init__(    self							)		->							Any:
																								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.ones([0]							)







																				def 			A      (    self   ,				lowerCAmelCase_							)		->							int:
																								self.pixel_values.to(lowerCAmelCase_							)
																								return self







														return Out()

										return extract







						def 			A      (    self							)		->							Union[str, Any]:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    'cpu'  # ensure determinism for the device-dependent torch.Generator
										_SCREAMING_SNAKE_CASE   :		str       		=    self.dummy_cond_unet
										_SCREAMING_SNAKE_CASE   :		Dict       		=    DDIMScheduler(
										    beta_start=0.00_085   ,				beta_end=0.012   ,				beta_schedule='scaled_linear'   ,				clip_sample=lowerCAmelCase_   ,				set_alpha_to_one=lowerCAmelCase_   ,				)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.dummy_vae
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.dummy_text_encoder
										_SCREAMING_SNAKE_CASE   :		Any       		=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

										# make sure here that pndm scheduler skips prk
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    StableDiffusionPipeline(
										    unet=lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				vae=lowerCAmelCase_   ,				text_encoder=lowerCAmelCase_   ,				tokenizer=lowerCAmelCase_   ,				safety_checker=lowerCAmelCase_   ,				feature_extractor=self.dummy_extractor   ,				)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    sd_pipe.to(lowerCAmelCase_							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'A painting of a squirrel eating a burger'

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    torch.Generator(device=lowerCAmelCase_							).manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    sd_pipe([prompt]   ,				generator=lowerCAmelCase_   ,				guidance_scale=6.0   ,				num_inference_steps=2   ,				output_type='np'							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    output.images

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    torch.Generator(device=lowerCAmelCase_							).manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe(
										    [prompt]   ,				generator=lowerCAmelCase_   ,				guidance_scale=6.0   ,				num_inference_steps=2   ,				output_type='np'   ,				return_dict=lowerCAmelCase_   ,				)[0]

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    image[0, -3:, -3:, -1]
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_SCREAMING_SNAKE_CASE   :		str       		=    np.array([0.5_756, 0.6_118, 0.5_005, 0.5_041, 0.5_471, 0.4_726, 0.4_976, 0.4_865, 0.4_864]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2







						def 			A      (    self							)		->							Union[str, Any]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    'cpu'  # ensure determinism for the device-dependent torch.Generator
										_SCREAMING_SNAKE_CASE   :		int       		=    self.dummy_cond_unet
										_SCREAMING_SNAKE_CASE   :		str       		=    PNDMScheduler(skip_prk_steps=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    self.dummy_vae
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.dummy_text_encoder
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

										# make sure here that pndm scheduler skips prk
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    StableDiffusionPipeline(
										    unet=lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				vae=lowerCAmelCase_   ,				text_encoder=lowerCAmelCase_   ,				tokenizer=lowerCAmelCase_   ,				safety_checker=lowerCAmelCase_   ,				feature_extractor=self.dummy_extractor   ,				)
										_SCREAMING_SNAKE_CASE   :		str       		=    sd_pipe.to(lowerCAmelCase_							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    torch.Generator(device=lowerCAmelCase_							).manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		str       		=    sd_pipe([prompt]   ,				generator=lowerCAmelCase_   ,				guidance_scale=6.0   ,				num_inference_steps=2   ,				output_type='np'							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    output.images

										_SCREAMING_SNAKE_CASE   :		Any       		=    torch.Generator(device=lowerCAmelCase_							).manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe(
										    [prompt]   ,				generator=lowerCAmelCase_   ,				guidance_scale=6.0   ,				num_inference_steps=2   ,				output_type='np'   ,				return_dict=lowerCAmelCase_   ,				)[0]

										_SCREAMING_SNAKE_CASE   :		int       		=    image[0, -3:, -3:, -1]
										_SCREAMING_SNAKE_CASE   :		str       		=    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    np.array([0.5_125, 0.5_716, 0.4_828, 0.5_060, 0.5_650, 0.4_768, 0.5_185, 0.4_895, 0.4_993]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    StableDiffusionPipeline.from_pretrained(
										    'hf-internal-testing/tiny-stable-diffusion-lms-pipe'   ,				safety_checker=lowerCAmelCase_							)
										assert isinstance(lowerCAmelCase_   ,				lowerCAmelCase_							)
										assert isinstance(pipe.scheduler   ,				lowerCAmelCase_							)
										assert pipe.safety_checker is None

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    pipe('example prompt'   ,				num_inference_steps=2							).images[0]
										assert image is not None

										# check that there's no error when saving a pipeline with one of the models being None
										with tempfile.TemporaryDirectory() as tmpdirname:
														pipe.save_pretrained(lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    StableDiffusionPipeline.from_pretrained(lowerCAmelCase_							)

										# sanity check that the pipeline still works
										assert pipe.safety_checker is None
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    pipe('example prompt'   ,				num_inference_steps=2							).images[0]
										assert image is not None







						@unittest.skipIf(torch_device != 'cuda'   ,				'This test requires a GPU'							)
						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Any       		=    self.dummy_cond_unet
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    PNDMScheduler(skip_prk_steps=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		str       		=    self.dummy_vae
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.dummy_text_encoder
										_SCREAMING_SNAKE_CASE   :		Any       		=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

										# put models in fp16
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    unet.half()
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    vae.half()
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    bert.half()

										# make sure here that pndm scheduler skips prk
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    StableDiffusionPipeline(
										    unet=lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				vae=lowerCAmelCase_   ,				text_encoder=lowerCAmelCase_   ,				tokenizer=lowerCAmelCase_   ,				safety_checker=lowerCAmelCase_   ,				feature_extractor=self.dummy_extractor   ,				)
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.to(lowerCAmelCase_							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    sd_pipe([prompt]   ,				num_inference_steps=2   ,				output_type='np'							).images

										assert image.shape == (1, 6_4, 6_4, 3)







@nightly
@require_torch_gpu
class _lowerCAmelCase						(    unittest.TestCase       ):







						def 			A      (    self							)		->							List[Any]:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







						def 			A      (    self							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'   ,				safety_checker=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    sd_pipe.to(lowerCAmelCase_							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    (
										    'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
										    ' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
										    ' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
										    ' children from bahnhof zoo, detailed '
										)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    4_0_0_3_6_6_0_3_4_6
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    7

										# without safety guidance (sld_guidance_scale = 0)
										_SCREAMING_SNAKE_CASE   :		str       		=    torch.manual_seed(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    sd_pipe(
										    [prompt]   ,				generator=lowerCAmelCase_   ,				guidance_scale=lowerCAmelCase_   ,				num_inference_steps=5_0   ,				output_type='np'   ,				width=5_1_2   ,				height=5_1_2   ,				sld_guidance_scale=0   ,				)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    output.images
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    image[0, -3:, -3:, -1]
										_SCREAMING_SNAKE_CASE   :		str       		=    [0.2_278, 0.2_231, 0.2_249, 0.2_333, 0.2_303, 0.1_885, 0.2_273, 0.2_144, 0.2_176]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2

										# without safety guidance (strong configuration)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    torch.manual_seed(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    sd_pipe(
										    [prompt]   ,				generator=lowerCAmelCase_   ,				guidance_scale=lowerCAmelCase_   ,				num_inference_steps=5_0   ,				output_type='np'   ,				width=5_1_2   ,				height=5_1_2   ,				sld_guidance_scale=2_0_0_0   ,				sld_warmup_steps=7   ,				sld_threshold=0.025   ,				sld_momentum_scale=0.5   ,				sld_mom_beta=0.7   ,				)

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    output.images
										_SCREAMING_SNAKE_CASE   :		Any       		=    image[0, -3:, -3:, -1]
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    [0.2_383, 0.2_276, 0.236, 0.2_192, 0.2_186, 0.2_053, 0.1_971, 0.1_901, 0.1_719]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'   ,				safety_checker=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    sd_pipe.to(lowerCAmelCase_							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		int       		=    'padme amidala taking a bath artwork, safe for work, no nudity'
										_SCREAMING_SNAKE_CASE   :		str       		=    2_7_3_4_9_7_1_7_5_5
										_SCREAMING_SNAKE_CASE   :		Dict       		=    7

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    torch.manual_seed(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    sd_pipe(
										    [prompt]   ,				generator=lowerCAmelCase_   ,				guidance_scale=lowerCAmelCase_   ,				num_inference_steps=5_0   ,				output_type='np'   ,				width=5_1_2   ,				height=5_1_2   ,				sld_guidance_scale=0   ,				)

										_SCREAMING_SNAKE_CASE   :		str       		=    output.images
										_SCREAMING_SNAKE_CASE   :		int       		=    image[0, -3:, -3:, -1]
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    [0.3_502, 0.3_622, 0.3_396, 0.3_642, 0.3_478, 0.3_318, 0.35, 0.3_348, 0.3_297]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    torch.manual_seed(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe(
										    [prompt]   ,				generator=lowerCAmelCase_   ,				guidance_scale=lowerCAmelCase_   ,				num_inference_steps=5_0   ,				output_type='np'   ,				width=5_1_2   ,				height=5_1_2   ,				sld_guidance_scale=2_0_0_0   ,				sld_warmup_steps=7   ,				sld_threshold=0.025   ,				sld_momentum_scale=0.5   ,				sld_mom_beta=0.7   ,				)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    output.images
										_SCREAMING_SNAKE_CASE   :		Any       		=    image[0, -3:, -3:, -1]
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    [0.5_531, 0.5_206, 0.4_895, 0.5_156, 0.5_182, 0.4_751, 0.4_802, 0.4_803, 0.4_443]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2







						def 			A      (    self							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    sd_pipe.to(lowerCAmelCase_							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    (
										    'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
										    ' leyendecker'
										)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    1_0_4_4_3_5_5_2_3_4
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    1_2

										_SCREAMING_SNAKE_CASE   :		int       		=    torch.manual_seed(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    sd_pipe(
										    [prompt]   ,				generator=lowerCAmelCase_   ,				guidance_scale=lowerCAmelCase_   ,				num_inference_steps=5_0   ,				output_type='np'   ,				width=5_1_2   ,				height=5_1_2   ,				sld_guidance_scale=0   ,				)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    output.images
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    image[0, -3:, -3:, -1]
										_SCREAMING_SNAKE_CASE   :		str       		=    np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]							)

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-7

										_SCREAMING_SNAKE_CASE   :		Any       		=    torch.manual_seed(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    sd_pipe(
										    [prompt]   ,				generator=lowerCAmelCase_   ,				guidance_scale=lowerCAmelCase_   ,				num_inference_steps=5_0   ,				output_type='np'   ,				width=5_1_2   ,				height=5_1_2   ,				sld_guidance_scale=2_0_0_0   ,				sld_warmup_steps=7   ,				sld_threshold=0.025   ,				sld_momentum_scale=0.5   ,				sld_mom_beta=0.7   ,				)

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    output.images
										_SCREAMING_SNAKE_CASE   :		int       		=    image[0, -3:, -3:, -1]
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    np.array([0.5_818, 0.6_285, 0.6_835, 0.6_019, 0.625, 0.6_754, 0.6_096, 0.6_334, 0.6_561]							)
										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2









"""simple docstring"""
import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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 _lowerCAmelCase						(    unittest.TestCase       ):







						def 			A      (    self							)		->							Any:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    'stabilityai/stable-diffusion-2'   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    sd_pipe(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				num_inference_steps=2_5   ,				jit=lowerCAmelCase_							)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										_SCREAMING_SNAKE_CASE   :		int       		=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]							)
										print(F"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		str       		=    'stabilityai/stable-diffusion-2'
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		int       		=    FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCAmelCase_   ,				subfolder='scheduler'							)
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[str]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)
										_SCREAMING_SNAKE_CASE   :		Any       		=    scheduler_params

										_SCREAMING_SNAKE_CASE   :		int       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		int       		=    sd_pipe(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				num_inference_steps=2_5   ,				jit=lowerCAmelCase_							)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										_SCREAMING_SNAKE_CASE   :		str       		=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										_SCREAMING_SNAKE_CASE   :		str       		=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										_SCREAMING_SNAKE_CASE   :		int       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]							)
										print(F"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2

