code: from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  from jax import random
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
  
  
  
  
  @flax.struct.dataclass
  class 					UpperCamelCase	:
  					# setable values
  					__UpperCamelCase						=   None
  					__UpperCamelCase						=   None
  					__UpperCamelCase						=   None
  
  
  
  
  					@classmethod
  					def 							UpperCamelCase_						( cls :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return cls()
  
  
  
  
  @dataclass
  class 					UpperCamelCase	(     __UpperCAmelCase      ):
  					__UpperCamelCase						=   42
  					__UpperCamelCase						=   42
  					__UpperCamelCase						=   None
  
  
  
  
  class 					UpperCamelCase	(     __UpperCAmelCase			,      __UpperCAmelCase      ):
  
  
  
  
  					@property
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return True
  
  
  
  
  					@register_to_config
  					def __init__( self :					Optional[int]      ,_lowerCAmelCase :					Tuple = 2_000      ,_lowerCAmelCase :					Optional[int] = 0.1_5      ,_lowerCAmelCase :					List[str] = 0.0_1      ,_lowerCAmelCase :					Optional[Any] = 1_348.0      ,_lowerCAmelCase :					Dict = 1E-5      ,_lowerCAmelCase :					str = 1      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							pass
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     ScoreSdeVeSchedulerState.create()
  							return self.set_sigmas(
  							    lowerCamelCase__      ,self.config.num_train_timesteps      ,self.config.sigma_min      ,self.config.sigma_max      ,self.config.sampling_eps      ,)
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[str]      ,_lowerCAmelCase :					Any      ,_lowerCAmelCase :					List[str]      ,_lowerCAmelCase :					int = ()      ,_lowerCAmelCase :					Union[str, Any] = None						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
  
  							__snake_case		       =     jnp.linspace(1      ,lowerCamelCase__      ,lowerCamelCase__						)
  							return state.replace(timesteps=lowerCamelCase__						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[str]      ,_lowerCAmelCase :					Optional[Any]      ,_lowerCAmelCase :					Optional[int]      ,_lowerCAmelCase :					int = None      ,_lowerCAmelCase :					str = None      ,_lowerCAmelCase :					str = None      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     sigma_min if sigma_min is not None else self.config.sigma_min
  							__snake_case		       =     sigma_max if sigma_max is not None else self.config.sigma_max
  							__snake_case		       =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
  							if state.timesteps is None:
  									__snake_case		       =     self.set_timesteps(lowerCamelCase__      ,lowerCamelCase__      ,lowerCamelCase__						)
  
  							__snake_case		       =     jnp.exp(jnp.linspace(jnp.log(lowerCamelCase__						)      ,jnp.log(lowerCamelCase__						)      ,lowerCamelCase__						)						)
  							__snake_case		       =     jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]						)
  
  							return state.replace(discrete_sigmas=lowerCamelCase__      ,sigmas=lowerCamelCase__						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any      ,_lowerCAmelCase :					Dict      ,_lowerCAmelCase :					List[str]      ,_lowerCAmelCase :					List[str]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return jnp.where(timesteps == 0      ,jnp.zeros_like(lowerCamelCase__						)      ,state.discrete_sigmas[timesteps - 1]						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[Any]      ,_lowerCAmelCase :					Tuple      ,_lowerCAmelCase :					Dict      ,_lowerCAmelCase :					Optional[Any]      ,_lowerCAmelCase :					Any      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					Union[str, Any] = True      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							if state.timesteps is None:
  									raise ValueError(
  									    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"						)
  
  							__snake_case		       =     timestep * jnp.ones(
  							    sample.shape[0]      ,)
  							__snake_case		       =     (timestep * (len(state.timesteps						) - 1)).long()
  
  							__snake_case		       =     state.discrete_sigmas[timesteps]
  							__snake_case		       =     self.get_adjacent_sigma(lowerCamelCase__      ,lowerCamelCase__      ,lowerCamelCase__						)
  							__snake_case		       =     jnp.zeros_like(lowerCamelCase__						)
  							__snake_case		       =     (sigma**2 - adjacent_sigma**2) ** 0.5
  
  							# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
  							# also equation 47 shows the analog from SDE models to ancestral sampling methods
  							__snake_case		       =     diffusion.flatten()
  							__snake_case		       =     broadcast_to_shape_from_left(lowerCamelCase__      ,sample.shape						)
  							__snake_case		       =     drift - diffusion**2 * model_output
  
  							#  equation 6: sample noise for the diffusion term of
  							__snake_case		       =     random.split(lowerCamelCase__      ,num=1						)
  							__snake_case		       =     random.normal(key=lowerCamelCase__      ,shape=sample.shape						)
  							__snake_case		       =     sample - drift  # subtract because `dt` is a small negative timestep
  							# TODO is the variable diffusion the correct scaling term for the noise?
  							__snake_case		       =     prev_sample_mean + diffusion * noise  # add impact of diffusion field g
  
  							if not return_dict:
  									return (prev_sample, prev_sample_mean, state)
  
  							return FlaxSdeVeOutput(prev_sample=lowerCamelCase__      ,prev_sample_mean=lowerCamelCase__      ,state=lowerCamelCase__						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					Tuple      ,_lowerCAmelCase :					Any      ,_lowerCAmelCase :					Any      ,_lowerCAmelCase :					Optional[Any]      ,_lowerCAmelCase :					Any = True      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							if state.timesteps is None:
  									raise ValueError(
  									    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"						)
  
  							# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
  							# sample noise for correction
  							__snake_case		       =     random.split(lowerCamelCase__      ,num=1						)
  							__snake_case		       =     random.normal(key=lowerCamelCase__      ,shape=sample.shape						)
  
  							# compute step size from the model_output, the noise, and the snr
  							__snake_case		       =     jnp.linalg.norm(lowerCamelCase__						)
  							__snake_case		       =     jnp.linalg.norm(lowerCamelCase__						)
  							__snake_case		       =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
  							__snake_case		       =     step_size * jnp.ones(sample.shape[0]						)
  
  							# compute corrected sample: model_output term and noise term
  							__snake_case		       =     step_size.flatten()
  							__snake_case		       =     broadcast_to_shape_from_left(lowerCamelCase__      ,sample.shape						)
  							__snake_case		       =     sample + step_size * model_output
  							__snake_case		       =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise
  
  							if not return_dict:
  									return (prev_sample, state)
  
  							return FlaxSdeVeOutput(prev_sample=lowerCamelCase__      ,state=lowerCamelCase__						)
  
  
  
  
  
  
  
  					def __len__( self :					Dict						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return self.config.num_train_timesteps
  
  
  
  
  
  
style_context: import gc
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPVisionConfig
  
  from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
  from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
  from diffusers.utils import floats_tensor, load_image, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  class 					UpperCamelCase	(     snake_case__			,      unittest.TestCase      ):
  					__UpperCamelCase						=   PaintByExamplePipeline
  					__UpperCamelCase						=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  					__UpperCamelCase						=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  					__UpperCamelCase						=   frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
  
  
  
  
  					def 							UpperCamelCase_						( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							torch.manual_seed(0						)
  							__snake_case		       =     UNetaDConditionModel(
  							    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,out_channels=4      ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,cross_attention_dim=32      ,)
  							__snake_case		       =     PNDMScheduler(skip_prk_steps=_lowerCAmelCase						)
  							torch.manual_seed(0						)
  							__snake_case		       =     AutoencoderKL(
  							    block_out_channels=[32, 64]      ,in_channels=3      ,out_channels=3      ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,latent_channels=4      ,)
  							torch.manual_seed(0						)
  							__snake_case		       =     CLIPVisionConfig(
  							    hidden_size=32      ,projection_dim=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,image_size=32      ,patch_size=4      ,)
  							__snake_case		       =     PaintByExampleImageEncoder(_lowerCAmelCase      ,proj_size=32						)
  							__snake_case		       =     CLIPImageProcessor(crop_size=32      ,size=32						)
  
  							__snake_case		       =     {
  							    "unet": unet,
  							    "scheduler": scheduler,
  							    "vae": vae,
  							    "image_encoder": image_encoder,
  							    "safety_checker": None,
  							    "feature_extractor": feature_extractor,
  							}
  							return components
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     np.array(image.convert("RGB"						)						)
  							__snake_case		       =     image[None].transpose(0      ,3      ,1      ,2						)
  							__snake_case		       =     torch.from_numpy(_lowerCAmelCase						).to(dtype=torch.floataa						) / 1_2_7.5 - 1.0
  							return image
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Any="cpu"      ,_lowerCAmelCase :					List[str]=0						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_lowerCAmelCase						)						).to(_lowerCAmelCase						)
  							__snake_case		       =     image.cpu().permute(0      ,2      ,3      ,1						)[0]
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						).resize((64, 64)						)
  							__snake_case		       =     Image.fromarray(np.uinta(image + 4						)						).convert("RGB"						).resize((64, 64)						)
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						).resize((32, 32)						)
  
  							if str(_lowerCAmelCase						).startswith("mps"						):
  									__snake_case		       =     torch.manual_seed(_lowerCAmelCase						)
  							else:
  									__snake_case		       =     torch.Generator(device=_lowerCAmelCase						).manual_seed(_lowerCAmelCase						)
  							__snake_case		       =     {
  							    "example_image": example_image,
  							    "image": init_image,
  							    "mask_image": mask_image,
  							    "generator": generator,
  							    "num_inference_steps": 2,
  							    "guidance_scale": 6.0,
  							    "output_type": "numpy",
  							}
  							return inputs
  
  
  
  
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_dummy_components()
  
  							# make sure here that pndm scheduler skips prk
  							__snake_case		       =     PaintByExamplePipeline(**_lowerCAmelCase						)
  							__snake_case		       =     pipe.to("cpu"						)
  							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
  
  							__snake_case		       =     self.get_dummy_inputs()
  							__snake_case		       =     pipe(**_lowerCAmelCase						)
  							__snake_case		       =     output.images
  
  							__snake_case		       =     image[0, -3:, -3:, -1]
  
  							assert image.shape == (1, 64, 64, 3)
  							__snake_case		       =     np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]						)
  
  							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
  
  
  
  
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     "cpu"
  							__snake_case		       =     self.get_dummy_inputs()
  							inputs.pop("mask_image"						)
  							__snake_case		       =     self.convert_to_pt(inputs.pop("image"						)						)
  							__snake_case		       =     image.clamp(0      ,1						) / 2
  
  							# make sure here that pndm scheduler skips prk
  							__snake_case		       =     PaintByExamplePipeline(**self.get_dummy_components()						)
  							__snake_case		       =     pipe.to(_lowerCAmelCase						)
  							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
  
  							__snake_case		       =     pipe(image=_lowerCAmelCase      ,mask_image=mask_image[:, 0]      ,**_lowerCAmelCase						)
  							__snake_case		       =     output.images
  
  							__snake_case		       =     image.cpu().permute(0      ,2      ,3      ,1						)[0]
  							__snake_case		       =     mask_image.cpu().permute(0      ,2      ,3      ,1						)[0]
  
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						)
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						)
  
  							__snake_case		       =     pipe(**self.get_dummy_inputs()						)
  							__snake_case		       =     output.images
  
  							assert out_a.shape == (1, 64, 64, 3)
  							assert np.abs(out_a.flatten() - out_a.flatten()						).max() < 5E-2
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().test_inference_batch_single_identical(expected_max_diff=3E-3						)
  
  
  
  
  @slow
  @require_torch_gpu
  class 					UpperCamelCase	(     unittest.TestCase      ):
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().tearDown()
  							gc.collect()
  							torch.cuda.empty_cache()
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     load_image(
  							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  							    "/paint_by_example/dog_in_bucket.png"						)
  							__snake_case		       =     load_image(
  							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  							    "/paint_by_example/mask.png"						)
  							__snake_case		       =     load_image(
  							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  							    "/paint_by_example/panda.jpg"						)
  
  							__snake_case		       =     PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"						)
  							__snake_case		       =     pipe.to(_lowerCAmelCase						)
  							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
  
  							__snake_case		       =     torch.manual_seed(321						)
  							__snake_case		       =     pipe(
  							    image=_lowerCAmelCase      ,mask_image=_lowerCAmelCase      ,example_image=_lowerCAmelCase      ,generator=_lowerCAmelCase      ,guidance_scale=5.0      ,num_inference_steps=50      ,output_type="np"      ,)
  
  							__snake_case		       =     output.images
  
  							__snake_case		       =     image[0, -3:, -3:, -1]
  
  							assert image.shape == (1, 512, 512, 3)
  							__snake_case		       =     np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]						)
  
  							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
  
  
  
  
  
  