



'''simple docstring'''



from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


lowerCamelCase	 =       logging.get_logger(__name__)  # pylint: disable=invalid-name





class _UpperCamelCase		(     A		):





				'''simple docstring'''





				def __init__(							self  :  Optional[int]  ,       _lowerCAmelCase  :  CLIPSegForImageSegmentation  ,       _lowerCAmelCase  :  CLIPSegProcessor  ,       _lowerCAmelCase  :  AutoencoderKL  ,       _lowerCAmelCase  :  CLIPTextModel  ,       _lowerCAmelCase  :  CLIPTokenizer  ,       _lowerCAmelCase  :  UNetaDConditionModel  ,       _lowerCAmelCase  :  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  ,       _lowerCAmelCase  :  StableDiffusionSafetyChecker  ,       _lowerCAmelCase  :  CLIPImageProcessor  ,       ):







											'''simple docstring'''

											super().__init__()

											if hasattr(scheduler.config  ,       'steps_offset') and scheduler.config.steps_offset != 1:
																		__lowercase       	=(
																		    f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
																		    f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
																		    'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
																		    ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
																		    ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
																		    ' file'
																		)
																		deprecate('steps_offset!=1'  ,       '1.0.0'  ,       _lowerCAmelCase  ,       standard_warn=_lowerCAmelCase)
																		__lowercase       	=dict(scheduler.config)
																		__lowercase       	=1
																		__lowercase       	=FrozenDict(_lowerCAmelCase)

											if hasattr(scheduler.config  ,       'skip_prk_steps') and scheduler.config.skip_prk_steps is False:
																		__lowercase       	=(
																		    f"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
																		    ' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'
																		    ' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'
																		    ' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'
																		    ' Hub, it would be very nice if you could open a Pull request for the'
																		    ' `scheduler/scheduler_config.json` file'
																		)
																		deprecate('skip_prk_steps not set'  ,       '1.0.0'  ,       _lowerCAmelCase  ,       standard_warn=_lowerCAmelCase)
																		__lowercase       	=dict(scheduler.config)
																		__lowercase       	=True
																		__lowercase       	=FrozenDict(_lowerCAmelCase)

											if safety_checker is None:
																		logger.warning(
																		    f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
																		    ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
																		    ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
																		    ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
																		    ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
																		    ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .')

											self.register_modules(
											    segmentation_model=_lowerCAmelCase  ,       segmentation_processor=_lowerCAmelCase  ,       vae=_lowerCAmelCase  ,       text_encoder=_lowerCAmelCase  ,       tokenizer=_lowerCAmelCase  ,       unet=_lowerCAmelCase  ,       scheduler=_lowerCAmelCase  ,       safety_checker=_lowerCAmelCase  ,       feature_extractor=_lowerCAmelCase  ,       )

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  Optional[Union[str, int]] = "auto"):







											'''simple docstring'''

											if slice_size == "auto":
																		# half the attention head size is usually a good trade-off between
																		# speed and memory
																		__lowercase       	=self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Any):







											'''simple docstring'''

											self.enable_attention_slicing(_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											if is_accelerate_available():
																		from accelerate import cpu_offload
											else:
																		raise ImportError('Please install accelerate via `pip install accelerate`')

											__lowercase       	=torch.device('cuda')

											for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
																		if cpu_offloaded_model is not None:
																									cpu_offload(_lowerCAmelCase  ,       _lowerCAmelCase)

				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 				__lowerCamelCase     (							self  :  Dict):







											'''simple docstring'''

											if self.device != torch.device('meta') or not hasattr(self.unet  ,       '_hf_hook'):
																		return self.device
											for module in self.unet.modules():
																		if (
																		    hasattr(_lowerCAmelCase  ,       '_hf_hook')
																		    and hasattr(module._hf_hook  ,       'execution_device')
																		    and module._hf_hook.execution_device is not None
																		):
																									return torch.device(module._hf_hook.execution_device)
											return self.device

				@torch.no_grad()
				def __call__(							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  Union[str, List[str]]  ,       _lowerCAmelCase  :  Union[torch.FloatTensor, PIL.Image.Image]  ,       _lowerCAmelCase  :  str  ,       _lowerCAmelCase  :  int = 5_1_2  ,       _lowerCAmelCase  :  int = 5_1_2  ,       _lowerCAmelCase  :  int = 5_0  ,       _lowerCAmelCase  :  float = 7.5  ,       _lowerCAmelCase  :  Optional[Union[str, List[str]]] = None  ,       _lowerCAmelCase  :  Optional[int] = 1  ,       _lowerCAmelCase  :  float = 0.0  ,       _lowerCAmelCase  :  Optional[torch.Generator] = None  ,       _lowerCAmelCase  :  Optional[torch.FloatTensor] = None  ,       _lowerCAmelCase  :  Optional[str] = "pil"  ,       _lowerCAmelCase  :  bool = True  ,       _lowerCAmelCase  :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,       _lowerCAmelCase  :  int = 1  ,       **_lowerCAmelCase  :  str  ,       ):







											'''simple docstring'''

											__lowercase       	=self.segmentation_processor(
											    text=[text]  ,       images=[image]  ,       padding='max_length'  ,       return_tensors='pt').to(self.device)
											__lowercase       	=self.segmentation_model(**_lowerCAmelCase)
											__lowercase       	=torch.sigmoid(outputs.logits).cpu().detach().unsqueeze(-1).numpy()
											__lowercase       	=self.numpy_to_pil(_lowerCAmelCase)[0].resize(image.size)

											# Run inpainting pipeline with the generated mask
											__lowercase       	=StableDiffusionInpaintPipeline(
											    vae=self.vae  ,       text_encoder=self.text_encoder  ,       tokenizer=self.tokenizer  ,       unet=self.unet  ,       scheduler=self.scheduler  ,       safety_checker=self.safety_checker  ,       feature_extractor=self.feature_extractor  ,       )
											return inpainting_pipeline(
											    prompt=_lowerCAmelCase  ,       image=_lowerCAmelCase  ,       mask_image=_lowerCAmelCase  ,       height=_lowerCAmelCase  ,       width=_lowerCAmelCase  ,       num_inference_steps=_lowerCAmelCase  ,       guidance_scale=_lowerCAmelCase  ,       negative_prompt=_lowerCAmelCase  ,       num_images_per_prompt=_lowerCAmelCase  ,       eta=_lowerCAmelCase  ,       generator=_lowerCAmelCase  ,       latents=_lowerCAmelCase  ,       output_type=_lowerCAmelCase  ,       return_dict=_lowerCAmelCase  ,       callback=_lowerCAmelCase  ,       callback_steps=_lowerCAmelCase  ,       )








'''simple docstring'''



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		):





				'''simple docstring'''





				def 				__lowerCamelCase     (							self  :  List[Any]):







											'''simple docstring'''

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

				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											__lowercase       	=load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/sd2-inpaint/init_image.png')
											__lowercase       	=load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png')

											__lowercase       	='xvjiarui/stable-diffusion-2-inpainting'
											__lowercase	,				__lowercase       	=FlaxStableDiffusionInpaintPipeline.from_pretrained(_lowerCAmelCase  ,       safety_checker=_lowerCAmelCase)

											__lowercase       	='Face of a yellow cat, high resolution, sitting on a park bench'

											__lowercase       	=jax.random.PRNGKey(0)
											__lowercase       	=5_0

											__lowercase       	=jax.device_count()
											__lowercase       	=num_samples * [prompt]
											__lowercase       	=num_samples * [init_image]
											__lowercase       	=num_samples * [mask_image]
											__lowercase	,				__lowercase	,				__lowercase       	=pipeline.prepare_inputs(_lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase)

											# shard inputs and rng
											__lowercase       	=replicate(_lowerCAmelCase)
											__lowercase       	=jax.random.split(_lowerCAmelCase  ,       jax.device_count())
											__lowercase       	=shard(_lowerCAmelCase)
											__lowercase       	=shard(_lowerCAmelCase)
											__lowercase       	=shard(_lowerCAmelCase)

											__lowercase       	=pipeline(
											    _lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase  ,       jit=_lowerCAmelCase)

											__lowercase       	=output.images.reshape(_lowerCAmelCase  ,       5_1_2  ,       5_1_2  ,       3)

											__lowercase       	=images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

											__lowercase       	=jnp.asarray(jax.device_get(image_slice.flatten()))
											__lowercase       	=jnp.array(
											    [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084])
											print(f"""output_slice: {output_slice}""")
											assert jnp.abs(output_slice - expected_slice).max() < 1e-2



