'''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 	_lowercase(  unittest.TestCase					):



   """simple docstring"""





   def       snake_case						(				self:     str						):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()





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

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

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

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

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

    # shard inputs and rng
    __UpperCAmelCase								=       replicate(_a						)
    __UpperCAmelCase								=       jax.random.split(_a      ,jax.device_count()						)
    __UpperCAmelCase								=       shard(_a						)
    __UpperCAmelCase								=       shard(_a						)
    __UpperCAmelCase								=       shard(_a						)

    __UpperCAmelCase								=       pipeline(
        _a      ,_a      ,_a      ,_a      ,_a      ,_a      ,jit=_a						)

    __UpperCAmelCase								=       output.images.reshape(_a      ,512      ,512      ,3						)

    __UpperCAmelCase								=       images[0, 253:256, 253:256, -1]

    __UpperCAmelCase								=       jnp.asarray(jax.device_get(image_slice.flatten()						)						)
    __UpperCAmelCase								=       jnp.array(
        [0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084]						)
    print(f"""output_slice: {output_slice}"""						)
    assert jnp.abs(output_slice - expected_slice						).max() < 1e-2








'''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


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






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      Union[str, Any]     ,      _a :      CLIPSegForImageSegmentation     ,      _a :      CLIPSegProcessor     ,      _a :      AutoencoderKL     ,      _a :      CLIPTextModel     ,      _a :      CLIPTokenizer     ,      _a :      UNetaDConditionModel     ,      _a :      Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,      _a :      StableDiffusionSafetyChecker     ,      _a :      CLIPImageProcessor     ,      )   ->   int:
    super().__init__()

    if hasattr(scheduler.config     ,      'steps_offset'  ) and scheduler.config.steps_offset != 1:
      __lowerCamelCase			:			int       			=					(
          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'     ,      _a     ,      standard_warn=_a  )
      __lowerCamelCase			:			int       			=					dict(scheduler.config  )
      __lowerCamelCase			:			Any       			=					1
      __lowerCamelCase			:			List[Any]       			=					FrozenDict(_a  )

    if hasattr(scheduler.config     ,      'skip_prk_steps'  ) and scheduler.config.skip_prk_steps is False:
      __lowerCamelCase			:			str       			=					(
          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'     ,      _a     ,      standard_warn=_a  )
      __lowerCamelCase			:			Dict       			=					dict(scheduler.config  )
      __lowerCamelCase			:			Tuple       			=					True
      __lowerCamelCase			:			Union[str, Any]       			=					FrozenDict(_a  )

    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=_a     ,      segmentation_processor=_a     ,      vae=_a     ,      text_encoder=_a     ,      tokenizer=_a     ,      unet=_a     ,      scheduler=_a     ,      safety_checker=_a     ,      feature_extractor=_a     ,      )




  def 		_lowercase	(					self :      Optional[int]     ,      _a :      Optional[Union[str, int]] = "auto"  )   ->   List[str]:
    if slice_size == "auto":
      # half the attention head size is usually a good trade-off between
      # speed and memory
      __lowerCamelCase			:			List[Any]       			=					self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(_a  )




  def 		_lowercase	(					self :      Optional[Any]  )   ->   int:
    self.enable_attention_slicing(_a  )




  def 		_lowercase	(					self :      str  )   ->   Any:
    if is_accelerate_available():
      from accelerate import cpu_offload
    else:
      raise ImportError('Please install accelerate via `pip install accelerate`'  )

    __lowerCamelCase			:			Dict       			=					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(_a     ,      _a  )




  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 		_lowercase	(					self :      Dict  )   ->   int:
    if self.device != torch.device('meta'  ) or not hasattr(self.unet     ,      '_hf_hook'  ):
      return self.device
    for module in self.unet.modules():
      if (
          hasattr(_a     ,      '_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 :      List[str]     ,      _a :      Union[str, List[str]]     ,      _a :      Union[torch.FloatTensor, PIL.Image.Image]     ,      _a :      str     ,      _a :      int = 512     ,      _a :      int = 512     ,      _a :      int = 50     ,      _a :      float = 7.5     ,      _a :      Optional[Union[str, List[str]]] = None     ,      _a :      Optional[int] = 1     ,      _a :      float = 0.0     ,      _a :      Optional[torch.Generator] = None     ,      _a :      Optional[torch.FloatTensor] = None     ,      _a :      Optional[str] = "pil"     ,      _a :      bool = True     ,      _a :      Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,      _a :      int = 1     ,      **_a :      Optional[Any]     ,      )   ->   int:
    __lowerCamelCase			:			Union[str, Any]       			=					self.segmentation_processor(
        text=[text]     ,      images=[image]     ,      padding='max_length'     ,      return_tensors='pt'  ).to(self.device  )
    __lowerCamelCase			:			Union[str, Any]       			=					self.segmentation_model(**_a  )
    __lowerCamelCase			:			List[str]       			=					torch.sigmoid(outputs.logits  ).cpu().detach().unsqueeze(-1  ).numpy()
    __lowerCamelCase			:			Optional[Any]       			=					self.numpy_to_pil(_a  )[0].resize(image.size  )

    # Run inpainting pipeline with the generated mask
    __lowerCamelCase			:			Tuple       			=					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=_a     ,      image=_a     ,      mask_image=_a     ,      height=_a     ,      width=_a     ,      num_inference_steps=_a     ,      guidance_scale=_a     ,      negative_prompt=_a     ,      num_images_per_prompt=_a     ,      eta=_a     ,      generator=_a     ,      latents=_a     ,      output_type=_a     ,      return_dict=_a     ,      callback=_a     ,      callback_steps=_a     ,      )






