




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     :   str						     =  logging.get_logger(__name__)  # pylint: disable=invalid-name
class  _A		(  __lowerCamelCase):

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        super().__init__()

        if hasattr(scheduler.config ,					'steps_offset'					) and scheduler.config.steps_offset != 1:
              SCREAMING_SNAKE_CASE_				:  List[str]           =     (
                  f"The configuration file of this scheduler: {scheduler} is outdated.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' ,					UpperCamelCase_ ,					standard_warn=UpperCamelCase_					)
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     dict(scheduler.config					)
              SCREAMING_SNAKE_CASE_				:  Tuple           =     1
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     FrozenDict(UpperCamelCase_					)

        if hasattr(scheduler.config ,					'skip_prk_steps'					) and scheduler.config.skip_prk_steps is False:
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     (
                  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' ,					UpperCamelCase_ ,					standard_warn=UpperCamelCase_					)
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     dict(scheduler.config					)
              SCREAMING_SNAKE_CASE_				:  Dict           =     True
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     FrozenDict(UpperCamelCase_					)

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

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE = "auto"					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        self.enable_attention_slicing(UpperCamelCase_					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

        SCREAMING_SNAKE_CASE_				:  Tuple           =     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(UpperCamelCase_ ,					UpperCamelCase_					)

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





        """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(UpperCamelCase_ ,					'_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 ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = 512 ,					_SCREAMING_SNAKE_CASE = 512 ,					_SCREAMING_SNAKE_CASE = 50 ,					_SCREAMING_SNAKE_CASE = 7.5 ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = 1 ,					_SCREAMING_SNAKE_CASE = 0.0 ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = "pil" ,					_SCREAMING_SNAKE_CASE = True ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = 1 ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     self.segmentation_processor(
            text=[text] ,					images=[image] ,					padding='max_length' ,					return_tensors='pt'					).to(self.device					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.segmentation_model(**UpperCamelCase_					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     torch.sigmoid(outputs.logits					).cpu().detach().unsqueeze(-1					).numpy()
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.numpy_to_pil(UpperCamelCase_					)[0].resize(image.size					)

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









from math import pi

def    A_  (							a					,   a  ):

      """simple docstring"""



      return 2 * pi * radius * (angle / 3_6_0)


if __name__ == "__main__":
    print(arc_length(90, 10))






