





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



class    A__  (			__snake_case     ):





      def __init__(					self			,				A_			,				A_			,				A_			,				A_			,				A_			,				A_			,				A_			,				A_			,				A_			,				):
       '''simple docstring'''




       super().__init__()

       if hasattr(scheduler.config			,				"steps_offset"  ) and scheduler.config.steps_offset != 1:
        UpperCamelCase      :   List[Any]			      =  (
            F"""The configuration file of this scheduler: {scheduler} is outdated.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_  )
        UpperCamelCase      :   List[Any]			      =  dict(scheduler.config  )
        UpperCamelCase      :   Dict			      =  1
        UpperCamelCase      :   str			      =  FrozenDict(A_  )

       if hasattr(scheduler.config			,				"skip_prk_steps"  ) and scheduler.config.skip_prk_steps is False:
        UpperCamelCase      :   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"			,				A_			,				standard_warn=A_  )
        UpperCamelCase      :   int			      =  dict(scheduler.config  )
        UpperCamelCase      :   Any			      =  True
        UpperCamelCase      :   Tuple			      =  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 			__UpperCamelCase(					self			,				A_ = "auto"  ):
       '''simple docstring'''




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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       self.enable_attention_slicing(A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




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

       UpperCamelCase      :   Any			      =  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 			__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(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			,				A_			,				A_			,				A_			,				A_ = 512			,				A_ = 512			,				A_ = 50			,				A_ = 7.5			,				A_ = None			,				A_ = 1			,				A_ = 0.0			,				A_ = None			,				A_ = None			,				A_ = "pil"			,				A_ = True			,				A_ = None			,				A_ = 1			,				**A_			,				):
       '''simple docstring'''




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

       # Run inpainting pipeline with the generated mask
       UpperCamelCase      :   Optional[int]			      =  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_			,				)







def 						A_							(	_lowerCAmelCase = 1000	)		->					int:
 UpperCamelCase      :   Optional[int]			      =  -1
 UpperCamelCase      :   int			      =  0
 for a in range(1				,  n // 3	):
  # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
  UpperCamelCase      :   Optional[Any]			      =  (n * n - 2 * a * n) // (2 * n - 2 * a)
  UpperCamelCase      :   List[str]			      =  n - a - b
  if c * c == (a * a + b * b):
   UpperCamelCase      :   Union[str, Any]			      =  a * b * c
   if candidate >= product:
    UpperCamelCase      :   Tuple			      =  candidate
 return product


if __name__ == "__main__":
    print(f"""{solution() = }""")
