






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


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


class      lowercase__     (			_UpperCAmelCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					Tuple:







							'''simple docstring'''





							super().__init__()

							if hasattr(scheduler.config		,    "steps_offset"       ) and scheduler.config.steps_offset != 1:
													lowerCAmelCase__       =						(
													    F"The configuration file of this scheduler: {scheduler} is outdated.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       )
													lowerCAmelCase__       =						dict(scheduler.config       )
													lowerCAmelCase__       =						1
													lowerCAmelCase__       =						FrozenDict(__UpperCAmelCase       )

							if hasattr(scheduler.config		,    "skip_prk_steps"       ) and scheduler.config.skip_prk_steps is False:
													lowerCAmelCase__       =						(
													    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       )
													lowerCAmelCase__       =						dict(scheduler.config       )
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						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		,    __UpperCAmelCase = "auto"       )->					Union[str, Any]:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							self.enable_attention_slicing(__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Union[str, Any]:







							'''simple docstring'''





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

							lowerCAmelCase__       =						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       )->					List[Any]:







							'''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		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = 512		,    __UpperCAmelCase = 512		,    __UpperCAmelCase = 50		,    __UpperCAmelCase = 7.5		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 1		,    __UpperCAmelCase = 0.0		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = "pil"		,    __UpperCAmelCase = True		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 1		,    **__UpperCAmelCase		,    )->					Optional[Any]:







							'''simple docstring'''





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

							# Run inpainting pipeline with the generated mask
							lowerCAmelCase__       =						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 __future__ import annotations

from collections.abc import Generator


def        _a  (     ) ->   Generator[int, None, None]:
						"""simple docstring"""







						lowerCAmelCase__       =						{}
						lowerCAmelCase__       =						2
						while True:
												lowerCAmelCase__       =						factor_map.pop(UpperCamelCase_    ,   UpperCamelCase_   )
												if factor:
																		lowerCAmelCase__       =						factor + prime
																		while x in factor_map:
																								x += factor
																		lowerCAmelCase__       =						factor
												else:
																		lowerCAmelCase__       =						prime
																		yield prime
												prime += 1


def        _a  (     UpperCamelCase_					:				float = 1e1_0   ) ->   int:
						"""simple docstring"""







						lowerCAmelCase__       =						sieve()

						lowerCAmelCase__       =						1
						while True:
												lowerCAmelCase__       =						next(UpperCamelCase_   )
												if (2 * prime * n) > limit:
																		return n
												# Ignore the next prime as the reminder will be 2.
												next(UpperCamelCase_   )
												n += 2


if __name__ == "__main__":
	print(solution())






