



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







class      _lowerCAmelCase							(      A_			):


	def __init__(self	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				):
				super().__init__()

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							    F'The configuration file of this scheduler: {scheduler} is outdated.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

				if hasattr(scheduler.config	,				"""skip_prk_steps"""							) and scheduler.config.skip_prk_steps is False:
							A_     :						List[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"""	,				snake_case__	,				standard_warn=snake_case__							)
							A_     :						Optional[int]     =					dict(scheduler.config							)
							A_     :						Tuple     =					True
							A_     :						str     =					FrozenDict(snake_case__							)

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


	def    _a     (self	,				lowercase = "auto"							):


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


	def    _a     (self							):
				self.enable_attention_slicing(snake_case__							)


	def    _a     (self							):


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

				A_     :						Optional[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(snake_case__	,				snake_case__							)


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


				if self.device != torch.device("""meta"""							) or not hasattr(self.unet	,				"""_hf_hook"""							):
							return self.device
				for module in self.unet.modules():
							if (
							    hasattr(snake_case__	,				"""_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	,				lowercase	,				lowercase	,				lowercase	,				lowercase = 512	,				lowercase = 512	,				lowercase = 50	,				lowercase = 7.5	,				lowercase = None	,				lowercase = 1	,				lowercase = 0.0	,				lowercase = None	,				lowercase = None	,				lowercase = "pil"	,				lowercase = True	,				lowercase = None	,				lowercase = 1	,				**lowercase	,				):
				A_     :						Optional[Any]     =					self.segmentation_processor(
				    text=[text]	,				images=[image]	,				padding="""max_length"""	,				return_tensors="""pt"""							).to(self.device							)
				A_     :						Dict     =					self.segmentation_model(**snake_case__							)
				A_     :						List[str]     =					torch.sigmoid(outputs.logits							).cpu().detach().unsqueeze(-1							).numpy()
				A_     :						Tuple     =					self.numpy_to_pil(snake_case__							)[0].resize(image.size							)

				# Run inpainting pipeline with the generated mask
				A_     :						Optional[Any]     =					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=snake_case__	,				image=snake_case__	,				mask_image=snake_case__	,				height=snake_case__	,				width=snake_case__	,				num_inference_steps=snake_case__	,				guidance_scale=snake_case__	,				negative_prompt=snake_case__	,				num_images_per_prompt=snake_case__	,				eta=snake_case__	,				generator=snake_case__	,				latents=snake_case__	,				output_type=snake_case__	,				return_dict=snake_case__	,				callback=snake_case__	,				callback_steps=snake_case__	,				)




'''simple docstring'''




from __future__ import annotations

from functools import lru_cache
from math import ceil

lowerCamelCase							:List[str]       = 1_0_0

lowerCamelCase							:Dict       = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCamelCase							:int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
						if prime not in primes:
												continue
						primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))



@lru_cache(maxsize=1_00   )
def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			if number_to_partition < 0:
						return set()
			elif number_to_partition == 0:
						return {1}

			A_     :						set[int]     =					set()
			A_     :						int
			A_     :						int

			for prime in primes:
						if prime > number_to_partition:
									continue
						for sub in partition(number_to_partition - prime   ):
									ret.add(sub * prime   )

			return ret



def      a      (			lowerCamelCase__ = 50_00   ):
			'''simple docstring'''





			for number_to_partition in range(1						,				lowerCamelCase__   ):
						if len(partition(lowerCamelCase__   )   ) > number_unique_partitions:
									return number_to_partition
			return None


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