


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


class 			lowerCamelCase_		(			lowerCAmelCase__   ):





				"""simple docstring"""







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

								if hasattr(scheduler.config       ,		"steps_offset"     ) and scheduler.config.steps_offset != 1:
												SCREAMING_SNAKE_CASE__     							=				(
												    f'''The configuration file of this scheduler: {scheduler} is outdated.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"       ,		_SCREAMING_SNAKE_CASE       ,		standard_warn=_SCREAMING_SNAKE_CASE     )
												SCREAMING_SNAKE_CASE__     							=				dict(scheduler.config     )
												SCREAMING_SNAKE_CASE__     							=				1
												SCREAMING_SNAKE_CASE__     							=				FrozenDict(_SCREAMING_SNAKE_CASE     )

								if hasattr(scheduler.config       ,		"skip_prk_steps"     ) and scheduler.config.skip_prk_steps is False:
												SCREAMING_SNAKE_CASE__     							=				(
												    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"       ,		_SCREAMING_SNAKE_CASE       ,		standard_warn=_SCREAMING_SNAKE_CASE     )
												SCREAMING_SNAKE_CASE__     							=				dict(scheduler.config     )
												SCREAMING_SNAKE_CASE__     							=				True
												SCREAMING_SNAKE_CASE__     							=				FrozenDict(_SCREAMING_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=_SCREAMING_SNAKE_CASE       ,		segmentation_processor=_SCREAMING_SNAKE_CASE       ,		vae=_SCREAMING_SNAKE_CASE       ,		text_encoder=_SCREAMING_SNAKE_CASE       ,		tokenizer=_SCREAMING_SNAKE_CASE       ,		unet=_SCREAMING_SNAKE_CASE       ,		scheduler=_SCREAMING_SNAKE_CASE       ,		safety_checker=_SCREAMING_SNAKE_CASE       ,		feature_extractor=_SCREAMING_SNAKE_CASE       ,		)



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



				def   lowerCAmelCase__		(						self     ):
								self.enable_attention_slicing(_SCREAMING_SNAKE_CASE     )



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

								SCREAMING_SNAKE_CASE__     							=				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(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE     )



				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def   lowerCAmelCase__		(						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(_SCREAMING_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       ,		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__       ,		):
								SCREAMING_SNAKE_CASE__     							=				self.segmentation_processor(
								    text=[text]       ,		images=[image]       ,		padding="max_length"       ,		return_tensors="pt"     ).to(self.device     )
								SCREAMING_SNAKE_CASE__     							=				self.segmentation_model(**_SCREAMING_SNAKE_CASE     )
								SCREAMING_SNAKE_CASE__     							=				torch.sigmoid(outputs.logits     ).cpu().detach().unsqueeze(-1     ).numpy()
								SCREAMING_SNAKE_CASE__     							=				self.numpy_to_pil(_SCREAMING_SNAKE_CASE     )[0].resize(image.size     )

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




"""simple docstring"""






from math import pi


def __lowercase     (      lowerCamelCase_  :      int						,					lowerCamelCase_  :      int  ):
				return 2 * pi * radius * (angle / 360)


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