
"""simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


lowercase		:						Union[str, Any] =					logging.get_logger(__name__)  # pylint: disable=invalid-name




class    _snake_case  (						__UpperCAmelCase       ):
							SCREAMING_SNAKE_CASE		     =						["""safety_checker""", """feature_extractor"""]






							def __init__(					self						:			str						, snake_case__						:			Any						, snake_case__						:			Union[str, Any]						, snake_case__						:			Tuple						, snake_case__						:			Union[str, Any]						, snake_case__						:			Optional[int]						, snake_case__						:			str						, snake_case__						:			int						, snake_case__						:			Any = True						, ):






												"""simple docstring"""



												super().__init__()
												if hasattr(scheduler.config						, """steps_offset"""    ) and scheduler.config.steps_offset != 1:
																	__a:						Tuple		    =					(
																	    f"""The configuration file of this scheduler: {scheduler} is outdated.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"""						, lowerCamelCase__						, standard_warn=lowerCamelCase__    )
																	__a:						Dict		    =					dict(scheduler.config    )
																	__a:						List[str]		    =					1
																	__a:						Optional[int]		    =					FrozenDict(lowerCamelCase__    )

												self.register_modules(
												    vae=lowerCamelCase__						, text_encoder=lowerCamelCase__						, tokenizer=lowerCamelCase__						, unet=lowerCamelCase__						, scheduler=lowerCamelCase__						, safety_checker=lowerCamelCase__						, feature_extractor=lowerCamelCase__						, )
												self.register_to_config(requires_safety_checker=lowerCamelCase__    )






							@property
							def 							__lowercase  (					self						:			Optional[int]    ):






												"""simple docstring"""



												return {k: getattr(self						, lowerCamelCase__    ) for k in self.config.keys() if not k.startswith("""_"""    )}






							def 							__lowercase  (					self						:			Tuple						, snake_case__						:			Optional[Any] = "auto"    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			int    ):






												"""simple docstring"""



												self.enable_attention_slicing(lowerCamelCase__    )






							@torch.no_grad()
							def 							__lowercase  (					self						:			int						, snake_case__						:			List[str]						, snake_case__						:			str						, snake_case__						:			int						, snake_case__						:			str = 0.8						, snake_case__						:			Any = 5_0						, snake_case__						:			str = 7.5						, snake_case__						:			Any = None						, snake_case__						:			str = 1						, snake_case__						:			Union[str, Any] = 0.0						, snake_case__						:			str = None						, snake_case__						:			int = "pil"						, snake_case__						:			Dict = True						, snake_case__						:			Optional[Any] = None						, snake_case__						:			Tuple = 1						, ):






												"""simple docstring"""



												return StableDiffusionInpaintPipelineLegacy(**self.components    )(
												    prompt=lowerCamelCase__						, image=lowerCamelCase__						, mask_image=lowerCamelCase__						, strength=lowerCamelCase__						, num_inference_steps=lowerCamelCase__						, guidance_scale=lowerCamelCase__						, negative_prompt=lowerCamelCase__						, num_images_per_prompt=lowerCamelCase__						, eta=lowerCamelCase__						, generator=lowerCamelCase__						, output_type=lowerCamelCase__						, return_dict=lowerCamelCase__						, callback=lowerCamelCase__						, )






							@torch.no_grad()
							def 							__lowercase  (					self						:			Tuple						, snake_case__						:			Any						, snake_case__						:			int						, snake_case__						:			Optional[int] = 0.8						, snake_case__						:			List[Any] = 5_0						, snake_case__						:			Tuple = 7.5						, snake_case__						:			Dict = None						, snake_case__						:			Tuple = 1						, snake_case__						:			Dict = 0.0						, snake_case__						:			Tuple = None						, snake_case__						:			int = "pil"						, snake_case__						:			int = True						, snake_case__						:			Dict = None						, snake_case__						:			List[str] = 1						, **snake_case__						:			List[str]						, ):






												"""simple docstring"""



												return StableDiffusionImgaImgPipeline(**self.components    )(
												    prompt=lowerCamelCase__						, image=lowerCamelCase__						, strength=lowerCamelCase__						, num_inference_steps=lowerCamelCase__						, guidance_scale=lowerCamelCase__						, negative_prompt=lowerCamelCase__						, num_images_per_prompt=lowerCamelCase__						, eta=lowerCamelCase__						, generator=lowerCamelCase__						, output_type=lowerCamelCase__						, return_dict=lowerCamelCase__						, callback=lowerCamelCase__						, callback_steps=lowerCamelCase__						, )






							@torch.no_grad()
							def 							__lowercase  (					self						:			Union[str, Any]						, snake_case__						:			Optional[int]						, snake_case__						:			int = 5_1_2						, snake_case__						:			Tuple = 5_1_2						, snake_case__						:			Optional[int] = 5_0						, snake_case__						:			List[str] = 7.5						, snake_case__						:			Any = None						, snake_case__						:			str = 1						, snake_case__						:			Dict = 0.0						, snake_case__						:			Optional[int] = None						, snake_case__						:			int = None						, snake_case__						:			Union[str, Any] = "pil"						, snake_case__						:			Optional[Any] = True						, snake_case__						:			List[Any] = None						, snake_case__						:			Optional[int] = 1						, ):






												"""simple docstring"""



												return StableDiffusionPipeline(**self.components    )(
												    prompt=lowerCamelCase__						, height=lowerCamelCase__						, width=lowerCamelCase__						, num_inference_steps=lowerCamelCase__						, guidance_scale=lowerCamelCase__						, negative_prompt=lowerCamelCase__						, num_images_per_prompt=lowerCamelCase__						, eta=lowerCamelCase__						, generator=lowerCamelCase__						, latents=lowerCamelCase__						, output_type=lowerCamelCase__						, return_dict=lowerCamelCase__						, callback=lowerCamelCase__						, callback_steps=lowerCamelCase__						, )


"""simple docstring"""
def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					for index, item in enumerate(lowerCamelCase_ ):
										if item == target:
															return index
					return -1


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					if not (0 <= high < len(lowerCamelCase_ ) and 0 <= low < len(lowerCamelCase_ )):
										raise Exception("""Invalid upper or lower bound!""" )
					if high < low:
										return -1
					if sequence[low] == target:
										return low
					if sequence[high] == target:
										return high
					return rec_linear_search(lowerCamelCase_	,				low + 1	,				high - 1	,				lowerCamelCase_ )


if __name__ == "__main__":
			lowercase		:						str =					input('Enter numbers separated by comma:\n').strip()
			lowercase		:						int =					[int(item.strip()) for item in user_input.split(',')]

			lowercase		:						Optional[Any] =					int(input('Enter a single number to be found in the list:\n').strip())
			lowercase		:						str =					linear_search(sequence, target)
			if result != -1:
						print(F"linear_search({sequence}, {target}) = {result}")
			else:
						print(F"{target} was not found in {sequence}")
