


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





class _snake_case (			a__ ):
							"""simple docstring"""





							__a				       =  ['''safety_checker''', '''feature_extractor''']







							def __init__( self      ,					__A      ,					__A      ,					__A      ,					__A      ,					__A      ,					__A      ,					__A      ,					__A = True      ,					)  ->List[Any]:



											"""simple docstring"""







											super().__init__()
											if hasattr(scheduler.config      ,					"steps_offset"						) and scheduler.config.steps_offset != 1:
															__lowerCamelCase		:   str           =       (
															    f'The configuration file of this scheduler: {scheduler} is outdated.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						)
															__lowerCamelCase		:   Union[str, Any]           =       dict(scheduler.config						)
															__lowerCamelCase		:   Tuple           =       1
															__lowerCamelCase		:   List[str]           =       FrozenDict(__A						)

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







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->Dict[str, Any]:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A = "auto"						)  ->Tuple:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											self.enable_attention_slicing(__A						)







							@torch.no_grad()
							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A      ,					__A = 0.8      ,					__A = 50      ,					__A = 7.5      ,					__A = None      ,					__A = 1      ,					__A = 0.0      ,					__A = None      ,					__A = "pil"      ,					__A = True      ,					__A = None      ,					__A = 1      ,					)  ->int:



											"""simple docstring"""







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







							@torch.no_grad()
							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A = 0.8      ,					__A = 50      ,					__A = 7.5      ,					__A = None      ,					__A = 1      ,					__A = 0.0      ,					__A = None      ,					__A = "pil"      ,					__A = True      ,					__A = None      ,					__A = 1      ,					**__A      ,					)  ->Optional[int]:



											"""simple docstring"""







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







							@torch.no_grad()
							def 		__SCREAMING_SNAKE_CASE( self      ,					__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      ,					)  ->List[str]:



											"""simple docstring"""







											return StableDiffusionPipeline(**self.components						)(
											    prompt=__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      ,					)









'''simple docstring'''




def 				a_							(    lowercase				,    lowercase     )      ->list:






				'''simple docstring'''

				__lowerCamelCase		:   int           =       len(lowercase     )
				__lowerCamelCase		:   int           =       len(lowercase     )
				__lowerCamelCase		:   int           =       (
				    first_input_list_length
				    if first_input_list_length > second_input_list_length
				    else second_input_list_length
				)
				__lowerCamelCase		:   list           =       []
				for char_count in range(lowercase     ):
								if char_count < first_input_list_length:
												output_result_list.append(first_input_list[char_count]     )
								if char_count < second_input_list_length:
												output_result_list.append(second_input_list[char_count]     )

				return output_result_list


if __name__ == "__main__":
				print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)





