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

class 				__a    (   __a					):



							'''simple docstring'''







							_lowerCamelCase				: Optional[Any]					      =      ["""safety_checker""", """feature_extractor"""]
							def __init__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase = True     ,						) ->       Any:




										'''simple docstring'''
										super().__init__()
										if hasattr(scheduler.config     ,						"steps_offset"					) and scheduler.config.steps_offset != 1:
													__lowercase						      =					(
													    f'''The configuration file of this scheduler: {scheduler} is outdated.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					)
													__lowercase						      =					dict(scheduler.config					)
													__lowercase						      =					1
													__lowercase						      =					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      SCREAMING_SNAKE_CASE	(    self					) ->       Dict[str, Any]:




										'''simple docstring'''
										return {k: getattr(self     ,						_lowerCamelCase					) for k in self.config.keys() if not k.startswith("_"					)}
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase = "auto"					) ->       Tuple:




										'''simple docstring'''
										if slice_size == "auto":
													# half the attention head size is usually a good trade-off between
													# speed and memory
													__lowercase						      =					self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Any:




										'''simple docstring'''
										self.enable_attention_slicing(_lowerCamelCase					)
							@torch.no_grad()
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase = 0.8     ,						_lowerCamelCase = 50     ,						_lowerCamelCase = 7.5     ,						_lowerCamelCase = None     ,						_lowerCamelCase = 1     ,						_lowerCamelCase = 0.0     ,						_lowerCamelCase = None     ,						_lowerCamelCase = "pil"     ,						_lowerCamelCase = True     ,						_lowerCamelCase = None     ,						_lowerCamelCase = 1     ,						) ->       Dict:




										'''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      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase = 0.8     ,						_lowerCamelCase = 50     ,						_lowerCamelCase = 7.5     ,						_lowerCamelCase = None     ,						_lowerCamelCase = 1     ,						_lowerCamelCase = 0.0     ,						_lowerCamelCase = None     ,						_lowerCamelCase = "pil"     ,						_lowerCamelCase = True     ,						_lowerCamelCase = None     ,						_lowerCamelCase = 1     ,						**_lowerCamelCase     ,						) ->       int:




										'''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      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase = 512     ,						_lowerCamelCase = 512     ,						_lowerCamelCase = 50     ,						_lowerCamelCase = 7.5     ,						_lowerCamelCase = None     ,						_lowerCamelCase = 1     ,						_lowerCamelCase = 0.0     ,						_lowerCamelCase = None     ,						_lowerCamelCase = None     ,						_lowerCamelCase = "pil"     ,						_lowerCamelCase = True     ,						_lowerCamelCase = None     ,						_lowerCamelCase = 1     ,						) ->       Dict:




										'''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__ (		__magic_name__ ,      __magic_name__			)	->list:
			__lowercase						      =					len(__magic_name__			)
			__lowercase						      =					len(__magic_name__			)
			__lowercase						      =					(
			    first_input_list_length
			    if first_input_list_length > second_input_list_length
			    else second_input_list_length
			)
			__lowercase						      =					[]
			for char_count in range(__magic_name__			):
						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=''' ''')





