






"""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


A_     :					List[Any]							=     logging.get_logger(__name__)  # pylint: disable=invalid-name



class 						a_ (      _a					):







      '''simple docstring'''

      lowerCamelCase__			:							int					=      ['safety_checker', 'feature_extractor']




      def __init__(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_ = True,	):

       '''simple docstring'''



       super().__init__()
       if hasattr(scheduler.config,	'steps_offset'   ) and scheduler.config.steps_offset != 1:
        lowerCamelCase__	:     Optional[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            f'''The configuration file of this scheduler: {scheduler} is outdated.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',	_SCREAMING_SNAKE_CASE,	standard_warn=_SCREAMING_SNAKE_CASE   )
        lowerCamelCase__	:     Union[str, Any]								=		dict(scheduler.config   )
        lowerCamelCase__	:     Optional[Any]								=		1
        lowerCamelCase__	:     int								=		FrozenDict(_SCREAMING_SNAKE_CASE   )

       self.register_modules(
           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,	)
       self.register_to_config(requires_safety_checker=_SCREAMING_SNAKE_CASE   )




      @property
      def      a__				(self   ):

       '''simple docstring'''



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




      def      a__				(self,	lowerCamelCase_ = "auto"   ):

       '''simple docstring'''



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




      def      a__				(self   ):

       '''simple docstring'''



       self.enable_attention_slicing(_SCREAMING_SNAKE_CASE   )




      @torch.no_grad()
      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_ = 0.8,	lowerCamelCase_ = 5_0,	lowerCamelCase_ = 7.5,	lowerCamelCase_ = None,	lowerCamelCase_ = 1,	lowerCamelCase_ = 0.0,	lowerCamelCase_ = None,	lowerCamelCase_ = "pil",	lowerCamelCase_ = True,	lowerCamelCase_ = None,	lowerCamelCase_ = 1,	):

       '''simple docstring'''



       return StableDiffusionInpaintPipelineLegacy(**self.components   )(
           prompt=_SCREAMING_SNAKE_CASE,	image=_SCREAMING_SNAKE_CASE,	mask_image=_SCREAMING_SNAKE_CASE,	strength=_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,	output_type=_SCREAMING_SNAKE_CASE,	return_dict=_SCREAMING_SNAKE_CASE,	callback=_SCREAMING_SNAKE_CASE,	)




      @torch.no_grad()
      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_ = 0.8,	lowerCamelCase_ = 5_0,	lowerCamelCase_ = 7.5,	lowerCamelCase_ = None,	lowerCamelCase_ = 1,	lowerCamelCase_ = 0.0,	lowerCamelCase_ = None,	lowerCamelCase_ = "pil",	lowerCamelCase_ = True,	lowerCamelCase_ = None,	lowerCamelCase_ = 1,	**lowerCamelCase_,	):

       '''simple docstring'''



       return StableDiffusionImgaImgPipeline(**self.components   )(
           prompt=_SCREAMING_SNAKE_CASE,	image=_SCREAMING_SNAKE_CASE,	strength=_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,	output_type=_SCREAMING_SNAKE_CASE,	return_dict=_SCREAMING_SNAKE_CASE,	callback=_SCREAMING_SNAKE_CASE,	callback_steps=_SCREAMING_SNAKE_CASE,	)





      @torch.no_grad()
      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_ = 5_1_2,	lowerCamelCase_ = 5_1_2,	lowerCamelCase_ = 5_0,	lowerCamelCase_ = 7.5,	lowerCamelCase_ = None,	lowerCamelCase_ = 1,	lowerCamelCase_ = 0.0,	lowerCamelCase_ = None,	lowerCamelCase_ = None,	lowerCamelCase_ = "pil",	lowerCamelCase_ = True,	lowerCamelCase_ = None,	lowerCamelCase_ = 1,	):

       '''simple docstring'''



       return StableDiffusionPipeline(**self.components   )(
           prompt=_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'''


__SCREAMING_SNAKE_CASE						=      {hex(i)[2:]: i for i in range(16)}  # Use [:2] to strip off the leading '0x'
def 					__magic_name__(       UpperCAmelCase__ :       str	):
						UpperCamelCase_     :	str					     =     hex_string.strip().lower()
						if not hex_string:
												raise ValueError("""Empty string was passed to the function"""	)
						UpperCamelCase_     :	Dict					     =     hex_string[0] == """-"""
						if is_negative:
												UpperCamelCase_     :	List[str]					     =     hex_string[1:]
						if not all(char in hex_table for char in hex_string	):
												raise ValueError("""Non-hexadecimal value was passed to the function"""	)
						UpperCamelCase_     :	int					     =     0
						for char in hex_string:
												UpperCamelCase_     :	Any					     =     16 * decimal_number + hex_table[char]
						return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
	from doctest import testmod

	testmod()




