




'''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


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

class   _snake_case( __UpperCAmelCase			):
 __snake_case:       Union[str, Any]									=				["""safety_checker""", """feature_extractor"""]






 def __init__(self					:     Optional[Any]						,		a					:     str						,		a					:     str						,		a					:     Dict						,		a					:     int						,		a					:     Dict						,		a					:     Optional[int]						,		a					:     Optional[Any]						,		a					:     Dict = True						,		)					->      Tuple:






    """simple docstring"""


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           f"""The configuration file of this scheduler: {scheduler} is outdated.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






    """simple docstring"""


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






 def 	_UpperCamelCase   (self					:     str						,		a					:     Union[str, Any] = "auto"						)					->      Union[str, Any]:






    """simple docstring"""


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






 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      str:






    """simple docstring"""


    self.enable_attention_slicing(lowerCamelCase__						)






 @torch.no_grad()
 def 	_UpperCamelCase   (self					:     Optional[int]						,		a					:     Optional[Any]						,		a					:     Tuple						,		a					:     str						,		a					:     Tuple = 0.8						,		a					:     Tuple = 50						,		a					:     Tuple = 7.5						,		a					:     Any = None						,		a					:     Union[str, Any] = 1						,		a					:     Dict = 0.0						,		a					:     Union[str, Any] = None						,		a					:     List[Any] = "pil"						,		a					:     Dict = True						,		a					:     Optional[int] = None						,		a					:     Union[str, Any] = 1						,		)					->      str:






    """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 	_UpperCamelCase   (self					:     List[Any]						,		a					:     Union[str, Any]						,		a					:     List[str]						,		a					:     int = 0.8						,		a					:     Dict = 50						,		a					:     List[str] = 7.5						,		a					:     Any = None						,		a					:     Union[str, Any] = 1						,		a					:     Tuple = 0.0						,		a					:     Optional[int] = None						,		a					:     List[Any] = "pil"						,		a					:     Optional[Any] = True						,		a					:     int = None						,		a					:     Optional[int] = 1						,		**a					:     Optional[int]						,		)					->      Any:






    """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 	_UpperCamelCase   (self					:     Any						,		a					:     Optional[int]						,		a					:     str = 5_12						,		a					:     str = 5_12						,		a					:     Any = 50						,		a					:     List[str] = 7.5						,		a					:     Any = None						,		a					:     Dict = 1						,		a					:     int = 0.0						,		a					:     str = None						,		a					:     List[Any] = None						,		a					:     Optional[int] = "pil"						,		a					:     Dict = True						,		a					:     List[str] = None						,		a					:     Any = 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 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   if not isinstance(UpperCAmelCase			,UpperCAmelCase	) or not all(
       isinstance(UpperCAmelCase			,UpperCAmelCase	) and item >= 0 for item in citations	):
      raise ValueError('The citations should be a list of non negative integers.'	)

   citations.sort()
   A__         =    len(UpperCAmelCase	)

   for i in range(UpperCAmelCase	):
      if citations[len_citations - 1 - i] <= i:
         return i

   return len_citations


if __name__ == "__main__":
    import doctest

    doctest.testmod()


