
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 	A__				(	_snake_case      ):
				lowercase            =    ["""safety_checker""", """feature_extractor"""]






				def __init__(		self   : int       ,	a   : AutoencoderKL       ,	a   : CLIPTextModel       ,	a   : CLIPTokenizer       ,	a   : UNetaDConditionModel       ,	a   : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,	a   : StableDiffusionSafetyChecker       ,	a   : CLIPImageProcessor       ,	a   : bool = True       ,	):






							'''simple docstring'''







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										    f'''The configuration file of this scheduler: {scheduler} is outdated.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

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






				@property
				def 				_lowerCamelCase						(		self   : str  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : List[Any]       ,	a   : Optional[Union[str, int]] = "auto"  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : str  ):






							'''simple docstring'''







							self.enable_attention_slicing(lowercase_  )






				@torch.no_grad()
				def 				_lowerCamelCase						(		self   : List[str]       ,	a   : Union[str, List[str]]       ,	a   : Union[torch.FloatTensor, PIL.Image.Image]       ,	a   : Union[torch.FloatTensor, PIL.Image.Image]       ,	a   : float = 0.8       ,	a   : Optional[int] = 50       ,	a   : Optional[float] = 7.5       ,	a   : Optional[Union[str, List[str]]] = None       ,	a   : Optional[int] = 1       ,	a   : Optional[float] = 0.0       ,	a   : Optional[torch.Generator] = None       ,	a   : Optional[str] = "pil"       ,	a   : bool = True       ,	a   : Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,	a   : int = 1       ,	):






							'''simple docstring'''







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






				@torch.no_grad()
				def 				_lowerCamelCase						(		self   : Any       ,	a   : Union[str, List[str]]       ,	a   : Union[torch.FloatTensor, PIL.Image.Image]       ,	a   : float = 0.8       ,	a   : Optional[int] = 50       ,	a   : Optional[float] = 7.5       ,	a   : Optional[Union[str, List[str]]] = None       ,	a   : Optional[int] = 1       ,	a   : Optional[float] = 0.0       ,	a   : Optional[torch.Generator] = None       ,	a   : Optional[str] = "pil"       ,	a   : bool = True       ,	a   : Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,	a   : int = 1       ,	**a   : Optional[int]       ,	):






							'''simple docstring'''







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






				@torch.no_grad()
				def 				_lowerCamelCase						(		self   : List[Any]       ,	a   : Union[str, List[str]]       ,	a   : int = 512       ,	a   : int = 512       ,	a   : int = 50       ,	a   : float = 7.5       ,	a   : Optional[Union[str, List[str]]] = None       ,	a   : Optional[int] = 1       ,	a   : float = 0.0       ,	a   : Optional[torch.Generator] = None       ,	a   : Optional[torch.FloatTensor] = None       ,	a   : Optional[str] = "pil"       ,	a   : bool = True       ,	a   : Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,	a   : int = 1       ,	):






							'''simple docstring'''







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


'''simple docstring'''

from __future__ import annotations




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 assert n > 1 and a > 0 and greatest_common_divisor(UpperCAmelCase_    ,			UpperCAmelCase_      ) == 1
 ((UpperCAmelCase)   ,					(UpperCAmelCase)   ,					(UpperCAmelCase))			:      Dict		   =					extended_gcd(UpperCAmelCase_    ,			UpperCAmelCase_      )  # Implemented below
 UpperCAmelCase			:      int		   =					(b * s) % n
 return x




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 ((UpperCAmelCase)   ,					(UpperCAmelCase))			:      int		   =					extended_euclid(UpperCAmelCase_    ,			UpperCAmelCase_      )  # Implemented below
 if b < 0:
  UpperCAmelCase			:      Optional[int]		   =					(b % n + n) % n
 return b




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      Tuple		   =					invert_modulo(UpperCAmelCase_    ,			UpperCAmelCase_      )
 UpperCAmelCase			:      List[Any]		   =					(b * s) % n
 return x




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 assert a >= 0 and b >= 0

 if b == 0:
  UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      Dict		   =					a, 1, 0
 else:
  ((UpperCAmelCase)   ,					(UpperCAmelCase)   ,					(UpperCAmelCase))			:      Optional[int]		   =					extended_gcd(UpperCAmelCase_    ,			a % b      )
  UpperCAmelCase			:      Dict		   =					q
  UpperCAmelCase			:      int		   =					p - q * (a // b)

 assert a % d == 0 and b % d == 0
 assert d == a * x + b * y

 return (d, x, y)




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 if b == 0:
  return (1, 0)
 ((UpperCAmelCase)   ,					(UpperCAmelCase))			:      Tuple		   =					extended_euclid(UpperCAmelCase_    ,			a % b      )
 UpperCAmelCase			:      Tuple		   =					a // b
 return (y, x - k * y)




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 if a < b:
  UpperCAmelCase   ,					UpperCAmelCase			:      Union[str, Any]		   =					b, a

 while a % b != 0:
  UpperCAmelCase   ,					UpperCAmelCase			:      int		   =					b, a % b

 return b


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

 testmod(name="modular_division", verbose=True)
 testmod(name="modular_division2", verbose=True)
 testmod(name="invert_modulo", verbose=True)
 testmod(name="extended_gcd", verbose=True)
 testmod(name="extended_euclid", verbose=True)
 testmod(name="greatest_common_divisor", verbose=True)




