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





class      _lowerCAmelCase (     __a	):
				_lowercase      		=['''safety_checker''', '''feature_extractor''']




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										    f"""The configuration file of this scheduler: {scheduler} is outdated.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

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




				@property
				def 					__a						( self					)    ->				Dict[str, Any]:
							return {k: getattr(self    ,    _UpperCamelCase					) for k in self.config.keys() if not k.startswith("_"					)}




				def 					__a						( self    ,    _UpperCamelCase = "auto"					)    ->				Dict:
							if slice_size == "auto":
										# half the attention head size is usually a good trade-off between
										# speed and memory
										lowerCAmelCase_      =     self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(_UpperCamelCase					)




				def 					__a						( self					)    ->				Dict:
							self.enable_attention_slicing(_UpperCamelCase					)




				@torch.no_grad()
				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase = 0.8    ,    _UpperCamelCase = 50    ,    _UpperCamelCase = 7.5    ,    _UpperCamelCase = None    ,    _UpperCamelCase = 1    ,    _UpperCamelCase = 0.0    ,    _UpperCamelCase = None    ,    _UpperCamelCase = "pil"    ,    _UpperCamelCase = True    ,    _UpperCamelCase = None    ,    _UpperCamelCase = 1    ,    )    ->				List[str]:
							# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
							return StableDiffusionInpaintPipelineLegacy(**self.components					)(
							    prompt=_UpperCamelCase    ,    image=_UpperCamelCase    ,    mask_image=_UpperCamelCase    ,    strength=_UpperCamelCase    ,    num_inference_steps=_UpperCamelCase    ,    guidance_scale=_UpperCamelCase    ,    negative_prompt=_UpperCamelCase    ,    num_images_per_prompt=_UpperCamelCase    ,    eta=_UpperCamelCase    ,    generator=_UpperCamelCase    ,    output_type=_UpperCamelCase    ,    return_dict=_UpperCamelCase    ,    callback=_UpperCamelCase    ,    )




				@torch.no_grad()
				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase = 0.8    ,    _UpperCamelCase = 50    ,    _UpperCamelCase = 7.5    ,    _UpperCamelCase = None    ,    _UpperCamelCase = 1    ,    _UpperCamelCase = 0.0    ,    _UpperCamelCase = None    ,    _UpperCamelCase = "pil"    ,    _UpperCamelCase = True    ,    _UpperCamelCase = None    ,    _UpperCamelCase = 1    ,    **_UpperCamelCase    ,    )    ->				Optional[Any]:
							# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
							return StableDiffusionImgaImgPipeline(**self.components					)(
							    prompt=_UpperCamelCase    ,    image=_UpperCamelCase    ,    strength=_UpperCamelCase    ,    num_inference_steps=_UpperCamelCase    ,    guidance_scale=_UpperCamelCase    ,    negative_prompt=_UpperCamelCase    ,    num_images_per_prompt=_UpperCamelCase    ,    eta=_UpperCamelCase    ,    generator=_UpperCamelCase    ,    output_type=_UpperCamelCase    ,    return_dict=_UpperCamelCase    ,    callback=_UpperCamelCase    ,    callback_steps=_UpperCamelCase    ,    )




				@torch.no_grad()
				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase = 512    ,    _UpperCamelCase = 512    ,    _UpperCamelCase = 50    ,    _UpperCamelCase = 7.5    ,    _UpperCamelCase = None    ,    _UpperCamelCase = 1    ,    _UpperCamelCase = 0.0    ,    _UpperCamelCase = None    ,    _UpperCamelCase = None    ,    _UpperCamelCase = "pil"    ,    _UpperCamelCase = True    ,    _UpperCamelCase = None    ,    _UpperCamelCase = 1    ,    )    ->				List[Any]:
							# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
							return StableDiffusionPipeline(**self.components					)(
							    prompt=_UpperCamelCase    ,    height=_UpperCamelCase    ,    width=_UpperCamelCase    ,    num_inference_steps=_UpperCamelCase    ,    guidance_scale=_UpperCamelCase    ,    negative_prompt=_UpperCamelCase    ,    num_images_per_prompt=_UpperCamelCase    ,    eta=_UpperCamelCase    ,    generator=_UpperCamelCase    ,    latents=_UpperCamelCase    ,    output_type=_UpperCamelCase    ,    return_dict=_UpperCamelCase    ,    callback=_UpperCamelCase    ,    callback_steps=_UpperCamelCase    ,    )





from __future__ import annotations






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			assert n > 1 and a > 0 and greatest_common_divisor(__lowerCAmelCase   ,							__lowerCAmelCase    ) == 1
			((lowerCAmelCase_)     ,  (lowerCAmelCase_)     ,  (lowerCAmelCase_))      =     extended_gcd(__lowerCAmelCase   ,							__lowerCAmelCase    )  # Implemented below
			lowerCAmelCase_      =     (b * s) % n
			return x






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			((lowerCAmelCase_)     ,  (lowerCAmelCase_))      =     extended_euclid(__lowerCAmelCase   ,							__lowerCAmelCase    )  # Implemented below
			if b < 0:
						lowerCAmelCase_      =     (b % n + n) % n
			return b






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			lowerCAmelCase_      =     invert_modulo(__lowerCAmelCase   ,							__lowerCAmelCase    )
			lowerCAmelCase_      =     (b * s) % n
			return x






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			assert a >= 0 and b >= 0

			if b == 0:
						lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_      =     a, 1, 0
			else:
						((lowerCAmelCase_)     ,  (lowerCAmelCase_)     ,  (lowerCAmelCase_))      =     extended_gcd(__lowerCAmelCase   ,							a % b    )
						lowerCAmelCase_      =     q
						lowerCAmelCase_      =     p - q * (a // b)

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

			return (d, x, y)






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			if b == 0:
						return (1, 0)
			((lowerCAmelCase_)     ,  (lowerCAmelCase_))      =     extended_euclid(__lowerCAmelCase   ,							a % b    )
			lowerCAmelCase_      =     a // b
			return (y, x - k * y)






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			if a < b:
						lowerCAmelCase_     ,  lowerCAmelCase_      =     b, a

			while a % b != 0:
						lowerCAmelCase_     ,  lowerCAmelCase_      =     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)



