






'''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      :     Optional[int]       	=logging.get_logger(__name__)  # pylint: disable=invalid-name

class _lowercase      (							__a						):
							a							 =			["""safety_checker""", """feature_extractor"""]




							def __init__(				self:							List[str]     ,				UpperCamelCase__:							AutoencoderKL     ,				UpperCamelCase__:							CLIPTextModel     ,				UpperCamelCase__:							CLIPTokenizer     ,				UpperCamelCase__:							UNetaDConditionModel     ,				UpperCamelCase__:							Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,				UpperCamelCase__:							StableDiffusionSafetyChecker     ,				UpperCamelCase__:							CLIPImageProcessor     ,				UpperCamelCase__:							bool = True     ,				):
														super().__init__()
														if hasattr(scheduler.config     ,				"""steps_offset"""	) and scheduler.config.steps_offset != 1:
																					lowerCamelCase__   :      Tuple			    =			(
																					    F'''The configuration file of this scheduler: {scheduler} is outdated.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			    =			dict(scheduler.config	)
																					lowerCamelCase__   :      List[str]			    =			1
																					lowerCamelCase__   :      List[Any]			    =			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 						lowerCamelCase_							(				self:							List[str]	):
														return {k: getattr(self     ,				UpperCamelCase__	) for k in self.config.keys() if not k.startswith("""_"""	)}




							def 						lowerCamelCase_							(				self:							int     ,				UpperCamelCase__:							Optional[Union[str, int]] = "auto"	):

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




							def 						lowerCamelCase_							(				self:							Any	):
														self.enable_attention_slicing(UpperCamelCase__	)




							@torch.no_grad()
							def 						lowerCamelCase_							(				self:							int     ,				UpperCamelCase__:							Union[str, List[str]]     ,				UpperCamelCase__:							Union[torch.FloatTensor, PIL.Image.Image]     ,				UpperCamelCase__:							Union[torch.FloatTensor, PIL.Image.Image]     ,				UpperCamelCase__:							float = 0.8     ,				UpperCamelCase__:							Optional[int] = 50     ,				UpperCamelCase__:							Optional[float] = 7.5     ,				UpperCamelCase__:							Optional[Union[str, List[str]]] = None     ,				UpperCamelCase__:							Optional[int] = 1     ,				UpperCamelCase__:							Optional[float] = 0.0     ,				UpperCamelCase__:							Optional[torch.Generator] = None     ,				UpperCamelCase__:							Optional[str] = "pil"     ,				UpperCamelCase__:							bool = True     ,				UpperCamelCase__:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,				UpperCamelCase__:							int = 1     ,				):
														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 						lowerCamelCase_							(				self:							str     ,				UpperCamelCase__:							Union[str, List[str]]     ,				UpperCamelCase__:							Union[torch.FloatTensor, PIL.Image.Image]     ,				UpperCamelCase__:							float = 0.8     ,				UpperCamelCase__:							Optional[int] = 50     ,				UpperCamelCase__:							Optional[float] = 7.5     ,				UpperCamelCase__:							Optional[Union[str, List[str]]] = None     ,				UpperCamelCase__:							Optional[int] = 1     ,				UpperCamelCase__:							Optional[float] = 0.0     ,				UpperCamelCase__:							Optional[torch.Generator] = None     ,				UpperCamelCase__:							Optional[str] = "pil"     ,				UpperCamelCase__:							bool = True     ,				UpperCamelCase__:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,				UpperCamelCase__:							int = 1     ,				**UpperCamelCase__:							int     ,				):
														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 						lowerCamelCase_							(				self:							Any     ,				UpperCamelCase__:							Union[str, List[str]]     ,				UpperCamelCase__:							int = 512     ,				UpperCamelCase__:							int = 512     ,				UpperCamelCase__:							int = 50     ,				UpperCamelCase__:							float = 7.5     ,				UpperCamelCase__:							Optional[Union[str, List[str]]] = None     ,				UpperCamelCase__:							Optional[int] = 1     ,				UpperCamelCase__:							float = 0.0     ,				UpperCamelCase__:							Optional[torch.Generator] = None     ,				UpperCamelCase__:							Optional[torch.FloatTensor] = None     ,				UpperCamelCase__:							Optional[str] = "pil"     ,				UpperCamelCase__:							bool = True     ,				UpperCamelCase__:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,				UpperCamelCase__:							int = 1     ,				):
														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__     ,				)







'''simple docstring'''







from __future__ import annotations





def 		snake_case_				(_a      :				list[list[int]]       ,  _a      :				list[list[int]]	):
    assert len(_a	) == len(_a	)

    UpperCAmelCase     			=						len(_a	)
    UpperCAmelCase     			=						list(range(_a	)	)
    UpperCAmelCase     			=						[-1] * n  # who the donor has donated to
    UpperCAmelCase     			=						[-1] * n  # who the recipient has received from
    UpperCAmelCase     			=						[0] * n

    while unmatched_donors:
        UpperCAmelCase     			=						unmatched_donors[0]
        UpperCAmelCase     			=						donor_pref[donor]
        UpperCAmelCase     			=						donor_preference[num_donations[donor]]
        num_donations[donor] += 1
        UpperCAmelCase     			=						recipient_pref[recipient]
        UpperCAmelCase     			=						rec_record[recipient]

        if prev_donor != -1:
            if rec_preference.index(_a	) > rec_preference.index(_a	):
                UpperCAmelCase     			=						donor
                UpperCAmelCase     			=						recipient
                unmatched_donors.append(_a	)
                unmatched_donors.remove(_a	)
        else:
            UpperCAmelCase     			=						donor
            UpperCAmelCase     			=						recipient
            unmatched_donors.remove(_a	)
    return donor_record




