




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


class      _a    (      __a					):
       __a							:	int  							=   ["""safety_checker""", """feature_extractor"""]
       def __init__(      self		:      List[str]  ,							lowercase		:      AutoencoderKL  ,							lowercase		:      CLIPTextModel  ,							lowercase		:      CLIPTokenizer  ,							lowercase		:      UNetaDConditionModel  ,							lowercase		:      Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  ,							lowercase		:      StableDiffusionSafetyChecker  ,							lowercase		:      CLIPImageProcessor  ,							lowercase		:      bool = True  ,							):



           '''simple docstring'''

           super().__init__()
           if hasattr(scheduler.config  ,							'''steps_offset'''							) and scheduler.config.steps_offset != 1:
               UpperCAmelCase     			=						(
                   f"The configuration file of this scheduler: {scheduler} is outdated.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							)
               UpperCAmelCase     			=						dict(scheduler.config							)
               UpperCAmelCase     			=						1
               UpperCAmelCase     			=						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        A (      self		:      List[str]							):



           '''simple docstring'''

           return {k: getattr(self  ,							lowercase							) for k in self.config.keys() if not k.startswith('''_'''							)}
       def        A (      self		:      int  ,							lowercase		:      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
               UpperCAmelCase     			=						self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(lowercase							)
       def        A (      self		:      Any							):



           '''simple docstring'''

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




