
"""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


SCREAMING_SNAKE_CASE_         =  logging.get_logger(__name__)  # pylint: disable=invalid-name
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE				:							Optional[int]							 =		["safety_checker", "feature_extractor"]




      def __init__(							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = True				,    )  ->   int:
          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                  F"The configuration file of this scheduler: {scheduler} is outdated.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							)  ->   Dict[str, Any]:
          return {k: getattr(self				,    lowercase_							) for k in self.config.keys() if not k.startswith('_'							)}




      def       a_ (							self				,    lowercase_ = "auto"							)  ->   Optional[int]:
          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							)  ->   List[Any]:
          self.enable_attention_slicing(lowercase_							)




      @torch.no_grad()
      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = 0.8				,    lowercase_ = 5_0				,    lowercase_ = 7.5				,    lowercase_ = None				,    lowercase_ = 1				,    lowercase_ = 0.0				,    lowercase_ = None				,    lowercase_ = "pil"				,    lowercase_ = True				,    lowercase_ = None				,    lowercase_ = 1				,    )  ->   Optional[int]:
          # 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=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				,    lowercase_				,    lowercase_				,    lowercase_ = 0.8				,    lowercase_ = 5_0				,    lowercase_ = 7.5				,    lowercase_ = None				,    lowercase_ = 1				,    lowercase_ = 0.0				,    lowercase_ = None				,    lowercase_ = "pil"				,    lowercase_ = True				,    lowercase_ = None				,    lowercase_ = 1				,    **lowercase_				,    )  ->   Tuple:
          # 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=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				,    lowercase_				,    lowercase_ = 5_1_2				,    lowercase_ = 5_1_2				,    lowercase_ = 5_0				,    lowercase_ = 7.5				,    lowercase_ = None				,    lowercase_ = 1				,    lowercase_ = 0.0				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = "pil"				,    lowercase_ = True				,    lowercase_ = None				,    lowercase_ = 1				,    )  ->   Optional[int]:
          # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
          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"""

SCREAMING_SNAKE_CASE_         =  {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

SCREAMING_SNAKE_CASE_         =  [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

SCREAMING_SNAKE_CASE_         =  [0 for x in range(len(graph))]
SCREAMING_SNAKE_CASE_         =  []




def       lowercase__  (   lowerCAmelCase			:   Optional[int]	,			lowerCAmelCase			:   Any	)	-> List[str]:






    """simple docstring"""




    UpperCAmelCase       =   1
    while stack:
        UpperCAmelCase       =   stack.pop()
        print(lowerCAmelCase	,			clothes[current_clothing]	)
        order += 1




def       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Dict	)	-> Optional[Any]:






    """simple docstring"""




    UpperCAmelCase       =   1
    for v in graph[u]:
        if not visited[v]:
            depth_first_search(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)

    stack.append(lowerCAmelCase	)




def       lowercase__  (   lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   Dict	)	-> Optional[Any]:






    """simple docstring"""




    for v in range(len(lowerCAmelCase	)	):
        if not visited[v]:
            depth_first_search(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)


if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)



