
"""simple docstring"""





def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				)
		for i in range(length - 1				):
				__SCREAMING_SNAKE_CASE            = i
				for k in range(i + 1  ,				UpperCamelCase_				):
						if collection[k] < collection[least]:
								__SCREAMING_SNAKE_CASE            = k
				if least != i:
						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = (collection[i], collection[least])
		return collection


if __name__ == "__main__":
							__magic_name__       =			input("Enter numbers separated by a comma:\n").strip()
							__magic_name__       =			[int(item) for item in user_input.split(",")]
							print(selection_sort(unsorted))






"""simple docstring"""




from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


_A			:       int   		=       """CompVis/stable-diffusion-v1-1"""
_A			:       Any   		=       """CompVis/stable-diffusion-v1-2"""
_A			:       Optional[int]   		=       """CompVis/stable-diffusion-v1-3"""
_A			:       Union[str, Any]   		=       """CompVis/stable-diffusion-v1-4"""

class 		a__						(      a_						):
   def __init__(			self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a = True	,    ):
          super()._init_()

          lowercase    :				Optional[Any]       		=							StableDiffusionPipeline.from_pretrained(_a				)
          lowercase    :				str       		=							StableDiffusionPipeline.from_pretrained(_a				)
          lowercase    :				Dict       		=							StableDiffusionPipeline.from_pretrained(_a				)
          lowercase    :				Union[str, Any]       		=							StableDiffusionPipeline(
              vae=_a	,    text_encoder=_a	,    tokenizer=_a	,    unet=_a	,    scheduler=_a	,    safety_checker=_a	,    feature_extractor=_a	,    requires_safety_checker=_a	,    )

          self.register_modules(pipelinea=self.pipea	,    pipelinea=self.pipea	,    pipelinea=self.pipea	,    pipelinea=self.pipea				)
   @property
   def 			__magic_name__			(			self				):
          return {k: getattr(self	,    _a				) for k in self.config.keys() if not k.startswith("_"				)}
   def 			__magic_name__			(			self	,    _a = "auto"				):
          if slice_size == "auto":
                 # half the attention head size is usually a good trade-off between
                 # speed and memory
                 lowercase    :				str       		=							self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(_a				)
   def 			__magic_name__			(			self				):
          self.enable_attention_slicing(_a				)
   @torch.no_grad()
   def 			__magic_name__			(			self	,    _a	,    _a = 512	,    _a = 512	,    _a = 50	,    _a = 7.5	,    _a = None	,    _a = 1	,    _a = 0.0	,    _a = None	,    _a = None	,    _a = "pil"	,    _a = True	,    _a = None	,    _a = 1	,    **_a	,    ):
          return self.pipea(
              prompt=_a	,    height=_a	,    width=_a	,    num_inference_steps=_a	,    guidance_scale=_a	,    negative_prompt=_a	,    num_images_per_prompt=_a	,    eta=_a	,    generator=_a	,    latents=_a	,    output_type=_a	,    return_dict=_a	,    callback=_a	,    callback_steps=_a	,    **_a	,    )
   @torch.no_grad()
   def 			__magic_name__			(			self	,    _a	,    _a = 512	,    _a = 512	,    _a = 50	,    _a = 7.5	,    _a = None	,    _a = 1	,    _a = 0.0	,    _a = None	,    _a = None	,    _a = "pil"	,    _a = True	,    _a = None	,    _a = 1	,    **_a	,    ):
          return self.pipea(
              prompt=_a	,    height=_a	,    width=_a	,    num_inference_steps=_a	,    guidance_scale=_a	,    negative_prompt=_a	,    num_images_per_prompt=_a	,    eta=_a	,    generator=_a	,    latents=_a	,    output_type=_a	,    return_dict=_a	,    callback=_a	,    callback_steps=_a	,    **_a	,    )
   @torch.no_grad()
   def 			__magic_name__			(			self	,    _a	,    _a = 512	,    _a = 512	,    _a = 50	,    _a = 7.5	,    _a = None	,    _a = 1	,    _a = 0.0	,    _a = None	,    _a = None	,    _a = "pil"	,    _a = True	,    _a = None	,    _a = 1	,    **_a	,    ):
          return self.pipea(
              prompt=_a	,    height=_a	,    width=_a	,    num_inference_steps=_a	,    guidance_scale=_a	,    negative_prompt=_a	,    num_images_per_prompt=_a	,    eta=_a	,    generator=_a	,    latents=_a	,    output_type=_a	,    return_dict=_a	,    callback=_a	,    callback_steps=_a	,    **_a	,    )
   @torch.no_grad()
   def 			__magic_name__			(			self	,    _a	,    _a = 512	,    _a = 512	,    _a = 50	,    _a = 7.5	,    _a = None	,    _a = 1	,    _a = 0.0	,    _a = None	,    _a = None	,    _a = "pil"	,    _a = True	,    _a = None	,    _a = 1	,    **_a	,    ):
          return self.pipea(
              prompt=_a	,    height=_a	,    width=_a	,    num_inference_steps=_a	,    guidance_scale=_a	,    negative_prompt=_a	,    num_images_per_prompt=_a	,    eta=_a	,    generator=_a	,    latents=_a	,    output_type=_a	,    return_dict=_a	,    callback=_a	,    callback_steps=_a	,    **_a	,    )

   @torch.no_grad()
   def 			__magic_name__			(			self	,    _a	,    _a = 512	,    _a = 512	,    _a = 50	,    _a = 7.5	,    _a = None	,    _a = 1	,    _a = 0.0	,    _a = None	,    _a = None	,    _a = "pil"	,    _a = True	,    _a = None	,    _a = 1	,    **_a	,    ):
          lowercase    :				List[Any]       		=							"cuda" if torch.cuda.is_available() else "cpu"
          self.to(_a				)

          # Checks if the height and width are divisible by 8 or not
          if height % 8 != 0 or width % 8 != 0:
                 raise ValueError(f"""`height` and `width` must be divisible by 8 but are {height} and {width}."""				)

          # Get first result from Stable Diffusion Checkpoint v1.1
          lowercase    :				List[Any]       		=							self.textaimg_sda_a(
              prompt=_a	,    height=_a	,    width=_a	,    num_inference_steps=_a	,    guidance_scale=_a	,    negative_prompt=_a	,    num_images_per_prompt=_a	,    eta=_a	,    generator=_a	,    latents=_a	,    output_type=_a	,    return_dict=_a	,    callback=_a	,    callback_steps=_a	,    **_a	,    )

          # Get first result from Stable Diffusion Checkpoint v1.2
          lowercase    :				Any       		=							self.textaimg_sda_a(
              prompt=_a	,    height=_a	,    width=_a	,    num_inference_steps=_a	,    guidance_scale=_a	,    negative_prompt=_a	,    num_images_per_prompt=_a	,    eta=_a	,    generator=_a	,    latents=_a	,    output_type=_a	,    return_dict=_a	,    callback=_a	,    callback_steps=_a	,    **_a	,    )

          # Get first result from Stable Diffusion Checkpoint v1.3
          lowercase    :				str       		=							self.textaimg_sda_a(
              prompt=_a	,    height=_a	,    width=_a	,    num_inference_steps=_a	,    guidance_scale=_a	,    negative_prompt=_a	,    num_images_per_prompt=_a	,    eta=_a	,    generator=_a	,    latents=_a	,    output_type=_a	,    return_dict=_a	,    callback=_a	,    callback_steps=_a	,    **_a	,    )

          # Get first result from Stable Diffusion Checkpoint v1.4
          lowercase    :				Optional[int]       		=							self.textaimg_sda_a(
              prompt=_a	,    height=_a	,    width=_a	,    num_inference_steps=_a	,    guidance_scale=_a	,    negative_prompt=_a	,    num_images_per_prompt=_a	,    eta=_a	,    generator=_a	,    latents=_a	,    output_type=_a	,    return_dict=_a	,    callback=_a	,    callback_steps=_a	,    **_a	,    )

          # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
          return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]				)


