




from __future__ import annotations

from math import pow, sqrt







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			dict[str, float]:



       """simple docstring"""

       if (resistance, reactance, impedance).count(0		) != 1:
              raise ValueError('''One and only one argument must be 0'''		)
       if resistance == 0:
              return {"resistance": sqrt(pow(lowercase_					,							2		) - pow(lowercase_					,							2		)		)}
       elif reactance == 0:
              return {"reactance": sqrt(pow(lowercase_					,							2		) - pow(lowercase_					,							2		)		)}
       elif impedance == 0:
              return {"impedance": sqrt(pow(lowercase_					,							2		) + pow(lowercase_					,							2		)		)}
       else:
              raise ValueError('''Exactly one argument must be 0'''		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()



"""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


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

class    a_   (  __UpperCamelCase      ):




					def __init__(						self    :  List[Any]	,    snake_case__    :  AutoencoderKL	,    snake_case__    :  CLIPTextModel	,    snake_case__    :  CLIPTokenizer	,    snake_case__    :  UNetaDConditionModel	,    snake_case__    :  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]	,    snake_case__    :  StableDiffusionSafetyChecker	,    snake_case__    :  CLIPImageProcessor	,    snake_case__    :  bool = True	,    ):
							super()._init_()

							lowerCAmelCase__   = StableDiffusionPipeline.from_pretrained(snake_case__  )
							lowerCAmelCase__   = StableDiffusionPipeline.from_pretrained(snake_case__  )
							lowerCAmelCase__   = StableDiffusionPipeline.from_pretrained(snake_case__  )
							lowerCAmelCase__   = StableDiffusionPipeline(
							    vae=snake_case__	,    text_encoder=snake_case__	,    tokenizer=snake_case__	,    unet=snake_case__	,    scheduler=snake_case__	,    safety_checker=snake_case__	,    feature_extractor=snake_case__	,    requires_safety_checker=snake_case__	,    )

							self.register_modules(pipelinea=self.pipea	,    pipelinea=self.pipea	,    pipelinea=self.pipea	,    pipelinea=self.pipea  )




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							return {k: getattr(self	,    snake_case__  ) for k in self.config.keys() if not k.startswith("""_"""  )}




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  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__   = self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							self.enable_attention_slicing(snake_case__  )




					@torch.no_grad()
					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    snake_case__    :  Union[str, List[str]]	,    snake_case__    :  int = 512	,    snake_case__    :  int = 512	,    snake_case__    :  int = 50	,    snake_case__    :  float = 7.5	,    snake_case__    :  Optional[Union[str, List[str]]] = None	,    snake_case__    :  Optional[int] = 1	,    snake_case__    :  float = 0.0	,    snake_case__    :  Optional[torch.Generator] = None	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,    snake_case__    :  int = 1	,    **snake_case__    :  List[Any]	,    ):
							return self.pipea(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )




					@torch.no_grad()
					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  Union[str, List[str]]	,    snake_case__    :  int = 512	,    snake_case__    :  int = 512	,    snake_case__    :  int = 50	,    snake_case__    :  float = 7.5	,    snake_case__    :  Optional[Union[str, List[str]]] = None	,    snake_case__    :  Optional[int] = 1	,    snake_case__    :  float = 0.0	,    snake_case__    :  Optional[torch.Generator] = None	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,    snake_case__    :  int = 1	,    **snake_case__    :  Tuple	,    ):
							return self.pipea(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )




					@torch.no_grad()
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]	,    snake_case__    :  Union[str, List[str]]	,    snake_case__    :  int = 512	,    snake_case__    :  int = 512	,    snake_case__    :  int = 50	,    snake_case__    :  float = 7.5	,    snake_case__    :  Optional[Union[str, List[str]]] = None	,    snake_case__    :  Optional[int] = 1	,    snake_case__    :  float = 0.0	,    snake_case__    :  Optional[torch.Generator] = None	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,    snake_case__    :  int = 1	,    **snake_case__    :  Optional[Any]	,    ):
							return self.pipea(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )




					@torch.no_grad()
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]	,    snake_case__    :  Union[str, List[str]]	,    snake_case__    :  int = 512	,    snake_case__    :  int = 512	,    snake_case__    :  int = 50	,    snake_case__    :  float = 7.5	,    snake_case__    :  Optional[Union[str, List[str]]] = None	,    snake_case__    :  Optional[int] = 1	,    snake_case__    :  float = 0.0	,    snake_case__    :  Optional[torch.Generator] = None	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,    snake_case__    :  int = 1	,    **snake_case__    :  str	,    ):
							return self.pipea(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )




					@torch.no_grad()
					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]	,    snake_case__    :  Union[str, List[str]]	,    snake_case__    :  int = 512	,    snake_case__    :  int = 512	,    snake_case__    :  int = 50	,    snake_case__    :  float = 7.5	,    snake_case__    :  Optional[Union[str, List[str]]] = None	,    snake_case__    :  Optional[int] = 1	,    snake_case__    :  float = 0.0	,    snake_case__    :  Optional[torch.Generator] = None	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,    snake_case__    :  int = 1	,    **snake_case__    :  Optional[Any]	,    ):
							lowerCAmelCase__   = """cuda""" if torch.cuda.is_available() else """cpu"""
							self.to(snake_case__  )

							# 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
							lowerCAmelCase__   = self.textaimg_sda_a(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )

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

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

							# Get first result from Stable Diffusion Checkpoint v1.4
							lowerCAmelCase__   = self.textaimg_sda_a(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )

							# 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]]  )




