





'''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


lowercase__   :				int      =   '''CompVis/stable-diffusion-v1-1'''
lowercase__   :				List[Any]      =   '''CompVis/stable-diffusion-v1-2'''
lowercase__   :				Dict      =   '''CompVis/stable-diffusion-v1-3'''
lowercase__   :				Tuple      =   '''CompVis/stable-diffusion-v1-4'''


class 			SCREAMING_SNAKE_CASE   (a__							):


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase = True						,     ):

          '''simple docstring'''






          super()._init_()

          __A						:			Union[str, Any]								=	StableDiffusionPipeline.from_pretrained(_UpperCAmelCase)
          __A						:			Union[str, Any]								=	StableDiffusionPipeline.from_pretrained(_UpperCAmelCase)
          __A						:			Any								=	StableDiffusionPipeline.from_pretrained(_UpperCAmelCase)
          __A						:			Any								=	StableDiffusionPipeline(
              vae=_UpperCAmelCase						,     text_encoder=_UpperCAmelCase						,     tokenizer=_UpperCAmelCase						,     unet=_UpperCAmelCase						,     scheduler=_UpperCAmelCase						,     safety_checker=_UpperCAmelCase						,     feature_extractor=_UpperCAmelCase						,     requires_safety_checker=_UpperCAmelCase						,     )

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


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return {k: getattr(self						,     _UpperCAmelCase) for k in self.config.keys() if not k.startswith('_')}


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase = "auto"):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          self.enable_attention_slicing(_UpperCAmelCase)


     @torch.no_grad()
     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase = 512						,     _UpperCAmelCase = 512						,     _UpperCAmelCase = 50						,     _UpperCAmelCase = 7.5						,     _UpperCAmelCase = None						,     _UpperCAmelCase = 1						,     _UpperCAmelCase = 0.0						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = "pil"						,     _UpperCAmelCase = True						,     _UpperCAmelCase = None						,     _UpperCAmelCase = 1						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          return self.pipea(
              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						,     **_UpperCAmelCase						,     )


     @torch.no_grad()
     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase = 512						,     _UpperCAmelCase = 512						,     _UpperCAmelCase = 50						,     _UpperCAmelCase = 7.5						,     _UpperCAmelCase = None						,     _UpperCAmelCase = 1						,     _UpperCAmelCase = 0.0						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = "pil"						,     _UpperCAmelCase = True						,     _UpperCAmelCase = None						,     _UpperCAmelCase = 1						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          return self.pipea(
              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						,     **_UpperCAmelCase						,     )


     @torch.no_grad()
     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase = 512						,     _UpperCAmelCase = 512						,     _UpperCAmelCase = 50						,     _UpperCAmelCase = 7.5						,     _UpperCAmelCase = None						,     _UpperCAmelCase = 1						,     _UpperCAmelCase = 0.0						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = "pil"						,     _UpperCAmelCase = True						,     _UpperCAmelCase = None						,     _UpperCAmelCase = 1						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          return self.pipea(
              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						,     **_UpperCAmelCase						,     )


     @torch.no_grad()
     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase = 512						,     _UpperCAmelCase = 512						,     _UpperCAmelCase = 50						,     _UpperCAmelCase = 7.5						,     _UpperCAmelCase = None						,     _UpperCAmelCase = 1						,     _UpperCAmelCase = 0.0						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = "pil"						,     _UpperCAmelCase = True						,     _UpperCAmelCase = None						,     _UpperCAmelCase = 1						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          return self.pipea(
              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						,     **_UpperCAmelCase						,     )


     @torch.no_grad()
     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase = 512						,     _UpperCAmelCase = 512						,     _UpperCAmelCase = 50						,     _UpperCAmelCase = 7.5						,     _UpperCAmelCase = None						,     _UpperCAmelCase = 1						,     _UpperCAmelCase = 0.0						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = "pil"						,     _UpperCAmelCase = True						,     _UpperCAmelCase = None						,     _UpperCAmelCase = 1						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          __A						:			Optional[Any]								=	'cuda' if torch.cuda.is_available() else 'cpu'
          self.to(_UpperCAmelCase)

          # 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
          __A						:			Dict								=	self.textaimg_sda_a(
              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						,     **_UpperCAmelCase						,     )

          # Get first result from Stable Diffusion Checkpoint v1.2
          __A						:			Any								=	self.textaimg_sda_a(
              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						,     **_UpperCAmelCase						,     )

          # Get first result from Stable Diffusion Checkpoint v1.3
          __A						:			Tuple								=	self.textaimg_sda_a(
              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						,     **_UpperCAmelCase						,     )

          # Get first result from Stable Diffusion Checkpoint v1.4
          __A						:			Dict								=	self.textaimg_sda_a(
              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						,     **_UpperCAmelCase						,     )

          # 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]])






'''simple docstring'''




from math import pi, sqrt, tan






def 	_lowerCAmelCase      (  __snake_case  :			float      )					->					float:
     if side_length < 0:
          raise ValueError('surface_area_cube() only accepts non-negative values'      )
     return 6 * side_length**2






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if length < 0 or breadth < 0 or height < 0:
          raise ValueError('surface_area_cuboid() only accepts non-negative values'      )
     return 2 * ((length * breadth) + (breadth * height) + (length * height))






def 	_lowerCAmelCase      (  __snake_case  :			float      )					->					float:
     if radius < 0:
          raise ValueError('surface_area_sphere() only accepts non-negative values'      )
     return 4 * pi * radius**2






def 	_lowerCAmelCase      (  __snake_case  :			float      )					->					float:
     if radius < 0:
          raise ValueError('surface_area_hemisphere() only accepts non-negative values'      )
     return 3 * pi * radius**2






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if radius < 0 or height < 0:
          raise ValueError('surface_area_cone() only accepts non-negative values'      )
     return pi * radius * (radius + (height**2 + radius**2) ** 0.5)






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if radius_a < 0 or radius_a < 0 or height < 0:
          raise ValueError(
              'surface_area_conical_frustum() only accepts non-negative values'      )
     __A						:			Union[str, Any]								=	(height**2 + (radius_a - radius_a) ** 2) ** 0.5
     return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if radius < 0 or height < 0:
          raise ValueError('surface_area_cylinder() only accepts non-negative values'      )
     return 2 * pi * radius * (height + radius)






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if torus_radius < 0 or tube_radius < 0:
          raise ValueError('surface_area_torus() only accepts non-negative values'      )
     if torus_radius < tube_radius:
          raise ValueError(
              'surface_area_torus() does not support spindle or self intersecting tori'      )
     return 4 * pow(__snake_case			,							2      ) * torus_radius * tube_radius






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if length < 0 or width < 0:
          raise ValueError('area_rectangle() only accepts non-negative values'      )
     return length * width






def 	_lowerCAmelCase      (  __snake_case  :			float      )					->					float:
     if side_length < 0:
          raise ValueError('area_square() only accepts non-negative values'      )
     return side_length**2






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if base < 0 or height < 0:
          raise ValueError('area_triangle() only accepts non-negative values'      )
     return (base * height) / 2






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if sidea < 0 or sidea < 0 or sidea < 0:
          raise ValueError('area_triangle_three_sides() only accepts non-negative values'      )
     elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
          raise ValueError('Given three sides do not form a triangle'      )
     __A						:			int								=	(sidea + sidea + sidea) / 2
     __A						:			Tuple								=	sqrt(
         semi_perimeter
         * (semi_perimeter - sidea)
         * (semi_perimeter - sidea)
         * (semi_perimeter - sidea)      )
     return area






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if base < 0 or height < 0:
          raise ValueError('area_parallelogram() only accepts non-negative values'      )
     return base * height






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if basea < 0 or basea < 0 or height < 0:
          raise ValueError('area_trapezium() only accepts non-negative values'      )
     return 1 / 2 * (basea + basea) * height






def 	_lowerCAmelCase      (  __snake_case  :			float      )					->					float:
     if radius < 0:
          raise ValueError('area_circle() only accepts non-negative values'      )
     return pi * radius**2






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if radius_x < 0 or radius_y < 0:
          raise ValueError('area_ellipse() only accepts non-negative values'      )
     return pi * radius_x * radius_y






def 	_lowerCAmelCase      (  __snake_case  :			float			,							__snake_case  :			float      )					->					float:
     if diagonal_a < 0 or diagonal_a < 0:
          raise ValueError('area_rhombus() only accepts non-negative values'      )
     return 1 / 2 * diagonal_a * diagonal_a






def 	_lowerCAmelCase      (  __snake_case  :			int			,							__snake_case  :			float      )					->					float:
     if not isinstance(__snake_case			,							__snake_case      ) or sides < 3:
          raise ValueError(
              'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides'      )
     elif length < 0:
          raise ValueError(
              'area_reg_polygon() only accepts non-negative values as \
length of a side'      )
     return (sides * length**2) / (4 * tan(pi / sides      ))
     return (sides * length**2) / (4 * tan(pi / sides      ))


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

  print('''[DEMO] Areas of various geometric shapes: \n''')
  print(f"""Rectangle: {area_rectangle(10, 20) = }""")
  print(f"""Square: {area_square(10) = }""")
  print(f"""Triangle: {area_triangle(10, 10) = }""")
  print(f"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
  print(f"""Parallelogram: {area_parallelogram(10, 20) = }""")
  print(f"""Rhombus: {area_rhombus(10, 20) = }""")
  print(f"""Trapezium: {area_trapezium(10, 20, 30) = }""")
  print(f"""Circle: {area_circle(20) = }""")
  print(f"""Ellipse: {area_ellipse(10, 20) = }""")
  print('''\nSurface Areas of various geometric shapes: \n''')
  print(f"""Cube: {surface_area_cube(20) = }""")
  print(f"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
  print(f"""Sphere: {surface_area_sphere(20) = }""")
  print(f"""Hemisphere: {surface_area_hemisphere(20) = }""")
  print(f"""Cone: {surface_area_cone(10, 20) = }""")
  print(f"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
  print(f"""Cylinder: {surface_area_cylinder(10, 20) = }""")
  print(f"""Torus: {surface_area_torus(20, 10) = }""")
  print(f"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
  print(f"""Square: {area_reg_polygon(4, 10) = }""")
  print(f"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")