
'''simple docstring'''
from math import pi, sqrt, tan







def  _A		(     snake_case					)      ->     float:
   if side_length < 0:
      raise ValueError("surface_area_cube() only accepts non-negative values"					)
   return 6 * side_length**2







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     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  _A		(     snake_case					)      ->     float:
   if radius < 0:
      raise ValueError("surface_area_sphere() only accepts non-negative values"					)
   return 4 * pi * radius**2







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







def  _A		(     snake_case		,							snake_case					)      ->     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  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     float:
   if radius_a < 0 or radius_a < 0 or height < 0:
      raise ValueError(
          "surface_area_conical_frustum() only accepts non-negative values"					)
   _lowercase      :				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  _A		(     snake_case		,							snake_case					)      ->     float:
   if radius < 0 or height < 0:
      raise ValueError("surface_area_cylinder() only accepts non-negative values"					)
   return 2 * pi * radius * (height + radius)







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







def  _A		(     snake_case					)      ->     float:
   if side_length < 0:
      raise ValueError("area_square() only accepts non-negative values"					)
   return side_length**2







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







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     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"					)
   _lowercase      :				Any      					=       (sidea + sidea + sidea) / 2
   _lowercase      :				List[str]      					=       sqrt(
       semi_perimeter
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)					)
   return area







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







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     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  _A		(     snake_case					)      ->     float:
   if radius < 0:
      raise ValueError("area_circle() only accepts non-negative values"					)
   return pi * radius**2







def  _A		(     snake_case		,							snake_case					)      ->     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  _A		(     snake_case		,							snake_case					)      ->     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  _A		(     snake_case		,							snake_case					)      ->     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) = }''')









'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class        a__  (							lowerCamelCase_  ,				lowerCamelCase_  ,				lowerCamelCase_  ,				unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					Dict     	=							StableUnCLIPImgaImgPipeline
   _SCREAMING_SNAKE_CASE   :					int     	=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   _SCREAMING_SNAKE_CASE   :					Any     	=							frozenset(
       []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							frozenset([]  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       32
      _lowercase      :				Any      					=       embedder_hidden_size

      # image encoding components

      _lowercase      :				Optional[int]      					=       CLIPImageProcessor(crop_size=32      ,    size=32  )

      torch.manual_seed(0  )
      _lowercase      :				Dict      					=       CLIPVisionModelWithProjection(
          CLIPVisionConfig(
              hidden_size=_UpperCamelCase      ,    projection_dim=_UpperCamelCase      ,    num_hidden_layers=5      ,    num_attention_heads=4      ,    image_size=32      ,    intermediate_size=37      ,    patch_size=1      ,    )  )

      # regular denoising components

      torch.manual_seed(0  )
      _lowercase      :				str      					=       StableUnCLIPImageNormalizer(embedding_dim=_UpperCamelCase  )
      _lowercase      :				Dict      					=       DDPMScheduler(beta_schedule="squaredcos_cap_v2"  )

      torch.manual_seed(0  )
      _lowercase      :				str      					=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

      torch.manual_seed(0  )
      _lowercase      :				Optional[Any]      					=       CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0      ,    eos_token_id=2      ,    hidden_size=_UpperCamelCase      ,    projection_dim=32      ,    intermediate_size=37      ,    layer_norm_eps=1E-0_5      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    pad_token_id=1      ,    vocab_size=1000      ,    )  )

      torch.manual_seed(0  )
      _lowercase      :				List[str]      					=       UNetaDConditionModel(
          sample_size=32      ,    in_channels=4      ,    out_channels=4      ,    down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")      ,    up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")      ,    block_out_channels=(32, 64)      ,    attention_head_dim=(2, 4)      ,    class_embed_type="projection"      ,    projection_class_embeddings_input_dim=embedder_projection_dim * 2      ,    cross_attention_dim=_UpperCamelCase      ,    layers_per_block=1      ,    upcast_attention=_UpperCamelCase      ,    use_linear_projection=_UpperCamelCase      ,    )

      torch.manual_seed(0  )
      _lowercase      :				int      					=       DDIMScheduler(
          beta_schedule="scaled_linear"      ,    beta_start=0.0_0_0_8_5      ,    beta_end=0.0_1_2      ,    prediction_type="v_prediction"      ,    set_alpha_to_one=_UpperCamelCase      ,    steps_offset=1      ,    )

      torch.manual_seed(0  )
      _lowercase      :				Dict      					=       AutoencoderKL()

      _lowercase      :				int      					=       {
          # image encoding components
          "feature_extractor": feature_extractor,
          "image_encoder": image_encoder.eval(),
          # image noising components
          "image_normalizer": image_normalizer.eval(),
          "image_noising_scheduler": image_noising_scheduler,
          # regular denoising components
          "tokenizer": tokenizer,
          "text_encoder": text_encoder.eval(),
          "unet": unet.eval(),
          "scheduler": scheduler,
          "vae": vae.eval(),
      }

      return components


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase=0      ,    _UpperCamelCase=True  ):





      """simple docstring"""





      if str(_UpperCamelCase  ).startswith("mps"  ):
         _lowercase      :				List[Any]      					=       torch.manual_seed(_UpperCamelCase  )
      else:
         _lowercase      :				List[str]      					=       torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )

      _lowercase      :				List[str]      					=       floats_tensor((1, 3, 32, 32)      ,    rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )

      if pil_image:
         _lowercase      :				Optional[Any]      					=       input_image * 0.5 + 0.5
         _lowercase      :				Optional[int]      					=       input_image.clamp(0      ,    1  )
         _lowercase      :				str      					=       input_image.cpu().permute(0      ,    2      ,    3      ,    1  ).float().numpy()
         _lowercase      :				int      					=       DiffusionPipeline.numpy_to_pil(_UpperCamelCase  )[0]

      return {
          "prompt": "An anime racoon running a marathon",
          "image": input_image,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "np",
      }


   @skip_mps
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       "cpu"  # ensure determinism for the device-dependent torch.Generator
      _lowercase      :				List[str]      					=       self.get_dummy_components()
      _lowercase      :				str      					=       StableUnCLIPImgaImgPipeline(**_UpperCamelCase  )
      _lowercase      :				int      					=       sd_pipe.to(_UpperCamelCase  )
      sd_pipe.set_progress_bar_config(disable=_UpperCamelCase  )

      _lowercase      :				str      					=       self.get_dummy_inputs(_UpperCamelCase  )
      inputs.update({"image_embeds": None}  )
      _lowercase      :				Tuple      					=       sd_pipe(**_UpperCamelCase  ).images
      _lowercase      :				str      					=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _lowercase      :				Tuple      					=       np.array([0.3_8_7_2, 0.7_2_2_4, 0.5_6_0_1, 0.4_7_4_1, 0.6_8_7_2, 0.5_8_1_4, 0.4_6_3_6, 0.3_8_6_7, 0.5_0_7_8]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       torch_device in ["cpu", "mps"]

      self._test_attention_slicing_forward_pass(test_max_difference=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       torch_device in ["cpu", "mps"]

      self._test_inference_batch_single_identical(test_max_difference=_UpperCamelCase  )






   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()      ,    reason="XFormers attention is only available with CUDA and `xformers` installed"      ,    )
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_UpperCamelCase  )




@slow
@require_torch_gpu
class        a__  (							unittest.TestCase  ):


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Any      					=       load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"  )

      _lowercase      :				Any      					=       load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"  )

      _lowercase      :				List[str]      					=       StableUnCLIPImgaImgPipeline.from_pretrained(
          "fusing/stable-unclip-2-1-l-img2img"      ,    torch_dtype=torch.floataa  )
      pipe.to(_UpperCamelCase  )
      pipe.set_progress_bar_config(disable=_UpperCamelCase  )
      # stable unclip will oom when integration tests are run on a V100,
      # so turn on memory savings
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _lowercase      :				int      					=       torch.Generator(device="cpu"  ).manual_seed(0  )
      _lowercase      :				List[Any]      					=       pipe(_UpperCamelCase      ,    "anime turle"      ,    generator=_UpperCamelCase      ,    output_type="np"  )

      _lowercase      :				str      					=       output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(_UpperCamelCase      ,    _UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"  )

      _lowercase      :				Any      					=       load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"  )

      _lowercase      :				Any      					=       StableUnCLIPImgaImgPipeline.from_pretrained(
          "fusing/stable-unclip-2-1-h-img2img"      ,    torch_dtype=torch.floataa  )
      pipe.to(_UpperCamelCase  )
      pipe.set_progress_bar_config(disable=_UpperCamelCase  )
      # stable unclip will oom when integration tests are run on a V100,
      # so turn on memory savings
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _lowercase      :				Optional[int]      					=       torch.Generator(device="cpu"  ).manual_seed(0  )
      _lowercase      :				Any      					=       pipe(_UpperCamelCase      ,    "anime turle"      ,    generator=_UpperCamelCase      ,    output_type="np"  )

      _lowercase      :				Tuple      					=       output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(_UpperCamelCase      ,    _UpperCamelCase  )






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"  )

      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      _lowercase      :				Optional[Any]      					=       StableUnCLIPImgaImgPipeline.from_pretrained(
          "fusing/stable-unclip-2-1-h-img2img"      ,    torch_dtype=torch.floataa  )
      _lowercase      :				Tuple      					=       pipe.to(_UpperCamelCase  )
      pipe.set_progress_bar_config(disable=_UpperCamelCase  )
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _lowercase      :				Optional[int]      					=       pipe(
          _UpperCamelCase      ,    "anime turtle"      ,    num_inference_steps=2      ,    output_type="np"      ,    )

      _lowercase      :				str      					=       torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 10**9






