



"""simple docstring"""
from __future__ import annotations

import math
from collections.abc import Callable



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = 100, )			->      float:
  a_     :      Union[str, Any] 						=     x_start
  a_     :      Optional[Any] 						=     fnc(_SCREAMING_SNAKE_CASE	)
  a_     :      Optional[int] 						=     0.0

  for _ in range(_SCREAMING_SNAKE_CASE	):
    # Approximates curve as a sequence of linear lines and sums their length
    a_     :      int 						=     (x_end - x_start) / steps + xa
    a_     :      Optional[int] 						=     fnc(_SCREAMING_SNAKE_CASE	)
    length += math.hypot(xa - xa, fxa - fxa	)

    # Increment step
    a_     :      Any 						=     xa
    a_     :      Union[str, Any] 						=     fxa

  return length


if __name__ == "__main__":



     def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      str:
       return math.sin(10 * x	)

     print("""f(x) = sin(10 * x)""")
     print("""The length of the curve from x = -10 to x = 10 is:""")
     SCREAMING_SNAKE_CASE_				= 10
     while i <= 10_00_00:
          print(F"""With {i} steps: {line_length(f, -10, 10, i)}""")
          i *= 10




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


SCREAMING_SNAKE_CASE_				= False




class 			snake_case_						(						unittest.TestCase				):
							pass




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






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"      )
									pipe.to(a_      )
									pipe.set_progress_bar_config(disable=a_      )

									a_     :      Optional[Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"      )
									a_     :      Optional[Any] 						=     torch.manual_seed(0      )
									a_     :      Dict 						=     pipe(
									    image=a_			,     generator=a_			,     guidance_scale=7.5			,     num_inference_steps=5_0			,     output_type="numpy"			,     ).images

									a_     :      List[Any] 						=     image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									a_     :      Any 						=     np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2