
'''simple docstring'''






from __future__ import annotations

import math
from collections.abc import Callable







def 		A_							(						_lowerCAmelCase :     Callable[[int | float], int | float]					,						_lowerCAmelCase :     int | float					,						_lowerCAmelCase :     int | float					,						_lowerCAmelCase :     int = 100					,						):



      """simple docstring"""




      _lowerCamelCase					: List[Any]		    = x_start
      _lowerCamelCase					: Optional[int]		    = fnc(SCREAMING_SNAKE_CASE_				)
      _lowerCamelCase					: Any		    = 0.0

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

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

      return length


if __name__ == "__main__":







 def 		A_							(						_lowerCAmelCase :     List[Any]				):



       """simple docstring"""




       return math.sin(10 * x				)

 print('f(x) = sin(10 * x)')
 print('The length of the curve from x = -10 to x = 10 is:')
 UpperCAmelCase_							: Any =							10
 while i <= 10_0000:
  print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
  i *= 10

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
		from .camera import create_pan_cameras
		from .pipeline_shap_e import ShapEPipeline
		from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
		from .renderer import (
		    BoundingBoxVolume,
		    ImportanceRaySampler,
		    MLPNeRFModelOutput,
		    MLPNeRSTFModel,
		    ShapEParamsProjModel,
		    ShapERenderer,
		    StratifiedRaySampler,
		    VoidNeRFModel,
		)