






from math import pi, sqrt, tan







def 			A__							(      _a  :					float	):





 '''simple docstring'''







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







def 			A__							(      _a  :					float		,  _a  :					float		,  _a  :					float	):





 '''simple docstring'''







 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__							(      _a  :					float	):





 '''simple docstring'''







 if radius < 0:
  raise ValueError("""surface_area_sphere() only accepts non-negative values"""	)
 return 4 * pi * radius**2







def 			A__							(      _a  :					float	):





 '''simple docstring'''







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







def 			A__							(      _a  :					float		,  _a  :					float	):





 '''simple docstring'''







 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__							(      _a  :					float		,  _a  :					float		,  _a  :					float	):





 '''simple docstring'''







 if radius_a < 0 or radius_a < 0 or height < 0:
  raise ValueError(
      """surface_area_conical_frustum() only accepts non-negative values"""	)
 snake_case__  :    List[str]							       =(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__							(      _a  :					float		,  _a  :					float	):





 '''simple docstring'''







 if radius < 0 or height < 0:
  raise ValueError("""surface_area_cylinder() only accepts non-negative values"""	)
 return 2 * pi * radius * (height + radius)







def 			A__							(      _a  :					float		,  _a  :					float	):





 '''simple docstring'''







 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(a_		,  2	) * torus_radius * tube_radius







def 			A__							(      _a  :					float		,  _a  :					float	):





 '''simple docstring'''







 if length < 0 or width < 0:
  raise ValueError("""area_rectangle() only accepts non-negative values"""	)
 return length * width







def 			A__							(      _a  :					float	):





 '''simple docstring'''







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







def 			A__							(      _a  :					float		,  _a  :					float	):





 '''simple docstring'''







 if base < 0 or height < 0:
  raise ValueError("""area_triangle() only accepts non-negative values"""	)
 return (base * height) / 2







def 			A__							(      _a  :					float		,  _a  :					float		,  _a  :					float	):





 '''simple docstring'''







 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"""	)
 snake_case__  :    str							       =(sidea + sidea + sidea) / 2
 snake_case__  :    Union[str, Any]							       =sqrt(
     semi_perimeter
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)	)
 return area







def 			A__							(      _a  :					float		,  _a  :					float	):





 '''simple docstring'''







 if base < 0 or height < 0:
  raise ValueError("""area_parallelogram() only accepts non-negative values"""	)
 return base * height







def 			A__							(      _a  :					float		,  _a  :					float		,  _a  :					float	):





 '''simple docstring'''







 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__							(      _a  :					float	):





 '''simple docstring'''







 if radius < 0:
  raise ValueError("""area_circle() only accepts non-negative values"""	)
 return pi * radius**2







def 			A__							(      _a  :					float		,  _a  :					float	):





 '''simple docstring'''







 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__							(      _a  :					float		,  _a  :					float	):





 '''simple docstring'''







 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__							(      _a  :					int		,  _a  :					float	):





 '''simple docstring'''







 if not isinstance(a_		,  a_	) 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) = }")













from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       import torch

       from transformers.modeling_outputs import BaseModelOutput

       from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)







@add_end_docstrings(_A						)
class  _lowercase   (   _A						):
      def __init__(     self				,			**a   ):
       super().__init__(**a   )

       if self.framework == "tf":
        raise ValueError(F"The {self.__class__} is only available in PyTorch."   )

       requires_backends(self				,			"""vision"""   )
       self.check_model_type(a   )
      def __call__(     self				,			a				,			a = None				,			**a				,			):
       if "text_queries" in kwargs:
        snake_case__  :    Any							       =kwargs.pop("""text_queries"""   )

       if isinstance(a				,			(str, Image.Image)   ):
        snake_case__  :    Union[str, Any]							       ={"""image""": image, """candidate_labels""": candidate_labels}
       else:
        snake_case__  :    str							       =image
       snake_case__  :    Union[str, Any]							       =super().__call__(a				,			**a   )
       return results
      def 						lowercase__					(     self				,			**a   ):
       snake_case__  :    Union[str, Any]							       ={}
       if "threshold" in kwargs:
        snake_case__  :    Optional[Any]							       =kwargs["""threshold"""]
       if "top_k" in kwargs:
        snake_case__  :    Dict							       =kwargs["""top_k"""]
       return {}, {}, postprocess_params
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    List[Any]							       =load_image(inputs["""image"""]   )
       snake_case__  :    Dict							       =inputs["""candidate_labels"""]
       if isinstance(a				,			a   ):
        snake_case__  :    str							       =candidate_labels.split(""","""   )

       snake_case__  :    Optional[Any]							       =torch.tensor([[image.height, image.width]]				,			dtype=torch.intaa   )
       for i, candidate_label in enumerate(a   ):
        snake_case__  :    Tuple							       =self.tokenizer(a				,			return_tensors=self.framework   )
        snake_case__  :    int							       =self.image_processor(a				,			return_tensors=self.framework   )
        yield {
            "is_last": i == len(a   ) - 1,
            "target_size": target_size,
            "candidate_label": candidate_label,
            **text_inputs,
            **image_features,
        }
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Tuple							       =model_inputs.pop("""target_size"""   )
       snake_case__  :    Tuple							       =model_inputs.pop("""candidate_label"""   )
       snake_case__  :    Dict							       =model_inputs.pop("""is_last"""   )

       snake_case__  :    Any							       =self.model(**a   )

       snake_case__  :    List[Any]							       ={"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
       return model_outputs
      def 						lowercase__					(     self				,			a				,			a=0.1				,			a=None   ):
       snake_case__  :    Optional[int]							       =[]
       for model_output in model_outputs:
        snake_case__  :    Tuple							       =model_output["""candidate_label"""]
        snake_case__  :    Optional[int]							       =BaseModelOutput(a   )
        snake_case__  :    int							       =self.image_processor.post_process_object_detection(
            outputs=a				,			threshold=a				,			target_sizes=model_output["""target_size"""]   )[0]

        for index in outputs["scores"].nonzero():
         snake_case__  :    int							       =outputs["""scores"""][index].item()
         snake_case__  :    Tuple							       =self._get_bounding_box(outputs["""boxes"""][index][0]   )

         snake_case__  :    Tuple							       ={"""score""": score, """label""": label, """box""": box}
         results.append(a   )

       snake_case__  :    Tuple							       =sorted(a				,			key=lambda a   : x["score"]				,			reverse=a   )
       if top_k:
        snake_case__  :    Union[str, Any]							       =results[:top_k]

       return results



      def 						lowercase__					(     self				,			a   ):
       if self.framework != "pt":
        raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch."""   )
       snake_case__       ,   snake_case__       ,   snake_case__       ,   snake_case__  :    Dict							       =box.int().tolist()
       snake_case__  :    int							       ={
           """xmin""": xmin,
           """ymin""": ymin,
           """xmax""": xmax,
           """ymax""": ymax,
       }
       return bbox





