
"""simple docstring"""





from __future__ import annotations

from math import pi, pow







def       __a	(						__lowerCamelCase    ):
   if side_length < 0:
      raise ValueError("vol_cube() only accepts non-negative values"    )
   return pow(__lowerCamelCase,						3    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   if height < 0 or radius < 0:
      raise ValueError("vol_spherical_cap() only accepts non-negative values"    )
   # Volume is 1/3 pi * height squared * (3 * radius - height)
   return 1 / 3 * pi * pow(__lowerCamelCase,						2    ) * (3 * radius - height)







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   if radius_a < 0 or radius_a < 0 or centers_distance < 0:
      raise ValueError("vol_spheres_intersect() only accepts non-negative values"    )
   if centers_distance == 0:
      return vol_sphere(min(__lowerCamelCase,						__lowerCamelCase    )    )

   UpperCAmelCase_   :   List[str]     	=       (
       (radius_a - radius_a + centers_distance)
       * (radius_a + radius_a - centers_distance)
       / (2 * centers_distance)
   )
   UpperCAmelCase_   :   Any     	=       (
       (radius_a - radius_a + centers_distance)
       * (radius_a + radius_a - centers_distance)
       / (2 * centers_distance)
   )

   return vol_spherical_cap(__lowerCamelCase,						__lowerCamelCase    ) + vol_spherical_cap(__lowerCamelCase,						__lowerCamelCase    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):

   if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
      raise ValueError(
          "vol_spheres_union() only accepts non-negative values, non-zero radius"    )

   if centers_distance == 0:
      return vol_sphere(max(__lowerCamelCase,						__lowerCamelCase    )    )

   return (
       vol_sphere(__lowerCamelCase    )
       + vol_sphere(__lowerCamelCase    )
       - vol_spheres_intersect(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   )







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   if width < 0 or height < 0 or length < 0:
      raise ValueError("vol_cuboid() only accepts non-negative values"    )
   return float(width * height * length    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   if height < 0 or area_of_base < 0:
      raise ValueError("vol_cone() only accepts non-negative values"    )
   return area_of_base * height / 3.0







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   if height < 0 or radius < 0:
      raise ValueError("vol_right_circ_cone() only accepts non-negative values"    )
   return pi * pow(__lowerCamelCase,						2    ) * height / 3.0







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   if height < 0 or area_of_base < 0:
      raise ValueError("vol_prism() only accepts non-negative values"    )
   return float(area_of_base * height    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   if height < 0 or area_of_base < 0:
      raise ValueError("vol_pyramid() only accepts non-negative values"    )
   return area_of_base * height / 3.0







def       __a	(						__lowerCamelCase    ):
   if radius < 0:
      raise ValueError("vol_sphere() only accepts non-negative values"    )
   # Volume is 4/3 * pi * radius cubed
   return 4 / 3 * pi * pow(__lowerCamelCase,						3    )







def       __a	(						__lowerCamelCase    ):
   if radius < 0:
      raise ValueError("vol_hemisphere() only accepts non-negative values"    )
   # Volume is radius cubed * pi * 2/3
   return pow(__lowerCamelCase,						3    ) * pi * 2 / 3







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   if height < 0 or radius < 0:
      raise ValueError("vol_circular_cylinder() only accepts non-negative values"    )
   # Volume is radius squared * height * pi
   return pow(__lowerCamelCase,						2    ) * height * pi







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   # Volume - (outer_radius squared - inner_radius squared) * pi * height
   if inner_radius < 0 or outer_radius < 0 or height < 0:
      raise ValueError(
          "vol_hollow_circular_cylinder() only accepts non-negative values"    )
   if outer_radius <= inner_radius:
      raise ValueError("outer_radius must be greater than inner_radius"    )
   return pi * (pow(__lowerCamelCase,						2    ) - pow(__lowerCamelCase,						2    )) * height







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   # Volume is 1/3 * pi * height *
   #           (radius_1 squared + radius_2 squared + radius_1 * radius_2)
   if radius_a < 0 or radius_a < 0 or height < 0:
      raise ValueError("vol_conical_frustum() only accepts non-negative values"    )
   return (
       1
       / 3
       * pi
       * height
       * (pow(__lowerCamelCase,						2    ) + pow(__lowerCamelCase,						2    ) + radius_a * radius_a)
   )







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   if torus_radius < 0 or tube_radius < 0:
      raise ValueError("vol_torus() only accepts non-negative values"    )
   return 2 * pow(__lowerCamelCase,						2    ) * torus_radius * pow(__lowerCamelCase,						2    )







def       __a	(						):
   print("Volumes:"    )
   print(f"""Cube: {vol_cube(2    ) = }"""    )  # = 8
   print(f"""Cuboid: {vol_cuboid(2,						2,						2    ) = }"""    )  # = 8
   print(f"""Cone: {vol_cone(2,						2    ) = }"""    )  # ~= 1.33
   print(f"""Right Circular Cone: {vol_right_circ_cone(2,						2    ) = }"""    )  # ~= 8.38
   print(f"""Prism: {vol_prism(2,						2    ) = }"""    )  # = 4
   print(f"""Pyramid: {vol_pyramid(2,						2    ) = }"""    )  # ~= 1.33
   print(f"""Sphere: {vol_sphere(2    ) = }"""    )  # ~= 33.5
   print(f"""Hemisphere: {vol_hemisphere(2    ) = }"""    )  # ~= 16.75
   print(f"""Circular Cylinder: {vol_circular_cylinder(2,						2    ) = }"""    )  # ~= 25.1
   print(f"""Torus: {vol_torus(2,						2    ) = }"""    )  # ~= 157.9
   print(f"""Conical Frustum: {vol_conical_frustum(2,						2,						4    ) = }"""    )  # ~= 58.6
   print(f"""Spherical cap: {vol_spherical_cap(1,						2    ) = }"""    )  # ~= 5.24
   print(f"""Spheres intersetion: {vol_spheres_intersect(2,						2,						1    ) = }"""    )  # ~= 21.21
   print(f"""Spheres union: {vol_spheres_union(2,						2,						1    ) = }"""    )  # ~= 45.81
   print(
       f"""Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1,						2,						3    ) = }"""    )  # ~= 28.3


if __name__ == "__main__":
    main()




"""simple docstring"""





import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
_a									=				logging.get_logger(__name__)







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   str     	=       WavLMForSequenceClassification.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   int     	=       downstream_dict["projector.weight"]
   UpperCAmelCase_   :   int     	=       downstream_dict["projector.bias"]
   UpperCAmelCase_   :   Optional[Any]     	=       downstream_dict["model.post_net.linear.weight"]
   UpperCAmelCase_   :   Optional[Any]     	=       downstream_dict["model.post_net.linear.bias"]
   return model







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       WavLMForAudioFrameClassification.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict["model.linear.weight"]
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["model.linear.bias"]
   return model







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Tuple     	=       WavLMForXVector.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["connector.weight"]
   UpperCAmelCase_   :   str     	=       downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
      UpperCAmelCase_   :   List[str]     	=       downstream_dict[
          f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   UpperCAmelCase_   :   Dict     	=       downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   UpperCAmelCase_   :   List[str]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   UpperCAmelCase_   :   Dict     	=       downstream_dict["objective.W"]
   return model







@torch.no_grad()
def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       torch.load(__lowerCamelCase,						map_location="cpu"    )

   UpperCAmelCase_   :   Union[str, Any]     	=       checkpoint["Downstream"]

   UpperCAmelCase_   :   List[str]     	=       WavLMConfig.from_pretrained(__lowerCamelCase    )
   UpperCAmelCase_   :   List[str]     	=       WavaVecaFeatureExtractor.from_pretrained(
       __lowerCamelCase,						return_attention_mask=__lowerCamelCase,						do_normalize=__lowerCamelCase    )

   UpperCAmelCase_   :   Any     	=       hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"    ):
      UpperCAmelCase_   :   Optional[int]     	=       convert_classification(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   elif arch.endswith("ForAudioFrameClassification"    ):
      UpperCAmelCase_   :   Dict     	=       convert_diarization(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   elif arch.endswith("ForXVector"    ):
      UpperCAmelCase_   :   List[Any]     	=       convert_xvector(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   else:
      raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""    )

   if hf_config.use_weighted_layer_sum:
      UpperCAmelCase_   :   Optional[int]     	=       checkpoint["Featurizer"]["weights"]

   hf_feature_extractor.save_pretrained(__lowerCamelCase    )
   hf_model.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
    _a									=				argparse.ArgumentParser()
    parser.add_argument(
        '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
    )
    parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
    parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
    parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
    _a									=				parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


