"""simple docstring"""







from __future__ import annotations

from math import pi, pow



def       A__    (	lowercase_     :	int | float						)  ->							float:
   if side_length < 0:
      raise ValueError('vol_cube() only accepts non-negative values'						)
   return pow(lowercase_      , 3						)



def       A__    (	lowercase_     :	float      , lowercase_     :	float						)  ->							float:
   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(lowercase_      , 2						) * (3 * radius - height)



def       A__    (	lowercase_     :	float      , lowercase_     :	float      , lowercase_     :	float						)  ->							float:
   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(lowercase_      , lowercase_						)						)

   __lowerCamelCase    		=					(
       (radius_a - radius_a + centers_distance)
       * (radius_a + radius_a - centers_distance)
       / (2 * centers_distance)
   )
   __lowerCamelCase    		=					(
       (radius_a - radius_a + centers_distance)
       * (radius_a + radius_a - centers_distance)
       / (2 * centers_distance)
   )

   return vol_spherical_cap(lowercase_      , lowercase_						) + vol_spherical_cap(lowercase_      , lowercase_						)



def       A__    (	lowercase_     :	float      , lowercase_     :	float      , lowercase_     :	float						)  ->							float:

   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(lowercase_      , lowercase_						)						)

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



def       A__    (	lowercase_     :	float      , lowercase_     :	float      , lowercase_     :	float						)  ->							float:
   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__    (	lowercase_     :	float      , lowercase_     :	float						)  ->							float:
   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__    (	lowercase_     :	float      , lowercase_     :	float						)  ->							float:
   if height < 0 or radius < 0:
      raise ValueError('vol_right_circ_cone() only accepts non-negative values'						)
   return pi * pow(lowercase_      , 2						) * height / 3.0



def       A__    (	lowercase_     :	float      , lowercase_     :	float						)  ->							float:
   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__    (	lowercase_     :	float      , lowercase_     :	float						)  ->							float:
   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__    (	lowercase_     :	float						)  ->							float:
   if radius < 0:
      raise ValueError('vol_sphere() only accepts non-negative values'						)
   # Volume is 4/3 * pi * radius cubed
   return 4 / 3 * pi * pow(lowercase_      , 3						)



def       A__    (	lowercase_     :	float						)  ->							float:
   if radius < 0:
      raise ValueError('vol_hemisphere() only accepts non-negative values'						)
   # Volume is radius cubed * pi * 2/3
   return pow(lowercase_      , 3						) * pi * 2 / 3



def       A__    (	lowercase_     :	float      , lowercase_     :	float						)  ->							float:
   if height < 0 or radius < 0:
      raise ValueError('vol_circular_cylinder() only accepts non-negative values'						)
   # Volume is radius squared * height * pi
   return pow(lowercase_      , 2						) * height * pi



def       A__    (	lowercase_     :	float      , lowercase_     :	float      , lowercase_     :	float						)  ->							float:
   # 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(lowercase_      , 2						) - pow(lowercase_      , 2						)) * height



def       A__    (	lowercase_     :	float      , lowercase_     :	float      , lowercase_     :	float						)  ->							float:
   # 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(lowercase_      , 2						) + pow(lowercase_      , 2						) + radius_a * radius_a)
   )



def       A__    (	lowercase_     :	float      , lowercase_     :	float						)  ->							float:
   if torus_radius < 0 or tube_radius < 0:
      raise ValueError('vol_torus() only accepts non-negative values'						)
   return 2 * pow(lowercase_      , 2						) * torus_radius * pow(lowercase_      , 2						)



def       A__    (	)  ->							Optional[Any]:
   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()
_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   logging.get_logger(__name__)



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	Any      , lowercase_     :	Optional[int]						)  ->							Union[str, Any]:
   __lowerCamelCase    		=					WavLMForSequenceClassification.from_pretrained(lowercase_      , config=lowercase_						)
   __lowerCamelCase    		=					downstream_dict['projector.weight']
   __lowerCamelCase    		=					downstream_dict['projector.bias']
   __lowerCamelCase    		=					downstream_dict['model.post_net.linear.weight']
   __lowerCamelCase    		=					downstream_dict['model.post_net.linear.bias']
   return model



def       A__    (	lowercase_     :	Dict      , lowercase_     :	str      , lowercase_     :	int						)  ->							List[Any]:
   __lowerCamelCase    		=					WavLMForAudioFrameClassification.from_pretrained(lowercase_      , config=lowercase_						)
   __lowerCamelCase    		=					downstream_dict['model.linear.weight']
   __lowerCamelCase    		=					downstream_dict['model.linear.bias']
   return model



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	Dict      , lowercase_     :	List[str]						)  ->							Tuple:
   __lowerCamelCase    		=					WavLMForXVector.from_pretrained(lowercase_      , config=lowercase_						)
   __lowerCamelCase    		=					downstream_dict['connector.weight']
   __lowerCamelCase    		=					downstream_dict['connector.bias']
   for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
      __lowerCamelCase    		=					downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      __lowerCamelCase    		=					downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   __lowerCamelCase    		=					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
   __lowerCamelCase    		=					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
   __lowerCamelCase    		=					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
   __lowerCamelCase    		=					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
   __lowerCamelCase    		=					downstream_dict['objective.W']
   return model



@torch.no_grad()
def       A__    (	lowercase_     :	str      , lowercase_     :	Optional[int]      , lowercase_     :	List[str]      , lowercase_     :	Union[str, Any]						)  ->							Optional[Any]:
   __lowerCamelCase    		=					torch.load(lowercase_      , map_location='cpu'						)

   __lowerCamelCase    		=					checkpoint['Downstream']

   __lowerCamelCase    		=					WavLMConfig.from_pretrained(lowercase_						)
   __lowerCamelCase    		=					WavaVecaFeatureExtractor.from_pretrained(
       lowercase_      , return_attention_mask=lowercase_      , do_normalize=lowercase_						)

   __lowerCamelCase    		=					hf_config.architectures[0]
   if arch.endswith('ForSequenceClassification'						):
      __lowerCamelCase    		=					convert_classification(lowercase_      , lowercase_      , lowercase_						)
   elif arch.endswith('ForAudioFrameClassification'						):
      __lowerCamelCase    		=					convert_diarization(lowercase_      , lowercase_      , lowercase_						)
   elif arch.endswith('ForXVector'						):
      __lowerCamelCase    		=					convert_xvector(lowercase_      , lowercase_      , lowercase_						)
   else:
      raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''						)

   if hf_config.use_weighted_layer_sum:
      __lowerCamelCase    		=					checkpoint['Featurizer']['weights']

   hf_feature_extractor.save_pretrained(lowercase_						)
   hf_model.save_pretrained(lowercase_						)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE	:		Optional[Any]   =   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.''')
     _SCREAMING_SNAKE_CASE	:		Dict   =   parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






