



"""simple docstring"""

import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
lowercase__					:						List[Any]												=			logging.get_logger(__name__)




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  List[str]       =			WavaVecaForSequenceClassification.from_pretrained(_a	,    config=_a					)
    snake_case_				:  List[str]       =			downstream_dict['''projector.weight''']
    snake_case_				:  int       =			downstream_dict['''projector.bias''']
    snake_case_				:  Union[str, Any]       =			downstream_dict['''model.post_net.linear.weight''']
    snake_case_				:  int       =			downstream_dict['''model.post_net.linear.bias''']
    return model




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Any       =			WavaVecaForAudioFrameClassification.from_pretrained(_a	,    config=_a					)
    snake_case_				:  Optional[int]       =			downstream_dict['''model.linear.weight''']
    snake_case_				:  Dict       =			downstream_dict['''model.linear.bias''']
    return model




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Optional[Any]       =			WavaVecaForXVector.from_pretrained(_a	,    config=_a					)
    snake_case_				:  List[Any]       =			downstream_dict['''connector.weight''']
    snake_case_				:  Tuple       =			downstream_dict['''connector.bias''']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
        snake_case_				:  List[Any]       =			downstream_dict[
            f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
        ]
        snake_case_				:  Dict       =			downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

    snake_case_				:  Tuple       =			downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
    snake_case_				:  List[str]       =			downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
    snake_case_				:  Tuple       =			downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
    snake_case_				:  Optional[int]       =			downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
    snake_case_				:  int       =			downstream_dict['''objective.W''']
    return model




@torch.no_grad()
def __lowercase    (							_a	,    _a	,    _a	,    _a					):
    snake_case_				:  Any       =			torch.load(_a	,    map_location='''cpu'''					)

    snake_case_				:  Tuple       =			checkpoint['''Downstream''']

    snake_case_				:  int       =			WavaVecaConfig.from_pretrained(_a					)
    snake_case_				:  List[str]       =			WavaVecaFeatureExtractor.from_pretrained(
        _a	,    return_attention_mask=_a	,    do_normalize=_a					)

    snake_case_				:  Tuple       =			hf_config.architectures[0]
    if arch.endswith('''ForSequenceClassification'''					):
        snake_case_				:  Tuple       =			convert_classification(_a	,    _a	,    _a					)
    elif arch.endswith('''ForAudioFrameClassification'''					):
        snake_case_				:  Optional[int]       =			convert_diarization(_a	,    _a	,    _a					)
    elif arch.endswith('''ForXVector'''					):
        snake_case_				:  Union[str, Any]       =			convert_xvector(_a	,    _a	,    _a					)
    else:
        raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}"					)

    if hf_config.use_weighted_layer_sum:
        snake_case_				:  Optional[int]       =			checkpoint['''Featurizer''']['''weights''']

    hf_feature_extractor.save_pretrained(_a					)
    hf_model.save_pretrained(_a					)


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







"""simple docstring"""

import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize




def __lowercase    (							_a					):
    return np.dot(_a	,    _a					)




class  _UpperCAmelCase :

 def __init__(  self	: int						,  *,
     lowercase_	: float = np.inf						,  lowercase_	: str = "linear"						,  lowercase_	: float = 0.0						,  ):
     snake_case_				:  Optional[Any]       =			regularization
     snake_case_				:  Tuple       =			gamma
     if kernel == "linear":
         snake_case_				:  int       =			self.__linear
     elif kernel == "rbf":
         if self.gamma == 0:
             raise ValueError('''rbf kernel requires gamma'''       )
         if not isinstance(self.gamma						,  (float, int)       ):
             raise ValueError('''gamma must be float or int'''       )
         if not self.gamma > 0:
             raise ValueError('''gamma must be > 0'''       )
         snake_case_				:  int       =			self.__rbf
         # in the future, there could be a default value like in sklearn
         # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
         # previously it was 1/(n_features)
     else:
         snake_case_				:  List[Any]       =			f"Unknown kernel: {kernel}"
         raise ValueError(lowercase_       )

 def 		_snake_case      (  self	: int						,  lowercase_	: ndarray						,  lowercase_	: ndarray       ):
     return np.dot(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: int						,  lowercase_	: ndarray						,  lowercase_	: ndarray       ):
     return np.exp(-(self.gamma * norm_squared(vectora - vectora       ))       )

 def 		_snake_case      (  self	: Any						,  lowercase_	: list[ndarray]						,  lowercase_	: ndarray       ):
     snake_case_				:  Union[str, Any]       =			observations
     snake_case_				:  int       =			classes

     # using Wolfe's Dual to calculate w.
     # Primal problem: minimize 1/2*norm_squared(w)
     #   constraint: yn(w . xn + b) >= 1
     #
     # With l a vector
     # Dual problem: maximize sum_n(ln) -
     #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
     #   constraint: self.C >= ln >= 0
     #           and sum_n(ln*yn) = 0
     # Then we get w using w = sum_n(ln*yn*xn)
     # At the end we can get b ~= mean(yn - w . xn)
     #
     # Since we use kernels, we only need l_star to calculate b
     # and to classify observations

     ((snake_case_),			)				:  List[str]       =			np.shape(lowercase_       )

     def to_minimize(lowercase_	: ndarray       ) -> float:
         snake_case_				:  Tuple       =			0
         ((snake_case_),			)				:  Optional[Any]       =			np.shape(lowercase_       )
         for i in range(lowercase_       ):
             for j in range(lowercase_       ):
                 s += (
                     candidate[i]
                     * candidate[j]
                     * classes[i]
                     * classes[j]
                     * self.kernel(observations[i]						,  observations[j]       )
                 )
         return 1 / 2 * s - sum(lowercase_       )

     snake_case_				:  Optional[Any]       =			LinearConstraint(lowercase_						,  0						,  0       )
     snake_case_				:  str       =			Bounds(0						,  self.regularization       )

     snake_case_				:  int       =			minimize(
         lowercase_						,  np.ones(lowercase_       )						,  bounds=lowercase_						,  constraints=[ly_contraint]       ).x
     snake_case_				:  Optional[Any]       =			l_star

     # calculating mean offset of separation plane to points
     snake_case_				:  List[Any]       =			0
     for i in range(lowercase_       ):
         for j in range(lowercase_       ):
             s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                 observations[i]						,  observations[j]       )
     snake_case_				:  Union[str, Any]       =			s / n






 def 		_snake_case      (  self	: List[str]						,  lowercase_	: ndarray       ):
     snake_case_				:  int       =			sum(
         self.optimum[n]
         * self.classes[n]
         * self.kernel(self.observations[n]						,  lowercase_       )
         for n in range(len(self.classes       )       )       )
     return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()





