






"""simple docstring"""







import unittest

import numpy as np

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case = None				,	):







      """simple docstring"""






      UpperCAmelCase          =	np.shape(_snake_case							)
      UpperCAmelCase          =	np.shape(_snake_case							)
      UpperCAmelCase          =	np.shape(_snake_case							)

      if shape_a[0] != shape_b[0]:
            UpperCAmelCase          =	(
                """Expected the same number of rows for A and B. """
                F'''Instead found A of size {shape_a} and B of size {shape_b}'''
            )
            raise ValueError(_snake_case							)

      if shape_b[1] != shape_c[1]:
            UpperCAmelCase          =	(
                """Expected the same number of columns for B and C. """
                F'''Instead found B of size {shape_b} and C of size {shape_c}'''
            )
            raise ValueError(_snake_case							)

      UpperCAmelCase          =	pseudo_inv
      if a_inv is None:
            try:
                  UpperCAmelCase          =	np.linalg.inv(_snake_case							)
            except np.linalg.LinAlgError:
                  raise ValueError(
                      """Input matrix A is not invertible. Cannot compute Schur complement."""							)

      return mat_c - mat_b.T @ a_inv @ mat_b







class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
          UpperCAmelCase          =	np.array([[0, 3], [3, 0], [2, 3]] )
          UpperCAmelCase          =	np.array([[2, 1], [6, 3]] )

          UpperCAmelCase          =	schur_complement(A     ,A     ,A )

          UpperCAmelCase          =	np.block([[a, b], [b.T, c]] )

          UpperCAmelCase          =	np.linalg.det(A )
          UpperCAmelCase          =	np.linalg.det(A )
          UpperCAmelCase          =	np.linalg.det(A )

          self.assertAlmostEqual(A     ,det_a * det_s )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
          UpperCAmelCase          =	np.array([[0, 3], [3, 0], [2, 3]] )
          UpperCAmelCase          =	np.array([[2, 1], [6, 3]] )

          with self.assertRaises(A ):
                schur_complement(A     ,A     ,A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
          UpperCAmelCase          =	np.array([[0, 3], [3, 0], [2, 3]] )
          UpperCAmelCase          =	np.array([[2, 1, 3], [6, 3, 5]] )

          with self.assertRaises(A ):
                schur_complement(A     ,A     ,A )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  unittest.main()











"""simple docstring"""







import argparse

import torch

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


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

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForSequenceClassification.from_pretrained(_snake_case				,	config=_snake_case							)
      UpperCAmelCase          =	downstream_dict["""projector.weight"""]
      UpperCAmelCase          =	downstream_dict["""projector.bias"""]
      UpperCAmelCase          =	downstream_dict["""model.post_net.linear.weight"""]
      UpperCAmelCase          =	downstream_dict["""model.post_net.linear.bias"""]
      return model

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForAudioFrameClassification.from_pretrained(_snake_case				,	config=_snake_case							)
      UpperCAmelCase          =	downstream_dict["""model.linear.weight"""]
      UpperCAmelCase          =	downstream_dict["""model.linear.bias"""]
      return model

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForXVector.from_pretrained(_snake_case				,	config=_snake_case							)
      UpperCAmelCase          =	downstream_dict["""connector.weight"""]
      UpperCAmelCase          =	downstream_dict["""connector.bias"""]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
            UpperCAmelCase          =	downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            UpperCAmelCase          =	downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      UpperCAmelCase          =	downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
      UpperCAmelCase          =	downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
      UpperCAmelCase          =	downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
      UpperCAmelCase          =	downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
      UpperCAmelCase          =	downstream_dict["""objective.W"""]
      return model

@torch.no_grad()
def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	torch.load(_snake_case				,	map_location="""cpu"""							)

      UpperCAmelCase          =	checkpoint["""Downstream"""]

      UpperCAmelCase          =	WavaVecaConfig.from_pretrained(_snake_case							)
      UpperCAmelCase          =	WavaVecaFeatureExtractor.from_pretrained(
          _snake_case				,	return_attention_mask=_snake_case				,	do_normalize=_snake_case							)

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

      if hf_config.use_weighted_layer_sum:
            UpperCAmelCase          =	checkpoint["""Featurizer"""]["""weights"""]

      hf_feature_extractor.save_pretrained(_snake_case							)
      hf_model.save_pretrained(_snake_case							)


if __name__ == "__main__":
  _UpperCamelCase					      =  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.""")
  _UpperCamelCase					      =  parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



