
import argparse

import torch

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


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


def 		_lowerCamelCase       (   _a			,   _a			,   _a  ):
  """simple docstring"""




  _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 		_lowerCamelCase       (   _a			,   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						WavLMForAudioFrameClassification.from_pretrained(lowercase__			,   config=lowercase__  )
  _lowerCamelCase      =						downstream_dict['''model.linear.weight''']
  _lowerCamelCase      =						downstream_dict['''model.linear.bias''']
  return model


def 		_lowerCamelCase       (   _a			,   _a			,   _a  ):
  """simple docstring"""




  _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 		_lowerCamelCase       (   _a			,   _a			,   _a			,   _a  ):
  """simple docstring"""




  _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__":
  _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)









from typing import Any

import numpy as np




def    UpperCAmelCase					(  lowercase__						:					np.ndarray	):



			'''simple docstring'''





			assert np.shape(lowercase__	)[0] == np.shape(lowercase__	)[1]

			# If matrix not symmetric, exit right away.
			if np.allclose(lowercase__				,					matrix.T	) is False:
						return False

			# Get eigenvalues and eignevectors for a symmetric matrix.
			a__				,       a__					=  np.linalg.eigh(lowercase__	)

			# Check sign of all eigenvalues.
			# np.all returns a value of type np.bool_
			return bool(np.all(eigen_values > 0	)	)




def    UpperCAmelCase					(  lowercase__						:					int	):



			'''simple docstring'''





			a__					=  np.random.randn(lowercase__				,					lowercase__	)
			a__					=  np.dot(lowercase__				,					random_matrix.T	)
			assert _is_matrix_spd(lowercase__	)
			return spd_matrix




def    UpperCAmelCase					(  lowercase__						:					np.ndarray				,					lowercase__						:					np.ndarray				,					lowercase__						:					int = 1000				,					lowercase__						:					float = 1e-8				,					):



			'''simple docstring'''





			assert np.shape(lowercase__	)[0] == np.shape(lowercase__	)[1]
			assert np.shape(lowercase__	)[0] == np.shape(lowercase__	)[0]
			assert _is_matrix_spd(lowercase__	)

			# Initialize solution guess, residual, search direction.
			a__					=  np.zeros((np.shape(lowercase__	)[0], 1)	)
			a__					=  np.copy(lowercase__	)
			a__					=  np.copy(lowercase__	)

			# Set initial errors in solution guess and residual.
			a__					=  1e9
			a__					=  1e9
			a__					=  1e9

			# Set iteration counter to threshold number of iterations.
			a__					=  0

			while error > tol:
						# Save this value so we only calculate the matrix-vector product once.
						a__					=  np.dot(lowercase__				,					lowercase__	)

						# The main algorithm.

						# Update search direction magnitude.
						a__					=  np.dot(ra.T				,					lowercase__	) / np.dot(pa.T				,					lowercase__	)
						# Update solution guess.
						a__					=  xa + alpha * pa
						# Calculate new residual.
						a__					=  ra - alpha * w
						# Calculate new Krylov subspace scale.
						a__					=  np.dot(r.T				,					lowercase__	) / np.dot(ra.T				,					lowercase__	)
						# Calculate new A conjuage search direction.
						a__					=  r + beta * pa

						# Calculate errors.
						a__					=  np.linalg.norm(r - ra	)
						a__					=  np.linalg.norm(x - xa	)
						a__					=  np.maximum(lowercase__				,					lowercase__	)

						# Update variables.
						a__					=  np.copy(lowercase__	)
						a__					=  np.copy(lowercase__	)
						a__					=  np.copy(lowercase__	)

						# Update number of iterations.
						iterations += 1
						if iterations > max_iterations:
									break

			return x




def    UpperCAmelCase					(  ):



			'''simple docstring'''





			a__					=  3
			a__					=  _create_spd_matrix(lowercase__	)
			a__					=  np.random.randn(lowercase__				,					1	)
			a__					=  np.dot(lowercase__				,					lowercase__	)

			# Numpy solution.
			a__					=  np.linalg.solve(lowercase__				,					lowercase__	)

			# Our implementation.
			a__					=  conjugate_gradient(lowercase__				,					lowercase__	)

			# Ensure both solutions are close to x_true (and therefore one another).
			assert np.linalg.norm(x_numpy - x_true	) <= 1e-6
			assert np.linalg.norm(x_conjugate_gradient - x_true	) <= 1e-6


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	test_conjugate_gradient()

