



"""simple docstring"""
import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

__a           =							["UtteranceLevel"]
@torch.no_grad()
def   A_    (     _lowercase,   _lowercase,   _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:List[Any]		=     torch.load(_lowercase,   map_location="""cpu"""      )
						if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
												raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""      )

						snake_case_					:Optional[Any]		=     checkpoint["""Downstream"""]

						snake_case_					:str		=     HubertConfig.from_pretrained(_lowercase      )
						snake_case_					:Union[str, Any]		=     HubertForSequenceClassification.from_pretrained(_lowercase,   config=_lowercase      )
						snake_case_					:Optional[int]		=     WavaVecaFeatureExtractor.from_pretrained(
						    _lowercase,   return_attention_mask=_lowercase,   do_normalize=_lowercase      )

						if hf_congfig.use_weighted_layer_sum:
												snake_case_					:str		=     checkpoint["""Featurizer"""]["""weights"""]

						snake_case_					:Union[str, Any]		=     downstream_dict["""projector.weight"""]
						snake_case_					:Optional[Any]		=     downstream_dict["""projector.bias"""]
						snake_case_					:str		=     downstream_dict["""model.post_net.linear.weight"""]
						snake_case_					:Optional[int]		=     downstream_dict["""model.post_net.linear.bias"""]

						hf_feature_extractor.save_pretrained(_lowercase      )
						hf_model.save_pretrained(_lowercase      )


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)







from typing import Any

import numpy as np





def     lowerCamelCase__  (  UpperCamelCase__      :							np.ndarray    )      ->			bool:



					'''simple docstring'''




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

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

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

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





def     lowerCamelCase__  (  UpperCamelCase__      :							int    )      ->			Any:



					'''simple docstring'''




					_snake_case						=					np.random.randn(UpperCamelCase__							,     UpperCamelCase__    )
					_snake_case						=					np.dot(UpperCamelCase__							,     random_matrix.T    )
					assert _is_matrix_spd(UpperCamelCase__    )
					return spd_matrix





def     lowerCamelCase__  (  UpperCamelCase__      :							np.ndarray							,     UpperCamelCase__      :							np.ndarray							,     UpperCamelCase__      :							int = 1_000							,     UpperCamelCase__      :							float = 1e-8							,     )      ->			Any:



					'''simple docstring'''




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

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

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

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

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

										# The main algorithm.

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

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

										# Update variables.
										_snake_case						=					np.copy(UpperCamelCase__    )
										_snake_case						=					np.copy(UpperCamelCase__    )
										_snake_case						=					np.copy(UpperCamelCase__    )

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

					return x





def     lowerCamelCase__  (  )      ->			None:



					'''simple docstring'''




					_snake_case						=					3
					_snake_case						=					_create_spd_matrix(UpperCamelCase__    )
					_snake_case						=					np.random.randn(UpperCamelCase__							,     1    )
					_snake_case						=					np.dot(UpperCamelCase__							,     UpperCamelCase__    )

					# Numpy solution.
					_snake_case						=					np.linalg.solve(UpperCamelCase__							,     UpperCamelCase__    )

					# Our implementation.
					_snake_case						=					conjugate_gradient(UpperCamelCase__							,     UpperCamelCase__    )

					# 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()



