
import argparse

import torch

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


logging.set_verbosity_info()
lowercase__							:							Any													=     logging.get_logger(__name__)




def 		A_	(							snake_case							:				Optional[int]	, snake_case							:				Union[str, Any]	, snake_case							:				Tuple	) ->      int:



						'''simple docstring'''

						__UpperCamelCase       					=     WavLMForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_	, config=SCREAMING_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							:				str	, snake_case							:				List[Any]	, snake_case							:				Tuple	) ->      Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     WavLMForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE_	, config=SCREAMING_SNAKE_CASE_	)
						__UpperCamelCase       					=     downstream_dict["model.linear.weight"]
						__UpperCamelCase       					=     downstream_dict["model.linear.bias"]
						return model




def 		A_	(							snake_case							:				Tuple	, snake_case							:				Dict	, snake_case							:				Optional[int]	) ->      Any:



						'''simple docstring'''

						__UpperCamelCase       					=     WavLMForXVector.from_pretrained(SCREAMING_SNAKE_CASE_	, config=SCREAMING_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							:				Tuple	, snake_case							:				int	, snake_case							:				int	, snake_case							:				str	) ->      List[str]:



						'''simple docstring'''

						__UpperCamelCase       					=     torch.load(SCREAMING_SNAKE_CASE_	, map_location='''cpu'''	)

						__UpperCamelCase       					=     checkpoint["Downstream"]

						__UpperCamelCase       					=     WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
						__UpperCamelCase       					=     WavaVecaFeatureExtractor.from_pretrained(
						    SCREAMING_SNAKE_CASE_	, return_attention_mask=SCREAMING_SNAKE_CASE_	, do_normalize=SCREAMING_SNAKE_CASE_	)

						__UpperCamelCase       					=     hf_config.architectures[0]
						if arch.endswith('''ForSequenceClassification'''	):
												__UpperCamelCase       					=     convert_classification(SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	)
						elif arch.endswith('''ForAudioFrameClassification'''	):
												__UpperCamelCase       					=     convert_diarization(SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	)
						elif arch.endswith('''ForXVector'''	):
												__UpperCamelCase       					=     convert_xvector(SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	, SCREAMING_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(SCREAMING_SNAKE_CASE_	)
						hf_model.save_pretrained(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
				lowercase__							:							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.")
				lowercase__							:							Dict													=     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 		A_	(							snake_case							:				np.ndarray	) ->      bool:



						'''simple docstring'''

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

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

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

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




def 		A_	(							snake_case							:				int	) ->      Any:



						'''simple docstring'''

						__UpperCamelCase       					=     np.random.randn(snake_case	, snake_case	)
						__UpperCamelCase       					=     np.dot(snake_case	, random_matrix.T	)
						assert _is_matrix_spd(snake_case	)
						return spd_matrix




def 		A_	(							snake_case							:				np.ndarray	, snake_case							:				np.ndarray	, snake_case							:				int = 1000	, snake_case							:				float = 1e-8	, ) ->      Any:



						'''simple docstring'''

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

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

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

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

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

												# The main algorithm.

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

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

												# Update variables.
												__UpperCamelCase       					=     np.copy(snake_case	)
												__UpperCamelCase       					=     np.copy(snake_case	)
												__UpperCamelCase       					=     np.copy(snake_case	)

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

						return x




def 		A_	(							) ->      None:



						'''simple docstring'''

						__UpperCamelCase       					=     3
						__UpperCamelCase       					=     _create_spd_matrix(snake_case	)
						__UpperCamelCase       					=     np.random.randn(snake_case	, 1	)
						__UpperCamelCase       					=     np.dot(snake_case	, snake_case	)

						# Numpy solution.
						__UpperCamelCase       					=     np.linalg.solve(snake_case	, snake_case	)

						# Our implementation.
						__UpperCamelCase       					=     conjugate_gradient(snake_case	, snake_case	)

						# 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()




