



import argparse

import torch

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:Optional[int]											=       logging.get_logger(__name__)


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Dict:







				"""simple docstring"""



				__A         =				WavLMForSequenceClassification.from_pretrained(a_	,     config=a_		)
				__A         =				downstream_dict["projector.weight"]
				__A         =				downstream_dict["projector.bias"]
				__A         =				downstream_dict["model.post_net.linear.weight"]
				__A         =				downstream_dict["model.post_net.linear.bias"]
				return model


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			List[Any]:







				"""simple docstring"""



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


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Optional[int]:







				"""simple docstring"""



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

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


@torch.no_grad()
def 		UpperCAmelCase    (			a_	,     a_	,     a_	,     a_		)	->			Optional[int]:







				"""simple docstring"""



				__A         =				torch.load(a_	,     map_location="cpu"		)

				__A         =				checkpoint["Downstream"]

				__A         =				WavLMConfig.from_pretrained(a_		)
				__A         =				WavaVecaFeatureExtractor.from_pretrained(
				    a_	,     return_attention_mask=a_	,     do_normalize=a_		)

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

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

				hf_feature_extractor.save_pretrained(a_		)
				hf_model.save_pretrained(a_		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple											=       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.')
					SCREAMING_SNAKE_CASE							:List[Any]											=       parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


def 		UpperCAmelCase    (			a_		)	->			int:







				"""simple docstring"""



				__A         =				str(a_		).strip()
				if not oct_string:
								raise ValueError("Empty string was passed to the function"		)
				__A         =				oct_string[0] == "-"
				if is_negative:
								__A         =				oct_string[1:]
				if not oct_string.isdigit() or not all(0 <= int(a_		) <= 7 for char in oct_string		):
								raise ValueError("Non-octal value was passed to the function"		)
				__A         =				0
				for char in oct_string:
								__A         =				8 * decimal_number + int(a_		)
				if is_negative:
								__A         =				-decimal_number
				return decimal_number


if __name__ == "__main__":
					from doctest import testmod

					testmod()
