


'''simple docstring'''

import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
_UpperCAmelCase				: Union[str, Any]							    =				logging.get_logger(__name__)

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				UniSpeechSatForSequenceClassification.from_pretrained(_lowerCamelCase,				config=_lowerCamelCase)
						__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 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				UniSpeechSatForAudioFrameClassification.from_pretrained(_lowerCamelCase,				config=_lowerCamelCase)
						__lowerCAmelCase							       =				downstream_dict['model.linear.weight']
						__lowerCAmelCase							       =				downstream_dict['model.linear.bias']
						return model

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				UniSpeechSatForXVector.from_pretrained(_lowerCamelCase,				config=_lowerCamelCase)
						__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 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				torch.load(_lowerCamelCase,				map_location='''cpu''')

						__lowerCAmelCase							       =				checkpoint['Downstream']

						__lowerCAmelCase							       =				UniSpeechSatConfig.from_pretrained(_lowerCamelCase)
						__lowerCAmelCase							       =				WavaVecaFeatureExtractor.from_pretrained(
						    _lowerCamelCase,				return_attention_mask=_lowerCamelCase,				do_normalize=_lowerCamelCase)

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


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





'''simple docstring'''

from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments

