






import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
a_		:						str             =    logging.get_logger(__name__)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= WavaVecaForSequenceClassification.from_pretrained(_UpperCAmelCase							,       config=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= downstream_dict['projector.weight']
			SCREAMING_SNAKE_CASE						= downstream_dict['projector.bias']
			SCREAMING_SNAKE_CASE						= downstream_dict['model.post_net.linear.weight']
			SCREAMING_SNAKE_CASE						= downstream_dict['model.post_net.linear.bias']
			return model






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= WavaVecaForAudioFrameClassification.from_pretrained(_UpperCAmelCase							,       config=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= downstream_dict['model.linear.weight']
			SCREAMING_SNAKE_CASE						= downstream_dict['model.linear.bias']
			return model






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= WavaVecaForXVector.from_pretrained(_UpperCAmelCase							,       config=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= downstream_dict['connector.weight']
			SCREAMING_SNAKE_CASE						= downstream_dict['connector.bias']
			for i, kernel_size in enumerate(hf_config.tdnn_kernel):
						SCREAMING_SNAKE_CASE						= downstream_dict[
						    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
						]
						SCREAMING_SNAKE_CASE						= downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

			SCREAMING_SNAKE_CASE						= downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
			SCREAMING_SNAKE_CASE						= downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
			SCREAMING_SNAKE_CASE						= downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
			SCREAMING_SNAKE_CASE						= downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
			SCREAMING_SNAKE_CASE						= downstream_dict['objective.W']
			return model






@torch.no_grad()
def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= torch.load(_UpperCAmelCase							,       map_location='cpu')

			SCREAMING_SNAKE_CASE						= checkpoint['Downstream']

			SCREAMING_SNAKE_CASE						= WavaVecaConfig.from_pretrained(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= WavaVecaFeatureExtractor.from_pretrained(
			    _UpperCAmelCase							,       return_attention_mask=_UpperCAmelCase							,       do_normalize=_UpperCAmelCase)

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

			if hf_config.use_weighted_layer_sum:
						SCREAMING_SNAKE_CASE						= checkpoint['Featurizer']['weights']

			hf_feature_extractor.save_pretrained(_UpperCAmelCase)
			hf_model.save_pretrained(_UpperCAmelCase)


if __name__ == "__main__":
				a_		:						int             =    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_		:						Dict             =    parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)












import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script

@require_cpu
class 		_snake_case  (  unittest.TestCase					):






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						debug_launcher(test_script.main)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						debug_launcher(test_ops.main)




