



import argparse

import torch

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


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






def      __lowercase       (			a__							,       a__							,       a__       )    ->			Optional[Any]:
							__SCREAMING_SNAKE_CASE									= WavaVecaForSequenceClassification.from_pretrained(lowercase__							,       config=lowercase__       )
							__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      __lowercase       (			a__							,       a__							,       a__       )    ->			Optional[int]:
							__SCREAMING_SNAKE_CASE									= WavaVecaForAudioFrameClassification.from_pretrained(lowercase__							,       config=lowercase__       )
							__SCREAMING_SNAKE_CASE									= downstream_dict["""model.linear.weight"""]
							__SCREAMING_SNAKE_CASE									= downstream_dict["""model.linear.bias"""]
							return model






def      __lowercase       (			a__							,       a__							,       a__       )    ->			Tuple:
							__SCREAMING_SNAKE_CASE									= WavaVecaForXVector.from_pretrained(lowercase__							,       config=lowercase__       )
							__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      __lowercase       (			a__							,       a__							,       a__							,       a__       )    ->			int:
							__SCREAMING_SNAKE_CASE									= torch.load(lowercase__							,       map_location='cpu'       )

							__SCREAMING_SNAKE_CASE									= checkpoint["""Downstream"""]

							__SCREAMING_SNAKE_CASE									= WavaVecaConfig.from_pretrained(lowercase__       )
							__SCREAMING_SNAKE_CASE									= WavaVecaFeatureExtractor.from_pretrained(
							    lowercase__							,       return_attention_mask=lowercase__							,       do_normalize=lowercase__       )

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


if __name__ == "__main__":
			lowerCAmelCase__							=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.''')
			lowerCAmelCase__							=parser.parse_args()
			convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)











lowerCAmelCase__		:					Optional[int]							=9.80_665






def      __lowercase       (			a__							,       a__							,       a__ = g       )    ->			float:

							if fluid_density <= 0:
														raise ValueError('Impossible fluid density'       )
							if volume < 0:
														raise ValueError('Impossible Object volume'       )
							if gravity <= 0:
														raise ValueError('Impossible Gravity'       )

							return fluid_density * gravity * volume


if __name__ == "__main__":
			import doctest

			# run doctest
			doctest.testmod()






