






import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase       :       Union[str, Any]        =				logging.get_logger(__name__)
def 					snake_case_						(   lowerCAmelCase_				:  Tuple				,				lowerCAmelCase_				:  Optional[int]				,				lowerCAmelCase_				:  Optional[int]					):
							__lowercase					:							Optional[int]			=   UniSpeechSatForSequenceClassification.from_pretrained(lowerCAmelCase_				,				config=lowerCAmelCase_					)
							__lowercase					:							str			=   downstream_dict["""projector.weight"""]
							__lowercase					:							Any			=   downstream_dict["""projector.bias"""]
							__lowercase					:							Dict			=   downstream_dict["""model.post_net.linear.weight"""]
							__lowercase					:							int			=   downstream_dict["""model.post_net.linear.bias"""]
							return model
def 					snake_case_						(   lowerCAmelCase_				:  Optional[Any]				,				lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  Optional[int]					):
							__lowercase					:							Dict			=   UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCAmelCase_				,				config=lowerCAmelCase_					)
							__lowercase					:							List[str]			=   downstream_dict["""model.linear.weight"""]
							__lowercase					:							Optional[int]			=   downstream_dict["""model.linear.bias"""]
							return model
def 					snake_case_						(   lowerCAmelCase_				:  int				,				lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  Optional[int]					):
							__lowercase					:							Union[str, Any]			=   UniSpeechSatForXVector.from_pretrained(lowerCAmelCase_				,				config=lowerCAmelCase_					)
							__lowercase					:							Tuple			=   downstream_dict["""connector.weight"""]
							__lowercase					:							Dict			=   downstream_dict["""connector.bias"""]
							for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
														__lowercase					:							Optional[Any]			=   downstream_dict[
														    F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
														]
														__lowercase					:							Dict			=   downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

							__lowercase					:							str			=   downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
							__lowercase					:							Optional[int]			=   downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
							__lowercase					:							Union[str, Any]			=   downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
							__lowercase					:							Optional[Any]			=   downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
							__lowercase					:							Optional[int]			=   downstream_dict["""objective.W"""]
							return model
@torch.no_grad()
def 					snake_case_						(   lowerCAmelCase_				:  Optional[int]				,				lowerCAmelCase_				:  int				,				lowerCAmelCase_				:  Any				,				lowerCAmelCase_				:  int					):
							__lowercase					:							int			=   torch.load(lowerCAmelCase_				,				map_location="""cpu"""					)

							__lowercase					:							Optional[int]			=   checkpoint["""Downstream"""]

							__lowercase					:							List[str]			=   UniSpeechSatConfig.from_pretrained(lowerCAmelCase_					)
							__lowercase					:							int			=   WavaVecaFeatureExtractor.from_pretrained(
							    lowerCAmelCase_				,				return_attention_mask=lowerCAmelCase_				,				do_normalize=lowerCAmelCase_					)

							__lowercase					:							int			=   hf_config.architectures[0]
							if arch.endswith("""ForSequenceClassification"""					):
														__lowercase					:							Any			=   convert_classification(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)
							elif arch.endswith("""ForAudioFrameClassification"""					):
														__lowercase					:							List[str]			=   convert_diarization(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)
							elif arch.endswith("""ForXVector"""					):
														__lowercase					:							List[str]			=   convert_xvector(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)
							else:
														raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"					)

							if hf_config.use_weighted_layer_sum:
														__lowercase					:							List[Any]			=   checkpoint["""Featurizer"""]["""weights"""]

							hf_feature_extractor.save_pretrained(lowerCAmelCase_					)
							hf_model.save_pretrained(lowerCAmelCase_					)


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







# flake8: noqa
# Lint as: python3
lowerCamelCase       :       Optional[Any]        =				[
    '''VerificationMode''',
    '''Version''',
    '''disable_progress_bar''',
    '''enable_progress_bar''',
    '''is_progress_bar_enabled''',
    '''experimental''',
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental