






"""simple docstring"""







import argparse

import torch

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


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


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Union[str, Any]:
				lowercase    :      Union[str, Any]       = WavaVecaForSequenceClassification.from_pretrained(__a  ,				config=__a )
				lowercase    :      List[Any]       = downstream_dict["""projector.weight"""]
				lowercase    :      List[str]       = downstream_dict["""projector.bias"""]
				lowercase    :      List[Any]       = downstream_dict["""model.post_net.linear.weight"""]
				lowercase    :      Union[str, Any]       = downstream_dict["""model.post_net.linear.bias"""]
				return model


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Dict:
				lowercase    :      Tuple       = WavaVecaForAudioFrameClassification.from_pretrained(__a  ,				config=__a )
				lowercase    :      str       = downstream_dict["""model.linear.weight"""]
				lowercase    :      str       = downstream_dict["""model.linear.bias"""]
				return model


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Optional[Any]:
				lowercase    :      Union[str, Any]       = WavaVecaForXVector.from_pretrained(__a  ,				config=__a )
				lowercase    :      Dict       = downstream_dict["""connector.weight"""]
				lowercase    :      Tuple       = downstream_dict["""connector.bias"""]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
								lowercase    :      str       = downstream_dict[
								    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
								]
								lowercase    :      List[str]       = downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

				lowercase    :      List[str]       = downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
				lowercase    :      Optional[Any]       = downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
				lowercase    :      int       = downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
				lowercase    :      Tuple       = downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
				lowercase    :      List[Any]       = downstream_dict["""objective.W"""]
				return model


@torch.no_grad()
def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A )     ->       Any:
				lowercase    :      Optional[int]       = torch.load(__a  ,				map_location="""cpu""" )

				lowercase    :      Optional[Any]       = checkpoint["""Downstream"""]

				lowercase    :      Optional[Any]       = WavaVecaConfig.from_pretrained(__a )
				lowercase    :      Dict       = WavaVecaFeatureExtractor.from_pretrained(
				    __a  ,				return_attention_mask=__a  ,				do_normalize=__a )

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

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

				hf_feature_extractor.save_pretrained(__a )
				hf_model.save_pretrained(__a )


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)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowerCAmelCase:						Optional[Any]   				=       {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						List[str]   				=       [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	_lowerCAmelCase:						List[Any]   				=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

