




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

UpperCamelCase__		=						['UtteranceLevel']






@torch.no_grad()
def  snake_case (							A						,       A						,       A						,       A      )							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		List[str]							       =							torch.load(A						,       map_location='cpu'      )
	if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
		raise NotImplementedError(f"The supported s3prl models are {SUPPORTED_MODELS}"      )

	lowerCamelCase_							:		Union[str, Any]							       =							checkpoint['Downstream']

	lowerCamelCase_							:		int							       =							HubertConfig.from_pretrained(A      )
	lowerCamelCase_							:		Optional[Any]							       =							HubertForSequenceClassification.from_pretrained(A						,       config=A      )
	lowerCamelCase_							:		str							       =							WavaVecaFeatureExtractor.from_pretrained(
	    A						,       return_attention_mask=A						,       do_normalize=A      )

	if hf_congfig.use_weighted_layer_sum:
		lowerCamelCase_							:		Optional[Any]							       =							checkpoint['Featurizer']['weights']

	lowerCamelCase_							:		List[str]							       =							downstream_dict['projector.weight']
	lowerCamelCase_							:		Tuple							       =							downstream_dict['projector.bias']
	lowerCamelCase_							:		Any							       =							downstream_dict['model.post_net.linear.weight']
	lowerCamelCase_							:		List[str]							       =							downstream_dict['model.post_net.linear.bias']

	hf_feature_extractor.save_pretrained(A      )
	hf_model.save_pretrained(A      )


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






UpperCamelCase__		=						{
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.0_009_842_073,
    "short-ton": 0.0_011_023_122,
    "pound": 2.2_046_244_202,
    "stone": 0.1_574_731_728,
    "ounce": 35.273_990_723,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.022136652e26,
}

UpperCamelCase__		=						{
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1_016.04_608,
    "short-ton": 907.184,
    "pound": 0.453_592,
    "stone": 6.35_029,
    "ounce": 0.0_283_495,
    "carrat": 0.0_002,
    "atomic-mass-unit": 1.660540199e-27,
}






def  snake_case (							A						,       A						,       A      )							->							float:




	'''simple docstring'''


	if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
		lowerCamelCase_							:		Union[str, Any]							       =							(
		    f"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
		    f"Supported values are: {', '.join(A      )}"
		)
		raise ValueError(A      )
	return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
							import doctest

							doctest.testmod()
