"""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    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							WavaVecaForSequenceClassification.from_pretrained(__lowerCamelCase				,			config=__lowerCamelCase )
							A__		   =							downstream_dict["""projector.weight"""]
							A__		   =							downstream_dict["""projector.bias"""]
							A__		   =							downstream_dict["""model.post_net.linear.weight"""]
							A__		   =							downstream_dict["""model.post_net.linear.bias"""]
							return model






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							WavaVecaForAudioFrameClassification.from_pretrained(__lowerCamelCase				,			config=__lowerCamelCase )
							A__		   =							downstream_dict["""model.linear.weight"""]
							A__		   =							downstream_dict["""model.linear.bias"""]
							return model






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





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

							A__		   =							downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
							A__		   =							downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
							A__		   =							downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
							A__		   =							downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
							A__		   =							downstream_dict["""objective.W"""]
							return model






@torch.no_grad()
def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							torch.load(__lowerCamelCase				,			map_location='cpu' )

							A__		   =							checkpoint["""Downstream"""]

							A__		   =							WavaVecaConfig.from_pretrained(__lowerCamelCase )
							A__		   =							WavaVecaFeatureExtractor.from_pretrained(
							    __lowerCamelCase				,			return_attention_mask=__lowerCamelCase				,			do_normalize=__lowerCamelCase )

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

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

							hf_feature_extractor.save_pretrained(__lowerCamelCase )
							hf_model.save_pretrained(__lowerCamelCase )


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 math import log

from scipy.constants import Boltzmann, physical_constants

a		:       Any			     = 300  # TEMPERATURE (unit = K)






def 				lowerCamelCase__      (    __lowerCamelCase			:     float  ,		__lowerCamelCase			:     float  ,		__lowerCamelCase			:     float  ,		):

  if donor_conc <= 0:
    raise ValueError("""Donor concentration should be positive"""		)
  elif acceptor_conc <= 0:
    raise ValueError("""Acceptor concentration should be positive"""		)
  elif intrinsic_conc <= 0:
    raise ValueError("""Intrinsic concentration should be positive"""		)
  elif donor_conc <= intrinsic_conc:
    raise ValueError(
        """Donor concentration should be greater than intrinsic concentration"""		)
  elif acceptor_conc <= intrinsic_conc:
    raise ValueError(
        """Acceptor concentration should be greater than intrinsic concentration"""		)
  else:
    return (
        Boltzmann
        * T
        * log((donor_conc * acceptor_conc) / intrinsic_conc**2		)
        / physical_constants["electron volt"][0]
    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


