"""simple docstring"""


import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase       :    int         =		logging.get_logger(__name__)


def   __UpperCAmelCase     (	_snake_case		:       Optional[Any],				_snake_case		:       str,				_snake_case		:       str       ):
   _lowercase							     =       WavLMForSequenceClassification.from_pretrained(_lowercase,				config=_lowercase       )
   _lowercase							     =       downstream_dict["projector.weight"]
   _lowercase							     =       downstream_dict["projector.bias"]
   _lowercase							     =       downstream_dict["model.post_net.linear.weight"]
   _lowercase							     =       downstream_dict["model.post_net.linear.bias"]
   return model


def   __UpperCAmelCase     (	_snake_case		:       Any,				_snake_case		:       Optional[int],				_snake_case		:       List[Any]       ):
   _lowercase							     =       WavLMForAudioFrameClassification.from_pretrained(_lowercase,				config=_lowercase       )
   _lowercase							     =       downstream_dict["model.linear.weight"]
   _lowercase							     =       downstream_dict["model.linear.bias"]
   return model


def   __UpperCAmelCase     (	_snake_case		:       List[str],				_snake_case		:       Union[str, Any],				_snake_case		:       Tuple       ):
   _lowercase							     =       WavLMForXVector.from_pretrained(_lowercase,				config=_lowercase       )
   _lowercase							     =       downstream_dict["connector.weight"]
   _lowercase							     =       downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
      _lowercase							     =       downstream_dict[
          f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      _lowercase							     =       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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


@torch.no_grad()
def   __UpperCAmelCase     (	_snake_case		:       Any,				_snake_case		:       int,				_snake_case		:       Dict,				_snake_case		:       Optional[int]       ):
   _lowercase							     =       torch.load(_lowercase,				map_location="cpu"       )

   _lowercase							     =       checkpoint["Downstream"]

   _lowercase							     =       WavLMConfig.from_pretrained(_lowercase       )
   _lowercase							     =       WavaVecaFeatureExtractor.from_pretrained(
       _lowercase,				return_attention_mask=_lowercase,				do_normalize=_lowercase       )

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

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

   hf_feature_extractor.save_pretrained(_lowercase       )
   hf_model.save_pretrained(_lowercase       )


if __name__ == "__main__":
  __UpperCamelCase       :    int         =		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       :    Optional[int]         =		parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






def      a    (   _lowercase :  Dict			,    _lowercase :  List[str]							)  ->List[Any]:
		if len(str(_lowercase							)							) == 1 or len(str(_lowercase							)							) == 1:
				return a * b

		UpperCamelCase__								=						max(len(str(_lowercase							)							)			,    len(str(_lowercase							)							)							)
		UpperCamelCase__								=						ma // 2

		UpperCamelCase__	,      UpperCamelCase__								=						divmod(_lowercase			,    1_0**ma							)
		UpperCamelCase__	,      UpperCamelCase__								=						divmod(_lowercase			,    1_0**ma							)

		UpperCamelCase__								=						karatsuba(_lowercase			,    _lowercase							)
		UpperCamelCase__								=						karatsuba((aa + aa)			,    (ba + ba)							)
		UpperCamelCase__								=						karatsuba(_lowercase			,    _lowercase							)

		return (z * 1_0 ** (2 * ma)) + ((y - z - x) * 1_0 ** (ma)) + (x)

def      a    (   )  ->List[Any]:
		print(karatsuba(1_5_4_6_3			,    2_3_4_8_9							)							)


if __name__ == "__main__":
				main()





