




'''simple docstring'''






import argparse

import torch

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


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






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Any:
      snake_case__       :   List[Any]	      =    WavaVecaForSequenceClassification.from_pretrained(__lowerCAmelCase				,      config=__lowerCAmelCase		)
      snake_case__       :   Optional[Any]	      =    downstream_dict['projector.weight']
      snake_case__       :   Optional[Any]	      =    downstream_dict['projector.bias']
      snake_case__       :   Optional[int]	      =    downstream_dict['model.post_net.linear.weight']
      snake_case__       :   List[str]	      =    downstream_dict['model.post_net.linear.bias']
      return model






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Optional[Any]:
      snake_case__       :   int	      =    WavaVecaForAudioFrameClassification.from_pretrained(__lowerCAmelCase				,      config=__lowerCAmelCase		)
      snake_case__       :   Dict	      =    downstream_dict['model.linear.weight']
      snake_case__       :   str	      =    downstream_dict['model.linear.bias']
      return model






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   int:
      snake_case__       :   Optional[int]	      =    WavaVecaForXVector.from_pretrained(__lowerCAmelCase				,      config=__lowerCAmelCase		)
      snake_case__       :   Any	      =    downstream_dict['connector.weight']
      snake_case__       :   str	      =    downstream_dict['connector.bias']
      for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
            snake_case__       :   int	      =    downstream_dict[
                f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
            ]
            snake_case__       :   str	      =    downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

      snake_case__       :   Optional[Any]	      =    downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
      snake_case__       :   Optional[int]	      =    downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
      snake_case__       :   List[Any]	      =    downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
      snake_case__       :   int	      =    downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
      snake_case__       :   Optional[int]	      =    downstream_dict['objective.W']
      return model






@torch.no_grad()
def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Optional[int]:
      snake_case__       :   str	      =    torch.load(__lowerCAmelCase				,      map_location='cpu'		)

      snake_case__       :   Tuple	      =    checkpoint['Downstream']

      snake_case__       :   Optional[int]	      =    WavaVecaConfig.from_pretrained(__lowerCAmelCase		)
      snake_case__       :   List[Any]	      =    WavaVecaFeatureExtractor.from_pretrained(
          __lowerCAmelCase				,      return_attention_mask=__lowerCAmelCase				,      do_normalize=__lowerCAmelCase		)

      snake_case__       :   str	      =    hf_config.architectures[0]
      if arch.endswith('ForSequenceClassification'		):
            snake_case__       :   Union[str, Any]	      =    convert_classification(__lowerCAmelCase				,      __lowerCAmelCase				,      __lowerCAmelCase		)
      elif arch.endswith('ForAudioFrameClassification'		):
            snake_case__       :   Tuple	      =    convert_diarization(__lowerCAmelCase				,      __lowerCAmelCase				,      __lowerCAmelCase		)
      elif arch.endswith('ForXVector'		):
            snake_case__       :   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:
            snake_case__       :   Dict	      =    checkpoint['Featurizer']['weights']

      hf_feature_extractor.save_pretrained(__lowerCAmelCase		)
      hf_model.save_pretrained(__lowerCAmelCase		)


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





from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_lowerCamelCase    :		Dict		      =    typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_lowerCamelCase    :		Any		      =    typing.Union[np.floataa, int, float]  # noqa: UP007

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     VectorOut:
					return np.sqrt(np.sum((np.asarray(__lowerCAmelCase   ) - np.asarray(__lowerCAmelCase   )) ** 2   )   )

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     VectorOut:
					return sum((va - va) ** 2 for va, va in zip(__lowerCAmelCase      ,	__lowerCAmelCase   )   ) ** (1 / 2)


if __name__ == "__main__":

			def        __a			(   )     ->     None:
								from timeit import timeit

								print('Without Numpy'   )
								print(
								    timeit(
								        'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'      ,	number=1_0000      ,	globals=globals()      ,	)   )
								print('With Numpy'   )
								print(
								    timeit(
								        'euclidean_distance([1, 2, 3], [4, 5, 6])'      ,	number=1_0000      ,	globals=globals()      ,	)   )



			benchmark()