

import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
a_    :						Optional[int]    		=      logging.get_logger(__name__)


def        __lowerCAmelCase    (       _UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  Any		,							_UpperCamelCase		:  List[Any]	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			UniSpeechSatForSequenceClassification.from_pretrained(_UpperCamelCase		,							config=_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			downstream_dict['projector.weight']
 SCREAMING_SNAKE_CASE								=			downstream_dict['projector.bias']
 SCREAMING_SNAKE_CASE								=			downstream_dict['model.post_net.linear.weight']
 SCREAMING_SNAKE_CASE								=			downstream_dict['model.post_net.linear.bias']
 return model


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  List[str]		,							_UpperCamelCase		:  Dict	)		->							Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			UniSpeechSatForAudioFrameClassification.from_pretrained(_UpperCamelCase		,							config=_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			downstream_dict['model.linear.weight']
 SCREAMING_SNAKE_CASE								=			downstream_dict['model.linear.bias']
 return model


def        __lowerCAmelCase    (       _UpperCamelCase		:  Any		,							_UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  List[str]	)		->							str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			UniSpeechSatForXVector.from_pretrained(_UpperCamelCase		,							config=_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			downstream_dict['connector.weight']
 SCREAMING_SNAKE_CASE								=			downstream_dict['connector.bias']
 for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
  SCREAMING_SNAKE_CASE								=			downstream_dict[
      f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
  ]
  SCREAMING_SNAKE_CASE								=			downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

 SCREAMING_SNAKE_CASE								=			downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
 SCREAMING_SNAKE_CASE								=			downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
 SCREAMING_SNAKE_CASE								=			downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
 SCREAMING_SNAKE_CASE								=			downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
 SCREAMING_SNAKE_CASE								=			downstream_dict['objective.W']
 return model


@torch.no_grad()
def        __lowerCAmelCase    (       _UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  str		,							_UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  List[Any]	)		->							List[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			torch.load(_UpperCamelCase		,							map_location='cpu'	)

 SCREAMING_SNAKE_CASE								=			checkpoint['Downstream']

 SCREAMING_SNAKE_CASE								=			UniSpeechSatConfig.from_pretrained(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			WavaVecaFeatureExtractor.from_pretrained(
     _UpperCamelCase		,							return_attention_mask=_UpperCamelCase		,							do_normalize=_UpperCamelCase	)

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

 if hf_config.use_weighted_layer_sum:
  SCREAMING_SNAKE_CASE								=			checkpoint['Featurizer']['weights']

 hf_feature_extractor.save_pretrained(_UpperCamelCase	)
 hf_model.save_pretrained(_UpperCamelCase	)


if __name__ == "__main__":
      a_    :						Tuple    		=      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_    :						str    		=      parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			abs(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			0
 while n > 0:
  res += n % 10
  n //= 10
 return res


def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			abs(_UpperCamelCase	)
 return n if n < 10 else n % 10 + sum_of_digits(n // 10	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							int:
 '''simple docstring'''


 return sum(int(_UpperCamelCase	) for c in str(abs(_UpperCamelCase	)	)	)


def        __lowerCAmelCase    (       )		->							None:
 '''simple docstring'''


 from collections.abc import Callable
 from timeit import timeit

 def benchmark_a_function(_UpperCamelCase		:  Callable		,							_UpperCamelCase		:  int	) -> None:
  SCREAMING_SNAKE_CASE								=			f"""{func.__name__}({value})"""
  SCREAMING_SNAKE_CASE								=			timeit(f"""__main__.{call}"""		,							setup='import __main__'	)
  print(f"""{call:56} = {func(_UpperCamelCase	)} -- {timing:.4f} seconds"""	)

 for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
  for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
   benchmark_a_function(_UpperCamelCase		,							_UpperCamelCase	)
  print()


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()

