



"""simple docstring"""






import argparse

import torch

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


logging.set_verbosity_info()
__UpperCAmelCase      :      Any   					=					logging.get_logger(__name__)
def      A      (			_A,     _A,     _A							):




     """simple docstring"""
     snake_case_						:Dict								=						WavLMForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE,     config=_SCREAMING_SNAKE_CASE							)
     snake_case_						:Dict								=						downstream_dict['projector.weight']
     snake_case_						:List[Any]								=						downstream_dict['projector.bias']
     snake_case_						:str								=						downstream_dict['model.post_net.linear.weight']
     snake_case_						:int								=						downstream_dict['model.post_net.linear.bias']
     return model
def      A      (			_A,     _A,     _A							):




     """simple docstring"""
     snake_case_						:List[str]								=						WavLMForAudioFrameClassification.from_pretrained(_SCREAMING_SNAKE_CASE,     config=_SCREAMING_SNAKE_CASE							)
     snake_case_						:Any								=						downstream_dict['model.linear.weight']
     snake_case_						:Tuple								=						downstream_dict['model.linear.bias']
     return model
def      A      (			_A,     _A,     _A							):




     """simple docstring"""
     snake_case_						:Optional[Any]								=						WavLMForXVector.from_pretrained(_SCREAMING_SNAKE_CASE,     config=_SCREAMING_SNAKE_CASE							)
     snake_case_						:List[str]								=						downstream_dict['connector.weight']
     snake_case_						:Optional[Any]								=						downstream_dict['connector.bias']
     for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
          snake_case_						:Optional[int]								=						downstream_dict[
              F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
          ]
          snake_case_						:Optional[int]								=						downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

     snake_case_						:int								=						downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
     snake_case_						:Union[str, Any]								=						downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
     snake_case_						:str								=						downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
     snake_case_						:int								=						downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
     snake_case_						:Union[str, Any]								=						downstream_dict['objective.W']
     return model
@torch.no_grad()
def      A      (			_A,     _A,     _A,     _A							):




     """simple docstring"""
     snake_case_						:Any								=						torch.load(_SCREAMING_SNAKE_CASE,     map_location="cpu"							)

     snake_case_						:Dict								=						checkpoint['Downstream']

     snake_case_						:Union[str, Any]								=						WavLMConfig.from_pretrained(_SCREAMING_SNAKE_CASE							)
     snake_case_						:str								=						WavaVecaFeatureExtractor.from_pretrained(
         _SCREAMING_SNAKE_CASE,     return_attention_mask=_SCREAMING_SNAKE_CASE,     do_normalize=_SCREAMING_SNAKE_CASE							)

     snake_case_						:str								=						hf_config.architectures[0]
     if arch.endswith("ForSequenceClassification"							):
          snake_case_						:Union[str, Any]								=						convert_classification(_SCREAMING_SNAKE_CASE,     _SCREAMING_SNAKE_CASE,     _SCREAMING_SNAKE_CASE							)
     elif arch.endswith("ForAudioFrameClassification"							):
          snake_case_						:Optional[int]								=						convert_diarization(_SCREAMING_SNAKE_CASE,     _SCREAMING_SNAKE_CASE,     _SCREAMING_SNAKE_CASE							)
     elif arch.endswith("ForXVector"							):
          snake_case_						:Dict								=						convert_xvector(_SCREAMING_SNAKE_CASE,     _SCREAMING_SNAKE_CASE,     _SCREAMING_SNAKE_CASE							)
     else:
          raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''							)

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

     hf_feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE							)
     hf_model.save_pretrained(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
    __UpperCAmelCase      :      Union[str, Any]   					=					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[Any]   					=					parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






"""simple docstring"""






def      A      (			_A,     _A = 1_000							):




					"""simple docstring"""
					if (not isinstance(_A,     _A							)) or (digit_position <= 0):
										raise ValueError("Digit position must be a positive integer"							)
					elif (not isinstance(_A,     _A							)) or (precision < 0):
										raise ValueError("Precision must be a nonnegative integer"							)

					# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
					# accurate
					snake_case_						:List[Any]								=						(
					    4 * _subsum(_A,     1,     _A							)
					    - 2 * _subsum(_A,     4,     _A							)
					    - _subsum(_A,     5,     _A							)
					    - _subsum(_A,     6,     _A							)
					)

					# return the first hex digit of the fractional part of the result
					return hex(int((sum_result % 1) * 16							)							)[2:]
def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					snake_case_						:int								=						0.0
					for sum_index in range(digit_pos_to_extract + precision							):
										snake_case_						:Optional[Any]								=						8 * sum_index + denominator_addend
										if sum_index < digit_pos_to_extract:
															# if the exponential term is an integer and we mod it by the denominator
															# before dividing, only the integer part of the sum will change;
															# the fractional part will not
															snake_case_						:List[Any]								=						pow(
															    16,     digit_pos_to_extract - 1 - sum_index,     _A							)
										else:
															snake_case_						:Optional[Any]								=						pow(16,     digit_pos_to_extract - 1 - sum_index							)
										total += exponential_term / denominator
					return total


if __name__ == "__main__":
				import doctest

				doctest.testmod()

