


import argparse

import torch

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


logging.set_verbosity_info()
__UpperCamelCase       :       Optional[Any]						       =				logging.get_logger(__name__)

def 			_a  (				SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				Dict     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   WavaVecaForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE    , config=SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Optional[int]          =   downstream_dict['''projector.weight''']
 UpperCamelCase__					:    Optional[int]          =   downstream_dict['''projector.bias''']
 UpperCamelCase__					:    Dict          =   downstream_dict['''model.post_net.linear.weight''']
 UpperCamelCase__					:    Optional[Any]          =   downstream_dict['''model.post_net.linear.bias''']
 return model

def 			_a  (				SCREAMING_SNAKE_CASE	:				Optional[int]    , SCREAMING_SNAKE_CASE	:				Dict    , SCREAMING_SNAKE_CASE	:				Union[str, Any]     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   WavaVecaForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE    , config=SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    List[Any]          =   downstream_dict['''model.linear.weight''']
 UpperCamelCase__					:    Tuple          =   downstream_dict['''model.linear.bias''']
 return model

def 			_a  (				SCREAMING_SNAKE_CASE	:				Optional[int]    , SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				List[Any]     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   WavaVecaForXVector.from_pretrained(SCREAMING_SNAKE_CASE    , config=SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Tuple          =   downstream_dict['''connector.weight''']
 UpperCamelCase__					:    Any          =   downstream_dict['''connector.bias''']
 for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
  UpperCamelCase__					:    int          =   downstream_dict[
      F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
  ]
  UpperCamelCase__					:    int          =   downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

 UpperCamelCase__					:    List[Any]          =   downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
 UpperCamelCase__					:    Any          =   downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
 UpperCamelCase__					:    Dict          =   downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
 UpperCamelCase__					:    Any          =   downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
 UpperCamelCase__					:    Union[str, Any]          =   downstream_dict['''objective.W''']
 return model

@torch.no_grad()
def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				List[str]    , SCREAMING_SNAKE_CASE	:				Dict    , SCREAMING_SNAKE_CASE	:				Any     ):







 """simple docstring"""



 UpperCamelCase__					:    List[str]          =   torch.load(SCREAMING_SNAKE_CASE    , map_location='''cpu'''     )

 UpperCamelCase__					:    Dict          =   checkpoint['''Downstream''']

 UpperCamelCase__					:    Optional[Any]          =   WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Tuple          =   WavaVecaFeatureExtractor.from_pretrained(
     SCREAMING_SNAKE_CASE    , return_attention_mask=SCREAMING_SNAKE_CASE    , do_normalize=SCREAMING_SNAKE_CASE     )

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

 hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE     )
 hf_model.save_pretrained(SCREAMING_SNAKE_CASE     )


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






from math import factorial, radians

def 			_a  (				SCREAMING_SNAKE_CASE	:				float    , SCREAMING_SNAKE_CASE	:				int = 18    , SCREAMING_SNAKE_CASE	:				int = 10     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

 # Converting from degrees to radians
 UpperCamelCase__					:    int          =   radians(SCREAMING_SNAKE_CASE     )

 UpperCamelCase__					:    Tuple          =   angle_in_radians
 UpperCamelCase__					:    str          =   3
 UpperCamelCase__					:    int          =   -1

 for _ in range(SCREAMING_SNAKE_CASE     ):
  result += (b * (angle_in_radians**a)) / factorial(SCREAMING_SNAKE_CASE     )

  UpperCamelCase__					:    List[Any]          =   -b  # One positive term and the next will be negative and so on...
  a += 2  # Increased by 2 for every term.

 return round(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       __import__("doctest").testmod()


