






class        __snake_case:



 def __init__(	self   :   List[str]	):

    """simple docstring"""






    lowercase__						:	dict[int, list[int]]         = {}
    # indexmap keys are an index and it's values are the number at that index
    lowercase__						:	dict[int, int]         = {}



 def 						UpperCAmelCase    (	self   :   Optional[Any] ,		UpperCAmelCase__   :   list | str | range ,		UpperCAmelCase__   :   int	):

    """simple docstring"""






    if isinstance(UpperCAmelCase__ ,		(range, str)	):
       lowercase__						:	int         = list(UpperCAmelCase__	)
    elif not isinstance(UpperCAmelCase__ ,		UpperCAmelCase__	):
       raise TypeError(
           """binary_search_delete() only accepts either a list, range or str"""	)

    lowercase__						:	str         = 0
    lowercase__						:	List[str]         = len(UpperCAmelCase__	) - 1

    while low <= high:
       lowercase__						:	List[str]         = (low + high) // 2
       if array[mid] == item:
          array.pop(UpperCAmelCase__	)
          return array
       elif array[mid] < item:
          lowercase__						:	List[Any]         = mid + 1
       else:
          lowercase__						:	str         = mid - 1
    raise ValueError(
        """Either the item is not in the array or the array was unsorted"""	)



 def 						UpperCAmelCase    (	self   :   List[str] ,		UpperCAmelCase__   :   list | str | range ,		UpperCAmelCase__   :   int	):

    """simple docstring"""






    if isinstance(UpperCAmelCase__ ,		(range, str)	):
       lowercase__						:	Any         = list(UpperCAmelCase__	)
    elif not isinstance(UpperCAmelCase__ ,		UpperCAmelCase__	):
       raise TypeError(
           """binary_search_insert() only accepts either a list, range or str"""	)

    lowercase__						:	Optional[int]         = 0
    lowercase__						:	Optional[Any]         = len(UpperCAmelCase__	) - 1

    while low <= high:
       lowercase__						:	str         = (low + high) // 2
       if array[mid] == index:
          # If the item already exists in the array,
          # insert it after the existing item
          array.insert(mid + 1 ,		UpperCAmelCase__	)
          return array
       elif array[mid] < index:
          lowercase__						:	str         = mid + 1
       else:
          lowercase__						:	Optional[Any]         = mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
    array.insert(UpperCAmelCase__ ,		UpperCAmelCase__	)
    return array



 def 						UpperCAmelCase    (	self   :   List[Any] ,		UpperCAmelCase__   :   int ,		UpperCAmelCase__   :   int	):

    """simple docstring"""






    if index in self.indexmap:
       lowercase__						:	Optional[int]         = self.indexmap[index]
       if len(self.numbermap[n]	) == 1:
          del self.numbermap[n]
       else:
          lowercase__						:	List[Any]         = self.binary_search_delete(self.numbermap[n] ,		UpperCAmelCase__	)

        # Set new index
    lowercase__						:	Optional[int]         = number

    # Number not seen before or empty so insert number value
    if number not in self.numbermap:
       lowercase__						:	Optional[Any]         = [index]

    # Here we need to perform a binary search insertion in order to insert
    # The item in the correct place
    else:
       lowercase__						:	int         = self.binary_search_insert(
           self.numbermap[number] ,		UpperCAmelCase__	)







 def 						UpperCAmelCase    (	self   :   Optional[int] ,		UpperCAmelCase__   :   int	):

    """simple docstring"""






    return self.numbermap.get(UpperCAmelCase__ ,		[-1]	)[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()










import argparse

import torch

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


logging.set_verbosity_info()
a    =       logging.get_logger(__name__)
def       a__						(  A		,				A		,				A					):
   lowercase__						:	int         = WavLMForSequenceClassification.from_pretrained(A		,				config=A					)
   lowercase__						:	Dict         = downstream_dict["""projector.weight"""]
   lowercase__						:	int         = downstream_dict["""projector.bias"""]
   lowercase__						:	Optional[Any]         = downstream_dict["""model.post_net.linear.weight"""]
   lowercase__						:	List[str]         = downstream_dict["""model.post_net.linear.bias"""]
   return model
def       a__						(  A		,				A		,				A					):
   lowercase__						:	Tuple         = WavLMForAudioFrameClassification.from_pretrained(A		,				config=A					)
   lowercase__						:	List[Any]         = downstream_dict["""model.linear.weight"""]
   lowercase__						:	Optional[int]         = downstream_dict["""model.linear.bias"""]
   return model
def       a__						(  A		,				A		,				A					):
   lowercase__						:	Optional[int]         = WavLMForXVector.from_pretrained(A		,				config=A					)
   lowercase__						:	List[str]         = downstream_dict["""connector.weight"""]
   lowercase__						:	int         = downstream_dict["""connector.bias"""]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
      lowercase__						:	Optional[Any]         = downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      lowercase__						:	List[str]         = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   lowercase__						:	Union[str, Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
   lowercase__						:	Optional[Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
   lowercase__						:	Any         = downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
   lowercase__						:	List[Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
   lowercase__						:	List[Any]         = downstream_dict["""objective.W"""]
   return model
@torch.no_grad()
def       a__						(  A		,				A		,				A		,				A					):
   lowercase__						:	List[Any]         = torch.load(A		,				map_location="""cpu"""					)

   lowercase__						:	Tuple         = checkpoint["""Downstream"""]

   lowercase__						:	List[str]         = WavLMConfig.from_pretrained(A					)
   lowercase__						:	Dict         = WavaVecaFeatureExtractor.from_pretrained(
       A		,				return_attention_mask=A		,				do_normalize=A					)

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

   if hf_config.use_weighted_layer_sum:
      lowercase__						:	Union[str, Any]         = checkpoint["""Featurizer"""]["""weights"""]

   hf_feature_extractor.save_pretrained(A					)
   hf_model.save_pretrained(A					)


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)


