






import argparse

import torch

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


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


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					List[Any]							)     ->  Any:
   __UpperCamelCase	:			str					=					UniSpeechSatForSequenceClassification.from_pretrained(__lowerCAmelCase					,							config=__lowerCAmelCase							)
   __UpperCamelCase	:			Tuple					=					downstream_dict["""projector.weight"""]
   __UpperCamelCase	:			List[str]					=					downstream_dict["""projector.bias"""]
   __UpperCamelCase	:			Union[str, Any]					=					downstream_dict["""model.post_net.linear.weight"""]
   __UpperCamelCase	:			Any					=					downstream_dict["""model.post_net.linear.bias"""]
   return model


def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					Tuple					,							__lowerCAmelCase							:					Optional[int]							)     ->  Union[str, Any]:
   __UpperCamelCase	:			Dict					=					UniSpeechSatForAudioFrameClassification.from_pretrained(__lowerCAmelCase					,							config=__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					downstream_dict["""model.linear.weight"""]
   __UpperCamelCase	:			List[Any]					=					downstream_dict["""model.linear.bias"""]
   return model


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					List[Any]							)     ->  Tuple:
   __UpperCamelCase	:			Union[str, Any]					=					UniSpeechSatForXVector.from_pretrained(__lowerCAmelCase					,							config=__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[Any]					=					downstream_dict["""connector.weight"""]
   __UpperCamelCase	:			List[str]					=					downstream_dict["""connector.bias"""]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
      __UpperCamelCase	:			Union[str, Any]					=					downstream_dict[
          f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
      ]
      __UpperCamelCase	:			Union[str, Any]					=					downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

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


@torch.no_grad()
def 				__lowerCamelCase     (							__lowerCAmelCase							:					Dict					,							__lowerCAmelCase							:					Optional[Any]					,							__lowerCAmelCase							:					Any					,							__lowerCAmelCase							:					str							)     ->  List[Any]:
   __UpperCamelCase	:			str					=					torch.load(__lowerCAmelCase					,							map_location="""cpu"""							)

   __UpperCamelCase	:			Any					=					checkpoint["""Downstream"""]

   __UpperCamelCase	:			Tuple					=					UniSpeechSatConfig.from_pretrained(__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[Any]					=					WavaVecaFeatureExtractor.from_pretrained(
       __lowerCAmelCase					,							return_attention_mask=__lowerCAmelCase					,							do_normalize=__lowerCAmelCase							)

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

   if hf_config.use_weighted_layer_sum:
      __UpperCamelCase	:			int					=					checkpoint["""Featurizer"""]["""weights"""]

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


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











from __future__ import annotations







class 		_A						:

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	Any=None			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					data
     __UpperCamelCase	:			Union[str, Any]					=					None

  def __repr__(    self						:	int			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					[]
     __UpperCamelCase	:			Optional[int]					=					self
     while temp:
        string_rep.append(f'{temp.data}'			)
        __UpperCamelCase	:			Dict					=					temp.next
     return "->".join(lowerCamelCase__			)







def 				__lowerCamelCase     (							__lowerCAmelCase							:					list							)     ->  Tuple:
   if not elements_list:
      raise Exception("""The Elements List is empty"""							)

   __UpperCamelCase	:			Dict					=					Node(elements_list[0]							)
   for i in range(1					,							len(__lowerCAmelCase							)							):
      __UpperCamelCase	:			List[str]					=					Node(elements_list[i]							)
      __UpperCamelCase	:			Optional[int]					=					current.next
   return head


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Node							)     ->  None:
   if head_node is not None and isinstance(__lowerCAmelCase					,							__lowerCAmelCase							):
      print_reverse(head_node.next							)
      print(head_node.data							)


def 				__lowerCamelCase     (							)     ->  Union[str, Any]:
   from doctest import testmod

   testmod()

   __UpperCamelCase	:			Any					=					make_linked_list([14, 52, 14, 12, 43]							)
   print("""Linked List:"""							)
   print(__lowerCAmelCase							)
   print("""Elements in Reverse:"""							)
   print_reverse(__lowerCAmelCase							)


if __name__ == "__main__":
       main()



