






import argparse

import torch

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


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







def       lowercase__	(					A_:						List[Any]  ,      A_:						List[Any]  ,      A_:						Dict		)	->				str:







       """simple docstring"""






       __UpperCAmelCase     				=WavaVecaForSequenceClassification.from_pretrained(A_  ,      config=A_		)
       __UpperCAmelCase     				=downstream_dict["""projector.weight"""]
       __UpperCAmelCase     				=downstream_dict["""projector.bias"""]
       __UpperCAmelCase     				=downstream_dict["""model.post_net.linear.weight"""]
       __UpperCAmelCase     				=downstream_dict["""model.post_net.linear.bias"""]
       return model







def       lowercase__	(					A_:						Any  ,      A_:						List[str]  ,      A_:						Any		)	->				Tuple:







       """simple docstring"""






       __UpperCAmelCase     				=WavaVecaForAudioFrameClassification.from_pretrained(A_  ,      config=A_		)
       __UpperCAmelCase     				=downstream_dict["""model.linear.weight"""]
       __UpperCAmelCase     				=downstream_dict["""model.linear.bias"""]
       return model







def       lowercase__	(					A_:						List[str]  ,      A_:						Optional[int]  ,      A_:						List[Any]		)	->				List[Any]:







       """simple docstring"""






       __UpperCAmelCase     				=WavaVecaForXVector.from_pretrained(A_  ,      config=A_		)
       __UpperCAmelCase     				=downstream_dict["""connector.weight"""]
       __UpperCAmelCase     				=downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
              __UpperCAmelCase     				=downstream_dict[
                  F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
              ]
              __UpperCAmelCase     				=downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

       __UpperCAmelCase     				=downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       __UpperCAmelCase     				=downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       __UpperCAmelCase     				=downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       __UpperCAmelCase     				=downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       __UpperCAmelCase     				=downstream_dict["""objective.W"""]
       return model







@torch.no_grad()
def       lowercase__	(					A_:						List[str]  ,      A_:						List[str]  ,      A_:						Any  ,      A_:						List[str]		)	->				Tuple:







       """simple docstring"""






       __UpperCAmelCase     				=torch.load(A_  ,      map_location="""cpu"""		)

       __UpperCAmelCase     				=checkpoint["""Downstream"""]

       __UpperCAmelCase     				=WavaVecaConfig.from_pretrained(A_		)
       __UpperCAmelCase     				=WavaVecaFeatureExtractor.from_pretrained(
           A_  ,      return_attention_mask=A_  ,      do_normalize=A_		)

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

       if hf_config.use_weighted_layer_sum:
              __UpperCAmelCase     				=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)








from __future__ import annotations

from typing import Any







class 						_snake_case    ( _snake_case				):
 pass







class 						_snake_case    :




 def __init__(						self   ,    _lowerCamelCase       ):
       a		:Any									=			data
       a		:Node | None									=			None




 def __iter__(						self       ):
       a		:Dict									=			self
       a		:Optional[int]									=			[]
       while node:
             if node in visited:
                   raise ContainsLoopError
             visited.append(_lowerCamelCase       )
             yield node.data
             a		:str									=			node.next_node




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       try:
             list(self       )
             return False
       except ContainsLoopError:
             return True


if __name__ == "__main__":
     snake_case							:       int										=	Node(1)
     snake_case							:       int										=	Node(2)
     snake_case							:       Optional[int]										=	Node(3)
     snake_case							:       List[str]										=	Node(4)
     print(root_node.has_loop)  # False
     snake_case							:       Optional[Any]										=	root_node.next_node
     print(root_node.has_loop)  # True

     snake_case							:       Dict										=	Node(5)
     snake_case							:       List[Any]										=	Node(6)
     snake_case							:       Optional[int]										=	Node(5)
     snake_case							:       Union[str, Any]										=	Node(6)
     print(root_node.has_loop)  # False

     snake_case							:       Tuple										=	Node(1)
     print(root_node.has_loop)  # False





