




from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass





@dataclass
class        lowerCamelCase__:
      UpperCAmelCase__ :      int
      UpperCAmelCase__ :      TreeNode | None       						=  None
      UpperCAmelCase__ :      TreeNode | None       						=  None




def    lowerCamelCase__			(		):





       '''simple docstring'''



       return TreeNode(3    ,     TreeNode(9      )    ,     TreeNode(20    ,     TreeNode(15      )    ,     TreeNode(7      )      )      )




def    lowerCamelCase__			(		A__			:   TreeNode      ):





       '''simple docstring'''



       def depth_first_search(
           A__			:   TreeNode | None    ,     A__			:   int    ,     A__			:   list[int]      ) -> None:
              if not root:
                     return

              if depth == len(A__      ):
                     right_view.append(root.val      )

              depth_first_search(root.right    ,     depth + 1    ,     A__      )
              depth_first_search(root.left    ,     depth + 1    ,     A__      )

       __lowerCamelCase  =		[]
       if not root:
              return right_view

       depth_first_search(A__    ,     0    ,     A__      )
       return right_view




def    lowerCamelCase__			(		A__			:   TreeNode      ):





       '''simple docstring'''



       def depth_first_search(
           A__			:   TreeNode | None    ,     A__			:   int    ,     A__			:   list[int]      ) -> None:
              if not root:
                     return

              if depth == len(A__      ):
                     left_view.append(root.val      )

              depth_first_search(root.left    ,     depth + 1    ,     A__      )
              depth_first_search(root.right    ,     depth + 1    ,     A__      )

       __lowerCamelCase  =		[]
       if not root:
              return left_view

       depth_first_search(A__    ,     0    ,     A__      )
       return left_view




def    lowerCamelCase__			(		A__			:   TreeNode      ):





       '''simple docstring'''



       def breadth_first_search(A__			:   TreeNode    ,     A__			:   list[int]      ) -> None:
              __lowerCamelCase  =		[(root, 0)]
              __lowerCamelCase  =		defaultdict(A__      )

              while queue:
                     __lowerCamelCase  =		queue.pop(0      )
                     __lowerCamelCase,  __lowerCamelCase  =		first

                     lookup[hd].append(node.val      )

                     if node.left:
                            queue.append((node.left, hd - 1)      )
                     if node.right:
                            queue.append((node.right, hd + 1)      )

              for pair in sorted(lookup.items()    ,     key=lambda A__      : each[0]      ):
                     top_view.append(pair[1][0]      )

       __lowerCamelCase  =		[]
       if not root:
              return top_view

       breadth_first_search(A__    ,     A__      )
       return top_view




def    lowerCamelCase__			(		A__			:   TreeNode      ):





       '''simple docstring'''



       from collections import defaultdict

       def breadth_first_search(A__			:   TreeNode    ,     A__			:   list[int]      ) -> None:
              __lowerCamelCase  =		[(root, 0)]
              __lowerCamelCase  =		defaultdict(A__      )

              while queue:
                     __lowerCamelCase  =		queue.pop(0      )
                     __lowerCamelCase,  __lowerCamelCase  =		first
                     lookup[hd].append(node.val      )

                     if node.left:
                            queue.append((node.left, hd - 1)      )
                     if node.right:
                            queue.append((node.right, hd + 1)      )

              for pair in sorted(lookup.items()    ,     key=lambda A__      : each[0]      ):
                     bottom_view.append(pair[1][-1]      )

       __lowerCamelCase  =		[]
       if not root:
              return bottom_view

       breadth_first_search(A__    ,     A__      )
       return bottom_view


if __name__ == "__main__":
       import doctest

       doctest.testmod()







import argparse

import torch

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


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




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Any    ,     A__			:   int      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForSequenceClassification.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""projector.weight"""]
       __lowerCamelCase  =		downstream_dict["""projector.bias"""]
       __lowerCamelCase  =		downstream_dict["""model.post_net.linear.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.post_net.linear.bias"""]
       return model




def    lowerCamelCase__			(		A__			:   Union[str, Any]    ,     A__			:   Any    ,     A__			:   List[Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForAudioFrameClassification.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""model.linear.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.linear.bias"""]
       return model




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Dict    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForXVector.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""connector.weight"""]
       __lowerCamelCase  =		downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
              __lowerCamelCase  =		downstream_dict[
                  f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
              ]
              __lowerCamelCase  =		downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

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




@torch.no_grad()
def    lowerCamelCase__			(		A__			:   Any    ,     A__			:   Tuple    ,     A__			:   int    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		torch.load(A__    ,     map_location="""cpu"""      )

       __lowerCamelCase  =		checkpoint["""Downstream"""]

       __lowerCamelCase  =		WavLMConfig.from_pretrained(A__      )
       __lowerCamelCase  =		WavaVecaFeatureExtractor.from_pretrained(
           A__    ,     return_attention_mask=A__    ,     do_normalize=A__      )

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

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

       hf_feature_extractor.save_pretrained(A__      )
       hf_model.save_pretrained(A__      )


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)

