'''simple docstring'''
import argparse

import torch

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


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



def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  Dict					):


      '''simple docstring'''


      UpperCAmelCase_            =  WavaVecaForSequenceClassification.from_pretrained(_UpperCamelCase							,    config=_UpperCamelCase					)
      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  __lowerCamelCase       (      _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  Dict							,    _UpperCamelCase  :  Union[str, Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  WavaVecaForAudioFrameClassification.from_pretrained(_UpperCamelCase							,    config=_UpperCamelCase					)
      UpperCAmelCase_            =  downstream_dict['''model.linear.weight''']
      UpperCAmelCase_            =  downstream_dict['''model.linear.bias''']
      return model



def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  List[Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  WavaVecaForXVector.from_pretrained(_UpperCamelCase							,    config=_UpperCamelCase					)
      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  __lowerCamelCase       (      _UpperCamelCase  :  Union[str, Any]							,    _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  Dict							,    _UpperCamelCase  :  Any					):


      '''simple docstring'''


      UpperCAmelCase_            =  torch.load(_UpperCamelCase							,    map_location='''cpu'''					)

      UpperCAmelCase_            =  checkpoint['''Downstream''']

      UpperCAmelCase_            =  WavaVecaConfig.from_pretrained(_UpperCamelCase					)
      UpperCAmelCase_            =  WavaVecaFeatureExtractor.from_pretrained(
          _UpperCamelCase							,    return_attention_mask=_UpperCamelCase							,    do_normalize=_UpperCamelCase					)

      UpperCAmelCase_            =  hf_config.architectures[0]
      if arch.endswith('''ForSequenceClassification'''					):
            UpperCAmelCase_            =  convert_classification(_UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					)
      elif arch.endswith('''ForAudioFrameClassification'''					):
            UpperCAmelCase_            =  convert_diarization(_UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					)
      elif arch.endswith('''ForXVector'''					):
            UpperCAmelCase_            =  convert_xvector(_UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					)
      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(_UpperCamelCase					)
      hf_model.save_pretrained(_UpperCamelCase					)


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




'''simple docstring'''
from heapq import heappop, heappush

import numpy as np



def  __lowerCamelCase       (      _UpperCamelCase  :  np.ndarray							,    _UpperCamelCase  :  tuple[int, int]							,    _UpperCamelCase  :  tuple[int, int]							,    _UpperCamelCase  :  bool							,    ):


      '''simple docstring'''


      UpperCAmelCase_      ,		UpperCAmelCase_            =  grid.shape
      UpperCAmelCase_            =  [-1, 1, 0, 0]
      UpperCAmelCase_            =  [0, 0, -1, 1]
      if allow_diagonal:
            dx += [-1, -1, 1, 1]
            dy += [-1, 1, -1, 1]

      UpperCAmelCase_      ,		UpperCAmelCase_            =  [(0, source)], set()
      UpperCAmelCase_            =  np.full((rows, cols)							,    np.inf					)
      UpperCAmelCase_            =  0
      UpperCAmelCase_            =  np.empty((rows, cols)							,    dtype=_UpperCamelCase					)
      UpperCAmelCase_            =  None

      while queue:
            ((UpperCAmelCase_)      ,		(UpperCAmelCase_))            =  heappop(_UpperCamelCase					)
            if (x, y) in visited:
                  continue
            visited.add((x, y)					)

            if (x, y) == destination:
                  UpperCAmelCase_            =  []
                  while (x, y) != source:
                        path.append((x, y)					)
                        UpperCAmelCase_      ,		UpperCAmelCase_            =  predecessors[x, y]
                  path.append(_UpperCamelCase					)  # add the source manually
                  path.reverse()
                  return matrix[destination], path

            for i in range(len(_UpperCamelCase					)					):
                  UpperCAmelCase_      ,		UpperCAmelCase_            =  x + dx[i], y + dy[i]
                  if 0 <= nx < rows and 0 <= ny < cols:
                        UpperCAmelCase_            =  grid[nx][ny]
                        if next_node == 1 and matrix[nx, ny] > dist + 1:
                              heappush(_UpperCamelCase							,    (dist + 1, (nx, ny))					)
                              UpperCAmelCase_            =  dist + 1
                              UpperCAmelCase_            =  (x, y)

      return np.inf, []


if __name__ == "__main__":
      import doctest

      doctest.testmod()





