




import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase__			:       Union[str, Any]         =							logging.get_logger(__name__)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[Any]      ,      __UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     Optional[int]							) ->			List[str]:
      SCREAMING_SNAKE_CASE_     						=      UniSpeechSatForSequenceClassification.from_pretrained(__UpperCAmelCase      ,      config=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['projector.weight']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['projector.bias']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['model.post_net.linear.weight']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['model.post_net.linear.bias']
      return model

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     Tuple							) ->			int:
      SCREAMING_SNAKE_CASE_     						=      UniSpeechSatForAudioFrameClassification.from_pretrained(__UpperCAmelCase      ,      config=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['model.linear.weight']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['model.linear.bias']
      return model

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Any      ,      __UpperCAmelCase						:     Any      ,      __UpperCAmelCase						:     int							) ->			str:
      SCREAMING_SNAKE_CASE_     						=      UniSpeechSatForXVector.from_pretrained(__UpperCAmelCase      ,      config=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['connector.weight']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['connector.bias']
      for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
            SCREAMING_SNAKE_CASE_     						=      downstream_dict[
                f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
            ]
            SCREAMING_SNAKE_CASE_     						=      downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

      SCREAMING_SNAKE_CASE_     						=      downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
      SCREAMING_SNAKE_CASE_     						=      downstream_dict['objective.W']
      return model

@torch.no_grad()
def  UpperCAmelCase_							(				__UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     Tuple      ,      __UpperCAmelCase						:     Union[str, Any]      ,      __UpperCAmelCase						:     Optional[int]							) ->			List[str]:
      SCREAMING_SNAKE_CASE_     						=      torch.load(__UpperCAmelCase      ,      map_location='cpu'							)

      SCREAMING_SNAKE_CASE_     						=      checkpoint['Downstream']

      SCREAMING_SNAKE_CASE_     						=      UniSpeechSatConfig.from_pretrained(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      WavaVecaFeatureExtractor.from_pretrained(
          __UpperCAmelCase      ,      return_attention_mask=__UpperCAmelCase      ,      do_normalize=__UpperCAmelCase							)

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

      if hf_config.use_weighted_layer_sum:
            SCREAMING_SNAKE_CASE_     						=      checkpoint['Featurizer']['weights']

      hf_feature_extractor.save_pretrained(__UpperCAmelCase							)
      hf_model.save_pretrained(__UpperCAmelCase							)


if __name__ == "__main__":
      lowerCamelCase__			:       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.')
      lowerCamelCase__			:       Dict         =							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 sys import maxsize
from typing import Generic, TypeVar

lowerCamelCase__			:       Dict         =							TypeVar('T')

def  UpperCAmelCase_							(				__UpperCAmelCase						:     int							) ->			int:
      return (position - 1) // 2

def  UpperCAmelCase_							(				__UpperCAmelCase						:     int							) ->			int:
      return (2 * position) + 1

def  UpperCAmelCase_							(				__UpperCAmelCase						:     int							) ->			int:
      return (2 * position) + 2





class 	lowerCamelCase_							(				Generic[T] ):


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      []
       SCREAMING_SNAKE_CASE_     						=      {}
       SCREAMING_SNAKE_CASE_     						=      0



 def __len__(							self  :		Optional[Any]						):
       return self.elements



 def __repr__(							self  :		Optional[int]						):
       return str(self.heap						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       # Check if the priority queue is empty
       return self.elements == 0



 def 				lowerCAmelCase_  (							self  :		Tuple    ,    _lowerCAmelCase  :		T    ,    _lowerCAmelCase  :		int						):
       # Add an element with given priority to the queue
       self.heap.append((elem, weight)						)
       SCREAMING_SNAKE_CASE_     						=      self.elements
       self.elements += 1
       self._bubble_up(_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       # Remove and return the element with lowest weight (highest priority)
       if self.elements > 1:
             self._swap_nodes(0    ,    self.elements - 1						)
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.heap.pop()
       del self.position_map[elem]
       self.elements -= 1
       if self.elements > 0:
             SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.heap[0]
             self._bubble_down(_lowerCAmelCase						)
       return elem



 def 				lowerCAmelCase_  (							self  :		Optional[int]    ,    _lowerCAmelCase  :		T    ,    _lowerCAmelCase  :		int						):
       # Update the weight of the given key
       SCREAMING_SNAKE_CASE_     						=      self.position_map[elem]
       SCREAMING_SNAKE_CASE_     						=      (elem, weight)
       if position > 0:
             SCREAMING_SNAKE_CASE_     						=      get_parent_position(_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.heap[parent_position]
             if parent_weight > weight:
                   self._bubble_up(_lowerCAmelCase						)
             else:
                   self._bubble_down(_lowerCAmelCase						)
       else:
             self._bubble_down(_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		T						):
       # Place a node at the proper position (upward movement) [to be used internally
       # only]
       SCREAMING_SNAKE_CASE_     						=      self.position_map[elem]
       if curr_pos == 0:
             return None
       SCREAMING_SNAKE_CASE_     						=      get_parent_position(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.heap[curr_pos]
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.heap[parent_position]
       if parent_weight > weight:
             self._swap_nodes(_lowerCAmelCase    ,    _lowerCAmelCase						)
             return self._bubble_up(_lowerCAmelCase						)
       return None



 def 				lowerCAmelCase_  (							self  :		List[Any]    ,    _lowerCAmelCase  :		T						):
       # Place a node at the proper position (downward movement) [to be used
       # internally only]
       SCREAMING_SNAKE_CASE_     						=      self.position_map[elem]
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.heap[curr_pos]
       SCREAMING_SNAKE_CASE_     						=      get_child_left_position(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      get_child_right_position(_lowerCAmelCase						)
       if child_left_position < self.elements and child_right_position < self.elements:
             SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.heap[child_left_position]
             SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.heap[child_right_position]
             if child_right_weight < child_left_weight and child_right_weight < weight:
                   self._swap_nodes(_lowerCAmelCase    ,    _lowerCAmelCase						)
                   return self._bubble_down(_lowerCAmelCase						)
       if child_left_position < self.elements:
             SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.heap[child_left_position]
             if child_left_weight < weight:
                   self._swap_nodes(_lowerCAmelCase    ,    _lowerCAmelCase						)
                   return self._bubble_down(_lowerCAmelCase						)
       else:
             return None
       if child_right_position < self.elements:
             SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.heap[child_right_position]
             if child_right_weight < weight:
                   self._swap_nodes(_lowerCAmelCase    ,    _lowerCAmelCase						)
                   return self._bubble_down(_lowerCAmelCase						)
       return None




 def 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		int						):
       # Swap the nodes at the given positions
       SCREAMING_SNAKE_CASE_     						=      self.heap[nodea_pos][0]
       SCREAMING_SNAKE_CASE_     						=      self.heap[nodea_pos][0]
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      (
           self.heap[nodea_pos],
           self.heap[nodea_pos],
       )
       SCREAMING_SNAKE_CASE_     						=      nodea_pos
       SCREAMING_SNAKE_CASE_     						=      nodea_pos





class 	lowerCamelCase_							(				Generic[T] ):


 '''simple docstring'''







 def __init__(							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_     						=      {}
       SCREAMING_SNAKE_CASE_     						=      0



 def __repr__(							self  :		Optional[int]						):
       return str(self.connections						)



 def __len__(							self  :		Tuple						):
       return self.nodes



 def 				lowerCAmelCase_  (							self  :		Dict    ,    _lowerCAmelCase  :		T						):
       # Add a node in the graph if it is not in the graph
       if node not in self.connections:
             SCREAMING_SNAKE_CASE_     						=      {}
             self.nodes += 1




 def 				lowerCAmelCase_  (							self  :		List[Any]    ,    _lowerCAmelCase  :		T    ,    _lowerCAmelCase  :		T    ,    _lowerCAmelCase  :		int						):
       # Add an edge between 2 nodes in the graph
       self.add_node(_lowerCAmelCase						)
       self.add_node(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      weight
       SCREAMING_SNAKE_CASE_     						=      weight


def  UpperCAmelCase_							(				__UpperCAmelCase						:     GraphUndirectedWeighted[T]      ,      ) ->			tuple[dict[T, int], dict[T, T | None]]:
      SCREAMING_SNAKE_CASE_     						=      {node: maxsize for node in graph.connections}
      SCREAMING_SNAKE_CASE_     						=      {node: None for node in graph.connections}

      SCREAMING_SNAKE_CASE_     						=      MinPriorityQueue()
      for node, weight in dist.items():
            priority_queue.push(__UpperCAmelCase      ,      __UpperCAmelCase							)

      if priority_queue.is_empty():
            return dist, parent

      # initialization
      SCREAMING_SNAKE_CASE_     						=      priority_queue.extract_min()
      SCREAMING_SNAKE_CASE_     						=      0
      for neighbour in graph.connections[node]:
            if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                  SCREAMING_SNAKE_CASE_     						=      dist[node] + graph.connections[node][neighbour]
                  priority_queue.update_key(__UpperCAmelCase      ,      dist[neighbour]							)
                  SCREAMING_SNAKE_CASE_     						=      node

    # running prim's algorithm
      while not priority_queue.is_empty():
            SCREAMING_SNAKE_CASE_     						=      priority_queue.extract_min()
            for neighbour in graph.connections[node]:
                  if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                        SCREAMING_SNAKE_CASE_     						=      dist[node] + graph.connections[node][neighbour]
                        priority_queue.update_key(__UpperCAmelCase      ,      dist[neighbour]							)
                        SCREAMING_SNAKE_CASE_     						=      node
      return dist, parent