

'''simple docstring'''






import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

snake_case_					  =	['UtteranceLevel']


@torch.no_grad()
def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Any  ,  SCREAMING_SNAKE_CASE_					:     Dict  ,  SCREAMING_SNAKE_CASE_					:     int  ,  SCREAMING_SNAKE_CASE_					:     List[str]   )							->       List[Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	torch.load(SCREAMING_SNAKE_CASE_  ,  map_location="cpu"   )
      if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
            raise NotImplementedError(F"The supported s3prl models are {SUPPORTED_MODELS}"   )

      SCREAMING_SNAKE_CASE_				:      str					=	checkpoint["Downstream"]

      SCREAMING_SNAKE_CASE_				:      List[Any]					=	HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE_   )
      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_  ,  config=SCREAMING_SNAKE_CASE_   )
      SCREAMING_SNAKE_CASE_				:      Dict					=	WavaVecaFeatureExtractor.from_pretrained(
          SCREAMING_SNAKE_CASE_  ,  return_attention_mask=SCREAMING_SNAKE_CASE_  ,  do_normalize=SCREAMING_SNAKE_CASE_   )

      if hf_congfig.use_weighted_layer_sum:
            SCREAMING_SNAKE_CASE_				:      List[str]					=	checkpoint["Featurizer"]["weights"]

      SCREAMING_SNAKE_CASE_				:      List[Any]					=	downstream_dict["projector.weight"]
      SCREAMING_SNAKE_CASE_				:      Tuple					=	downstream_dict["projector.bias"]
      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	downstream_dict["model.post_net.linear.weight"]
      SCREAMING_SNAKE_CASE_				:      List[Any]					=	downstream_dict["model.post_net.linear.bias"]

      hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_   )
      hf_model.save_pretrained(SCREAMING_SNAKE_CASE_   )


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




'''simple docstring'''






import heapq


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     List[str]  ,  SCREAMING_SNAKE_CASE_					:     Any  ,  SCREAMING_SNAKE_CASE_					:     Tuple   )							->       Union[str, Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	[(0, start)]  # cost from start node,end node
      SCREAMING_SNAKE_CASE_				:      str					=	set()
      while heap:
            ((SCREAMING_SNAKE_CASE_),						(SCREAMING_SNAKE_CASE_))				:      Optional[int]					=	heapq.heappop(SCREAMING_SNAKE_CASE_   )
            if u in visited:
                  continue
            visited.add(SCREAMING_SNAKE_CASE_   )
            if u == end:
                  return cost
            for v, c in graph[u]:
                  if v in visited:
                        continue
                  SCREAMING_SNAKE_CASE_				:      int					=	cost + c
                  heapq.heappush(SCREAMING_SNAKE_CASE_  ,  (next_item, v)   )
      return -1


snake_case_					  =	{
    'A': [['B', 2], ['C', 5]],
    'B': [['A', 2], ['D', 3], ['E', 1], ['F', 1]],
    'C': [['A', 5], ['F', 3]],
    'D': [['B', 3]],
    'E': [['B', 4], ['F', 3]],
    'F': [['C', 3], ['E', 3]],
}
snake_case_					  =	{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
snake_case_					  =	{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

snake_case_					  =	dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

snake_case_					  =	dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

snake_case_					  =	dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
      import doctest

      doctest.testmod()

