import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE  :		Tuple        =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :		str        =      ["UtteranceLevel"]



@torch.no_grad()
def 			A__						(  lowercase_    :  Union[str, Any]			,   lowercase_    :  Optional[int]			,   lowercase_    :  Union[str, Any]			,   lowercase_    :  Dict	)     ->       Union[str, Any]:
   __lowerCamelCase								=				torch.load(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}'''	)

   __lowerCamelCase								=				checkpoint['''Downstream''']

   __lowerCamelCase								=				HubertConfig.from_pretrained(snake_case_	)
   __lowerCamelCase								=				HubertForSequenceClassification.from_pretrained(snake_case_			,   config=snake_case_	)
   __lowerCamelCase								=				WavaVecaFeatureExtractor.from_pretrained(
       snake_case_			,   return_attention_mask=snake_case_			,   do_normalize=snake_case_	)

   if hf_congfig.use_weighted_layer_sum:
      __lowerCamelCase								=				checkpoint['''Featurizer''']['''weights''']

   __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''']

   hf_feature_extractor.save_pretrained(snake_case_	)
   hf_model.save_pretrained(snake_case_	)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE  :		Tuple        =      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.''')
     _SCREAMING_SNAKE_CASE  :		List[Any]        =      parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







"""simple docstring"""







_SCREAMING_SNAKE_CASE  :		Optional[Any]        =      {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

_SCREAMING_SNAKE_CASE  :		List[str]        =      [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_SCREAMING_SNAKE_CASE  :		Optional[Any]        =      [0 for x in range(len(graph))]
_SCREAMING_SNAKE_CASE  :		List[Any]        =      []



def 			A__						(  lowercase_    :  Optional[int]			,   lowercase_    :  List[str]	)     ->       List[str]:
   __lowerCamelCase								=				1
   while stack:
      __lowerCamelCase								=				stack.pop()
      print(lowercase_			,   clothes[current_clothing]	)
      order += 1



def 			A__						(  lowercase_    :  List[Any]			,   lowercase_    :  List[str]			,   lowercase_    :  Union[str, Any]	)     ->       List[Any]:
   __lowerCamelCase								=				1
   for v in graph[u]:
      if not visited[v]:
         depth_first_search(lowercase_			,   lowercase_			,   lowercase_	)

   stack.append(lowercase_	)



def 			A__						(  lowercase_    :  Dict			,   lowercase_    :  Optional[Any]	)     ->       Union[str, Any]:
   for v in range(len(lowercase_	)	):
      if not visited[v]:
         depth_first_search(lowercase_			,   lowercase_			,   lowercase_	)


if __name__ == "__main__":
     topological_sort(graph, visited)
     print(stack)
     print_stack(stack, clothes)






