

"""simple docstring"""
import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

A									=						["""UtteranceLevel"""]







@torch.no_grad()
def       UpperCamelCase_					(     lowerCamelCase							:					Tuple     , lowerCamelCase							:					Any     , lowerCamelCase							:					List[Any]     , lowerCamelCase							:					Tuple   )						->     Optional[Any]:



       """simple docstring"""







       __magic_name__			:		Tuple        =     torch.load(lowerCamelCase     , map_location='''cpu'''   )
       if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
              raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""   )

       __magic_name__			:		Union[str, Any]        =     checkpoint['''Downstream''']

       __magic_name__			:		Tuple        =     HubertConfig.from_pretrained(lowerCamelCase   )
       __magic_name__			:		List[Any]        =     HubertForSequenceClassification.from_pretrained(lowerCamelCase     , config=lowerCamelCase   )
       __magic_name__			:		Optional[int]        =     WavaVecaFeatureExtractor.from_pretrained(
           lowerCamelCase     , return_attention_mask=lowerCamelCase     , do_normalize=lowerCamelCase   )

       if hf_congfig.use_weighted_layer_sum:
              __magic_name__			:		List[str]        =     checkpoint['''Featurizer''']['''weights''']

       __magic_name__			:		Any        =     downstream_dict['''projector.weight''']
       __magic_name__			:		List[Any]        =     downstream_dict['''projector.bias''']
       __magic_name__			:		Optional[int]        =     downstream_dict['''model.post_net.linear.weight''']
       __magic_name__			:		Any        =     downstream_dict['''model.post_net.linear.bias''']

       hf_feature_extractor.save_pretrained(lowerCamelCase   )
       hf_model.save_pretrained(lowerCamelCase   )


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



"""simple docstring"""
from collections import defaultdict







def       UpperCamelCase_					(     lowerCamelCase							:					str   )						->     dict:



       """simple docstring"""







       __magic_name__			:		defaultdict[str, int]        =     defaultdict(lowerCamelCase   )
       # Creating a dictionary containing count of each word
       for word in sentence.split():
              occurrence[word] += 1
       return occurrence


if __name__ == "__main__":
 for word, count in word_occurrence("""INPUT STRING""").items():
  print(F"""{word}: {count}""")
