

import numpy as np

_a			=       [
    ['''a''', '''b''', '''c''', '''d''', '''e'''],
    ['''f''', '''g''', '''h''', '''i''', '''k'''],
    ['''l''', '''m''', '''n''', '''o''', '''p'''],
    ['''q''', '''r''', '''s''', '''t''', '''u'''],
    ['''v''', '''w''', '''x''', '''y''', '''z'''],
]






class 							A_			:


     def __init__(							self      :		int )							->     None:
      __lowerCAmelCase:       Dict            =				np.array(lowercase_ )


     def   UpperCAmelCase  (							self      :		str      ,  UpperCAmelCase      :		Optional[Any] )							->     np.ndarray:
      __lowerCAmelCase     ,							__lowerCAmelCase:       str            =				np.where(letter == self.SQUARE )
      __lowerCAmelCase:       Union[str, Any]            =				np.concatenate([indexa + 1, indexa + 1] )
      return indexes


     def   UpperCAmelCase  (							self      :		Dict      ,  UpperCAmelCase      :		int      ,  UpperCAmelCase      :		Any )							->     str:
      return self.SQUARE[indexa - 1, indexa - 1]


     def   UpperCAmelCase  (							self      :		int      ,  UpperCAmelCase      :		str )							->     str:
      __lowerCAmelCase:       Optional[int]            =				message.lower()
      __lowerCAmelCase:       Any            =				message.replace('j'      ,  'i' )

      __lowerCAmelCase:       int            =				''
      for letter_index in range(len(lowercase_ ) ):
       if message[letter_index] != " ":
        __lowerCAmelCase:       Tuple            =				self.letter_to_numbers(message[letter_index] )
        __lowerCAmelCase:       Dict            =				encoded_message + str(numbers[0] ) + str(numbers[1] )
       elif message[letter_index] == " ":
        __lowerCAmelCase:       Tuple            =				encoded_message + ' '

      return encoded_message


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		int )							->     str:
      __lowerCAmelCase:       Any            =				message.replace(' '      ,  '  ' )
      __lowerCAmelCase:       Optional[Any]            =				''
      for numbers_index in range(int(len(lowercase_ ) / 2 ) ):
       if message[numbers_index * 2] != " ":
        __lowerCAmelCase:       Any            =				message[numbers_index * 2]
        __lowerCAmelCase:       Any            =				message[numbers_index * 2 + 1]

        __lowerCAmelCase:       Dict            =				self.numbers_to_letter(int(lowercase_ )      ,  int(lowercase_ ) )
        __lowerCAmelCase:       str            =				decoded_message + letter
       elif message[numbers_index * 2] == " ":
        __lowerCAmelCase:       Union[str, Any]            =				decoded_message + ' '

      return decoded_message








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 _a			(	SCREAMING_SNAKE_CASE		:      Union[str, Any]   , SCREAMING_SNAKE_CASE		:      Optional[int]   , SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      str		)   ->   str:

 """simple docstring"""






 __lowerCAmelCase:       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}'''		)

 __lowerCAmelCase:       str            =				checkpoint['Downstream']

 __lowerCAmelCase:       List[str]            =				HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Any            =				HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE   , config=SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[int]            =				WavaVecaFeatureExtractor.from_pretrained(
     SCREAMING_SNAKE_CASE   , return_attention_mask=SCREAMING_SNAKE_CASE   , do_normalize=SCREAMING_SNAKE_CASE		)

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

 __lowerCAmelCase:       str            =				downstream_dict['projector.weight']
 __lowerCAmelCase:       Any            =				downstream_dict['projector.bias']
 __lowerCAmelCase:       str            =				downstream_dict['model.post_net.linear.weight']
 __lowerCAmelCase:       str            =				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__":
  _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)





