

def _a			(	SCREAMING_SNAKE_CASE		:      int		)   ->   str:

 """simple docstring"""






 __lowerCAmelCase:       str            =				1
 __lowerCAmelCase:       Optional[int]            =				0
 __lowerCAmelCase:       str            =				1
 __lowerCAmelCase:       List[str]            =				1
 __lowerCAmelCase:       Union[str, Any]            =				3
 __lowerCAmelCase:       Optional[Any]            =				3
 __lowerCAmelCase:       Any            =				limit
 __lowerCAmelCase:       str            =				0

 __lowerCAmelCase:       int            =				""
 while counter != decimal + 1:
  if 4 * q + r - t < n * t:
   result += str(SCREAMING_SNAKE_CASE_		)
   if counter == 0:
    result += "."

   if decimal == counter:
    break

   counter += 1
   __lowerCAmelCase:       Tuple            =				10 * (r - n * t)
   __lowerCAmelCase:       Optional[Any]            =				((10 * (3 * q + r)) // t) - 10 * n
   q *= 10
   __lowerCAmelCase:       Optional[Any]            =				nr
  else:
   __lowerCAmelCase:       Union[str, Any]            =				(2 * q + r) * l
   __lowerCAmelCase:       Any            =				(q * (7 * k) + 2 + (r * l)) // (t * l)
   q *= k
   t *= l
   l += 2
   k += 1
   __lowerCAmelCase:       List[Any]            =				nn
   __lowerCAmelCase:       Any            =				nr
 return result







def _a			(	)   ->   None:

 """simple docstring"""






 print(f'''{calculate_pi(50		) = }'''		)
 import doctest

 doctest.testmod()


if __name__ == "__main__":
  main()








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)





