






"""simple docstring"""





import functools
from math import sqrt
from time import time






def 							A(    snake_case_		,  *snake_case_		,  **snake_case_						):
       """simple docstring"""





       lowercase__:							Optional[int]         =     time()
       lowercase__:							int         =     func(*snake_case_		,  **snake_case_						)
       lowercase__:							Tuple         =     time()
       if int(end - start						) > 0:
              print(F"""{func.__name__} runtime: {(end - start):0.4f} s"""						)
       else:
              print(F"""{func.__name__} runtime: {(end - start) * 1000:0.4f} ms"""						)
       return output






def 							A(    snake_case_						):
       """simple docstring"""





       if n < 0:
              raise Exception("n is negative"						)
       if n == 0:
              return [0]
       lowercase__:							Any         =     [0, 1]
       for _ in range(n - 1						):
              fib.append(fib[-1] + fib[-2]						)
       return fib






def 							A(    snake_case_						):
       """simple docstring"""





       def fib_recursive_term(snake_case_						) -> int:
              if i < 0:
                     raise Exception("n is negative"						)
              if i < 2:
                     return i
              return fib_recursive_term(i - 1						) + fib_recursive_term(i - 2						)

       if n < 0:
              raise Exception("n is negative"						)
       return [fib_recursive_term(snake_case_						) for i in range(n + 1						)]






def 							A(    snake_case_						):
       """simple docstring"""





       @functools.cache
       def fib_recursive_term(snake_case_						) -> int:
              if i < 0:
                     raise Exception("n is negative"						)
              if i < 2:
                     return i
              return fib_recursive_term(i - 1						) + fib_recursive_term(i - 2						)

       if n < 0:
              raise Exception("n is negative"						)
       return [fib_recursive_term(snake_case_						) for i in range(n + 1						)]






def 							A(    snake_case_						):
       """simple docstring"""





       if n < 0:
              raise Exception("n is negative"						)
       # Cache must be outside recursuive function
       # other it will reset every time it calls itself.
       lowercase__:							dict[int, int]         =     {0: 0, 1: 1, 2: 1}  # Prefilled cache

       def rec_fn_memoized(snake_case_						) -> int:
              if num in cache:
                     return cache[num]

              lowercase__:							Any         =     rec_fn_memoized(num - 1						) + rec_fn_memoized(num - 2						)
              lowercase__:							List[str]         =     value
              return value

       return [rec_fn_memoized(snake_case_						) for i in range(n + 1						)]






def 							A(    snake_case_						):
       """simple docstring"""





       if n < 0:
              raise Exception("n is negative"						)
       if n >= 1475:
              raise Exception("n is too large"						)
       lowercase__:							Any         =     sqrt(5						)
       lowercase__:							Optional[int]         =     (1 + sqrt_a) / 2
       return [round(phi**i / sqrt_a						) for i in range(n + 1						)]


if __name__ == "__main__":
  UpperCamelCase					=     30
  time_func(fib_iterative, num)
  time_func(fib_recursive, num)  # Around 3s runtime
  time_func(fib_recursive_cached, num)  # Around 0ms runtime
  time_func(fib_memoization, num)
  time_func(fib_binet, num)







import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_SCREAMING_SNAKE_CASE     =     ["""UtteranceLevel"""]
@torch.no_grad()
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->int:

  '''simple docstring'''




  a__  		=  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}"       )

  a__  		=  checkpoint["Downstream"]

  a__  		=  HubertConfig.from_pretrained(lowerCamelCase__       )
  a__  		=  HubertForSequenceClassification.from_pretrained(lowerCamelCase__			,					config=lowerCamelCase__       )
  a__  		=  WavaVecaFeatureExtractor.from_pretrained(
      lowerCamelCase__			,					return_attention_mask=lowerCamelCase__			,					do_normalize=lowerCamelCase__       )

  if hf_congfig.use_weighted_layer_sum:
    a__  		=  checkpoint["Featurizer"]["weights"]

  a__  		=  downstream_dict["projector.weight"]
  a__  		=  downstream_dict["projector.bias"]
  a__  		=  downstream_dict["model.post_net.linear.weight"]
  a__  		=  downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(lowerCamelCase__       )
  hf_model.save_pretrained(lowerCamelCase__       )


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

