






"""simple docstring"""





import functools
from math import sqrt
from time import time






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





       lowercase__:							Union[str, Any]         =     time()
       lowercase__:							Dict         =     func(*__lowerCamelCase		,  **__lowerCamelCase						)
       lowercase__:							str         =     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__:							Optional[int]         =     [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(__lowerCamelCase						) 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(__lowerCamelCase						) 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__:							List[Any]         =     rec_fn_memoized(num - 1						) + rec_fn_memoized(num - 2						)
              lowercase__:							List[Any]         =     value
              return value

       return [rec_fn_memoized(__lowerCamelCase						) 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__:							Any         =     (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)








"""simple docstring"""





import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   str     	=       WavLMForSequenceClassification.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   int     	=       downstream_dict["projector.weight"]
   UpperCAmelCase_   :   int     	=       downstream_dict["projector.bias"]
   UpperCAmelCase_   :   Optional[Any]     	=       downstream_dict["model.post_net.linear.weight"]
   UpperCAmelCase_   :   Optional[Any]     	=       downstream_dict["model.post_net.linear.bias"]
   return model







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       WavLMForAudioFrameClassification.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict["model.linear.weight"]
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["model.linear.bias"]
   return model







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Tuple     	=       WavLMForXVector.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["connector.weight"]
   UpperCAmelCase_   :   str     	=       downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
      UpperCAmelCase_   :   List[str]     	=       downstream_dict[
          f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   UpperCAmelCase_   :   Dict     	=       downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   UpperCAmelCase_   :   List[str]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   UpperCAmelCase_   :   Dict     	=       downstream_dict["objective.W"]
   return model







@torch.no_grad()
def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       torch.load(__lowerCamelCase,						map_location="cpu"    )

   UpperCAmelCase_   :   Union[str, Any]     	=       checkpoint["Downstream"]

   UpperCAmelCase_   :   List[str]     	=       WavLMConfig.from_pretrained(__lowerCamelCase    )
   UpperCAmelCase_   :   List[str]     	=       WavaVecaFeatureExtractor.from_pretrained(
       __lowerCamelCase,						return_attention_mask=__lowerCamelCase,						do_normalize=__lowerCamelCase    )

   UpperCAmelCase_   :   Any     	=       hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"    ):
      UpperCAmelCase_   :   Optional[int]     	=       convert_classification(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   elif arch.endswith("ForAudioFrameClassification"    ):
      UpperCAmelCase_   :   Dict     	=       convert_diarization(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   elif arch.endswith("ForXVector"    ):
      UpperCAmelCase_   :   List[Any]     	=       convert_xvector(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   else:
      raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""    )

   if hf_config.use_weighted_layer_sum:
      UpperCAmelCase_   :   Optional[int]     	=       checkpoint["Featurizer"]["weights"]

   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)


