




import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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




def 					lowerCamelCase__       (						A__       :					Tuple  ,			A__       :					int  ,			A__       :					Any	):





       '''simple docstring'''



       __lowerCamelCase   =						WavaVecaForSequenceClassification.from_pretrained(A__  ,			config=A__	)
       __lowerCamelCase   =						downstream_dict["""projector.weight"""]
       __lowerCamelCase   =						downstream_dict["""projector.bias"""]
       __lowerCamelCase   =						downstream_dict["""model.post_net.linear.weight"""]
       __lowerCamelCase   =						downstream_dict["""model.post_net.linear.bias"""]
       return model




def 					lowerCamelCase__       (						A__       :					Optional[Any]  ,			A__       :					Optional[Any]  ,			A__       :					Union[str, Any]	):





       '''simple docstring'''



       __lowerCamelCase   =						WavaVecaForAudioFrameClassification.from_pretrained(A__  ,			config=A__	)
       __lowerCamelCase   =						downstream_dict["""model.linear.weight"""]
       __lowerCamelCase   =						downstream_dict["""model.linear.bias"""]
       return model




def 					lowerCamelCase__       (						A__       :					Optional[int]  ,			A__       :					Tuple  ,			A__       :					Dict	):





       '''simple docstring'''



       __lowerCamelCase   =						WavaVecaForXVector.from_pretrained(A__  ,			config=A__	)
       __lowerCamelCase   =						downstream_dict["""connector.weight"""]
       __lowerCamelCase   =						downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
              __lowerCamelCase   =						downstream_dict[
                  f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
              ]
              __lowerCamelCase   =						downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

       __lowerCamelCase   =						downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       __lowerCamelCase   =						downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       __lowerCamelCase   =						downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       __lowerCamelCase   =						downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       __lowerCamelCase   =						downstream_dict["""objective.W"""]
       return model




@torch.no_grad()
def 					lowerCamelCase__       (						A__       :					List[str]  ,			A__       :					Dict  ,			A__       :					List[str]  ,			A__       :					Tuple	):





       '''simple docstring'''



       __lowerCamelCase   =						torch.load(A__  ,			map_location="""cpu"""	)

       __lowerCamelCase   =						checkpoint["""Downstream"""]

       __lowerCamelCase   =						WavaVecaConfig.from_pretrained(A__	)
       __lowerCamelCase   =						WavaVecaFeatureExtractor.from_pretrained(
           A__  ,			return_attention_mask=A__  ,			do_normalize=A__	)

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

       if hf_config.use_weighted_layer_sum:
              __lowerCamelCase   =						checkpoint["""Featurizer"""]["""weights"""]

       hf_feature_extractor.save_pretrained(A__	)
       hf_model.save_pretrained(A__	)


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


'''simple docstring'''




def 	UpperCamelCase_							(					_UpperCAmelCase  :      int			,	_UpperCAmelCase  :      int    )    ->    int:


       """simple docstring"""







       _UpperCAmelCase     :  List[str]							      =    1  # To kept the Calculated Value
       # Since C(n, k) = C(n, n-k)
       if k > (n - k):
              _UpperCAmelCase     :  Any							      =    n - k
       # Calculate C(n,k)
       for i in range(_UpperCAmelCase    ):
              result *= n - i
              result //= i + 1
       return result

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    int:


       """simple docstring"""







       return binomial_coefficient(2 * node_count			,	_UpperCAmelCase    ) // (node_count + 1)

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    int:


       """simple docstring"""







       if n < 0:
              raise ValueError("factorial() not defined for negative values"    )
       _UpperCAmelCase     :  List[str]							      =    1
       for i in range(1			,	n + 1    ):
              result *= i
       return result

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    int:


       """simple docstring"""







       return catalan_number(_UpperCAmelCase    ) * factorial(_UpperCAmelCase    )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     Dict     =  int(input("""Enter the number of nodes: """).strip() or 0)
  if node_count <= 0:
    raise ValueError("""We need some nodes to work with.""")
  print(
      F'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
      F'binary trees and {catalan_number(node_count)} binary search trees.'
  )

