


'''simple docstring'''



import argparse

import torch

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


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







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__       ):
			UpperCAmelCase__	:       Dict   =  WavaVecaForSequenceClassification.from_pretrained(_UpperCamelCase			,   config=_UpperCamelCase       )
			UpperCAmelCase__	:       List[Any]   =  downstream_dict['''projector.weight''']
			UpperCAmelCase__	:       Dict   =  downstream_dict['''projector.bias''']
			UpperCAmelCase__	:       int   =  downstream_dict['''model.post_net.linear.weight''']
			UpperCAmelCase__	:       str   =  downstream_dict['''model.post_net.linear.bias''']
			return model







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__       ):
			UpperCAmelCase__	:       int   =  WavaVecaForAudioFrameClassification.from_pretrained(_UpperCamelCase			,   config=_UpperCamelCase       )
			UpperCAmelCase__	:       List[Any]   =  downstream_dict['''model.linear.weight''']
			UpperCAmelCase__	:       Dict   =  downstream_dict['''model.linear.bias''']
			return model







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__       ):
			UpperCAmelCase__	:       Optional[Any]   =  WavaVecaForXVector.from_pretrained(_UpperCamelCase			,   config=_UpperCamelCase       )
			UpperCAmelCase__	:       Dict   =  downstream_dict['''connector.weight''']
			UpperCAmelCase__	:       Dict   =  downstream_dict['''connector.bias''']
			for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
						UpperCAmelCase__	:       Dict   =  downstream_dict[
						    f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
						]
						UpperCAmelCase__	:       Dict   =  downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

			UpperCAmelCase__	:       int   =  downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
			UpperCAmelCase__	:       Any   =  downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
			UpperCAmelCase__	:       int   =  downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
			UpperCAmelCase__	:       Optional[int]   =  downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
			UpperCAmelCase__	:       Optional[Any]   =  downstream_dict['''objective.W''']
			return model







@torch.no_grad()
def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__       ):
			UpperCAmelCase__	:       str   =  torch.load(_UpperCamelCase			,   map_location="""cpu"""       )

			UpperCAmelCase__	:       Dict   =  checkpoint['''Downstream''']

			UpperCAmelCase__	:       str   =  WavaVecaConfig.from_pretrained(_UpperCamelCase       )
			UpperCAmelCase__	:       Any   =  WavaVecaFeatureExtractor.from_pretrained(
			    _UpperCamelCase			,   return_attention_mask=_UpperCamelCase			,   do_normalize=_UpperCamelCase       )

			UpperCAmelCase__	:       List[str]   =  hf_config.architectures[0]
			if arch.endswith("""ForSequenceClassification"""       ):
						UpperCAmelCase__	:       Any   =  convert_classification(_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase       )
			elif arch.endswith("""ForAudioFrameClassification"""       ):
						UpperCAmelCase__	:       str   =  convert_diarization(_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase       )
			elif arch.endswith("""ForXVector"""       ):
						UpperCAmelCase__	:       Dict   =  convert_xvector(_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase       )
			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(_UpperCamelCase       )
			hf_model.save_pretrained(_UpperCamelCase       )


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)

"""simple docstring"""




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  stooge(_UpperCamelCase			,					0			,					len(_UpperCamelCase    ) - 1    )
  return arr




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _lowercase			,					_lowercase:      Optional[Any]   			=  arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _lowercase:      Tuple   			=  (int)((h - i + 1) / 3    )

    # Recursively sort first 2/3 elements
    stooge(_UpperCamelCase			,					_UpperCamelCase			,					(h - t)    )

    # Recursively sort last 2/3 elements
    stooge(_UpperCamelCase			,					i + t			,					(_UpperCamelCase)    )

    # Recursively sort first 2/3 elements
    stooge(_UpperCamelCase			,					_UpperCamelCase			,					(h - t)    )


if __name__ == "__main__":
  A__ :       Dict				= input('Enter numbers separated by a comma:\n').strip()
  A__ :       Optional[Any]				= [int(item) for item in user_input.split(',')]
  print(stooge_sort(unsorted))
