



import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Tuple	,   _snake_case	:List[Any]	,   _snake_case	:Optional[Any]				)  ->     List[str]:
   _A      =       UniSpeechSatForSequenceClassification.from_pretrained(_snake_case	,   config=_snake_case				)
   _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''']
   return model

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Dict	,   _snake_case	:Union[str, Any]	,   _snake_case	:List[str]				)  ->     str:
   _A      =       UniSpeechSatForAudioFrameClassification.from_pretrained(_snake_case	,   config=_snake_case				)
   _A      =       downstream_dict['''model.linear.weight''']
   _A      =       downstream_dict['''model.linear.bias''']
   return model

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]	,   _snake_case	:str	,   _snake_case	:str				)  ->     List[str]:
   _A      =       UniSpeechSatForXVector.from_pretrained(_snake_case	,   config=_snake_case				)
   _A      =       downstream_dict['''connector.weight''']
   _A      =       downstream_dict['''connector.bias''']
   for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
      _A      =       downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      _A      =       downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   _A      =       downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
   _A      =       downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
   _A      =       downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
   _A      =       downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
   _A      =       downstream_dict['''objective.W''']
   return model

@torch.no_grad()
def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Any	,   _snake_case	:List[str]	,   _snake_case	:Union[str, Any]	,   _snake_case	:List[str]				)  ->     Any:
   _A      =       torch.load(_snake_case	,   map_location='''cpu'''				)

   _A      =       checkpoint['''Downstream''']

   _A      =       UniSpeechSatConfig.from_pretrained(_snake_case				)
   _A      =       WavaVecaFeatureExtractor.from_pretrained(
       _snake_case	,   return_attention_mask=_snake_case	,   do_normalize=_snake_case				)

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

   if hf_config.use_weighted_layer_sum:
      _A      =       checkpoint['''Featurizer''']['''weights''']

   hf_feature_extractor.save_pretrained(_snake_case				)
   hf_model.save_pretrained(_snake_case				)


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 							lowerCamelCase	(					_snake_case	:    Optional[Any]  ,_snake_case	:    Union[str, Any]  ,_snake_case	:    Dict  ,_snake_case	:    List[str]  ,_snake_case	:    List[str]  ,_snake_case	:    List[str]							):





    '''simple docstring'''


    if index == r:
        for j in range(_snake_case							):
            print(data[j]  ,end=" "							)
        print(" "							)
        return
    #  When no more elements are there to put in data[]
    if i >= n:
        return
    # current is included, put next at next location
    lowercase__									=    arr[i]
    combination_util(_snake_case  ,_snake_case  ,_snake_case  ,index + 1  ,_snake_case  ,i + 1							)
    # current is excluded, replace it with
    # next (Note that i+1 is passed, but
    # index is not changed)
    combination_util(_snake_case  ,_snake_case  ,_snake_case  ,_snake_case  ,_snake_case  ,i + 1							)
    # The main function that prints all combinations
    # of size r in arr[] of size n. This function
    # mainly uses combinationUtil()




def 							lowerCamelCase	(					_snake_case	:    Optional[Any]  ,_snake_case	:    Tuple  ,_snake_case	:    Union[str, Any]							):





    '''simple docstring'''


    lowercase__									=    [0] * r
    # Print all combination using temporary array 'data[]'
    combination_util(_snake_case  ,_snake_case  ,_snake_case  ,0  ,_snake_case  ,0							)


if __name__ == "__main__":
 # Driver code to check the function above
 SCREAMING_SNAKE_CASE__          =  [10, 20, 30, 40, 50]
 print_combination(arr, len(arr), 3)
 # This code is contributed by Ambuj sahu




