





import argparse

import torch

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


logging.set_verbosity_info()
UpperCAmelCase_           =	logging.get_logger(__name__)
def      A__       (    SCREAMING_SNAKE_CASE_   :      Optional[int]				,	SCREAMING_SNAKE_CASE_   :      Optional[Any]				,	SCREAMING_SNAKE_CASE_   :      Tuple						)  ->  Optional[Any]:






   """simple docstring"""







   _UpperCAmelCase           =   WavaVecaForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_				,	config=SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   downstream_dict['''projector.weight''']
   _UpperCAmelCase           =   downstream_dict['''projector.bias''']
   _UpperCAmelCase           =   downstream_dict['''model.post_net.linear.weight''']
   _UpperCAmelCase           =   downstream_dict['''model.post_net.linear.bias''']
   return model
def      A__       (    SCREAMING_SNAKE_CASE_   :      List[Any]				,	SCREAMING_SNAKE_CASE_   :      Optional[Any]				,	SCREAMING_SNAKE_CASE_   :      Optional[Any]						)  ->  Dict:






   """simple docstring"""







   _UpperCAmelCase           =   WavaVecaForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE_				,	config=SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   downstream_dict['''model.linear.weight''']
   _UpperCAmelCase           =   downstream_dict['''model.linear.bias''']
   return model
def      A__       (    SCREAMING_SNAKE_CASE_   :      Any				,	SCREAMING_SNAKE_CASE_   :      str				,	SCREAMING_SNAKE_CASE_   :      Union[str, Any]						)  ->  int:






   """simple docstring"""







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

   _UpperCAmelCase           =   downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
   _UpperCAmelCase           =   downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
   _UpperCAmelCase           =   downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
   _UpperCAmelCase           =   downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
   _UpperCAmelCase           =   downstream_dict['''objective.W''']
   return model
@torch.no_grad()
def      A__       (    SCREAMING_SNAKE_CASE_   :      List[Any]				,	SCREAMING_SNAKE_CASE_   :      Any				,	SCREAMING_SNAKE_CASE_   :      Optional[int]				,	SCREAMING_SNAKE_CASE_   :      Dict						)  ->  Optional[Any]:






   """simple docstring"""







   _UpperCAmelCase           =   torch.load(SCREAMING_SNAKE_CASE_				,	map_location='''cpu'''						)

   _UpperCAmelCase           =   checkpoint['''Downstream''']

   _UpperCAmelCase           =   WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   WavaVecaFeatureExtractor.from_pretrained(
       SCREAMING_SNAKE_CASE_				,	return_attention_mask=SCREAMING_SNAKE_CASE_				,	do_normalize=SCREAMING_SNAKE_CASE_						)

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

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

   hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_						)
   hf_model.save_pretrained(SCREAMING_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'''





import numpy as np

from transformers import Pipeline






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Any ):






			"""simple docstring"""




			__A=	np.max(_SCREAMING_SNAKE_CASE,axis=-1,keepdims=_SCREAMING_SNAKE_CASE )
			__A=	np.exp(outputs - maxes )
			return shifted_exp / shifted_exp.sum(axis=-1,keepdims=_SCREAMING_SNAKE_CASE )






class 						a__					(					a_   ):





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    **lowerCAmelCase_							:			List[str]	)      ->		List[str]:
						__A=	{}
						if "second_text" in kwargs:
									__A=	kwargs['second_text']
						return preprocess_kwargs, {}, {}
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Optional[int]=None	)      ->		int:
						return self.tokenizer(lowerCAmelCase_			,    text_pair=lowerCAmelCase_			,    return_tensors=self.framework	)
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			List[str]	)      ->		Tuple:
						return self.model(**lowerCAmelCase_	)

			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Dict	)      ->		Optional[int]:
						__A=	model_outputs.logits[0].numpy()
						__A=	softmax(lowerCAmelCase_	)

						__A=	np.argmax(lowerCAmelCase_	)
						__A=	self.model.config.idalabel[best_class]
						__A=	probabilities[best_class].item()
						__A=	logits.tolist()
						return {"label": label, "score": score, "logits": logits}



