


"""simple docstring"""






import argparse

import torch

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


logging.set_verbosity_info()
_lowerCAmelCase				:  Any 					=     logging.get_logger(__name__)







def     lowerCamelCase_(						_lowerCamelCase       ,						_lowerCamelCase       ,						_lowerCamelCase    )  ->			List[Any]:
   '''simple docstring'''





   _lowerCamelCase					:							List[Any]				=			WavLMForSequenceClassification.from_pretrained(lowerCAmelCase_       ,						config=lowerCAmelCase_    )
   _lowerCamelCase					:							List[Any]				=			downstream_dict["projector.weight"]
   _lowerCamelCase					:							Union[str, Any]				=			downstream_dict["projector.bias"]
   _lowerCamelCase					:							Dict				=			downstream_dict["model.post_net.linear.weight"]
   _lowerCamelCase					:							Tuple				=			downstream_dict["model.post_net.linear.bias"]
   return model







def     lowerCamelCase_(						_lowerCamelCase       ,						_lowerCamelCase       ,						_lowerCamelCase    )  ->			str:
   '''simple docstring'''





   _lowerCamelCase					:							Dict				=			WavLMForAudioFrameClassification.from_pretrained(lowerCAmelCase_       ,						config=lowerCAmelCase_    )
   _lowerCamelCase					:							Optional[Any]				=			downstream_dict["model.linear.weight"]
   _lowerCamelCase					:							Union[str, Any]				=			downstream_dict["model.linear.bias"]
   return model







def     lowerCamelCase_(						_lowerCamelCase       ,						_lowerCamelCase       ,						_lowerCamelCase    )  ->			Any:
   '''simple docstring'''





   _lowerCamelCase					:							Union[str, Any]				=			WavLMForXVector.from_pretrained(lowerCAmelCase_       ,						config=lowerCAmelCase_    )
   _lowerCamelCase					:							Optional[Any]				=			downstream_dict["connector.weight"]
   _lowerCamelCase					:							List[str]				=			downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
      _lowerCamelCase					:							Optional[Any]				=			downstream_dict[
          F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      _lowerCamelCase					:							Tuple				=			downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   _lowerCamelCase					:							Tuple				=			downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   _lowerCamelCase					:							Any				=			downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   _lowerCamelCase					:							Tuple				=			downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   _lowerCamelCase					:							Dict				=			downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   _lowerCamelCase					:							Optional[Any]				=			downstream_dict["objective.W"]
   return model







@torch.no_grad()
def     lowerCamelCase_(						_lowerCamelCase       ,						_lowerCamelCase       ,						_lowerCamelCase       ,						_lowerCamelCase    )  ->			List[Any]:
   '''simple docstring'''





   _lowerCamelCase					:							Optional[int]				=			torch.load(lowerCAmelCase_       ,						map_location="cpu"    )

   _lowerCamelCase					:							List[Any]				=			checkpoint["Downstream"]

   _lowerCamelCase					:							List[Any]				=			WavLMConfig.from_pretrained(lowerCAmelCase_    )
   _lowerCamelCase					:							Optional[Any]				=			WavaVecaFeatureExtractor.from_pretrained(
       lowerCAmelCase_       ,						return_attention_mask=lowerCAmelCase_       ,						do_normalize=lowerCAmelCase_    )

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

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

   hf_feature_extractor.save_pretrained(lowerCAmelCase_    )
   hf_model.save_pretrained(lowerCAmelCase_    )


if __name__ == "__main__":
  _lowerCAmelCase				:  Union[str, Any] 					=     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.''')
  _lowerCAmelCase				:  List[str] 					=     parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def 							_A   (						lowerCAmelCase_     :					Optional[Any]   ,				lowerCAmelCase_     :					Tuple			):
     """simple docstring"""

     lowerCAmelCase__		= CanineConfig()
     lowerCAmelCase__		= CanineModel(lowerCAmelCase_			)
     model.eval()

     print(F'Building PyTorch model from configuration: {config}'			)

     # Load weights from tf checkpoint
     load_tf_weights_in_canine(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_			)

     # Save pytorch-model (weights and configuration)
     print(F'Save PyTorch model to {pytorch_dump_path}'			)
     model.save_pretrained(lowerCAmelCase_			)

     # Save tokenizer files
     lowerCAmelCase__		= CanineTokenizer()
     print(F'Save tokenizer files to {pytorch_dump_path}'			)
     tokenizer.save_pretrained(lowerCAmelCase_			)


if __name__ == "__main__":
     UpperCamelCase								=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path',
         default=None,
         type=str,
         required=True,
         help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
     )
     parser.add_argument(
         '--pytorch_dump_path',
         default=None,
         type=str,
         required=True,
         help='Path to a folder where the PyTorch model will be placed.',
     )
     UpperCamelCase								=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
