



import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_UpperCamelCase							    =						['UtteranceLevel']

@torch.no_grad()
def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				str:
  __lowerCamelCase	:	int   	=			torch.load(_lowerCAmelCase	,map_location='cpu'	)
  if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
    raise NotImplementedError(F'The supported s3prl models are {SUPPORTED_MODELS}'	)

  __lowerCamelCase	:	str   	=			checkpoint['Downstream']

  __lowerCamelCase	:	Union[str, Any]   	=			HubertConfig.from_pretrained(_lowerCAmelCase	)
  __lowerCamelCase	:	Any   	=			HubertForSequenceClassification.from_pretrained(_lowerCAmelCase	,config=_lowerCAmelCase	)
  __lowerCamelCase	:	List[Any]   	=			WavaVecaFeatureExtractor.from_pretrained(
      _lowerCAmelCase	,return_attention_mask=_lowerCAmelCase	,do_normalize=_lowerCAmelCase	)

  if hf_congfig.use_weighted_layer_sum:
    __lowerCamelCase	:	int   	=			checkpoint['Featurizer']['weights']

  __lowerCamelCase	:	int   	=			downstream_dict['projector.weight']
  __lowerCamelCase	:	str   	=			downstream_dict['projector.bias']
  __lowerCamelCase	:	Any   	=			downstream_dict['model.post_net.linear.weight']
  __lowerCamelCase	:	List[str]   	=			downstream_dict['model.post_net.linear.bias']

  hf_feature_extractor.save_pretrained(_lowerCAmelCase	)
  hf_model.save_pretrained(_lowerCAmelCase	)


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 argparse

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


logging.set_verbosity_info()

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				List[Any]:
  # Initialize PyTorch model
  __lowerCamelCase	:	Union[str, Any]   	=			CanineConfig()
  __lowerCamelCase	:	Optional[Any]   	=			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	:	str   	=			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)






