

import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
  import transformer_engine.pytorch as te







def _a			(	SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      str=True   , SCREAMING_SNAKE_CASE		:      Tuple=True   , SCREAMING_SNAKE_CASE		:      Union[str, Any]=True		)   ->   List[str]:

 """simple docstring"""






 if not is_fpa_available():
  raise ImportError('Using `convert_model` requires transformer_engine to be installed.'		)
 for name, module in model.named_children():
  if isinstance(SCREAMING_SNAKE_CASE   , nn.Linear		) and to_transformer_engine and _convert_linear:
   # Return early if the linear layer weights are not multiples of 16
   if any(p % 16 != 0 for p in module.weight.shape		):
    return
   __lowerCAmelCase:       Optional[Any]            =				module.bias is not None
   __lowerCAmelCase:       Union[str, Any]            =				te.Linear(
       module.in_features   , module.out_features   , bias=SCREAMING_SNAKE_CASE   , params_dtype=module.weight.dtype		)
   __lowerCAmelCase:       Any            =				module.weight.data.clone()
   if has_bias:
    __lowerCAmelCase:       Tuple            =				module.bias.data.clone()

   setattr(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
  elif isinstance(SCREAMING_SNAKE_CASE   , nn.LayerNorm		) and to_transformer_engine and _convert_ln:
   __lowerCAmelCase:       str            =				te.LayerNorm(module.normalized_shape[0]   , eps=module.eps   , params_dtype=module.weight.dtype		)
   __lowerCAmelCase:       Union[str, Any]            =				module.weight.data.clone()
   __lowerCAmelCase:       Tuple            =				module.bias.data.clone()

   setattr(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
  elif isinstance(SCREAMING_SNAKE_CASE   , te.Linear		) and not to_transformer_engine and _convert_linear:
   __lowerCAmelCase:       Optional[int]            =				module.bias is not None
   __lowerCAmelCase:       str            =				nn.Linear(
       module.in_features   , module.out_features   , bias=SCREAMING_SNAKE_CASE   , params_dtype=module.weight.dtype		)
   __lowerCAmelCase:       Dict            =				module.weight.data.clone()
   if has_bias:
    __lowerCAmelCase:       Any            =				module.bias.data.clone()

   setattr(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
  elif isinstance(SCREAMING_SNAKE_CASE   , te.LayerNorm		) and not to_transformer_engine and _convert_ln:
   __lowerCAmelCase:       Union[str, Any]            =				nn.LayerNorm(module.normalized_shape[0]   , eps=module.eps   , params_dtype=module.weight.dtype		)
   __lowerCAmelCase:       Optional[Any]            =				module.weight.data.clone()
   __lowerCAmelCase:       Union[str, Any]            =				module.bias.data.clone()

   setattr(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
  else:
   convert_model(
       SCREAMING_SNAKE_CASE   , to_transformer_engine=SCREAMING_SNAKE_CASE   , _convert_linear=_convert_linear   , _convert_ln=_convert_ln   , )







def _a			(	SCREAMING_SNAKE_CASE		:      int		)   ->   int:

 """simple docstring"""






 if not is_fpa_available():
  raise ImportError('Using `has_transformer_engine_layers` requires transformer_engine to be installed.'		)
 for m in model.modules():
  if isinstance(SCREAMING_SNAKE_CASE   , (te.LayerNorm, te.Linear)		):
   return True
 return False








import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_a			=       ['''UtteranceLevel''']







@torch.no_grad()
def _a			(	SCREAMING_SNAKE_CASE		:      Union[str, Any]   , SCREAMING_SNAKE_CASE		:      Optional[int]   , SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      str		)   ->   str:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				torch.load(SCREAMING_SNAKE_CASE   , 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:       List[str]            =				HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Any            =				HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE   , config=SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[int]            =				WavaVecaFeatureExtractor.from_pretrained(
     SCREAMING_SNAKE_CASE   , return_attention_mask=SCREAMING_SNAKE_CASE   , do_normalize=SCREAMING_SNAKE_CASE		)

 if hf_congfig.use_weighted_layer_sum:
  __lowerCAmelCase:       Dict            =				checkpoint['Featurizer']['weights']

 __lowerCAmelCase:       str            =				downstream_dict['projector.weight']
 __lowerCAmelCase:       Any            =				downstream_dict['projector.bias']
 __lowerCAmelCase:       str            =				downstream_dict['model.post_net.linear.weight']
 __lowerCAmelCase:       str            =				downstream_dict['model.post_net.linear.bias']

 hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE		)
 hf_model.save_pretrained(SCREAMING_SNAKE_CASE		)


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)





