

import argparse

import torch

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


logging.set_verbosity_info()
lowercase_   							=  logging.get_logger(__name__)
def 	a (     A__      :  Tuple ,  A__      :  Optional[int] ,  A__      :  Optional[int]	)       -> Union[str, Any]:





      """simple docstring"""



      _lowercase      			=UniSpeechSatForSequenceClassification.from_pretrained(A__ ,  config=A__	)
      _lowercase      			=downstream_dict['projector.weight']
      _lowercase      			=downstream_dict['projector.bias']
      _lowercase      			=downstream_dict['model.post_net.linear.weight']
      _lowercase      			=downstream_dict['model.post_net.linear.bias']
      return model
def 	a (     A__      :  str ,  A__      :  Tuple ,  A__      :  str	)       -> Any:





      """simple docstring"""



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





      """simple docstring"""



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

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





      """simple docstring"""



      _lowercase      			=torch.load(A__ ,  map_location='cpu'	)

      _lowercase      			=checkpoint['Downstream']

      _lowercase      			=UniSpeechSatConfig.from_pretrained(A__	)
      _lowercase      			=WavaVecaFeatureExtractor.from_pretrained(
          A__ ,  return_attention_mask=A__ ,  do_normalize=A__	)

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

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

      hf_feature_extractor.save_pretrained(A__	)
      hf_model.save_pretrained(A__	)


if __name__ == "__main__":
  lowercase_   							=  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.')
  lowercase_   							=  parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')





class 					__lowerCAmelCase	:




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = True				,  lowerCAmelCase = False   )    ->					str:
        '''simple docstring'''






        _lowercase      			=scheduler
        _lowercase      			=optimizers if isinstance(lowerCAmelCase				,  (list, tuple)   ) else [optimizers]
        _lowercase      			=split_batches
        _lowercase      			=step_with_optimizer
        _lowercase      			=GradientState()




  def     A__	(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					List[Any]:
        '''simple docstring'''






        if not self.step_with_optimizer:
              # No link between scheduler and optimizer -> just step
              self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )
              return

        # Otherwise, first make sure the optimizer was stepped.
        if not self.gradient_state.sync_gradients:
              if self.gradient_state.adjust_scheduler:
                    self.scheduler._step_count += 1
              return

        for opt in self.optimizers:
              if opt.step_was_skipped:
                    return
        if self.split_batches:
              # Split batches -> the training dataloader batch size is not changed so one step per training step
              self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )
        else:
              # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
              # num_processes steps per training step
              _lowercase      			=AcceleratorState().num_processes
              for _ in range(lowerCAmelCase   ):
                    # Special case when using OneCycle and `drop_last` was not used
                    if hasattr(self.scheduler				,  'total_steps'   ):
                          if self.scheduler._step_count <= self.scheduler.total_steps:
                                self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )
                    else:
                          self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        return self.scheduler.get_last_lr()




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        return self.scheduler.state_dict()




  def     A__	(							self				,  lowerCAmelCase   )    ->					str:
        '''simple docstring'''






        self.scheduler.load_state_dict(lowerCAmelCase   )




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        return self.scheduler.get_lr()




  def     A__	(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        return self.scheduler.print_lr(*lowerCAmelCase				,  **lowerCAmelCase   )


