"""simple docstring"""







import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm



def       A__    (	lowercase_     :	List[Any]      , lowercase_     :	int						)  ->							Optional[int]:
   if isinstance(__lowerCamelCase      , (list, tuple)						):
      return honor_type(__lowerCamelCase      , (move_to_device(__lowerCamelCase      , __lowerCamelCase						) for t in state)						)
   elif isinstance(__lowerCamelCase      , __lowerCamelCase						):
      return type(__lowerCamelCase						)({k: move_to_device(__lowerCamelCase      , __lowerCamelCase						) for k, v in state.items()}						)
   elif isinstance(__lowerCamelCase      , torch.Tensor						):
      return state.to(__lowerCamelCase						)
   return state






class      _SCREAMING_SNAKE_CASE							(			torch.optim.Optimizer				):



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=None	):


      '''simple docstring'''

      __lowerCamelCase    		=					optimizer
      __lowerCamelCase    		=					scaler
      __lowerCamelCase    		=					AcceleratorState()
      __lowerCamelCase    		=					GradientState()
      __lowerCamelCase    		=					device_placement
      __lowerCamelCase    		=					False
      __lowerCamelCase    		=					None

      # Handle device placement
      if device_placement:
         __lowerCamelCase    		=					self.optimizer.state_dict()
         if self.accelerator_state.distributed_type == DistributedType.TPU:
            xm.send_cpu_data_to_device(lowercase_ ,				self.accelerator_state.device	)
         else:
            __lowerCamelCase    		=					move_to_device(lowercase_ ,				self.accelerator_state.device	)
         self.optimizer.load_state_dict(lowercase_	)







   @property
   def    _A				(	self	):


      '''simple docstring'''

      return self.optimizer.state







   @state.setter
   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					state







   @property
   def    _A				(	self	):


      '''simple docstring'''

      return self.optimizer.param_groups







   @param_groups.setter
   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					param_groups







   @property
   def    _A				(	self	):


      '''simple docstring'''

      return self.optimizer.defaults







   @defaults.setter
   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					defaults







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      self.optimizer.add_param_group(lowercase_	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
         xm.send_cpu_data_to_device(lowercase_ ,				self.accelerator_state.device	)
      self.optimizer.load_state_dict(lowercase_	)







   def    _A				(	self	):


      '''simple docstring'''

      return self.optimizer.state_dict()







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE=None	):


      '''simple docstring'''

      if self.gradient_state.sync_gradients:
         __lowerCamelCase    		=					"set_to_none" in inspect.signature(self.optimizer.zero_grad	).parameters
         if accept_arg:
            if set_to_none is None:
               __lowerCamelCase    		=					False
            self.optimizer.zero_grad(set_to_none=lowercase_	)
         else:
            if set_to_none is not None:
               raise ValueError('`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'	)
            self.optimizer.zero_grad()







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE=None	):


      '''simple docstring'''

      if self.gradient_state.sync_gradients:
         if self.accelerator_state.distributed_type == DistributedType.TPU:
            __lowerCamelCase    		=					{"closure": closure} if closure is not None else {}
            xm.optimizer_step(self.optimizer ,				optimizer_args=lowercase_	)
         elif self.scaler is not None:
            __lowerCamelCase    		=					False
            if self._last_scale is None:
               # `get_scale` is an async operation requiring full synchronization
               # on CPU and GPUs before finishing. As a result, we store away
               # the prior one to reduce the call overhead
               __lowerCamelCase    		=					self.scaler.get_scale()
               __lowerCamelCase    		=					True
            self.scaler.step(self.optimizer ,				lowercase_	)
            self.scaler.update()
            __lowerCamelCase    		=					self.scaler.get_scale()
            if not new_scale:
               # If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
               __lowerCamelCase    		=					scale_after < self._last_scale
            __lowerCamelCase    		=					scale_after

         else:
            self.optimizer.step(lowercase_	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      for param_group in self.optimizer.param_groups:
         __lowerCamelCase    		=					[parameters_map.get(lowercase_ ,				lowercase_	) for p in param_group["params"]]







   @property
   def    _A				(	self	):


      '''simple docstring'''

      warnings.warn(
          'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
          '`optimizer.step_was_skipped` instead.' ,				lowercase_ ,				)
      return self._is_overflow







   @property
   def    _A				(	self	):


      '''simple docstring'''

      return self._is_overflow







   def __getstate__(	self	):


      '''simple docstring'''

      return self.__dict__.copy()







   def __setstate__(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      self.__dict__.update(lowercase_	)








"""simple docstring"""





import argparse

import torch

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


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







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   str     	=       WavLMForSequenceClassification.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   int     	=       downstream_dict["projector.weight"]
   UpperCAmelCase_   :   int     	=       downstream_dict["projector.bias"]
   UpperCAmelCase_   :   Optional[Any]     	=       downstream_dict["model.post_net.linear.weight"]
   UpperCAmelCase_   :   Optional[Any]     	=       downstream_dict["model.post_net.linear.bias"]
   return model







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       WavLMForAudioFrameClassification.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict["model.linear.weight"]
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["model.linear.bias"]
   return model







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Tuple     	=       WavLMForXVector.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["connector.weight"]
   UpperCAmelCase_   :   str     	=       downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
      UpperCAmelCase_   :   List[str]     	=       downstream_dict[
          f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   UpperCAmelCase_   :   Dict     	=       downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   UpperCAmelCase_   :   List[str]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   UpperCAmelCase_   :   Dict     	=       downstream_dict["objective.W"]
   return model







@torch.no_grad()
def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       torch.load(__lowerCamelCase,						map_location="cpu"    )

   UpperCAmelCase_   :   Union[str, Any]     	=       checkpoint["Downstream"]

   UpperCAmelCase_   :   List[str]     	=       WavLMConfig.from_pretrained(__lowerCamelCase    )
   UpperCAmelCase_   :   List[str]     	=       WavaVecaFeatureExtractor.from_pretrained(
       __lowerCamelCase,						return_attention_mask=__lowerCamelCase,						do_normalize=__lowerCamelCase    )

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

   if hf_config.use_weighted_layer_sum:
      UpperCAmelCase_   :   Optional[int]     	=       checkpoint["Featurizer"]["weights"]

   hf_feature_extractor.save_pretrained(__lowerCamelCase    )
   hf_model.save_pretrained(__lowerCamelCase    )


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)


