



"""simple docstring"""





import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


SCREAMING_SNAKE_CASE				:     List[Any]          =	logging.get_logger(__name__)


class       __lowerCamelCase	(   nn.Module    ):







  def __init__(self				):




     '''simple docstring'''




     super().__init__()
     if version.parse(torch.__version__				) < version.parse("""1.12.0"""				):
        raise ImportError(
            f"""You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use """
            """PytorchGELUTanh. Please upgrade torch."""				)







  def 							A__						(self       ,	lowerCamelCase				):




     '''simple docstring'''




     return nn.functional.gelu(__A       ,	approximate="""tanh"""				)





class       __lowerCamelCase	(   nn.Module    ):







  def 							A__						(self       ,	lowerCamelCase				):




     '''simple docstring'''




     return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.04_4715 * torch.pow(__A       ,	3.0				))				))





class       __lowerCamelCase	(   nn.Module    ):







  def __init__(self       ,	lowerCamelCase = False				):




     '''simple docstring'''




     super().__init__()
     if use_gelu_python:
        _lowerCAmelCase					     =		self._gelu_python
     else:
        _lowerCAmelCase					     =		nn.functional.gelu







  def 							A__						(self       ,	lowerCamelCase				):




     '''simple docstring'''




     return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0				)				))







  def 							A__						(self       ,	lowerCamelCase				):




     '''simple docstring'''




     return self.act(__A				)





class       __lowerCamelCase	(   nn.Module    ):







  def 							A__						(self       ,	lowerCamelCase				):




     '''simple docstring'''




     return 0.5 * input * (1.0 + torch.tanh(input * 0.79_7884_5608 * (1.0 + 0.04_4715 * input * input)				))





class       __lowerCamelCase	(   nn.Module    ):







  def 							A__						(self       ,	lowerCamelCase				):




     '''simple docstring'''




     return input * torch.sigmoid(1.702 * input				)





class       __lowerCamelCase	(   nn.Module    ):







  def __init__(self       ,	lowerCamelCase       ,	lowerCamelCase				):




     '''simple docstring'''




     if min > max:
        raise ValueError(f"""min should be < max (got min: {min}, max: {max})"""				)

     super().__init__()
     _lowerCAmelCase					     =		min
     _lowerCAmelCase					     =		max







  def 							A__						(self       ,	lowerCamelCase				):




     '''simple docstring'''




     return torch.clip(gelu(__A				)       ,	self.min       ,	self.max				)





class       __lowerCamelCase	(   nn.Module    ):







  def __init__(self				):




     '''simple docstring'''




     super().__init__()
     _lowerCAmelCase					     =		math.sqrt(2 / math.pi				)







  def 							A__						(self       ,	lowerCamelCase				):




     '''simple docstring'''




     return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_4715 * torch.pow(__A       ,	3				))				))





class       __lowerCamelCase	(   nn.Module    ):







  def 							A__						(self       ,	lowerCamelCase				):




     '''simple docstring'''




     return nn.functional.silu(__A				)





class       __lowerCamelCase	(   nn.Module    ):







  def __init__(self				):




     '''simple docstring'''




     super().__init__()
     if version.parse(torch.__version__				) < version.parse("""1.9.0"""				):
        _lowerCAmelCase					     =		self._mish_python
     else:
        _lowerCAmelCase					     =		nn.functional.mish







  def 							A__						(self       ,	lowerCamelCase				):




     '''simple docstring'''




     return input * torch.tanh(nn.functional.softplus(__A				)				)







  def 							A__						(self       ,	lowerCamelCase				):




     '''simple docstring'''




     return self.act(__A				)





class       __lowerCamelCase	(   nn.Module    ):







  def 							A__						(self       ,	lowerCamelCase				):




     '''simple docstring'''




     return input





class       __lowerCamelCase	(   nn.Module    ):







  def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase=0.70_7107       ,	lowerCamelCase=0.28_2095				):




     '''simple docstring'''




     _lowerCAmelCase					     =		(input - mu).div(sigma * math.sqrt(2.0				)				)
     return 0.5 * (1.0 + torch.erf(__A				))





class       __lowerCamelCase	(   nn.Module    ):







  def 							A__						(self       ,	lowerCamelCase				):




     '''simple docstring'''




     _lowerCAmelCase					     =		nn.functional.relu(__A				)
     _lowerCAmelCase					     =		torch.square(__A				)
     return squared





class       __lowerCamelCase	(   _SCREAMING_SNAKE_CASE    ):







  def __getitem__(self       ,	lowerCamelCase				):




     '''simple docstring'''




     _lowerCAmelCase					     =		super().__getitem__(__A				)
     _lowerCAmelCase     ,						_lowerCAmelCase					     =		content if isinstance(__A       ,	__A				) else (content, {})
     return cls(**__A				)





SCREAMING_SNAKE_CASE				:     List[str]          =	{
    """gelu""": GELUActivation,
    """gelu_10""": (ClippedGELUActivation, {"""min""": -1_0, """max""": 1_0}),
    """gelu_fast""": FastGELUActivation,
    """gelu_new""": NewGELUActivation,
    """gelu_python""": (GELUActivation, {"""use_gelu_python""": True}),
    """gelu_pytorch_tanh""": PytorchGELUTanh,
    """gelu_accurate""": AccurateGELUActivation,
    """laplace""": LaplaceActivation,
    """linear""": LinearActivation,
    """mish""": MishActivation,
    """quick_gelu""": QuickGELUActivation,
    """relu""": nn.ReLU,
    """relu2""": ReLUSquaredActivation,
    """relu6""": nn.ReLUa,
    """sigmoid""": nn.Sigmoid,
    """silu""": SiLUActivation,
    """swish""": SiLUActivation,
    """tanh""": nn.Tanh,
}
SCREAMING_SNAKE_CASE				:     Optional[int]          =	ClassInstantier(ACTaCLS)






def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   )							->	Dict:






   """simple docstring"""


   if activation_string in ACTaFN:
      return ACTaFN[activation_string]
   else:
      raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()   )}"""   )


# For backwards compatibility with: from activations import gelu_python
SCREAMING_SNAKE_CASE				:     Dict          =	get_activation('''gelu_python''')
SCREAMING_SNAKE_CASE				:     Optional[Any]          =	get_activation('''gelu_new''')
SCREAMING_SNAKE_CASE				:     Dict          =	get_activation('''gelu''')
SCREAMING_SNAKE_CASE				:     str          =	get_activation('''gelu_fast''')
SCREAMING_SNAKE_CASE				:     Any          =	get_activation('''quick_gelu''')
SCREAMING_SNAKE_CASE				:     Dict          =	get_activation('''silu''')
SCREAMING_SNAKE_CASE				:     List[Any]          =	get_activation('''mish''')



SCREAMING_SNAKE_CASE				:     Union[str, Any]          =	get_activation('''linear''')




"""simple docstring"""





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE				:     str          =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:     List[str]          =	{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}






def 	__UpperCAmelCase					(       snake_case_  :  List[str]   ,	snake_case_  :  Dict   ,	snake_case_  :  Any   ,	snake_case_  :  str   ,	snake_case_  :  Tuple   )							->	str:






   """simple docstring"""


   for attribute in key.split("""."""   ):
      _lowerCAmelCase					     =		getattr(snake_case_   ,	snake_case_   )

   if weight_type is not None:
      _lowerCAmelCase					     =		getattr(snake_case_   ,	snake_case_   ).shape
   else:
      _lowerCAmelCase					     =		hf_pointer.shape

   assert hf_shape == value.shape, (
       F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
       F""" {value.shape} for {full_name}"""
   )

   if weight_type == "weight":
      _lowerCAmelCase					     =		value
   elif weight_type == "weight_g":
      _lowerCAmelCase					     =		value
   elif weight_type == "weight_v":
      _lowerCAmelCase					     =		value
   elif weight_type == "bias":
      _lowerCAmelCase					     =		value
   else:
      _lowerCAmelCase					     =		value

   logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""   )






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  Any   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		[]
   _lowerCAmelCase					     =		fairseq_model.state_dict()

   _lowerCAmelCase					     =		hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      _lowerCAmelCase					     =		False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   ,	hf_model.config.feat_extract_norm == """group"""   ,	)
         _lowerCAmelCase					     =		True
      else:
         for key, mapped_key in MAPPING.items():
            _lowerCAmelCase					     =		mapped_key

            if key in name:
               _lowerCAmelCase					     =		True
               if "*" in mapped_key:
                  _lowerCAmelCase					     =		name.split(snake_case_   )[0].split("""."""   )[-2]
                  _lowerCAmelCase					     =		mapped_key.replace("""*"""   ,	snake_case_   )
               if "weight_g" in name:
                  _lowerCAmelCase					     =		"""weight_g"""
               elif "weight_v" in name:
                  _lowerCAmelCase					     =		"""weight_v"""
               elif "weight" in name:
                  _lowerCAmelCase					     =		"""weight"""
               elif "bias" in name:
                  _lowerCAmelCase					     =		"""bias"""
               else:
                  _lowerCAmelCase					     =		None
               set_recursively(snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   )
            continue
      if not is_used:
         unused_weights.append(snake_case_   )

   logger.warning(F"""Unused weights: {unused_weights}"""   )






def 	__UpperCAmelCase					(       snake_case_  :  Any   ,	snake_case_  :  List[Any]   ,	snake_case_  :  Dict   ,	snake_case_  :  Union[str, Any]   ,	snake_case_  :  Optional[Any]   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		full_name.split("""conv_layers."""   )[-1]
   _lowerCAmelCase					     =		name.split("""."""   )
   _lowerCAmelCase					     =		int(items[0]   )
   _lowerCAmelCase					     =		int(items[1]   )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
             " found."
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
   else:
      unused_weights.append(snake_case_   )






def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   )							->	Any:






   """simple docstring"""


   _lowerCAmelCase					     =		HubertConfig()
   _lowerCAmelCase					     =		model.config

   _lowerCAmelCase					     =		fs_config.activation_dropout
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		fs_config.attention_dropout
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		eval(fs_config.extractor_conv_feature_layers   )
   _lowerCAmelCase					     =		[x[0] for x in conv_layers]
   _lowerCAmelCase					     =		[x[1] for x in conv_layers]
   _lowerCAmelCase					     =		[x[2] for x in conv_layers]
   _lowerCAmelCase					     =		"""gelu"""
   _lowerCAmelCase					     =		"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		fs_config.activation_fn
   _lowerCAmelCase					     =		fs_config.dropout
   _lowerCAmelCase					     =		fs_config.encoder_embed_dim
   _lowerCAmelCase					     =		0.0_2
   _lowerCAmelCase					     =		fs_config.encoder_ffn_embed_dim
   _lowerCAmelCase					     =		1e-5
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		fs_config.encoder_attention_heads
   _lowerCAmelCase					     =		fs_config.conv_pos_groups
   _lowerCAmelCase					     =		fs_config.conv_pos
   _lowerCAmelCase					     =		len(snake_case_   )
   _lowerCAmelCase					     =		fs_config.encoder_layers

   return config






@torch.no_grad()
def 	__UpperCAmelCase					(       snake_case_  :  Optional[Any]   ,	snake_case_  :  Union[str, Any]=None   )							->	List[str]:






   """simple docstring"""


   _lowerCAmelCase					     =		distilhubert().model.model

   if config_path is not None:
      _lowerCAmelCase					     =		HubertConfig.from_pretrained(snake_case_   )
   else:
      _lowerCAmelCase					     =		convert_config(snake_case_   )
   _lowerCAmelCase					     =		model.eval()

   _lowerCAmelCase					     =		WavaVecaFeatureExtractor(
       feature_size=1   ,	sampling_rate=16000   ,	padding_value=0   ,	do_normalize=snake_case_   ,	return_attention_mask=snake_case_   ,	)
   _lowerCAmelCase					     =		HubertModel(snake_case_   )

   recursively_load_weights(snake_case_   ,	snake_case_   )

   feature_extractor.save_pretrained(snake_case_   )
   hf_model.save_pretrained(snake_case_   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE				:     List[str]          =	argparse.ArgumentParser()
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  SCREAMING_SNAKE_CASE				:     Optional[int]          =	parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)