





"""simple docstring"""




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

lowerCamelCase_					:						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__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  for attribute in key.split('.' ):
    A_  :	Optional[Any]    						=  getattr(_UpperCAmelCase			, _UpperCAmelCase )

  if weight_type is not None:
    A_  :	List[str]    						=  getattr(_UpperCAmelCase			, _UpperCAmelCase ).shape
  else:
    A_  :	Any    						=  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":
    A_  :	Tuple    						=  value
  elif weight_type == "weight_g":
    A_  :	str    						=  value
  elif weight_type == "weight_v":
    A_  :	Any    						=  value
  elif weight_type == "bias":
    A_  :	List[str]    						=  value
  else:
    A_  :	str    						=  value

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





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	List[Any]    						=  []
  A_  :	Dict    						=  fairseq_model.state_dict()

  A_  :	Any    						=  hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    A_  :	Optional[int]    						=  False
    if "conv_layers" in name:
      load_conv_layer(
          _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, hf_model.config.feat_extract_norm == 'group'			, )
      A_  :	Union[str, Any]    						=  True
    else:
      for key, mapped_key in MAPPING.items():
        A_  :	int    						=  mapped_key

        if key in name:
          A_  :	str    						=  True
          if "*" in mapped_key:
            A_  :	Union[str, Any]    						=  name.split(_UpperCAmelCase )[0].split('.' )[-2]
            A_  :	Tuple    						=  mapped_key.replace('*'			, _UpperCAmelCase )
          if "weight_g" in name:
            A_  :	List[Any]    						=  'weight_g'
          elif "weight_v" in name:
            A_  :	Tuple    						=  'weight_v'
          elif "weight" in name:
            A_  :	Optional[Any]    						=  'weight'
          elif "bias" in name:
            A_  :	List[Any]    						=  'bias'
          else:
            A_  :	List[str]    						=  None
          set_recursively(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )
        continue
    if not is_used:
      unused_weights.append(_UpperCAmelCase )

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





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Dict    						=  full_name.split('conv_layers.' )[-1]
  A_  :	Tuple    						=  name.split('.' )
  A_  :	Dict    						=  int(items[0] )
  A_  :	str    						=  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."""
      )
      A_  :	Optional[int]    						=  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."""
      )
      A_  :	str    						=  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."
      )
      A_  :	int    						=  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."""
      )
      A_  :	Union[str, Any]    						=  value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
  else:
    unused_weights.append(_UpperCAmelCase )





def 						UpperCAmelCase__		(					_UpperCAmelCase ):






  """simple docstring"""





  A_  :	Any    						=  HubertConfig()
  A_  :	Tuple    						=  model.config

  A_  :	Optional[int]    						=  fs_config.activation_dropout
  A_  :	Dict    						=  False
  A_  :	List[str]    						=  fs_config.attention_dropout
  A_  :	List[Any]    						=  False
  A_  :	str    						=  eval(fs_config.extractor_conv_feature_layers )
  A_  :	Tuple    						=  [x[0] for x in conv_layers]
  A_  :	Dict    						=  [x[1] for x in conv_layers]
  A_  :	Tuple    						=  [x[2] for x in conv_layers]
  A_  :	str    						=  'gelu'
  A_  :	str    						=  'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
  A_  :	Union[str, Any]    						=  False
  A_  :	int    						=  0.0
  A_  :	Any    						=  0.0
  A_  :	Tuple    						=  fs_config.activation_fn
  A_  :	int    						=  fs_config.dropout
  A_  :	Dict    						=  fs_config.encoder_embed_dim
  A_  :	Optional[int]    						=  0.02
  A_  :	Any    						=  fs_config.encoder_ffn_embed_dim
  A_  :	List[Any]    						=  1E-5
  A_  :	Union[str, Any]    						=  0.0
  A_  :	Tuple    						=  fs_config.encoder_attention_heads
  A_  :	Optional[int]    						=  fs_config.conv_pos_groups
  A_  :	List[str]    						=  fs_config.conv_pos
  A_  :	List[Any]    						=  len(_UpperCAmelCase )
  A_  :	str    						=  fs_config.encoder_layers

  return config





@torch.no_grad()
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase=None ):






  """simple docstring"""





  A_  :	Optional[int]    						=  distilhubert().model.model

  if config_path is not None:
    A_  :	Optional[int]    						=  HubertConfig.from_pretrained(_UpperCAmelCase )
  else:
    A_  :	Union[str, Any]    						=  convert_config(_UpperCAmelCase )
  A_  :	List[str]    						=  model.eval()

  A_  :	Optional[Any]    						=  WavaVecaFeatureExtractor(
      feature_size=1			, sampling_rate=16000			, padding_value=0			, do_normalize=_UpperCAmelCase			, return_attention_mask=_UpperCAmelCase			, )
  A_  :	str    						=  HubertModel(_UpperCAmelCase )

  recursively_load_weights(_UpperCAmelCase			, _UpperCAmelCase )

  feature_extractor.save_pretrained(_UpperCAmelCase )
  hf_model.save_pretrained(_UpperCAmelCase )


if __name__ == "__main__":
   lowerCamelCase_					:						Union[str, Any]      = 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')
   lowerCamelCase_					:						str      = parser.parse_args()
   convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_					:						List[str]      = logging.get_logger(__name__)

lowerCamelCase_					:						Optional[int]      = {
    'google/electra-small-generator': 'https://huggingface.co/google/electra-small-generator/resolve/main/config.json',
    'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/config.json',
    'google/electra-large-generator': 'https://huggingface.co/google/electra-large-generator/resolve/main/config.json',
    'google/electra-small-discriminator': (
        'https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'
    ),
    'google/electra-base-discriminator': (
        'https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'
    ),
    'google/electra-large-discriminator': (
        'https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'
    ),
}

class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			Tuple        =       """electra"""

      def __init__(					self ,				snake_case_=3_0_5_2_2 ,				snake_case_=1_2_8 ,				snake_case_=2_5_6 ,				snake_case_=1_2 ,				snake_case_=4 ,				snake_case_=1_0_2_4 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=5_1_2 ,				snake_case_=2 ,				snake_case_=0.02 ,				snake_case_=1E-12 ,				snake_case_="first" ,				snake_case_=True ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0 ,				snake_case_="absolute" ,				snake_case_=True ,				snake_case_=None ,				**snake_case_ ,				):

        """simple docstring"""

        super().__init__(pad_token_id=snake_case_ ,				**snake_case_  )

        A_  :	Optional[Any]    						=  vocab_size
        A_  :	Any    						=  embedding_size
        A_  :	Optional[int]    						=  hidden_size
        A_  :	Tuple    						=  num_hidden_layers
        A_  :	Tuple    						=  num_attention_heads
        A_  :	List[Any]    						=  intermediate_size
        A_  :	Any    						=  hidden_act
        A_  :	str    						=  hidden_dropout_prob
        A_  :	Any    						=  attention_probs_dropout_prob
        A_  :	str    						=  max_position_embeddings
        A_  :	Optional[Any]    						=  type_vocab_size
        A_  :	Dict    						=  initializer_range
        A_  :	Optional[int]    						=  layer_norm_eps

        A_  :	int    						=  summary_type
        A_  :	List[str]    						=  summary_use_proj
        A_  :	Optional[Any]    						=  summary_activation
        A_  :	Optional[Any]    						=  summary_last_dropout
        A_  :	List[Any]    						=  position_embedding_type
        A_  :	Union[str, Any]    						=  use_cache
        A_  :	Tuple    						=  classifier_dropout


class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        if self.task == "multiple-choice":
          A_  :	int    						=  {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
          A_  :	Optional[Any]    						=  {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
                ('token_type_ids', dynamic_axis),
            ]  )