
'''simple docstring'''





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
a__		: Dict  							=							logging.get_logger(__name__)

a__		: Tuple  							=							{
    "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 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      str:







      """simple docstring"""






      for attribute in key.split('.'						):
            __A													=      getattr(UpperCAmelCase   ,	UpperCAmelCase						)

      if weight_type is not None:
            __A													=      getattr(UpperCAmelCase   ,	UpperCAmelCase						).shape
      else:
            __A													=      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													=      value
      elif weight_type == "weight_g":
            __A													=      value
      elif weight_type == "weight_v":
            __A													=      value
      elif weight_type == "bias":
            __A													=      value
      else:
            __A													=      value

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





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      str:







      """simple docstring"""
      __A													=      []
      __A													=      fairseq_model.state_dict()

      __A													=      hf_model.feature_extractor

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

                        if key in name:
                              __A													=      True
                              if "*" in mapped_key:
                                    __A													=      name.split(UpperCAmelCase						)[0].split('.'						)[-2]
                                    __A													=      mapped_key.replace('*'   ,	UpperCAmelCase						)
                              if "weight_g" in name:
                                    __A													=      'weight_g'
                              elif "weight_v" in name:
                                    __A													=      'weight_v'
                              elif "weight" in name:
                                    __A													=      'weight'
                              elif "bias" in name:
                                    __A													=      'bias'
                              else:
                                    __A													=      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 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Optional[int]:







      """simple docstring"""
      __A													=      full_name.split('conv_layers.'						)[-1]
      __A													=      name.split('.'						)
      __A													=      int(items[0]						)
      __A													=      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													=      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													=      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													=      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													=      value
                  logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'						)
      else:
            unused_weights.append(UpperCAmelCase						)





def 							snake_case				(		UpperCAmelCase						)->      str:







      """simple docstring"""
      __A													=      HubertConfig()
      __A													=      model.config

      __A													=      fs_config.activation_dropout
      __A													=      False
      __A													=      fs_config.attention_dropout
      __A													=      False
      __A													=      eval(fs_config.extractor_conv_feature_layers						)
      __A													=      [x[0] for x in conv_layers]
      __A													=      [x[1] for x in conv_layers]
      __A													=      [x[2] for x in conv_layers]
      __A													=      'gelu'
      __A													=      'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
      __A													=      False
      __A													=      0.0
      __A													=      0.0
      __A													=      fs_config.activation_fn
      __A													=      fs_config.dropout
      __A													=      fs_config.encoder_embed_dim
      __A													=      0.02
      __A													=      fs_config.encoder_ffn_embed_dim
      __A													=      1e-5
      __A													=      0.0
      __A													=      fs_config.encoder_attention_heads
      __A													=      fs_config.conv_pos_groups
      __A													=      fs_config.conv_pos
      __A													=      len(UpperCAmelCase						)
      __A													=      fs_config.encoder_layers

      return config





@torch.no_grad()
def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase=None						)->      Optional[int]:







      """simple docstring"""
      __A													=      distilhubert().model.model

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

      __A													=      WavaVecaFeatureExtractor(
          feature_size=1   ,	sampling_rate=1_6_0_0_0   ,	padding_value=0   ,	do_normalize=UpperCAmelCase   ,	return_attention_mask=UpperCAmelCase   ,	)
      __A													=      HubertModel(UpperCAmelCase						)

      recursively_load_weights(UpperCAmelCase   ,	UpperCAmelCase						)

      feature_extractor.save_pretrained(UpperCAmelCase						)
      hf_model.save_pretrained(UpperCAmelCase						)


if __name__ == "__main__":
 a__		: List[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")
 a__		: Any  							=							parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)









import numpy as np







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

 """simple docstring"""






 __lowerCAmelCase:  Union[str, Any]        = np.exp(SCREAMING_SNAKE_CASE						)

 # Add up the all the exponentials
 __lowerCAmelCase:  Dict        = np.sum(SCREAMING_SNAKE_CASE						)

 # Divide every exponent by the sum of all exponents
 __lowerCAmelCase:  int        = exponent_vector / sum_of_exponents

 return softmax_vector


if __name__ == "__main__":
  print(softmax((0,)))





