

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase							:  Optional[Any]     =				logging.get_logger(__name__)

_lowerCamelCase							:  int     =				{
    "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      a__	(						UpperCAmelCase				:     List[Any]							,					UpperCAmelCase				:     str							,					UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     Optional[int]     )    ->		List[str]:
  for attribute in key.split('''.'''     ):
    UpperCAmelCase			:				Union[str, Any]										=					getattr(UpperCAmelCase							,					UpperCAmelCase     )

  if weight_type is not None:
    UpperCAmelCase			:				Optional[int]										=					getattr(UpperCAmelCase							,					UpperCAmelCase     ).shape
  else:
    UpperCAmelCase			:				List[str]										=					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":
    UpperCAmelCase			:				Any										=					value
  elif weight_type == "weight_g":
    UpperCAmelCase			:				Tuple										=					value
  elif weight_type == "weight_v":
    UpperCAmelCase			:				Union[str, Any]										=					value
  elif weight_type == "bias":
    UpperCAmelCase			:				Dict										=					value
  else:
    UpperCAmelCase			:				Dict										=					value

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

def      a__	(						UpperCAmelCase				:     Tuple							,					UpperCAmelCase				:     List[Any]     )    ->		Optional[int]:
  UpperCAmelCase			:				Optional[Any]										=					[]
  UpperCAmelCase			:				List[Any]										=					fairseq_model.state_dict()

  UpperCAmelCase			:				int										=					hf_model.feature_extractor

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

        if key in name:
          UpperCAmelCase			:				int										=					True
          if "*" in mapped_key:
            UpperCAmelCase			:				List[Any]										=					name.split(UpperCAmelCase     )[0].split('''.'''     )[-2]
            UpperCAmelCase			:				Union[str, Any]										=					mapped_key.replace('''*'''							,					UpperCAmelCase     )
          if "weight_g" in name:
            UpperCAmelCase			:				str										=					'''weight_g'''
          elif "weight_v" in name:
            UpperCAmelCase			:				Union[str, Any]										=					'''weight_v'''
          elif "weight" in name:
            UpperCAmelCase			:				str										=					'''weight'''
          elif "bias" in name:
            UpperCAmelCase			:				Optional[Any]										=					'''bias'''
          else:
            UpperCAmelCase			:				List[Any]										=					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      a__	(						UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     Tuple							,					UpperCAmelCase				:     Union[str, Any]							,					UpperCAmelCase				:     Tuple     )    ->		Dict:
  UpperCAmelCase			:				int										=					full_name.split('''conv_layers.'''     )[-1]
  UpperCAmelCase			:				List[str]										=					name.split('''.'''     )
  UpperCAmelCase			:				Union[str, Any]										=					int(items[0]     )
  UpperCAmelCase			:				Optional[int]										=					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.'''
      )
      UpperCAmelCase			:				str										=					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.'''
      )
      UpperCAmelCase			:				Optional[Any]										=					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."
      )
      UpperCAmelCase			:				Optional[Any]										=					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.'''
      )
      UpperCAmelCase			:				List[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      a__	(						UpperCAmelCase				:     Union[str, Any]     )    ->		Optional[Any]:
  UpperCAmelCase			:				List[str]										=					HubertConfig()
  UpperCAmelCase			:				Any										=					model.config

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

  return config

@torch.no_grad()
def      a__	(						UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Dict=None     )    ->		Optional[int]:
  UpperCAmelCase			:				int										=					distilhubert().model.model

  if config_path is not None:
    UpperCAmelCase			:				List[Any]										=					HubertConfig.from_pretrained(UpperCAmelCase     )
  else:
    UpperCAmelCase			:				Optional[int]										=					convert_config(UpperCAmelCase     )
  UpperCAmelCase			:				Tuple										=					model.eval()

  UpperCAmelCase			:				int										=					WavaVecaFeatureExtractor(
      feature_size=1							,					sampling_rate=16_000							,					padding_value=0							,					do_normalize=UpperCAmelCase							,					return_attention_mask=UpperCAmelCase							,					)
  UpperCAmelCase			:				int										=					HubertModel(UpperCAmelCase     )

  recursively_load_weights(UpperCAmelCase							,					UpperCAmelCase     )

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


if __name__ == "__main__":
     _lowerCamelCase							:  Dict     =				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							:  Any     =				parser.parse_args()
     convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)








def      a__	(						UpperCAmelCase				:     list     )    ->		list:
  UpperCAmelCase			:				Tuple										=					len(UpperCAmelCase     )

  # Set the gap value to be decreased by a factor of 1.3
  # after each iteration
  UpperCAmelCase			:				Optional[Any]										=					1.3

  # Continue sorting until the gap is 1
  while gap > 1:
    # Decrease the gap value
    UpperCAmelCase			:				int										=					int(gap / shrink     )

    # Sort the elements using insertion sort
    for i in range(UpperCAmelCase							,					len(UpperCAmelCase     )     ):
      UpperCAmelCase			:				Union[str, Any]										=					collection[i]
      UpperCAmelCase			:				List[Any]										=					i
      while j >= gap and collection[j - gap] > temp:
        UpperCAmelCase			:				int										=					collection[j - gap]
        j -= gap
      UpperCAmelCase			:				List[Any]										=					temp

  return collection


if __name__ == "__main__":
     import doctest

     doctest.testmod()





