"""simple docstring"""




import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


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

_a      :		Optional[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.grep_linear': 'encoder.layers.*.attention.gru_rel_pos_linear',
    'self_attn.relative_attention_bias': 'encoder.layers.*.attention.rel_attn_embed',
    'self_attn.grep_a': 'encoder.layers.*.attention.gru_rel_pos_const',
    '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',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
_a      :		List[Any]           =   [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any			,_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Optional[int]			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       str	)       ->      List[str]:
  for attribute in key.split("""."""	):
    _lowerCAmelCase						:       Tuple  			=     getattr(_lowerCamelCase			,_lowerCamelCase	)

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

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





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict			,_lowerCamelCase						:       Dict	)       ->      Any:
  _lowerCAmelCase						:       int  			=     []
  _lowerCAmelCase						:       Dict  			=     fairseq_model.state_dict()

  _lowerCAmelCase						:       int  			=     hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    _lowerCAmelCase						:       int  			=     False
    if "conv_layers" in name:
      load_conv_layer(
          _lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,hf_model.config.feat_extract_norm == """group"""			,)
      _lowerCAmelCase						:       Union[str, Any]  			=     True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split("""w2v_model."""	)[-1] == name.split("""."""	)[0]:
          _lowerCAmelCase						:       List[Any]  			=     True
          if "*" in mapped_key:
            _lowerCAmelCase						:       int  			=     name.split(_lowerCamelCase	)[0].split("""."""	)[-2]
            _lowerCAmelCase						:       Any  			=     mapped_key.replace("""*"""			,_lowerCamelCase	)
          if "weight_g" in name:
            _lowerCAmelCase						:       int  			=     """weight_g"""
          elif "weight_v" in name:
            _lowerCAmelCase						:       List[str]  			=     """weight_v"""
          elif "bias" in name and "relative_attention_bias" not in name:
            _lowerCAmelCase						:       Tuple  			=     """bias"""
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            _lowerCAmelCase						:       str  			=     """weight"""
          else:
            _lowerCAmelCase						:       Dict  			=     None

          set_recursively(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
        continue
    if not is_used:
      unused_weights.append(_lowerCamelCase	)

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





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





@torch.no_grad()
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       str=None	)       ->      Dict:
  # load the pre-trained checkpoints
  _lowerCAmelCase						:       int  			=     torch.load(_lowerCamelCase	)
  _lowerCAmelCase						:       List[str]  			=     WavLMConfigOrig(checkpoint["""cfg"""]	)
  _lowerCAmelCase						:       Tuple  			=     WavLMOrig(_lowerCamelCase	)
  model.load_state_dict(checkpoint["""model"""]	)
  model.eval()

  if config_path is not None:
    _lowerCAmelCase						:       Any  			=     WavLMConfig.from_pretrained(_lowerCamelCase	)
  else:
    _lowerCAmelCase						:       Any  			=     WavLMConfig()

  _lowerCAmelCase						:       Union[str, Any]  			=     WavLMModel(_lowerCamelCase	)

  recursively_load_weights(_lowerCamelCase			,_lowerCamelCase	)

  hf_wavlm.save_pretrained(_lowerCamelCase	)


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





"""simple docstring"""




from math import ceil





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Union[str, Any]	)       ->      int:
  _lowerCAmelCase						:       Dict  			=     list(range(0			,_lowerCamelCase	)	)

  _lowerCAmelCase						:       Tuple  			=     [item for sublist in list(device_map.values()	) for item in sublist]

  # Duplicate check
  _lowerCAmelCase						:       Union[str, Any]  			=     []
  for i in device_map_blocks:
    if device_map_blocks.count(_lowerCamelCase	) > 1 and i not in duplicate_blocks:
      duplicate_blocks.append(_lowerCamelCase	)
    # Missing blocks
  _lowerCAmelCase						:       int  			=     [i for i in blocks if i not in device_map_blocks]
  _lowerCAmelCase						:       List[Any]  			=     [i for i in device_map_blocks if i not in blocks]

  if len(_lowerCamelCase	) != 0:
    raise ValueError(
        """Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
        """ These attention blocks were specified more than once: """ + str(_lowerCamelCase	)	)
  if len(_lowerCamelCase	) != 0:
    raise ValueError(
        """There are attention blocks for this model that are not specified in the device_map. Add these attention """
        """blocks to a device on the device_map: """ + str(_lowerCamelCase	)	)
  if len(_lowerCamelCase	) != 0:
    raise ValueError(
        """The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
        + str(_lowerCamelCase	)	)






def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int			,_lowerCamelCase						:       Tuple	)       ->      str:
  _lowerCAmelCase						:       Optional[Any]  			=     list(range(_lowerCamelCase	)	)
  _lowerCAmelCase						:       Optional[Any]  			=     int(ceil(n_layers / len(_lowerCamelCase	)	)	)
  _lowerCAmelCase						:       Optional[int]  			=     [layers[i : i + n_blocks] for i in range(0			,_lowerCamelCase			,_lowerCamelCase	)]

  return dict(zip(_lowerCamelCase			,_lowerCamelCase	)	)




