

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      		=     logging.get_logger(__name__)

__A      		=     {
    '''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      		=     [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Dict:



  """simple docstring"""






  for attribute in key.split('''.'''					):
    _snake_case						=							getattr(_UpperCamelCase			,	_UpperCamelCase					)

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

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



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     Dict:



  """simple docstring"""






  _snake_case						=							[]
  _snake_case						=							fairseq_model.state_dict()

  _snake_case						=							hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    _snake_case						=							False
    if "conv_layers" in name:
      load_conv_layer(
          _UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	hf_model.config.feat_extract_norm == '''group'''			,	)
      _snake_case						=							True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split('''w2v_model.'''					)[-1] == name.split('''.'''					)[0]:
          _snake_case						=							True
          if "*" in mapped_key:
            _snake_case						=							name.split(_UpperCamelCase					)[0].split('''.'''					)[-2]
            _snake_case						=							mapped_key.replace('''*'''			,	_UpperCamelCase					)
          if "weight_g" in name:
            _snake_case						=							'''weight_g'''
          elif "weight_v" in name:
            _snake_case						=							'''weight_v'''
          elif "bias" in name and "relative_attention_bias" not in name:
            _snake_case						=							'''bias'''
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            _snake_case						=							'''weight'''
          else:
            _snake_case						=							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					)							->     Union[str, Any]:



  """simple docstring"""






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



@torch.no_grad()
def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase=None					)							->     Tuple:



  """simple docstring"""






  _snake_case						=							torch.load(_UpperCamelCase					)
  _snake_case						=							WavLMConfigOrig(checkpoint['''cfg''']					)
  _snake_case						=							WavLMOrig(_UpperCamelCase					)
  model.load_state_dict(checkpoint['''model''']					)
  model.eval()

  if config_path is not None:
    _snake_case						=							WavLMConfig.from_pretrained(_UpperCamelCase					)
  else:
    _snake_case						=							WavLMConfig()

  _snake_case						=							WavLMModel(_UpperCamelCase					)

  recursively_load_weights(_UpperCamelCase			,	_UpperCamelCase					)

  hf_wavlm.save_pretrained(_UpperCamelCase					)


if __name__ == "__main__":
 __A      		=     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      		=     parser.parse_args()
 convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Optional[Any]:



  """simple docstring"""






  _snake_case						=							AlbertConfig.from_json_file(_UpperCamelCase					)
  print(F"""Building PyTorch model from configuration: {config}"""					)
  _snake_case						=							AlbertForPreTraining(_UpperCamelCase					)

  # Load weights from tf checkpoint
  load_tf_weights_in_albert(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)

  # Save pytorch-model
  print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
  torch.save(model.state_dict()			,	_UpperCamelCase					)


if __name__ == "__main__":
 __A      		=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--albert_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained ALBERT model. \n'''
         '''This specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 __A      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)

