


"""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()
__snake_case    					=					logging.get_logger(__name__)

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


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[Any]       ,		lowerCamelCase__	:							Dict       ,		lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							List[str]  ):
 for attribute in key.split("""."""  ):
  lowercase__       :       Union[str, Any]				=						getattr(lowerCamelCase__       ,		lowerCamelCase__  )

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

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


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int  ):
 lowercase__       :       Tuple				=						[]
 lowercase__       :       Optional[Any]				=						fairseq_model.state_dict()

 lowercase__       :       List[Any]				=						hf_model.feature_extractor

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


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

 if config_path is not None:
  lowercase__       :       int				=						WavLMConfig.from_pretrained(lowerCamelCase__  )
 else:
  lowercase__       :       Optional[int]				=						WavLMConfig()

 lowercase__       :       int				=						WavLMModel(lowerCamelCase__  )

 recursively_load_weights(lowerCamelCase__       ,		lowerCamelCase__  )

 hf_wavlm.save_pretrained(lowerCamelCase__  )


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



"""simple docstring"""


import logging
import os
import threading
import time


try:
   import warnings
except ImportError:
   __snake_case    					=					None

try:
   import msvcrt
except ImportError:
   __snake_case    					=					None

try:
   import fcntl
except ImportError:
   __snake_case    					=					None


# Backward compatibility
# ------------------------------------------------
try:
   TimeoutError
except NameError:
   __snake_case    					=					OSError


# Data
# ------------------------------------------------
__snake_case    					=					[
    'Timeout',
    'BaseFileLock',
    'WindowsFileLock',
    'UnixFileLock',
    'SoftFileLock',
    'FileLock',
]

__snake_case    					=					'3.0.12'


__snake_case    					=					None


def    _lowerCamelCase   (     ):
 global _logger
 lowercase__       :       Tuple				=						_logger or logging.getLogger(__name__  )
 return _logger

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__						)      ->							Optional[int]:
    lowercase__       :       Union[str, Any]				=						lock_file
    return None







   def __str__(						self						)      ->							List[Any]:
    lowercase__       :       Tuple				=						F'''The file lock \'{self.lock_file}\' could not be acquired.'''
    return temp

class 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__						)      ->							Optional[Any]:
    lowercase__       :       str				=						lock
    return None


   def __enter__(						self						)      ->							List[Any]:
    return self.lock







   def __exit__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Any:
    self.lock.release()
    return None

class 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=-1 ,     lowerCamelCase__=None						)      ->							Optional[Any]:
    lowercase__       :       List[Any]				=						max_filename_length if max_filename_length is not None else 255
    # Hash the filename if it's too long
    lowercase__       :       Union[str, Any]				=						self.hash_filename_if_too_long(lowerCamelCase__ ,     lowerCamelCase__						)
    # The path to the lock file.
    lowercase__       :       int				=						lock_file

    # The file descriptor for the *_lock_file* as it is returned by the
    # os.open() function.
    # This file lock is only NOT None, if the object currently holds the
    # lock.
    lowercase__       :       Dict				=						None

    # The default timeout value.
    lowercase__       :       Optional[Any]				=						timeout

    # We use this lock primarily for the lock counter.
    lowercase__       :       Optional[int]				=						threading.Lock()

    # The lock counter is used for implementing the nested locking
    # mechanism. Whenever the lock is acquired, the counter is increased and
    # the lock is only released, when this value is 0 again.
    lowercase__       :       Union[str, Any]				=						0
    return None


   @property
   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    return self._lock_file


   @property
   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    return self._timeout


   @timeout.setter
   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							Optional[Any]:
    lowercase__       :       Union[str, Any]				=						float(lowerCamelCase__						)
    return None


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    raise NotImplementedError()


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    raise NotImplementedError()


   @property
   def 					UpperCAmelCase__(						self						)      ->							str:
    return self._lock_file_fd is not None


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__=None ,     lowerCamelCase__=0.05						)      ->							List[str]:
    # Use the default timeout, if no timeout is provided.
    if timeout is None:
     lowercase__       :       int				=						self.timeout

    # Increment the number right at the beginning.
    # We can still undo it, if something fails.
    with self._thread_lock:
     self._lock_counter += 1

    lowercase__       :       Tuple				=						id(self						)
    lowercase__       :       Any				=						self._lock_file
    lowercase__       :       Union[str, Any]				=						time.time()
    try:
     while True:
      with self._thread_lock:
       if not self.is_locked:
        logger().debug(F'''Attempting to acquire lock {lock_id} on {lock_filename}'''						)
        self._acquire()

      if self.is_locked:
       logger().debug(F'''Lock {lock_id} acquired on {lock_filename}'''						)
       break
      elif timeout >= 0 and time.time() - start_time > timeout:
       logger().debug(F'''Timeout on acquiring lock {lock_id} on {lock_filename}'''						)
       raise Timeout(self._lock_file						)
      else:
       logger().debug(
           F'''Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...'''						)
       time.sleep(lowerCamelCase__						)
    except:  # noqa
     # Something did go wrong, so decrement the counter.
     with self._thread_lock:
      lowercase__       :       Any				=						max(0 ,     self._lock_counter - 1						)

     raise
    return _Acquire_ReturnProxy(lock=self						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__=False						)      ->							int:
    with self._thread_lock:
     if self.is_locked:
      self._lock_counter -= 1

      if self._lock_counter == 0 or force:
       lowercase__       :       Tuple				=						id(self						)
       lowercase__       :       int				=						self._lock_file

       logger().debug(F'''Attempting to release lock {lock_id} on {lock_filename}'''						)
       self._release()
       lowercase__       :       str				=						0
       logger().debug(F'''Lock {lock_id} released on {lock_filename}'''						)

    return None


   def __enter__(						self						)      ->							Dict:
    self.acquire()
    return self


   def __exit__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Dict:
    self.release()
    return None


   def __del__(						self						)      ->							int:
    self.release(force=lowerCamelCase__						)
    return None







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							str:
    lowercase__       :       Optional[int]				=						os.path.basename(lowerCamelCase__						)
    if len(lowerCamelCase__						) > max_length and max_length > 0:
     lowercase__       :       Union[str, Any]				=						os.path.dirname(lowerCamelCase__						)
     lowercase__       :       List[Any]				=						str(hash(lowerCamelCase__						)						)
     lowercase__       :       Optional[int]				=						filename[: max_length - len(lowerCamelCase__						) - 8] + """...""" + hashed_filename + """.lock"""
     return os.path.join(lowerCamelCase__ ,     lowerCamelCase__						)
    else:
     return path

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=-1 ,     lowerCamelCase__=None						)      ->							Tuple:
    from .file_utils import relative_to_absolute_path

    super().__init__(lowerCamelCase__ ,     timeout=lowerCamelCase__ ,     max_filename_length=lowerCamelCase__						)
    lowercase__       :       List[Any]				=						"""\\\\?\\""" + relative_to_absolute_path(self.lock_file						)


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       Union[str, Any]				=						os.O_RDWR | os.O_CREAT | os.O_TRUNC

    try:
     lowercase__       :       Dict				=						os.open(self._lock_file ,     lowerCamelCase__						)
    except OSError:
     pass
    else:
     try:
      msvcrt.locking(lowerCamelCase__ ,     msvcrt.LK_NBLCK ,     1						)
     except OSError:
      os.close(lowerCamelCase__						)
     else:
      lowercase__       :       Optional[Any]				=						fd
    return None







   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       int				=						self._lock_file_fd
    lowercase__       :       Any				=						None
    msvcrt.locking(lowerCamelCase__ ,     msvcrt.LK_UNLCK ,     1						)
    os.close(lowerCamelCase__						)

    try:
     os.remove(self._lock_file						)
    # Probably another instance of the application
    # that acquired the file lock.
    except OSError:
     pass
    return None

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=-1 ,     lowerCamelCase__=None						)      ->							List[str]:
    lowercase__       :       Optional[Any]				=						os.statvfs(os.path.dirname(lowerCamelCase__						)						).f_namemax
    super().__init__(lowerCamelCase__ ,     timeout=lowerCamelCase__ ,     max_filename_length=lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       List[Any]				=						os.O_RDWR | os.O_CREAT | os.O_TRUNC
    lowercase__       :       List[Any]				=						os.open(self._lock_file ,     lowerCamelCase__						)

    try:
     fcntl.flock(lowerCamelCase__ ,     fcntl.LOCK_EX | fcntl.LOCK_NB						)
    except OSError:
     os.close(lowerCamelCase__						)
    else:
     lowercase__       :       Any				=						fd
    return None







   def 					UpperCAmelCase__(						self						)      ->							str:
    # Do not remove the lockfile:
    #
    #   https://github.com/benediktschmitt/py-filelock/issues/31
    #   https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
    lowercase__       :       Optional[int]				=						self._lock_file_fd
    lowercase__       :       Optional[Any]				=						None
    fcntl.flock(lowerCamelCase__ ,     fcntl.LOCK_UN						)
    os.close(lowerCamelCase__						)
    return None

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       Tuple				=						os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
    try:
     lowercase__       :       Any				=						os.open(self._lock_file ,     lowerCamelCase__						)
    except OSError:
     pass
    else:
     lowercase__       :       Union[str, Any]				=						fd
    return None







   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    os.close(self._lock_file_fd						)
    lowercase__       :       Optional[Any]				=						None

    try:
     os.remove(self._lock_file						)
    # The file is already deleted and that's what we want.
    except OSError:
     pass
    return None
__snake_case    					=					None

if msvcrt:
   __snake_case    					=					WindowsFileLock
elif fcntl:
   __snake_case    					=					UnixFileLock
else:
   __snake_case    					=					SoftFileLock

   if warnings is not None:
      warnings.warn('only soft file lock is available')