



"""simple docstring"""
import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length

def       _snake_case						(						UpperCamelCase						:		Optional[int]						):
 UpperCAmelCase	:					int	=	int(UpperCamelCase						)
 UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	t // 3600, (t // 60) % 60, t % 60
 return F"{h}:{m:02d}:{s:02d}" if h != 0 else F"{m:02d}:{s:02d}"

def       _snake_case						(						UpperCamelCase						:		Optional[int]			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		Optional[int]			,		UpperCamelCase						:		int=300						):
 # docstyle-ignore
 return F"\n    <div>\n      {prefix}\n      <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>\n      {label}\n    </div>\n    "

def       _snake_case						(						UpperCamelCase						:		Dict						):
 UpperCAmelCase	:					str	=	"""<table border=\"1\" class=\"dataframe\">\n"""
 html_code += """  <thead>\n <tr style="text-align: left;">\n"""
 for i in items[0]:
  html_code += F"      <th>{i}</th>\n"
 html_code += "    </tr>\n  </thead>\n  <tbody>\n"
 for line in items[1:]:
  html_code += "    <tr>\n"
  for elt in line:
   UpperCAmelCase	:					Optional[Any]	=	F"{elt:.6f}" if isinstance(UpperCamelCase			,		UpperCamelCase						) else str(UpperCamelCase						)
   html_code += F"      <td>{elt}</td>\n"
  html_code += "    </tr>\n"
 html_code += "  </tbody>\n</table><p>"
 return html_code



class       SCREAMING_SNAKE_CASE__  :
   __lowerCAmelCase  :   Optional[int]						=						5
   __lowerCAmelCase  :   Tuple						=						0.2

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = True      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = 300      ,     )  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	total
    UpperCAmelCase	:					Optional[int]	=	"""""" if prefix is None else prefix
    UpperCAmelCase	:					Union[str, Any]	=	leave
    UpperCAmelCase	:					List[Any]	=	parent
    UpperCAmelCase	:					Dict	=	width
    UpperCAmelCase	:					int	=	None
    UpperCAmelCase	:					List[Any]	=	None
    UpperCAmelCase	:					Dict	=	None

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = False      ,     _SCREAMING_SNAKE_CASE = None						)  ->      Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	value
    if comment is not None:
     UpperCAmelCase	:					Dict	=	comment
    if self.last_value is None:
     UpperCAmelCase	:					List[str]	=	time.time()
     UpperCAmelCase	:					int	=	value
     UpperCAmelCase	:					List[Any]	=	None
     UpperCAmelCase	:					Union[str, Any]	=	self.warmup
     UpperCAmelCase	:					Any	=	1
     self.update_bar(_SCREAMING_SNAKE_CASE						)
    elif value <= self.last_value and not force_update:
     return
    elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for      ,     self.total						):
     if self.first_calls > 0:
      self.first_calls -= 1
     UpperCAmelCase	:					int	=	time.time()
     UpperCAmelCase	:					Dict	=	current_time - self.start_time
     # We could have value = self.start_value if the update is called twixe with the same start value.
     if value > self.start_value:
      UpperCAmelCase	:					Any	=	self.elapsed_time / (value - self.start_value)
     else:
      UpperCAmelCase	:					Any	=	None
     if value >= self.total:
      UpperCAmelCase	:					Tuple	=	self.total
      UpperCAmelCase	:					Dict	=	None
      if not self.leave:
       self.close()
     elif self.average_time_per_item is not None:
      UpperCAmelCase	:					Dict	=	self.average_time_per_item * (self.total - value)
     self.update_bar(_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Any	=	value
     UpperCAmelCase	:					Union[str, Any]	=	current_time
     if self.average_time_per_item is None:
      UpperCAmelCase	:					List[Any]	=	1
     else:
      UpperCAmelCase	:					Dict	=	max(int(self.update_every / self.average_time_per_item						)      ,     1						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	""" """ * (len(str(self.total						)						) - len(str(_SCREAMING_SNAKE_CASE						)						)) + str(_SCREAMING_SNAKE_CASE						)
    if self.elapsed_time is None:
     UpperCAmelCase	:					List[Any]	=	F"[{spaced_value}/{self.total} : < :"
    elif self.predicted_remaining is None:
     UpperCAmelCase	:					Union[str, Any]	=	F"[{spaced_value}/{self.total} {format_time(self.elapsed_time						)}"
    else:
     UpperCAmelCase	:					Optional[int]	=	(
         F"[{spaced_value}/{self.total} {format_time(self.elapsed_time						)} <"
         F" {format_time(self.predicted_remaining						)}"
     )
     self.label += F", {1/self.average_time_per_item:.2f} it/s"
    self.label += "]" if self.comment is None or len(self.comment						) == 0 else F", {self.comment}]"
    self.display()

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	html_progress_bar(self.value      ,     self.total      ,     self.prefix      ,     self.label      ,     self.width						)
    if self.parent is not None:
     # If this is a child bar, the parent will take care of the display.
     self.parent.display()
     return
    if self.output is None:
     UpperCAmelCase	:					Dict	=	disp.display(disp.HTML(self.html_code						)      ,     display_id=_SCREAMING_SNAKE_CASE						)
    else:
     self.output.update(disp.HTML(self.html_code						)						)




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    if self.parent is None and self.output is not None:
     self.output.update(disp.HTML(""""""						)						)



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None						)  ->      str:





    '''simple docstring'''




    super().__init__(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[Any]	=	None if column_names is None else [column_names]
    UpperCAmelCase	:					List[Any]	=	None

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	html_progress_bar(self.value      ,     self.total      ,     self.prefix      ,     self.label      ,     self.width						)
    if self.inner_table is not None:
     self.html_code += text_to_html_table(self.inner_table						)
    if self.child_bar is not None:
     self.html_code += self.child_bar.html_code
    if self.output is None:
     UpperCAmelCase	:					Any	=	disp.display(disp.HTML(self.html_code						)      ,     display_id=_SCREAMING_SNAKE_CASE						)
    else:
     self.output.update(disp.HTML(self.html_code						)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    if self.inner_table is None:
     UpperCAmelCase	:					Tuple	=	[list(values.keys()						), list(values.values()						)]
    else:
     UpperCAmelCase	:					str	=	self.inner_table[0]
     if len(self.inner_table						) == 1:
      # We give a chance to update the column names at the first iteration
      for key in values.keys():
       if key not in columns:
        columns.append(_SCREAMING_SNAKE_CASE						)
      UpperCAmelCase	:					Optional[Any]	=	columns
     self.inner_table.append([values[c] for c in columns]						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=300						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	NotebookProgressBar(_SCREAMING_SNAKE_CASE      ,     prefix=_SCREAMING_SNAKE_CASE      ,     parent=self      ,     width=_SCREAMING_SNAKE_CASE						)
    return self.child_bar




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	None
    self.display()



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

   def __init__(   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	None
    UpperCAmelCase	:					Any	=	None
    UpperCAmelCase	:					Any	=	False

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	"""Epoch""" if args.evaluation_strategy == IntervalStrategy.EPOCH else """Step"""
    UpperCAmelCase	:					Optional[Any]	=	0
    UpperCAmelCase	:					Optional[int]	=	0
    UpperCAmelCase	:					int	=	[self.first_column] + ["""Training Loss"""]
    if args.evaluation_strategy != IntervalStrategy.NO:
     column_names.append("""Validation Loss"""						)
    UpperCAmelCase	:					int	=	NotebookTrainingTracker(state.max_steps      ,     _SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	int(state.epoch						) if int(state.epoch						) == state.epoch else F"{state.epoch:.2f}"
    self.training_tracker.update(
        state.global_step + 1      ,     comment=F"Epoch {epoch}/{state.num_train_epochs}"      ,     force_update=self._force_next_update      ,     )
    UpperCAmelCase	:					int	=	False

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None      ,     **_SCREAMING_SNAKE_CASE						)  ->      Optional[int]:





    '''simple docstring'''




    if not has_length(_SCREAMING_SNAKE_CASE						):
     return
    if self.prediction_bar is None:
     if self.training_tracker is not None:
      UpperCAmelCase	:					int	=	self.training_tracker.add_child(len(_SCREAMING_SNAKE_CASE						)						)
     else:
      UpperCAmelCase	:					Union[str, Any]	=	NotebookProgressBar(len(_SCREAMING_SNAKE_CASE						)						)
     self.prediction_bar.update(1						)
    else:
     self.prediction_bar.update(self.prediction_bar.value + 1						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      List[str]:





    '''simple docstring'''




    if self.prediction_bar is not None:
     self.prediction_bar.close()
    UpperCAmelCase	:					int	=	None

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None      ,     **_SCREAMING_SNAKE_CASE						)  ->      str:





    '''simple docstring'''




    if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
     UpperCAmelCase	:					List[Any]	=	{"""Training Loss""": logs["""loss"""]}
     # First column is necessarily Step sine we're not in epoch eval strategy
     UpperCAmelCase	:					Optional[Any]	=	state.global_step
     self.training_tracker.write_line(_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None      ,     **_SCREAMING_SNAKE_CASE						)  ->      str:





    '''simple docstring'''




    if self.training_tracker is not None:
     UpperCAmelCase	:					Any	=	{"""Training Loss""": """No log""", """Validation Loss""": """No log"""}
     for log in reversed(state.log_history						):
      if "loss" in log:
       UpperCAmelCase	:					Optional[int]	=	log["""loss"""]
       break

     if self.first_column == "Epoch":
      UpperCAmelCase	:					Dict	=	int(state.epoch						)
     else:
      UpperCAmelCase	:					int	=	state.global_step
     UpperCAmelCase	:					Union[str, Any]	=	"""eval"""
     for k in metrics:
      if k.endswith("""_loss"""						):
       UpperCAmelCase	:					Optional[Any]	=	re.sub(r"""\_loss$"""      ,     """"""      ,     _SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Tuple	=	metrics.pop("""total_flos"""      ,     _SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Union[str, Any]	=	metrics.pop("""epoch"""      ,     _SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Dict	=	metrics.pop(F"{metric_key_prefix}_runtime"      ,     _SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Optional[int]	=	metrics.pop(F"{metric_key_prefix}_samples_per_second"      ,     _SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					str	=	metrics.pop(F"{metric_key_prefix}_steps_per_second"      ,     _SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Optional[Any]	=	metrics.pop(F"{metric_key_prefix}_jit_compilation_time"      ,     _SCREAMING_SNAKE_CASE						)
     for k, v in metrics.items():
      if k == F"{metric_key_prefix}_loss":
       UpperCAmelCase	:					Tuple	=	v
      else:
       UpperCAmelCase	:					Union[str, Any]	=	k.split("""_"""						)
       UpperCAmelCase	:					List[str]	=	""" """.join([part.capitalize() for part in splits[1:]]						)
       UpperCAmelCase	:					List[str]	=	v
     self.training_tracker.write_line(_SCREAMING_SNAKE_CASE						)
     self.training_tracker.remove_child()
     UpperCAmelCase	:					Union[str, Any]	=	None
     # Evaluation takes a long time so we should force the next update.
     UpperCAmelCase	:					Union[str, Any]	=	True




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      Tuple:





    '''simple docstring'''




    self.training_tracker.update(
        state.global_step      ,     comment=F"Epoch {int(state.epoch						)}/{state.num_train_epochs}"      ,     force_update=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[int]	=	None





"""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:   Optional[int]								=		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.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[str]								=		[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def       _snake_case						(						UpperCamelCase						:		str			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Optional[int]			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Any						):
 for attribute in key.split("""."""						):
  UpperCAmelCase	:					Optional[Any]	=	getattr(UpperCamelCase			,		UpperCamelCase						)

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

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

def       _snake_case						(						UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		Optional[Any]						):
 UpperCAmelCase	:					Tuple	=	[]
 UpperCAmelCase	:					Any	=	fairseq_model.state_dict()

 UpperCAmelCase	:					Tuple	=	hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCAmelCase	:					str	=	False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		hf_model.config.feat_extract_norm == """group"""			,		)
   UpperCAmelCase	:					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]:
     UpperCAmelCase	:					Dict	=	True
     if "*" in mapped_key:
      UpperCAmelCase	:					str	=	name.split(UpperCamelCase						)[0].split("""."""						)[-2]
      UpperCAmelCase	:					Tuple	=	mapped_key.replace("""*"""			,		UpperCamelCase						)
     if "weight_g" in name:
      UpperCAmelCase	:					Any	=	"""weight_g"""
     elif "weight_v" in name:
      UpperCAmelCase	:					Optional[Any]	=	"""weight_v"""
     elif "bias" in name and "relative_attention_bias" not in name:
      UpperCAmelCase	:					Union[str, Any]	=	"""bias"""
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      UpperCAmelCase	:					str	=	"""weight"""
     else:
      UpperCAmelCase	:					Optional[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       _snake_case						(						UpperCamelCase						:		Optional[Any]			,		UpperCamelCase						:		Dict			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		Any						):
 UpperCAmelCase	:					str	=	full_name.split("""conv_layers."""						)[-1]
 UpperCAmelCase	:					Dict	=	name.split("""."""						)
 UpperCAmelCase	:					List[str]	=	int(items[0]						)
 UpperCAmelCase	:					Any	=	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	:					Optional[Any]	=	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	:					Tuple	=	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	:					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."
   )
   UpperCAmelCase	:					Optional[Any]	=	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						:		int			,		UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		List[Any]=None						):
 # load the pre-trained checkpoints
 UpperCAmelCase	:					List[Any]	=	torch.load(UpperCamelCase						)
 UpperCAmelCase	:					List[str]	=	WavLMConfigOrig(checkpoint["""cfg"""]						)
 UpperCAmelCase	:					Optional[int]	=	WavLMOrig(UpperCamelCase						)
 model.load_state_dict(checkpoint["""model"""]						)
 model.eval()

 if config_path is not None:
  UpperCAmelCase	:					List[str]	=	WavLMConfig.from_pretrained(UpperCamelCase						)
 else:
  UpperCAmelCase	:					List[Any]	=	WavLMConfig()

 UpperCAmelCase	:					Any	=	WavLMModel(UpperCamelCase						)

 recursively_load_weights(UpperCamelCase			,		UpperCamelCase						)

 hf_wavlm.save_pretrained(UpperCamelCase						)


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