





'''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_		:  List[Any]		 =						logging.get_logger(__name__)

A_		:  Optional[Any]		 =						{
    "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        UpperCamelCase__		(						__magic_name__  :   Tuple		,       __magic_name__  :   Optional[Any]		,       __magic_name__  :   Union[str, Any]		,       __magic_name__  :   int		,       __magic_name__  :   Dict      )				->			Optional[int]:
 '''simple docstring'''






 for attribute in key.split("""."""      ):
  snake_case__     :					Optional[int]    =  getattr(__magic_name__		,       __magic_name__      )

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

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



def        UpperCamelCase__		(						__magic_name__  :   Any		,       __magic_name__  :   List[Any]      )				->			str:
 '''simple docstring'''






 snake_case__     :					List[str]    =  []
 snake_case__     :					Any    =  fairseq_model.state_dict()

 snake_case__     :					Union[str, Any]    =  hf_model.feature_extractor

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

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

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



def        UpperCamelCase__		(						__magic_name__  :   Dict		,       __magic_name__  :   Union[str, Any]		,       __magic_name__  :   Tuple		,       __magic_name__  :   Any		,       __magic_name__  :   Tuple      )				->			Dict:
 '''simple docstring'''






 snake_case__     :					int    =  full_name.split("""conv_layers."""      )[-1]
 snake_case__     :					str    =  name.split("""."""      )
 snake_case__     :					Union[str, Any]    =  int(items[0]      )
 snake_case__     :					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."
   )
   snake_case__     :					Dict    =  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__     :					Optional[int]    =  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__     :					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."
   )
   snake_case__     :					Dict    =  value
   logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
 else:
  unused_weights.append(__magic_name__      )



@torch.no_grad()
def        UpperCamelCase__		(						__magic_name__  :   List[str]		,       __magic_name__  :   Union[str, Any]		,       __magic_name__  :   Union[str, Any]=None      )				->			int:
 '''simple docstring'''






 snake_case__     :					Union[str, Any]    =  torch.load(__magic_name__      )
 snake_case__     :					str    =  WavLMConfigOrig(checkpoint["""cfg"""]      )
 snake_case__     :					Tuple    =  WavLMOrig(__magic_name__      )
 model.load_state_dict(checkpoint["""model"""]      )
 model.eval()

 if config_path is not None:
  snake_case__     :					Union[str, Any]    =  WavLMConfig.from_pretrained(__magic_name__      )
 else:
  snake_case__     :					Tuple    =  WavLMConfig()

 snake_case__     :					str    =  WavLMModel(__magic_name__      )

 recursively_load_weights(__magic_name__		,       __magic_name__      )

 hf_wavlm.save_pretrained(__magic_name__      )


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_		:  Optional[int]		 =						parser.parse_args()
    convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator




def    lowerCAmelCase_  (		lowercase:				Dict		)     ->					Any:







 '''simple docstring'''
 _UpperCamelCase:  Optional[Any]    						=			fname.split(os.path.sep		)[-1]
 return re.search(R'''^(.*)_\d+\.jpg$'''	,       lowercase		).groups()[0]




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   def __init__(						self       :	Dict		,	_lowercase       :	Any		,	_lowercase       :	Any=None		,	_lowercase       :	List[str]=None				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			file_names
    _UpperCamelCase:  List[Any]    						=			image_transform
    _UpperCamelCase:  Tuple    						=			label_to_id



   def __len__(						self       :	List[str]				):



    """simple docstring"""



    return len(self.file_names				)



   def __getitem__(						self       :	List[str]		,	_lowercase       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			self.file_names[idx]
    _UpperCamelCase:  Optional[int]    						=			PIL.Image.open(_lowercase				)
    _UpperCamelCase:  List[str]    						=			raw_image.convert('''RGB'''				)
    if self.image_transform is not None:
     _UpperCamelCase:  Optional[Any]    						=			self.image_transform(_lowercase				)
    _UpperCamelCase:  Tuple    						=			extract_label(_lowercase				)
    if self.label_to_id is not None:
     _UpperCamelCase:  Any    						=			self.label_to_id[label]
    return {"image": image, "label": label}




def    lowerCAmelCase_  (		lowercase:				Optional[Any]	,       lowercase:				Any		)     ->					str:







 '''simple docstring'''
 # Initialize accelerator
 if args.with_tracking:
  _UpperCamelCase:  Tuple    						=			Accelerator(
      cpu=args.cpu	,       mixed_precision=args.mixed_precision	,       log_with='''all'''	,       project_dir=args.project_dir		)
 else:
  _UpperCamelCase:  Dict    						=			Accelerator(cpu=args.cpu	,       mixed_precision=args.mixed_precision		)

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 _UpperCamelCase:  List[str]    						=			config['''lr''']
 _UpperCamelCase:  int    						=			int(config['''num_epochs''']		)
 _UpperCamelCase:  Optional[Any]    						=			int(config['''seed''']		)
 _UpperCamelCase:  Optional[Any]    						=			int(config['''batch_size''']		)
 _UpperCamelCase:  List[str]    						=			config['''image_size''']
 if not isinstance(lowercase	,       (list, tuple)		):
  _UpperCamelCase:  Optional[int]    						=			(image_size, image_size)

 # Parse out whether we are saving every epoch or after a certain number of batches
 if hasattr(args.checkpointing_steps	,       '''isdigit'''		):
  if args.checkpointing_steps == "epoch":
   _UpperCamelCase:  Any    						=			args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   _UpperCamelCase:  List[str]    						=			int(args.checkpointing_steps		)
  else:
   raise ValueError(
       F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""		)
 else:
  _UpperCamelCase:  Any    						=			None

 # We need to initialize the trackers we use, and also store our configuration
 if args.with_tracking:
  _UpperCamelCase:  Union[str, Any]    						=			os.path.split(lowercase		)[-1].split('''.'''		)[0]
  accelerator.init_trackers(lowercase	,       lowercase		)

 # Grab all the image filenames
 _UpperCamelCase:  List[str]    						=			[os.path.join(args.data_dir	,       lowercase		) for fname in os.listdir(args.data_dir		) if fname.endswith('''.jpg'''		)]

 # Build the label correspondences
 _UpperCamelCase:  Optional[Any]    						=			[extract_label(lowercase		) for fname in file_names]
 _UpperCamelCase:  int    						=			list(set(lowercase		)		)
 id_to_label.sort()
 _UpperCamelCase:  Tuple    						=			{lbl: i for i, lbl in enumerate(lowercase		)}

 # Set the seed before splitting the data.
 np.random.seed(lowercase		)
 torch.manual_seed(lowercase		)
 torch.cuda.manual_seed_all(lowercase		)

 # Split our filenames between train and validation
 _UpperCamelCase:  List[str]    						=			np.random.permutation(len(lowercase		)		)
 _UpperCamelCase:  Dict    						=			int(0.8 * len(lowercase		)		)
 _UpperCamelCase:  Optional[int]    						=			random_perm[:cut]
 _UpperCamelCase:  Optional[int]    						=			random_perm[cut:]

 # For training we use a simple RandomResizedCrop
 _UpperCamelCase:  List[Any]    						=			Compose([RandomResizedCrop(lowercase	,       scale=(0.5, 1.0)		), ToTensor()]		)
 _UpperCamelCase:  int    						=			PetsDataset(
     [file_names[i] for i in train_split]	,       image_transform=lowercase	,       label_to_id=lowercase		)

 # For evaluation, we use a deterministic Resize
 _UpperCamelCase:  Dict    						=			Compose([Resize(lowercase		), ToTensor()]		)
 _UpperCamelCase:  str    						=			PetsDataset([file_names[i] for i in eval_split]	,       image_transform=lowercase	,       label_to_id=lowercase		)

 # Instantiate dataloaders.
 _UpperCamelCase:  str    						=			DataLoader(lowercase	,       shuffle=lowercase	,       batch_size=lowercase	,       num_workers=4		)
 _UpperCamelCase:  str    						=			DataLoader(lowercase	,       shuffle=lowercase	,       batch_size=lowercase	,       num_workers=4		)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 _UpperCamelCase:  str    						=			create_model('''resnet50d'''	,       pretrained=lowercase	,       num_classes=len(lowercase		)		)

 # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
 # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
 # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
 _UpperCamelCase:  Optional[int]    						=			model.to(accelerator.device		)

 # Freezing the base model
 for param in model.parameters():
  _UpperCamelCase:  Optional[Any]    						=			False
 for param in model.get_classifier().parameters():
  _UpperCamelCase:  Optional[int]    						=			True

 # We normalize the batches of images to be a bit faster.
 _UpperCamelCase:  Union[str, Any]    						=			torch.tensor(model.default_cfg['''mean''']		)[None, :, None, None].to(accelerator.device		)
 _UpperCamelCase:  List[Any]    						=			torch.tensor(model.default_cfg['''std''']		)[None, :, None, None].to(accelerator.device		)

 # Instantiate optimizer
 _UpperCamelCase:  List[str]    						=			torch.optim.Adam(params=model.parameters()	,       lr=lr / 25		)

 # Instantiate learning rate scheduler
 _UpperCamelCase:  Any    						=			OneCycleLR(optimizer=lowercase	,       max_lr=lowercase	,       epochs=lowercase	,       steps_per_epoch=len(lowercase		)		)

 # Prepare everything
 # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
 # prepare method.
 _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  List[str]    						=			accelerator.prepare(
     lowercase	,       lowercase	,       lowercase	,       lowercase	,       lowercase		)
 # We need to keep track of how many total steps we have iterated over
 _UpperCamelCase:  Tuple    						=			0
 # We also need to keep track of the starting epoch so files are named properly
 _UpperCamelCase:  List[str]    						=			0

 # Potentially load in the weights and states from a previous save
 if args.resume_from_checkpoint:
  if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
   accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""		)
   accelerator.load_state(args.resume_from_checkpoint		)
   _UpperCamelCase:  Union[str, Any]    						=			os.path.basename(args.resume_from_checkpoint		)
  else:
   # Get the most recent checkpoint
   _UpperCamelCase:  Union[str, Any]    						=			[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
   dirs.sort(key=os.path.getctime		)
   _UpperCamelCase:  Union[str, Any]    						=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  _UpperCamelCase:  List[Any]    						=			os.path.splitext(lowercase		)[0]

  if "epoch" in training_difference:
   _UpperCamelCase:  Any    						=			int(training_difference.replace('''epoch_'''	,       ''''''		)		) + 1
   _UpperCamelCase:  List[str]    						=			None
  else:
   _UpperCamelCase:  int    						=			int(training_difference.replace('''step_'''	,       ''''''		)		)
   _UpperCamelCase:  List[Any]    						=			resume_step // len(lowercase		)
   resume_step -= starting_epoch * len(lowercase		)

    # Now we train the model
 for epoch in range(lowercase	,       lowercase		):
  model.train()
  if args.with_tracking:
   _UpperCamelCase:  Optional[int]    						=			0
  if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
   # We need to skip steps until we reach the resumed step
   _UpperCamelCase:  Union[str, Any]    						=			accelerator.skip_first_batches(lowercase	,       lowercase		)
   overall_step += resume_step
  else:
   # After the first iteration though, we need to go back to the original dataloader
   _UpperCamelCase:  Optional[int]    						=			train_dataloader
  for batch in active_dataloader:
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   _UpperCamelCase:  Optional[Any]    						=			{k: v.to(accelerator.device		) for k, v in batch.items()}
   _UpperCamelCase:  int    						=			(batch['''image'''] - mean) / std
   _UpperCamelCase:  List[str]    						=			model(lowercase		)
   _UpperCamelCase:  Dict    						=			torch.nn.functional.cross_entropy(lowercase	,       batch['''label''']		)
   # We keep track of the loss at each epoch
   if args.with_tracking:
    total_loss += loss.detach().float()
   accelerator.backward(lowercase		)
   optimizer.step()
   lr_scheduler.step()
   optimizer.zero_grad()
   overall_step += 1
   if isinstance(lowercase	,       lowercase		):
    _UpperCamelCase:  Any    						=			F"""step_{overall_step}"""
    if overall_step % checkpointing_steps == 0:
     if args.output_dir is not None:
      _UpperCamelCase:  List[Any]    						=			os.path.join(args.output_dir	,       lowercase		)
     accelerator.save_state(lowercase		)
  model.eval()
  _UpperCamelCase:  List[str]    						=			0
  _UpperCamelCase:  Union[str, Any]    						=			0
  for step, batch in enumerate(lowercase		):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   _UpperCamelCase:  Optional[Any]    						=			{k: v.to(accelerator.device		) for k, v in batch.items()}
   _UpperCamelCase:  Union[str, Any]    						=			(batch['''image'''] - mean) / std
   with torch.no_grad():
    _UpperCamelCase:  List[Any]    						=			model(lowercase		)
   _UpperCamelCase:  Optional[Any]    						=			outputs.argmax(dim=-1		)
   _UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			accelerator.gather_for_metrics((predictions, batch['''label'''])		)
   _UpperCamelCase:  List[str]    						=			predictions == references
   num_elems += accurate_preds.shape[0]
   accurate += accurate_preds.long().sum()

  _UpperCamelCase:  Optional[int]    						=			accurate.item() / num_elems
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""		)
  if args.with_tracking:
   accelerator.log(
       {
           '''accuracy''': 100 * eval_metric,
           '''train_loss''': total_loss.item() / len(lowercase		),
           '''epoch''': epoch,
       }	,       step=lowercase	,       )
  if checkpointing_steps == "epoch":
   _UpperCamelCase:  Tuple    						=			F"""epoch_{epoch}"""
   if args.output_dir is not None:
    _UpperCamelCase:  Any    						=			os.path.join(args.output_dir	,       lowercase		)
   accelerator.save_state(lowercase		)

 if args.with_tracking:
  accelerator.end_training()




def    lowerCAmelCase_  (		)     ->					Union[str, Any]:







 '''simple docstring'''
 _UpperCamelCase:  str    						=			argparse.ArgumentParser(description='''Simple example of training script.'''		)
 parser.add_argument('''--data_dir'''	,       required=lowercase	,       help='''The data folder on disk.'''		)
 parser.add_argument('''--fp16'''	,       action='''store_true'''	,       help='''If passed, will use FP16 training.'''		)
 parser.add_argument(
     '''--mixed_precision'''	,       type=lowercase	,       default=lowercase	,       choices=['''no''', '''fp16''', '''bf16''', '''fp8''']	,       help='''Whether to use mixed precision. Choose'''
     '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
     '''and an Nvidia Ampere GPU.'''	,       )
 parser.add_argument('''--cpu'''	,       action='''store_true'''	,       help='''If passed, will train on the CPU.'''		)
 parser.add_argument(
     '''--checkpointing_steps'''	,       type=lowercase	,       default=lowercase	,       help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''	,       )
 parser.add_argument(
     '''--output_dir'''	,       type=lowercase	,       default='''.'''	,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''	,       )
 parser.add_argument(
     '''--resume_from_checkpoint'''	,       type=lowercase	,       default=lowercase	,       help='''If the training should continue from a checkpoint folder.'''	,       )
 parser.add_argument(
     '''--with_tracking'''	,       action='''store_true'''	,       help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''	,       )
 parser.add_argument(
     '''--project_dir'''	,       type=lowercase	,       default='''logs'''	,       help='''Location on where to store experiment tracking logs` and relevent project information'''	,       )
 _UpperCamelCase:  Any    						=			parser.parse_args()
 _UpperCamelCase:  Optional[int]    						=			{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
 training_function(lowercase	,       lowercase		)


if __name__ == "__main__":
  main()