




"""simple docstring"""





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      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							fname.split(os.path.sep   )[-1]
      return re.search(r"^(.*)_\d+\.jpg$"			,    lowerCAmelCase__   ).groups()[0]
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       def __init__(							self    :						Tuple	,       _UpperCAmelCase    :						Tuple	,       _UpperCAmelCase    :						str=None	,       _UpperCAmelCase    :						int=None					)				->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							file_names
             UpperCAmelCase_    				=							image_transform
             UpperCAmelCase_    				=							label_to_id



       def __len__(							self    :						Any					)				->		int:


             '''simple docstring'''




             return len(self.file_names					)







       def __getitem__(							self    :						Optional[int]	,       _UpperCAmelCase    :						int					)				->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.file_names[idx]
             UpperCAmelCase_    				=							PIL.Image.open(_UpperCAmelCase					)
             UpperCAmelCase_    				=							raw_image.convert("RGB"					)
             if self.image_transform is not None:
                   UpperCAmelCase_    				=							self.image_transform(_UpperCAmelCase					)
             UpperCAmelCase_    				=							extract_label(_UpperCAmelCase					)
             if self.label_to_id is not None:
                   UpperCAmelCase_    				=							self.label_to_id[label]
             return {"image": image, "label": label}



def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      # Initialize accelerator
      if args.with_tracking:
            UpperCAmelCase_    				=							Accelerator(
                cpu=args.cpu			,    mixed_precision=args.mixed_precision			,    log_with="all"			,    project_dir=args.project_dir   )
      else:
            UpperCAmelCase_    				=							Accelerator(cpu=args.cpu			,    mixed_precision=args.mixed_precision   )

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCAmelCase_    				=							config["lr"]
      UpperCAmelCase_    				=							int(config["num_epochs"]   )
      UpperCAmelCase_    				=							int(config["seed"]   )
      UpperCAmelCase_    				=							int(config["batch_size"]   )
      UpperCAmelCase_    				=							config["image_size"]
      if not isinstance(lowerCAmelCase__			,    (list, tuple)   ):
            UpperCAmelCase_    				=							(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_    				=							args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                  UpperCAmelCase_    				=							int(args.checkpointing_steps   )
            else:
                  raise ValueError(
                      f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""   )
      else:
            UpperCAmelCase_    				=							None

      # We need to initialize the trackers we use, and also store our configuration
      if args.with_tracking:
            UpperCAmelCase_    				=							os.path.split(lowerCAmelCase__   )[-1].split("."   )[0]
            accelerator.init_trackers(lowerCAmelCase__			,    lowerCAmelCase__   )

      # Grab all the image filenames
      UpperCAmelCase_    				=							[os.path.join(args.data_dir			,    lowerCAmelCase__   ) for fname in os.listdir(args.data_dir   ) if fname.endswith(".jpg"   )]

      # Build the label correspondences
      UpperCAmelCase_    				=							[extract_label(lowerCAmelCase__   ) for fname in file_names]
      UpperCAmelCase_    				=							list(set(lowerCAmelCase__   )   )
      id_to_label.sort()
      UpperCAmelCase_    				=							{lbl: i for i, lbl in enumerate(lowerCAmelCase__   )}

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

      # Split our filenames between train and validation
      UpperCAmelCase_    				=							np.random.permutation(len(lowerCAmelCase__   )   )
      UpperCAmelCase_    				=							int(0.8 * len(lowerCAmelCase__   )   )
      UpperCAmelCase_    				=							random_perm[:cut]
      UpperCAmelCase_    				=							random_perm[cut:]

      # For training we use a simple RandomResizedCrop
      UpperCAmelCase_    				=							Compose([RandomResizedCrop(lowerCAmelCase__			,    scale=(0.5, 1.0)   ), ToTensor()]   )
      UpperCAmelCase_    				=							PetsDataset(
          [file_names[i] for i in train_split]			,    image_transform=lowerCAmelCase__			,    label_to_id=lowerCAmelCase__   )

      # For evaluation, we use a deterministic Resize
      UpperCAmelCase_    				=							Compose([Resize(lowerCAmelCase__   ), ToTensor()]   )
      UpperCAmelCase_    				=							PetsDataset([file_names[i] for i in eval_split]			,    image_transform=lowerCAmelCase__			,    label_to_id=lowerCAmelCase__   )

      # Instantiate dataloaders.
      UpperCAmelCase_    				=							DataLoader(lowerCAmelCase__			,    shuffle=lowerCAmelCase__			,    batch_size=lowerCAmelCase__			,    num_workers=4   )
      UpperCAmelCase_    				=							DataLoader(lowerCAmelCase__			,    shuffle=lowerCAmelCase__			,    batch_size=lowerCAmelCase__			,    num_workers=4   )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCAmelCase_    				=							create_model("resnet50d"			,    pretrained=lowerCAmelCase__			,    num_classes=len(lowerCAmelCase__   )   )

      # 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_    				=							model.to(accelerator.device   )

      # Freezing the base model
      for param in model.parameters():
            UpperCAmelCase_    				=							False
      for param in model.get_classifier().parameters():
            UpperCAmelCase_    				=							True

      # We normalize the batches of images to be a bit faster.
      UpperCAmelCase_    				=							torch.tensor(model.default_cfg["mean"]   )[None, :, None, None].to(accelerator.device   )
      UpperCAmelCase_    				=							torch.tensor(model.default_cfg["std"]   )[None, :, None, None].to(accelerator.device   )

      # Instantiate optimizer
      UpperCAmelCase_    				=							torch.optim.Adam(params=model.parameters()			,    lr=lr / 25   )

      # Instantiate learning rate scheduler
      UpperCAmelCase_    				=							OneCycleLR(optimizer=lowerCAmelCase__			,    max_lr=lowerCAmelCase__			,    epochs=lowerCAmelCase__			,    steps_per_epoch=len(lowerCAmelCase__   )   )

      # 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_    				=							accelerator.prepare(
          lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
      # We need to keep track of how many total steps we have iterated over
      UpperCAmelCase_    				=							0
      # We also need to keep track of the starting epoch so files are named properly
      UpperCAmelCase_    				=							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_    				=							os.path.basename(args.resume_from_checkpoint   )
            else:
                  # Get the most recent checkpoint
                  UpperCAmelCase_    				=							[f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
                  dirs.sort(key=os.path.getctime   )
                  UpperCAmelCase_    				=							dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            UpperCAmelCase_    				=							os.path.splitext(lowerCAmelCase__   )[0]

            if "epoch" in training_difference:
                  UpperCAmelCase_    				=							int(training_difference.replace("epoch_"			,    ""   )   ) + 1
                  UpperCAmelCase_    				=							None
            else:
                  UpperCAmelCase_    				=							int(training_difference.replace("step_"			,    ""   )   )
                  UpperCAmelCase_    				=							resume_step // len(lowerCAmelCase__   )
                  resume_step -= starting_epoch * len(lowerCAmelCase__   )

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

            UpperCAmelCase_    				=							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(lowerCAmelCase__   ),
                          "epoch": epoch,
                      }			,    step=lowerCAmelCase__			,    )
            if checkpointing_steps == "epoch":
                  UpperCAmelCase_    				=							f"""epoch_{epoch}"""
                  if args.output_dir is not None:
                        UpperCAmelCase_    				=							os.path.join(args.output_dir			,    lowerCAmelCase__   )
                  accelerator.save_state(lowerCAmelCase__   )

      if args.with_tracking:
            accelerator.end_training()
def      a__			(   ):
      UpperCAmelCase_    				=							argparse.ArgumentParser(description="Simple example of training script."   )
      parser.add_argument("--data_dir"			,    required=lowerCAmelCase__			,    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=lowerCAmelCase__			,    default=lowerCAmelCase__			,    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=lowerCAmelCase__			,    default=lowerCAmelCase__			,    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=lowerCAmelCase__			,    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=lowerCAmelCase__			,    default=lowerCAmelCase__			,    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=lowerCAmelCase__			,    default="logs"			,    help="Location on where to store experiment tracking logs` and relevent project information"			,    )
      UpperCAmelCase_    				=							parser.parse_args()
      UpperCAmelCase_    				=							{"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
      training_function(lowerCAmelCase__			,    lowerCAmelCase__   )


if __name__ == "__main__":
  main()









"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """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""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
lowerCamelCase      =		[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      for attribute in key.split("."   ):
            UpperCAmelCase_    				=							getattr(lowerCAmelCase__			,    lowerCAmelCase__   )

      if weight_type is not None:
            UpperCAmelCase_    				=							getattr(lowerCAmelCase__			,    lowerCAmelCase__   ).shape
      else:
            UpperCAmelCase_    				=							hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                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_    				=							value
      elif weight_type == "weight_g":
            UpperCAmelCase_    				=							value
      elif weight_type == "weight_v":
            UpperCAmelCase_    				=							value
      elif weight_type == "bias":
            UpperCAmelCase_    				=							value
      elif weight_type == "running_mean":
            UpperCAmelCase_    				=							value
      elif weight_type == "running_var":
            UpperCAmelCase_    				=							value
      elif weight_type == "num_batches_tracked":
            UpperCAmelCase_    				=							value
      elif weight_type == "inv_freq":
            UpperCAmelCase_    				=							value
      else:
            UpperCAmelCase_    				=							value

      logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""   )
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[]
      UpperCAmelCase_    				=							fairseq_model.state_dict()

      UpperCAmelCase_    				=							hf_model.wavaveca_conformer.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCAmelCase_    				=							False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    hf_model.config.feat_extract_norm == "group"			,    )
                  UpperCAmelCase_    				=							True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCAmelCase_    				=							"wav2vec2_conformer." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                        if key in name or key.split("w2v_model."   )[-1] == name.split("."   )[0]:
                              UpperCAmelCase_    				=							True
                              if "*" in mapped_key:
                                    UpperCAmelCase_    				=							name.split(lowerCAmelCase__   )[0].split("."   )[-2]
                                    UpperCAmelCase_    				=							mapped_key.replace("*"			,    lowerCAmelCase__   )
                              if "pos_bias_u" in name:
                                    UpperCAmelCase_    				=							None
                              elif "pos_bias_v" in name:
                                    UpperCAmelCase_    				=							None
                              elif "weight_g" in name:
                                    UpperCAmelCase_    				=							"weight_g"
                              elif "weight_v" in name:
                                    UpperCAmelCase_    				=							"weight_v"
                              elif "bias" in name:
                                    UpperCAmelCase_    				=							"bias"
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    UpperCAmelCase_    				=							"weight"
                              elif "running_mean" in name:
                                    UpperCAmelCase_    				=							"running_mean"
                              elif "inv_freq" in name:
                                    UpperCAmelCase_    				=							"inv_freq"
                              elif "running_var" in name:
                                    UpperCAmelCase_    				=							"running_var"
                              elif "num_batches_tracked" in name:
                                    UpperCAmelCase_    				=							"num_batches_tracked"
                              else:
                                    UpperCAmelCase_    				=							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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							full_name.split("conv_layers."   )[-1]
      UpperCAmelCase_    				=							name.split("."   )
      UpperCAmelCase_    				=							int(items[0]   )
      UpperCAmelCase_    				=							int(items[1]   )

      if type_id == 0:
            if "bias" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                        raise ValueError(
                            f"""{full_name} has size {value.shape}, but"""
                            f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""   )
                  UpperCAmelCase_    				=							value
                  logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                        raise ValueError(
                            f"""{full_name} has size {value.shape}, but"""
                            f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""   )
                  UpperCAmelCase_    				=							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:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                        raise ValueError(
                            f"""{full_name} has size {value.shape}, but"""
                            f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""   )
                  UpperCAmelCase_    				=							value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                        raise ValueError(
                            f"""{full_name} has size {value.shape}, but"""
                            f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""   )
                  UpperCAmelCase_    				=							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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=None			,    lowerCAmelCase__=None			,    lowerCAmelCase__=True   ):
      if config_path is not None:
            UpperCAmelCase_    				=							WavaVecaConformerConfig.from_pretrained(lowerCAmelCase__			,    hidden_act="swish"   )
      else:
            UpperCAmelCase_    				=							WavaVecaConformerConfig()

      if "rope" in checkpoint_path:
            UpperCAmelCase_    				=							"rotary"

      if is_finetuned:
            if dict_path:
                  UpperCAmelCase_    				=							Dictionary.load(lowerCAmelCase__   )

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  UpperCAmelCase_    				=							target_dict.pad_index
                  UpperCAmelCase_    				=							target_dict.bos_index
                  UpperCAmelCase_    				=							target_dict.eos_index
                  UpperCAmelCase_    				=							len(target_dict.symbols   )
                  UpperCAmelCase_    				=							os.path.join(lowerCAmelCase__			,    "vocab.json"   )
                  if not os.path.isdir(lowerCAmelCase__   ):
                        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(lowerCAmelCase__   )   )
                        return
                  os.makedirs(lowerCAmelCase__			,    exist_ok=lowerCAmelCase__   )
                  UpperCAmelCase_    				=							target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  UpperCAmelCase_    				=							0
                  UpperCAmelCase_    				=							1
                  with open(lowerCAmelCase__			,    "w"			,    encoding="utf-8"   ) as vocab_handle:
                        json.dump(lowerCAmelCase__			,    lowerCAmelCase__   )
                  UpperCAmelCase_    				=							WavaVecaCTCTokenizer(
                      lowerCAmelCase__			,    unk_token=target_dict.unk_word			,    pad_token=target_dict.pad_word			,    bos_token=target_dict.bos_word			,    eos_token=target_dict.eos_word			,    word_delimiter_token="|"			,    do_lower_case=lowerCAmelCase__			,    )
                  UpperCAmelCase_    				=							True if config.feat_extract_norm == "layer" else False
                  UpperCAmelCase_    				=							WavaVecaFeatureExtractor(
                      feature_size=1			,    sampling_rate=16000			,    padding_value=0			,    do_normalize=lowerCAmelCase__			,    return_attention_mask=lowerCAmelCase__			,    )
                  UpperCAmelCase_    				=							WavaVecaProcessor(feature_extractor=lowerCAmelCase__			,    tokenizer=lowerCAmelCase__   )
                  processor.save_pretrained(lowerCAmelCase__   )

            UpperCAmelCase_    				=							WavaVecaConformerForCTC(lowerCAmelCase__   )
      else:
            UpperCAmelCase_    				=							WavaVecaConformerForPreTraining(lowerCAmelCase__   )

      if is_finetuned:
            UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_    				=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]			,    arg_overrides={"data": "/".join(dict_path.split("/"   )[:-1]   )}   )
      else:
            UpperCAmelCase_    				=							argparse.Namespace(task="audio_pretraining"   )
            UpperCAmelCase_    				=							fairseq.tasks.setup_task(lowerCAmelCase__   )

            UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_    				=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,    task=lowerCAmelCase__   )

      UpperCAmelCase_    				=							model[0].eval()

      recursively_load_weights(lowerCAmelCase__			,    lowerCAmelCase__			,    not is_finetuned   )

      hf_wavavec.save_pretrained(lowerCAmelCase__   )


if __name__ == "__main__":
  lowerCamelCase      =		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("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
  parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
  parser.add_argument(
      """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
  )
  lowerCamelCase      =		parser.parse_args()
  convert_wavaveca_conformer_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )




