code: 
  
  
  import argparse
  import os
  
  import evaluate
  import torch
  from datasets import load_dataset
  from torch.optim import AdamW
  from torch.utils.data import DataLoader
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
  
  from accelerate import Accelerator, DistributedType
  
  
  ########################################################################
  # This is a fully working simple example to use Accelerate,
  # specifically showcasing the checkpointing capability,
  # and builds off the `nlp_example.py` script.
  #
  # This example trains a Bert base model on GLUE MRPC
  # in any of the following settings (with the same script):
  #   - single CPU or single GPU
  #   - multi GPUS (using PyTorch distributed mode)
  #   - (multi) TPUs
  #   - fp16 (mixed-precision) or fp32 (normal precision)
  #
  # To help focus on the differences in the code, building `DataLoaders`
  # was refactored into its own function.
  # New additions from the base script can be found quickly by
  # looking for the # New Code # tags
  #
  # To run it in each of these various modes, follow the instructions
  # in the readme for examples:
  # https://github.com/huggingface/accelerate/tree/main/examples
  #
  ########################################################################
  
  _SCREAMING_SNAKE_CASE    :				str       =					16
  _SCREAMING_SNAKE_CASE    :				Dict       =					32
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_ = 16       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       AutoTokenizer.from_pretrained('''bert-base-cased'''       )
       snake_case					=       load_dataset('''glue'''							,'''mrpc'''       )
  
       def tokenize_function(UpperCamelCase_       ):
            # max_length=None => use the model max length (it's actually the default)
            snake_case					=       tokenizer(examples['''sentence1''']							,examples['''sentence2''']							,truncation=UpperCamelCase__							,max_length=UpperCamelCase__       )
            return outputs
  
       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
            snake_case					=       datasets.map(
                UpperCamelCase__							,batched=UpperCamelCase__							,remove_columns=['''idx''', '''sentence1''', '''sentence2''']							,)
  
       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       snake_case					=       tokenized_datasets.rename_column('''label'''							,'''labels'''       )
  
       def collate_fn(UpperCamelCase_       ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            snake_case					=       1_28 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                 snake_case					=       16
            elif accelerator.mixed_precision != "no":
                 snake_case					=       8
            else:
                 snake_case					=       None
  
            return tokenizer.pad(
                UpperCamelCase__							,padding='''longest'''							,max_length=UpperCamelCase__							,pad_to_multiple_of=UpperCamelCase__							,return_tensors='''pt'''							,)
  
       # Instantiate dataloaders.
       snake_case					=       DataLoader(
           tokenized_datasets['''train''']							,shuffle=UpperCamelCase__							,collate_fn=UpperCamelCase__							,batch_size=UpperCamelCase__       )
       snake_case					=       DataLoader(
           tokenized_datasets['''validation''']							,shuffle=UpperCamelCase__							,collate_fn=UpperCamelCase__							,batch_size=UpperCamelCase__       )
  
       return train_dataloader, eval_dataloader
  
  
  # For testing only
  if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
   from accelerate.test_utils.training import mocked_dataloaders
  
   _SCREAMING_SNAKE_CASE    :				int       =					mocked_dataloaders  # noqa: F811
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''							,UpperCamelCase__       ) == "1":
            snake_case					=       2
       # Initialize accelerator
       snake_case					=       Accelerator(cpu=args.cpu							,mixed_precision=args.mixed_precision       )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       snake_case					=       config["lr"]
       snake_case					=       int(config['''num_epochs''']       )
       snake_case					=       int(config['''seed''']       )
       snake_case					=       int(config['''batch_size''']       )
  
       # New Code #
       # 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":
                 snake_case					=       args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                 snake_case					=       int(args.checkpointing_steps       )
            else:
                 raise ValueError(
                     F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''       )
       else:
            snake_case					=       None
  
       set_seed(UpperCamelCase__       )
  
       snake_case					=       get_dataloaders(UpperCamelCase__							,UpperCamelCase__       )
       snake_case					=       evaluate.load('''glue'''							,'''mrpc'''       )
  
       # If the batch size is too big we use gradient accumulation
       snake_case					=       1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            snake_case					=       batch_size // MAX_GPU_BATCH_SIZE
            snake_case					=       MAX_GPU_BATCH_SIZE
  
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       snake_case					=       AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''							,return_dict=UpperCamelCase__       )
  
       # 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).
       snake_case					=       model.to(accelerator.device       )
  
       # Instantiate optimizer
       snake_case					=       AdamW(params=model.parameters()							,lr=UpperCamelCase__       )
  
       # Instantiate scheduler
       snake_case					=       get_linear_schedule_with_warmup(
           optimizer=UpperCamelCase__							,num_warmup_steps=1_00							,num_training_steps=(len(UpperCamelCase__       ) * num_epochs) // gradient_accumulation_steps							,)
  
       # 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.
       snake_case					=       accelerator.prepare(
           UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__							,UpperCamelCase__       )
  
       # New Code #
       # We need to keep track of how many total steps we have iterated over
       snake_case					=       0
       # We also need to keep track of the stating epoch so files are named properly
       snake_case					=       0
  
       # We need to load the checkpoint back in before training here with `load_state`
       # The total number of epochs is adjusted based on where the state is being loaded from,
       # as we assume continuation of the same training script
       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       )
                 snake_case					=       os.path.basename(args.resume_from_checkpoint       )
            else:
                 # Get the most recent checkpoint
                 snake_case					=       [f.name for f in os.scandir(os.getcwd()       ) if f.is_dir()]
                 dirs.sort(key=os.path.getctime       )
                 snake_case					=       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            snake_case					=       os.path.splitext(UpperCamelCase__       )[0]
  
            if "epoch" in training_difference:
                 snake_case					=       int(training_difference.replace('''epoch_'''							,''''''       )       ) + 1
                 snake_case					=       None
            else:
                 snake_case					=       int(training_difference.replace('''step_'''							,''''''       )       )
                 snake_case					=       resume_step // len(UpperCamelCase__       )
                 resume_step -= starting_epoch * len(UpperCamelCase__       )
  
      # Now we train the model
       for epoch in range(UpperCamelCase__							,UpperCamelCase__       ):
            model.train()
            # New Code #
            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
                 snake_case					=       accelerator.skip_first_batches(UpperCamelCase__							,UpperCamelCase__       )
                 overall_step += resume_step
            else:
                 # After the first iteration though, we need to go back to the original dataloader
                 snake_case					=       train_dataloader
            for step, batch in enumerate(UpperCamelCase__       ):
                 # We could avoid this line since we set the accelerator with `device_placement=True`.
                 batch.to(accelerator.device       )
                 snake_case					=       model(**UpperCamelCase__       )
                 snake_case					=       outputs.loss
                 snake_case					=       loss / gradient_accumulation_steps
                 accelerator.backward(UpperCamelCase__       )
                 if step % gradient_accumulation_steps == 0:
                      optimizer.step()
                      lr_scheduler.step()
                      optimizer.zero_grad()
                 # New Code #
                 overall_step += 1
  
                 # New Code #
                 # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                 # These are saved to folders named `step_{overall_step}`
                 # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                 # If mixed precision was used, will also save a "scalar.bin" file
                 if isinstance(UpperCamelCase__							,UpperCamelCase__       ):
                      snake_case					=       F'''step_{overall_step}'''
                      if overall_step % checkpointing_steps == 0:
                           if args.output_dir is not None:
                                snake_case					=       os.path.join(args.output_dir							,UpperCamelCase__       )
                           accelerator.save_state(UpperCamelCase__       )
  
            model.eval()
            for step, batch in enumerate(UpperCamelCase__       ):
                 # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                 batch.to(accelerator.device       )
                 with torch.no_grad():
                      snake_case					=       model(**UpperCamelCase__       )
                 snake_case					=       outputs.logits.argmax(dim=-1       )
                 snake_case					=       accelerator.gather_for_metrics((predictions, batch['''labels'''])       )
                 metric.add_batch(
                     predictions=UpperCamelCase__							,references=UpperCamelCase__							,)
  
            snake_case					=       metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'''epoch {epoch}:'''							,UpperCamelCase__       )
  
            # New Code #
            # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
            # These are saved to folders named `epoch_{epoch}`
            # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
            # If mixed precision was used, will also save a "scalar.bin" file
            if checkpointing_steps == "epoch":
                 snake_case					=       F'''epoch_{epoch}'''
                 if args.output_dir is not None:
                      snake_case					=       os.path.join(args.output_dir							,UpperCamelCase__       )
                 accelerator.save_state(UpperCamelCase__       )
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       argparse.ArgumentParser(description='''Simple example of training script.'''       )
       parser.add_argument(
           '''--mixed_precision'''							,type=UpperCamelCase__							,default=UpperCamelCase__							,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=UpperCamelCase__							,default=UpperCamelCase__							,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=UpperCamelCase__							,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=UpperCamelCase__							,default=UpperCamelCase__							,help='''If the training should continue from a checkpoint folder.'''							,)
       snake_case					=       parser.parse_args()
       snake_case					=       {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
       training_function(UpperCamelCase__							,UpperCamelCase__       )
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
  
style_context: 
  
  
  import argparse
  import json
  import os
  
  import fairseq
  import torch
  from fairseq.data import Dictionary
  
  # Register SEW's fairseq modules
  from sew_asapp import tasks  # noqa: F401
  
  from transformers import (
      SEWDConfig,
      SEWDForCTC,
      SEWDModel,
      WavaVecaCTCTokenizer,
      WavaVecaFeatureExtractor,
      WavaVecaProcessor,
      logging,
  )
  
  
  logging.set_verbosity_info()
  _SCREAMING_SNAKE_CASE    :				List[str]       =					logging.get_logger(__name__)
  
  _SCREAMING_SNAKE_CASE    :				Dict       =					{
      "post_extract_proj": "feature_projection",
      "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
      "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
      "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
      "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
      "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
      "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
      "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
      "output.dense": "encoder.encoder.layer.*.output.dense",
      "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
      "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
      "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
      "encoder.upsample.0": "encoder.upsample.projection",
      "encoder.layer_norm": "encoder.layer_norm",
      "w2v_model.layer_norm": "layer_norm",
      "w2v_encoder.proj": "lm_head",
      "mask_emb": "masked_spec_embed",
  }
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for attribute in key.split('''.'''       ):
            snake_case					=       getattr(UpperCamelCase_							,UpperCamelCase_       )
  
       if weight_type is not None:
            snake_case					=       getattr(UpperCamelCase_							,UpperCamelCase_       ).shape
       else:
            snake_case					=       hf_pointer.shape
  
       assert hf_shape == value.shape, (
           F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
           F''' {value.shape} for {full_name}'''
       )
  
       if weight_type == "weight":
            snake_case					=       value
       elif weight_type == "weight_g":
            snake_case					=       value
       elif weight_type == "weight_v":
            snake_case					=       value
       elif weight_type == "bias":
            snake_case					=       value
       else:
            snake_case					=       value
  
       logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       snake_case					=       fairseq_model.state_dict()
  
       snake_case					=       hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor
  
       for name, value in fairseq_dict.items():
            snake_case					=       False
            if "conv_layers" in name:
                 load_conv_layer(
                     UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,hf_model.config.feat_extract_norm == '''group'''							,)
                 snake_case					=       True
            else:
                 for key, mapped_key in MAPPING.items():
                      snake_case					=       '''sew_d.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key
  
                      if key in name or key.split('''w2v_model.'''       )[-1] == name.split('''.'''       )[0]:
                           snake_case					=       True
                           if "*" in mapped_key:
                                snake_case					=       name.split(UpperCamelCase_       )[0].split('''.'''       )[-2]
                                if not layer_index.isnumeric():
                                     continue
                                snake_case					=       mapped_key.replace('''*'''							,UpperCamelCase_       )
                           if "weight_g" in name:
                                snake_case					=       '''weight_g'''
                           elif "weight_v" in name:
                                snake_case					=       '''weight_v'''
                           elif "weight" in name:
                                snake_case					=       '''weight'''
                           elif "bias" in name:
                                snake_case					=       '''bias'''
                           else:
                                snake_case					=       None
                           set_recursively(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
                      continue
            if not is_used:
                 unused_weights.append(UpperCamelCase_       )
  
       logger.warning(F'''Unused weights: {unused_weights}'''       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       full_name.split('''conv_layers.'''       )[-1]
       snake_case					=       name.split('''.'''       )
       snake_case					=       int(items[0]       )
       snake_case					=       int(items[1]       )
  
       if type_id == 0:
            if "bias" in name:
                 assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                     F'''{full_name} has size {value.shape}, but'''
                     F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
                 )
                 snake_case					=       value
                 logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''       )
            elif "weight" in name:
                 assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                     F'''{full_name} has size {value.shape}, but'''
                     F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
                 )
                 snake_case					=       value
                 logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''       )
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                 assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                     F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                     " found."
                 )
                 snake_case					=       value
                 logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
            elif "weight" in name:
                 assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                     F'''{full_name} has size {value.shape}, but'''
                     F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
                 )
                 snake_case					=       value
                 logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
       else:
            unused_weights.append(UpperCamelCase_       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       SEWDConfig()
       if is_finetuned:
            snake_case					=       model.wav_encoder.wav_model.cfg
       else:
            snake_case					=       model.cfg
  
       snake_case					=       fs_config.conv_bias
       snake_case					=       eval(fs_config.conv_feature_layers       )
       snake_case					=       [x[0] for x in conv_layers]
       snake_case					=       [x[1] for x in conv_layers]
       snake_case					=       [x[2] for x in conv_layers]
       snake_case					=       '''gelu'''
       snake_case					=       '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
       snake_case					=       0.0
       snake_case					=       fs_config.activation_fn.name
       snake_case					=       fs_config.encoder_embed_dim
       snake_case					=       0.02
       snake_case					=       fs_config.encoder_ffn_embed_dim
       snake_case					=       1e-5
       snake_case					=       fs_config.encoder_layerdrop
       snake_case					=       fs_config.encoder_attention_heads
       snake_case					=       fs_config.conv_pos_groups
       snake_case					=       fs_config.conv_pos
       snake_case					=       len(UpperCamelCase_       )
       snake_case					=       fs_config.encoder_layers
       snake_case					=       fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       snake_case					=       fs_config.max_position_embeddings
       snake_case					=       fs_config.position_buckets
       snake_case					=       fs_config.share_att_key
       snake_case					=       fs_config.relative_attention
       snake_case					=       fs_config.position_biased_input
       snake_case					=       tuple(fs_config.pos_att_type.split('''|'''       )       )
       snake_case					=       fs_config.norm_rel_ebd
  
       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
            snake_case					=       model.cfg
            snake_case					=       fs_config.final_dropout
            snake_case					=       fs_config.layerdrop
       snake_case					=       fs_config.activation_dropout
       snake_case					=       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       snake_case					=       fs_config.attention_dropout
       snake_case					=       fs_config.dropout_input
       snake_case					=       fs_config.dropout
       snake_case					=       fs_config.mask_channel_length
       snake_case					=       fs_config.mask_channel_prob
       snake_case					=       fs_config.mask_length
       snake_case					=       fs_config.mask_prob
  
       snake_case					=       '''Wav2Vec2FeatureExtractor'''
       snake_case					=       '''Wav2Vec2CTCTokenizer'''
  
       return config
  @torch.no_grad()
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=None							,UpperCamelCase_=None							,UpperCamelCase_=True       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if is_finetuned:
            snake_case				,						snake_case				,						snake_case					=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]							,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''       )[:-1]       )}       )
       else:
            snake_case				,						snake_case				,						snake_case					=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )
  
       if config_path is not None:
            snake_case					=       SEWDConfig.from_pretrained(UpperCamelCase_       )
       else:
            snake_case					=       convert_config(model[0]							,UpperCamelCase_       )
       snake_case					=       model[0].eval()
  
       snake_case					=       True if config.feat_extract_norm == '''layer''' else False
       snake_case					=       WavaVecaFeatureExtractor(
           feature_size=1							,sampling_rate=1_60_00							,padding_value=0							,do_normalize=UpperCamelCase_							,return_attention_mask=UpperCamelCase_							,)
  
       if is_finetuned:
            if dict_path:
                 snake_case					=       Dictionary.load(UpperCamelCase_       )
  
                 # important change bos & pad token id since CTC symbol is <pad> and
                 # not <s> as in fairseq
                 snake_case					=       target_dict.pad_index
                 snake_case					=       target_dict.bos_index
                 snake_case					=       target_dict.pad_index
                 snake_case					=       target_dict.bos_index
                 snake_case					=       target_dict.eos_index
                 snake_case					=       len(target_dict.symbols       )
                 snake_case					=       os.path.join(UpperCamelCase_							,'''vocab.json'''       )
                 if not os.path.isdir(UpperCamelCase_       ):
                      logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCamelCase_       )       )
                      return
                 os.makedirs(UpperCamelCase_							,exist_ok=UpperCamelCase_       )
                 with open(UpperCamelCase_							,'''w'''							,encoding='''utf-8'''       ) as vocab_handle:
                      json.dump(target_dict.indices							,UpperCamelCase_       )
                 snake_case					=       WavaVecaCTCTokenizer(
                     UpperCamelCase_							,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=UpperCamelCase_							,)
                 snake_case					=       WavaVecaProcessor(feature_extractor=UpperCamelCase_							,tokenizer=UpperCamelCase_       )
                 processor.save_pretrained(UpperCamelCase_       )
  
            snake_case					=       SEWDForCTC(UpperCamelCase_       )
       else:
            snake_case					=       SEWDModel(UpperCamelCase_       )
            feature_extractor.save_pretrained(UpperCamelCase_       )
  
       recursively_load_weights(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
  
       hf_model.save_pretrained(UpperCamelCase_       )
  
  
  if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE    :				List[Any]       =					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(
       "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
   )
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )
  
  
  
  
  