import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoFeatureExtractor,
    AutoModelForAudioClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


lowerCamelCase__  =					logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.14.0''', '''To fix: pip install -r examples/pytorch/audio-classification/requirements.txt''')


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ = 16_000 )      ->str:




  '''simple docstring'''

  _UpperCamelCase           =    int(round(sample_rate * max_length ) )
  if len(_UpperCamelCase ) <= sample_length:
    return wav
  _UpperCamelCase           =    randint(0    ,     len(_UpperCamelCase ) - sample_length - 1 )
  return wav[random_offset : random_offset + sample_length]




@dataclass
class 					_UpperCAmelCase			:



 '''simple docstring'''




 __A			       =					field(default=SCREAMING_SNAKE_CASE__,			metadata={'''help''': '''Name of a dataset from the datasets package'''}  )
 __A			       =					field(
     default=SCREAMING_SNAKE_CASE__,			metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}  )
 __A			       =					field(
     default=SCREAMING_SNAKE_CASE__,			metadata={'''help''': '''A file containing the training audio paths and labels.'''}  )
 __A			       =					field(
     default=SCREAMING_SNAKE_CASE__,			metadata={'''help''': '''A file containing the validation audio paths and labels.'''}  )
 __A			       =					field(
     default='''train''',			metadata={
         '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
     },			)
 __A			       =					field(
     default='''validation''',			metadata={
         '''help''': (
             '''The name of the training data set split to use (via the datasets library). Defaults to \'validation\''''
         )
     },			)
 __A			       =					field(
     default='''audio''',			metadata={'''help''': '''The name of the dataset column containing the audio data. Defaults to \'audio\''''},			)
 __A			       =					field(
     default='''label''',			metadata={'''help''': '''The name of the dataset column containing the labels. Defaults to \'label\''''}  )
 __A			       =					field(
     default=SCREAMING_SNAKE_CASE__,			metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of training examples to this '''
             '''value if set.'''
         )
     },			)
 __A			       =					field(
     default=SCREAMING_SNAKE_CASE__,			metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
             '''value if set.'''
         )
     },			)
 __A			       =					field(
     default=20,			metadata={'''help''': '''Audio clips will be randomly cut to this length during training if the value is set.'''},			)




@dataclass
class 					_UpperCAmelCase			:



 '''simple docstring'''




 __A			       =					field(
     default='''facebook/wav2vec2-base''',			metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''},			)
 __A			       =					field(
     default=SCREAMING_SNAKE_CASE__,			metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
 __A			       =					field(
     default=SCREAMING_SNAKE_CASE__,			metadata={'''help''': '''Where do you want to store the pretrained models downloaded from the Hub'''}  )
 __A			       =					field(
     default='''main''',			metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''},			)
 __A			       =					field(
     default=SCREAMING_SNAKE_CASE__,			metadata={'''help''': '''Name or path of preprocessor config.'''}  )
 __A			       =					field(
     default=SCREAMING_SNAKE_CASE__,			metadata={'''help''': '''Whether to freeze the feature encoder layers of the model.'''}  )
 __A			       =					field(
     default=SCREAMING_SNAKE_CASE__,			metadata={'''help''': '''Whether to generate an attention mask in the feature extractor.'''}  )
 __A			       =					field(
     default=SCREAMING_SNAKE_CASE__,			metadata={
         '''help''': (
             '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
             '''with private models).'''
         )
     },			)
 __A			       =					field(
     default=SCREAMING_SNAKE_CASE__,			metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}  )
 __A			       =					field(
     default=SCREAMING_SNAKE_CASE__,			metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''},			)







 def 		__UpperCAmelCase				(		self				:   Dict)						->	int:


   """simple docstring"""







   if not self.freeze_feature_extractor and self.freeze_feature_encoder:
     warnings.warn(
         "The argument `--freeze_feature_extractor` is deprecated and "
         "will be removed in a future version. Use `--freeze_feature_encoder`"
         "instead. Setting `freeze_feature_encoder==True`."							, A_							, )
   if self.freeze_feature_extractor and not self.freeze_feature_encoder:
     raise ValueError(
         "The argument `--freeze_feature_extractor` is deprecated and "
         "should not be used in combination with `--freeze_feature_encoder`."
         "Only make use of `--freeze_feature_encoder`.")


def 				lowerCAmelCase__       (			)      ->Tuple:




  '''simple docstring'''

  _UpperCamelCase           =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
  if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    _UpperCamelCase           =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
  else:
    _UpperCamelCase           =    parser.parse_args_into_dataclasses()

  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry("run_audio_classification"    ,     _UpperCamelCase    ,     _UpperCamelCase )

  # Setup logging
  logging.basicConfig(
      format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,     datefmt="%m/%d/%Y %H:%M:%S"    ,     handlers=[logging.StreamHandler(sys.stdout )]    ,     )

  if training_args.should_log:
    # The default of training_args.log_level is passive, so we set log level at info here to have that default.
    transformers.utils.logging.set_verbosity_info()

  _UpperCamelCase           =    training_args.get_process_log_level()
  logger.setLevel(_UpperCamelCase )
  transformers.utils.logging.set_verbosity(_UpperCamelCase )
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()

  # Log on each process the small summary:
  logger.warning(
      f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} '
      + f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
  logger.info(f'Training/evaluation parameters {training_args}' )

  # Set seed before initializing model.
  set_seed(training_args.seed )

  # Detecting last checkpoint.
  _UpperCamelCase           =    None
  if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
    _UpperCamelCase           =    get_last_checkpoint(training_args.output_dir )
    if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
      raise ValueError(
          f'Output directory ({training_args.output_dir}) already exists and is not empty. '
          "Use --overwrite_output_dir to train from scratch." )
    elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
      logger.info(
          f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
          "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )

    # Initialize our dataset and prepare it for the audio classification task.
  _UpperCamelCase           =    DatasetDict()
  _UpperCamelCase           =    load_dataset(
      data_args.dataset_name    ,     data_args.dataset_config_name    ,     split=data_args.train_split_name    ,     use_auth_token=True if model_args.use_auth_token else None    ,     )
  _UpperCamelCase           =    load_dataset(
      data_args.dataset_name    ,     data_args.dataset_config_name    ,     split=data_args.eval_split_name    ,     use_auth_token=True if model_args.use_auth_token else None    ,     )

  if data_args.audio_column_name not in raw_datasets["train"].column_names:
    raise ValueError(
        f'--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '
        "Make sure to set `--audio_column_name` to the correct audio column - one of "
        f'{", ".join(raw_datasets["train"].column_names )}.' )

  if data_args.label_column_name not in raw_datasets["train"].column_names:
    raise ValueError(
        f'--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '
        "Make sure to set `--label_column_name` to the correct text column - one of "
        f'{", ".join(raw_datasets["train"].column_names )}.' )

  # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
  # transformer outputs in the classifier, but it doesn't always lead to better accuracy
  _UpperCamelCase           =    AutoFeatureExtractor.from_pretrained(
      model_args.feature_extractor_name or model_args.model_name_or_path    ,     return_attention_mask=model_args.attention_mask    ,     cache_dir=model_args.cache_dir    ,     revision=model_args.model_revision    ,     use_auth_token=True if model_args.use_auth_token else None    ,     )

  # `datasets` takes care of automatically loading and resampling the audio,
  # so we just need to set the correct target sampling rate.
  _UpperCamelCase           =    raw_datasets.cast_column(
      data_args.audio_column_name    ,     datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )

  _UpperCamelCase           =    feature_extractor.model_input_names[0]

  def train_transforms(a__ ):
    _UpperCamelCase           =    []
    for audio in batch[data_args.audio_column_name]:
      _UpperCamelCase           =    random_subsample(
          audio["array"]    ,     max_length=data_args.max_length_seconds    ,     sample_rate=feature_extractor.sampling_rate )
      subsampled_wavs.append(_UpperCamelCase )
    _UpperCamelCase           =    feature_extractor(_UpperCamelCase    ,     sampling_rate=feature_extractor.sampling_rate )
    _UpperCamelCase           =    {model_input_name: inputs.get(_UpperCamelCase )}
    _UpperCamelCase           =    list(batch[data_args.label_column_name] )

    return output_batch

  def val_transforms(a__ ):
    _UpperCamelCase           =    [audio['''array'''] for audio in batch[data_args.audio_column_name]]
    _UpperCamelCase           =    feature_extractor(_UpperCamelCase    ,     sampling_rate=feature_extractor.sampling_rate )
    _UpperCamelCase           =    {model_input_name: inputs.get(_UpperCamelCase )}
    _UpperCamelCase           =    list(batch[data_args.label_column_name] )

    return output_batch

  # Prepare label mappings.
  # We'll include these in the model's config to get human readable labels in the Inference API.
  _UpperCamelCase           =    raw_datasets['''train'''].features[data_args.label_column_name].names
  _UpperCamelCase           =    {}, {}
  for i, label in enumerate(_UpperCamelCase ):
    _UpperCamelCase           =    str(_UpperCamelCase )
    _UpperCamelCase           =    label

  # Load the accuracy metric from the datasets package
  _UpperCamelCase           =    evaluate.load("accuracy" )

  # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
  # `predictions` and `label_ids` fields) and has to return a dictionary string to float.
  def compute_metrics(a__ ):
    _UpperCamelCase           =    np.argmax(eval_pred.predictions    ,     axis=1 )
    return metric.compute(predictions=_UpperCamelCase    ,     references=eval_pred.label_ids )

  _UpperCamelCase           =    AutoConfig.from_pretrained(
      model_args.config_name or model_args.model_name_or_path    ,     num_labels=len(_UpperCamelCase )    ,     labelaid=_UpperCamelCase    ,     idalabel=_UpperCamelCase    ,     finetuning_task="audio-classification"    ,     cache_dir=model_args.cache_dir    ,     revision=model_args.model_revision    ,     use_auth_token=True if model_args.use_auth_token else None    ,     )
  _UpperCamelCase           =    AutoModelForAudioClassification.from_pretrained(
      model_args.model_name_or_path    ,     from_tf=bool(".ckpt" in model_args.model_name_or_path )    ,     config=_UpperCamelCase    ,     cache_dir=model_args.cache_dir    ,     revision=model_args.model_revision    ,     use_auth_token=True if model_args.use_auth_token else None    ,     ignore_mismatched_sizes=model_args.ignore_mismatched_sizes    ,     )

  # freeze the convolutional waveform encoder
  if model_args.freeze_feature_encoder:
    model.freeze_feature_encoder()

  if training_args.do_train:
    if data_args.max_train_samples is not None:
      _UpperCamelCase           =    (
          raw_datasets['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
      )
    # Set the training transforms
    raw_datasets["train"].set_transform(_UpperCamelCase    ,     output_all_columns=_UpperCamelCase )

  if training_args.do_eval:
    if data_args.max_eval_samples is not None:
      _UpperCamelCase           =    (
          raw_datasets['''eval'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
      )
    # Set the validation transforms
    raw_datasets["eval"].set_transform(_UpperCamelCase    ,     output_all_columns=_UpperCamelCase )

  # Initialize our trainer
  _UpperCamelCase           =    Trainer(
      model=_UpperCamelCase    ,     args=_UpperCamelCase    ,     train_dataset=raw_datasets["train"] if training_args.do_train else None    ,     eval_dataset=raw_datasets["eval"] if training_args.do_eval else None    ,     compute_metrics=_UpperCamelCase    ,     tokenizer=_UpperCamelCase    ,     )

  # Training
  if training_args.do_train:
    _UpperCamelCase           =    None
    if training_args.resume_from_checkpoint is not None:
      _UpperCamelCase           =    training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      _UpperCamelCase           =    last_checkpoint
    _UpperCamelCase           =    trainer.train(resume_from_checkpoint=_UpperCamelCase )
    trainer.save_model()
    trainer.log_metrics("train"    ,     train_result.metrics )
    trainer.save_metrics("train"    ,     train_result.metrics )
    trainer.save_state()

  # Evaluation
  if training_args.do_eval:
    _UpperCamelCase           =    trainer.evaluate()
    trainer.log_metrics("eval"    ,     _UpperCamelCase )
    trainer.save_metrics("eval"    ,     _UpperCamelCase )

  # Write model card and (optionally) push to hub
  _UpperCamelCase           =    {
      '''finetuned_from''': model_args.model_name_or_path,
      '''tasks''': '''audio-classification''',
      '''dataset''': data_args.dataset_name,
      '''tags''': ['''audio-classification'''],
  }
  if training_args.push_to_hub:
    trainer.push_to_hub(**_UpperCamelCase )
  else:
    trainer.create_model_card(**_UpperCamelCase )


if __name__ == "__main__":
   main()






"""simple docstring"""




import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


A__ :       List[Any]				= 1_6
A__ :       Union[str, Any]				= 3_2




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase = 16    ):






  """simple docstring"""



  _lowercase:      Any   			=  AutoTokenizer.from_pretrained('''bert-base-cased'''    )
  _lowercase:      List[str]   			=  load_dataset('''glue'''			,					'''mrpc'''    )

  def tokenize_function(_UpperCamelCase    ):
    # max_length=None => use the model max length (it's actually the default)
    _lowercase:      Union[str, Any]   			=  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():
    _lowercase:      Tuple   			=  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
  _lowercase:      Optional[Any]   			=  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.
    _lowercase:      Union[str, Any]   			=  128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _lowercase:      int   			=  16
    elif accelerator.mixed_precision != "no":
      _lowercase:      Tuple   			=  8
    else:
      _lowercase:      str   			=  None

    return tokenizer.pad(
        _UpperCamelCase			,					padding='''longest'''			,					max_length=_UpperCamelCase			,					pad_to_multiple_of=_UpperCamelCase			,					return_tensors='''pt'''			,					)

  # Instantiate dataloaders.
  _lowercase:      List[Any]   			=  DataLoader(
      tokenized_datasets['''train''']			,					shuffle=_UpperCamelCase			,					collate_fn=_UpperCamelCase			,					batch_size=_UpperCamelCase    )
  _lowercase:      int   			=  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

  A__ :       List[Any]				= mocked_dataloaders  # noqa: F811




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,					_UpperCamelCase    ) == "1":
    _lowercase:      Tuple   			=  2
  # Initialize accelerator
  _lowercase:      Optional[Any]   			=  Accelerator(cpu=args.cpu			,					mixed_precision=args.mixed_precision    )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowercase:      Optional[Any]   			=  config['''lr''']
  _lowercase:      Tuple   			=  int(config['''num_epochs''']    )
  _lowercase:      Any   			=  int(config['''seed''']    )
  _lowercase:      List[Any]   			=  int(config['''batch_size''']    )

  _lowercase:      Optional[int]   			=  evaluate.load('''glue'''			,					'''mrpc'''    )

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=_UpperCamelCase    )
  def inner_training_loop(_UpperCamelCase    ):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(_UpperCamelCase    )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowercase:      Optional[int]   			=  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).
    _lowercase:      List[Any]   			=  model.to(accelerator.device    )

    # Instantiate optimizer
    _lowercase:      List[str]   			=  AdamW(params=model.parameters()			,					lr=_UpperCamelCase    )
    _lowercase			,					_lowercase:      Tuple   			=  get_dataloaders(_UpperCamelCase			,					_UpperCamelCase    )

    # Instantiate scheduler
    _lowercase:      str   			=  get_linear_schedule_with_warmup(
        optimizer=_UpperCamelCase			,					num_warmup_steps=100			,					num_training_steps=(len(_UpperCamelCase    ) * num_epochs)			,					)

    # 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.
    _lowercase			,					_lowercase			,					_lowercase			,					_lowercase			,					_lowercase:      Union[str, Any]   			=  accelerator.prepare(
        _UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )

    # Now we train the model
    for epoch in range(_UpperCamelCase    ):
      model.train()
      for step, batch in enumerate(_UpperCamelCase    ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device    )
        _lowercase:      Tuple   			=  model(**_UpperCamelCase    )
        _lowercase:      Union[str, Any]   			=  outputs.loss
        accelerator.backward(_UpperCamelCase    )
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(_UpperCamelCase    ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device    )
        with torch.no_grad():
          _lowercase:      str   			=  model(**_UpperCamelCase    )
        _lowercase:      int   			=  outputs.logits.argmax(dim=-1    )
        _lowercase			,					_lowercase:      List[str]   			=  accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
        metric.add_batch(
            predictions=_UpperCamelCase			,					references=_UpperCamelCase			,					)

      _lowercase:      int   			=  metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''			,					_UpperCamelCase    )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Dict   			=  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.'''    )
  _lowercase:      Tuple   			=  parser.parse_args()
  _lowercase:      str   			=  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(_UpperCamelCase			,					_UpperCamelCase    )


if __name__ == "__main__":
  main()
