




"""simple docstring"""





import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


_lowercase		 =      logging.getLogger(__name__)

@dataclass
class  __lowercase    :


				'''simple docstring'''


				_lowerCAmelCase		     =field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}		)
				_lowerCAmelCase		     =field(
				    default=snake_case_  ,				metadata={"help": "Pretrained config name or path if not the same as model_name"}		)
				_lowerCAmelCase		     =field(
				    default="NER"  ,				metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}		)
				_lowerCAmelCase		     =field(
				    default=snake_case_  ,				metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}		)
				_lowerCAmelCase		     =field(default=snake_case_  ,				metadata={"help": "Set this flag to use fast tokenization."}		)
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				_lowerCAmelCase		     =field(
				    default=snake_case_  ,				metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,				)







@dataclass
class  __lowercase    :


				'''simple docstring'''


				_lowerCAmelCase		     =field(
				    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}		)
				_lowerCAmelCase		     =field(
				    metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}		)
				_lowerCAmelCase		     =field(
				    default=128  ,				metadata={
				        "help": (
				            "The maximum total input sequence length after tokenization. Sequences longer "
				            "than this will be truncated, sequences shorter will be padded."
				        )
				    }  ,				)
				_lowerCAmelCase		     =field(
				    default=snake_case_  ,				metadata={"help": "Overwrite the cached training and evaluation sets"}		)







def 						lowerCAmelCase							(	):
						'''simple docstring'''


						snake_case__    :   Dict							     =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
						snake_case__     ,		snake_case__     ,		snake_case__    :   Dict							     =   parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir						)
						    and os.listdir(training_args.output_dir						)
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
												    ''' --overwrite_output_dir to overcome.'''						)

						snake_case__    :   Tuple							     =   import_module('''tasks'''						)

						try:
												snake_case__    :   Optional[Any]							     =   getattr(lowerCamelCase ,     model_args.task_type						)
												snake_case__    :   TokenClassificationTask							     =   token_classification_task_clazz()
						except AttributeError:
												raise ValueError(
												    f'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
												    f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''						)

						# Setup logging
						logging.basicConfig(
						    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,     datefmt='''%m/%d/%Y %H:%M:%S''' ,     level=logging.INFO ,     )
						logger.info(
						    '''n_replicas: %s, distributed training: %s, 16-bits training: %s''' ,     training_args.n_replicas ,     bool(training_args.n_replicas > 1						) ,     training_args.fpaa ,     )
						logger.info('''Training/evaluation parameters %s''' ,     lowerCamelCase						)

						# Prepare Token Classification task
						snake_case__    :   Tuple							     =   token_classification_task.get_labels(data_args.labels						)
						snake_case__    :   Dict[int, str]							     =   dict(enumerate(lowerCamelCase						)						)
						snake_case__    :   List[Any]							     =   len(lowerCamelCase						)

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						snake_case__    :   int							     =   AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,     num_labels=lowerCamelCase ,     idalabel=lowerCamelCase ,     labelaid={label: i for i, label in enumerate(lowerCamelCase						)} ,     cache_dir=model_args.cache_dir ,     )
						snake_case__    :   Optional[Any]							     =   AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,     cache_dir=model_args.cache_dir ,     use_fast=model_args.use_fast ,     )

						with training_args.strategy.scope():
												snake_case__    :   Dict							     =   TFAutoModelForTokenClassification.from_pretrained(
												    model_args.model_name_or_path ,     from_pt=bool('''.bin''' in model_args.model_name_or_path						) ,     config=lowerCamelCase ,     cache_dir=model_args.cache_dir ,     )

						# Get datasets
						snake_case__    :   Tuple							     =   (
						    TFTokenClassificationDataset(
						        token_classification_task=lowerCamelCase ,     data_dir=data_args.data_dir ,     tokenizer=lowerCamelCase ,     labels=lowerCamelCase ,     model_type=config.model_type ,     max_seq_length=data_args.max_seq_length ,     overwrite_cache=data_args.overwrite_cache ,     mode=Split.train ,     )
						    if training_args.do_train
						    else None
						)
						snake_case__    :   str							     =   (
						    TFTokenClassificationDataset(
						        token_classification_task=lowerCamelCase ,     data_dir=data_args.data_dir ,     tokenizer=lowerCamelCase ,     labels=lowerCamelCase ,     model_type=config.model_type ,     max_seq_length=data_args.max_seq_length ,     overwrite_cache=data_args.overwrite_cache ,     mode=Split.dev ,     )
						    if training_args.do_eval
						    else None
						)

						def align_predictions(lowerCamelCase ,     lowerCamelCase						) -> Tuple[List[int], List[int]]:
												snake_case__    :   Optional[int]							     =   np.argmax(lowerCamelCase ,     axis=2						)
												snake_case__     ,		snake_case__    :   Any							     =   preds.shape
												snake_case__    :   Optional[Any]							     =   [[] for _ in range(lowerCamelCase						)]
												snake_case__    :   Any							     =   [[] for _ in range(lowerCamelCase						)]

												for i in range(lowerCamelCase						):
																		for j in range(lowerCamelCase						):
																								if label_ids[i, j] != -100:
																														out_label_list[i].append(label_map[label_ids[i][j]]						)
																														preds_list[i].append(label_map[preds[i][j]]						)

												return preds_list, out_label_list

						def compute_metrics(lowerCamelCase						) -> Dict:
												snake_case__     ,		snake_case__    :   Union[str, Any]							     =   align_predictions(p.predictions ,     p.label_ids						)

												return {
												    "precision": precision_score(lowerCamelCase ,     lowerCamelCase						),
												    "recall": recall_score(lowerCamelCase ,     lowerCamelCase						),
												    "f1": fa_score(lowerCamelCase ,     lowerCamelCase						),
												}

						# Initialize our Trainer
						snake_case__    :   Optional[int]							     =   TFTrainer(
						    model=lowerCamelCase ,     args=lowerCamelCase ,     train_dataset=train_dataset.get_dataset() if train_dataset else None ,     eval_dataset=eval_dataset.get_dataset() if eval_dataset else None ,     compute_metrics=lowerCamelCase ,     )

						# Training
						if training_args.do_train:
												trainer.train()
												trainer.save_model()
												tokenizer.save_pretrained(training_args.output_dir						)

						# Evaluation
						snake_case__    :   int							     =   {}
						if training_args.do_eval:
												logger.info('''*** Evaluate ***'''						)

												snake_case__    :   Optional[int]							     =   trainer.evaluate()
												snake_case__    :   int							     =   os.path.join(training_args.output_dir ,     '''eval_results.txt'''						)

												with open(lowerCamelCase ,     '''w'''						) as writer:
																		logger.info('''***** Eval results *****'''						)

																		for key, value in result.items():
																								logger.info('''  %s = %s''' ,     lowerCamelCase ,     lowerCamelCase						)
																								writer.write('''%s = %s\n''' % (key, value)						)

																		results.update(lowerCamelCase						)

    # Predict
						if training_args.do_predict:
												snake_case__    :   Dict							     =   TFTokenClassificationDataset(
												    token_classification_task=lowerCamelCase ,     data_dir=data_args.data_dir ,     tokenizer=lowerCamelCase ,     labels=lowerCamelCase ,     model_type=config.model_type ,     max_seq_length=data_args.max_seq_length ,     overwrite_cache=data_args.overwrite_cache ,     mode=Split.test ,     )

												snake_case__     ,		snake_case__     ,		snake_case__    :   Dict							     =   trainer.predict(test_dataset.get_dataset()						)
												snake_case__     ,		snake_case__    :   Optional[Any]							     =   align_predictions(lowerCamelCase ,     lowerCamelCase						)
												snake_case__    :   Any							     =   classification_report(lowerCamelCase ,     lowerCamelCase						)

												logger.info('''\n%s''' ,     lowerCamelCase						)

												snake_case__    :   Optional[Any]							     =   os.path.join(training_args.output_dir ,     '''test_results.txt'''						)

												with open(lowerCamelCase ,     '''w'''						) as writer:
																		writer.write('''%s\n''' % report						)

												# Save predictions
												snake_case__    :   Tuple							     =   os.path.join(training_args.output_dir ,     '''test_predictions.txt'''						)

												with open(lowerCamelCase ,     '''w'''						) as writer:
																		with open(os.path.join(data_args.data_dir ,     '''test.txt'''						) ,     '''r'''						) as f:
																								snake_case__    :   str							     =   0

																								for line in f:
																														if line.startswith('''-DOCSTART-'''						) or line == "" or line == "\n":
																																				writer.write(lowerCamelCase						)

																																				if not preds_list[example_id]:
																																										example_id += 1
																														elif preds_list[example_id]:
																																				snake_case__    :   Dict							     =   line.split()[0] + ''' ''' + preds_list[example_id].pop(0						) + '''\n'''

																																				writer.write(lowerCamelCase						)
																														else:
																																				logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.''' ,     line.split()[0]						)

						return results


if __name__ == "__main__":
							main()








"""simple docstring"""





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
#
########################################################################

_lowercase		 =      16
_lowercase		 =      32

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase = 16						):
						'''simple docstring'''


						snake_case__    :   int							     =   AutoTokenizer.from_pretrained('''bert-base-cased'''						)
						snake_case__    :   Dict							     =   load_dataset('''glue''' ,     '''mrpc'''						)

						def tokenize_function(lowerCamelCase						):
												# max_length=None => use the model max length (it's actually the default)
												snake_case__    :   Optional[int]							     =   tokenizer(examples['''sentence1'''] ,     examples['''sentence2'''] ,     truncation=lowerCamelCase ,     max_length=lowerCamelCase						)
												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__    :   Tuple							     =   datasets.map(
												    lowerCamelCase ,     batched=lowerCamelCase ,     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__    :   str							     =   tokenized_datasets.rename_column('''label''' ,     '''labels'''						)

						def collate_fn(lowerCamelCase						):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												snake_case__    :   Tuple							     =   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":
																		snake_case__    :   Optional[Any]							     =   16
												elif accelerator.mixed_precision != "no":
																		snake_case__    :   Dict							     =   8
												else:
																		snake_case__    :   str							     =   None

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

						# Instantiate dataloaders.
						snake_case__    :   List[Any]							     =   DataLoader(
						    tokenized_datasets['''train'''] ,     shuffle=lowerCamelCase ,     collate_fn=lowerCamelCase ,     batch_size=lowerCamelCase						)
						snake_case__    :   Tuple							     =   DataLoader(
						    tokenized_datasets['''validation'''] ,     shuffle=lowerCamelCase ,     collate_fn=lowerCamelCase ,     batch_size=lowerCamelCase						)

						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

							_lowercase		 =      mocked_dataloaders  # noqa: F811

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,     lowerCamelCase						) == "1":
												snake_case__    :   Any							     =   2
						# Initialize accelerator
						snake_case__    :   List[Any]							     =   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__    :   Any							     =   config['''lr''']
						snake_case__    :   int							     =   int(config['''num_epochs''']						)
						snake_case__    :   List[str]							     =   int(config['''seed''']						)
						snake_case__    :   Dict							     =   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__    :   Optional[Any]							     =   args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		snake_case__    :   Union[str, Any]							     =   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__    :   Union[str, Any]							     =   None

						set_seed(lowerCamelCase						)

						snake_case__     ,		snake_case__    :   int							     =   get_dataloaders(lowerCamelCase ,     lowerCamelCase						)
						snake_case__    :   Optional[Any]							     =   evaluate.load('''glue''' ,     '''mrpc'''						)

						# If the batch size is too big we use gradient accumulation
						snake_case__    :   Optional[int]							     =   1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												snake_case__    :   Any							     =   batch_size // MAX_GPU_BATCH_SIZE
												snake_case__    :   Union[str, Any]							     =   MAX_GPU_BATCH_SIZE

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						snake_case__    :   Tuple							     =   AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,     return_dict=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).
						snake_case__    :   Dict							     =   model.to(accelerator.device						)

						# Instantiate optimizer
						snake_case__    :   Optional[int]							     =   AdamW(params=model.parameters() ,     lr=lowerCamelCase						)

						# Instantiate scheduler
						snake_case__    :   Optional[Any]							     =   get_linear_schedule_with_warmup(
						    optimizer=lowerCamelCase ,     num_warmup_steps=100 ,     num_training_steps=(len(lowerCamelCase						) * 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__     ,		snake_case__     ,		snake_case__     ,		snake_case__     ,		snake_case__    :   Optional[Any]							     =   accelerator.prepare(
						    lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						)

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

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

    # Now we train the model
						for epoch in range(lowerCamelCase ,     lowerCamelCase						):
												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__    :   int							     =   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
																		snake_case__    :   Tuple							     =   train_dataloader
												for step, batch in enumerate(lowerCamelCase						):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device						)
																		snake_case__    :   Tuple							     =   model(**lowerCamelCase						)
																		snake_case__    :   Optional[int]							     =   outputs.loss
																		snake_case__    :   Union[str, Any]							     =   loss / gradient_accumulation_steps
																		accelerator.backward(lowerCamelCase						)
																		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(lowerCamelCase ,     lowerCamelCase						):
																								snake_case__    :   Tuple							     =   f'''step_{overall_step}'''
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				snake_case__    :   Optional[int]							     =   os.path.join(args.output_dir ,     lowerCamelCase						)
																														accelerator.save_state(lowerCamelCase						)

												model.eval()
												for step, batch in enumerate(lowerCamelCase						):
																		# 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__    :   List[str]							     =   model(**lowerCamelCase						)
																		snake_case__    :   Optional[int]							     =   outputs.logits.argmax(dim=-1						)
																		snake_case__     ,		snake_case__    :   Dict							     =   accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
																		metric.add_batch(
																		    predictions=lowerCamelCase ,     references=lowerCamelCase ,     )

												snake_case__    :   str							     =   metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'''epoch {epoch}:''' ,     lowerCamelCase						)

												# 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__    :   str							     =   f'''epoch_{epoch}'''
																		if args.output_dir is not None:
																								snake_case__    :   Optional[int]							     =   os.path.join(args.output_dir ,     lowerCamelCase						)
																		accelerator.save_state(lowerCamelCase						)

def 						lowerCAmelCase							(	):
						'''simple docstring'''


						snake_case__    :   Tuple							     =   argparse.ArgumentParser(description='''Simple example of training script.'''						)
						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.''' ,     )
						snake_case__    :   List[str]							     =   parser.parse_args()
						snake_case__    :   str							     =   {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(lowerCamelCase ,     lowerCamelCase						)


if __name__ == "__main__":
							main()


