



import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel







def    _snake_case (       __snake_case=3_2					,  __snake_case=1_0					,  __snake_case=1_0_0					,  __snake_case=1_0_2_6					,  __snake_case=True					,  __snake_case="data/tokenized_stories_train_wikitext103.jbl"					,  __snake_case="igf_context_pairs.jbl"					,  )     ->     str:






      '''simple docstring'''







      set_seed(3    )
      # generate train_data and objective_set
      UpperCAmelCase_      ,      UpperCAmelCase_  :	Union[str, Any]				      =		generate_datasets(
          __snake_case					,  __snake_case					,  number=__snake_case					,  min_len=1_0_2_6					,  trim=__snake_case    )
      # keeps model same across runs
      set_seed(4    )
      # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
      # can we train on GPU?
      UpperCAmelCase_  :	Any				      =		torch.device("cuda:0" if torch.cuda.is_available() else "cpu"    )

      # load pretrained model
      UpperCAmelCase_  :	int				      =		load_gpta("gpt2"    ).to(__snake_case    )
      print("computing perplexity on objective set"    )
      UpperCAmelCase_  :	Optional[int]				      =		compute_perplexity(__snake_case					,  __snake_case					,  __snake_case    ).item()
      print("perplexity on objective set:"					,  __snake_case    )

      # collect igf pairs and save to file demo.jbl
      collect_objective_set(__snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )

      # clean up, delete model and data we don't need anymore
      del model, train_data, objective_set
      torch.cuda.empty_cache()







def    _snake_case (       __snake_case					,  __snake_case=1_5					,  __snake_case=1_2_8					,  __snake_case=1_0_0					,  __snake_case="igf_model.pt"					,  )     ->     Optional[Any]:






      '''simple docstring'''







      set_seed(4_2    )

      # Load pre-trained model
      UpperCAmelCase_  :	int				      =		GPTaLMHeadModel.from_pretrained("gpt2"    )

      # Initialize secondary learner to use embedding weights of model
      UpperCAmelCase_  :	str				      =		SecondaryLearner(__snake_case    )

      # Train secondary learner
      UpperCAmelCase_  :	Optional[int]				      =		train_secondary_learner(
          __snake_case					,  __snake_case					,  max_epochs=__snake_case					,  batch_size=__snake_case					,  eval_freq=1_0_0					,  igf_model_path=__snake_case					,  )

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

      return secondary_learner







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case=3_2					,  __snake_case=1_0_0_0					,  __snake_case=1_6					,  __snake_case=1.0					,  __snake_case=recopy_gpta					,  __snake_case=None					,  __snake_case=1_0					,  __snake_case="gpt2_finetuned.pt"					,  )     ->     List[Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	List[Any]				      =		torch.device("cuda:0" if torch.cuda.is_available() else "cpu"    )
      UpperCAmelCase_  :	Optional[Any]				      =		RandomSampler(__snake_case    )
      UpperCAmelCase_  :	int				      =		DataLoader(__snake_case					,  sampler=__snake_case    )

      UpperCAmelCase_  :	List[Any]				      =		max_steps // (len(__snake_case    )) + 1
      UpperCAmelCase_  :	Optional[Any]				      =		0
      UpperCAmelCase_  :	Dict				      =		torch.zeros((1, context_len)					,  dtype=torch.long					,  device=__snake_case    )
      UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_  :	Dict				      =		recopy_model(__snake_case					,  __snake_case					,  __snake_case    )

      model.train()
      if secondary_learner is not None:
            secondary_learner.to(__snake_case    )
            secondary_learner.eval()
      UpperCAmelCase_  :	Union[str, Any]				      =		[]
      UpperCAmelCase_  :	Optional[int]				      =		0

      UpperCAmelCase_  :	str				      =		[]
      UpperCAmelCase_  :	List[Any]				      =		[]

      # Compute the performance of the transformer model at the beginning
      UpperCAmelCase_  :	Optional[int]				      =		compute_perplexity(__snake_case					,  __snake_case					,  __snake_case    )
      test_perps.append(__snake_case    )
      print("Test perplexity, step"					,  __snake_case					,  ":"					,  __snake_case    )
      for epoch in range(int(__snake_case    )    ):
            for step, example in enumerate(__snake_case    ):
                  torch.cuda.empty_cache()
                  UpperCAmelCase_  :	Optional[Any]				      =		random.randint(0					,  example.size(2    ) - context_len - 1    )
                  UpperCAmelCase_  :	str				      =		example[0, 0, start : start + context_len]
                  lm_optimizer.zero_grad()
                  UpperCAmelCase_  :	int				      =		model(__snake_case					,  labels=__snake_case    )
                  UpperCAmelCase_  :	Union[str, Any]				      =		True

                  if secondary_learner is not None:
                        UpperCAmelCase_  :	Optional[int]				      =		secondary_learner.forward(
                            torch.tensor(__snake_case					,  dtype=torch.long					,  device=__snake_case    ).unsqueeze(0    )    )[0].item()
                        observed_qs.append(float(__snake_case    )    )

                        # Here we implement the simple non-constant threshold for the predicted IG(X) value
                        # We will decay the selectivity of our secondary learner filter from
                        # 1 standard deviation above average to 1 below average after 10 batches.

                        if global_step == 1_0:
                              UpperCAmelCase_  :	str				      =		-1
                        if predicted_q < threshold:
                              UpperCAmelCase_  :	str				      =		False

            # If we passed the filter, add the context to the batch!
                  if do_backprop:
                        contexts.append(np.array(context.cpu()    )    )
                        UpperCAmelCase_  :	Any				      =		outputs[0]
                        lm_loss.backward()
                        examples += 1

                  del outputs

                  # Once the batch is filled with enough contexts, backprop on the batch.
                  if examples == batch_size:
                        torch.cuda.empty_cache()
                        UpperCAmelCase_  :	Dict				      =		0
                        # Do LM backprop
                        torch.nn.utils.clip_grad_norm_(model.parameters()					,  3.0    )
                        lm_optimizer.step()
                        lm_scheduler.step()  # Update learning rate schedule
                        global_step += 1
                        # Compute the performance of the transformer model at this batch
                        if global_step % eval_interval == 0:
                              UpperCAmelCase_  :	Tuple				      =		compute_perplexity(__snake_case					,  __snake_case					,  __snake_case    )
                              test_perps.append(__snake_case    )

                              print("Test perplexity, step"					,  __snake_case					,  ":"					,  __snake_case    )
            # Break out of the loop after 60 batches
                  if max_steps > 0 and global_step > 6_0:
                        break
            if max_steps > 0 and global_step > 6_0:
                  break

    # save finetuned transformer model
      torch.save(model.state_dict()					,  __snake_case    )
      torch.cuda.empty_cache()
      # Do some cleaning up so we can reinitialize for the next run of this function
      del lm_optimizer
      del lm_scheduler
      return model







def    _snake_case (       )     ->     Dict:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[int]				      =		argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"    )

      # Required parameters
      parser.add_argument(
          "--data_dir"					,  default=__snake_case					,  type=__snake_case					,  required=__snake_case					,  help="The input data dir. Should contain data files for WikiText."					,  )
      parser.add_argument(
          "--model_name_or_path"					,  default=__snake_case					,  type=__snake_case					,  required=__snake_case					,  help="Path to pretrained model or model identifier from huggingface.co/models"					,  )
      parser.add_argument(
          "--data_file"					,  type=__snake_case					,  default=__snake_case					,  help=(
              "A jbl file containing tokenized data which can be split as objective dataset, "
              "train_dataset and test_dataset."
          )					,  )

      parser.add_argument(
          "--igf_data_file"					,  type=__snake_case					,  default=__snake_case					,  help="A jbl file containing the context and information gain pairs to train secondary learner."					,  )

      parser.add_argument(
          "--output_dir"					,  default=__snake_case					,  type=__snake_case					,  required=__snake_case					,  help="The output directory where the final fine-tuned model is stored."					,  )

      parser.add_argument(
          "--tokenizer_name"					,  default=__snake_case					,  type=__snake_case					,  help="Pretrained tokenizer name or path if not the same as model_name"					,  )
      parser.add_argument("--seed"					,  type=__snake_case					,  default=__snake_case					,  help="A seed for reproducible training."    )

      parser.add_argument(
          "--context_len"					,  default=3_2					,  type=__snake_case					,  help=(
              "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          )					,  )

      parser.add_argument(
          "--size_objective_set"					,  default=1_0_0					,  type=__snake_case					,  help="number of articles that are long enough to be used as our objective set"					,  )
      parser.add_argument(
          "--eval_freq"					,  default=1_0_0					,  type=__snake_case					,  help="secondary model evaluation is triggered at eval_freq"    )

      parser.add_argument("--max_steps"					,  default=1_0_0_0					,  type=__snake_case					,  help="To calculate training epochs"    )

      parser.add_argument(
          "--secondary_learner_batch_size"					,  default=1_2_8					,  type=__snake_case					,  help="batch size of training data for secondary learner"					,  )

      parser.add_argument(
          "--batch_size"					,  default=1_6					,  type=__snake_case					,  help="batch size of training data of language model(gpt2) "    )

      parser.add_argument(
          "--eval_interval"					,  default=1_0					,  type=__snake_case					,  help=(
              "decay the selectivity of our secondary learner filter from"
              "1 standard deviation above average to 1 below average after 10 batches"
          )					,  )

      parser.add_argument(
          "--number"					,  default=1_0_0					,  type=__snake_case					,  help="The number of examples split to be used as objective_set/test_data"    )

      parser.add_argument(
          "--min_len"					,  default=1_0_2_6					,  type=__snake_case					,  help="The minimum length of the article to be used as objective set"    )

      parser.add_argument(
          "--secondary_learner_max_epochs"					,  default=1_5					,  type=__snake_case					,  help="number of epochs to train secondary learner"    )

      parser.add_argument("--trim"					,  default=__snake_case					,  type=__snake_case					,  help="truncate the example if it exceeds context length"    )

      parser.add_argument(
          "--threshold"					,  default=1.0					,  type=__snake_case					,  help=(
              "The threshold value used by secondary learner to filter the train_data and allow only"
              " informative data as input to the model"
          )					,  )

      parser.add_argument("--finetuned_model_name"					,  default="gpt2_finetuned.pt"					,  type=__snake_case					,  help="finetuned_model_name"    )

      parser.add_argument(
          "--recopy_model"					,  default=__snake_case					,  type=__snake_case					,  help="Reset the model to the original pretrained GPT-2 weights after each iteration"					,  )

      # function calls
      # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
      generate_n_pairs(
          context_len=3_2					,  max_steps=1_0					,  size_objective_set=1_0_0					,  min_len=1_0_2_6					,  trim=__snake_case					,  data_file="data/tokenized_stories_train_wikitext103.jbl"					,  igf_data_file="igf_context_pairs.jbl"					,  )

      # Load train data for secondary learner
      UpperCAmelCase_  :	List[str]				      =		joblib.load("data/IGF_values.jbl"    )

      # Train secondary learner
      UpperCAmelCase_  :	Tuple				      =		training_secondary_learner(
          __snake_case					,  secondary_learner_max_epochs=1_5					,  secondary_learner_batch_size=1_2_8					,  eval_freq=1_0_0					,  igf_model_path="igf_model.pt"					,  )

      # load pretrained gpt2 model
      UpperCAmelCase_  :	str				      =		GPTaLMHeadModel.from_pretrained("gpt2"    )
      set_seed(4_2    )

      # Generate train and test data to train and evaluate gpt2 model
      UpperCAmelCase_      ,      UpperCAmelCase_  :	List[str]				      =		generate_datasets(
          context_len=3_2					,  file="data/tokenized_stories_train_wikitext103.jbl"					,  number=1_0_0					,  min_len=1_0_2_6					,  trim=__snake_case    )

      # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
      finetune(
          __snake_case					,  __snake_case					,  __snake_case					,  context_len=3_2					,  max_steps=1_0_0_0					,  batch_size=1_6					,  threshold=1.0					,  recopy_model=__snake_case					,  secondary_learner=__snake_case					,  eval_interval=1_0					,  finetuned_model_name="gpt2_finetuned.pt"					,  )


if __name__ == "__main__":
   main()





import argparse
from typing import List

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

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 how to perform Cross Validation,
# 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
#
########################################################################


__lowerCamelCase         =			16
__lowerCamelCase         =			32







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case = 1_6    )     ->     Tuple:






      '''simple docstring'''







      UpperCAmelCase_  :	int				      =		AutoTokenizer.from_pretrained("bert-base-cased"    )
      UpperCAmelCase_  :	List[Any]				      =		DatasetDict(
          {
              "train": dataset["train"].select(__snake_case    ),
              "validation": dataset["train"].select(__snake_case    ),
              "test": dataset["validation"],
          }    )

      def tokenize_function(__snake_case    ):
            # max_length=None => use the model max length (it's actually the default)
            UpperCAmelCase_  :	List[Any]				      =		tokenizer(examples["sentence1"]					,  examples["sentence2"]					,  truncation=__snake_case					,  max_length=__snake_case    )
            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():
            UpperCAmelCase_  :	int				      =		datasets.map(
                __snake_case					,  batched=__snake_case					,  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
      UpperCAmelCase_  :	Tuple				      =		tokenized_datasets.rename_column("label"					,  "labels"    )

      def collate_fn(__snake_case    ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            UpperCAmelCase_  :	Dict				      =		1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  UpperCAmelCase_  :	Optional[Any]				      =		1_6
            elif accelerator.mixed_precision != "no":
                  UpperCAmelCase_  :	str				      =		8
            else:
                  UpperCAmelCase_  :	Dict				      =		None

            return tokenizer.pad(
                __snake_case					,  padding="longest"					,  max_length=__snake_case					,  pad_to_multiple_of=__snake_case					,  return_tensors="pt"					,  )

      # Instantiate dataloaders.
      UpperCAmelCase_  :	List[str]				      =		DataLoader(
          tokenized_datasets["train"]					,  shuffle=__snake_case					,  collate_fn=__snake_case					,  batch_size=__snake_case    )
      UpperCAmelCase_  :	Union[str, Any]				      =		DataLoader(
          tokenized_datasets["validation"]					,  shuffle=__snake_case					,  collate_fn=__snake_case					,  batch_size=__snake_case    )

      UpperCAmelCase_  :	str				      =		DataLoader(
          tokenized_datasets["test"]					,  shuffle=__snake_case					,  collate_fn=__snake_case					,  batch_size=__snake_case    )

      return train_dataloader, eval_dataloader, test_dataloader







def    _snake_case (       __snake_case					,  __snake_case    )     ->     List[str]:






      '''simple docstring'''







      UpperCAmelCase_  :	Tuple				      =		[]
      # Download the dataset
      UpperCAmelCase_  :	Optional[int]				      =		load_dataset("glue"					,  "mrpc"    )
      # Create our splits
      UpperCAmelCase_  :	Optional[Any]				      =		StratifiedKFold(n_splits=int(args.num_folds    )    )
      # Initialize accelerator
      UpperCAmelCase_  :	Tuple				      =		Accelerator(cpu=args.cpu					,  mixed_precision=args.mixed_precision    )
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCAmelCase_  :	Optional[int]				      =		config["lr"]
      UpperCAmelCase_  :	Dict				      =		int(config["num_epochs"]    )
      UpperCAmelCase_  :	Union[str, Any]				      =		int(config["seed"]    )
      UpperCAmelCase_  :	Optional[Any]				      =		int(config["batch_size"]    )

      UpperCAmelCase_  :	Optional[Any]				      =		evaluate.load("glue"					,  "mrpc"    )

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

      set_seed(__snake_case    )

      # New Code #
      # Create our folds:
      UpperCAmelCase_  :	int				      =		kfold.split(np.zeros(datasets["train"].num_rows    )					,  datasets["train"]["label"]    )
      UpperCAmelCase_  :	Dict				      =		[]
      # Iterate over them
      for i, (train_idxs, valid_idxs) in enumerate(__snake_case    ):
            UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_  :	str				      =		get_fold_dataloaders(
                __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  )
            # Instantiate the model (we build the model here so that the seed also control new weights initialization)
            UpperCAmelCase_  :	Optional[int]				      =		AutoModelForSequenceClassification.from_pretrained("bert-base-cased"					,  return_dict=__snake_case    )

            # 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_  :	Union[str, Any]				      =		model.to(accelerator.device    )

            # Instantiate optimizer
            UpperCAmelCase_  :	Tuple				      =		AdamW(params=model.parameters()					,  lr=__snake_case    )

            # Instantiate scheduler
            UpperCAmelCase_  :	str				      =		get_linear_schedule_with_warmup(
                optimizer=__snake_case					,  num_warmup_steps=1_0_0					,  num_training_steps=(len(__snake_case    ) * 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.
            UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_  :	Union[str, Any]				      =		accelerator.prepare(
                __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )

            # Now we train the model
            for epoch in range(__snake_case    ):
                  model.train()
                  for step, batch in enumerate(__snake_case    ):
                        # We could avoid this line since we set the accelerator with `device_placement=True`.
                        batch.to(accelerator.device    )
                        UpperCAmelCase_  :	Optional[Any]				      =		model(**__snake_case    )
                        UpperCAmelCase_  :	Dict				      =		outputs.loss
                        UpperCAmelCase_  :	List[Any]				      =		loss / gradient_accumulation_steps
                        accelerator.backward(__snake_case    )
                        if step % gradient_accumulation_steps == 0:
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()

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

                  UpperCAmelCase_  :	Optional[Any]				      =		metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(F"""epoch {epoch}:"""					,  __snake_case    )

            # New Code #
            # We also run predictions on the test set at the very end
            UpperCAmelCase_  :	Union[str, Any]				      =		[]
            for step, batch in enumerate(__snake_case    ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device    )
                  with torch.no_grad():
                        UpperCAmelCase_  :	Tuple				      =		model(**__snake_case    )
                  UpperCAmelCase_  :	Optional[int]				      =		outputs.logits
                  UpperCAmelCase_      ,      UpperCAmelCase_  :	Optional[int]				      =		accelerator.gather_for_metrics((predictions, batch["labels"])    )
                  fold_predictions.append(predictions.cpu()    )
                  if i == 0:
                        # We need all of the test predictions
                        test_references.append(references.cpu()    )
        # Use accelerator.print to print only on the main process.
            test_predictions.append(torch.cat(__snake_case					,  dim=0    )    )
            # We now need to release all our memory and get rid of the current model, optimizer, etc
            accelerator.free_memory()
      # New Code #
      # Finally we check the accuracy of our folded results:
      UpperCAmelCase_  :	Union[str, Any]				      =		torch.cat(__snake_case					,  dim=0    )
      UpperCAmelCase_  :	str				      =		torch.stack(__snake_case					,  dim=0    ).sum(dim=0    ).div(int(args.num_folds    )    ).argmax(dim=-1    )
      UpperCAmelCase_  :	List[str]				      =		metric.compute(predictions=__snake_case					,  references=__snake_case    )
      accelerator.print("Average test metrics from all folds:"					,  __snake_case    )







def    _snake_case (       )     ->     str:






      '''simple docstring'''







      UpperCAmelCase_  :	Union[str, Any]				      =		argparse.ArgumentParser(description="Simple example of training script."    )
      parser.add_argument(
          "--mixed_precision"					,  type=__snake_case					,  default=__snake_case					,  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."    )
      # New Code #
      parser.add_argument("--num_folds"					,  type=__snake_case					,  default=3					,  help="The number of splits to perform across the dataset"    )
      UpperCAmelCase_  :	Any				      =		parser.parse_args()
      UpperCAmelCase_  :	List[Any]				      =		{"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
      training_function(__snake_case					,  __snake_case    )


if __name__ == "__main__":
   main()
