"""simple docstring"""





import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase_     =	get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')




@require_sentencepiece
@require_tokenizers
class  _snake_case		(    __snake_case   ,       unittest.TestCase ):







    """simple docstring"""






    a			=						GPTSwaTokenizer
    a			=						False
    a			=						True
    a			=						False





    def 		_lowerCAmelCase		(      self							:							Optional[int]):



           """simple docstring"""







           super().setUp()

           # We have a SentencePiece fixture for testing
           _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    GPTSwaTokenizer(_A      ,    eos_token="""<unk>"""      ,    bos_token="""<unk>"""      ,    pad_token="""<unk>""")

           tokenizer.save_pretrained(self.tmpdirname)





    def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							Dict):



           """simple docstring"""







           _SCREAMING_SNAKE_CASE		:						Any			  =    """This is a test"""
           _SCREAMING_SNAKE_CASE		:						Any			  =    """This is a test"""
           return input_text, output_text





    def 		_lowerCAmelCase		(      self							:							Any):



           """simple docstring"""







           _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    """<s>"""
           _SCREAMING_SNAKE_CASE		:						Dict			  =    1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A)      ,    _A)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A)      ,    _A)





    def 		_lowerCAmelCase		(      self							:							Tuple):



           """simple docstring"""







           _SCREAMING_SNAKE_CASE		:						str			  =    list(self.get_tokenizer().get_vocab().keys())

           self.assertEqual(vocab_keys[0]      ,    """<unk>""")
           self.assertEqual(vocab_keys[1]      ,    """<s>""")
           self.assertEqual(vocab_keys[-1]      ,    """j""")
           self.assertEqual(len(_A)      ,    2_0_0_0)





    def 		_lowerCAmelCase		(      self							:							Dict):



           """simple docstring"""







           self.assertEqual(self.get_tokenizer().vocab_size      ,    2_0_0_0)





    def 		_lowerCAmelCase		(      self							:							Optional[Any]):



           """simple docstring"""







           _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    GPTSwaTokenizer(_A)

           _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    tokenizer.tokenize("""This is a test""")
           self.assertListEqual(_A      ,    ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])

           self.assertListEqual(tokenizer.convert_tokens_to_ids(_A)      ,    [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2])

           _SCREAMING_SNAKE_CASE		:						List[Any]			  =    tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
           # fmt: off
           self.assertListEqual(
               _A      ,    ["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]      ,    )
           # fmt: on

           _SCREAMING_SNAKE_CASE		:						Any			  =    tokenizer.convert_tokens_to_ids(_A)
           self.assertListEqual(
               _A      ,    [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0]      ,    )

           _SCREAMING_SNAKE_CASE		:						str			  =    tokenizer.convert_ids_to_tokens(_A)
           # fmt: off
           self.assertListEqual(
               _A      ,    ["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""])
           # fmt: on





    def 		_lowerCAmelCase		(      self							:							int):



           """simple docstring"""







           _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    GPTSwaTokenizer(_A)
           _SCREAMING_SNAKE_CASE		:						int			  =    ["""This is a test""", """I was born in 92000, and this is falsé."""]
           _SCREAMING_SNAKE_CASE		:						Tuple			  =    [
               [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2],
               [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0],
           ]

           # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
           for text, expected_ids in zip(_A      ,    _A):
                  self.assertListEqual(tokenizer.encode_fast(_A)      ,    _A)

           # Test that decode_fast returns the input text
           for text, token_ids in zip(_A      ,    _A):
                  self.assertEqual(tokenizer.decode_fast(_A)      ,    _A)





    @slow
    def 		_lowerCAmelCase		(      self							:							Dict):



           """simple docstring"""







           _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    [
               """<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')""",
               """Hey there, how are you doing this fine day?""",
               """This is a text with a trailing spaces followed by a dot     .""",
               """Häj sväjs lillebrör! =)""",
               """Det är inget fel på Mr. Cool""",
           ]

           # fmt: off
           _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {"""input_ids""": [[6_3_4_2_3, 5, 6_8_1_1, 1_4_9_5_4, 2_8_2, 8_1_6, 3_8_2_1, 6_3_4_6_6, 6_3_4_2_5, 6_3_4_6_2, 1_8, 6_3_9_7_8, 6_7_8, 3_0_1, 1_3_2_0, 6_3_4_2_3, 6_3_4_5_5, 6_3_4_5_8, 1_8, 6_3_9_8_2, 4_2_4_6, 3_9_4_0, 1_9_0_1, 4_7_7_8_9, 5_5_4_7, 1_8_9_9_4], [1_9_6_3_0, 1_1_0_0, 6_3_4_4_6, 1_3_4_2, 6_3_3, 5_4_4, 4_4_8_8, 5_9_3, 5_1_0_2, 2_4_1_6, 6_3_4_9_5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_6_5_2, 4_2_8, 2_6_8, 1_9_3_6, 5_1_5, 2_6_8, 5_8_5_9_3, 2_2_4_1_3, 9_1_0_6, 5_4_6, 2_6_8, 3_3_2_1_3, 6_3_9_7_9, 6_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5_1_3_0, 6_3_4_5_0, 9_2_4, 6_3_4_4_9, 2_2_4_9, 4_0_6_2, 1_5_5_8, 3_1_8, 6_3_5_0_4, 2_1_4_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_0_9, 3_7_7, 2_8_2_7, 2_5_5_9, 3_3_2, 6_5_7_5, 6_3_4_4_3, 2_6_8_0_1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
           # fmt: on
           self.tokenizer_integration_test_util(
               expected_encoding=_A      ,    model_name="""AI-Sweden/gpt-sw3-126m"""      ,    sequences=_A      ,    )



"""simple docstring"""





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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 16  )->    str:
							_SCREAMING_SNAKE_CASE		:						int			  =    AutoTokenizer.from_pretrained("""bert-base-cased"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    DatasetDict(
							    {
							        """train""": dataset["""train"""].select(__SCREAMING_SNAKE_CASE  ),
							        """validation""": dataset["""train"""].select(__SCREAMING_SNAKE_CASE  ),
							        """test""": dataset["""validation"""],
							    }  )

							def tokenize_function(__SCREAMING_SNAKE_CASE  ):
														# max_length=None => use the model max length (it's actually the default)
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    tokenizer(examples["""sentence1"""]     ,	examples["""sentence2"""]     ,	truncation=__SCREAMING_SNAKE_CASE     ,	max_length=__SCREAMING_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():
														_SCREAMING_SNAKE_CASE		:						str			  =    datasets.map(
														    __SCREAMING_SNAKE_CASE     ,	batched=__SCREAMING_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
							_SCREAMING_SNAKE_CASE		:						Any			  =    tokenized_datasets.rename_column("""label"""     ,	"""labels"""  )

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

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

							# Instantiate dataloaders.
							_SCREAMING_SNAKE_CASE		:						int			  =    DataLoader(
							    tokenized_datasets["""train"""]     ,	shuffle=__SCREAMING_SNAKE_CASE     ,	collate_fn=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    DataLoader(
							    tokenized_datasets["""validation"""]     ,	shuffle=__SCREAMING_SNAKE_CASE     ,	collate_fn=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Dict			  =    DataLoader(
							    tokenized_datasets["""test"""]     ,	shuffle=__SCREAMING_SNAKE_CASE     ,	collate_fn=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE  )

							return train_dataloader, eval_dataloader, test_dataloader



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

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    evaluate.load("""glue"""     ,	"""mrpc"""  )

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

							set_seed(__SCREAMING_SNAKE_CASE  )

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

														# Instantiate optimizer
														_SCREAMING_SNAKE_CASE		:						int			  =    AdamW(params=model.parameters()     ,	lr=__SCREAMING_SNAKE_CASE  )

														# Instantiate scheduler
														_SCREAMING_SNAKE_CASE		:						int			  =    get_linear_schedule_with_warmup(
														    optimizer=__SCREAMING_SNAKE_CASE     ,	num_warmup_steps=100     ,	num_training_steps=(len(__SCREAMING_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.
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    accelerator.prepare(
														    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

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

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

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

														# New Code #
														# We also run predictions on the test set at the very end
														_SCREAMING_SNAKE_CASE		:						str			  =    []
														for step, batch in enumerate(__SCREAMING_SNAKE_CASE  ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device  )
																					with torch.no_grad():
																												_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(**__SCREAMING_SNAKE_CASE  )
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    outputs.logits
																					_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[Any]			  =    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(__SCREAMING_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:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    torch.cat(__SCREAMING_SNAKE_CASE     ,	dim=0  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.stack(__SCREAMING_SNAKE_CASE     ,	dim=0  ).sum(dim=0  ).div(int(args.num_folds  )  ).argmax(dim=-1  )
							_SCREAMING_SNAKE_CASE		:						int			  =    metric.compute(predictions=__SCREAMING_SNAKE_CASE     ,	references=__SCREAMING_SNAKE_CASE  )
							accelerator.print("""Average test metrics from all folds:"""     ,	__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_()->    Optional[Any]:
							_SCREAMING_SNAKE_CASE		:						Any			  =    argparse.ArgumentParser(description="""Simple example of training script."""  )
							parser.add_argument(
							    """--mixed_precision"""     ,	type=__SCREAMING_SNAKE_CASE     ,	default=__SCREAMING_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=__SCREAMING_SNAKE_CASE     ,	default=3     ,	help="""The number of splits to perform across the dataset"""  )
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    parser.parse_args()
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
			main()


