


"""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
#
########################################################################


__SCREAMING_SNAKE_CASE							:      str						=      16
__SCREAMING_SNAKE_CASE							:      Optional[int]						=      32







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = 16			)      ->			Dict:
							snake_case_          =						AutoTokenizer.from_pretrained("""bert-base-cased"""			)
							snake_case_          =						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)
														snake_case_          =						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():
														snake_case_          =						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
							snake_case_          =						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.
														snake_case_          =						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_          =						16
														elif accelerator.mixed_precision != "no":
																					snake_case_          =						8
														else:
																					snake_case_          =						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.
							snake_case_          =						DataLoader(
							    tokenized_datasets["""train"""]			,	shuffle=_SCREAMING_SNAKE_CASE			,	collate_fn=_SCREAMING_SNAKE_CASE			,	batch_size=_SCREAMING_SNAKE_CASE			)
							snake_case_          =						DataLoader(
							    tokenized_datasets["""validation"""]			,	shuffle=_SCREAMING_SNAKE_CASE			,	collate_fn=_SCREAMING_SNAKE_CASE			,	batch_size=_SCREAMING_SNAKE_CASE			)

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

							snake_case_          =						evaluate.load("""glue"""			,	"""mrpc"""			)

							# If the batch size is too big we use gradient accumulation
							snake_case_          =						1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														snake_case_          =						batch_size // MAX_GPU_BATCH_SIZE
														snake_case_          =						MAX_GPU_BATCH_SIZE

							set_seed(_SCREAMING_SNAKE_CASE			)

							# New Code #
							# Create our folds:
							snake_case_          =						kfold.split(np.zeros(datasets["""train"""].num_rows			)			,	datasets["""train"""]["""label"""]			)
							snake_case_          =						[]
							# Iterate over them
							for i, (train_idxs, valid_idxs) in enumerate(_SCREAMING_SNAKE_CASE			):
														snake_case_    ,  snake_case_    ,  snake_case_          =						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)
														snake_case_          =						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).
														snake_case_          =						model.to(accelerator.device			)

														# Instantiate optimizer
														snake_case_          =						AdamW(params=model.parameters()			,	lr=_SCREAMING_SNAKE_CASE			)

														# Instantiate scheduler
														snake_case_          =						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.
														snake_case_    ,  snake_case_    ,  snake_case_    ,  snake_case_    ,  snake_case_          =						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			)
																												snake_case_          =						model(**_SCREAMING_SNAKE_CASE			)
																												snake_case_          =						outputs.loss
																												snake_case_          =						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():
																																			snake_case_          =						model(**_SCREAMING_SNAKE_CASE			)
																												snake_case_          =						outputs.logits.argmax(dim=-1			)
																												snake_case_    ,  snake_case_          =						accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
																												metric.add_batch(
																												    predictions=_SCREAMING_SNAKE_CASE			,	references=_SCREAMING_SNAKE_CASE			,	)

																					snake_case_          =						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
														snake_case_          =						[]
														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():
																												snake_case_          =						model(**_SCREAMING_SNAKE_CASE			)
																					snake_case_          =						outputs.logits
																					snake_case_    ,  snake_case_          =						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:
							snake_case_          =						torch.cat(_SCREAMING_SNAKE_CASE			,	dim=0			)
							snake_case_          =						torch.stack(_SCREAMING_SNAKE_CASE			,	dim=0			).sum(dim=0			).div(int(args.num_folds			)			).argmax(dim=-1			)
							snake_case_          =						metric.compute(predictions=_SCREAMING_SNAKE_CASE			,	references=_SCREAMING_SNAKE_CASE			)
							accelerator.print("""Average test metrics from all folds:"""			,	_SCREAMING_SNAKE_CASE			)







def   _a			( )      ->			str:
							snake_case_          =						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"""			)
							snake_case_          =						parser.parse_args()
							snake_case_          =						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				main()







"""simple docstring"""
from __future__ import annotations

from collections.abc import Iterator





class        __A       :





							'''simple docstring'''

							def __init__(			self							: Any						,    UpperCAmelCase_							: int    )   ->None:



														"""simple docstring"""



														snake_case_          =						value
														snake_case_          =						None
														snake_case_          =						None





class        __A       :





							'''simple docstring'''

							def __init__(			self							: List[Any]						,    UpperCAmelCase_							: Node    )   ->None:



														"""simple docstring"""



														snake_case_          =						tree



							def 						lowerCAmelCase    (			self							: List[str]						,    UpperCAmelCase_							: Node | None    )   ->int:



														"""simple docstring"""



														if node is None:
																					return 0
														return node.value + (
														    self.depth_first_search(node.left    ) + self.depth_first_search(node.right    )
														)



							def __iter__(			self							: str    )   ->Iterator[int]:



														"""simple docstring"""



														yield self.depth_first_search(self.tree    )


if __name__ == "__main__":
				import doctest

				doctest.testmod()



