



"""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
# and perform gradient accumulation
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCamelCase     =							16
UpperCamelCase     =							32

def       _lowerCamelCase				(							UpperCAmelCase_ :       Accelerator,	UpperCAmelCase_ :       int = 16     )	-> List[str]:






	"""simple docstring"""





	A__											=					AutoTokenizer.from_pretrained("bert-base-cased"     )
	A__											=					load_dataset("glue",	"mrpc"     )

	def tokenize_function(UpperCAmelCase_ :       List[Any]     ):
		# max_length=None => use the model max length (it's actually the default)
		A__											=					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():
		A__											=					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
	A__											=					tokenized_datasets.rename_column("label",	"labels"     )

	def collate_fn(UpperCAmelCase_ :       str     ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		A__											=					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":
			A__											=					16
		elif accelerator.mixed_precision != "no":
			A__											=					8
		else:
			A__											=					None

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

	# Instantiate dataloaders.
	A__											=					DataLoader(
	    tokenized_datasets["train"],	shuffle=UpperCAmelCase_,	collate_fn=UpperCAmelCase_,	batch_size=UpperCAmelCase_     )
	A__											=					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

			UpperCamelCase     =							mocked_dataloaders  # noqa: F811

def       _lowerCamelCase				(							UpperCAmelCase_ :       List[Any],	UpperCAmelCase_ :       Tuple     )	-> Dict:






	"""simple docstring"""





	if os.environ.get("TESTING_MOCKED_DATALOADERS",	UpperCAmelCase_     ) == "1":
		A__											=					2
	# New Code #
	A__											=					int(args.gradient_accumulation_steps     )
	# Initialize accelerator
	A__											=					Accelerator(
	    cpu=args.cpu,	mixed_precision=args.mixed_precision,	gradient_accumulation_steps=UpperCAmelCase_     )
	if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
		raise NotImplementedError(
		    "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"     )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A__											=					config["lr"]
	A__											=					int(config["num_epochs"]     )
	A__											=					int(config["seed"]     )
	A__											=					int(config["batch_size"]     )

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

	set_seed(UpperCAmelCase_     )
	A__							,							A__											=					get_dataloaders(UpperCAmelCase_,	UpperCAmelCase_     )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A__											=					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).
	A__											=					model.to(accelerator.device     )

	# Instantiate optimizer
	A__											=					AdamW(params=model.parameters(),	lr=UpperCAmelCase_     )

	# Instantiate scheduler
	A__											=					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.
	A__							,							A__							,							A__							,							A__							,							A__											=					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     )
			# New code #
			# We use the new `accumulate` context manager to perform gradient accumulation
			# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
			with accelerator.accumulate(UpperCAmelCase_     ):
				A__											=					model(**UpperCAmelCase_     )
				A__											=					output.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():
				A__											=					model(**UpperCAmelCase_     )
			A__											=					outputs.logits.argmax(dim=-1     )
			A__							,							A__											=					accelerator.gather_for_metrics((predictions, batch["labels"])     )
			metric.add_batch(
			    predictions=UpperCAmelCase_,	references=UpperCAmelCase_,	)

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

def       _lowerCamelCase				(							)	-> int:






	"""simple docstring"""





	A__											=					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.",	)
	# New Code #
	parser.add_argument(
	    "--gradient_accumulation_steps",	type=UpperCAmelCase_,	default=1,	help="The number of minibatches to be ran before gradients are accumulated.",	)
	parser.add_argument("--cpu",	action="store_true",	help="If passed, will train on the CPU."     )
	A__											=					parser.parse_args()
	A__											=					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(UpperCAmelCase_,	UpperCAmelCase_     )


if __name__ == "__main__":
			main()









"""simple docstring"""







def       _lowerCamelCase				(							UpperCAmelCase_ :       Optional[Any]     )	-> Dict:






	"""simple docstring"""





	A__											=					[]
	A__											=					set({"(", "[", "{"}     )
	A__											=					set({")", "]", "}"}     )
	A__											=					{"{": "}", "[": "]", "(": ")"}

	for i in range(len(UpperCAmelCase_     )     ):
		if s[i] in open_brackets:
			stack.append(s[i]     )

		elif s[i] in closed_brackets and (
		    len(UpperCAmelCase_     ) == 0 or (len(UpperCAmelCase_     ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(UpperCAmelCase_     ) == 0

def       _lowerCamelCase				(							)	-> str:






	"""simple docstring"""





	A__											=					input("Enter sequence of brackets: "     )
	if is_balanced(UpperCAmelCase_     ):
		print(UpperCAmelCase_,	"is balanced"     )
	else:
		print(UpperCAmelCase_,	"is not balanced"     )


if __name__ == "__main__":
			main()




