





"""simple docstring"""





import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCAmelCase__     			=			16
lowerCAmelCase__     			=			32


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 16  ,	_SCREAMING_SNAKE_CASE = "bert-base-cased"    ):




		"""simple docstring"""


		UpperCamelCase   =	AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	load_dataset("glue"  ,	"mrpc"    )

		def tokenize_function(_SCREAMING_SNAKE_CASE    ):
				# max_length=None => use the model max length (it's actually the default)
				UpperCamelCase   =	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
		UpperCamelCase   =	datasets.map(
		    _SCREAMING_SNAKE_CASE  ,	batched=_SCREAMING_SNAKE_CASE  ,	remove_columns=["idx", "sentence1", "sentence2"]  ,	load_from_cache_file=_SCREAMING_SNAKE_CASE    )

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		UpperCamelCase   =	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.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(_SCREAMING_SNAKE_CASE  ,	padding="max_length"  ,	max_length=128  ,	return_tensors="pt"    )
				return tokenizer.pad(_SCREAMING_SNAKE_CASE  ,	padding="longest"  ,	return_tensors="pt"    )

		# Instantiate dataloaders.
		UpperCamelCase   =	DataLoader(
		    tokenized_datasets["train"]  ,	shuffle=_SCREAMING_SNAKE_CASE  ,	collate_fn=_SCREAMING_SNAKE_CASE  ,	batch_size=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	DataLoader(
		    tokenized_datasets["validation"]  ,	shuffle=_SCREAMING_SNAKE_CASE  ,	collate_fn=_SCREAMING_SNAKE_CASE  ,	batch_size=_SCREAMING_SNAKE_CASE    )

		return train_dataloader, eval_dataloader


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		model.eval()
		UpperCamelCase   =	0
		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():
						UpperCamelCase   =	model(**_SCREAMING_SNAKE_CASE    )
				UpperCamelCase   =	outputs.logits.argmax(dim=-1    )
				# It is slightly faster to call this once, than multiple times
				UpperCamelCase   ,				UpperCamelCase   =	accelerator.gather(
				    (predictions, batch["labels"])    )  # If we are in a multiprocess environment, the last batch has duplicates
				if accelerator.use_distributed:
						if step == len(_SCREAMING_SNAKE_CASE    ) - 1:
								UpperCamelCase   =	predictions[: len(eval_dataloader.dataset    ) - samples_seen]
								UpperCamelCase   =	references[: len(eval_dataloader.dataset    ) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=_SCREAMING_SNAKE_CASE  ,	references=_SCREAMING_SNAKE_CASE  ,	)

		UpperCamelCase   =	metric.compute()
		return eval_metric["accuracy"]


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		UpperCamelCase   =	config["lr"]
		UpperCamelCase   =	int(config["num_epochs"]    )
		UpperCamelCase   =	int(config["seed"]    )
		UpperCamelCase   =	int(config["batch_size"]    )
		UpperCamelCase   =	args.model_name_or_path

		set_seed(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   ,				UpperCamelCase   =	get_dataloaders(_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)
		UpperCamelCase   =	AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE  ,	return_dict=_SCREAMING_SNAKE_CASE    )

		# Instantiate optimizer
		UpperCamelCase   =	(
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		UpperCamelCase   =	optimizer_cls(params=model.parameters()  ,	lr=_SCREAMING_SNAKE_CASE    )

		if accelerator.state.deepspeed_plugin is not None:
				UpperCamelCase   =	accelerator.state.deepspeed_plugin.deepspeed_config[
				    "gradient_accumulation_steps"
				]
		else:
				UpperCamelCase   =	1
		UpperCamelCase   =	(len(_SCREAMING_SNAKE_CASE    ) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				UpperCamelCase   =	get_linear_schedule_with_warmup(
				    optimizer=_SCREAMING_SNAKE_CASE  ,	num_warmup_steps=0  ,	num_training_steps=_SCREAMING_SNAKE_CASE  ,	)
		else:
				UpperCamelCase   =	DummyScheduler(_SCREAMING_SNAKE_CASE  ,	total_num_steps=_SCREAMING_SNAKE_CASE  ,	warmup_num_steps=0    )

		# 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   =	accelerator.prepare(
		    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

		# We need to keep track of how many total steps we have iterated over
		UpperCamelCase   =	0
		# We also need to keep track of the stating epoch so files are named properly
		UpperCamelCase   =	0
		UpperCamelCase   =	evaluate.load("glue"  ,	"mrpc"    )
		UpperCamelCase   =	num_epochs

		if args.partial_train_epoch is not None:
				UpperCamelCase   =	args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint    )
				UpperCamelCase   =	args.resume_from_checkpoint.split("epoch_"    )[1]
				UpperCamelCase   =	""
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				UpperCamelCase   =	int(_SCREAMING_SNAKE_CASE    ) + 1
				UpperCamelCase   =	evaluation_loop(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
				accelerator.print("resumed checkpoint performance:"  ,	_SCREAMING_SNAKE_CASE    )
				accelerator.print("resumed checkpoint's scheduler's lr:"  ,	lr_scheduler.get_lr()[0]    )
				accelerator.print("resumed optimizers's lr:"  ,	optimizer.param_groups[0]["lr"]    )
				with open(os.path.join(args.output_dir  ,	F"state_{starting_epoch-1}.json"    )  ,	"r"    ) as f:
						UpperCamelCase   =	json.load(_SCREAMING_SNAKE_CASE    )
						assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["lr"] == lr_scheduler.get_lr()[0]
						), "Scheduler learning rate mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
						), "Optimizer learning rate mismatch, loading from checkpoint failed"
						assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
						return

    # Now we train the model
		UpperCamelCase   =	{}
		for epoch in range(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):
				model.train()
				for step, batch in enumerate(_SCREAMING_SNAKE_CASE    ):
						UpperCamelCase   =	model(**_SCREAMING_SNAKE_CASE    )
						UpperCamelCase   =	outputs.loss
						UpperCamelCase   =	loss / gradient_accumulation_steps
						accelerator.backward(_SCREAMING_SNAKE_CASE    )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1
				UpperCamelCase   =	F"epoch_{epoch}"
				UpperCamelCase   =	os.path.join(args.output_dir  ,	_SCREAMING_SNAKE_CASE    )
				accelerator.save_state(_SCREAMING_SNAKE_CASE    )
				UpperCamelCase   =	evaluation_loop(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
				UpperCamelCase   =	accuracy
				UpperCamelCase   =	lr_scheduler.get_lr()[0]
				UpperCamelCase   =	optimizer.param_groups[0]["lr"]
				UpperCamelCase   =	epoch
				UpperCamelCase   =	overall_step
				accelerator.print(F"epoch {epoch}:"  ,	_SCREAMING_SNAKE_CASE    )

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						with open(os.path.join(args.output_dir  ,	F"state_{epoch}.json"    )  ,	"w"    ) as f:
								json.dump(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )


def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."    )
		parser.add_argument(
		    "--model_name_or_path"  ,	type=_SCREAMING_SNAKE_CASE  ,	default="bert-base-cased"  ,	help="Path to pretrained model or model identifier from huggingface.co/models."  ,	required=_SCREAMING_SNAKE_CASE  ,	)
		parser.add_argument(
		    "--output_dir"  ,	type=_SCREAMING_SNAKE_CASE  ,	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=_SCREAMING_SNAKE_CASE  ,	default=_SCREAMING_SNAKE_CASE  ,	help="If the training should continue from a checkpoint folder."  ,	)
		parser.add_argument(
		    "--partial_train_epoch"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=_SCREAMING_SNAKE_CASE  ,	help="If passed, the training will stop after this number of epochs."  ,	)
		parser.add_argument(
		    "--num_epochs"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=2  ,	help="Number of train epochs."  ,	)
		UpperCamelCase   =	parser.parse_args()
		UpperCamelCase   =	{"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

		training_function(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						main()













"""simple docstring"""





def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		return number & 1 == 0


if __name__ == "__main__":
						import doctest

						doctest.testmod()






