



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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


lowercase_						=       16
lowercase_						=       32



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_ = 16					):
			lowercase__   					=			AutoTokenizer.from_pretrained("bert-base-cased"					)
			lowercase__   					=			load_dataset("glue"  ,					"mrpc"					)

			def tokenize_function(SCREAMING_SNAKE_CASE_					):
						# max_length=None => use the model max length (it's actually the default)
						lowercase__   					=			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():
						lowercase__   					=			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
			lowercase__   					=			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.
						lowercase__   					=			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":
									lowercase__   					=			16
						elif accelerator.mixed_precision != "no":
									lowercase__   					=			8
						else:
									lowercase__   					=			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.
			lowercase__   					=			DataLoader(
			    tokenized_datasets["train"]  ,					shuffle=SCREAMING_SNAKE_CASE_  ,					collate_fn=SCREAMING_SNAKE_CASE_  ,					batch_size=SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			DataLoader(
			    tokenized_datasets["validation"]  ,					shuffle=SCREAMING_SNAKE_CASE_  ,					collate_fn=SCREAMING_SNAKE_CASE_  ,					batch_size=SCREAMING_SNAKE_CASE_					)

			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

							lowercase_						=       mocked_dataloaders  # noqa: F811



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			# For testing only
			if os.environ.get("TESTING_MOCKED_DATALOADERS"  ,					SCREAMING_SNAKE_CASE_					) == "1":
						lowercase__   					=			2
			# New Code #
			lowercase__   					=			int(args.gradient_accumulation_steps					)
			lowercase__   					=			int(args.local_sgd_steps					)
			# Initialize accelerator
			lowercase__   					=			Accelerator(
			    cpu=args.cpu  ,					mixed_precision=args.mixed_precision  ,					gradient_accumulation_steps=SCREAMING_SNAKE_CASE_					)
			if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
						raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"					)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowercase__   					=			config["lr"]
			lowercase__   					=			int(config["num_epochs"]					)
			lowercase__   					=			int(config["seed"]					)
			lowercase__   					=			int(config["batch_size"]					)

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

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

			# Instantiate optimizer
			lowercase__   					=			AdamW(params=model.parameters()  ,					lr=SCREAMING_SNAKE_CASE_					)

			# Instantiate scheduler
			lowercase__   					=			get_linear_schedule_with_warmup(
			    optimizer=SCREAMING_SNAKE_CASE_  ,					num_warmup_steps=100  ,					num_training_steps=(len(SCREAMING_SNAKE_CASE_					) * 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.
			lowercase__					,    lowercase__					,    lowercase__					,    lowercase__					,    lowercase__   					=			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()
						with LocalSGD(
						    accelerator=SCREAMING_SNAKE_CASE_  ,					model=SCREAMING_SNAKE_CASE_  ,					local_sgd_steps=SCREAMING_SNAKE_CASE_  ,					enabled=local_sgd_steps is not None					) as local_sgd:
									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					)
												# 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(SCREAMING_SNAKE_CASE_					):
															lowercase__   					=			model(**SCREAMING_SNAKE_CASE_					)
															lowercase__   					=			output.loss
															accelerator.backward(SCREAMING_SNAKE_CASE_					)
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()
															# LocalSGD-specific line
															local_sgd.step()

						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():
												lowercase__   					=			model(**SCREAMING_SNAKE_CASE_					)
									lowercase__   					=			outputs.logits.argmax(dim=-1					)
									lowercase__					,    lowercase__   					=			accelerator.gather_for_metrics((predictions, batch["labels"])					)
									metric.add_batch(
									    predictions=SCREAMING_SNAKE_CASE_  ,					references=SCREAMING_SNAKE_CASE_  ,					)

						lowercase__   					=			metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''  ,					SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       ):
			lowercase__   					=			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."  ,					)
			# New Code #
			parser.add_argument(
			    "--gradient_accumulation_steps"  ,					type=SCREAMING_SNAKE_CASE_  ,					default=1  ,					help="The number of minibatches to be ran before gradients are accumulated."  ,					)
			parser.add_argument(
			    "--local_sgd_steps"  ,					type=SCREAMING_SNAKE_CASE_  ,					default=8  ,					help="Number of local SGD steps or None to disable local SGD"					)
			parser.add_argument("--cpu"  ,					action="store_true"  ,					help="If passed, will train on the CPU."					)
			lowercase__   					=			parser.parse_args()
			lowercase__   					=			{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
							main()









from __future__ import annotations



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return len(set(SCREAMING_SNAKE_CASE_					)					) == len(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




