


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


_lowerCamelCase     =  16
_lowerCamelCase     =  32


def __lowercase     (      lowerCamelCase_  :      Accelerator						,					lowerCamelCase_  :      int = 16  ):
				SCREAMING_SNAKE_CASE__     							=				AutoTokenizer.from_pretrained("bert-base-cased"  )
				SCREAMING_SNAKE_CASE__     							=				load_dataset("glue"						,					"mrpc"  )

				def tokenize_function(lowerCamelCase_  :      int  ):
								# max_length=None => use the model max length (it's actually the default)
								SCREAMING_SNAKE_CASE__     							=				tokenizer(examples["sentence1"]						,					examples["sentence2"]						,					truncation=lowerCamelCase_						,					max_length=lowerCamelCase_  )
								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__     							=				datasets.map(
								    lowerCamelCase_						,					batched=lowerCamelCase_						,					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__     							=				tokenized_datasets.rename_column("label"						,					"labels"  )

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

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

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

				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

		_lowerCamelCase     =  mocked_dataloaders  # noqa: F811


def __lowercase     (      lowerCamelCase_  :      str						,					lowerCamelCase_  :      Tuple  ):
				# For testing only
				if os.environ.get("TESTING_MOCKED_DATALOADERS"						,					lowerCamelCase_  ) == "1":
								SCREAMING_SNAKE_CASE__     							=				2
				# New Code #
				SCREAMING_SNAKE_CASE__     							=				int(args.gradient_accumulation_steps  )
				SCREAMING_SNAKE_CASE__     							=				int(args.local_sgd_steps  )
				# Initialize accelerator
				SCREAMING_SNAKE_CASE__     							=				Accelerator(
				    cpu=args.cpu						,					mixed_precision=args.mixed_precision						,					gradient_accumulation_steps=lowerCamelCase_  )
				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
				SCREAMING_SNAKE_CASE__     							=				config['lr']
				SCREAMING_SNAKE_CASE__     							=				int(config["num_epochs"]  )
				SCREAMING_SNAKE_CASE__     							=				int(config["seed"]  )
				SCREAMING_SNAKE_CASE__     							=				int(config["batch_size"]  )

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

				set_seed(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				get_dataloaders(lowerCamelCase_						,					lowerCamelCase_  )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				SCREAMING_SNAKE_CASE__     							=				AutoModelForSequenceClassification.from_pretrained("bert-base-cased"						,					return_dict=lowerCamelCase_  )

				# 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__     							=				model.to(accelerator.device  )

				# Instantiate optimizer
				SCREAMING_SNAKE_CASE__     							=				AdamW(params=model.parameters()						,					lr=lowerCamelCase_  )

				# Instantiate scheduler
				SCREAMING_SNAKE_CASE__     							=				get_linear_schedule_with_warmup(
				    optimizer=lowerCamelCase_						,					num_warmup_steps=100						,					num_training_steps=(len(lowerCamelCase_  ) * 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.
				SCREAMING_SNAKE_CASE__     							=				accelerator.prepare(
				    lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_  )

				# Now we train the model
				for epoch in range(lowerCamelCase_  ):
								model.train()
								with LocalSGD(
								    accelerator=lowerCamelCase_						,					model=lowerCamelCase_						,					local_sgd_steps=lowerCamelCase_						,					enabled=local_sgd_steps is not None  ) as local_sgd:
												for step, batch in enumerate(lowerCamelCase_  ):
																# 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(lowerCamelCase_  ):
																				SCREAMING_SNAKE_CASE__     							=				model(**lowerCamelCase_  )
																				SCREAMING_SNAKE_CASE__     							=				output.loss
																				accelerator.backward(lowerCamelCase_  )
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

								model.eval()
								for step, batch in enumerate(lowerCamelCase_  ):
												# 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__     							=				model(**lowerCamelCase_  )
												SCREAMING_SNAKE_CASE__     							=				outputs.logits.argmax(dim=-1  )
												SCREAMING_SNAKE_CASE__     							=				accelerator.gather_for_metrics((predictions, batch["labels"])  )
												metric.add_batch(
												    predictions=lowerCamelCase_						,					references=lowerCamelCase_						,					)

								SCREAMING_SNAKE_CASE__     							=				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'''epoch {epoch}:'''						,					lowerCamelCase_  )


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				argparse.ArgumentParser(description="Simple example of training script."  )
				parser.add_argument(
				    "--mixed_precision"						,					type=lowerCamelCase_						,					default=lowerCamelCase_						,					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=lowerCamelCase_						,					default=1						,					help="The number of minibatches to be ran before gradients are accumulated."						,					)
				parser.add_argument(
				    "--local_sgd_steps"						,					type=lowerCamelCase_						,					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."  )
				SCREAMING_SNAKE_CASE__     							=				parser.parse_args()
				SCREAMING_SNAKE_CASE__     							=				{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
				training_function(lowerCamelCase_						,					lowerCamelCase_  )


if __name__ == "__main__":
		main()




"""simple docstring"""






import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase     =  500000

_lowerCamelCase     ,     _lowerCamelCase     =  os.path.split(__file__)
_lowerCamelCase     =  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def __lowercase     (      lowerCamelCase_  :      datasets.Dataset						,					**lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				dataset.map(**lowerCamelCase_  )


@get_duration
def __lowercase     (      lowerCamelCase_  :      datasets.Dataset						,					**lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				dataset.filter(**lowerCamelCase_  )


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				{"num examples": SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								SCREAMING_SNAKE_CASE__     							=				datasets.Features({"text": datasets.Value("string"  ), "numbers": datasets.Value("float32"  )}  )
								SCREAMING_SNAKE_CASE__     							=				generate_example_dataset(
								    os.path.join(lowerCamelCase_						,					"dataset.arrow"  )						,					lowerCamelCase_						,					num_examples=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				transformers.AutoTokenizer.from_pretrained("bert-base-cased"						,					use_fast=lowerCamelCase_  )

								def tokenize(lowerCamelCase_  :      Union[str, Any]  ):
												return tokenizer(examples["text"]  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="numpy"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="pandas"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="torch"						,					columns="numbers"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="tensorflow"						,					columns="numbers"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lowerCamelCase_						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				filter(lowerCamelCase_  )

								# Activate later when tokenizer support batched inputs
								# with dataset.formatted_as(type='numpy'):
								#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

				with open(lowerCamelCase_						,					"wb"  ) as f:
								f.write(json.dumps(lowerCamelCase_  ).encode("utf-8"  )  )


if __name__ == "__main__":  # useful to run the profiler
	benchmark_map_filter()
