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# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os

import torch
from torch.optim import AdamW
from torch.utils.data import DataLoader

import evaluate
from accelerate import Accelerator, DistributedType
from datasets import load_dataset
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


MAX_GPU_BATCH_SIZE = 16
EVAL_BATCH_SIZE = 32


def training_function(config, args):
    # Initialize accelerator
    if args.with_tracking:
        accelerator = Accelerator(
            cpu=args.cpu, mixed_precision=args.mixed_precision, log_with="all", logging_dir=args.logging_dir
        )
    else:
        accelerator = Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision)

    if hasattr(args.checkpointing_steps, "isdigit"):
        if args.checkpointing_steps == "epoch":
            checkpointing_steps = args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            checkpointing_steps = int(args.checkpointing_steps)
        else:
            raise ValueError(
                f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."
            )
    else:
        checkpointing_steps = None
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    lr = config["lr"]
    num_epochs = int(config["num_epochs"])
    seed = int(config["seed"])
    batch_size = int(config["batch_size"])

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        run = os.path.split(__file__)[-1].split(".")[0]
        accelerator.init_trackers(run, config)

    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
    datasets = load_dataset("glue", "mrpc")
    metric = evaluate.load("glue", "mrpc")

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

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

    def collate_fn(examples):
        # 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(examples, padding="max_length", max_length=128, return_tensors="pt")
        return tokenizer.pad(examples, padding="longest", return_tensors="pt")

    # Instantiate dataloaders.
    train_dataloader = DataLoader(
        tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size
    )
    eval_dataloader = DataLoader(
        tokenized_datasets["validation"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE
    )

    set_seed(seed)

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

    # 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).
    model = model.to(accelerator.device)

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

    # Instantiate scheduler
    lr_scheduler = get_linear_schedule_with_warmup(
        optimizer=optimizer,
        num_warmup_steps=100,
        num_training_steps=(len(train_dataloader) * 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.
    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
        model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
    )

    # We need to keep track of how many total steps we have iterated over
    overall_step = 0
    # We also need to keep track of the stating epoch so files are named properly
    starting_epoch = 0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}")
            accelerator.load_state(args.resume_from_checkpoint)
            path = os.path.basename(args.resume_from_checkpoint)
        else:
            # Get the most recent checkpoint
            dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
            dirs.sort(key=os.path.getctime)
            path = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        training_difference = os.path.splitext(path)[0]

        if "epoch" in training_difference:
            starting_epoch = int(training_difference.replace("epoch_", "")) + 1
            resume_step = None
        else:
            resume_step = int(training_difference.replace("step_", ""))
            starting_epoch = resume_step // len(train_dataloader)
            resume_step -= starting_epoch * len(train_dataloader)

    # Now we train the model
    for epoch in range(starting_epoch, num_epochs):
        model.train()
        if args.with_tracking:
            total_loss = 0
        for step, batch in enumerate(train_dataloader):
            # We need to skip steps until we reach the resumed step
            if args.resume_from_checkpoint and epoch == starting_epoch:
                if resume_step is not None and step < resume_step:
                    overall_step += 1
                    continue
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device)
            outputs = model(**batch)
            loss = outputs.loss
            loss = loss / gradient_accumulation_steps
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(loss)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

            if isinstance(checkpointing_steps, int):
                output_dir = f"step_{overall_step}"
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        output_dir = os.path.join(args.output_dir, output_dir)
                    accelerator.save_state(output_dir)

        model.eval()
        for step, batch in enumerate(eval_dataloader):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device)
            with torch.no_grad():
                outputs = model(**batch)
            predictions = outputs.logits.argmax(dim=-1)
            predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
            metric.add_batch(
                predictions=predictions,
                references=references,
            )

        eval_metric = metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"epoch {epoch}:", eval_metric)
        if args.with_tracking:
            accelerator.log(
                {
                    "accuracy": eval_metric["accuracy"],
                    "f1": eval_metric["f1"],
                    "train_loss": total_loss.item() / len(train_dataloader),
                    "epoch": epoch,
                },
                step=epoch,
            )

        if checkpointing_steps == "epoch":
            output_dir = f"epoch_{epoch}"
            if args.output_dir is not None:
                output_dir = os.path.join(args.output_dir, output_dir)
            accelerator.save_state(output_dir)

    if args.with_tracking:
        accelerator.end_training()


def main():
    parser = argparse.ArgumentParser(description="Simple example of training script.")
    parser.add_argument(
        "--mixed_precision",
        type=str,
        default="no",
        choices=["no", "fp16", "bf16"],
        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.")
    parser.add_argument(
        "--checkpointing_steps",
        type=str,
        default=None,
        help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help="If the training should continue from a checkpoint folder.",
    )
    parser.add_argument(
        "--with_tracking",
        action="store_true",
        help="Whether to load in all available experiment trackers from the environment and use them for logging.",
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default=".",
        help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",
    )
    parser.add_argument(
        "--logging_dir",
        type=str,
        default="logs",
        help="Location on where to store experiment tracking logs`",
    )
    args = parser.parse_args()
    config = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(config, args)


if __name__ == "__main__":
    main()

	# coding=utf-8
	# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	import argparse
	import os

	import torch
	from torch.optim import AdamW
	from torch.utils.data import DataLoader

	import evaluate
	from accelerate import Accelerator, DistributedType
	from datasets import load_dataset
	from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed


	########################################################################
	# This is a fully working simple example to use Accelerate
	#
	# 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)
	#
	# This example also demonstrates the checkpointing and sharding capabilities
	#
	# 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
	#
	########################################################################


	MAX_GPU_BATCH_SIZE = 16
	EVAL_BATCH_SIZE = 32


	def training_function(config, args):
	# Initialize accelerator
	if args.with_tracking:
	accelerator = Accelerator(
	cpu=args.cpu, mixed_precision=args.mixed_precision, log_with="all", logging_dir=args.logging_dir
	)
	else:
	accelerator = Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision)

	if hasattr(args.checkpointing_steps, "isdigit"):
	if args.checkpointing_steps == "epoch":
	checkpointing_steps = args.checkpointing_steps
	elif args.checkpointing_steps.isdigit():
	checkpointing_steps = int(args.checkpointing_steps)
	else:
	raise ValueError(
	f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."
	)
	else:
	checkpointing_steps = None
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	lr = config["lr"]
	num_epochs = int(config["num_epochs"])
	seed = int(config["seed"])
	batch_size = int(config["batch_size"])

	# We need to initialize the trackers we use, and also store our configuration
	if args.with_tracking:
	run = os.path.split(__file__)[-1].split(".")[0]
	accelerator.init_trackers(run, config)

	tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
	datasets = load_dataset("glue", "mrpc")
	metric = evaluate.load("glue", "mrpc")

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

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

	def collate_fn(examples):
	# 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(examples, padding="max_length", max_length=128, return_tensors="pt")
	return tokenizer.pad(examples, padding="longest", return_tensors="pt")

	# Instantiate dataloaders.
	train_dataloader = DataLoader(
	tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size
	)
	eval_dataloader = DataLoader(
	tokenized_datasets["validation"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE
	)

	set_seed(seed)

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

	# 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).
	model = model.to(accelerator.device)

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

	# Instantiate scheduler
	lr_scheduler = get_linear_schedule_with_warmup(
	optimizer=optimizer,
	num_warmup_steps=100,
	num_training_steps=(len(train_dataloader) * 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.
	model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
	model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
	)

	# We need to keep track of how many total steps we have iterated over
	overall_step = 0
	# We also need to keep track of the stating epoch so files are named properly
	starting_epoch = 0

	# Potentially load in the weights and states from a previous save
	if args.resume_from_checkpoint:
	if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
	accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}")
	accelerator.load_state(args.resume_from_checkpoint)
	path = os.path.basename(args.resume_from_checkpoint)
	else:
	# Get the most recent checkpoint
	dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
	dirs.sort(key=os.path.getctime)
	path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
	# Extract `epoch_{i}` or `step_{i}`
	training_difference = os.path.splitext(path)[0]

	if "epoch" in training_difference:
	starting_epoch = int(training_difference.replace("epoch_", "")) + 1
	resume_step = None
	else:
	resume_step = int(training_difference.replace("step_", ""))
	starting_epoch = resume_step // len(train_dataloader)
	resume_step -= starting_epoch * len(train_dataloader)

	# Now we train the model
	for epoch in range(starting_epoch, num_epochs):
	model.train()
	if args.with_tracking:
	total_loss = 0
	for step, batch in enumerate(train_dataloader):
	# We need to skip steps until we reach the resumed step
	if args.resume_from_checkpoint and epoch == starting_epoch:
	if resume_step is not None and step < resume_step:
	overall_step += 1
	continue
	# We could avoid this line since we set the accelerator with `device_placement=True`.
	batch.to(accelerator.device)
	outputs = model(**batch)
	loss = outputs.loss
	loss = loss / gradient_accumulation_steps
	# We keep track of the loss at each epoch
	if args.with_tracking:
	total_loss += loss.detach().float()
	accelerator.backward(loss)
	if step % gradient_accumulation_steps == 0:
	optimizer.step()
	lr_scheduler.step()
	optimizer.zero_grad()

	overall_step += 1

	if isinstance(checkpointing_steps, int):
	output_dir = f"step_{overall_step}"
	if overall_step % checkpointing_steps == 0:
	if args.output_dir is not None:
	output_dir = os.path.join(args.output_dir, output_dir)
	accelerator.save_state(output_dir)

	model.eval()
	for step, batch in enumerate(eval_dataloader):
	# We could avoid this line since we set the accelerator with `device_placement=True`.
	batch.to(accelerator.device)
	with torch.no_grad():
	outputs = model(**batch)
	predictions = outputs.logits.argmax(dim=-1)
	predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
	metric.add_batch(
	predictions=predictions,
	references=references,
	)

	eval_metric = metric.compute()
	# Use accelerator.print to print only on the main process.
	accelerator.print(f"epoch {epoch}:", eval_metric)
	if args.with_tracking:
	accelerator.log(
	{
	"accuracy": eval_metric["accuracy"],
	"f1": eval_metric["f1"],
	"train_loss": total_loss.item() / len(train_dataloader),
	"epoch": epoch,
	},
	step=epoch,
	)

	if checkpointing_steps == "epoch":
	output_dir = f"epoch_{epoch}"
	if args.output_dir is not None:
	output_dir = os.path.join(args.output_dir, output_dir)
	accelerator.save_state(output_dir)

	if args.with_tracking:
	accelerator.end_training()


	def main():
	parser = argparse.ArgumentParser(description="Simple example of training script.")
	parser.add_argument(
	"--mixed_precision",
	type=str,
	default="no",
	choices=["no", "fp16", "bf16"],
	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.")
	parser.add_argument(
	"--checkpointing_steps",
	type=str,
	default=None,
	help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
	)
	parser.add_argument(
	"--resume_from_checkpoint",
	type=str,
	default=None,
	help="If the training should continue from a checkpoint folder.",
	)
	parser.add_argument(
	"--with_tracking",
	action="store_true",
	help="Whether to load in all available experiment trackers from the environment and use them for logging.",
	)
	parser.add_argument(
	"--output_dir",
	type=str,
	default=".",
	help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",
	)
	parser.add_argument(
	"--logging_dir",
	type=str,
	default="logs",
	help="Location on where to store experiment tracking logs`",
	)
	args = parser.parse_args()
	config = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(config, args)


	if __name__ == "__main__":
	main()