


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


A  :     int    					=    1_6
A  :     Dict    					=    3_2


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase = 16					,				_UpperCamelCase = "bert-base-cased"   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    AutoTokenizer.from_pretrained(__lowerCAmelCase   )
			__lowerCAmelCase       					=    load_dataset("glue"					,				"mrpc"   )

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

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			__lowerCAmelCase       					=    tokenized_datasets.rename_column("label"					,				"labels"   )

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

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

			return train_dataloader, eval_dataloader


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




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

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


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    Accelerator()

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

			set_seed(__lowerCAmelCase   )
			__lowerCAmelCase       					=    get_dataloaders(__lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase   )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__lowerCAmelCase       					=    AutoModelForSequenceClassification.from_pretrained(__lowerCAmelCase					,				return_dict=__lowerCAmelCase   )

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

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

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

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

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

			if args.resume_from_checkpoint:
						accelerator.load_state(args.resume_from_checkpoint   )
						__lowerCAmelCase       					=    args.resume_from_checkpoint.split("epoch_"   )[1]
						__lowerCAmelCase       					=    ""
						for char in epoch_string:
									if char.isdigit():
												state_epoch_num += char
									else:
												break
						__lowerCAmelCase       					=    int(__lowerCAmelCase   ) + 1
						__lowerCAmelCase       					=    evaluation_loop(__lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase   )
						accelerator.print("resumed checkpoint performance:"					,				__lowerCAmelCase   )
						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:
									__lowerCAmelCase       					=    json.load(__lowerCAmelCase   )
									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
			__lowerCAmelCase       					=    {}
			for epoch in range(__lowerCAmelCase					,				__lowerCAmelCase   ):
						model.train()
						for step, batch in enumerate(__lowerCAmelCase   ):
									__lowerCAmelCase       					=    model(**__lowerCAmelCase   )
									__lowerCAmelCase       					=    outputs.loss
									__lowerCAmelCase       					=    loss / gradient_accumulation_steps
									accelerator.backward(__lowerCAmelCase   )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

						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(__lowerCAmelCase					,				__lowerCAmelCase   )


def _lowerCamelCase			(	):

			'''simple docstring'''




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

			training_function(__lowerCAmelCase					,				__lowerCAmelCase   )


if __name__ == "__main__":
	main()








"""simple docstring"""


import gc
import threading
import time

import psutil
import torch
class 						_UpperCamelCase						:
							'''simple docstring'''







							def __init__(  self			):
										__lowerCAmelCase       					=    psutil.Process()
										__lowerCAmelCase       					=    False

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    -1

										while True:
													__lowerCAmelCase       					=    max(self.process.memory_info().rss					,     self.cpu_memory_peak			)

													# can't sleep or will not catch the peak right (this comment is here on purpose)
													if not self.peak_monitoring:
																break

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    True
										__lowerCAmelCase       					=    threading.Thread(target=self.peak_monitor			)
										__lowerCAmelCase       					=    True
										self.thread.start()



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    False
										self.thread.join()
										return self.cpu_memory_peak
A  :     Any    					=    PeakCPUMemory()


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    {"time": time.time()}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			__lowerCAmelCase       					=    psutil.Process().memory_info().rss
			cpu_peak_tracker.start()

			# GPU mem
			for i in range(torch.cuda.device_count()   ):
						__lowerCAmelCase       					=    torch.cuda.memory_allocated(_UpperCamelCase   )
			torch.cuda.reset_peak_memory_stats()

			return measures


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    {"time": time.time() - start_measures["time"]}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			__lowerCAmelCase       					=    (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
			__lowerCAmelCase       					=    (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

			# GPU mem
			for i in range(torch.cuda.device_count()   ):
						__lowerCAmelCase       					=    (torch.cuda.memory_allocated(_UpperCamelCase   ) - start_measures[str(_UpperCamelCase   )]) / 2**20
						__lowerCAmelCase       					=    (torch.cuda.max_memory_allocated(_UpperCamelCase   ) - start_measures[str(_UpperCamelCase   )]) / 2**20

			return measures


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			print(f"{description}:"   )
			print(f"- Time: {measures['time']:.2f}s"   )
			for i in range(torch.cuda.device_count()   ):
						print(f"- GPU {i} allocated: {measures[str(_UpperCamelCase   )]:.2f}MiB"   )
						__lowerCAmelCase       					=    measures[f"{i}-peak"]
						print(f"- GPU {i} peak: {peak:.2f}MiB"   )
			print(f"- CPU RAM allocated: {measures['cpu']:.2f}MiB"   )
			print(f"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"   )




