


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							:					Optional[int]     						=     16
A							:					Dict     						=     32





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase = 16      ,	_lowerCAmelCase = "bert-base-cased" )						->	List[Any]:


						'''simple docstring'''






						__snake_case				=		AutoTokenizer.from_pretrained(_lowerCAmelCase )
						__snake_case				=		load_dataset("glue"      ,	"mrpc" )

						def tokenize_function(_lowerCAmelCase ):
												# max_length=None => use the model max length (it's actually the default)
												__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
						__snake_case				=		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
						__snake_case				=		tokenized_datasets.rename_column("label"      ,	"labels" )

						def collate_fn(_lowerCAmelCase ):
												# 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.
						__snake_case				=		DataLoader(
						    tokenized_datasets["train"]      ,	shuffle=_lowerCAmelCase      ,	collate_fn=_lowerCAmelCase      ,	batch_size=_lowerCAmelCase )
						__snake_case				=		DataLoader(
						    tokenized_datasets["validation"]      ,	shuffle=_lowerCAmelCase      ,	collate_fn=_lowerCAmelCase      ,	batch_size=_lowerCAmelCase )

						return train_dataloader, eval_dataloader





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	int:


						'''simple docstring'''






						__snake_case				=		Accelerator()

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

						set_seed(_lowerCAmelCase )
						__snake_case		,					__snake_case				=		get_dataloaders(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )

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

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

						if accelerator.state.deepspeed_plugin is not None:
												__snake_case				=		accelerator.state.deepspeed_plugin.deepspeed_config[
												    "gradient_accumulation_steps"
												]
						else:
												__snake_case				=		1
						__snake_case				=		(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
						):
												__snake_case				=		get_linear_schedule_with_warmup(
												    optimizer=_lowerCAmelCase      ,	num_warmup_steps=0      ,	num_training_steps=_lowerCAmelCase      ,	)
						else:
												__snake_case				=		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.
						__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case				=		accelerator.prepare(
						    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )

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

						# Now we train the model
						__snake_case				=		evaluate.load("glue"      ,	"mrpc" )
						__snake_case				=		0
						__snake_case				=		{}
						for epoch in range(_lowerCAmelCase      ,	_lowerCAmelCase ):
												model.train()
												for step, batch in enumerate(_lowerCAmelCase ):
																		__snake_case				=		model(**_lowerCAmelCase )
																		__snake_case				=		outputs.loss
																		__snake_case				=		loss / gradient_accumulation_steps
																		accelerator.backward(_lowerCAmelCase )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

												model.eval()
												__snake_case				=		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():
																								__snake_case				=		model(**_lowerCAmelCase )
																		__snake_case				=		outputs.logits.argmax(dim=-1 )
																		# It is slightly faster to call this once, than multiple times
																		__snake_case		,					__snake_case				=		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:
																														__snake_case				=		predictions[: len(eval_dataloader.dataset ) - samples_seen]
																														__snake_case				=		references[: len(eval_dataloader.dataset ) - samples_seen]
																								else:
																														samples_seen += references.shape[0]
																		metric.add_batch(
																		    predictions=_lowerCAmelCase      ,	references=_lowerCAmelCase      ,	)

												__snake_case				=		metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''      ,	_lowerCAmelCase )
												__snake_case				=		eval_metric["accuracy"]

												if best_performance < eval_metric["accuracy"]:
																		__snake_case				=		eval_metric["accuracy"]

						if args.performance_lower_bound is not None:
												assert (
												    args.performance_lower_bound <= best_performance
												), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
												with open(os.path.join(args.output_dir      ,	"all_results.json" )      ,	"w" ) as f:
																		json.dump(_lowerCAmelCase      ,	_lowerCAmelCase )





def 	_lowerCAmelCase       (    )						->	int:


						'''simple docstring'''






						__snake_case				=		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(
						    "--performance_lower_bound"      ,	type=_lowerCAmelCase      ,	default=_lowerCAmelCase      ,	help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."      ,	)
						parser.add_argument(
						    "--num_epochs"      ,	type=_lowerCAmelCase      ,	default=3      ,	help="Number of train epochs."      ,	)
						__snake_case				=		parser.parse_args()
						__snake_case				=		{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
						training_function(_lowerCAmelCase      ,	_lowerCAmelCase )


if __name__ == "__main__":
	main()









import os
from typing import Dict, List, Tuple, TypeVar, Union


A							:					List[Any]     						=     TypeVar('T')

A							:					Dict     						=     Union[List[T], Tuple[T, ...]]
A							:					Any     						=     Union[T, List[T], Dict[str, T]]
A							:					Optional[int]     						=     Union[str, bytes, os.PathLike]





