

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


lowerCAmelCase__								=		1_6
lowerCAmelCase__								=		3_2






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Accelerator							,       UpperCAmelCase_							:     int = 1_6							,       UpperCAmelCase_							:     str = "bert-base-cased"	)						->				str:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						AutoTokenizer.from_pretrained(UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[int]			=						load_dataset('glue'							,       'mrpc'	)

	def tokenize_function(UpperCAmelCase_							:     Any	):
		# max_length=None => use the model max length (it's actually the default)
		_UpperCamelCase   :		int			=						tokenizer(examples['sentence1']							,       examples['sentence2']							,       truncation=UpperCAmelCase_							,       max_length=UpperCAmelCase_	)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	_UpperCamelCase   :		Any			=						datasets.map(
	    UpperCAmelCase_							,       batched=UpperCAmelCase_							,       remove_columns=['idx', 'sentence1', 'sentence2']							,       load_from_cache_file=UpperCAmelCase_	)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	_UpperCamelCase   :		Union[str, Any]			=						tokenized_datasets.rename_column('label'							,       'labels'	)

	def collate_fn(UpperCAmelCase_							:     Any	):
		# 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(UpperCAmelCase_							,       padding='max_length'							,       max_length=1_2_8							,       return_tensors='pt'	)
		return tokenizer.pad(UpperCAmelCase_							,       padding='longest'							,       return_tensors='pt'	)

	# Instantiate dataloaders.
	_UpperCamelCase   :		Any			=						DataLoader(
	    tokenized_datasets['train']							,       shuffle=UpperCAmelCase_							,       collate_fn=UpperCAmelCase_							,       batch_size=UpperCAmelCase_	)
	_UpperCamelCase   :		List[str]			=						DataLoader(
	    tokenized_datasets['validation']							,       shuffle=UpperCAmelCase_							,       collate_fn=UpperCAmelCase_							,       batch_size=UpperCAmelCase_	)

	return train_dataloader, eval_dataloader






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Any	)						->				List[Any]:




	'''simple docstring'''




	model.eval()
	_UpperCamelCase   :		int			=						0
	for step, batch in enumerate(UpperCAmelCase_	):
		# We could avoid this line since we set the accelerator with `device_placement=True`.
		batch.to(accelerator.device	)
		with torch.no_grad():
			_UpperCamelCase   :		List[Any]			=						model(**UpperCAmelCase_	)
		_UpperCamelCase   :		List[Any]			=						outputs.logits.argmax(dim=-1	)
		# It is slightly faster to call this once, than multiple times
		_UpperCamelCase   :		int			=						accelerator.gather(
		    (predictions, batch['labels'])	)  # If we are in a multiprocess environment, the last batch has duplicates
		if accelerator.use_distributed:
			if step == len(UpperCAmelCase_	) - 1:
				_UpperCamelCase   :		Optional[int]			=						predictions[: len(eval_dataloader.dataset	) - samples_seen]
				_UpperCamelCase   :		str			=						references[: len(eval_dataloader.dataset	) - samples_seen]
			else:
				samples_seen += references.shape[0]
		metric.add_batch(
		    predictions=UpperCAmelCase_							,       references=UpperCAmelCase_							,       )

	_UpperCamelCase   :		Optional[Any]			=						metric.compute()
	return eval_metric["accuracy"]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     Any	)						->				List[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	_UpperCamelCase   :		Any			=						config['lr']
	_UpperCamelCase   :		List[str]			=						int(config['num_epochs']	)
	_UpperCamelCase   :		Optional[int]			=						int(config['seed']	)
	_UpperCamelCase   :		List[Any]			=						int(config['batch_size']	)
	_UpperCamelCase   :		Optional[Any]			=						args.model_name_or_path

	set_seed(UpperCAmelCase_	)
	_UpperCamelCase   :		List[str]			=						get_dataloaders(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	_UpperCamelCase   :		Optional[Any]			=						AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase_							,       return_dict=UpperCAmelCase_	)

	# Instantiate optimizer
	_UpperCamelCase   :		Tuple			=						(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	_UpperCamelCase   :		Dict			=						optimizer_cls(params=model.parameters()							,       lr=UpperCAmelCase_	)

	if accelerator.state.deepspeed_plugin is not None:
		_UpperCamelCase   :		Any			=						accelerator.state.deepspeed_plugin.deepspeed_config[
		    'gradient_accumulation_steps'
		]
	else:
		_UpperCamelCase   :		Optional[Any]			=						1
	_UpperCamelCase   :		List[Any]			=						(len(UpperCAmelCase_	) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		_UpperCamelCase   :		Optional[int]			=						get_linear_schedule_with_warmup(
		    optimizer=UpperCAmelCase_							,       num_warmup_steps=0							,       num_training_steps=UpperCAmelCase_							,       )
	else:
		_UpperCamelCase   :		List[Any]			=						DummyScheduler(UpperCAmelCase_							,       total_num_steps=UpperCAmelCase_							,       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.
	_UpperCamelCase   :		Any			=						accelerator.prepare(
	    UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)

	# We need to keep track of how many total steps we have iterated over
	_UpperCamelCase   :		Optional[int]			=						0
	# We also need to keep track of the stating epoch so files are named properly
	_UpperCamelCase   :		List[str]			=						0
	_UpperCamelCase   :		Any			=						evaluate.load('glue'							,       'mrpc'	)
	_UpperCamelCase   :		int			=						num_epochs

	if args.partial_train_epoch is not None:
		_UpperCamelCase   :		int			=						args.partial_train_epoch

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

			overall_step += 1
		_UpperCamelCase   :		int			=						F'''epoch_{epoch}'''
		_UpperCamelCase   :		Tuple			=						os.path.join(args.output_dir							,       UpperCAmelCase_	)
		accelerator.save_state(UpperCAmelCase_	)
		_UpperCamelCase   :		Any			=						evaluation_loop(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
		_UpperCamelCase   :		int			=						accuracy
		_UpperCamelCase   :		Tuple			=						lr_scheduler.get_lr()[0]
		_UpperCamelCase   :		int			=						optimizer.param_groups[0]['lr']
		_UpperCamelCase   :		int			=						epoch
		_UpperCamelCase   :		List[Any]			=						overall_step
		accelerator.print(F'''epoch {epoch}:'''							,       UpperCAmelCase_	)

		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(UpperCAmelCase_							,       UpperCAmelCase_	)






def 					lowerCamelCase_     (							)						->				Any:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[int]			=						argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'	)
	parser.add_argument(
	    '--model_name_or_path'							,       type=UpperCAmelCase_							,       default='bert-base-cased'							,       help='Path to pretrained model or model identifier from huggingface.co/models.'							,       required=UpperCAmelCase_							,       )
	parser.add_argument(
	    '--output_dir'							,       type=UpperCAmelCase_							,       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=UpperCAmelCase_							,       default=UpperCAmelCase_							,       help='If the training should continue from a checkpoint folder.'							,       )
	parser.add_argument(
	    '--partial_train_epoch'							,       type=UpperCAmelCase_							,       default=UpperCAmelCase_							,       help='If passed, the training will stop after this number of epochs.'							,       )
	parser.add_argument(
	    '--num_epochs'							,       type=UpperCAmelCase_							,       default=2							,       help='Number of train epochs.'							,       )
	_UpperCamelCase   :		Optional[int]			=						parser.parse_args()
	_UpperCamelCase   :		Union[str, Any]			=						{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}

	training_function(UpperCAmelCase_							,       UpperCAmelCase_	)


if __name__ == "__main__":
							main()







from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels




