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


SCREAMING_SNAKE_CASE									=						16
SCREAMING_SNAKE_CASE									=						32


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase = 16     ,__UpperCAmelCase = "bert-base-cased"			):






	"""simple docstring"""





	_lowercase :    Any							=							AutoTokenizer.from_pretrained(lowerCAmelCase__			)
	_lowercase :    Union[str, Any]							=							load_dataset('glue'     ,'mrpc'			)

	def tokenize_function(__UpperCAmelCase			):
		# max_length=None => use the model max length (it's actually the default)
		_lowercase :    Tuple							=							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
	_lowercase :    Optional[int]							=							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
	_lowercase :    str							=							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.
	_lowercase :    Tuple							=							DataLoader(
	    tokenized_datasets['train']     ,shuffle=lowerCAmelCase__     ,collate_fn=lowerCAmelCase__     ,batch_size=lowerCAmelCase__			)
	_lowercase :    Any							=							DataLoader(
	    tokenized_datasets['validation']     ,shuffle=lowerCAmelCase__     ,collate_fn=lowerCAmelCase__     ,batch_size=lowerCAmelCase__			)

	return train_dataloader, eval_dataloader


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Tuple							=							Accelerator()

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

	set_seed(lowerCAmelCase__			)
	_lowercase				,      _lowercase :    int							=							get_dataloaders(lowerCAmelCase__     ,lowerCAmelCase__     ,lowerCAmelCase__			)

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

	# Instantiate optimizer
	_lowercase :    Optional[Any]							=							(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	_lowercase :    str							=							optimizer_cls(params=model.parameters()     ,lr=lowerCAmelCase__			)

	if accelerator.state.deepspeed_plugin is not None:
		_lowercase :    Union[str, Any]							=							accelerator.state.deepspeed_plugin.deepspeed_config[
		    'gradient_accumulation_steps'
		]
	else:
		_lowercase :    List[str]							=							1
	_lowercase :    Optional[int]							=							(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
	):
		_lowercase :    Optional[Any]							=							get_linear_schedule_with_warmup(
		    optimizer=lowerCAmelCase__     ,num_warmup_steps=0     ,num_training_steps=lowerCAmelCase__     ,)
	else:
		_lowercase :    Optional[int]							=							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.
	_lowercase				,      _lowercase				,      _lowercase				,      _lowercase				,      _lowercase :    int							=							accelerator.prepare(
	    lowerCAmelCase__     ,lowerCAmelCase__     ,lowerCAmelCase__     ,lowerCAmelCase__     ,lowerCAmelCase__			)

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

	# Now we train the model
	_lowercase :    Any							=							evaluate.load('glue'     ,'mrpc'			)
	_lowercase :    List[str]							=							0
	_lowercase :    str							=							{}
	for epoch in range(lowerCAmelCase__     ,lowerCAmelCase__			):
		model.train()
		for step, batch in enumerate(lowerCAmelCase__			):
			_lowercase :    Any							=							model(**lowerCAmelCase__			)
			_lowercase :    str							=							outputs.loss
			_lowercase :    List[Any]							=							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()
		_lowercase :    Optional[Any]							=							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():
				_lowercase :    Any							=							model(**lowerCAmelCase__			)
			_lowercase :    int							=							outputs.logits.argmax(dim=-1			)
			# It is slightly faster to call this once, than multiple times
			_lowercase				,      _lowercase :    str							=							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:
					_lowercase :    Union[str, Any]							=							predictions[: len(eval_dataloader.dataset			) - samples_seen]
					_lowercase :    List[Any]							=							references[: len(eval_dataloader.dataset			) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=lowerCAmelCase__     ,references=lowerCAmelCase__     ,)

		_lowercase :    List[Any]							=							metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'''epoch {epoch}:'''     ,lowerCAmelCase__			)
		_lowercase :    Union[str, Any]							=							eval_metric['accuracy']

		if best_performance < eval_metric["accuracy"]:
			_lowercase :    List[Any]							=							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(		):






	"""simple docstring"""





	_lowercase :    List[str]							=							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.'     ,)
	_lowercase :    List[Any]							=							parser.parse_args()
	_lowercase :    int							=							{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
	training_function(lowerCAmelCase__     ,lowerCAmelCase__			)


if __name__ == "__main__":
						main()




"""simple docstring"""







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							len(__UpperCAmelCase			)
	_lowercase :    Any							=							len(__UpperCAmelCase			)
	_lowercase :    List[Any]							=							[[False for _ in range(m + 1			)] for _ in range(n + 1			)]
	_lowercase :    Union[str, Any]							=							True
	for i in range(__UpperCAmelCase			):
		for j in range(m + 1			):
			if dp[i][j]:
				if j < m and a[i].upper() == b[j]:
					_lowercase :    Optional[int]							=							True
				if a[i].islower():
					_lowercase :    Union[str, Any]							=							True
	return dp[n][m]


if __name__ == "__main__":
						import doctest

						doctest.testmod()



