



'''simple docstring'''




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


UpperCamelCase__       :	int    = 16
UpperCamelCase__       :	str    = 32


def 				lowerCAmelCase_   (	_lowerCamelCase:   Accelerator       ,      _lowerCamelCase:   int = 16       ,      _lowerCamelCase:   str = "bert-base-cased"					):
		__SCREAMING_SNAKE_CASE       :		str           =		AutoTokenizer.from_pretrained(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Tuple           =		load_dataset("""glue"""       ,      """mrpc"""					)

		def tokenize_function(_lowerCamelCase:   Optional[Any]					):
				# max_length=None => use the model max length (it's actually the default)
				__SCREAMING_SNAKE_CASE       :		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
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		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
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		tokenized_datasets.rename_column("""label"""       ,      """labels"""					)

		def collate_fn(_lowerCamelCase:   Tuple					):
				# 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=1_28       ,      return_tensors="""pt"""					)
				return tokenizer.pad(_lowerCamelCase       ,      padding="""longest"""       ,      return_tensors="""pt"""					)

		# Instantiate dataloaders.
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		DataLoader(
		    tokenized_datasets["""train"""]       ,      shuffle=_lowerCamelCase       ,      collate_fn=_lowerCamelCase       ,      batch_size=_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Any           =		DataLoader(
		    tokenized_datasets["""validation"""]       ,      shuffle=_lowerCamelCase       ,      collate_fn=_lowerCamelCase       ,      batch_size=_lowerCamelCase					)

		return train_dataloader, eval_dataloader


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[int]       ,      _lowerCamelCase:   Optional[Any]					):
		# Initialize accelerator
		__SCREAMING_SNAKE_CASE       :		Tuple           =		Accelerator()

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

		set_seed(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		List[Any]           =		get_dataloaders(_lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase					)

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

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

		if accelerator.state.deepspeed_plugin is not None:
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		accelerator.state.deepspeed_plugin.deepspeed_config[
				    """gradient_accumulation_steps"""
				]
		else:
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		1
		__SCREAMING_SNAKE_CASE       :		str           =		(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
		):
				__SCREAMING_SNAKE_CASE       :		Tuple           =		get_linear_schedule_with_warmup(
				    optimizer=_lowerCamelCase       ,      num_warmup_steps=0       ,      num_training_steps=_lowerCamelCase       ,      )
		else:
				__SCREAMING_SNAKE_CASE       :		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.
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		int           =		accelerator.prepare(
		    _lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase					)

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

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

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(F"epoch {epoch}:"       ,      _lowerCamelCase					)
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		eval_metric["""accuracy"""]

				if best_performance < eval_metric["accuracy"]:
						__SCREAMING_SNAKE_CASE       :		Dict           =		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_   (	):
		__SCREAMING_SNAKE_CASE       :		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."""       ,      )
		__SCREAMING_SNAKE_CASE       :		List[str]           =		parser.parse_args()
		__SCREAMING_SNAKE_CASE       :		Tuple           =		{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
		training_function(_lowerCamelCase       ,      _lowerCamelCase					)


if __name__ == "__main__":
						main()




'''simple docstring'''




UpperCamelCase__       :	Optional[Any]    = [
    (10_00, '''M'''),
    (9_00, '''CM'''),
    (5_00, '''D'''),
    (4_00, '''CD'''),
    (1_00, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 1_00, """D""": 5_00, """M""": 10_00}
		__SCREAMING_SNAKE_CASE       :		Tuple           =		0
		__SCREAMING_SNAKE_CASE       :		str           =		0
		while place < len(_lowerCamelCase					):
				if (place + 1 < len(_lowerCamelCase					)) and (vals[roman[place]] < vals[roman[place + 1]]):
						total += vals[roman[place + 1]] - vals[roman[place]]
						place += 2
				else:
						total += vals[roman[place]]
						place += 1
		return total


def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		Any           =		[]
		for arabic, roman in ROMAN:
				((__SCREAMING_SNAKE_CASE)       ,						(__SCREAMING_SNAKE_CASE))       :		str           =		divmod(_lowerCamelCase       ,      _lowerCamelCase					)
				result.append(roman * factor					)
				if number == 0:
						break
		return "".join(_lowerCamelCase					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()