


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


__snake_case      = 16
__snake_case      = 32






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		Any							,	SCREAMING_SNAKE_CASE_ :		int = 16							,	SCREAMING_SNAKE_CASE_ :		Dict = "bert-base-cased"						):





			"""simple docstring"""




			UpperCamelCase								=       AutoTokenizer.from_pretrained(UpperCAmelCase__						)
			UpperCamelCase								=       load_dataset("""glue"""							,	"""mrpc"""						)

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

			def collate_fn(SCREAMING_SNAKE_CASE_ :		int						):
						# 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=128							,	return_tensors="""pt"""						)
						return tokenizer.pad(UpperCAmelCase__							,	padding="""longest"""							,	return_tensors="""pt"""						)

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

			return train_dataloader, eval_dataloader






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		Optional[int]							,	SCREAMING_SNAKE_CASE_ :		str						):





			"""simple docstring"""




			UpperCamelCase								=       Accelerator()

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

			set_seed(UpperCAmelCase__						)
			UpperCamelCase	,	UpperCamelCase								=       get_dataloaders(UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__						)

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

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

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

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

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

									overall_step += 1

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

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

						if best_performance < eval_metric["accuracy"]:
									UpperCamelCase								=       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(UpperCAmelCase__							,	UpperCAmelCase__						)






def 				_lowercase     (		):





			"""simple docstring"""




			UpperCamelCase								=       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(
			    """--performance_lower_bound"""							,	type=UpperCAmelCase__							,	default=UpperCAmelCase__							,	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=UpperCAmelCase__							,	default=3							,	help="""Number of train epochs."""							,	)
			UpperCamelCase								=       parser.parse_args()
			UpperCamelCase								=       {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
			training_function(UpperCAmelCase__							,	UpperCAmelCase__						)


if __name__ == "__main__":
			main()







'''simple docstring'''







import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""",							[None, 4_00 * 2**20, 6_00 * 2**20]					)
@pytest.mark.parametrize("""input_in_memory_max_size""",							["""default""", 0, 1_00 * 2**20, 9_00 * 2**20]					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       if input_in_memory_max_size != "default":
              monkeypatch.setattr(datasets.config,							"""IN_MEMORY_MAX_SIZE""",							UpperCAmelCase__					)
       A_						       =   datasets.config.IN_MEMORY_MAX_SIZE
       if input_in_memory_max_size == "default":
              assert in_memory_max_size == 0
       else:
              assert in_memory_max_size == input_in_memory_max_size
       if dataset_size and in_memory_max_size:
              A_						       =   dataset_size < in_memory_max_size
       else:
              A_						       =   False
       A_						       =   is_small_dataset(UpperCAmelCase__					)
       assert result == expected
