






import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a_		:    str   							=     16
a_		:    List[Any]   							=     32






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase = 16):
			SCREAMING_SNAKE_CASE							=			AutoTokenizer.from_pretrained('bert-base-cased')
			SCREAMING_SNAKE_CASE							=			load_dataset('glue'				,							'mrpc')

			def tokenize_function(_UpperCAmelCase):
						# max_length=None => use the model max length (it's actually the default)
						SCREAMING_SNAKE_CASE							=			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
			# starting with the main process first:
			with accelerator.main_process_first():
						SCREAMING_SNAKE_CASE							=			datasets.map(
						    _UpperCAmelCase				,							batched=_UpperCAmelCase				,							remove_columns=['idx', 'sentence1', 'sentence2']				,							)

			# 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							=			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.
						SCREAMING_SNAKE_CASE							=			128 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									SCREAMING_SNAKE_CASE							=			16
						elif accelerator.mixed_precision != "no":
									SCREAMING_SNAKE_CASE							=			8
						else:
									SCREAMING_SNAKE_CASE							=			None

						return tokenizer.pad(
						    _UpperCAmelCase				,							padding='longest'				,							max_length=_UpperCAmelCase				,							pad_to_multiple_of=_UpperCAmelCase				,							return_tensors='pt'				,							)

			# Instantiate dataloaders.
			SCREAMING_SNAKE_CASE							=			DataLoader(
			    tokenized_datasets['train']				,							shuffle=_UpperCAmelCase				,							collate_fn=_UpperCAmelCase				,							batch_size=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE							=			DataLoader(
			    tokenized_datasets['validation']				,							shuffle=_UpperCAmelCase				,							collate_fn=_UpperCAmelCase				,							batch_size=_UpperCAmelCase)

			return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
				from accelerate.test_utils.training import mocked_dataloaders

				a_		:    Optional[Any]   							=     mocked_dataloaders  # noqa: F811






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase):
			# For testing only
			if os.environ.get('TESTING_MOCKED_DATALOADERS'				,							_UpperCAmelCase) == "1":
						SCREAMING_SNAKE_CASE							=			2
			# Initialize accelerator
			SCREAMING_SNAKE_CASE							=			Accelerator(cpu=args.cpu				,							mixed_precision=args.mixed_precision)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			SCREAMING_SNAKE_CASE							=			config['lr']
			SCREAMING_SNAKE_CASE							=			int(config['num_epochs'])
			SCREAMING_SNAKE_CASE							=			int(config['seed'])
			SCREAMING_SNAKE_CASE							=			int(config['batch_size'])

			SCREAMING_SNAKE_CASE							=			evaluate.load('glue'				,							'mrpc')

			# New Code #
			# We now can define an inner training loop function. It should take a batch size as the only parameter,
			# and build the dataloaders in there.
			# It also gets our decorator
			@find_executable_batch_size(starting_batch_size=_UpperCAmelCase)
			def inner_training_loop(_UpperCAmelCase):
						# And now just move everything below under this function
						# We need to bring in the Accelerator object from earlier
						nonlocal accelerator
						# And reset all of its attributes that could hold onto any memory:
						accelerator.free_memory()

						# Then we can declare the model, optimizer, and everything else:
						set_seed(_UpperCAmelCase)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						SCREAMING_SNAKE_CASE							=			AutoModelForSequenceClassification.from_pretrained('bert-base-cased'				,							return_dict=_UpperCAmelCase)

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						SCREAMING_SNAKE_CASE							=			model.to(accelerator.device)

						# Instantiate optimizer
						SCREAMING_SNAKE_CASE							=			AdamW(params=model.parameters()				,							lr=_UpperCAmelCase)
						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			get_dataloaders(_UpperCAmelCase				,							_UpperCAmelCase)

						# Instantiate scheduler
						SCREAMING_SNAKE_CASE							=			get_linear_schedule_with_warmup(
						    optimizer=_UpperCAmelCase				,							num_warmup_steps=100				,							num_training_steps=(len(_UpperCAmelCase) * num_epochs)				,							)

						# 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							=			accelerator.prepare(
						    _UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase)

						# Now we train the model
						for epoch in range(_UpperCAmelCase):
									model.train()
									for step, batch in enumerate(_UpperCAmelCase):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device)
												SCREAMING_SNAKE_CASE							=			model(**_UpperCAmelCase)
												SCREAMING_SNAKE_CASE							=			outputs.loss
												accelerator.backward(_UpperCAmelCase)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									model.eval()
									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():
															SCREAMING_SNAKE_CASE							=			model(**_UpperCAmelCase)
												SCREAMING_SNAKE_CASE							=			outputs.logits.argmax(dim=-1)
												SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			accelerator.gather_for_metrics((predictions, batch['labels']))
												metric.add_batch(
												    predictions=_UpperCAmelCase				,							references=_UpperCAmelCase				,							)

									SCREAMING_SNAKE_CASE							=			metric.compute()
									# Use accelerator.print to print only on the main process.
									accelerator.print(F'''epoch {epoch}:'''				,							_UpperCAmelCase)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
			inner_training_loop()






def 					lowerCamelCase__					():
			SCREAMING_SNAKE_CASE							=			argparse.ArgumentParser(description='Simple example of training script.')
			parser.add_argument(
			    '--mixed_precision'				,							type=_UpperCAmelCase				,							default=_UpperCAmelCase				,							choices=['no', 'fp16', 'bf16', 'fp8']				,							help='Whether to use mixed precision. Choose'
			    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
			    'and an Nvidia Ampere GPU.'				,							)
			parser.add_argument('--cpu'				,							action='store_true'				,							help='If passed, will train on the CPU.')
			SCREAMING_SNAKE_CASE							=			parser.parse_args()
			SCREAMING_SNAKE_CASE							=			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(_UpperCAmelCase				,							_UpperCAmelCase)


if __name__ == "__main__":
				main()








'''simple docstring'''
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