





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
#
########################################################################


_lowerCamelCase   				=			16
_lowerCamelCase   				=			32






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Accelerator					,   UpperCamelCase__:							int = 16    ):
		SCREAMING_SNAKE_CASE__			=      AutoTokenizer.from_pretrained("""bert-base-cased"""    )
		SCREAMING_SNAKE_CASE__			=      load_dataset("""glue"""					,   """mrpc"""    )

		def tokenize_function(UpperCamelCase__:							Optional[Any]    ):
				# 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__:							str    ):
				# 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

	_lowerCamelCase   				=			mocked_dataloaders  # noqa: F811






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							str					,   UpperCamelCase__:							str    ):
		# 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__:							Any    ):
				# 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 				SCREAMING_SNAKE_CASE__							(       ):
		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()






import warnings
from functools import wraps
from typing import Callable






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Callable    ):

		@wraps(UpperCamelCase__    )
		def _inner_fn(*UpperCamelCase__:							Dict					,   **UpperCamelCase__:							Any    ):
				warnings.warn(
				    (f'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')					,   UpperCamelCase__					,   )
				return fn(*UpperCamelCase__					,   **UpperCamelCase__    )

		return _inner_fn