





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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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     			=     32






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Dict   ,				UpperCamelCase__:							Dict = 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=lowercase_   ,				max_length=lowercase_      )
				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(
				    lowercase_   ,				batched=lowercase_   ,				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__:							Optional[int]      ):
				# 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(
				    lowercase_   ,				padding="""longest"""   ,				pad_to_multiple_of=lowercase_   ,				return_tensors="""pt"""   ,				)

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

		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__:							List[Any]   ,				UpperCamelCase__:							str      ):

		if os.environ.get("""TESTING_MOCKED_DATALOADERS"""   ,				lowercase_      ) == "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 use the `find_executable_batch_size` decorator, passing in the desired observed batch size
		# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
		# half each time. From this, we can calculate the number of gradient accumulation steps needed
		# and modify the Accelerator object as a result
		@find_executable_batch_size(starting_batch_size=int(lowercase_      )      )
		def inner_training_loop(UpperCamelCase__:							List[Any]      ):
				# Since we need to modify the outside accelerator object, we need to bring it
				# to the local scope
				nonlocal accelerator

				# We can calculate the number of gradient accumulation steps based on the current
				# batch size vs the starting batch size
				SCREAMING_SNAKE_CASE__								= observed_batch_size // batch_size

				# And then set it in the Accelerator directly:
				SCREAMING_SNAKE_CASE__								= num_gradient_accumulation_steps

				# Next we need to free all of the stored model references in the Accelerator each time
				accelerator.free_memory()

				# And set the seed so our results are reproducable each reset
				set_seed(lowercase_      )

				# 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=lowercase_      )

				# 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=lowercase_      )
				SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= get_dataloaders(lowercase_   ,				lowercase_      )

				# Instantiate scheduler
				SCREAMING_SNAKE_CASE__								= get_linear_schedule_with_warmup(
				    optimizer=lowercase_   ,				num_warmup_steps=100   ,				num_training_steps=(len(lowercase_      ) * 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(
				    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_      )

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

						model.eval()
						for step, batch in enumerate(lowercase_      ):
								# 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(**lowercase_      )
								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=lowercase_   ,				references=lowercase_   ,				)

						SCREAMING_SNAKE_CASE__								= metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''   ,				lowercase_      )

						 # 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=lowercase_   ,				default=lowercase_   ,				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()
		# New Code #
		# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
		SCREAMING_SNAKE_CASE__								= {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 256}
		training_function(lowercase_   ,				lowercase_      )


if __name__ == "__main__":
	main()






from typing import Callable






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Callable      ):

		def wrapper(UpperCamelCase__:							List[Any]      ):
				SCREAMING_SNAKE_CASE__								= function_with_docstring.__doc__
				return target_function

		return wrapper