


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

_a									=				32





def lowerCAmelCase__(__snake_case	,__snake_case = 16							)    ->				int:







	'''simple docstring'''







	lowerCamelCase__        =		AutoTokenizer.from_pretrained('''bert-base-cased'''							)
	lowerCamelCase__        =		load_dataset('''glue'''	,'''mrpc'''							)

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

	def collate_fn(__snake_case							):
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			lowerCamelCase__        =		16
		elif accelerator.mixed_precision != "no":
			lowerCamelCase__        =		8
		else:
			lowerCamelCase__        =		None

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

	# Instantiate dataloaders.
	lowerCamelCase__        =		DataLoader(
	    tokenized_datasets['''train''']	,shuffle=__UpperCamelCase	,collate_fn=__UpperCamelCase	,batch_size=__UpperCamelCase							)
	lowerCamelCase__        =		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									=				mocked_dataloaders  # noqa: F811





def lowerCAmelCase__(__snake_case	,__snake_case							)    ->				List[Any]:







	'''simple docstring'''







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

	lowerCamelCase__        =		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(__UpperCamelCase							)							)
	def inner_training_loop(__snake_case							):
		# 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
		lowerCamelCase__        =		observed_batch_size // batch_size

		# And then set it in the Accelerator directly:
		lowerCamelCase__        =		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(__UpperCamelCase							)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCamelCase__        =		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).
		lowerCamelCase__        =		model.to(accelerator.device							)

		# Instantiate optimizer
		lowerCamelCase__        =		AdamW(params=model.parameters()	,lr=__UpperCamelCase							)
		lowerCamelCase__    ,     lowerCamelCase__        =		get_dataloaders(__UpperCamelCase	,__UpperCamelCase							)

		# Instantiate scheduler
		lowerCamelCase__        =		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.
		lowerCamelCase__    ,     lowerCamelCase__    ,     lowerCamelCase__    ,     lowerCamelCase__    ,     lowerCamelCase__        =		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							):
				# And perform gradient accumulation
				with accelerator.accumulate(__UpperCamelCase							):
					# We could avoid this line since we set the accelerator with `device_placement=True`.
					batch.to(accelerator.device							)
					lowerCamelCase__        =		model(**__UpperCamelCase							)
					lowerCamelCase__        =		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():
					lowerCamelCase__        =		model(**__UpperCamelCase							)
				lowerCamelCase__        =		outputs.logits.argmax(dim=-1							)
				lowerCamelCase__    ,     lowerCamelCase__        =		accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
				metric.add_batch(
				    predictions=__UpperCamelCase	,references=__UpperCamelCase	,)

			lowerCamelCase__        =		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__()    ->				Any:







	'''simple docstring'''







	lowerCamelCase__        =		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.'''							)
	lowerCamelCase__        =		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
	lowerCamelCase__        =		{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
	training_function(__UpperCamelCase	,__UpperCamelCase							)


if __name__ == "__main__":
			main()










def lowerCAmelCase__(__snake_case							)    ->				int:







	'''simple docstring'''







	lowerCamelCase__        =		str(__snake_case							).strip()
	if not bin_string:
		raise ValueError('''Empty string was passed to the function'''							)
	lowerCamelCase__        =		bin_string[0] == '''-'''
	if is_negative:
		lowerCamelCase__        =		bin_string[1:]
	if not all(char in '''01''' for char in bin_string							):
		raise ValueError('''Non-binary value was passed to the function'''							)
	lowerCamelCase__        =		0
	for char in bin_string:
		lowerCamelCase__        =		2 * decimal_number + int(__snake_case							)
	return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
			from doctest import testmod

			testmod()



