


import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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)
#
# 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      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = 16	):


		"""simple docstring"""




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

		def tokenize_function(SCREAMING_SNAKE_CASE	):
				# max_length=None => use the model max length (it's actually the default)
				lowercase__				= tokenizer(examples['''sentence1''']		,							examples['''sentence2''']		,							truncation=SCREAMING_SNAKE_CASE		,							max_length=SCREAMING_SNAKE_CASE	)
				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():
				lowercase__				= datasets.map(
				    SCREAMING_SNAKE_CASE		,							batched=SCREAMING_SNAKE_CASE		,							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
		lowercase__				= tokenized_datasets.rename_column('''label'''		,							'''labels'''	)

		def collate_fn(SCREAMING_SNAKE_CASE	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				lowercase__				= 1_28 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						lowercase__				= 16
				elif accelerator.mixed_precision != "no":
						lowercase__				= 8
				else:
						lowercase__				= None

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

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

		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      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,							SCREAMING_SNAKE_CASE	) == "1":
				lowercase__				= 2
		# New Code #
		lowercase__				= int(args.gradient_accumulation_steps	)
		# Initialize accelerator
		lowercase__				= Accelerator(
		    cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							gradient_accumulation_steps=SCREAMING_SNAKE_CASE	)
		if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
				raise NotImplementedError(
				    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''	)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowercase__				= config['''lr''']
		lowercase__				= int(config['''num_epochs''']	)
		lowercase__				= int(config['''seed''']	)
		lowercase__				= int(config['''batch_size''']	)

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

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

		# 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).
		lowercase__				= model.to(accelerator.device	)

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

		# Instantiate scheduler
		lowercase__				= get_linear_schedule_with_warmup(
		    optimizer=SCREAMING_SNAKE_CASE		,							num_warmup_steps=1_00		,							num_training_steps=(len(SCREAMING_SNAKE_CASE	) * 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.
		lowercase__	,						lowercase__	,						lowercase__	,						lowercase__	,						lowercase__				= accelerator.prepare(
		    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)

		# Now we train the model
		for epoch in range(SCREAMING_SNAKE_CASE	):
				model.train()
				for step, batch in enumerate(SCREAMING_SNAKE_CASE	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						# New code #
						# We use the new `accumulate` context manager to perform gradient accumulation
						# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
						with accelerator.accumulate(SCREAMING_SNAKE_CASE	):
								lowercase__				= model(**SCREAMING_SNAKE_CASE	)
								lowercase__				= output.loss
								accelerator.backward(SCREAMING_SNAKE_CASE	)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				for step, batch in enumerate(SCREAMING_SNAKE_CASE	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						with torch.no_grad():
								lowercase__				= model(**SCREAMING_SNAKE_CASE	)
						lowercase__				= outputs.logits.argmax(dim=-1	)
						lowercase__	,						lowercase__				= accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
						metric.add_batch(
						    predictions=SCREAMING_SNAKE_CASE		,							references=SCREAMING_SNAKE_CASE		,							)

				lowercase__				= metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'epoch {epoch}:'		,							SCREAMING_SNAKE_CASE	)
def      _a					( ):


		"""simple docstring"""




		lowercase__				= argparse.ArgumentParser(description='''Simple example of training script.'''	)
		parser.add_argument(
		    '''--mixed_precision'''		,							type=SCREAMING_SNAKE_CASE		,							default=SCREAMING_SNAKE_CASE		,							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.'''		,							)
		# New Code #
		parser.add_argument(
		    '''--gradient_accumulation_steps'''		,							type=SCREAMING_SNAKE_CASE		,							default=1		,							help='''The number of minibatches to be ran before gradients are accumulated.'''		,							)
		parser.add_argument('''--cpu'''		,							action='''store_true'''		,							help='''If passed, will train on the CPU.'''	)
		lowercase__				= parser.parse_args()
		lowercase__				= {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
			main()





from __future__ import annotations

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							2
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							[]
	while i * i <= n:
		if n % i:
			i += 1
		else:
			n //= i
			factors.append(_a)
	if n > 1:
		factors.append(_a)
	return factors


if __name__ == "__main__":
			import doctest

			doctest.testmod()