"""simple docstring"""







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    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase = 16		):
	__lowerCAmelCase      :						str						      =				AutoTokenizer.from_pretrained('bert-base-cased'		)
	__lowerCAmelCase      :						str						      =				load_dataset('glue'	,  'mrpc'		)

	def tokenize_function(_UpperCamelCase		):
		# max_length=None => use the model max length (it's actually the default)
		__lowerCAmelCase      :						List[str]						      =				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      :						Tuple						      =				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      :						List[Any]						      =				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.
		__lowerCAmelCase      :						int						      =				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":
			__lowerCAmelCase      :						Optional[Any]						      =				16
		elif accelerator.mixed_precision != "no":
			__lowerCAmelCase      :						Optional[Any]						      =				8
		else:
			__lowerCAmelCase      :						int						      =				None

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

	# Instantiate dataloaders.
	__lowerCAmelCase      :						List[Any]						      =				DataLoader(
	    tokenized_datasets['train']	,  shuffle=_UpperCamelCase	,  collate_fn=_UpperCamelCase	,  batch_size=_UpperCamelCase		)
	__lowerCAmelCase      :						str						      =				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    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	# For testing only
	if os.environ.get('TESTING_MOCKED_DATALOADERS'	,  _UpperCamelCase		) == "1":
		__lowerCAmelCase      :						Dict						      =				2
	# New Code #
	__lowerCAmelCase      :						Dict						      =				int(args.gradient_accumulation_steps		)
	# Initialize accelerator
	__lowerCAmelCase      :						List[Any]						      =				Accelerator(
	    cpu=args.cpu	,  mixed_precision=args.mixed_precision	,  gradient_accumulation_steps=_UpperCamelCase		)
	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
	__lowerCAmelCase      :						Optional[Any]						      =				config['lr']
	__lowerCAmelCase      :						List[str]						      =				int(config['num_epochs']		)
	__lowerCAmelCase      :						Dict						      =				int(config['seed']		)
	__lowerCAmelCase      :						Dict						      =				int(config['batch_size']		)

	__lowerCAmelCase      :						Tuple						      =				evaluate.load('glue'	,  'mrpc'		)

	set_seed(_UpperCamelCase		)
	__lowerCAmelCase      :						Union[str, Any]						      =				get_dataloaders(_UpperCamelCase	,  _UpperCamelCase		)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__lowerCAmelCase      :						Optional[Any]						      =				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      :						Optional[int]						      =				model.to(accelerator.device		)

	# Instantiate optimizer
	__lowerCAmelCase      :						Union[str, Any]						      =				AdamW(params=model.parameters()	,  lr=_UpperCamelCase		)

	# Instantiate scheduler
	__lowerCAmelCase      :						int						      =				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      :						int						      =				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		)
			# 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(_UpperCamelCase		):
				__lowerCAmelCase      :						Tuple						      =				model(**_UpperCamelCase		)
				__lowerCAmelCase      :						Union[str, Any]						      =				output.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      :						List[str]						      =				model(**_UpperCamelCase		)
			__lowerCAmelCase      :						Tuple						      =				outputs.logits.argmax(dim=-1		)
			__lowerCAmelCase      :						Tuple						      =				accelerator.gather_for_metrics((predictions, batch['labels'])		)
			metric.add_batch(
			    predictions=_UpperCamelCase	,  references=_UpperCamelCase	,  )

		__lowerCAmelCase      :						int						      =				metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"epoch {epoch}:"	,  _UpperCamelCase		)
def    __lowerCAmelCase    ():
	__lowerCAmelCase      :						str						      =				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.'	,  )
	# New Code #
	parser.add_argument(
	    '--gradient_accumulation_steps'	,  type=_UpperCamelCase	,  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.'		)
	__lowerCAmelCase      :						List[Any]						      =				parser.parse_args()
	__lowerCAmelCase      :						Optional[Any]						      =				{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(_UpperCamelCase	,  _UpperCamelCase		)


if __name__ == "__main__":
							main()

"""simple docstring"""







class 			A__	:

		def __init__(				self	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						List[Any]						      =				size
			__lowerCAmelCase      :						str						      =				[0] * size
			__lowerCAmelCase      :						Any						      =				[0] * size

		@staticmethod
		def 							__lowerCamelCase							(				_SCREAMING_SNAKE_CASE			):
			return index | (index + 1)

		@staticmethod
		def 							__lowerCamelCase							(				_SCREAMING_SNAKE_CASE			):
			return (index & (index + 1)) - 1

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Any						      =				value
			while index < self.size:
				__lowerCAmelCase      :						Dict						      =				self.get_prev(_SCREAMING_SNAKE_CASE			) + 1
				if current_left_border == index:
					__lowerCAmelCase      :						Any						      =				value
				else:
					__lowerCAmelCase      :						Any						      =				max(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						List[Any]						      =				self.get_next(_SCREAMING_SNAKE_CASE			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			right -= 1  # Because of right is exclusive
			__lowerCAmelCase      :						Optional[int]						      =				0
			while left <= right:
				__lowerCAmelCase      :						Optional[int]						      =				self.get_prev(_SCREAMING_SNAKE_CASE			)
				if left <= current_left:
					__lowerCAmelCase      :						Optional[Any]						      =				max(_SCREAMING_SNAKE_CASE	,		self.tree[right]			)
					__lowerCAmelCase      :						Optional[Any]						      =				current_left
				else:
					__lowerCAmelCase      :						List[str]						      =				max(_SCREAMING_SNAKE_CASE	,		self.arr[right]			)
					right -= 1
			return result


if __name__ == "__main__":
							import doctest

							doctest.testmod()