



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

UpperCAmelCase__			:   Tuple      			=    32


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase = 16			):
				_UpperCAmelCase						:  Any     					=	AutoTokenizer.from_pretrained('''bert-base-cased'''			)
				_UpperCAmelCase						:  Any     					=	load_dataset('''glue'''      ,					'''mrpc'''			)

				def tokenize_function(_UpperCamelCase			):
								# max_length=None => use the model max length (it's actually the default)
								_UpperCAmelCase						:  Any     					=	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():
								_UpperCAmelCase						:  List[str]     					=	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
				_UpperCAmelCase						:  Optional[Any]     					=	tokenized_datasets.rename_column('''label'''      ,					'''labels'''			)

				def collate_fn(_UpperCamelCase			):
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												_UpperCAmelCase						:  Dict     					=	16
								elif accelerator.mixed_precision != "no":
												_UpperCAmelCase						:  Optional[Any]     					=	8
								else:
												_UpperCAmelCase						:  Optional[Any]     					=	None

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

				# Instantiate dataloaders.
				_UpperCAmelCase						:  List[Any]     					=	DataLoader(
				    tokenized_datasets['''train''']      ,					shuffle=_UpperCamelCase      ,					collate_fn=_UpperCamelCase      ,					batch_size=_UpperCamelCase			)
				_UpperCAmelCase						:  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

					UpperCAmelCase__			:   Any      			=    mocked_dataloaders  # noqa: F811


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				# For testing only
				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''      ,					_UpperCamelCase			) == "1":
								_UpperCAmelCase						:  Dict     					=	2
				# Initialize accelerator
				_UpperCAmelCase						:  Optional[Any]     					=	Accelerator(cpu=args.cpu      ,					mixed_precision=args.mixed_precision			)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_UpperCAmelCase						:  Any     					=	config['''lr''']
				_UpperCAmelCase						:  Optional[int]     					=	int(config['''num_epochs''']			)
				_UpperCAmelCase						:  List[Any]     					=	int(config['''seed''']			)
				_UpperCAmelCase						:  Union[str, Any]     					=	int(config['''batch_size''']			)

				_UpperCAmelCase						:  Dict     					=	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(_UpperCamelCase			):
								# 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
								_UpperCAmelCase						:  str     					=	observed_batch_size // batch_size

								# And then set it in the Accelerator directly:
								_UpperCAmelCase						:  Optional[Any]     					=	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)
								_UpperCAmelCase						:  List[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).
								_UpperCAmelCase						:  int     					=	model.to(accelerator.device			)

								# Instantiate optimizer
								_UpperCAmelCase						:  Tuple     					=	AdamW(params=model.parameters()      ,					lr=_UpperCamelCase			)
								_UpperCAmelCase					, _UpperCAmelCase						:  Optional[int]     					=	get_dataloaders(_UpperCamelCase      ,					_UpperCamelCase			)

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

												_UpperCAmelCase						:  Union[str, Any]     					=	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 	_A	(   ):
				_UpperCAmelCase						:  List[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.'''      ,					)
				parser.add_argument('''--cpu'''      ,					action='''store_true'''      ,					help='''If passed, will train on the CPU.'''			)
				_UpperCAmelCase						:  Optional[int]     					=	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
				_UpperCAmelCase						:  Optional[Any]     					=	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
				training_function(_UpperCamelCase      ,					_UpperCamelCase			)


if __name__ == "__main__":
					main()





class 							lowerCAmelCase_		:







		def __init__(    self  :   int       ,		UpperCAmelCase_  :   int					)       ->		None:


						'''simple docstring'''







						_UpperCAmelCase						:  Tuple     					=	data
						_UpperCAmelCase						:  BinaryTreeNode | None     					=	None
						_UpperCAmelCase						:  BinaryTreeNode | None     					=	None


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				if node is None:
								_UpperCAmelCase						:  Union[str, Any]     					=	BinaryTreeNode(_UpperCamelCase			)
								return node

				# binary search tree is not empty,
				# so we will insert it into the tree
				# if new_value is less than value of data in node,
				#  add it to left subtree and proceed recursively
				if new_value < node.data:
								_UpperCAmelCase						:  Any     					=	insert(node.left_child      ,					_UpperCamelCase			)
				else:
								# if new_value is greater than value of data in node,
								#  add it to right subtree and proceed recursively
								_UpperCAmelCase						:  List[Any]     					=	insert(node.right_child      ,					_UpperCamelCase			)
				return node


def 	_A	(   _UpperCamelCase			):  # if node is None,return
				if node:
								_UpperCAmelCase						:  str     					=	inorder(node.left_child			)
								_UpperCAmelCase						:  Union[str, Any]     					=	[*inorder_array, node.data]
								_UpperCAmelCase						:  Optional[int]     					=	inorder_array + inorder(node.right_child			)
				else:
								_UpperCAmelCase						:  Tuple     					=	[]
				return inorder_array


def 	_A	(   ):
				_UpperCAmelCase						:  Dict     					=	insert(_UpperCamelCase      ,					15			)
				insert(_UpperCamelCase      ,					10			)
				insert(_UpperCamelCase      ,					25			)
				insert(_UpperCamelCase      ,					6			)
				insert(_UpperCamelCase      ,					14			)
				insert(_UpperCamelCase      ,					20			)
				insert(_UpperCamelCase      ,					60			)
				return root


def 	_A	(   ):
				# main function
				_UpperCAmelCase						:  Tuple     					=	make_tree()
				print('''Printing values of binary search tree in Inorder Traversal.'''			)
				inorder(_UpperCamelCase			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
