



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,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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       =1_6
__A       =3_2

def 			a		( _UpperCAmelCase				:   Accelerator					,  _UpperCAmelCase				:   int = 16		):







		'''simple docstring'''





		__UpperCAmelCase       :	Tuple						=      AutoTokenizer.from_pretrained('''bert-base-cased'''		)
		__UpperCAmelCase       :	Optional[Any]						=      load_dataset('''glue'''					,  '''mrpc'''		)

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

		def collate_fn(_UpperCAmelCase				:   Optional[Any]		):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				__UpperCAmelCase       :	Optional[int]						=      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":
						__UpperCAmelCase       :	Tuple						=      16
				elif accelerator.mixed_precision != "no":
						__UpperCAmelCase       :	List[Any]						=      8
				else:
						__UpperCAmelCase       :	Any						=      None

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

		# Instantiate dataloaders.
		__UpperCAmelCase       :	Tuple						=      DataLoader(
		    tokenized_datasets['''train''']					,  shuffle=_UpperCAmelCase					,  collate_fn=_UpperCAmelCase					,  batch_size=_UpperCAmelCase		)
		__UpperCAmelCase       :	List[Any]						=      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 			a		( _UpperCAmelCase				:   int					,  _UpperCAmelCase				:   Dict		):







		'''simple docstring'''





		if os.environ.get('''TESTING_MOCKED_DATALOADERS'''					,  _UpperCAmelCase		) == "1":
				__UpperCAmelCase       :	int						=      2
		# Initialize accelerator
		__UpperCAmelCase       :	Union[str, 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       :	Union[str, Any]						=      config['''lr''']
		__UpperCAmelCase       :	int						=      int(config['''num_epochs''']		)
		__UpperCAmelCase       :	List[str]						=      int(config['''seed''']		)
		__UpperCAmelCase       :	str						=      int(config['''batch_size''']		)

		# New Code #
		# Parse out whether we are saving every epoch or after a certain number of batches
		if hasattr(args.checkpointing_steps					,  '''isdigit'''		):
				if args.checkpointing_steps == "epoch":
						__UpperCAmelCase       :	str						=      args.checkpointing_steps
				elif args.checkpointing_steps.isdigit():
						__UpperCAmelCase       :	Union[str, Any]						=      int(args.checkpointing_steps		)
				else:
						raise ValueError(
						    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'		)
		else:
				__UpperCAmelCase       :	Optional[int]						=      None

		set_seed(_UpperCAmelCase		)

		__UpperCAmelCase				,   __UpperCAmelCase       :	Tuple						=      get_dataloaders(_UpperCAmelCase					,  _UpperCAmelCase		)
		__UpperCAmelCase       :	str						=      evaluate.load('''glue'''					,  '''mrpc'''		)

		# If the batch size is too big we use gradient accumulation
		__UpperCAmelCase       :	List[Any]						=      1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				__UpperCAmelCase       :	Any						=      batch_size // MAX_GPU_BATCH_SIZE
				__UpperCAmelCase       :	List[Any]						=      MAX_GPU_BATCH_SIZE

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

		# Instantiate optimizer
		__UpperCAmelCase       :	List[Any]						=      AdamW(params=model.parameters()					,  lr=_UpperCAmelCase		)

		# Instantiate scheduler
		__UpperCAmelCase       :	Optional[int]						=      get_linear_schedule_with_warmup(
		    optimizer=_UpperCAmelCase					,  num_warmup_steps=1_00					,  num_training_steps=(len(_UpperCAmelCase		) * num_epochs) // gradient_accumulation_steps					,  )

		# 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       :	Optional[Any]						=      accelerator.prepare(
		    _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase		)

		# New Code #
		# We need to keep track of how many total steps we have iterated over
		__UpperCAmelCase       :	Optional[int]						=      0
		# We also need to keep track of the stating epoch so files are named properly
		__UpperCAmelCase       :	Tuple						=      0

		# We need to load the checkpoint back in before training here with `load_state`
		# The total number of epochs is adjusted based on where the state is being loaded from,
		# as we assume continuation of the same training script
		if args.resume_from_checkpoint:
				if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
						accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}'		)
						accelerator.load_state(args.resume_from_checkpoint		)
						__UpperCAmelCase       :	List[Any]						=      os.path.basename(args.resume_from_checkpoint		)
				else:
						# Get the most recent checkpoint
						__UpperCAmelCase       :	List[str]						=      [f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
						dirs.sort(key=os.path.getctime		)
						__UpperCAmelCase       :	str						=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract `epoch_{i}` or `step_{i}`
				__UpperCAmelCase       :	Union[str, Any]						=      os.path.splitext(_UpperCAmelCase		)[0]

				if "epoch" in training_difference:
						__UpperCAmelCase       :	Union[str, Any]						=      int(training_difference.replace('''epoch_'''					,  ''''''		)		) + 1
						__UpperCAmelCase       :	List[str]						=      None
				else:
						__UpperCAmelCase       :	str						=      int(training_difference.replace('''step_'''					,  ''''''		)		)
						__UpperCAmelCase       :	Dict						=      resume_step // len(_UpperCAmelCase		)
						resume_step -= starting_epoch * len(_UpperCAmelCase		)

    # Now we train the model
		for epoch in range(_UpperCAmelCase					,  _UpperCAmelCase		):
				model.train()
				# New Code #
				if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
						# We need to skip steps until we reach the resumed step
						__UpperCAmelCase       :	Any						=      accelerator.skip_first_batches(_UpperCAmelCase					,  _UpperCAmelCase		)
						overall_step += resume_step
				else:
						# After the first iteration though, we need to go back to the original dataloader
						__UpperCAmelCase       :	Optional[Any]						=      train_dataloader
				for step, batch in enumerate(_UpperCAmelCase		):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device		)
						__UpperCAmelCase       :	List[str]						=      model(**_UpperCAmelCase		)
						__UpperCAmelCase       :	str						=      outputs.loss
						__UpperCAmelCase       :	Any						=      loss / gradient_accumulation_steps
						accelerator.backward(_UpperCAmelCase		)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()
						# New Code #
						overall_step += 1

						# New Code #
						# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
						# These are saved to folders named `step_{overall_step}`
						# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
						# If mixed precision was used, will also save a "scalar.bin" file
						if isinstance(_UpperCAmelCase					,  _UpperCAmelCase		):
								__UpperCAmelCase       :	Any						=      f'step_{overall_step}'
								if overall_step % checkpointing_steps == 0:
										if args.output_dir is not None:
												__UpperCAmelCase       :	Optional[int]						=      os.path.join(args.output_dir					,  _UpperCAmelCase		)
										accelerator.save_state(_UpperCAmelCase		)

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

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

				# New Code #
				# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
				# These are saved to folders named `epoch_{epoch}`
				# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
				# If mixed precision was used, will also save a "scalar.bin" file
				if checkpointing_steps == "epoch":
						__UpperCAmelCase       :	List[Any]						=      f'epoch_{epoch}'
						if args.output_dir is not None:
								__UpperCAmelCase       :	Dict						=      os.path.join(args.output_dir					,  _UpperCAmelCase		)
						accelerator.save_state(_UpperCAmelCase		)

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Optional[Any]						=      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.'''		)
		parser.add_argument(
		    '''--checkpointing_steps'''					,  type=_UpperCAmelCase					,  default=_UpperCAmelCase					,  help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''					,  )
		parser.add_argument(
		    '''--output_dir'''					,  type=_UpperCAmelCase					,  default='''.'''					,  help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''					,  )
		parser.add_argument(
		    '''--resume_from_checkpoint'''					,  type=_UpperCAmelCase					,  default=_UpperCAmelCase					,  help='''If the training should continue from a checkpoint folder.'''					,  )
		__UpperCAmelCase       :	Union[str, Any]						=      parser.parse_args()
		__UpperCAmelCase       :	Dict						=      {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(_UpperCAmelCase					,  _UpperCAmelCase		)


if __name__ == "__main__":
				main()










class 						UpperCAmelCase__     :




			'''simple docstring'''






			def __init__(  self				:					List[Any]			,			a_				:					int					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      data
					__UpperCAmelCase       :	BinaryTreeNode | None						=      None
					__UpperCAmelCase       :	BinaryTreeNode | None						=      None

def 			a		( _UpperCAmelCase				:   BinaryTreeNode | None					,  _UpperCAmelCase				:   int		):







		'''simple docstring'''





		if node is None:
				__UpperCAmelCase       :	List[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       :	int						=      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       :	str						=      insert(node.right_child					,  _UpperCAmelCase		)
		return node

def 			a		( _UpperCAmelCase				:   None | BinaryTreeNode		):  # if node is None,return







		'''simple docstring'''





		if node:
				__UpperCAmelCase       :	Optional[int]						=      inorder(node.left_child		)
				__UpperCAmelCase       :	int						=      [*inorder_array, node.data]
				__UpperCAmelCase       :	Optional[Any]						=      inorder_array + inorder(node.right_child		)
		else:
				__UpperCAmelCase       :	List[Any]						=      []
		return inorder_array

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Any						=      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		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      make_tree()
		print('''Printing values of binary search tree in Inorder Traversal.'''		)
		inorder(_UpperCAmelCase		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()





