





'''simple docstring'''

import argparse

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
#
# 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 : List[Any]											=			16
__lowerCamelCase : Union[str, Any]											=			32







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_ = 16							):

				"""simple docstring"""






				lowercase         = AutoTokenizer.from_pretrained("bert-base-cased"							)
				lowercase         = load_dataset("glue"		,							"mrpc"							)

				def tokenize_function(lowerCAmelCase_							):
								# max_length=None => use the model max length (it's actually the default)
								lowercase         = tokenizer(examples["sentence1"]		,							examples["sentence2"]		,							truncation=lowerCAmelCase_		,							max_length=lowerCAmelCase_							)
								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(
								    lowerCAmelCase_		,							batched=lowerCAmelCase_		,							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(lowerCAmelCase_							):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowercase         = 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":
												lowercase         = 16
								elif accelerator.mixed_precision != "no":
												lowercase         = 8
								else:
												lowercase         = None

								return tokenizer.pad(
								    lowerCAmelCase_		,							padding="longest"		,							max_length=lowerCAmelCase_		,							pad_to_multiple_of=lowerCAmelCase_		,							return_tensors="pt"		,							)

				# Instantiate dataloaders.
				lowercase         = DataLoader(
				    tokenized_datasets["train"]		,							shuffle=lowerCAmelCase_		,							collate_fn=lowerCAmelCase_		,							batch_size=lowerCAmelCase_		,							drop_last=lowerCAmelCase_							)
				lowercase         = DataLoader(
				    tokenized_datasets["validation"]		,							shuffle=lowerCAmelCase_		,							collate_fn=lowerCAmelCase_		,							batch_size=lowerCAmelCase_		,							drop_last=(accelerator.mixed_precision == "fp8")		,							)

				return train_dataloader, eval_dataloader







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = Accelerator(cpu=args.cpu		,							mixed_precision=args.mixed_precision							)
				# 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"							)

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

				set_seed(lowerCAmelCase_							)
				lowercase   ,							lowercase         = get_dataloaders(lowerCAmelCase_		,							lowerCAmelCase_							)
				# 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=lowerCAmelCase_							)

				# 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=lowerCAmelCase_							)

				# Instantiate scheduler
				lowercase         = get_linear_schedule_with_warmup(
				    optimizer=lowerCAmelCase_		,							num_warmup_steps=100		,							num_training_steps=(len(lowerCAmelCase_							) * 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.

				lowercase   ,							lowercase   ,							lowercase   ,							lowercase   ,							lowercase         = accelerator.prepare(
				    lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							)

				# Now we train the model
				for epoch in range(lowerCAmelCase_							):
								model.train()
								for step, batch in enumerate(lowerCAmelCase_							):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device							)
												lowercase         = model(**lowerCAmelCase_							)
												lowercase         = outputs.loss
												lowercase         = loss / gradient_accumulation_steps
												accelerator.backward(lowerCAmelCase_							)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

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







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = argparse.ArgumentParser(description="Simple example of training script."							)
				parser.add_argument(
				    "--mixed_precision"		,							type=lowerCAmelCase_		,							default=lowerCAmelCase_		,							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."							)
				lowercase         = parser.parse_args()
				lowercase         = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
				training_function(lowerCAmelCase_		,							lowerCAmelCase_							)


if __name__ == "__main__":
			main()











'''simple docstring'''

from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any

@dataclass
class       UpperCAmelCase :
		UpperCAmelCase					:	int
		UpperCAmelCase					:	Node | None 			=		None
		UpperCAmelCase					:	Node | None 			=		None







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = Node(1							)
				lowercase         = Node(2							)
				lowercase         = Node(3							)
				lowercase         = Node(4							)
				lowercase         = Node(5							)
				return tree







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return [root.data, *preorder(root.left							), *preorder(root.right							)] if root else []







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return postorder(root.left							) + postorder(root.right							) + [root.data] if root else []







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return [*inorder(root.left							), root.data, *inorder(root.right							)] if root else []







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return (max(height(root.left							)		,							height(root.right							)							) + 1) if root else 0







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = []

				if root is None:
								return output

				lowercase         = deque([root]							)

				while process_queue:
								lowercase         = process_queue.popleft()
								output.append(node.data							)

								if node.left:
												process_queue.append(node.left							)
								if node.right:
												process_queue.append(node.right							)
				return output







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = []

				def populate_output(lowerCAmelCase_		,							lowerCAmelCase_							) -> None:
								if not root:
												return
								if level == 1:
												output.append(root.data							)
								elif level > 1:
												populate_output(root.left		,							level - 1							)
												populate_output(root.right		,							level - 1							)

				populate_output(lowerCAmelCase_		,							lowerCAmelCase_							)
				return output







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = []

				def populate_output(lowerCAmelCase_		,							lowerCAmelCase_							) -> None:
								if root is None:
												return
								if level == 1:
												output.append(root.data							)
								elif level > 1:
												populate_output(root.right		,							level - 1							)
												populate_output(root.left		,							level - 1							)

				populate_output(lowerCAmelCase_		,							lowerCAmelCase_							)
				return output







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				if root is None:
								return []

				lowercase         = []

				lowercase         = 0
				lowercase         = height(lowerCAmelCase_							)

				for h in range(1		,							height_tree + 1							):
								if not flag:
												output.append(get_nodes_from_left_to_right(lowerCAmelCase_		,							lowerCAmelCase_							)							)
												lowercase         = 1
								else:
												output.append(get_nodes_from_right_to_left(lowerCAmelCase_		,							lowerCAmelCase_							)							)
												lowercase         = 0

				return output







def 							UpperCAmelCase_    (	):  # Main function for testing.

				"""simple docstring"""






				lowercase         = make_tree()
				print(f'In-order Traversal: {inorder(lowerCAmelCase_							)}'							)
				print(f'Pre-order Traversal: {preorder(lowerCAmelCase_							)}'							)
				print(f'Post-order Traversal: {postorder(lowerCAmelCase_							)}'		,							"\n"							)

				print(f'Height of Tree: {height(lowerCAmelCase_							)}'		,							"\n"							)

				print("Complete Level Order Traversal: "							)
				print(level_order(lowerCAmelCase_							)		,							"\n"							)

				print("Level-wise order Traversal: "							)

				for level in range(1		,							height(lowerCAmelCase_							) + 1							):
								print(f'Level {level}:'		,							get_nodes_from_left_to_right(lowerCAmelCase_		,							level=lowerCAmelCase_							)							)

				print("\nZigZag order Traversal: "							)
				print(zigzag(lowerCAmelCase_							)							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()




