Utilities and Helpers

def convert_tensor(input_, device=None, non_blocking=False):
    """
    Convert input to tensor and move to specified device.
    
    Parameters:
    - input_: input data (tensor, numpy array, list, etc.)
    - device: target device (torch.device, string, or None)
    - non_blocking: whether to use non-blocking transfer
    
    Returns:
    Converted tensor on target device
    """

def to_onehot(indices, num_classes):
    """
    Convert indices to one-hot encoding.
    
    Parameters:
    - indices: tensor of class indices
    - num_classes: total number of classes
    
    Returns:
    One-hot encoded tensor
    """

def apply_to_tensor(input_, func):
    """
    Apply function to all tensors in nested structure.
    
    Parameters:
    - input_: nested structure containing tensors
    - func: function to apply to each tensor
    
    Returns:
    Structure with function applied to all tensors
    """

def apply_to_type(input_, input_type, func):
    """
    Apply function to all objects of specific type in nested structure.
    
    Parameters:
    - input_: nested structure
    - input_type: type to match
    - func: function to apply to matched objects
    
    Returns:
    Structure with function applied to matched objects
    """

def setup_logger(name=None, level=logging.INFO, stream=None, format="%(asctime)s %(name)s %(levelname)s %(message)s", filepath=None, distributed_rank=None):
    """
    Setup logger with specified configuration.
    
    Parameters:
    - name: logger name (default: None for root logger)
    - level: logging level (default: logging.INFO)
    - stream: output stream (default: None for sys.stderr)
    - format: log message format string
    - filepath: path to log file (optional)
    - distributed_rank: rank for distributed training (optional)
    
    Returns:
    Configured logger instance
    """

def manual_seed(seed):
    """
    Set manual seed for reproducibility across all relevant libraries.
    
    Parameters:
    - seed: random seed value
    
    Sets seeds for:
    - Python random module
    - NumPy random state
    - PyTorch random state
    - PyTorch CUDA random state (if available)
    """

from ignite.utils import convert_tensor
import torch

# Convert various inputs to tensors
data = [1, 2, 3, 4, 5]
tensor_data = convert_tensor(data)  # Convert list to tensor

# Move to specific device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tensor_on_device = convert_tensor(data, device=device)

# Non-blocking transfer for performance
tensor_nb = convert_tensor(data, device=device, non_blocking=True)

# Works with numpy arrays
import numpy as np
np_array = np.array([1.0, 2.0, 3.0])
tensor_from_np = convert_tensor(np_array, device=device)

from ignite.utils import to_onehot
import torch

# Convert class indices to one-hot
indices = torch.tensor([0, 1, 2, 1, 0])
num_classes = 3
onehot = to_onehot(indices, num_classes)
print(onehot)
# Output: tensor([[1, 0, 0],
#                 [0, 1, 0],
#                 [0, 0, 1],
#                 [0, 1, 0],
#                 [1, 0, 0]])

# Works with batch dimensions
batch_indices = torch.tensor([[0, 1], [2, 1]])
batch_onehot = to_onehot(batch_indices, num_classes)
print(batch_onehot.shape)  # torch.Size([2, 2, 3])

from ignite.utils import apply_to_tensor, apply_to_type
import torch

# Apply function to all tensors in nested structure
data = {
    'input': torch.randn(10, 5),
    'target': torch.randint(0, 2, (10,)),
    'metadata': {
        'weights': torch.ones(10),
        'info': 'some string'
    }
}

# Move all tensors to GPU
gpu_data = apply_to_tensor(data, lambda x: x.cuda() if torch.cuda.is_available() else x)

# Apply function to specific types
def double_tensors(x):
    return x * 2

doubled_data = apply_to_type(data, torch.Tensor, double_tensors)

# Custom type matching
class CustomClass:
    def __init__(self, value):
        self.value = value

data_with_custom = {
    'tensor': torch.randn(5),
    'custom': CustomClass(42),
    'nested': {
        'another_custom': CustomClass(24)
    }
}

def modify_custom(obj):
    obj.value *= 2
    return obj

modified_data = apply_to_type(data_with_custom, CustomClass, modify_custom)

from ignite.utils import setup_logger
import logging

# Basic logger setup
logger = setup_logger()
logger.info("This is a basic log message")

# Custom logger with specific level
debug_logger = setup_logger(name="debug_logger", level=logging.DEBUG)
debug_logger.debug("Debug message")

# Logger with file output
file_logger = setup_logger(
    name="file_logger",
    level=logging.INFO,
    filepath="training.log",
    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s"
)
file_logger.info("This will be written to training.log")

# Distributed training logger (only logs on rank 0)
import ignite.distributed as idist

dist_logger = setup_logger(
    name="distributed_logger",
    distributed_rank=idist.rank()
)
dist_logger.info("This only appears on rank 0")

from ignite.utils import manual_seed

# Set seed for reproducibility
manual_seed(42)

# Now all random operations will be reproducible
import torch
import numpy as np
import random

# These will produce the same results across runs
torch_random = torch.randn(3, 3)
np_random = np.random.randn(3, 3)
py_random = random.random()

# For complete reproducibility in training
manual_seed(42)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

# Your training code here - results will be reproducible

from ignite.engine import Engine, Events
from ignite.utils import convert_tensor, setup_logger

# Setup logger for training
logger = setup_logger(name="trainer")

def train_step(engine, batch):
    model.train()
    optimizer.zero_grad()
    
    # Use convert_tensor for device management
    x, y = batch
    x = convert_tensor(x, device=device, non_blocking=True)
    y = convert_tensor(y, device=device, non_blocking=True)
    
    y_pred = model(x)
    loss = criterion(y_pred, y)
    loss.backward()
    optimizer.step()
    
    return loss.item()

trainer = Engine(train_step)

@trainer.on(Events.EPOCH_COMPLETED)
def log_results(engine):
    logger.info(f"Epoch {engine.state.epoch} completed")

# Set reproducible seed before training
manual_seed(42)
trainer.run(train_loader, max_epochs=10)

from ignite.utils import apply_to_tensor
import torch

def prepare_batch(batch, device=None, non_blocking=False):
    """Custom batch preparation function."""
    # Move all tensors in batch to device
    def move_to_device(tensor):
        if device is not None:
            return tensor.to(device, non_blocking=non_blocking)
        return tensor
    
    return apply_to_tensor(batch, move_to_device)

# Use in training
def train_step(engine, batch):
    batch = prepare_batch(batch, device=device, non_blocking=True)
    # ... rest of training step