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from typing import Protocol, Any
import torch
from tqdm import tqdm

class DataProvider(Protocol):
    def get_train_loader(self) -> Any:
        ...

class Trainer:
    def __init__(self, model: torch.nn.Module, optimizer: torch.optim.Optimizer, criterion: Any, epochs: int = 5):
        self.model = model
        self.optimizer = optimizer
        self.criterion = criterion
        self.epochs = epochs

    def fit(self, data_provider: DataProvider):
        train_loader = data_provider.get_train_loader()

        # Detect device (MPS for Mac M4, CUDA for Nvidia, or CPU)
        device = torch.device("mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu")
        print(f"Training on device: {device}")

        # push model to the accelaration device
        self.model.to(device)

        for epoch in range(self.epochs):
            self.model.train()
            running_loss = 0.0

            pbar = tqdm(train_loader, desc=f"Epoch {epoch+1}/{self.epochs}", leave=False)

            for batch in pbar:
                # the inputs are the images, and the targets are the labels (the answers )
                inputs, targets = batch
                # clears the stored gradients from the previous step
                # necessary because PyTorch accumulates the weights on backward
                # if we don't clean this up, it will explode the weights corrupting the training
                self.optimizer.zero_grad()
                # pushes image data into Apple Silicon chip or equivalent
                inputs = inputs.to(device)
                targets = targets.to(device)
                # feeds the model with inputs through model's layers to generate new model predictions
                outputs = self.model(inputs)
                # compares models outputs with the answers
                loss = self.criterion(outputs, targets)
                loss.backward()
                self.optimizer.step()

                running_loss += loss.item()

                pbar.set_postfix(loss=f"{loss.item():.4f}")

                avg_loss = running_loss / len(train_loader)

            avg_loss = running_loss / len(train_loader)
            print(f"Epoch [{epoch+1}/{self.epochs}] - Loss: {avg_loss:.4f}")

	from typing import Protocol, Any
	import torch
	from tqdm import tqdm

	class DataProvider(Protocol):
	def get_train_loader(self) -> Any:
	...

	class Trainer:
	def __init__(self, model: torch.nn.Module, optimizer: torch.optim.Optimizer, criterion: Any, epochs: int = 5):
	self.model = model
	self.optimizer = optimizer
	self.criterion = criterion
	self.epochs = epochs

	def fit(self, data_provider: DataProvider):
	train_loader = data_provider.get_train_loader()

	# Detect device (MPS for Mac M4, CUDA for Nvidia, or CPU)
	device = torch.device("mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu")
	print(f"Training on device: {device}")

	# push model to the accelaration device
	self.model.to(device)

	for epoch in range(self.epochs):
	self.model.train()
	running_loss = 0.0

	pbar = tqdm(train_loader, desc=f"Epoch {epoch+1}/{self.epochs}", leave=False)

	for batch in pbar:
	# the inputs are the images, and the targets are the labels (the answers )
	inputs, targets = batch
	# clears the stored gradients from the previous step
	# necessary because PyTorch accumulates the weights on backward
	# if we don't clean this up, it will explode the weights corrupting the training
	self.optimizer.zero_grad()
	# pushes image data into Apple Silicon chip or equivalent
	inputs = inputs.to(device)
	targets = targets.to(device)
	# feeds the model with inputs through model's layers to generate new model predictions
	outputs = self.model(inputs)
	# compares models outputs with the answers
	loss = self.criterion(outputs, targets)
	loss.backward()
	self.optimizer.step()

	running_loss += loss.item()

	pbar.set_postfix(loss=f"{loss.item():.4f}")

	avg_loss = running_loss / len(train_loader)

	avg_loss = running_loss / len(train_loader)
	print(f"Epoch [{epoch+1}/{self.epochs}] - Loss: {avg_loss:.4f}")