//! ML model templates (Python)

use crate::utils::to_pascal_case;

/// Generate classifier model template
pub fn classifier_model(name: &str) -> String {
    format!(
        r#"""
{} - Image Classifier Model

Auto-generated model template.
"""

import torch
import torch.nn as nn
import torchvision.transforms as transforms


class {}(nn.Module):
    """Simple CNN classifier"""

    def __init__(self, num_classes=10):
        super().__init__()

        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )

        self.classifier = nn.Sequential(
            nn.Linear(64 * 56 * 56, 512),
            nn.ReLU(inplace=True),
            nn.Dropout(0.5),
            nn.Linear(512, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x


def load_model(checkpoint_path=None):
    """Load model from checkpoint"""
    model = {}()

    if checkpoint_path:
        model.load_state_dict(torch.load(checkpoint_path))
        model.eval()

    return model


def preprocess(image):
    """Preprocess image for inference"""
    transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                           std=[0.229, 0.224, 0.225]),
    ])
    return transform(image).unsqueeze(0)
"#,
        name,
        to_pascal_case(name),
        to_pascal_case(name)
    )
}

/// Generate detector model template
pub fn detector_model(name: &str) -> String {
    format!(
        r#"""
{} - Object Detector Model

Auto-generated model template.
"""

import torch
import torch.nn as nn
import torchvision.transforms as transforms


class {}(nn.Module):
    """Simple object detector"""

    def __init__(self, num_classes=80):
        super().__init__()

        # TODO: Implement detector architecture
        # Consider using pretrained models like:
        # - torchvision.models.detection.fasterrcnn_resnet50_fpn
        # - torchvision.models.detection.retinanet_resnet50_fpn
        pass

    def forward(self, images):
        # TODO: Implement forward pass
        # Return: List[Dict[str, Tensor]] with keys:
        #   - boxes: [N, 4] (x1, y1, x2, y2)
        #   - labels: [N]
        #   - scores: [N]
        pass


def load_model(checkpoint_path=None):
    """Load model from checkpoint"""
    model = {}()

    if checkpoint_path:
        model.load_state_dict(torch.load(checkpoint_path))
        model.eval()

    return model


def preprocess(image):
    """Preprocess image for inference"""
    transform = transforms.Compose([
        transforms.ToTensor(),
    ])
    return transform(image)
"#,
        name,
        to_pascal_case(name),
        to_pascal_case(name)
    )
}

/// Generate RL policy template
pub fn rl_policy(name: &str) -> String {
    format!(
        r#"""
{} - RL Policy

Auto-generated policy template.
"""

from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv
import gymnasium as gym


def create_env():
    """Create training environment"""
    # TODO: Replace with your custom environment
    env = gym.make("CartPole-v1")
    return env


def train_policy(total_timesteps=100000):
    """Train RL policy"""

    # Create environment
    env = DummyVecEnv([create_env])

    # Create model
    model = PPO(
        "MlpPolicy",
        env,
        learning_rate=0.0003,
        n_steps=2048,
        batch_size=64,
        n_epochs=10,
        gamma=0.99,
        gae_lambda=0.95,
        verbose=1,
    )

    # Train
    model.learn(total_timesteps=total_timesteps)

    # Save
    model.save("{}_policy")

    return model


def load_policy(path="{}_policy"):
    """Load trained policy"""
    model = PPO.load(path)
    return model


if __name__ == "__main__":
    # Train policy
    model = train_policy()

    # Test policy
    env = create_env()
    obs, _ = env.reset()

    for _ in range(1000):
        action, _ = model.predict(obs, deterministic=True)
        obs, reward, terminated, truncated, info = env.step(action)

        if terminated or truncated:
            obs, _ = env.reset()

    env.close()
"#,
        name, name, name
    )
}

/// Generate LLM integration template
pub fn llm(name: &str) -> String {
    format!(
        r#"""
{} - LLM Integration

Auto-generated LLM template.
"""

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch


class {}:
    """LLM wrapper for robot control"""

    def __init__(self, model_name="gpt2"):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"

        # Load model and tokenizer
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForCausalLM.from_pretrained(model_name)
        self.model.to(self.device)
        self.model.eval()

    def generate_response(self, prompt, max_length=100):
        """Generate text response"""

        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)

        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_length=max_length,
                num_return_sequences=1,
                temperature=0.7,
                do_sample=True,
            )

        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        return response

    def parse_command(self, text):
        """Parse natural language command"""
        # TODO: Implement command parsing
        # Extract robot actions from LLM response
        pass


if __name__ == "__main__":
    llm = {}()

    # Test generation
    prompt = "Robot, please pick up the red cup"
    response = llm.generate_response(prompt)
    print(f"Response: {{response}}")
"#,
        name,
        to_pascal_case(name),
        to_pascal_case(name)
    )
}
