# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
#   - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch

# This is a summary file containing the main takeaways from chapter 6.

import urllib.request
import zipfile
import os
from pathlib import Path
import time

import matplotlib.pyplot as plt
import pandas as pd
import tiktoken
import torch
from torch.utils.data import Dataset, DataLoader

from gpt_download import download_and_load_gpt2
from previous_chapters import GPTModel, load_weights_into_gpt


def download_and_unzip_spam_data(url, zip_path, extracted_path, data_file_path):
    if data_file_path.exists():
        print(f"{data_file_path} already exists. Skipping download and extraction.")
        return

    # Downloading the file
    with urllib.request.urlopen(url) as response:
        with open(zip_path, "wb") as out_file:
            out_file.write(response.read())

    # Unzipping the file
    with zipfile.ZipFile(zip_path, "r") as zip_ref:
        zip_ref.extractall(extracted_path)

    # Add .tsv file extension
    original_file_path = Path(extracted_path) / "SMSSpamCollection"
    os.rename(original_file_path, data_file_path)
    print(f"File downloaded and saved as {data_file_path}")


def create_balanced_dataset(df):
    # Count the instances of "spam"
    num_spam = df[df["Label"] == "spam"].shape[0]

    # Randomly sample "ham" instances to match the number of "spam" instances
    ham_subset = df[df["Label"] == "ham"].sample(num_spam, random_state=123)

    # Combine ham "subset" with "spam"
    balanced_df = pd.concat([ham_subset, df[df["Label"] == "spam"]])

    return balanced_df


def random_split(df, train_frac, validation_frac):
    # Shuffle the entire DataFrame
    df = df.sample(frac=1, random_state=123).reset_index(drop=True)

    # Calculate split indices
    train_end = int(len(df) * train_frac)
    validation_end = train_end + int(len(df) * validation_frac)

    # Split the DataFrame
    train_df = df[:train_end]
    validation_df = df[train_end:validation_end]
    test_df = df[validation_end:]

    return train_df, validation_df, test_df


class SpamDataset(Dataset):
    def __init__(self, csv_file, tokenizer, max_length=None, pad_token_id=50256):
        self.data = pd.read_csv(csv_file)

        # Pre-tokenize texts
        self.encoded_texts = [
            tokenizer.encode(text) for text in self.data["Text"]
        ]

        if max_length is None:
            self.max_length = self._longest_encoded_length()
        else:
            self.max_length = max_length
            # Truncate sequences if they are longer than max_length
            self.encoded_texts = [
                encoded_text[:self.max_length]
                for encoded_text in self.encoded_texts
            ]

        # Pad sequences to the longest sequence
        self.encoded_texts = [
            encoded_text + [pad_token_id] * (self.max_length - len(encoded_text))
            for encoded_text in self.encoded_texts
        ]

    def __getitem__(self, index):
        encoded = self.encoded_texts[index]
        label = self.data.iloc[index]["Label"]
        return (
            torch.tensor(encoded, dtype=torch.long),
            torch.tensor(label, dtype=torch.long)
        )

    def __len__(self):
        return len(self.data)

    def _longest_encoded_length(self):
        max_length = 0
        for encoded_text in self.encoded_texts:
            encoded_length = len(encoded_text)
            if encoded_length > max_length:
                max_length = encoded_length
        return max_length
        # Note: A more pythonic version to implement this method
        # is the following, which is also used in the next chapter:
        # return max(len(encoded_text) for encoded_text in self.encoded_texts)


def calc_accuracy_loader(data_loader, model, device, num_batches=None):
    model.eval()
    correct_predictions, num_examples = 0, 0

    if num_batches is None:
        num_batches = len(data_loader)
    else:
        num_batches = min(num_batches, len(data_loader))
    for i, (input_batch, target_batch) in enumerate(data_loader):
        if i < num_batches:
            input_batch, target_batch = input_batch.to(device), target_batch.to(device)

            with torch.no_grad():
                logits = model(input_batch)[:, -1, :]  # Logits of last output token
            predicted_labels = torch.argmax(logits, dim=-1)

            num_examples += predicted_labels.shape[0]
            correct_predictions += (predicted_labels == target_batch).sum().item()
        else:
            break
    return correct_predictions / num_examples


def calc_loss_batch(input_batch, target_batch, model, device):
    input_batch, target_batch = input_batch.to(device), target_batch.to(device)
    logits = model(input_batch)[:, -1, :]  # Logits of last output token
    loss = torch.nn.functional.cross_entropy(logits, target_batch)
    return loss


def calc_loss_loader(data_loader, model, device, num_batches=None):
    total_loss = 0.
    if len(data_loader) == 0:
        return float("nan")
    elif num_batches is None:
        num_batches = len(data_loader)
    else:
        num_batches = min(num_batches, len(data_loader))
    for i, (input_batch, target_batch) in enumerate(data_loader):
        if i < num_batches:
            loss = calc_loss_batch(input_batch, target_batch, model, device)
            total_loss += loss.item()
        else:
            break
    return total_loss / num_batches


def evaluate_model(model, train_loader, val_loader, device, eval_iter):
    model.eval()
    with torch.no_grad():
        train_loss = calc_loss_loader(train_loader, model, device, num_batches=eval_iter)
        val_loss = calc_loss_loader(val_loader, model, device, num_batches=eval_iter)
    model.train()
    return train_loss, val_loss


def train_classifier_simple(model, train_loader, val_loader, optimizer, device, num_epochs,
                            eval_freq, eval_iter, tokenizer):
    # Initialize lists to track losses and tokens seen
    train_losses, val_losses, train_accs, val_accs = [], [], [], []
    examples_seen, global_step = 0, -1

    # Main training loop
    for epoch in range(num_epochs):
        model.train()  # Set model to training mode

        for input_batch, target_batch in train_loader:
            optimizer.zero_grad()  # Reset loss gradients from previous batch iteration
            loss = calc_loss_batch(input_batch, target_batch, model, device)
            loss.backward()  # Calculate loss gradients
            optimizer.step()  # Update model weights using loss gradients
            examples_seen += input_batch.shape[0]  # New: track examples instead of tokens
            global_step += 1

            # Optional evaluation step
            if global_step % eval_freq == 0:
                train_loss, val_loss = evaluate_model(
                    model, train_loader, val_loader, device, eval_iter)
                train_losses.append(train_loss)
                val_losses.append(val_loss)
                print(f"Ep {epoch+1} (Step {global_step:06d}): "
                      f"Train loss {train_loss:.3f}, Val loss {val_loss:.3f}")

        # Calculate accuracy after each epoch
        train_accuracy = calc_accuracy_loader(train_loader, model, device, num_batches=eval_iter)
        val_accuracy = calc_accuracy_loader(val_loader, model, device, num_batches=eval_iter)
        print(f"Training accuracy: {train_accuracy*100:.2f}% | ", end="")
        print(f"Validation accuracy: {val_accuracy*100:.2f}%")
        train_accs.append(train_accuracy)
        val_accs.append(val_accuracy)

    return train_losses, val_losses, train_accs, val_accs, examples_seen


def plot_values(epochs_seen, examples_seen, train_values, val_values, label="loss"):
    fig, ax1 = plt.subplots(figsize=(5, 3))

    # Plot training and validation loss against epochs
    ax1.plot(epochs_seen, train_values, label=f"Training {label}")
    ax1.plot(epochs_seen, val_values, linestyle="-.", label=f"Validation {label}")
    ax1.set_xlabel("Epochs")
    ax1.set_ylabel(label.capitalize())
    ax1.legend()

    # Create a second x-axis for tokens seen
    ax2 = ax1.twiny()  # Create a second x-axis that shares the same y-axis
    ax2.plot(examples_seen, train_values, alpha=0)  # Invisible plot for aligning ticks
    ax2.set_xlabel("Examples seen")

    fig.tight_layout()  # Adjust layout to make room
    plt.savefig(f"{label}-plot.pdf")
    # plt.show()


if __name__ == "__main__":

    import argparse

    parser = argparse.ArgumentParser(
        description="Finetune a GPT model for classification"
    )
    parser.add_argument(
        "--test_mode",
        default=False,
        action="store_true",
        help=("This flag runs the model in test mode for internal testing purposes. "
              "Otherwise, it runs the model as it is used in the chapter (recommended).")
    )
    args = parser.parse_args()

    ########################################
    # Download and prepare dataset
    ########################################

    url = "https://archive.ics.uci.edu/static/public/228/sms+spam+collection.zip"
    zip_path = "sms_spam_collection.zip"
    extracted_path = "sms_spam_collection"
    data_file_path = Path(extracted_path) / "SMSSpamCollection.tsv"

    try:
        download_and_unzip_spam_data(url, zip_path, extracted_path, data_file_path)
    except (urllib.error.HTTPError, urllib.error.URLError, TimeoutError) as e:
        print(f"Primary URL failed: {e}. Trying backup URL...")
        url = "https://f001.backblazeb2.com/file/LLMs-from-scratch/sms%2Bspam%2Bcollection.zip"
        download_and_unzip_spam_data(url, zip_path, extracted_path, data_file_path)

    df = pd.read_csv(data_file_path, sep="\t", header=None, names=["Label", "Text"])
    balanced_df = create_balanced_dataset(df)
    balanced_df["Label"] = balanced_df["Label"].map({"ham": 0, "spam": 1})

    train_df, validation_df, test_df = random_split(balanced_df, 0.7, 0.1)
    train_df.to_csv("train.csv", index=None)
    validation_df.to_csv("validation.csv", index=None)
    test_df.to_csv("test.csv", index=None)

    ########################################
    # Create data loaders
    ########################################
    tokenizer = tiktoken.get_encoding("gpt2")

    train_dataset = SpamDataset(
        csv_file="train.csv",
        max_length=None,
        tokenizer=tokenizer
    )

    val_dataset = SpamDataset(
        csv_file="validation.csv",
        max_length=train_dataset.max_length,
        tokenizer=tokenizer
    )

    test_dataset = SpamDataset(
        csv_file="test.csv",
        max_length=train_dataset.max_length,
        tokenizer=tokenizer
    )

    num_workers = 0
    batch_size = 8

    torch.manual_seed(123)

    train_loader = DataLoader(
        dataset=train_dataset,
        batch_size=batch_size,
        shuffle=True,
        num_workers=num_workers,
        drop_last=True,
    )

    val_loader = DataLoader(
        dataset=val_dataset,
        batch_size=batch_size,
        num_workers=num_workers,
        drop_last=False,
    )

    test_loader = DataLoader(
        dataset=test_dataset,
        batch_size=batch_size,
        num_workers=num_workers,
        drop_last=False,
    )

    ########################################
    # Load pretrained model
    ########################################

    # Small GPT model for testing purposes
    if args.test_mode:
        BASE_CONFIG = {
            "vocab_size": 50257,
            "context_length": 120,
            "drop_rate": 0.0,
            "qkv_bias": False,
            "emb_dim": 12,
            "n_layers": 1,
            "n_heads": 2
        }
        model = GPTModel(BASE_CONFIG)
        model.eval()
        device = "cpu"

    # Code as it is used in the main chapter
    else:
        CHOOSE_MODEL = "gpt2-small (124M)"
        INPUT_PROMPT = "Every effort moves"

        BASE_CONFIG = {
            "vocab_size": 50257,     # Vocabulary size
            "context_length": 1024,  # Context length
            "drop_rate": 0.0,        # Dropout rate
            "qkv_bias": True         # Query-key-value bias
        }

        model_configs = {
            "gpt2-small (124M)": {"emb_dim": 768, "n_layers": 12, "n_heads": 12},
            "gpt2-medium (355M)": {"emb_dim": 1024, "n_layers": 24, "n_heads": 16},
            "gpt2-large (774M)": {"emb_dim": 1280, "n_layers": 36, "n_heads": 20},
            "gpt2-xl (1558M)": {"emb_dim": 1600, "n_layers": 48, "n_heads": 25},
        }

        BASE_CONFIG.update(model_configs[CHOOSE_MODEL])

        assert train_dataset.max_length <= BASE_CONFIG["context_length"], (
            f"Dataset length {train_dataset.max_length} exceeds model's context "
            f"length {BASE_CONFIG['context_length']}. Reinitialize data sets with "
            f"`max_length={BASE_CONFIG['context_length']}`"
        )

        model_size = CHOOSE_MODEL.split(" ")[-1].lstrip("(").rstrip(")")
        settings, params = download_and_load_gpt2(model_size=model_size, models_dir="gpt2")

        model = GPTModel(BASE_CONFIG)
        load_weights_into_gpt(model, params)
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    ########################################
    # Modify and pretrained model
    ########################################

    for param in model.parameters():
        param.requires_grad = False

    torch.manual_seed(123)

    num_classes = 2
    model.out_head = torch.nn.Linear(in_features=BASE_CONFIG["emb_dim"], out_features=num_classes)
    model.to(device)

    for param in model.trf_blocks[-1].parameters():
        param.requires_grad = True

    for param in model.final_norm.parameters():
        param.requires_grad = True

    ########################################
    # Finetune modified model
    ########################################

    start_time = time.time()
    torch.manual_seed(123)

    optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5, weight_decay=0.1)

    num_epochs = 5
    train_losses, val_losses, train_accs, val_accs, examples_seen = train_classifier_simple(
        model, train_loader, val_loader, optimizer, device,
        num_epochs=num_epochs, eval_freq=50, eval_iter=5,
        tokenizer=tokenizer
    )

    end_time = time.time()
    execution_time_minutes = (end_time - start_time) / 60
    print(f"Training completed in {execution_time_minutes:.2f} minutes.")

    ########################################
    # Plot results
    ########################################

    # loss plot
    epochs_tensor = torch.linspace(0, num_epochs, len(train_losses))
    examples_seen_tensor = torch.linspace(0, examples_seen, len(train_losses))
    plot_values(epochs_tensor, examples_seen_tensor, train_losses, val_losses)

    # accuracy plot
    epochs_tensor = torch.linspace(0, num_epochs, len(train_accs))
    examples_seen_tensor = torch.linspace(0, examples_seen, len(train_accs))
    plot_values(epochs_tensor, examples_seen_tensor, train_accs, val_accs, label="accuracy")
