#!/usr/bin/env python3
# pairwise_compare.py
import logging
import random
import lzma

import torch
from torch import nn
from torch.nn import functional as F

import re
import pandas as pd
import matplotlib.pyplot as plt

import pairwise_comp_nn as comp_nn

# early pytorch device setup
DEVICE = torch.accelerator.current_accelerator() if torch.accelerator.is_available() else "cpu"

# Valves
DIMENSIONS = 2
TRAIN_STEPS = 5000
TRAIN_BATCHSZ = 8192
TRAIN_PROGRESS = 10
BATCH_LOWER = -100.0
BATCH_UPPER = 100.0
DO_VERBOSE_EARLY_TRAIN = False

# Files
MODEL_PATH = "./files/pwcomp.model"
LOGGING_PATH = "./files/output.log"
EMBED_CHART_PATH = "./files/embedding_chart.png"
EMBEDDINGS_DATA_PATH = "./files/embedding_data.csv"
TRAINING_LOG_PATH = "./files/training.log.xz"

def parse_training_log(file_path: str) -> pd.DataFrame:
    text: str = ""
    with lzma.open(file_path, mode='rt') as f:
        text = f.read()
        

    pattern = re.compile(r"step=\s*(\d+)\s+loss=([0-9.]+)\s+acc=([0-9.]+)")
    rows = [(int(s), float(l), float(a)) for s, l, a in pattern.findall(text)]
    df = pd.DataFrame(rows, columns=["step", "loss", "acc"]).sort_values("step").reset_index(drop=True)

    # Avoid log(0) issues for loss plot by clamping at a tiny positive value
    eps = 1e-10
    df["loss_clamped"] = df["loss"].clip(lower=eps)

    return df

def plt_loss_tstep(df: pd.DataFrame) -> None:
    # Plot 1: Loss
    plt.figure(figsize=(8, 4))
    plt.plot(df["step"], df["loss_clamped"])
    plt.yscale("log")
    plt.xlabel("Step")
    plt.ylabel("Loss (log scale)")
    plt.title("Training Loss vs Step")
    plt.tight_layout()
    plt.savefig('./files/training_loss_v_step.png')
    plt.close()

    return None

def plt_acc_tstep(df: pd.DataFrame, eps=1e-10) -> None:
    # Plot 2: Accuracy
    df["err"] = (1.0 - df["acc"]).clip(lower=eps)
    plt.figure(figsize=(8, 4))
    plt.plot(df["step"], df["err"])
    plt.yscale("log")
    plt.xlabel("Step")
    plt.ylabel("Error rate (1 - accuracy) (log scale)")
    plt.title("Training Error Rate vs Step")
    plt.tight_layout()
    plt.savefig('./files/training_error_v_step.png')
    plt.close()

    return None

def plt_embeddings(model: comp_nn.PairwiseComparator) -> None:
    import csv

    log.info("Starting embeddings sweep...")
    # samples for embedding mapping
    with torch.no_grad():
        xs = torch.arange(
            BATCH_LOWER,
            BATCH_UPPER + 1.0,
            0.1,
        ).unsqueeze(1).to(DEVICE)   # shape: (N, 1)

        embeddings = model.embed(xs)  # shape: (N, d)
    
    # move data back to CPU for plotting
    embeddings = embeddings.cpu()
    xs = xs.cpu()

    for i in range(embeddings.shape[1]):
        plt.plot(xs.squeeze(), embeddings[:, i], label=f"dim {i}")
        
    plt.legend()
    plt.savefig(EMBED_CHART_PATH)
    plt.close()

    # save all our embeddings data to csv
    csv_data = list(zip(xs.squeeze().tolist(), embeddings.tolist()))
    with open(file=EMBEDDINGS_DATA_PATH, mode="w", newline='') as f:
        csv_file = csv.writer(f)
        csv_file.writerows(csv_data)

    return None
    
def get_torch_info() -> None:
    log.info("PyTorch Version: %s", torch.__version__)
    log.info("HIP Version: %s", torch.version.hip)
    log.info("CUDA support: %s", torch.cuda.is_available())

    if torch.cuda.is_available():
        log.info("CUDA device detected: %s", torch.cuda.get_device_name(0))

    log.info("Using %s compute mode", DEVICE)

def set_seed(seed: int) -> None:
    random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)

# Data: pairs (a, b) with label y = 1 if a > b else 0 -> (a,b,y)
def sample_batch(batch_size: int, low=BATCH_LOWER, high=BATCH_UPPER, epsi=1e-4) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    a = (high - low) * torch.rand(batch_size, 1) + low
    b = (high - low) * torch.rand(batch_size, 1) + low

    epsi = 1e-4
    y = torch.where(a > b + epsi, 1.0,
        torch.where(a < b - epsi, 0.0, 0.5))

    return a, b, y
    
def training_entry():
    # all prng seeds to 0 for deterministic outputs durring testing
    # the seed should initialized normally otherwise
    set_seed(0)

    model = comp_nn.PairwiseComparator(d=DIMENSIONS).to(DEVICE)
    opt = torch.optim.AdamW(model.parameters(), lr=8e-4, weight_decay=1e-3)

    log.info(f"Using {TRAINING_LOG_PATH} as the logging destination for training...")
    with lzma.open(TRAINING_LOG_PATH, mode='wt') as tlog:
        # training loop
        training_start_time = datetime.datetime.now()
        for step in range(TRAIN_STEPS):
            a, b, y = sample_batch(TRAIN_BATCHSZ)
            a, b, y = a.to(DEVICE), b.to(DEVICE), y.to(DEVICE)

            logits = model(a, b)
            loss_fn = F.binary_cross_entropy_with_logits(logits, y)

            opt.zero_grad()
            loss_fn.backward()
            opt.step()

            if step % TRAIN_PROGRESS == 0:
                with torch.no_grad():
                    pred = (torch.sigmoid(logits) > 0.5).float()
                    acc = (pred == y).float().mean().item()
                tlog.write(f"step={step:5d} loss={loss_fn.item():.7f} acc={acc:.7f}\n")

                # also print to normal text log occasionally to show some activity.
                if step % 2500 == 0:
                    log.info(f"still training... step={step} of {TRAIN_STEPS}")

    training_end_time = datetime.datetime.now()
    log.info(f"Training steps complete. Start time: {training_start_time} End time: {training_end_time}")

    # evaluate final model accuracy on fresh pairs
    with torch.no_grad():
        a, b, y = sample_batch(TRAIN_BATCHSZ*4)
        a, b, y = a.to(DEVICE), b.to(DEVICE), y.to(DEVICE)
        logits = model(a, b)
        pred = (torch.sigmoid(logits) > 0.5).float()
        errors = (pred != y).sum().item()
        acc = (pred == y).float().mean().item()
    log.info(f"Final test acc: {acc} errors: {errors}")

    # embed model dimensions into the model serialization
    torch.save({"state_dict": model.state_dict(), "d": DIMENSIONS}, MODEL_PATH)
    log.info(f"Saved PyTorch Model State to {MODEL_PATH}")

def infer_entry():
    model_ckpt = torch.load(MODEL_PATH, map_location=DEVICE)
    model = comp_nn.PairwiseComparator(d=model_ckpt["d"]).to(DEVICE)
    model.load_state_dict(model_ckpt["state_dict"])
    model.eval()

    # sample pairs
    pairs = [(1, 2), (10, 3), (5, 5), (10, 35), (-64, 11), (300, 162), (2, 0), (2, 1), (3, 1), (4, 1), (3, 10),(30, 1), (0, 0), (-162, 237),
             (10, 20), (100, 30), (50, 50), (100, 350), (-640, 110), (30, -420), (200, 0), (92, 5), (30, 17), (42, 10), (30, 100),(30, 1), (0, 400), (-42, -42)]
    a = torch.tensor([[p[0]] for p in pairs], dtype=torch.float32, device=DEVICE)
    b = torch.tensor([[p[1]] for p in pairs], dtype=torch.float32, device=DEVICE)

    # sanity check before inference
    log.debug(f"a.device: {a.device} model.device: {next(model.parameters()).device}")

    with torch.no_grad():
        probs = torch.sigmoid(model(a, b))

    log.info(f"Output probabilities for {pairs.__len__()} pairs")
    for (x, y), p in zip(pairs, probs):
        log.info(f"P({x} > {y}) = {p.item():.3f}")

    plt_embeddings(model)

if __name__ == '__main__':
    import sys
    import os
    import datetime

    if not os.path.exists("./files/"):
        os.mkdir("./files")
        
    log = logging.getLogger(__name__)
    logging.basicConfig(filename=LOGGING_PATH, level=logging.INFO)
    
    log.info(f"Log opened {datetime.datetime.now()}")
    get_torch_info()

    name = os.path.basename(sys.argv[0])
    if name == 'train.py':
        training_entry()
    elif name == 'infer.py':
        infer_entry()
    else:
        # alt call patern
        # python3 pairwise_compare.py train
        # python3 pairwise_compare.py infer
        # python3 pairwise_compare.py graphs
        if len(sys.argv) > 1:
            mode = sys.argv[1].strip().lower()
            if mode == "train":
                training_entry()
            elif mode == "infer":
                infer_entry()
            elif mode == "graphs":
                data = parse_training_log(TRAINING_LOG_PATH)
                plt_loss_tstep(data)
                plt_acc_tstep(data)
            else:
                log.error(f"Unknown operation: {mode}")
                log.error("Invalid call syntax, call script as \"train.py\" or \"infer.py\" or as pairwise_compare.py <mode> where mode is \"train\" or \"infer\"")
        else:
            log.error("Not enough arguments passed to script; call as train.py or infer.py or as pairwise_compare.py <mode> where mode is \"train\" or \"infer\"")

    log.info(f"Log closed {datetime.datetime.now()}")