import json
import numpy as np
import matplotlib.pyplot as plt

def load_graded(path):
    with open(path) as f:
        data = json.load(f)
    return [item["graded_list"] for item in data]

def bootstrap_pass_at_k(graded_lists, k, n_bootstrap=10000, rng=None):
    if rng is None:
        rng = np.random.default_rng(42)
    problem_scores = []
    for outcomes in graded_lists:
        outcomes_arr = np.array(outcomes, dtype=bool)
        n = len(outcomes_arr)
        samples = rng.integers(0, n, size=(n_bootstrap, k))
        any_pass = outcomes_arr[samples].any(axis=1)
        problem_scores.append(any_pass.mean())
    return np.mean(problem_scores)

with open("output/base_model_evals.json") as f:
    base_evals = json.load(f)
one_shot = base_evals["results"]["generic_base_one_shot"]

maxrl_steps = [100, 200, 300, 400]
tailrl_steps = [100, 200, 300, 400]
k_values = [1, 2, 4, 8, 16]
left_k_values = [1, 16]

maxrl_data = {}
for step in maxrl_steps:
    path = f"output/maxrl_binary_{step}/Scenario.codegeneration_16_0.6_eval_all.json"
    graded = load_graded(path)
    rng = np.random.default_rng(42)
    maxrl_data[step] = {k: bootstrap_pass_at_k(graded, k, rng=rng) for k in k_values}

tailrl_data = {}
for step in tailrl_steps:
    path = f"output/tailrl_cont_{step}/Scenario.codegeneration_16_0.6_eval_all.json"
    graded = load_graded(path)
    rng = np.random.default_rng(42)
    tailrl_data[step] = {k: bootstrap_pass_at_k(graded, k, rng=rng) for k in k_values}

results_json = {
    "base_one_shot": {f"pass@{k}": one_shot[f"pass@{k}"] for k in k_values},
    "maxrl_binary": {str(step): {f"pass@{k}": float(v) for k, v in ks.items()} for step, ks in maxrl_data.items()},
    "tailrl_cont": {str(step): {f"pass@{k}": float(v) for k, v in ks.items()} for step, ks in tailrl_data.items()},
}
results_path = "output/pass_at_k_results.json"
with open(results_path, "w") as f:
    json.dump(results_json, f, indent=2)
print(f"Saved {results_path}")

fig, (ax_left, ax_right) = plt.subplots(1, 2, figsize=(14, 5))

maxrl_colors = {'p@1': 'steelblue', 'p@16': 'navy'}
tailrl_colors = {'p@1': 'darkorange', 'p@16': 'saddlebrown'}
markers = {1: 'o', 16: 's'}

# --- Left plot: p@1 and p@16 vs steps ---
for k in left_k_values:
    vals_maxrl = [maxrl_data[s][k] for s in maxrl_steps]
    vals_tailrl = [tailrl_data[s][k] for s in tailrl_steps]
    ax_left.plot(maxrl_steps, vals_maxrl, marker=markers[k], color=maxrl_colors[f'p@{k}'],
                 linewidth=2, markersize=7, label=f"maxrl p@{k}")
    ax_left.plot(tailrl_steps, vals_tailrl, marker=markers[k], color=tailrl_colors[f'p@{k}'],
                 linewidth=2, markersize=7, linestyle='--', label=f"tailrl p@{k}")

for k, ls in zip(left_k_values, ['-', '--']):
    ax_left.axhline(one_shot[f"pass@{k}"], color='gray', linewidth=1.5,
                    linestyle=ls, label=f"base one-shot p@{k}")

ax_left.set_xlabel("Training Steps", fontsize=12)
ax_left.set_ylabel("pass@k", fontsize=12)
ax_left.set_title("Performance vs Training Steps", fontsize=13)
ax_left.set_xticks(sorted(set(maxrl_steps + tailrl_steps)))
ax_left.legend(fontsize=10, loc="lower right")
ax_left.grid(True, alpha=0.3)

# --- Right plot: pass@k at last checkpoint (step 400) ---
last_maxrl = [maxrl_data[400][k] for k in k_values]
last_tailrl = [tailrl_data[400][k] for k in k_values]

base_one_shot_vals = [one_shot[f"pass@{k}"] for k in k_values]
ax_right.plot(k_values, base_one_shot_vals, marker='^', color='gray',
              linewidth=2, markersize=8, linestyle='--', label="base one-shot")
for k, v in zip(k_values, base_one_shot_vals):
    ax_right.annotate(f"{v:.3f}", (k, v), textcoords="offset points", xytext=(5, 6),
                      fontsize=8, color='gray')

ax_right.plot(k_values, last_maxrl, marker='o', color='steelblue',
              linewidth=2, markersize=8, label="maxrl_binary_400")
ax_right.plot(k_values, last_tailrl, marker='s', color='darkorange',
              linewidth=2, markersize=8, label="tailrl_cont_400")
for k, v in zip(k_values, last_maxrl):
    ax_right.annotate(f"{v:.3f}", (k, v), textcoords="offset points", xytext=(5, 6),
                      fontsize=8, color='steelblue')
for k, v in zip(k_values, last_tailrl):
    ax_right.annotate(f"{v:.3f}", (k, v), textcoords="offset points", xytext=(5, -14),
                      fontsize=8, color='darkorange')

ax_right.set_xscale("log", base=2)
ax_right.set_xticks(k_values)
ax_right.set_xticklabels([str(k) for k in k_values])
ax_right.set_xlabel("k", fontsize=12)
ax_right.set_ylabel("pass@k", fontsize=12)
ax_right.set_title("pass@k at Last Checkpoint", fontsize=13)
all_right = last_maxrl + last_tailrl + base_one_shot_vals
ax_right.set_ylim(min(all_right) * 0.9, max(all_right) * 1.15)
ax_right.legend(fontsize=10)
ax_right.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig("output/pass_at_k_comparison.png", dpi=150)
print("Saved output/pass_at_k_comparison.png")