import os
import random
import numpy as np
import torch
from datasets import Dataset
import wandb

SEED = 42

def set_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False
    os.environ["PYTHONHASHSEED"] = str(seed)

set_seed(SEED)

os.environ["HF_HOME"] = "/root/hf_cache"
os.environ["HF_DATASETS_CACHE"] = "/root/hf_cache/datasets"

BLOCK_SIZE = 2048
DATA_DIR = "/data/copypaste_2048"

NUM_TRAIN = 500_000
NUM_VAL = 1_000
NUM_TEST = 1_000

VOCAB_SIZE = 250
DELIM_TOKEN = 251
PAD_TOKEN = 0

def generate_copy_paste_samples(num_samples, seed):
    def gen():
        rng = np.random.default_rng(seed)
        max_seq_len = (BLOCK_SIZE - 1) // 2
        
        for _ in range(num_samples):
            seq_len = rng.integers(10, max_seq_len + 1)
            seq = rng.integers(1, VOCAB_SIZE + 1, size=seq_len)
            
            input_ids = np.full(BLOCK_SIZE, PAD_TOKEN, dtype=np.int64)
            labels = np.full(BLOCK_SIZE, -100, dtype=np.int64)
            
            input_ids[:seq_len] = seq
            input_ids[seq_len] = DELIM_TOKEN
            input_ids[seq_len+1 : seq_len+1+seq_len] = seq 
            
            labels[seq_len+1 : seq_len+1+seq_len] = seq
            attention_mask = np.zeros(BLOCK_SIZE, dtype=np.int64)
            attention_mask[:seq_len+1+seq_len] = 1
            
            yield {
                "input_ids": input_ids.tolist(), 
                "attention_mask": attention_mask.tolist(),
                "labels": labels.tolist()
            }
    return gen

if __name__ == "__main__":
    print(f"Generating Copy-Paste dataset with SEED: {SEED} (Block size: {BLOCK_SIZE})...")
    
    ds_train = Dataset.from_generator(generate_copy_paste_samples(NUM_TRAIN, SEED))
    ds_val = Dataset.from_generator(generate_copy_paste_samples(NUM_VAL, SEED + 1))
    ds_test = Dataset.from_generator(generate_copy_paste_samples(NUM_TEST, SEED + 2))

    os.makedirs(DATA_DIR, exist_ok=True)
    
    print(f"Saving finalized datasets to {DATA_DIR}...")
    ds_train.save_to_disk(f"{DATA_DIR}/train")
    ds_val.save_to_disk(f"{DATA_DIR}/val")
    ds_test.save_to_disk(f"{DATA_DIR}/test") 

    print(f"Final Train blocks count: {len(ds_train)} (~{len(ds_train) * BLOCK_SIZE / 1e8:.2f}B tokens)")
    print(f"Final Val blocks count: {len(ds_val)}")
    print(f"Final Test blocks count: {len(ds_test)}")