import json
import os
import sys

from datasets import load_dataset
from ppo_hh import create_reward_fn
from datasets import load_from_disk
import random
import numpy as np
import torch
import trlx
from trlx.data.default_configs import (
    ILQLConfig,
    ModelConfig,
    OptimizerConfig,
    SchedulerConfig,
    TokenizerConfig,
    TrainConfig,
    TRLConfig,
)
RANDOM_SEED = 0
MODEL_SIZE = "125M"
OUTPUT_DIR = "./output"


random.seed(RANDOM_SEED)
np.random.seed(RANDOM_SEED)
torch.manual_seed(RANDOM_SEED)
torch.cuda.manual_seed(RANDOM_SEED) 

default_config = TRLConfig(
    train=TrainConfig(
        seq_length=1000,
        batch_size=1,
        epochs=100,
        total_steps=20000,
        checkpoint_interval=1000,
        eval_interval=1000,
        pipeline="PromptPipeline",
        trainer="AccelerateILQLTrainer",
        checkpoint_dir="checkpoints/ilql_hh",
        seed=RANDOM_SEED,
    ),
    model=ModelConfig(model_path="EleutherAI/gpt-j-6B", num_layers_unfrozen=2),
    tokenizer=TokenizerConfig(tokenizer_path="EleutherAI/gpt-j-6B", truncation_side="left"),
    optimizer=OptimizerConfig(name="adamw", kwargs=dict(lr=1e-6, betas=(0.9, 0.95), eps=1.0e-8, weight_decay=1.0e-6)),
    scheduler=SchedulerConfig(name="cosine_annealing", kwargs=dict(T_max=1000000000, eta_min=1e-6)),
    method=ILQLConfig(
        name="ilqlconfig",
        tau=0.6,
        gamma=0.99,
        cql_scale=0.1,
        awac_scale=1,
        alpha=0.0001,
        beta=0,
        steps_for_target_q_sync=1,
        two_qs=True,
        gen_kwargs=dict(max_new_tokens=128, top_k=20, beta=[1, 4], temperature=1.0),
    ),
)





config_name = MODEL_SIZE 
if config_name == "125M":
    default_config.train.batch_size = 4
    default_config.model.model_path = "Dahoas/pythia-125M-static-sft" 
    default_config.tokenizer.tokenizer_path = "EleutherAI/gpt-neox-20b"
elif config_name == "1B":
    default_config.train.batch_size = 1
    default_config.model.model_path = "Dahoas/pythia-1B-static-sft"
    default_config.tokenizer.tokenizer_path = "EleutherAI/gpt-neox-20b"
elif config_name == "6B":
    default_config.train.batch_size = 1
    default_config.model.model_path = "Dahoas/pythia-6B-static-sft"
    default_config.tokenizer.tokenizer_path = "EleutherAI/gpt-neox-20b"
elif config_name == "20B":
    default_config.train.batch_size = 1
    default_config.train.total_steps = 3000
    default_config.train.checkpoint_dir = "checkpoints/ilql_hh_20B"
    default_config.model.model_path = "EleutherAI/gpt-neox-20b"
    default_config.tokenizer.tokenizer_path = "EleutherAI/gpt-neox-20b"

reward_fn = create_reward_fn()
def preprocess(sample):
    sample["prompt_output"] = [
        [sample["prompt"], sample["chosen"]],
        [sample["prompt"], sample["rejected"]],
    ]
    prompt_res = [
        sample["prompt"] + sample["chosen"],
        sample["prompt"] + sample["rejected"],
    ]
    reward = reward_fn(prompt_res, sample["prompt"], sample["rejected"])

    sample["reward"] = reward
    return sample


def main(hparams={}):
    output_dir = OUTPUT_DIR
    config = TRLConfig.update(default_config, hparams)
    config.train.rollout_logging_dir = output_dir
    config.train.checkpoint_dir = output_dir
    config.train.logging_dir = output_dir
    config.train.tracker = "tensorboard"
    
    dataset = load_dataset("Dahoas/rm-static").map(preprocess)
    prompts_outputs = sum(dataset["train"]["prompt_output"], [])

    rewards = sum(dataset["train"]["reward"], [])
    eval_prompts = [prompt_output[0][0] for prompt_output in dataset["test"]["prompt_output"]][:280]

    trlx.train(
        samples=prompts_outputs,
        rewards=rewards,
        config=config,
        eval_prompts=eval_prompts,
        metric_fn=lambda **kwargs: {"reward": reward_fn(**kwargs)},
        stop_sequences=["Human:", "human:", "Assistant:", "assistant:"],
    )


if __name__ == "__main__":
    hparams = {} if len(sys.argv) == 1 else json.loads(sys.argv[1])
    main(hparams)
