import pprint
from functools import partial

from tqdm import tqdm, trange
import numpy as np
import mlxu

import jax
import jax.numpy as jnp
from jax.experimental.pjit import pjit, with_sharding_constraint
from jax.sharding import PartitionSpec as PS
from flax.training.train_state import TrainState

from EasyLM.data import DatasetFactory
from EasyLM.checkpoint import StreamingCheckpointer
from EasyLM.optimizers import OptimizerFactory
from EasyLM.jax_utils import (
    JaxRNG, JaxDistributedConfig, next_rng, match_partition_rules,
    cross_entropy_loss_and_accuracy, global_norm, get_float_dtype_by_name,
    set_random_seed, average_metrics, get_weight_decay_mask,
    make_shard_and_gather_fns, tree_apply
)
from EasyLM.models.gptj.gptj_model import GPTJConfig, FlaxGPTJForCausalLMModule


FLAGS, FLAGS_DEF = mlxu.define_flags_with_default(
    seed=42,
    mesh_dim='1,-1,1',
    dtype='fp32',
    total_steps=10000,
    load_gptj_config='',
    update_gptj_config='',
    load_checkpoint='',
    load_dataset_state='',
    log_freq=50,
    save_model_freq=0,
    save_milestone_freq=0,
    eval_steps=0,
    tokenizer=GPTJConfig.get_tokenizer_config(),
    train_dataset=DatasetFactory.get_default_config(),
    eval_dataset=DatasetFactory.get_default_config(),
    optimizer=OptimizerFactory.get_default_config(),
    checkpointer=StreamingCheckpointer.get_default_config(),
    gptj=GPTJConfig.get_default_config(),
    logger=mlxu.WandBLogger.get_default_config(),
    log_all_worker=False,
    jax_distributed=JaxDistributedConfig.get_default_config(),
)


def main(argv):
    JaxDistributedConfig.initialize(FLAGS.jax_distributed)
    variant = mlxu.get_user_flags(FLAGS, FLAGS_DEF)
    flags_config_dict = mlxu.user_flags_to_config_dict(FLAGS, FLAGS_DEF)
    logger = mlxu.WandBLogger(
        config=FLAGS.logger,
        variant=variant,
        enable=FLAGS.log_all_worker or (jax.process_index() == 0),
    )
    set_random_seed(FLAGS.seed)

    tokenizer = GPTJConfig.get_tokenizer(FLAGS.tokenizer)
    dataset = DatasetFactory.load_dataset(FLAGS.train_dataset, tokenizer)
    if FLAGS.load_dataset_state != '':
        dataset.load_state_dict(mlxu.load_pickle(FLAGS.load_dataset_state))

    if FLAGS.eval_steps > 0:
        eval_dataset = DatasetFactory.load_dataset(
            FLAGS.eval_dataset, dataset.tokenizer
        )
        eval_iterator = iter(eval_dataset)

    seq_length = dataset.seq_length

    if FLAGS.load_gptj_config != '':
        gptj_config = GPTJConfig.load_config(FLAGS.load_gptj_config)
    else:
        gptj_config = GPTJConfig(**FLAGS.gptj)

    if FLAGS.update_gptj_config != '':
        gptj_config.update(dict(eval(FLAGS.update_gptj_config)))

    gptj_config.update(dict(
        bos_token_id=dataset.tokenizer.bos_token_id,
        eos_token_id=dataset.tokenizer.eos_token_id,
    ))
    if gptj_config.vocab_size < dataset.vocab_size:
        gptj_config.update(dict(vocab_size=dataset.vocab_size))

    model = FlaxGPTJForCausalLMModule(
        gptj_config, dtype=get_float_dtype_by_name(FLAGS.dtype)
    )

    optimizer, optimizer_info = OptimizerFactory.get_optimizer(
        FLAGS.optimizer,
        get_weight_decay_mask(GPTJConfig.get_weight_decay_exclusions()),
    )

    def create_trainstate_from_params(params):
        return TrainState.create(params=params, tx=optimizer, apply_fn=None)

    def init_fn(rng):
        rng_generator = JaxRNG(rng)
        params = model.init(
            input_ids=jnp.zeros((4, seq_length), dtype=jnp.int32),
            position_ids=jnp.zeros((4, seq_length), dtype=jnp.int32),
            attention_mask=jnp.ones((4, seq_length), dtype=jnp.int32),
            rngs=rng_generator(gptj_config.rng_keys()),
        )
        return TrainState.create(params=params, tx=optimizer, apply_fn=None)

    def train_step(train_state, rng, batch):
        rng_generator = JaxRNG(rng)
        batch = with_sharding_constraint(batch, PS(('dp', 'fsdp')))
        def loss_and_accuracy(params):
            logits = model.apply(
                params, batch['input_tokens'], deterministic=False,
                rngs=rng_generator(gptj_config.rng_keys()),
            ).logits
            return cross_entropy_loss_and_accuracy(
                logits, batch['target_tokens'], batch['loss_masks']
            )
        grad_fn = jax.value_and_grad(loss_and_accuracy, has_aux=True)
        (loss, accuracy), grads = grad_fn(train_state.params)
        train_state = train_state.apply_gradients(grads=grads)
        metrics = dict(
            loss=loss,
            accuracy=accuracy,
            learning_rate=optimizer_info['learning_rate_schedule'](train_state.step),
            gradient_norm=global_norm(grads),
            param_norm=global_norm(train_state.params),
        )
        return train_state, rng_generator(), metrics

    def eval_step(train_state, rng, batch):
        rng_generator = JaxRNG(rng)
        batch = with_sharding_constraint(batch, PS(('dp', 'fsdp')))
        logits = model.apply(
            train_state.params, batch['input_tokens'], deterministic=True,
            rngs=rng_generator(gptj_config.rng_keys()),
        ).logits
        loss, accuracy = cross_entropy_loss_and_accuracy(
            logits, batch['target_tokens'], batch['loss_masks']
        )
        metrics = dict(
            eval_loss=loss,
            eval_accuracy=accuracy,
        )
        return rng_generator(), metrics

    train_state_shapes = jax.eval_shape(init_fn, next_rng())
    train_state_partition = match_partition_rules(
        GPTJConfig.get_partition_rules(), train_state_shapes
    )

    shard_fns, gather_fns = make_shard_and_gather_fns(
        train_state_partition, train_state_shapes
    )
    checkpointer = StreamingCheckpointer(
        FLAGS.checkpointer, logger.output_dir,
        enable=jax.process_index() == 0,
    )

    sharded_init_fn = pjit(
        init_fn,
        in_shardings=PS(),
        out_shardings=train_state_partition
    )

    sharded_create_trainstate_from_params = pjit(
        create_trainstate_from_params,
        in_shardings=(train_state_partition.params, ),
        out_shardings=train_state_partition,
        donate_argnums=(0, ),
    )

    sharded_train_step = pjit(
        train_step,
        in_shardings=(train_state_partition, PS(), PS()),
        out_shardings=(train_state_partition, PS(), PS()),
        donate_argnums=(0, 1),
    )

    sharded_eval_step = pjit(
        eval_step,
        in_shardings=(train_state_partition, PS(), PS()),
        out_shardings=(PS(), PS()),
        donate_argnums=(1,),
    )

    def save_checkpoint(train_state, milestone=False):
        step = int(jax.device_get(train_state.step))
        metadata = dict(
            step=step,
            variant=variant,
            flags=flags_config_dict,
            gptj_config=gptj_config.to_dict(),
        )
        checkpointer.save_all(
            train_state=train_state,
            gather_fns=gather_fns,
            metadata=metadata,
            dataset=dataset.get_state_dict(),
            milestone=milestone,
        )

    mesh = GPTJConfig.get_jax_mesh(FLAGS.mesh_dim)
    with mesh:
        train_state, restored_params = None, None
        if FLAGS.load_checkpoint != '':
            load_type, load_path = FLAGS.load_checkpoint.split('::', 1)
            if load_type == 'huggingface':
                restored_params = tree_apply(
                    shard_fns.params, gptj_config.load_pretrained(load_path)
                )
                train_state = None
            else:
                train_state, restored_params = checkpointer.load_trainstate_checkpoint(
                    FLAGS.load_checkpoint, train_state_shapes, shard_fns
                )

        if train_state is None and restored_params is None:
            # Initialize from scratch
            train_state = sharded_init_fn(next_rng())
        elif train_state is None and restored_params is not None:
            # Restore from params but initialize train_state
            train_state = sharded_create_trainstate_from_params(restored_params)
            del restored_params

        start_step = int(jax.device_get(train_state.step))

        if FLAGS.save_model_freq > 0:
            save_checkpoint(train_state)

        sharded_rng = next_rng()

        step_counter = trange(start_step, FLAGS.total_steps, ncols=0)

        for step, (batch, dataset_metrics) in zip(step_counter, dataset):
            train_state, sharded_rng, metrics = sharded_train_step(
                train_state, sharded_rng, batch
            )

            if step % FLAGS.log_freq == 0:
                if FLAGS.eval_steps > 0:
                    eval_metric_list = []
                    for _ in range(FLAGS.eval_steps):
                        eval_batch, _ = next(eval_iterator)
                        sharded_rng, eval_metrics = sharded_eval_step(
                            train_state, sharded_rng, eval_batch
                        )
                        eval_metric_list.append(eval_metrics)
                    metrics.update(average_metrics(eval_metric_list))

                log_metrics = {"step": step}
                log_metrics.update(metrics)
                log_metrics.update(dataset_metrics)
                log_metrics = jax.device_get(log_metrics)
                logger.log(log_metrics)
                tqdm.write("\n" + pprint.pformat(log_metrics) + "\n")

            if FLAGS.save_milestone_freq > 0 and (step + 1) % FLAGS.save_milestone_freq == 0:
                save_checkpoint(train_state, milestone=True)
            elif FLAGS.save_model_freq > 0 and (step + 1) % FLAGS.save_model_freq == 0:
                save_checkpoint(train_state)

        if FLAGS.save_model_freq > 0:
            save_checkpoint(train_state)


if __name__ == "__main__":
    mlxu.run(main)
