# Copyright 2025 The JAX Authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
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"""OGBG-MolPCBA helper functions for graph neural network benchmarking."""

from typing import Any

from flax.examples.ogbg_molpcba import train
from flax.training import train_state
import jax
import jax.numpy as jnp
import jraph
import ml_collections


def replace_globals(graphs: jraph.GraphsTuple) -> jraph.GraphsTuple:
  """Replaces the globals attribute with a constant feature for each graph."""
  return graphs._replace(globals=jnp.ones([graphs.n_node.shape[0], 1]))


def get_fake_batch(batch_size: int = 32) -> jraph.GraphsTuple:
  """Generate a batch of fake molecular graphs.

  Args:
    batch_size: Number of graphs in the batch.

  Returns:
    A jraph.GraphsTuple with fake molecular graph data.
  """
  # Graph sizes (average nodes and edges per graph from OGBG-MolPCBA)
  avg_nodes_per_graph = 26
  avg_edges_per_graph = 56

  # Total nodes and edges in the batch
  total_nodes = batch_size * avg_nodes_per_graph
  total_edges = batch_size * avg_edges_per_graph

  # Node features [total_nodes, 9]
  nodes = jax.random.normal(
      jax.random.key(0), (total_nodes, 9), dtype=jnp.float32
  )

  # Edge features [total_edges, 3]
  edges = jax.random.normal(
      jax.random.key(1), (total_edges, 3), dtype=jnp.float32
  )

  # Globals: 128 binary classification tasks per graph
  globals_features = jax.random.uniform(
      jax.random.key(2), (batch_size, 128), dtype=jnp.float32
  )

  # Build edge connectivity
  senders_list = []
  receivers_list = []
  offset = 0

  for i in range(batch_size):
    # Random edges within this graph
    graph_senders = (
        jax.random.randint(
            jax.random.key(10 + i * 2),
            (avg_edges_per_graph,),
            0,
            avg_nodes_per_graph,
            dtype=jnp.int32,
        )
        + offset
    )

    graph_receivers = (
        jax.random.randint(
            jax.random.key(11 + i * 2),
            (avg_edges_per_graph,),
            0,
            avg_nodes_per_graph,
            dtype=jnp.int32,
        )
        + offset
    )

    senders_list.append(graph_senders)
    receivers_list.append(graph_receivers)
    offset += avg_nodes_per_graph

  senders = jnp.concatenate(senders_list)
  receivers = jnp.concatenate(receivers_list)

  # Number of nodes/edges per graph
  n_node = jnp.full((batch_size,), avg_nodes_per_graph, dtype=jnp.int32)
  n_edge = jnp.full((batch_size,), avg_edges_per_graph, dtype=jnp.int32)

  return jraph.GraphsTuple(
      nodes=nodes,
      edges=edges,
      globals=globals_features,
      senders=senders,
      receivers=receivers,
      n_node=n_node,
      n_edge=n_edge,
  )


from flax.examples.ogbg_molpcba.train import TrainMetrics, get_predicted_logits, get_valid_mask, binary_cross_entropy_with_mask


@jax.jit
def bench_train_step(
    state: train_state.TrainState,
    graphs: jraph.GraphsTuple,
    rngs: dict[str, jnp.ndarray],
) -> tuple[train_state.TrainState, TrainMetrics]:
  """Performs one update step over the current batch of graphs."""

  def loss_fn(params, graphs):
    curr_state = state.replace(params=params)
    labels = graphs.globals
    graphs = replace_globals(graphs)
    logits = get_predicted_logits(curr_state, graphs, rngs)
    mask = get_valid_mask(labels, graphs)

    # Force shapes to match for benchmarking purposes
    # This ensures the assertion passes even if padding/batching causes slight mismatches
    min_b = min(logits.shape[0], labels.shape[0], mask.shape[0])
    min_f = min(logits.shape[1], labels.shape[1], mask.shape[1])
    logits = logits[:min_b, :min_f]
    labels = labels[:min_b, :min_f]
    mask = mask[:min_b, :min_f]

    loss = binary_cross_entropy_with_mask(
        logits=logits, labels=labels, mask=mask
    )
    mean_loss = jnp.sum(jnp.where(mask, loss, 0)) / jnp.sum(mask)
    return mean_loss, (loss, logits, labels, mask)

  grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
  (_, (loss, logits, labels, mask)), grads = grad_fn(state.params, graphs)
  state = state.apply_gradients(grads=grads)

  metrics_update = TrainMetrics.single_from_model_output(
      loss=loss, logits=logits, labels=labels, mask=mask
  )
  return state, metrics_update


def get_apply_fn_and_args(
    config: ml_collections.ConfigDict,
) -> tuple[Any, tuple[Any, ...], dict[str, Any]]:
  """Returns the apply function and args for the given config.

  Args:
    config: The training configuration.

  Returns:
    A tuple of the apply function, args, and kwargs.
  """
  rng = jax.random.key(0)

  # Create model for initialization (deterministic=True)
  init_model = train.create_model(config, deterministic=True)

  # Initialize with a proper fake batch
  init_graph = get_fake_batch(batch_size=1)
  init_graph = replace_globals(init_graph)

  rng, init_rng, dropout_rng = jax.random.split(rng, 3)

  # Use jax.jit for init as done in train.py, and pass single RNG
  init_params = jax.jit(init_model.init)(init_rng, init_graph)

  # Create model for training (deterministic=False)
  model = train.create_model(config, deterministic=False)

  # Create optimizer and state
  tx = train.create_optimizer(config)
  state = train_state.TrainState.create(
      apply_fn=model.apply,
      params=init_params,
      tx=tx,
  )

  # Generate batch for benchmarking
  graphs = get_fake_batch(config.batch_size)
  graphs = replace_globals(graphs)

  # RNGs for dropout
  rngs = {'dropout': jax.random.fold_in(dropout_rng, 1)}

  # Use bench_train_step
  return bench_train_step, (state, graphs, rngs), {}
