# pylint: disable=g-bad-file-header
# Copyright 2019 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""A simple actor-critic agent implemented in JAX + Haiku."""

from typing import Any, Callable, NamedTuple, Tuple

from bsuite.baselines import base
from bsuite.baselines.utils import sequence

import dm_env
from dm_env import specs
import haiku as hk
import jax
import jax.numpy as jnp
import optax
import rlax

Logits = jnp.ndarray
Value = jnp.ndarray
PolicyValueNet = Callable[[jnp.ndarray], Tuple[Logits, Value]]


class TrainingState(NamedTuple):
  params: hk.Params
  opt_state: Any


class ActorCritic(base.Agent):
  """Feed-forward actor-critic agent."""

  def __init__(
      self,
      obs_spec: specs.Array,
      action_spec: specs.DiscreteArray,
      network: PolicyValueNet,
      optimizer: optax.GradientTransformation,
      rng: hk.PRNGSequence,
      sequence_length: int,
      discount: float,
      td_lambda: float,
  ):

    # Define loss function.
    def loss(trajectory: sequence.Trajectory) -> jnp.ndarray:
      """"Actor-critic loss."""
      logits, values = network(trajectory.observations)
      td_errors = rlax.td_lambda(
          v_tm1=values[:-1],
          r_t=trajectory.rewards,
          discount_t=trajectory.discounts * discount,
          v_t=values[1:],
          lambda_=jnp.array(td_lambda),
      )
      critic_loss = jnp.mean(td_errors**2)
      actor_loss = rlax.policy_gradient_loss(
          logits_t=logits[:-1],
          a_t=trajectory.actions,
          adv_t=td_errors,
          w_t=jnp.ones_like(td_errors))

      return actor_loss + critic_loss

    # Transform the loss into a pure function.
    loss_fn = hk.without_apply_rng(hk.transform(loss)).apply

    # Define update function.
    @jax.jit
    def sgd_step(state: TrainingState,
                 trajectory: sequence.Trajectory) -> TrainingState:
      """Does a step of SGD over a trajectory."""
      gradients = jax.grad(loss_fn)(state.params, trajectory)
      updates, new_opt_state = optimizer.update(gradients, state.opt_state)
      new_params = optax.apply_updates(state.params, updates)
      return TrainingState(params=new_params, opt_state=new_opt_state)

    # Initialize network parameters and optimiser state.
    init, forward = hk.without_apply_rng(hk.transform(network))
    dummy_observation = jnp.zeros((1, *obs_spec.shape), dtype=jnp.float32)
    initial_params = init(next(rng), dummy_observation)
    initial_opt_state = optimizer.init(initial_params)

    # Internalize state.
    self._state = TrainingState(initial_params, initial_opt_state)
    self._forward = jax.jit(forward)
    self._buffer = sequence.Buffer(obs_spec, action_spec, sequence_length)
    self._sgd_step = sgd_step
    self._rng = rng

  def select_action(self, timestep: dm_env.TimeStep) -> base.Action:
    """Selects actions according to a softmax policy."""
    key = next(self._rng)
    observation = timestep.observation[None, ...]
    logits, _ = self._forward(self._state.params, observation)
    action = jax.random.categorical(key, logits).squeeze()
    return int(action)

  def update(
      self,
      timestep: dm_env.TimeStep,
      action: base.Action,
      new_timestep: dm_env.TimeStep,
  ):
    """Adds a transition to the trajectory buffer and periodically does SGD."""
    self._buffer.append(timestep, action, new_timestep)
    if self._buffer.full() or new_timestep.last():
      trajectory = self._buffer.drain()
      self._state = self._sgd_step(self._state, trajectory)


def default_agent(obs_spec: specs.Array,
                  action_spec: specs.DiscreteArray,
                  seed: int = 0) -> base.Agent:
  """Creates an actor-critic agent with default hyperparameters."""

  def network(inputs: jnp.ndarray) -> Tuple[Logits, Value]:
    flat_inputs = hk.Flatten()(inputs)
    torso = hk.nets.MLP([64, 64])
    policy_head = hk.Linear(action_spec.num_values)
    value_head = hk.Linear(1)
    embedding = torso(flat_inputs)
    logits = policy_head(embedding)
    value = value_head(embedding)
    return logits, jnp.squeeze(value, axis=-1)

  return ActorCritic(
      obs_spec=obs_spec,
      action_spec=action_spec,
      network=network,
      optimizer=optax.adam(3e-3),
      rng=hk.PRNGSequence(seed),
      sequence_length=32,
      discount=0.99,
      td_lambda=0.9,
  )
