# Path: deluca/lung/controllers/_expiratory.py
# class Expiratory(Controller):
#   """Expiratory."""
#   waveform: BreathWaveform = deluca.field(jaxed=False)
# 
#   # TODO(dsuo): Handle dataclass initialization of jax objects
#   def setup(self):
#     if self.waveform is None:
#       self.waveform = BreathWaveform.create()
# 
#   @jax.jit
#   def __call__(self, state, obs, *args, **kwargs):
#     time = obs.time
#     u_out = jax.lax.cond(
#         self.waveform.is_in(time), lambda x: 0, lambda x: 1,
#         jnp.zeros_like(time))
#     new_dt = jnp.max(jnp.array([DEFAULT_DT, time - proper_time(state.time)]))
#     new_time = time
#     new_steps = state.steps + 1
#     state = state.replace(time=new_time, steps=new_steps, dt=new_dt)
#     return state, u_out
# 
# Path: deluca/lung/core.py
# class BreathWaveform(deluca.Obj):
#   r"""Waveform generator with shape |‾\_."""
#   peep: float = deluca.field(5., jaxed=False)
#   pip: float = deluca.field(35., jaxed=False)
#   bpm: int = deluca.field(20, jaxed=False)
#   fp: jnp.array = deluca.field(jaxed=False)
#   xp: jnp.array = deluca.field(jaxed=False)
#   in_bounds: Tuple[int, int] = deluca.field((0, 1), jaxed=False)
#   ex_bounds: Tuple[int, int] = deluca.field((2, 4), jaxed=False)
#   period: float = deluca.field(jaxed=False)
#   dt: float = deluca.field(DEFAULT_DT, jaxed=False)
#   dtype: jax._src.numpy.lax_numpy._ScalarMeta = deluca.field(
#       jnp.float32, jaxed=False)
# 
#   def setup(self):
#     if self.fp is None:
#       self.fp = jnp.array([self.pip, self.pip, self.peep, self.peep, self.pip])
#     if self.xp is None:
#       self.xp = jnp.array(DEFAULT_XP)
#     self.period = 60 / self.bpm
# 
#   def at(self, t):
# 
#     @functools.partial(jax.jit, static_argnums=(3,))
#     def static_interp(t, xp, fp, period):
#       return jnp.interp(t, xp, fp, period=period)
# 
#     return static_interp(t, self.xp, self.fp, self.period).astype(self.dtype)
# 
#   def elapsed(self, t):
#     return t % self.period
# 
#   def is_in(self, t):
#     return self.elapsed(t) <= self.xp[self.in_bounds[1]]
# 
#   def is_ex(self, t):
#     return not self.is_in(t)
# 
# Path: deluca/lung/utils/scripts/test_controller.py
# @jax.jit
# def test_controller(controller, sim, pips, peep):
#   """Test controller."""
#   # new_controller = controller.replace(use_leaky_clamp=False)
#   score = 0.0
#   horizon = 29
#   for pip in pips:
#     waveform = BreathWaveform.create(peep=peep, pip=pip)
#     result = run_controller_scan(
#         controller,
#         T=horizon,
#         abort=horizon,
#         env=sim,
#         waveform=waveform,
#         init_controller=True,
#     )
#     analyzer = Analyzer(result)
#     preds = analyzer.pressure  # shape = (29,)
#     truth = analyzer.target  # shape = (29,)
#     # print('preds.shape: %s', str(preds.shape))
#     # print('truth.shape: %s', str(truth.shape))
#     score += jnp.abs(preds - truth).mean()
#   score = score / len(pips)
#   return score


import copy
import functools
import jax
import jax.numpy as jnp
import optax
from clu import metric_writers
from deluca.lung.controllers._expiratory import Expiratory
from deluca.lung.core import BreathWaveform
from deluca.lung.utils.scripts.test_controller import test_controller

  tt = jnp.linspace(0, duration, int(duration / dt))
  losses = []
  for epoch in range(epochs):
    if pip_feed == "parallel":
      value, grad = jax.value_and_grad(rollout_parallel)(controller, sim, tt,
                                                         use_noise, peep,
                                                         jnp.array(pips),
                                                         loss_fn)
      updates, optim_state = optim.update(grad, optim_state, controller)
      controller = optax.apply_updates(controller, updates)
      per_step_loss = value / len(tt)
      losses.append(per_step_loss)
      if epoch % print_loss == 0:
        # make new controller with trained parameters and normal clamp
        score = test_controller(controller, sim, pips, peep)
        print(f"Epoch: {epoch}\tLoss: {score:.2f}")
        if tensorboard_dir is not None:
          summary_writer.write_scalars(epoch, {"score": score})
    if pip_feed == "sequential":
      for pip in pips:
        value, grad = jax.value_and_grad(rollout)(controller, sim, tt,
                                                  use_noise, peep, pip,
                                                  loss_fn, jnp.array(0.))
        updates, optim_state = optim.update(grad, optim_state, controller)
        controller = optax.apply_updates(controller, updates)
        per_step_loss = value / len(tt)
        losses.append(per_step_loss)
        if epoch % print_loss == 0:
          # make new controller with trained parameters and normal clamp

# Path: deluca/lung/controllers/_expiratory.py
# class Expiratory(Controller):
#   """Expiratory."""
#   waveform: BreathWaveform = deluca.field(jaxed=False)
# 
#   # TODO(dsuo): Handle dataclass initialization of jax objects
#   def setup(self):
#     if self.waveform is None:
#       self.waveform = BreathWaveform.create()
# 
#   @jax.jit
#   def __call__(self, state, obs, *args, **kwargs):
#     time = obs.time
#     u_out = jax.lax.cond(
#         self.waveform.is_in(time), lambda x: 0, lambda x: 1,
#         jnp.zeros_like(time))
#     new_dt = jnp.max(jnp.array([DEFAULT_DT, time - proper_time(state.time)]))
#     new_time = time
#     new_steps = state.steps + 1
#     state = state.replace(time=new_time, steps=new_steps, dt=new_dt)
#     return state, u_out
# 
# Path: deluca/lung/core.py
# class BreathWaveform(deluca.Obj):
#   r"""Waveform generator with shape |‾\_."""
#   peep: float = deluca.field(5., jaxed=False)
#   pip: float = deluca.field(35., jaxed=False)
#   bpm: int = deluca.field(20, jaxed=False)
#   fp: jnp.array = deluca.field(jaxed=False)
#   xp: jnp.array = deluca.field(jaxed=False)
#   in_bounds: Tuple[int, int] = deluca.field((0, 1), jaxed=False)
#   ex_bounds: Tuple[int, int] = deluca.field((2, 4), jaxed=False)
#   period: float = deluca.field(jaxed=False)
#   dt: float = deluca.field(DEFAULT_DT, jaxed=False)
#   dtype: jax._src.numpy.lax_numpy._ScalarMeta = deluca.field(
#       jnp.float32, jaxed=False)
# 
#   def setup(self):
#     if self.fp is None:
#       self.fp = jnp.array([self.pip, self.pip, self.peep, self.peep, self.pip])
#     if self.xp is None:
#       self.xp = jnp.array(DEFAULT_XP)
#     self.period = 60 / self.bpm
# 
#   def at(self, t):
# 
#     @functools.partial(jax.jit, static_argnums=(3,))
#     def static_interp(t, xp, fp, period):
#       return jnp.interp(t, xp, fp, period=period)
# 
#     return static_interp(t, self.xp, self.fp, self.period).astype(self.dtype)
# 
#   def elapsed(self, t):
#     return t % self.period
# 
#   def is_in(self, t):
#     return self.elapsed(t) <= self.xp[self.in_bounds[1]]
# 
#   def is_ex(self, t):
#     return not self.is_in(t)
# 
# Path: deluca/lung/utils/scripts/test_controller.py
# @jax.jit
# def test_controller(controller, sim, pips, peep):
#   """Test controller."""
#   # new_controller = controller.replace(use_leaky_clamp=False)
#   score = 0.0
#   horizon = 29
#   for pip in pips:
#     waveform = BreathWaveform.create(peep=peep, pip=pip)
#     result = run_controller_scan(
#         controller,
#         T=horizon,
#         abort=horizon,
#         env=sim,
#         waveform=waveform,
#         init_controller=True,
#     )
#     analyzer = Analyzer(result)
#     preds = analyzer.pressure  # shape = (29,)
#     truth = analyzer.target  # shape = (29,)
#     # print('preds.shape: %s', str(preds.shape))
#     # print('truth.shape: %s', str(truth.shape))
#     score += jnp.abs(preds - truth).mean()
#   score = score / len(pips)
#   return score


# Path: deluca/lung/utils/scripts/train_controller.py
import copy
import functools
import jax
import jax.numpy as jnp
import optax
from clu import metric_writers
from deluca.lung.controllers._expiratory import Expiratory
from deluca.lung.core import BreathWaveform
from deluca.lung.utils.scripts.test_controller import test_controller


  tt = jnp.linspace(0, duration, int(duration / dt))
  losses = []
  for epoch in range(epochs):
    if pip_feed == "parallel":
      value, grad = jax.value_and_grad(rollout_parallel)(controller, sim, tt,
                                                         use_noise, peep,
                                                         jnp.array(pips),
                                                         loss_fn)
      updates, optim_state = optim.update(grad, optim_state, controller)
      controller = optax.apply_updates(controller, updates)
      per_step_loss = value / len(tt)
      losses.append(per_step_loss)
      if epoch % print_loss == 0:
        # make new controller with trained parameters and normal clamp
        score = test_controller(controller, sim, pips, peep)
        print(f"Epoch: {epoch}\tLoss: {score:.2f}")
        if tensorboard_dir is not None:
          summary_writer.write_scalars(epoch, {"score": score})
    if pip_feed == "sequential":
      for pip in pips:
        value, grad = jax.value_and_grad(rollout)(controller, sim, tt,
                                                  use_noise, peep, pip,
                                                  loss_fn, jnp.array(0.))
        updates, optim_state = optim.update(grad, optim_state, controller)
        controller = optax.apply_updates(controller, updates)
        per_step_loss = value / len(tt)
        losses.append(per_step_loss)
        if epoch % print_loss == 0:
          # make new controller with trained parameters and normal clamp

          score = test_controller(controller, sim, pips, peep)