# Copyright 2023 DeepMind Technologies Limited.
#
# 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.
"""Wrappers for Predictors which allow them to work with normalized data.

The Predictor which is wrapped sees normalized inputs and targets, and makes
normalized predictions. The wrapper handles translating the predictions back
to the original domain.
"""

import logging
from typing import Optional, Tuple

from graphcast import predictor_base
from graphcast import xarray_tree
import xarray


def normalize(values: xarray.Dataset,
              scales: xarray.Dataset,
              locations: Optional[xarray.Dataset],
              ) -> xarray.Dataset:
  """Normalize variables using the given scales and (optionally) locations."""
  def normalize_array(array):
    if array.name is None:
      raise ValueError(
          "Can't look up normalization constants because array has no name.")
    if locations is not None:
      if array.name in locations:
        array = array - locations[array.name].astype(array.dtype)
      else:
        logging.warning('No normalization location found for %s', array.name)
    if array.name in scales:
      array = array / scales[array.name].astype(array.dtype)
    else:
      logging.warning('No normalization scale found for %s', array.name)
    return array
  return xarray_tree.map_structure(normalize_array, values)


def unnormalize(values: xarray.Dataset,
                scales: xarray.Dataset,
                locations: Optional[xarray.Dataset],
                ) -> xarray.Dataset:
  """Unnormalize variables using the given scales and (optionally) locations."""
  def unnormalize_array(array):
    if array.name is None:
      raise ValueError(
          "Can't look up normalization constants because array has no name.")
    if array.name in scales:
      array = array * scales[array.name].astype(array.dtype)
    else:
      logging.warning('No normalization scale found for %s', array.name)
    if locations is not None:
      if array.name in locations:
        array = array + locations[array.name].astype(array.dtype)
      else:
        logging.warning('No normalization location found for %s', array.name)
    return array
  return xarray_tree.map_structure(unnormalize_array, values)


class InputsAndResiduals(predictor_base.Predictor):
  """Wraps with a residual connection, normalizing inputs and target residuals.

  The inner predictor is given inputs that are normalized using `locations`
  and `scales` to roughly zero-mean unit variance.

  For target variables that are present in the inputs, the inner predictor is
  trained to predict residuals (target - last_frame_of_input) that have been
  normalized using `residual_scales` (and optionally `residual_locations`) to
  roughly unit variance / zero mean.

  This replaces `residual.Predictor` in the case where you want normalization
  that's based on the scales of the residuals.

  Since we return the underlying predictor's loss on the normalized residuals,
  if the underlying predictor is a sum of per-variable losses, the normalization
  will affect the relative weighting of the per-variable loss terms (hopefully
  in a good way).

  For target variables *not* present in the inputs, the inner predictor is
  trained to predict targets directly, that have been normalized in the same
  way as the inputs.

  The transforms applied to the targets (the residual connection and the
  normalization) are applied in reverse to the predictions before returning
  them.
  """

  def __init__(
      self,
      predictor: predictor_base.Predictor,
      stddev_by_level: xarray.Dataset,
      mean_by_level: xarray.Dataset,
      diffs_stddev_by_level: xarray.Dataset):
    self._predictor = predictor
    self._scales = stddev_by_level
    self._locations = mean_by_level
    self._residual_scales = diffs_stddev_by_level
    self._residual_locations = None

  def _unnormalize_prediction_and_add_input(self, inputs, norm_prediction):
    if norm_prediction.sizes.get('time') != 1:
      raise ValueError(
          'normalization.InputsAndResiduals only supports predicting a '
          'single timestep.')
    if norm_prediction.name in inputs:
      # Residuals are assumed to be predicted as normalized (unit variance),
      # but the scale and location they need mapping to is that of the residuals
      # not of the values themselves.
      prediction = unnormalize(
          norm_prediction, self._residual_scales, self._residual_locations)
      # A prediction for which we have a corresponding input -- we are
      # predicting the residual:
      last_input = inputs[norm_prediction.name].isel(time=-1)
      prediction += last_input
      return prediction
    else:
      # A predicted variable which is not an input variable. We are predicting
      # it directly, so unnormalize it directly to the target scale/location:
      return unnormalize(norm_prediction, self._scales, self._locations)

  def _subtract_input_and_normalize_target(self, inputs, target):
    if target.sizes.get('time') != 1:
      raise ValueError(
          'normalization.InputsAndResiduals only supports wrapping predictors'
          'that predict a single timestep.')
    if target.name in inputs:
      target_residual = target
      last_input = inputs[target.name].isel(time=-1)
      target_residual -= last_input
      return normalize(
          target_residual, self._residual_scales, self._residual_locations)
    else:
      return normalize(target, self._scales, self._locations)

  def __call__(self,
               inputs: xarray.Dataset,
               targets_template: xarray.Dataset,
               forcings: xarray.Dataset,
               **kwargs
               ) -> xarray.Dataset:
    norm_inputs = normalize(inputs, self._scales, self._locations)
    norm_forcings = normalize(forcings, self._scales, self._locations)
    norm_predictions = self._predictor(
        norm_inputs, targets_template, forcings=norm_forcings, **kwargs)
    return xarray_tree.map_structure(
        lambda pred: self._unnormalize_prediction_and_add_input(inputs, pred),
        norm_predictions)

  def loss(self,
           inputs: xarray.Dataset,
           targets: xarray.Dataset,
           forcings: xarray.Dataset,
           **kwargs,
           ) -> predictor_base.LossAndDiagnostics:
    """Returns the loss computed on normalized inputs and targets."""
    norm_inputs = normalize(inputs, self._scales, self._locations)
    norm_forcings = normalize(forcings, self._scales, self._locations)
    norm_target_residuals = xarray_tree.map_structure(
        lambda t: self._subtract_input_and_normalize_target(inputs, t),
        targets)
    return self._predictor.loss(
        norm_inputs, norm_target_residuals, forcings=norm_forcings, **kwargs)

  def loss_and_predictions(  # pytype: disable=signature-mismatch  # jax-ndarray
      self,
      inputs: xarray.Dataset,
      targets: xarray.Dataset,
      forcings: xarray.Dataset,
      **kwargs,
      ) -> Tuple[predictor_base.LossAndDiagnostics,
                 xarray.Dataset]:
    """The loss computed on normalized data, with unnormalized predictions."""
    norm_inputs = normalize(inputs, self._scales, self._locations)
    norm_forcings = normalize(forcings, self._scales, self._locations)
    norm_target_residuals = xarray_tree.map_structure(
        lambda t: self._subtract_input_and_normalize_target(inputs, t),
        targets)
    (loss, scalars), norm_predictions = self._predictor.loss_and_predictions(
        norm_inputs, norm_target_residuals, forcings=norm_forcings, **kwargs)
    predictions = xarray_tree.map_structure(
        lambda pred: self._unnormalize_prediction_and_add_input(inputs, pred),
        norm_predictions)
    return (loss, scalars), predictions
