'''
This module contains different variations of the Physics Informer Neural Network model using the JaxModel API
'''
import numpy as np
import time
from typing import Any, Callable, Iterable, List, Optional, Tuple, Union

from collections.abc import Sequence as SequenceCollection

from deepchem.data import Dataset
from deepchem.models.losses import Loss
from deepchem.models.optimizers import Optimizer
from deepchem.utils.typing import LossFn, OneOrMany
from deepchem.trans.transformers import Transformer, undo_transforms

# JAX dependencies
import jax
import jax.numpy as jnp
import haiku as hk
import optax
from deepchem.models.jax_models.jax_model import JaxModel
from deepchem.models.jax_models.jax_model import create_default_gradient_fn, create_default_eval_fn

import logging
import warnings

logger = logging.getLogger(__name__)


def create_default_update_fn(optimizer: optax.GradientTransformation,
                             model_loss: Callable):
  """
  This function calls the update function, to implement the backpropagation
  """

  @jax.jit
  def update(params, opt_state, batch, target, weights,
             rng) -> Tuple[hk.Params, optax.OptState, jnp.ndarray]:
    batch_loss, grads = jax.value_and_grad(model_loss)(params, target, weights,
                                                       rng, *batch)
    updates, opt_state = optimizer.update(grads, opt_state)
    new_params = optax.apply_updates(params, updates)
    return new_params, opt_state, batch_loss

  return update


class PINNModel(JaxModel):
  """
  This is class is derived from the JaxModel class and methods are also very similar to JaxModel,
  but it has the option of passing multiple arguments(Done using *args) suitable for PINNs model.
  Ex - Approximating f(x, y, z, t) satisfying a Linear differential equation.

  This model is recommended for linear partial differential equations but if you can accurately write
  the gradient function in Jax depending on your use case, then it will work as well.

  This class requires two functions apart from the usual function definition and weights

  [1] **grad_fn** : Each PINNs have a different strategy for calculating its final losses. This
  function tells the PINNModel how to go about computing the derivatives for backpropagation.
  It should follow this format:

  >>>
  >> def gradient_fn(forward_fn, loss_outputs, initial_data):
  >>
  >>  def model_loss(params, target, weights, rng, ...):
  >>
  >>    # write code using the arguments.
  >>    # ... indicates the variable number of positional arguments.
  >>    return
  >>
  >>  return model_loss

  "..." can be replaced with various arguments like (x, y, z, y) but should match with eval_fn

  [2] **eval_fn**: Function for defining how the model needs to compute during inference.
  It should follow this format

  >>>
  >> def create_eval_fn(forward_fn, params):
  >>  def eval_model(..., rng=None):
  >>    # write code here using arguments
  >>
  >>    return
  >>  return eval_model

  "..." can be replaced with various arguments like (x, y, z, y) but should match with grad_fn

  [3] boundary_data:
  For a detailed example, check out - deepchem/models/jax_models/tests/test_pinn.py where we have
  solved f'(x) = -sin(x)

  References
  ----------
  .. [1] Raissi et. al. "Physics-informed neural networks: A deep learning framework for solving
     forward and inverse problems involving nonlinear partial differential equations" Journal of
     Computational Physics https://doi.org/10.1016/j.jcp.2018.10.045

  .. [2] Raissi et. al. "Physics Informed Deep Learning (Part I): Data-driven
     Solutions of Nonlinear Partial Differential Equations" arXiv preprint arXiv:1711.10561

  Notes
  -----
  This class requires Jax, Haiku and Optax to be installed.
  """

  def __init__(self,
               forward_fn: hk.State,
               params: hk.Params,
               initial_data: dict = {},
               output_types: Optional[List[str]] = None,
               batch_size: int = 100,
               learning_rate: float = 0.001,
               optimizer: Union[optax.GradientTransformation, Optimizer] = None,
               grad_fn: Callable = create_default_gradient_fn,
               update_fn: Callable = create_default_update_fn,
               eval_fn: Callable = create_default_eval_fn,
               rng=jax.random.PRNGKey(1),
               log_frequency: int = 100,
               **kwargs):
    """
    Parameters
    ----------
    forward_fn: hk.State or Function
      Any Jax based model that has a `apply` method for computing the network. Currently
      only haiku models are supported.
    params: hk.Params
      The parameter of the Jax based networks
    initial_data: dict
      This acts as a session variable which will be passed as a dictionary in grad_fn
    output_types: list of strings, optional (default None)
      the type of each output from the model, as described above
    batch_size: int, optional (default 100)
      default batch size for training and evaluating
    learning_rate: float or LearningRateSchedule, optional (default 0.001)
      the learning rate to use for fitting.  If optimizer is specified, this is
      ignored.
    optimizer: optax object
      For the time being, it is optax object
    grad_fn: Callable (default create_default_gradient_fn)
      It defines how the loss function and gradients need to be calculated for the PINNs model
    update_fn: Callable (default create_default_update_fn)
      It defines how the weights need to be updated using backpropogation. We have used optax library
      for optimisation operations. Its reccomended to leave this default.
    eval_fn: Callable (default create_default_eval_fn)
      Function for defining on how the model needs to compute during inference.
    rng: jax.random.PRNGKey, optional (default 1)
      A default global PRNG key to use for drawing random numbers.
    log_frequency: int, optional (default 100)
      The frequency at which to log data. Data is logged using
      `logging` by default.
    """
    warnings.warn(
        'PinnModel is still in active development and we could change the design of the API in the future.'
    )
    self.boundary_data = initial_data
    super(PINNModel,
          self).__init__(forward_fn, params, None, output_types, batch_size,
                         learning_rate, optimizer, grad_fn, update_fn, eval_fn,
                         rng, log_frequency, **kwargs)

  def fit_generator(self,
                    generator: Iterable[Tuple[Any, Any, Any]],
                    loss: Union[Loss, LossFn] = None,
                    callbacks: Union[Callable, List[Callable]] = [],
                    all_losses: Optional[List[float]] = None) -> float:
    if not isinstance(callbacks, SequenceCollection):
      callbacks = [callbacks]
    self._ensure_built()
    avg_loss = 0.0
    last_avg_loss = 0.0
    averaged_batches = 0
    if loss is None:
      loss = self._loss_fn
    model_loss_fn = self._create_gradient_fn(self.forward_fn,
                                             self._loss_outputs,
                                             self.boundary_data)
    grad_update = self._create_update_fn(self.optimizer, model_loss_fn)

    params, opt_state = self._get_trainable_params()
    rng = self.rng
    time1 = time.time()

    # Main training loop

    for batch in generator:
      inputs, labels, weights = self._prepare_batch(batch)

      if isinstance(inputs, list) and len(inputs) == 1:
        inputs = inputs[0]

      if isinstance(labels, list) and len(labels) == 1:
        labels = labels[0]

      if isinstance(weights, list) and len(weights) == 1:
        weights = weights[0]

      params, opt_state, batch_loss = grad_update(params,
                                                  opt_state,
                                                  inputs,
                                                  labels,
                                                  weights,
                                                  rng=rng)
      rng, _ = jax.random.split(rng)
      avg_loss += jax.device_get(batch_loss)
      self._global_step += 1
      current_step = self._global_step
      averaged_batches += 1
      should_log = (current_step % self.log_frequency == 0)

      if should_log:
        avg_loss = float(avg_loss) / averaged_batches
        logger.info('Ending global_step %d: Average loss %g' %
                    (current_step, avg_loss))
        if all_losses is not None:
          all_losses.append(avg_loss)
        # Capture the last avg_loss in case of return since we're resetting to 0 now
        last_avg_loss = avg_loss
        avg_loss = 0.0
        averaged_batches = 0
      for c in callbacks:
        c(self, current_step)

    # Report final results.
    if averaged_batches > 0:
      avg_loss = float(avg_loss) / averaged_batches
      logger.info('Ending global_step %d: Average loss %g' %
                  (current_step, avg_loss))
      if all_losses is not None:
        all_losses.append(avg_loss)
      last_avg_loss = avg_loss

    time2 = time.time()
    logger.info("TIMING: model fitting took %0.3f s" % (time2 - time1))
    self._set_trainable_params(params, opt_state)
    return last_avg_loss

  def _prepare_batch(self, batch):
    inputs, labels, weights = batch
    inputs = [
        x.astype(np.float32) if x.dtype == np.float64 else x for x in inputs
    ]
    inputs = [np.split(x, x.shape[1], 1) for x in inputs]
    if labels is not None:
      labels = [
          x.astype(np.float32) if x.dtype == np.float64 else x for x in labels
      ]
    else:
      labels = []

    if weights is not None:
      weights = [
          x.astype(np.float32) if x.dtype == np.float64 else x for x in weights
      ]
    else:
      weights = []

    return (inputs, labels, weights)

  def default_generator(
      self,
      dataset: Dataset,
      epochs: int = 1,
      mode: str = 'fit',
      deterministic: bool = True,
      pad_batches: bool = True) -> Iterable[Tuple[List, List, List]]:
    """Create a generator that iterates batches for a dataset.
    Subclasses may override this method to customize how model inputs are
    generated from the data.

    Parameters
    ----------
    dataset: Dataset
      the data to iterate
    epochs: int
      the number of times to iterate over the full dataset
    mode: str
      allowed values are 'fit' (called during training), 'predict' (called
      during prediction), and 'uncertainty' (called during uncertainty
      prediction)
    deterministic: bool
      whether to iterate over the dataset in order, or randomly shuffle the
      data for each epoch
    pad_batches: bool
      whether to pad each batch up to this model's preferred batch size
    Returns
    -------
    a generator that iterates batches, each represented as a tuple of lists:
    ([inputs], [outputs], [weights])
    """

    for epoch in range(epochs):
      for (X_b, y_b, w_b, _) in dataset.iterbatches(batch_size=self.batch_size,
                                                    deterministic=deterministic,
                                                    pad_batches=pad_batches):
        yield ([X_b], [y_b], [w_b])

  def _predict(self, generator: Iterable[Tuple[Any, Any, Any]],
               transformers: List[Transformer], uncertainty: bool,
               other_output_types: Optional[OneOrMany[str]]):
    """
    Predict outputs for data provided by a generator.
    This is the private implementation of prediction.  Do not
    call it directly. Instead call one of the public prediction
    methods.
    Parameters
    ----------
    generator: generator
      this should generate batches, each represented as a tuple of the form
      (inputs, labels, weights).
    transformers: List[dc.trans.Transformers]
      Transformers that the input data has been transformed by.  The output
      is passed through these transformers to undo the transformations.
    uncertainty: bool
      specifies whether this is being called as part of estimating uncertainty.
      If True, it sets the training flag so that dropout will be enabled, and
      returns the values of the uncertainty outputs.
    other_output_types: list, optional
      Provides a list of other output_types (strings) to predict from model.
    Returns
    -------
    A NumpyArray if the model produces a single output, or a list of arrays otherwise.
    """
    results: Optional[List[List[np.ndarray]]] = None
    variances: Optional[List[List[np.ndarray]]] = None
    if uncertainty and (other_output_types is not None):
      raise ValueError(
          'This model cannot compute uncertainties and other output types simultaneously. Please invoke one at a time.'
      )
    if uncertainty:
      if self._variance_outputs is None or len(self._variance_outputs) == 0:
        raise ValueError('This model cannot compute uncertainties')
      if len(self._variance_outputs) != len(self._prediction_outputs):
        raise ValueError(
            'The number of variances must exactly match the number of outputs')
    if other_output_types:
      if self._other_outputs is None or len(self._other_outputs) == 0:
        raise ValueError(
            'This model cannot compute other outputs since no other output_types were specified.'
        )
    self._ensure_built()
    eval_fn = self._create_eval_fn(self.forward_fn, self.params)
    rng = self.rng

    for batch in generator:
      inputs, _, _ = self._prepare_batch(batch)

      if isinstance(inputs, list) and len(inputs) == 1:
        inputs = inputs[0]

      output_values = eval_fn(*inputs, rng)
      if isinstance(output_values, jnp.ndarray):
        output_values = [output_values]
      output_values = [jax.device_get(t) for t in output_values]

      # Apply tranformers and record results.
      if uncertainty:
        var = [output_values[i] for i in self._variance_outputs]
        if variances is None:
          variances = [var]
        else:
          for i, t in enumerate(var):
            variances[i].append(t)

      access_values = []
      if other_output_types:
        access_values += self._other_outputs
      elif self._prediction_outputs is not None:
        access_values += self._prediction_outputs

      if len(access_values) > 0:
        output_values = [output_values[i] for i in access_values]

      if len(transformers) > 0:
        if len(output_values) > 1:
          raise ValueError(
              "predict() does not support Transformers for models with multiple outputs."
          )
        elif len(output_values) == 1:
          output_values = [undo_transforms(output_values[0], transformers)]

      if results is None:
        results = [[] for i in range(len(output_values))]
      for i, t in enumerate(output_values):
        results[i].append(t)

    # Concatenate arrays to create the final results.
    final_results = []
    final_variances = []
    if results is not None:
      for r in results:
        final_results.append(np.concatenate(r, axis=0))
    if uncertainty and variances is not None:
      for v in variances:
        final_variances.append(np.concatenate(v, axis=0))
      return zip(final_results, final_variances)
    if len(final_results) == 1:
      return final_results[0]
    else:
      return final_results
