"""
Contains class for gaussian process hyperparameter optimizations.
"""
import os
import logging
import tempfile
from typing import Dict, List, Optional, Tuple, Union

from deepchem.data import Dataset
from deepchem.trans import Transformer
from deepchem.metrics import Metric
from deepchem.hyper.base_classes import HyperparamOpt
from deepchem.hyper.base_classes import _convert_hyperparam_dict_to_filename

logger = logging.getLogger(__name__)


def compute_parameter_range(params_dict: Dict,
                            search_range: Union[int, float, Dict]
                           ) -> Dict[str, Tuple[str, List[float]]]:
  """Convenience Function to compute parameter search space.

  Parameters
  ----------
  params_dict: Dict
    Dictionary mapping strings to Ints/Floats. An explicit list of
    parameters is computed with `search_range`. The optimization range
    computed is specified in the documentation for `search_range`
    below.
  search_range: int/float/Dict (default 4)
    The `search_range` specifies the range of parameter values to
    search for. If `search_range` is an int/float, it is used as the
    global search range for parameters. This creates a search
    problem on the following space:

    optimization on [initial value / search_range,
                     initial value * search_range]

    If `search_range` is a dict, it must contain the same keys as
    for `params_dict`. In this case, `search_range` specifies a
    per-parameter search range. This is useful in case some
    parameters have a larger natural range than others. For a given
    hyperparameter `hp` this would create the following search
    range:

    optimization on hp on [initial value[hp] / search_range[hp],
                           initial value[hp] * search_range[hp]]

  Returns
  -------
  param_range: Dict
    Dictionary mapping hyperparameter names to tuples. Each tuple is
    of form `(value_type, value_range)` where `value_type` is a string
    that is either "int" or "cont" and `value_range` is a list of two
    elements of the form `[low, hi]`. This format is expected by
    pyGPGO which `GaussianProcessHyperparamOpt` uses to perform
    optimization.
  """
  # Range of optimization
  param_range = {}
  if isinstance(search_range, dict):
    if sorted(params_dict.keys()) != sorted(search_range.keys()):
      raise ValueError(
          "If search_range is provided as a dictionary, it must have the same keys as params_dict."
      )
  elif (not isinstance(search_range, int)) and (not isinstance(
      search_range, float)):
    raise ValueError("search_range must be a dict or int or float.")
  for hp, value in params_dict.items():
    if isinstance(search_range, dict):
      hp_search_range = search_range[hp]
    else:
      # We know from guard above that this is an int/float
      hp_search_range = search_range
    if isinstance(value, int):
      value_range = [value // hp_search_range, value * hp_search_range]
      param_range[hp] = ("int", value_range)
    elif isinstance(value, float):
      value_range = [value / hp_search_range, value * hp_search_range]
      param_range[hp] = ("cont", value_range)
  return param_range


class GaussianProcessHyperparamOpt(HyperparamOpt):
  """
  Gaussian Process Global Optimization(GPGO)

  This class uses Gaussian Process optimization to select
  hyperparameters. Underneath the hood it uses pyGPGO to optimize
  models. If you don't have pyGPGO installed, you won't be able to use
  this class.

  Note that `params_dict` has a different semantics than for
  `GridHyperparamOpt`. `param_dict[hp]` must be an int/float and is
  used as the center of a search range.

  Examples
  --------
  This example shows the type of constructor function expected.

  >>> import sklearn
  >>> import deepchem as dc
  >>> optimizer = dc.hyper.GaussianProcessHyperparamOpt(lambda **p: dc.models.GraphConvModel(n_tasks=1, **p))

  Here's a more sophisticated example that shows how to optimize only
  some parameters of a model. In this case, we have some parameters we
  want to optimize, and others which we don't. To handle this type of
  search, we create a `model_builder` which hard codes some arguments
  (in this case, `n_tasks` and `n_features` which are properties of a
  dataset and not hyperparameters to search over.)

  >>> def model_builder(**model_params):
  ...   n_layers = model_params['layers']
  ...   layer_width = model_params['width']
  ...   dropout = model_params['dropout']
  ...   return dc.models.MultitaskClassifier(
  ...     n_tasks=5,
  ...     n_features=100,
  ...     layer_sizes=[layer_width]*n_layers,
  ...     dropouts=dropout
  ...   )
  >>> optimizer = dc.hyper.GaussianProcessHyperparamOpt(model_builder)

  Notes
  -----
  This class requires pyGPGO to be installed.
  """

  def hyperparam_search(self,
                        params_dict: Dict,
                        train_dataset: Dataset,
                        valid_dataset: Dataset,
                        metric: Metric,
                        output_transformers: List[Transformer] = [],
                        use_max: bool = True,
                        logdir: Optional[str] = None,
                        max_iter: int = 20,
                        search_range: Union[int, float, Dict] = 4,
                        logfile: Optional[str] = None,
                        **kwargs):
    """Perform hyperparameter search using a gaussian process.

    Parameters
    ----------
    params_dict: Dict
      Maps hyperparameter names (strings) to possible parameter
      values. The semantics of this list are different than for
      `GridHyperparamOpt`. `params_dict[hp]` must map to an int/float,
      which is used as the center of a search with radius
      `search_range` since pyGPGO can only optimize numerical
      hyperparameters.
    train_dataset: Dataset
      dataset used for training
    valid_dataset: Dataset
      dataset used for validation(optimization on valid scores)
    metric: Metric
      metric used for evaluation
    output_transformers: list[Transformer]
      Transformers for evaluation. This argument is needed since
      `train_dataset` and `valid_dataset` may have been transformed
      for learning and need the transform to be inverted before
      the metric can be evaluated on a model.
    use_max: bool, (default True)
      Specifies whether to maximize or minimize `metric`.
      maximization(True) or minimization(False)
    logdir: str, optional, (default None)
      The directory in which to store created models. If not set, will
      use a temporary directory.
    max_iter: int, (default 20)
      number of optimization trials
    search_range: int/float/Dict (default 4)
      The `search_range` specifies the range of parameter values to
      search for. If `search_range` is an int/float, it is used as the
      global search range for parameters. This creates a search
      problem on the following space:

      optimization on [initial value / search_range,
                       initial value * search_range]

      If `search_range` is a dict, it must contain the same keys as
      for `params_dict`. In this case, `search_range` specifies a
      per-parameter search range. This is useful in case some
      parameters have a larger natural range than others. For a given
      hyperparameter `hp` this would create the following search
      range:

      optimization on hp on [initial value[hp] / search_range[hp],
                             initial value[hp] * search_range[hp]]
    logfile: str, optional (default None)
      Name of logfile to write results to. If specified, this is must
      be a valid file. If not specified, results of hyperparameter
      search will be written to `logdir/.txt`.


    Returns
    -------
    Tuple[`best_model`, `best_hyperparams`, `all_scores`]
      `(best_model, best_hyperparams, all_scores)` where `best_model` is
      an instance of `dc.model.Model`, `best_hyperparams` is a
      dictionary of parameters, and `all_scores` is a dictionary mapping
      string representations of hyperparameter sets to validation
      scores.
    """
    try:
      from pyGPGO.covfunc import matern32
      from pyGPGO.acquisition import Acquisition
      from pyGPGO.surrogates.GaussianProcess import GaussianProcess
      from pyGPGO.GPGO import GPGO
    except ModuleNotFoundError:
      raise ImportError("This class requires pyGPGO to be installed.")

    # Specify logfile
    log_file = None
    if logfile:
      log_file = logfile
    elif logdir is not None:
      # Make logdir if it doesn't exist.
      if not os.path.exists(logdir):
        os.makedirs(logdir, exist_ok=True)
      log_file = os.path.join(logdir, "results.txt")

    # setup range
    param_range = compute_parameter_range(params_dict, search_range)
    param_keys = list(param_range.keys())

    # Stores all results
    all_results = {}
    # Store all model references so we don't have to reload
    all_models = {}
    # Stores all model locations
    model_locations = {}

    # Demarcating internal function for readability
    def optimizing_function(**placeholders):
      """Private Optimizing function

      Take in hyper parameter values and return valid set performances

      Parameters
      ----------
      placeholders: keyword arguments
        Should be various hyperparameters as specified in `param_keys` above.

      Returns:
      --------
      valid_scores: float
        valid set performances
      """
      hyper_parameters = {}
      for hp in param_keys:
        if param_range[hp][0] == "int":
          # param values are always float in BO, so this line converts float to int
          # see : https://github.com/josejimenezluna/pyGPGO/issues/10
          hyper_parameters[hp] = int(placeholders[hp])
        else:
          hyper_parameters[hp] = float(placeholders[hp])
      logger.info("Running hyperparameter set: %s" % str(hyper_parameters))
      if log_file:
        with open(log_file, 'w+') as f:
          # Record hyperparameters
          f.write("Parameters: %s" % str(hyper_parameters))
          f.write('\n')

      hp_str = _convert_hyperparam_dict_to_filename(hyper_parameters)
      if logdir is not None:
        filename = "model%s" % hp_str
        model_dir = os.path.join(logdir, filename)
        logger.info("model_dir is %s" % model_dir)
        try:
          os.makedirs(model_dir)
        except OSError:
          if not os.path.isdir(model_dir):
            logger.info("Error creating model_dir, using tempfile directory")
            model_dir = tempfile.mkdtemp()
      else:
        model_dir = tempfile.mkdtemp()
      # Add it on to the information needed for the constructor
      hyper_parameters["model_dir"] = model_dir
      model = self.model_builder(**hyper_parameters)
      model.fit(train_dataset)
      try:
        model.save()
      # Some models autosave
      except NotImplementedError:
        pass

      multitask_scores = model.evaluate(valid_dataset, [metric],
                                        output_transformers)
      score = multitask_scores[metric.name]

      if log_file:
        with open(log_file, 'a') as f:
          # Record performances
          f.write("Score: %s" % str(score))
          f.write('\n')
      # Store all results
      all_results[hp_str] = score
      # Store reference to model
      all_models[hp_str] = model
      model_locations[hp_str] = model_dir
      # GPGO maximize performance by default
      # set performance to its negative value for minimization
      if use_max:
        return score
      else:
        return -score

    # execute GPGO
    cov = matern32()
    gp = GaussianProcess(cov)
    acq = Acquisition(mode='ExpectedImprovement')
    gpgo = GPGO(gp, acq, optimizing_function, param_range)
    logger.info("Max number of iteration: %i" % max_iter)
    gpgo.run(max_iter=max_iter)

    hp_opt, valid_performance_opt = gpgo.getResult()
    hyper_parameters = {}
    for hp in param_keys:
      if param_range[hp][0] == "int":
        hyper_parameters[hp] = int(hp_opt[hp])
      else:
        # FIXME: Incompatible types in assignment
        hyper_parameters[hp] = float(hp_opt[hp])  # type: ignore
    hp_str = _convert_hyperparam_dict_to_filename(hyper_parameters)

    # Let's fetch the model with the best parameters
    best_model = all_models[hp_str]

    # Compare best model to default hyperparameters
    if log_file:
      with open(log_file, 'a') as f:
        # Record hyperparameters
        f.write("params_dict:")
        f.write(str(params_dict))
        f.write('\n')

    # Return default hyperparameters
    return best_model, hyper_parameters, all_results
