"""
This module provide basic data management functionalities
"""

from abc import abstractmethod
from torch.utils.data import Dataset
from torch_geometric.data import Data
from ..graph import Graph, LabelBatch


class PinaDatasetFactory:
    """
    Factory class for the PINA dataset.

    Depending on the type inside the conditions, it creates a different dataset
    object:

    - :class:`PinaTensorDataset` for `torch.Tensor`
    - :class:`PinaGraphDataset` for `list` of `torch_geometric.data.Data`
        objects
    """

    def __new__(cls, conditions_dict, **kwargs):
        """
        Instantiate the appropriate subclass of :class:`PinaDataset`.

        If a graph is present in the conditions, returns a
        :class:`PinaGraphDataset`,  otherwise returns a
        :class:`PinaTensorDataset`.

        :param dict conditions_dict: Dictionary containing the conditions.
        :return: A subclass of :class:`PinaDataset`.
        :rtype: :class:`PinaTensorDataset` | :class:`PinaGraphDataset`

        :raises ValueError: If an empty dictionary is provided.
        """

        # Check if conditions_dict is empty
        if len(conditions_dict) == 0:
            raise ValueError("No conditions provided")

        # Check is a Graph is present in the conditions
        is_graph = cls._is_graph_dataset(conditions_dict)
        if is_graph:
            # If a Graph is present, return a PinaGraphDataset
            return PinaGraphDataset(conditions_dict, **kwargs)
        # If no Graph is present, return a PinaTensorDataset
        return PinaTensorDataset(conditions_dict, **kwargs)

    @staticmethod
    def _is_graph_dataset(conditions_dict):
        """
        Check if a graph is present in the conditions.

        :param conditions_dict: Dictionary containing the conditions.
        :type conditions_dict: dict
        :return: True if a graph is present in the conditions, False otherwise
        :rtype: bool
        """

        # Iterate over the conditions dictionary
        for v in conditions_dict.values():
            # Iterate over the values of the current condition
            for cond in v.values():
                # Check if the current value is a list of Data objects
                if isinstance(cond, (Data, Graph, list, tuple)):
                    return True
        return False


class PinaDataset(Dataset):
    """
    Abstract class for the PINA dataset
    """

    def __init__(
        self, conditions_dict, max_conditions_lengths, automatic_batching
    ):
        """
        Initialize the :class:`PinaDataset`.

        Stores the conditions dictionary, the maximum number of conditions to
        consider, and the automatic batching flag.

        :param dict conditions_dict: Dictionary containing the conditions.
        :param dict max_conditions_lengths: Maximum number of data points to
            consider in a single batch for each condition.
        :param bool automatic_batching: Whether PyTorch automatic batching is
            enabled in :class:`PinaDataModule`.
        """

        # Store the conditions dictionary
        self.conditions_dict = conditions_dict
        # Store the maximum number of conditions to consider
        self.max_conditions_lengths = max_conditions_lengths
        # Store length of each condition
        self.conditions_length = {
            k: len(v["input"]) for k, v in self.conditions_dict.items()
        }
        # Store the maximum length of the dataset
        self.length = max(self.conditions_length.values())
        # Dynamically set the getitem function based on automatic batching
        if automatic_batching:
            self._getitem_func = self._getitem_int
        else:
            self._getitem_func = self._getitem_dummy

    def _get_max_len(self):
        """
        Returns the length of the longest condition in the dataset

        :return: Length of the longest condition in the dataset
        :rtype: int
        """

        max_len = 0
        for condition in self.conditions_dict.values():
            max_len = max(max_len, len(condition["input"]))
        return max_len

    def __len__(self):
        return self.length

    def __getitem__(self, idx):
        return self._getitem_func(idx)

    def _getitem_dummy(self, idx):
        """
        Return the index itself. This is used when automatic batching is
        disabled to postpone the data retrieval to the dataloader.

        :param idx: Index
        :type idx: int
        :return: Index
        :rtype: int
        """

        # If automatic batching is disabled, return the data at the given index
        return idx

    def _getitem_int(self, idx):
        """
        Return the data at the given index in the dataset. This is used when
        automatic batching is enabled.

        :param int idx: Index
        :return: A dictionary containing the data at the given index
        :rtype: dict
        """

        # If automatic batching is enabled, return the data at the given index
        return {
            k: {k_data: v[k_data][idx % len(v["input"])] for k_data in v.keys()}
            for k, v in self.conditions_dict.items()
        }

    def get_all_data(self):
        """
        Return all data in the dataset

        :return: All data in the dataset
        :rtype: dict
        """
        index = list(range(len(self)))
        return self.fetch_from_idx_list(index)

    def fetch_from_idx_list(self, idx):
        """
        Return data from the dataset given a list of indices

        :param idx: List of indices
        :type idx: list
        :return: Data from the dataset
        :rtype: dict
        """
        to_return_dict = {}
        for condition, data in self.conditions_dict.items():
            # Get the indices for the current condition
            cond_idx = idx[: self.max_conditions_lengths[condition]]
            # Get the length of the current condition
            condition_len = self.conditions_length[condition]
            # If the length of the dataset is greater than the length of the
            # current condition, repeat the indices
            if self.length > condition_len:
                cond_idx = [idx % condition_len for idx in cond_idx]
            # Retrieve the data from the current condition
            to_return_dict[condition] = self._retrive_data(data, cond_idx)
        return to_return_dict

    @abstractmethod
    def _retrive_data(self, data, idx_list):
        """
        Retrieve data from the dataset given a list of indices

        :param dict data: Dictionary containing the data
        :param list idx_list: List of indices to retrieve
        :return: Dictionary containing the data at the given indices
        :rtype: dict
        """


class PinaTensorDataset(PinaDataset):
    """
    Class for the PINA dataset with torch.Tensor data
    """

    # Override _retrive_data method for torch.Tensor data
    def _retrive_data(self, data, idx_list):
        """
        Retrieve data from the dataset given a list of indices

        :param data: Dictionary containing the data
            (only torch.Tensor/LableTensor)
        :type data: dict
        :param list(int) idx_list: indices to retrieve
        :return: Dictionary containing the data at the given indices
        :rtype: dict
        """

        return {k: v[idx_list] for k, v in data.items()}

    @property
    def input(self):
        """
        Method to return all input points from the dataset.

        :return: Dictionary containing the input points
        :rtype: dict
        """
        return {k: v["input"] for k, v in self.conditions_dict.items()}


class PinaGraphDataset(PinaDataset):
    """
    Class for the PINA dataset with torch_geometric.data.Data data
    """

    def _create_graph_batch(self, data):
        """
        Create a LabelBatch object from a list of Data objects.

        :param data: List of Data or Graph objects
        :type data: list(Data) | list(Graph)
        :return: LabelBatch object all the graph collated in a single batch
            disconnected graphs.
        :rtype: LabelBatch
        """
        batch = LabelBatch.from_data_list(data)
        return batch

    def _create_tensor_batch(self, data):
        """
        Create a torch.Tensor object from a list of torch.Tensor objects.

        :param data: torch.Tensor object of shape (N, ...) where N is the
            number of data points.
        :type data: torch.Tensor | LabelTensor
        :return: reshaped torch.Tensor or LabelTensor object
        :rtype: torch.Tensor | LabelTensor
        """
        out = data.reshape(-1, *data.shape[2:])
        return out

    def create_batch(self, data):
        """
        Create a Batch object from a list of Data objects.

        :param data: List of Data objects
        :type data: list
        :return: Batch object
        :rtype: Batch or PinaBatch
        """

        if isinstance(data[0], Data):
            return self._create_graph_batch(data)
        return self._create_tensor_batch(data)

    # Override _retrive_data method for graph handling
    def _retrive_data(self, data, idx_list):
        """
        Retrieve data from the dataset given a list of indices

        :param dict data: dictionary containing the data
        :param list idx_list: list of indices to retrieve
        :return: dictionary containing the data at the given indices
        :rtype: dict
        """
        # Return the data from the current condition
        # If the data is a list of Data objects, create a Batch object
        # If the data is a list of torch.Tensor objects, create a torch.Tensor
        return {
            k: (
                self._create_graph_batch([v[i] for i in idx_list])
                if isinstance(v, list)
                else self._create_tensor_batch(v[idx_list])
            )
            for k, v in data.items()
        }