"""Unified data structure for input and ouput of all the stages in loading process."""

from dataclasses import dataclass
from typing import Dict, List, Tuple, Union

import torch

import dgl

from .base import etype_str_to_tuple
from .sampled_subgraph import SampledSubgraph

__all__ = ["MiniBatch"]


@dataclass
class MiniBatch:
    r"""A composite data class for data structure in the graphbolt. It is
    designed to facilitate the exchange of data among different components
    involved in processing data. The purpose of this class is to unify the
    representation of input and output data across different stages, ensuring
    consistency and ease of use throughout the loading process."""

    seed_nodes: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
    """
    Representation of seed nodes used for sampling in the graph.
    - If `seed_nodes` is a tensor: It indicates the graph is homogeneous.
    - If `seed_nodes` is a dictionary: The keys should be node type and the
      value should be corresponding heterogeneous node ids.
    """

    node_pairs: Union[
        Tuple[torch.Tensor, torch.Tensor],
        Dict[str, Tuple[torch.Tensor, torch.Tensor]],
    ] = None
    """
    Representation of seed node pairs utilized in link prediction tasks.
    - If `node_pairs` is a tuple: It indicates a homogeneous graph where each
      tuple contains two tensors representing source-destination node pairs.
    - If `node_pairs` is a dictionary: The keys should be edge type, and the
      value should be a tuple of tensors representing node pairs of the given
      type.
    """

    labels: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
    """
    Labels associated with seed nodes / node pairs in the graph.
    - If `labels` is a tensor: It indicates the graph is homogeneous. The value
      should be corresponding labels to given 'seed_nodes' or 'node_pairs'.
    - If `labels` is a dictionary: The keys should be node or edge type and the
      value should be corresponding labels to given 'seed_nodes' or 'node_pairs'.
    """

    negative_srcs: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
    """
    Representation of negative samples for the head nodes in the link
    prediction task.
    - If `negative_srcs` is a tensor: It indicates a homogeneous graph.
    - If `negative_srcs` is a dictionary: The key should be edge type, and the
      value should correspond to the negative samples for head nodes of the
      given type.
    """

    negative_dsts: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
    """
    Representation of negative samples for the tail nodes in the link
    prediction task.
    - If `negative_dsts` is a tensor: It indicates a homogeneous graph.
    - If `negative_dsts` is a dictionary: The key should be edge type, and the
      value should correspond to the negative samples for head nodes of the
      given type.
    """

    sampled_subgraphs: List[SampledSubgraph] = None
    """A list of 'SampledSubgraph's, each one corresponding to one layer,
    representing a subset of a larger graph structure.
    """

    input_nodes: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
    """A representation of input nodes in the outermost layer. Conatins all nodes
       in the 'sampled_subgraphs'.
    - If `input_nodes` is a tensor: It indicates the graph is homogeneous.
    - If `input_nodes` is a dictionary: The keys should be node type and the
      value should be corresponding heterogeneous node id.
    """

    node_features: Union[
        Dict[str, torch.Tensor], Dict[Tuple[str, str], torch.Tensor]
    ] = None
    """A representation of node features.
      - If keys are single strings: It means the graph is homogeneous, and the
      keys are feature names.
      - If keys are tuples: It means the graph is heterogeneous, and the keys
      are tuples of '(node_type, feature_name)'.
    """

    edge_features: List[
        Union[Dict[str, torch.Tensor], Dict[Tuple[str, str], torch.Tensor]]
    ] = None
    """Edge features associated with the 'sampled_subgraphs'.
      - If keys are single strings: It means the graph is homogeneous, and the
      keys are feature names.
      - If keys are tuples: It means the graph is heterogeneous, and the keys
      are tuples of '(edge_type, feature_name)'. Note, edge type is single
      string of format 'str:str:str'.
    """

    compacted_node_pairs: Union[
        Tuple[torch.Tensor, torch.Tensor],
        Dict[str, Tuple[torch.Tensor, torch.Tensor]],
    ] = None
    """
    Representation of compacted node pairs corresponding to 'node_pairs', where
    all node ids inside are compacted.
    """

    compacted_negative_srcs: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
    """
    Representation of compacted nodes corresponding to 'negative_srcs', where
    all node ids inside are compacted.
    """

    compacted_negative_dsts: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
    """
    Representation of compacted nodes corresponding to 'negative_dsts', where
    all node ids inside are compacted.
    """

    def to_dgl_blocks(self):
        """Transforming a `MiniBatch` into DGL blocks necessitates constructing a
        graphical structure and assigning features to the nodes and edges within
        the blocks.
        """
        if not self.sampled_subgraphs:
            return None

        is_heterogeneous = isinstance(
            self.sampled_subgraphs[0].node_pairs, Dict
        )

        blocks = []
        for subgraph in self.sampled_subgraphs:
            reverse_row_node_ids = subgraph.reverse_row_node_ids
            assert (
                reverse_row_node_ids is not None
            ), "Missing `reverse_row_node_ids` in sampled subgraph."
            reverse_column_node_ids = subgraph.reverse_column_node_ids
            assert (
                reverse_column_node_ids is not None
            ), "Missing `reverse_column_node_ids` in sampled subgraph."
            if is_heterogeneous:
                node_pairs = {
                    etype_str_to_tuple(etype): v
                    for etype, v in subgraph.node_pairs.items()
                }
                num_src_nodes = {
                    ntype: nodes.size(0)
                    for ntype, nodes in reverse_row_node_ids.items()
                }
                num_dst_nodes = {
                    ntype: nodes.size(0)
                    for ntype, nodes in reverse_column_node_ids.items()
                }
            else:
                node_pairs = subgraph.node_pairs
                num_src_nodes = reverse_row_node_ids.size(0)
                num_dst_nodes = reverse_column_node_ids.size(0)
            blocks.append(
                dgl.create_block(
                    node_pairs,
                    num_src_nodes=num_src_nodes,
                    num_dst_nodes=num_dst_nodes,
                )
            )

        if is_heterogeneous:
            # Assign node features to the outermost layer's source nodes.
            if self.node_features:
                for (
                    node_type,
                    feature_name,
                ), feature in self.node_features.items():
                    blocks[0].srcnodes[node_type].data[feature_name] = feature
            # Assign edge features.
            if self.edge_features:
                for block, edge_feature in zip(blocks, self.edge_features):
                    for (
                        edge_type,
                        feature_name,
                    ), feature in edge_feature.items():
                        block.edges[etype_str_to_tuple(edge_type)].data[
                            feature_name
                        ] = feature
            # Assign reverse node ids to the outermost layer's source nodes.
            for node_type, reverse_ids in self.sampled_subgraphs[
                0
            ].reverse_row_node_ids.items():
                blocks[0].srcnodes[node_type].data[dgl.NID] = reverse_ids
            # Assign reverse edges ids.
            for block, subgraph in zip(blocks, self.sampled_subgraphs):
                if subgraph.reverse_edge_ids:
                    for (
                        edge_type,
                        reverse_ids,
                    ) in subgraph.reverse_edge_ids.items():
                        block.edges[etype_str_to_tuple(edge_type)].data[
                            dgl.EID
                        ] = reverse_ids
        else:
            # Assign node features to the outermost layer's source nodes.
            if self.node_features:
                for feature_name, feature in self.node_features.items():
                    blocks[0].srcdata[feature_name] = feature
            # Assign edge features.
            if self.edge_features:
                for block, edge_feature in zip(blocks, self.edge_features):
                    for feature_name, feature in edge_feature.items():
                        block.edata[feature_name] = feature
            blocks[0].srcdata[dgl.NID] = self.sampled_subgraphs[
                0
            ].reverse_row_node_ids
            # Assign reverse edges ids.
            for block, subgraph in zip(blocks, self.sampled_subgraphs):
                if subgraph.reverse_edge_ids is not None:
                    block.edata[dgl.EID] = subgraph.reverse_edge_ids

        return blocks
