from __future__ import annotations

import warnings
from itertools import combinations, permutations
from typing import Dict, List, Tuple

import networkx as nx

from numpy import ndarray

from ...graph.GraphClass import CausalGraph
from ...utils.BackgroundKnowledge import BackgroundKnowledge
from ...utils.cit import *
from ...utils.PCUtils import Helper, Meek, SkeletonDiscovery, UCSepset
from ...utils.PCUtils.BackgroundKnowledgeOrientUtils import \
    orient_by_background_knowledge


def pc(
    data: ndarray, 
    alpha=0.05, 
    indep_test=fisherz, 
    stable: bool = True, 
    uc_rule: int = 0, 
    uc_priority: int = 2,
    mvpc: bool = False, 
    correction_name: str = 'MV_Crtn_Fisher_Z',
    background_knowledge: BackgroundKnowledge | None = None, 
    verbose: bool = False, 
    show_progress: bool = True,
    node_names: List[str] | None = None,
    **kwargs
):
    if data.shape[0] < data.shape[1]:
        warnings.warn("The number of features is much larger than the sample size!")

    if mvpc:  # missing value PC
        if indep_test == fisherz:
            indep_test = mv_fisherz
        return mvpc_alg(data=data, node_names=node_names, alpha=alpha, indep_test=indep_test, correction_name=correction_name, stable=stable,
                        uc_rule=uc_rule, uc_priority=uc_priority, background_knowledge=background_knowledge,
                        verbose=verbose,
                        show_progress=show_progress, **kwargs)
    else:
        return pc_alg(data=data, node_names=node_names, alpha=alpha, indep_test=indep_test, stable=stable, uc_rule=uc_rule,
                      uc_priority=uc_priority, background_knowledge=background_knowledge, verbose=verbose,
                      show_progress=show_progress, **kwargs)


def pc_alg(
    data: ndarray,
    node_names: List[str] | None,
    alpha: float,
    indep_test: str,
    stable: bool,
    uc_rule: int,
    uc_priority: int,
    background_knowledge: BackgroundKnowledge | None = None,
    verbose: bool = False,
    show_progress: bool = True,
    **kwargs
) -> CausalGraph:
    """
    Perform Peter-Clark (PC) algorithm for causal discovery

    Parameters
    ----------
    data : data set (numpy ndarray), shape (n_samples, n_features). The input data, where n_samples is the number of samples and n_features is the number of features.
    node_names: Shape [n_features]. The name for each feature (each feature is represented as a Node in the graph, so it's also the node name)
    alpha : float, desired significance level of independence tests (p_value) in (0, 1)
    indep_test : str, the name of the independence test being used
            ["fisherz", "chisq", "gsq", "kci"]
           - "fisherz": Fisher's Z conditional independence test
           - "chisq": Chi-squared conditional independence test
           - "gsq": G-squared conditional independence test
           - "kci": Kernel-based conditional independence test
    stable : run stabilized skeleton discovery if True (default = True)
    uc_rule : how unshielded colliders are oriented
           0: run uc_sepset
           1: run maxP
           2: run definiteMaxP
    uc_priority : rule of resolving conflicts between unshielded colliders
           -1: whatever is default in uc_rule
           0: overwrite
           1: orient bi-directed
           2. prioritize existing colliders
           3. prioritize stronger colliders
           4. prioritize stronger* colliers
    background_knowledge : background knowledge
    verbose : True if verbose output should be printed.
    show_progress : True iff the algorithm progress should be show in console.

    Returns
    -------
    cg : a CausalGraph object, where cg.G.graph[j,i]=1 and cg.G.graph[i,j]=-1 indicates  i --> j ,
                    cg.G.graph[i,j] = cg.G.graph[j,i] = -1 indicates i --- j,
                    cg.G.graph[i,j] = cg.G.graph[j,i] = 1 indicates i <-> j.

    """

    start = time.time()
    indep_test = CIT(data, indep_test, **kwargs)
    cg_1 = SkeletonDiscovery.skeleton_discovery(data, alpha, indep_test, stable,
                                                background_knowledge=background_knowledge, verbose=verbose,
                                                show_progress=show_progress, node_names=node_names)

    if background_knowledge is not None:
        orient_by_background_knowledge(cg_1, background_knowledge)

    if uc_rule == 0:
        if uc_priority != -1:
            cg_2 = UCSepset.uc_sepset(cg_1, uc_priority, background_knowledge=background_knowledge)
        else:
            cg_2 = UCSepset.uc_sepset(cg_1, background_knowledge=background_knowledge)
        cg = Meek.meek(cg_2, background_knowledge=background_knowledge)

    elif uc_rule == 1:
        if uc_priority != -1:
            cg_2 = UCSepset.maxp(cg_1, uc_priority, background_knowledge=background_knowledge)
        else:
            cg_2 = UCSepset.maxp(cg_1, background_knowledge=background_knowledge)
        cg = Meek.meek(cg_2, background_knowledge=background_knowledge)

    elif uc_rule == 2:
        if uc_priority != -1:
            cg_2 = UCSepset.definite_maxp(cg_1, alpha, uc_priority, background_knowledge=background_knowledge)
        else:
            cg_2 = UCSepset.definite_maxp(cg_1, alpha, background_knowledge=background_knowledge)
        cg_before = Meek.definite_meek(cg_2, background_knowledge=background_knowledge)
        cg = Meek.meek(cg_before, background_knowledge=background_knowledge)
    else:
        raise ValueError("uc_rule should be in [0, 1, 2]")
    end = time.time()

    cg.PC_elapsed = end - start

    return cg


def mvpc_alg(
    data: ndarray,
    node_names: List[str] | None,
    alpha: float,
    indep_test: str,
    correction_name: str,
    stable: bool,
    uc_rule: int,
    uc_priority: int,
    background_knowledge: BackgroundKnowledge | None = None,
    verbose: bool = False,
    show_progress: bool = True,
    **kwargs,
) -> CausalGraph:
    """
    Perform missing value Peter-Clark (PC) algorithm for causal discovery

    Parameters
    ----------
    data : data set (numpy ndarray), shape (n_samples, n_features). The input data, where n_samples is the number of samples and n_features is the number of features.
    node_names: Shape [n_features]. The name for each feature (each feature is represented as a Node in the graph, so it's also the node name)
    alpha :  float, desired significance level of independence tests (p_value) in (0,1)
    indep_test : str, name of the test-wise deletion independence test being used
            ["mv_fisherz", "mv_g_sq"]
            - mv_fisherz: Fisher's Z conditional independence test
            - mv_g_sq: G-squared conditional independence test (TODO: under development)
    correction_name : correction_name: name of the missingness correction
            [MV_Crtn_Fisher_Z, MV_Crtn_G_sq, MV_DRW_Fisher_Z, MV_DRW_G_sq]
            - "MV_Crtn_Fisher_Z": Permutation based correction method
            - "MV_Crtn_G_sq": G-squared conditional independence test (TODO: under development)
            - "MV_DRW_Fisher_Z": density ratio weighting based correction method (TODO: under development)
            - "MV_DRW_G_sq": G-squared conditional independence test (TODO: under development)
    stable : run stabilized skeleton discovery if True (default = True)
    uc_rule : how unshielded colliders are oriented
           0: run uc_sepset
           1: run maxP
           2: run definiteMaxP
    uc_priority : rule of resolving conflicts between unshielded colliders
           -1: whatever is default in uc_rule
           0: overwrite
           1: orient bi-directed
           2. prioritize existing colliders
           3. prioritize stronger colliders
           4. prioritize stronger* colliers
    background_knowledge: background knowledge
    verbose : True iff verbose output should be printed.
    show_progress : True iff the algorithm progress should be show in console.

    Returns
    -------
    cg : a CausalGraph object, where cg.G.graph[j,i]=1 and cg.G.graph[i,j]=-1 indicates  i --> j ,
                    cg.G.graph[i,j] = cg.G.graph[j,i] = -1 indicates i --- j,
                    cg.G.graph[i,j] = cg.G.graph[j,i] = 1 indicates i <-> j.

    """

    start = time.time()
    indep_test = CIT(data, indep_test, **kwargs)
    ## Step 1: detect the direct causes of missingness indicators
    prt_m = get_parent_missingness_pairs(data, alpha, indep_test, stable)
    # print('Finish detecting the parents of missingness indicators.  ')

    ## Step 2:
    ## a) Run PC algorithm with the 1st step skeleton;
    cg_pre = SkeletonDiscovery.skeleton_discovery(data, alpha, indep_test, stable,
                                                  background_knowledge=background_knowledge,
                                                  verbose=verbose, show_progress=show_progress, node_names=node_names)
    if background_knowledge is not None:
        orient_by_background_knowledge(cg_pre, background_knowledge)

    cg_pre.to_nx_skeleton()
    # print('Finish skeleton search with test-wise deletion.')

    ## b) Correction of the extra edges
    cg_corr = skeleton_correction(data, alpha, correction_name, cg_pre, prt_m, stable)
    # print('Finish missingness correction.')

    if background_knowledge is not None:
        orient_by_background_knowledge(cg_corr, background_knowledge)

    ## Step 3: Orient the edges
    if uc_rule == 0:
        if uc_priority != -1:
            cg_2 = UCSepset.uc_sepset(cg_corr, uc_priority, background_knowledge=background_knowledge)
        else:
            cg_2 = UCSepset.uc_sepset(cg_corr, background_knowledge=background_knowledge)
        cg = Meek.meek(cg_2, background_knowledge=background_knowledge)

    elif uc_rule == 1:
        if uc_priority != -1:
            cg_2 = UCSepset.maxp(cg_corr, uc_priority, background_knowledge=background_knowledge)
        else:
            cg_2 = UCSepset.maxp(cg_corr, background_knowledge=background_knowledge)
        cg = Meek.meek(cg_2, background_knowledge=background_knowledge)

    elif uc_rule == 2:
        if uc_priority != -1:
            cg_2 = UCSepset.definite_maxp(cg_corr, alpha, uc_priority, background_knowledge=background_knowledge)
        else:
            cg_2 = UCSepset.definite_maxp(cg_corr, alpha, background_knowledge=background_knowledge)
        cg_before = Meek.definite_meek(cg_2, background_knowledge=background_knowledge)
        cg = Meek.meek(cg_before, background_knowledge=background_knowledge)
    else:
        raise ValueError("uc_rule should be in [0, 1, 2]")
    end = time.time()

    cg.PC_elapsed = end - start

    return cg


#######################################################################################################################
## *********** Functions for Step 1 ***********
def get_parent_missingness_pairs(data: ndarray, alpha: float, indep_test, stable: bool = True) -> Dict[str, list]:
    """
    Detect the parents of missingness indicators
    If a missingness indicator has no parent, it will not be included in the result
    :param data: data set (numpy ndarray)
    :param alpha: desired significance level in (0, 1) (float)
    :param indep_test: name of the test-wise deletion independence test being used
        - "MV_Fisher_Z": Fisher's Z conditional independence test
        - "MV_G_sq": G-squared conditional independence test (TODO: under development)
    :param stable: run stabilized skeleton discovery if True (default = True)
    :return:
    cg: a CausalGraph object
    """
    parent_missingness_pairs = {'prt': [], 'm': []}

    ## Get the index of missingness indicators
    missingness_index = get_missingness_index(data)

    ## Get the index of parents of missingness indicators
    # If the missingness indicator has no parent, then it will not be collected in prt_m
    for missingness_i in missingness_index:
        parent_of_missingness_i = detect_parent(missingness_i, data, alpha, indep_test, stable)
        if not isempty(parent_of_missingness_i):
            parent_missingness_pairs['prt'].append(parent_of_missingness_i)
            parent_missingness_pairs['m'].append(missingness_i)
    return parent_missingness_pairs


def isempty(prt_r) -> bool:
    """Test whether the parent of a missingness indicator is empty"""
    return len(prt_r) == 0


def get_missingness_index(data: ndarray) -> List[int]:
    """Detect the parents of missingness indicators
    :param data: data set (numpy ndarray)
    :return:
    missingness_index: list, the index of missingness indicators
    """

    missingness_index = []
    _, ncol = np.shape(data)
    for i in range(ncol):
        if np.isnan(data[:, i]).any():
            missingness_index.append(i)
    return missingness_index


def detect_parent(r: int, data_: ndarray, alpha: float, indep_test, stable: bool = True) -> ndarray:
    """Detect the parents of a missingness indicator
    :param r: the missingness indicator
    :param data_: data set (numpy ndarray)
    :param alpha: desired significance level in (0, 1) (float)
    :param indep_test: name of the test-wise deletion independence test being used
        - "MV_Fisher_Z": Fisher's Z conditional independence test
        - "MV_G_sq": G-squared conditional independence test (TODO: under development)
    :param stable: run stabilized skeleton discovery if True (default = True)
    : return:
    prt: parent of the missingness indicator, r
    """
    ## TODO: in the test-wise deletion CI test, if test between a binary and a continuous variable,
    #  there can be the case where the binary variable only take one value after deletion.
    #  It is because the assumption is violated.

    ## *********** Adaptation 0 ***********
    # For avoid changing the original data
    data = data_.copy()
    ## *********** End ***********

    assert type(data) == np.ndarray
    assert 0 < alpha < 1

    ## *********** Adaptation 1 ***********
    # data
    ## Replace the variable r with its missingness indicator
    ## If r is not a missingness indicator, return [].
    data[:, r] = np.isnan(data[:, r]).astype(float)  # True is missing; false is not missing
    if sum(data[:, r]) == 0 or sum(data[:, r]) == len(data[:, r]):
        return np.empty(0)
    ## *********** End ***********

    no_of_var = data.shape[1]
    cg = CausalGraph(no_of_var)
    cg.set_ind_test(CIT(data, indep_test.method))

    node_ids = range(no_of_var)
    pair_of_variables = list(permutations(node_ids, 2))

    depth = -1
    while cg.max_degree() - 1 > depth:
        depth += 1
        edge_removal = []
        for (x, y) in pair_of_variables:

            ## *********** Adaptation 2 ***********
            # the skeleton search
            ## Only test which variable is the neighbor of r
            if x != r:
                continue
            ## *********** End ***********

            Neigh_x = cg.neighbors(x)
            if y not in Neigh_x:
                continue
            else:
                Neigh_x = np.delete(Neigh_x, np.where(Neigh_x == y))

            if len(Neigh_x) >= depth:
                for S in combinations(Neigh_x, depth):
                    p = cg.ci_test(x, y, S)
                    if p > alpha:
                        if not stable:  # Unstable: Remove x---y right away
                            edge1 = cg.G.get_edge(cg.G.nodes[x], cg.G.nodes[y])
                            if edge1 is not None:
                                cg.G.remove_edge(edge1)
                            edge2 = cg.G.get_edge(cg.G.nodes[y], cg.G.nodes[x])
                            if edge2 is not None:
                                cg.G.remove_edge(edge2)
                        else:  # Stable: x---y will be removed only
                            edge_removal.append((x, y))  # after all conditioning sets at
                            edge_removal.append((y, x))  # depth l have been considered
                            Helper.append_value(cg.sepset, x, y, S)
                            Helper.append_value(cg.sepset, y, x, S)
                        break

        for (x, y) in list(set(edge_removal)):
            edge1 = cg.G.get_edge(cg.G.nodes[x], cg.G.nodes[y])
            if edge1 is not None:
                cg.G.remove_edge(edge1)

    ## *********** Adaptation 3 ***********
    ## extract the parent of r from the graph
    cg.to_nx_skeleton()
    cg_skel_adj = nx.to_numpy_array(cg.nx_skel).astype(int)
    prt = get_parent(r, cg_skel_adj)
    ## *********** End ***********

    return prt


def get_parent(r: int, cg_skel_adj: ndarray) -> ndarray:
    """Get the neighbors of missingness indicators which are the parents
    :param r: the missingness indicator index
    :param cg_skel_adj: adjacancy matrix of a causal skeleton
    :return:
    prt: list, parents of the missingness indicator r
    """
    num_var = len(cg_skel_adj[0, :])
    indx = np.array([i for i in range(num_var)])
    prt = indx[cg_skel_adj[r, :] == 1]
    return prt


## *********** END ***********
#######################################################################################################################

def skeleton_correction(data: ndarray, alpha: float, test_with_correction_name: str, init_cg: CausalGraph, prt_m: dict,
                        stable: bool = True) -> CausalGraph:
    """Perform skeleton discovery
    :param data: data set (numpy ndarray)
    :param alpha: desired significance level in (0, 1) (float)
    :param test_with_correction_name: name of the independence test being used
           - "MV_Crtn_Fisher_Z": Fisher's Z conditional independence test
           - "MV_Crtn_G_sq": G-squared conditional independence test
    :param stable: run stabilized skeleton discovery if True (default = True)
    :return:
    cg: a CausalGraph object
    """

    assert type(data) == np.ndarray
    assert 0 < alpha < 1
    assert test_with_correction_name in ["MV_Crtn_Fisher_Z", "MV_Crtn_G_sq"]

    ## *********** Adaption 1 ***********
    no_of_var = data.shape[1]

    ## Initialize the graph with the result of test-wise deletion skeletion search
    cg = init_cg

    if test_with_correction_name in ["MV_Crtn_Fisher_Z", "MV_Crtn_G_sq"]:
        cg.set_ind_test(CIT(data, "mc_fisherz"))
    # No need of the correlation matrix if using test-wise deletion test
    cg.prt_m = prt_m
    ## *********** Adaption 1 ***********

    node_ids = range(no_of_var)
    pair_of_variables = list(permutations(node_ids, 2))

    depth = -1
    while cg.max_degree() - 1 > depth:
        depth += 1
        edge_removal = []
        for (x, y) in pair_of_variables:
            Neigh_x = cg.neighbors(x)
            if y not in Neigh_x:
                continue
            else:
                Neigh_x = np.delete(Neigh_x, np.where(Neigh_x == y))

            if len(Neigh_x) >= depth:
                for S in combinations(Neigh_x, depth):
                    p = cg.ci_test(x, y, S)
                    if p > alpha:
                        if not stable:  # Unstable: Remove x---y right away
                            edge1 = cg.G.get_edge(cg.G.nodes[x], cg.G.nodes[y])
                            if edge1 is not None:
                                cg.G.remove_edge(edge1)
                            edge2 = cg.G.get_edge(cg.G.nodes[y], cg.G.nodes[x])
                            if edge2 is not None:
                                cg.G.remove_edge(edge2)
                        else:  # Stable: x---y will be removed only
                            edge_removal.append((x, y))  # after all conditioning sets at
                            edge_removal.append((y, x))  # depth l have been considered
                            Helper.append_value(cg.sepset, x, y, S)
                            Helper.append_value(cg.sepset, y, x, S)
                        break

        for (x, y) in list(set(edge_removal)):
            edge1 = cg.G.get_edge(cg.G.nodes[x], cg.G.nodes[y])
            if edge1 is not None:
                cg.G.remove_edge(edge1)

    return cg


#######################################################################################################################

# *********** Evaluation util ***********

def get_adjacancy_matrix(g: CausalGraph) -> ndarray:
    return nx.to_numpy_array(g.nx_graph).astype(int)


def matrix_diff(cg1: CausalGraph, cg2: CausalGraph) -> (float, List[Tuple[int, int]]):
    adj1 = get_adjacancy_matrix(cg1)
    adj2 = get_adjacancy_matrix(cg2)
    count = 0
    diff_ls = []
    for i in range(len(adj1[:, ])):
        for j in range(len(adj2[:, ])):
            if adj1[i, j] != adj2[i, j]:
                diff_ls.append((i, j))
                count += 1
    return count / 2, diff_ls
