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# pylint: disable=import-error,too-many-locals,too-many-statements,too-many-branches,unused-variable
"""Dynamic programming tuner."""
import sys
import numpy as np

from ._base import MAX_OUTPUT_NODES
from .base_graph_tuner import BaseGraphTuner
from .dynamic_programming_stage import DPStage
from .utils import has_multiple_inputs, is_boundary_node

if sys.version_info[0] == 3:
    import queue
else:
    import Queue as queue

class DPTuner(BaseGraphTuner):
    """Tuner which uses dynamic programming to solve MDP problem.

    Note: currently dynamic programming is used to solve this MDP problem. However,
    this problem is intrinsically non-polynomial. DP can't apply for more complicated
    models, such as networks with many element-wise sum operators. In this case, switch
    to heuristic algorithm such as PBQP tuner.
    """
    def __init__(self, *args, **kwargs):
        """Create a dynamic programming tuner.
        """
        super(DPTuner, self).__init__(*args, **kwargs)
        self._num_states = self._max_num_states = None
        self._stage_dict = {}
        self._dep_dict = {}
        self._counted_nodes_set = set()

        self._global_data_dict = {
            "dtype": self._dtype,
            "counted_nodes_set": self._counted_nodes_set,
            "stage_dict": self._stage_dict,
            "in_nodes_dict": self._in_nodes_dict,
            "out_nodes_dict": self._out_nodes_dict,
            "dep_dict": self._dep_dict,
            "node_list": self._node_list,
            "input_shapes": self._input_shapes,
            "layout_transform_interlayer_cost": self._layout_transform_interlayer_cost
        }

    def _check_num_states(self, num_states):
        """Track the number of states."""
        self._num_states += num_states
        if self._max_num_states is not None:
            if self._num_states > self._max_num_states:
                raise RuntimeError("Too many states detected while running dynamic "
                                   "programming: got %d states but upper limit is %d." %
                                   (self._num_states, self._max_num_states))

    def _forward(self):
        """Forward pass in DP to generate states for all stages.
        """
        self._logger.info("Start forward pass...")
        for node_idx in sorted(self._in_nodes_dict.keys()):
            stage = DPStage(idx=node_idx, target_ops=self._target_ops,
                            **self._global_data_dict)
            self._check_num_states(stage.full_states.size)
            self._stage_dict[node_idx] = stage
        self._logger.info("Finished forward pass.")

    def _backward(self):
        """Backward pass in DP to generate optimal solution.
        """
        self._logger.info("Start backward pass...")
        input_names = self._input_shapes.keys()
        optimal_record_dict = {}
        # Pick optimal schedule for output nodes
        output_idx_list = []
        for key, val in self._out_nodes_dict.items():
            if not val:
                output_idx_list.append(key)

        # Restrict number of output nodes to avoid numpy reshape error
        if len(output_idx_list) > MAX_OUTPUT_NODES:
            msg = "The number of outputs in graph is larger than upper " \
                  "limit: %s vs %s. Usually this is caused by too many " \
                  "LAYOUT_FIXED_OP in graph. Switch to greedily select schedule." \
                  "No action required at this moment. We will continuously improve graph tuner" \
                  % (len(output_idx_list), MAX_OUTPUT_NODES)
            self._logger.warning(msg)
            self._optimal_record_dict = {key : 0 for key in self._in_nodes_dict}
            return

        states_list, aligned_node_list = DPStage.align_states(output_idx_list, self._stage_dict,
                                                              self._node_list)
        num_states = states_list[0][3].size
        self._check_num_states(num_states * len(output_idx_list))
        aligned_node_shape = states_list[0][3].shape
        min_time = 0
        min_pos = -1
        for states in states_list:
            min_time += np.amax(states[3])
        flatten_states_list = [current_states[3].flatten() for current_states in states_list]
        for i in range(num_states):
            current_time = 0
            for j, current_states in enumerate(states_list):
                current_time += flatten_states_list[j][i]
            if min_time > current_time:
                min_time = current_time
                min_pos = i
        for i, states in enumerate(states_list):
            current_major_axis = states[1]
            current_sch_idx = (min_pos % (states[2] *
                                          aligned_node_shape[current_major_axis])) // states[2]
            optimal_record_dict[aligned_node_list[i]] = current_sch_idx
        # Pick optimal schedule for dependencies of output nodes
        for i in range(len(states_list), len(aligned_node_list)):
            multiplier = 1
            for j in range(i + 1, len(aligned_node_list)):
                multiplier *= aligned_node_shape[j]
            optimal_record_dict[aligned_node_list[i]] = \
                min_pos // multiplier % aligned_node_shape[i]

        # Backward pass to get optimal schedules for other nodes
        bfs_q = queue.Queue()
        visited = set()
        for out_idx in output_idx_list:
            bfs_q.put(out_idx)
        while not bfs_q.empty():
            node_idx = bfs_q.get()
            visited.add(node_idx)
            node = self._node_list[node_idx]
            if is_boundary_node(node, input_names):
                continue
            optimal_sch_idx = optimal_record_dict[node_idx]
            full_states = self._stage_dict[node_idx].full_states
            if not has_multiple_inputs(self._node_list, node_idx, input_names, self._opt_out_op):
                input_idx = self._in_nodes_dict[node_idx][0]
                input_node = self._node_list[input_idx]
                if is_boundary_node(input_node, input_names):
                    continue
                if input_idx not in visited:
                    bfs_q.put(input_idx)
                    if input_idx not in optimal_record_dict:
                        dep_list = self._stage_dict[node_idx].dep
                        dep_idx = tuple([optimal_record_dict[item] for item in dep_list])
                        tmp = np.argmin(full_states, axis=1)
                        optimal_input_sch_idx = tmp[(optimal_sch_idx,) + dep_idx]
                        optimal_record_dict[input_idx] = optimal_input_sch_idx
            else:
                input_idx_list = self._in_nodes_dict[node_idx]
                optimal_record_dict[input_idx_list[0]] = optimal_sch_idx
                full_states_idx = self._stage_dict[node_idx].full_states_idx
                tmp = full_states[optimal_sch_idx]
                new_states_idx, new_states_pos = [], []
                visited_states_idx, visited_states_pos = [], []
                for i in range(1, len(full_states_idx)):
                    if full_states_idx[i] in optimal_record_dict:
                        visited_states_idx.append(full_states_idx[i])
                        visited_states_pos.append(i - 1)
                    else:
                        new_states_idx.append(full_states_idx[i])
                        new_states_pos.append(i - 1)
                if visited_states_idx:
                    tmp = np.transpose(tmp, tuple(visited_states_pos + new_states_pos))
                    tmp = tmp[tuple([optimal_record_dict[idx] for idx in visited_states_idx])]
                min_pos = np.argmin(tmp)
                multiplier = 1
                for i in range(len(new_states_idx)):
                    multiplier *= full_states.shape[new_states_pos[i] + 1]
                for pos, idx in zip(new_states_pos, new_states_idx):
                    multiplier //= full_states.shape[pos + 1]
                    optimal_record_dict[idx] = min_pos // multiplier
                    min_pos %= multiplier
                for input_idx in input_idx_list:
                    if input_idx not in visited:
                        bfs_q.put(input_idx)

        self._optimal_record_dict = optimal_record_dict
        for node_idx, _ in self._in_nodes_dict.items():
            if self._node_list[node_idx]["op"] not in self._target_ops:
                continue
        self._logger.info("Finished backward pass...")

    def run(self, **kwargs):
        """Run dynamic programming solver.
        """
        max_num_states = None if "max_num_states" not in kwargs else kwargs["max_num_states"]
        self._num_states = 0
        self._max_num_states = max_num_states
        self._logger.info("Start to run dynamic programming algorithm...")
        self._forward()
        self._backward()
        self._logger.info("Finished DPExecutor run.")
