# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

from typing import Collection, Optional, Tuple, List
import copy

from overrides import overrides

from archai.nas.model_desc_builder import ModelDescBuilder
from archai.nas.operations import Op
from archai.nas.model_desc import ConvMacroParams, CellDesc, CellType, OpDesc, \
                                  EdgeDesc, TensorShape, TensorShapes, NodeDesc
from archai.algos.darts.mixed_op import MixedOp
from archai.common.config import Config

class DartsModelDescBuilder(ModelDescBuilder):
    @overrides
    def pre_build(self, conf_model_desc:Config)->None:
        Op.register_op('mixed_op',
                       lambda op_desc, arch_params, affine:
                           MixedOp(op_desc, arch_params, affine))

    @overrides
    def build_nodes(self, stem_shapes:TensorShapes, conf_cell:Config,
                    cell_index:int, cell_type:CellType, node_count:int,
                    in_shape:TensorShape, out_shape:TensorShape) \
                        ->Tuple[TensorShapes, List[NodeDesc]]:

        assert in_shape[0]==out_shape[0]

        reduction = (cell_type==CellType.Reduction)

        nodes:List[NodeDesc] =  []
        conv_params = ConvMacroParams(in_shape[0], out_shape[0])

        # add mixed op for each edge in each node
        # how does the stride works? For all ops connected to s0 and s1, we apply
        # reduction in WxH. All ops connected elsewhere automatically gets
        # reduced WxH (because all subsequent states are derived from s0 and s1).
        # Note that channel is increased via conv_params for the cell
        for i in range(node_count):
            edges=[]
            for j in range(i+2):
                op_desc = OpDesc('mixed_op',
                                    params={
                                        'conv': conv_params,
                                        'stride': 2 if reduction and j < 2 else 1
                                    }, in_len=1, trainables=None, children=None)
                edge = EdgeDesc(op_desc, input_ids=[j])
                edges.append(edge)
            nodes.append(NodeDesc(edges=edges, conv_params=conv_params))

        out_shapes = [copy.deepcopy(out_shape) for _  in range(node_count)]

        return out_shapes, nodes




