from copy import deepcopy
from typing import Dict, List

import torch
from torch.fx.node import Node

from colossalai.auto_parallel.meta_profiler import MetaInfo
from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
    CommAction,
    CommType,
    OperationData,
    OperationDataType,
    TrainCycleItem,
)
from colossalai.device.device_mesh import DeviceMesh
from colossalai.tensor.comm_spec import CommSpec
from colossalai.tensor.shape_consistency import ShapeConsistencyManager
from colossalai.tensor.sharding_spec import ShardingSpec

shape_consistency_manager = ShapeConsistencyManager()


def runtime_apply(node: Node, origin_dict: Dict, input_dict: Dict, node_index: int, user_node_index: int):
    """
    This method will be invoked during runtime to do the shape consistency, which make sure the activations is converted into
    the user node expected form.
    """
    origin_sharding_spec = origin_dict[node_index]
    target_sharding_spec = input_dict[node_index][user_node_index]
    return shape_consistency_manager.apply_for_autoparallel_runtime(node, origin_sharding_spec, target_sharding_spec)


def runtime_apply_for_iterable_object(node: Node, origin_dict: Dict, input_dict: Dict, node_index: int,
                                      user_node_index: int):
    """
    This method will be invoked during runtime to do the shape consistency, which makes sure the activations in type of tuple or list
    is converted into the user node expected form.
    """
    rst = []
    for index, (origin_sharding_spec,
                target_sharding_spec) in enumerate(zip(origin_dict[node_index],
                                                       input_dict[node_index][user_node_index])):
        rst.append(
            shape_consistency_manager.apply_for_autoparallel_runtime(node[index], origin_sharding_spec,
                                                                     target_sharding_spec))
    rst = type(node)(rst)
    return rst


def construct_meta_info(node: Node, user_node: Node) -> MetaInfo:
    """
    This method is used to construct `MetaInto` for shape consistency node
    TODO: Actually we could attain the cost information from resharding cost in node
    handler, we should modify this part in the future.
    """

    def compute_shape(sharding_spec: ShardingSpec):
        shape = sharding_spec.entire_shape
        new_shape = []
        for dim, shard in sharding_spec.dim_partition_dict.items():
            new_shape.append(shape[dim] // len(shard))
        return new_shape

    meta_info = MetaInfo()
    origin_sharding_spec, target_sharding_spec = node.sharding_spec, user_node.sharding_spec
    _, comm_action_sequence, total_cost = shape_consistency_manager.shape_consistency(
        origin_sharding_spec, target_sharding_spec)

    # NOTE: the cost in shape_consistency_manager.mem_cost is the count in number of numel
    # get mem cost for MetaInfo
    mem_cost = shape_consistency_manager.mem_cost(comm_action_sequence)
    element_length = node._meta_data.element_size()
    mem_cost.fwd.activation *= element_length
    mem_cost.fwd.temp *= element_length
    mem_cost.bwd.activation *= element_length
    mem_cost.bwd.temp *= element_length
    mem_cost.total.activation *= element_length

    meta_info.memory_cost = mem_cost

    # get computation cost for MetaInfo
    compute_cost = TrainCycleItem(total_cost['forward'], total_cost['backward'], total_cost['total'])
    meta_info.compute_cost = compute_cost

    # get tensor shape for MetaInfo
    input_shape = compute_shape(origin_sharding_spec)
    output_shape = compute_shape(target_sharding_spec)

    meta_info.fwd_in = [torch.rand(input_shape, device='meta')]
    meta_info.fwd_buffer = []
    meta_info.fwd_out = [torch.rand(output_shape, device='meta')]

    return meta_info


def runtime_comm_spec_apply(tensor: torch.Tensor, comm_actions_dict: Dict, node_index: int, op_data_name: str):
    """
    This method will be invoked during runtime to apply the comm action following the instruction of comm spec.
    """
    comm_action = comm_actions_dict[node_index][op_data_name]
    if isinstance(comm_action.comm_spec, CommSpec):
        rst = comm_action.comm_spec.covert_spec_to_action(tensor)
    else:
        origin_sharding_spec = comm_action.comm_spec['src_spec']
        tgt_sharding_spec = comm_action.comm_spec['tgt_spec']
        rst = shape_consistency_manager.apply_for_autoparallel_runtime(tensor, origin_sharding_spec, tgt_sharding_spec)
    return rst


def _preprocess_graph(nodes: List[Node]):
    """
    This method is used to extract all the placeholders with sharding information,
    and mapping the nodes into the index of the origin graph.
    """
    # mapping the node into the origin graph index
    node_to_index_dict = {}
    index = 0
    for node in nodes:
        if node.target == 'sharding_spec_convert_dict':
            input_dict_node = node
            continue
        if node.target == 'origin_node_sharding_spec_dict':
            origin_dict_node = node
            continue
        if node.target == 'comm_actions_dict':
            comm_actions_dict_node = node
            continue
        if not hasattr(node, 'best_strategy'):
            continue
        node_to_index_dict[node] = index
        index += 1

    return input_dict_node, origin_dict_node, comm_actions_dict_node, node_to_index_dict


def _shape_consistency_apply(gm: torch.fx.GraphModule):
    """
    This pass is used to add the shape consistency node to the origin graph.
    """
    mod_graph = gm.graph
    nodes = tuple(mod_graph.nodes)

    input_dict_node, origin_dict_node, _, node_to_index_dict = _preprocess_graph(nodes)

    for node in nodes:
        if not hasattr(node, 'best_strategy') or node.op == 'output':
            continue

        for user_node_index, user_node in enumerate(node.strategies_vector.successor_nodes):
            if isinstance(node.sharding_spec, (list, tuple)):
                assert isinstance(
                    node.target_sharding_specs,
                    (list,
                     tuple)), 'target sharding specs should be tuple or list when node.sharding_spec is tuple or list'
                total_difference = 0
                for sharding_spec, target_sharding_spec in zip(node.sharding_spec,
                                                               node.target_sharding_specs[user_node_index]):
                    total_difference += sharding_spec.sharding_sequence_difference(target_sharding_spec)
                if total_difference == 0:
                    continue
                with mod_graph.inserting_before(user_node):
                    shape_consistency_node = mod_graph.create_node('call_function',
                                                                   runtime_apply_for_iterable_object,
                                                                   args=(node, origin_dict_node, input_dict_node,
                                                                         node_to_index_dict[node], user_node_index))

            else:
                assert isinstance(node.sharding_spec,
                                  ShardingSpec), 'node.sharding_spec should be type of ShardingSpec, tuple or list.'
                if node.sharding_spec.sharding_sequence_difference(node.target_sharding_specs[user_node_index]) == 0:
                    continue
                with mod_graph.inserting_before(user_node):
                    shape_consistency_node = mod_graph.create_node('call_function',
                                                                   runtime_apply,
                                                                   args=(node, origin_dict_node, input_dict_node,
                                                                         node_to_index_dict[node], user_node_index))
                    # meta_info = construct_meta_info(node, user_node)
                    # setattr(shape_consistency_node, 'best_metainfo', meta_info)

            new_args = list(user_node.args)
            new_kwargs = dict(user_node.kwargs)
            # the origin node may be a positional argument or key word argument of user node
            if node in new_args:
                # substitute the origin node with shape_consistency_node
                origin_index_args = new_args.index(node)
                new_args[origin_index_args] = shape_consistency_node
                user_node.args = tuple(new_args)
            elif str(node) in new_kwargs:
                # substitute the origin node with shape_consistency_node
                new_kwargs[str(node)] = shape_consistency_node
                user_node.kwargs = new_kwargs

    return gm


def _comm_spec_apply(gm: torch.fx.GraphModule):
    """
    This pass is used to add the comm spec apply node to the origin graph.
    """
    mod_graph = gm.graph
    nodes = tuple(mod_graph.nodes)

    _, _, comm_actions_dict_node, node_to_index_dict = _preprocess_graph(nodes)

    for node in nodes:
        if not hasattr(node, 'best_strategy') or node.op == 'output':
            continue

        comm_actions = node.best_strategy.communication_actions
        for op_data, comm_action in comm_actions.items():

            if comm_action.comm_type == CommType.HOOK:
                continue
            if comm_action.comm_type == CommType.BEFORE:
                if op_data.type == OperationDataType.OUTPUT:
                    comm_object = node
                elif comm_action.key_for_kwarg is not None:
                    comm_object = node.kwargs[comm_action.key_for_kwarg]
                else:
                    comm_object = node.args[comm_action.arg_index]
                with mod_graph.inserting_before(node):
                    comm_spec_apply_node = mod_graph.create_node('call_function',
                                                                 runtime_comm_spec_apply,
                                                                 args=(comm_object, comm_actions_dict_node,
                                                                       node_to_index_dict[node], op_data.name))
                # the origin node may be a positional argument or key word argument of user node
                if comm_action.key_for_kwarg is not None:
                    # substitute the origin node with comm_spec_apply_node
                    new_kwargs = dict(node.kwargs)
                    new_kwargs[comm_action.key_for_kwarg] = comm_spec_apply_node
                    node.kwargs = new_kwargs
                else:
                    # substitute the origin node with comm_spec_apply_node
                    new_args = list(node.args)
                    new_args[comm_action.arg_index] = comm_spec_apply_node
                    node.args = tuple(new_args)

            elif comm_action.comm_type == CommType.AFTER:
                with mod_graph.inserting_after(node):
                    comm_spec_apply_node = mod_graph.create_node('call_function',
                                                                 runtime_comm_spec_apply,
                                                                 args=(node, comm_actions_dict_node,
                                                                       node_to_index_dict[node], op_data.name))
                user_list = list(node.users.keys())
                for user in user_list:
                    if user == comm_spec_apply_node:
                        continue
                    new_args = list(user.args)
                    new_kwargs = dict(user.kwargs)
                    # the origin node may be a positional argument or key word argument of user node
                    if node in new_args:
                        # substitute the origin node with comm_spec_apply_node
                        new_args[new_args.index(node)] = comm_spec_apply_node
                        user.args = tuple(new_args)
                    elif str(node) in new_kwargs:
                        # substitute the origin node with comm_spec_apply_node
                        new_kwargs[str(node)] = comm_spec_apply_node
                        user.kwargs = new_kwargs
    return gm


def runtime_apply_pass(gm: torch.fx.GraphModule):
    """
    The method manages all the passes acting on the distributed training runtime.
    """
    gm = _shape_consistency_apply(gm)
    gm = _comm_spec_apply(gm)

    return gm
