from typing import Dict, List

from torch.fx import Node

from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
    MemoryCost,
    OperationData,
    ShardingStrategy,
    TrainCycleItem,
)
from colossalai.device.device_mesh import DeviceMesh

from .strategy_generator import OutputStrategyGenerator

__all__ = ['OutputGenerator']


class OutputGenerator(OutputStrategyGenerator):
    """
    OutputGenerator is a generic class to generate strategies for Output Node.
    """

    def __init__(self, operation_data_mapping: Dict[str, OperationData], device_mesh: DeviceMesh,
                 predecessor_nodes: List[Node], output_option: str):
        super().__init__(operation_data_mapping, device_mesh, predecessor_nodes)
        self.output_option = output_option

    def validate(self) -> bool:
        return super().validate()

    def update_compute_cost(self, strategy: ShardingStrategy):
        compute_cost = TrainCycleItem(fwd=10, bwd=10, total=20)
        strategy.compute_cost = compute_cost

    def update_memory_cost(self, strategy: ShardingStrategy):
        '''
        Compute the memory cost per device with this specific strategy.
        '''
        fwd_mem_cost = MemoryCost(activation=0, parameter=0)

        bwd_mem_cost = MemoryCost(activation=0, parameter=0)

        # compute total cost
        total_mem_cost = MemoryCost(activation=0, parameter=0)
        memory_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)
        strategy.memory_cost = memory_cost

    def replica_strategy(self) -> List[ShardingStrategy]:
        """
        Generate replica strategy for output node.
        """
        dim_partition_dict_mapping = {
            "output": {},
        }
        for index, _ in enumerate(self.predecessor_nodes):
            mapping_name = f"input_{index}"
            dim_partition_dict_mapping[mapping_name] = {}

        communication_action_mapping = {}
        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)

        name = 'Replica Output'

        strategy = self.get_sharding_strategy(name=name,
                                              sharding_spec_mapping=sharding_spec_mapping,
                                              communication_action_mapping=communication_action_mapping)
        return strategy

    def distributed_strategy(self, mesh_list: List[List[int]] = None) -> List[ShardingStrategy]:
        """
        Generate distributed strategy for output node.
        """
        # TODO: need to take care of the case when the first element of output only need to be sharded.
        output_op_data = self.op_data['output']
        if isinstance(output_op_data.data, tuple):
            length = len(output_op_data.data)
            dim_partition_dict_mapping = {
                "output": [{
                    0: mesh_list
                }] * length,
            }
        else:
            dim_partition_dict_mapping = {
                "output": {
                    0: mesh_list
                },
            }
        for index, _ in enumerate(self.predecessor_nodes):
            mapping_name = f"input_{index}"
            dim_partition_dict_mapping[mapping_name] = {0: mesh_list}

        communication_action_mapping = {}
        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)

        name = 'Distributed Output'

        strategy = self.get_sharding_strategy(name=name,
                                              sharding_spec_mapping=sharding_spec_mapping,
                                              communication_action_mapping=communication_action_mapping)
        return strategy

    def collate_strategies(self) -> List[ShardingStrategy]:
        strategy_list = []
        mesh_list = [0, 1]
        if self.output_option == 'replicated':
            strategy_list.append(self.replica_strategy())
        elif self.output_option == 'distributed':
            strategy_list.append(self.distributed_strategy(mesh_list))

        return strategy_list
