from typing import Any, Dict, Iterator, Optional, Tuple, Union

import torch
from torch import nn

from colossalai.nn.parallel.layers import ColoEmbedding, ColoLinear, register_colo_module
from colossalai.tensor import ColoParameter, ColoTensor, ProcessGroup

from .utils import InsertPostInitMethodToModuleSubClasses

# find named_params includes replica


def _named_params_with_replica(
    module: nn.Module,
    prefix: str = '',
    recurse: bool = True,
) -> Iterator[Tuple[str, Union[nn.Parameter, ColoTensor]]]:
    modules = module.named_modules(prefix=prefix) if recurse else [(prefix, module)]

    for mod_prefix, mod in modules:
        for name, val in mod._parameters.items():
            if val is None:
                continue
            name = mod_prefix + ('.' if mod_prefix else '') + name
            yield name, val


def _convert_to_coloparam(param: torch.nn.Parameter,
                          device: torch.device,
                          dtype=torch.float,
                          default_pg: Optional[ProcessGroup] = None,
                          default_dist_spec: Optional[Any] = None) -> ColoParameter:

    if isinstance(param, ColoParameter):
        return param
    # detaching tensor is necessary for optimizers.
    requires_grad = param.requires_grad
    # param is the global tensor.
    
    if param.device.type == "meta":
        colo_param = ColoParameter(param, requires_grad=requires_grad)
    else:    
        colo_param = ColoParameter(param.to(device=device, dtype=dtype), requires_grad=requires_grad)
      

    # if default_shard_plan exists, shard the param during initialization.
    # This can reduce the model size after initialization.
    # NOTE() embedding usually can not be correctly sharded. So I use except to handle
    # the param that can not be sharded by the default plan
    if default_pg is not None:
        colo_param.set_process_group(default_pg)

    if default_dist_spec is not None:
        try:
            colo_param.set_dist_spec(default_dist_spec)
        except:
            pass
    return colo_param


def ColoModulize(module):
    """
    Replacing the parameters() and named_parameters() with our customized ones
    """

    module._colo_visited = True


class ColoInitContext(InsertPostInitMethodToModuleSubClasses):

    def __init__(self,
                 device: torch.device = torch.device('cpu'),
                 dtype: torch.dtype = torch.float,
                 default_pg: Optional[ProcessGroup] = None,
                 default_dist_spec=None):
        """
        Args:
            device (torch.device): the device where parameters initialized are resident. Defaults to torch.device('cpu').
            dtype (torch.dtype): the dtype of parameters initialized. Defults to torch.float.
            default_pg (ProcessGroup): the default process group for all initialized parameters.
            default_dist_spec: the default distributed specifications.
        """
        super().__init__()
        self._device = device
        self._dtype = dtype

        self._register_colo_modules()
        self._default_pg = default_pg
        self._default_dist_spec = default_dist_spec

    def _register_colo_modules(self):
        register_colo_module(torch.nn.Linear, ColoLinear())
        register_colo_module(torch.nn.Embedding, ColoEmbedding())

    def _pre_context_exec(self):
        pass

    def _post_init_method(self, module: torch.nn.Module, *args, **kwargs):
        """
        The function to call at the end of the constructor of each module.
        FIXME(fjr) The module may be passed to this function multiple times?
        """
        name_list = []
        for name, param in _named_params_with_replica(module):
            if isinstance(param, ColoTensor):
                continue

            split = name.rfind('.')
            if split >= 0:    # param in submodule
                module_name = name[:split]
                param_name = name[split + 1:]
            else:
                module_name = ''    # param in current module
                param_name = name
            name_list.append((module_name, param_name))

        replaced_tensors = dict(
        )    # record mapping between (torch.Tensor, ColoTensor) to distinguish the same reference
        for module_name, param_name in name_list:
            submodule = module.get_submodule(module_name)
            param = submodule.get_parameter(param_name)
            if param in replaced_tensors:
                colo_param = replaced_tensors[param]
            else:
                colo_param = _convert_to_coloparam(param, self._device, self._dtype, self._default_pg,
                                                   self._default_dist_spec)
                replaced_tensors[param] = colo_param
            delattr(submodule, param_name)
            setattr(submodule, param_name, colo_param)
            colo_param.shared_param_modules.append(submodule)
        
        meta_param_flag = 0
        meta_buffer_flag = 0
        for param in module.parameters():
            if param.device.type=="meta":
                meta_param_flag = 1
            if meta_param_flag == 1 and param.device.type!="meta":
                raise ValueError("Meta parameters and valued parameters can not  be in the same model")
            
        for buffer in module.buffers():
            if buffer.device.type=="meta":
                meta_buffer_flag = 1
            if meta_buffer_flag == 1 and buffer.device.type!="meta":
                raise ValueError("Meta buffers and valued buffers can not be in the same model")
        
        if meta_param_flag==1 and meta_buffer_flag==1:
            pass
        elif meta_buffer_flag==0 and meta_param_flag==1:
             for name, buf in module.named_buffers():
                module._buffers[name] = module._buffers[name].to(device=self._device)
        elif meta_param_flag==0 and meta_buffer_flag==1:
            for name, param in module.named_parameters():
                module._parameters[name] = module._parameters[name].to(device=self._device)
        else:
            module.to(self._device)
 

def post_process_colo_init_ctx(model: torch.nn.Module,
                               device: torch.device = torch.device('cpu'),
                               dtype: torch.dtype = torch.float,
                               default_pg: Optional[ProcessGroup] = None,
                               default_dist_spec=None):
    """post_process_colo_init_ctx

    This function is called after `ColoInitContext`.

    Args:
        model (torch.nn.module): the model
        device (torch.device, optional): device type of the model params. Defaults to torch.device('cpu').
        dtype (torch.dtype, optional): dtype of the model params. Defaults to torch.float.
        default_pg (Optional[ProcessGroup], optional): default process group. Defaults to None. Inidicates a DP-only process group.
        default_dist_spec (Any, optional): default dist spec of params. Defaults to None.

    Raises:
        RuntimeError: raise error if
    """

    torch_params = []
    for n, p in model.named_parameters():
        if not isinstance(p, ColoParameter):
            # print(f"{n} is not a ColoParameter. We are going to converting it to ColoParameter")
            torch_params.append((n, p))

    for (n, param) in torch_params:
        name_list = n.split('.')
        module = model
        for i in range(len(name_list) - 1):
            module = module._modules[name_list[i]]
        delattr(module, name_list[-1])
        setattr(module, name_list[-1], _convert_to_coloparam(param, device, dtype, default_pg, default_dist_spec))

    del torch_params
    for n, p in model.named_parameters():
        if not isinstance(p, ColoTensor):
            raise RuntimeError
