import torch
import numpy as np


def set_seed(seed):
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
    import random
    random.seed(seed)


def soft_update_from_to(source, target, tau):
    for target_param, param in zip(target.parameters(), source.parameters()):
        target_param.data.copy_(
            target_param.data * (1.0 - tau) + param.data * tau
        )


def copy_model_params_from_to(source, target):
    for target_param, param in zip(target.parameters(), source.parameters()):
        target_param.data.copy_(param.data)


def fanin_init(tensor, scale=1):
    size = tensor.size()
    if len(size) == 2:
        fan_in = size[0]
    elif len(size) > 2:
        fan_in = np.prod(size[1:])
    else:
        raise Exception("Shape must be have dimension at least 2.")
    bound = scale / np.sqrt(fan_in)
    return tensor.data.uniform_(-bound, bound)


def orthogonal_init(tensor, gain=0.01):
    torch.nn.init.orthogonal_(tensor, gain=gain)


def fanin_init_weights_like(tensor):
    size = tensor.size()
    if len(size) == 2:
        fan_in = size[0]
    elif len(size) > 2:
        fan_in = np.prod(size[1:])
    else:
        raise Exception("Shape must be have dimension at least 2.")
    bound = 1. / np.sqrt(fan_in)
    new_tensor = FloatTensor(tensor.size())
    new_tensor.uniform_(-bound, bound)
    return new_tensor


"""
GPU wrappers
"""

_use_gpu = False
device = None
_gpu_id = 0


def set_gpu_mode(mode, gpu_id=0):
    global _use_gpu
    global device
    global _gpu_id
    _gpu_id = gpu_id
    _use_gpu = mode
    device = torch.device("cuda:" + str(gpu_id) if _use_gpu else "cpu")
    return device

def gpu_enabled():
    return _use_gpu


def set_device(gpu_id):
    torch.cuda.set_device(gpu_id)


# noinspection PyPep8Naming
def FloatTensor(*args, torch_device=None, **kwargs):
    if torch_device is None:
        torch_device = device
    return torch.FloatTensor(*args, **kwargs, device=torch_device)


def from_numpy(*args, **kwargs):
    return torch.from_numpy(*args, **kwargs).float().to(device)


def get_numpy(tensor):
    return tensor.to('cpu').detach().numpy()


def zeros(*sizes, torch_device=None, **kwargs):
    if torch_device is None:
        torch_device = device
    return torch.zeros(*sizes, **kwargs, device=torch_device)


def ones(*sizes, torch_device=None, **kwargs):
    if torch_device is None:
        torch_device = device
    return torch.ones(*sizes, **kwargs, device=torch_device)


def ones_like(*args, torch_device=None, **kwargs):
    if torch_device is None:
        torch_device = device
    return torch.ones_like(*args, **kwargs, device=torch_device)


def rand(*args, torch_device=None, **kwargs):
    if torch_device is None:
        torch_device = device
    return torch.rand(*args, **kwargs, device=torch_device)


def randn(*args, torch_device=None, **kwargs):
    if torch_device is None:
        torch_device = device
    return torch.randn(*args, **kwargs, device=torch_device)


def zeros_like(*args, torch_device=None, **kwargs):
    if torch_device is None:
        torch_device = device
    return torch.zeros_like(*args, **kwargs, device=torch_device)


def tensor(*args, torch_device=None, **kwargs):
    if torch_device is None:
        torch_device = device
    return torch.tensor(*args, **kwargs, device=torch_device)


def normal(*args, **kwargs):
    return torch.normal(*args, **kwargs).to(device)


def eval_np(module, *args, **kwargs):
    """
    Eval this module with a numpy interface

    Same as a call to __call__ except all Variable input/outputs are
    replaced with numpy equivalents.

    Assumes the output is either a single object or a tuple of objects.
    """
    torch_args = tuple(torch_ify(x) for x in args)
    torch_kwargs = {k: torch_ify(v) for k, v in kwargs.items()}
    outputs = module(*torch_args, **torch_kwargs)
    if isinstance(outputs, tuple):
        return tuple(np_ify(x) for x in outputs)
    else:
        return np_ify(outputs)


def torch_ify(np_array_or_other):
    if isinstance(np_array_or_other, np.ndarray):
        return from_numpy(np_array_or_other)
    else:
        return np_array_or_other


def np_ify(tensor_or_other):
    if isinstance(tensor_or_other, torch.autograd.Variable):
        return get_numpy(tensor_or_other)
    else:
        return tensor_or_other


def _elem_or_tuple_to_variable(elem_or_tuple):
    if isinstance(elem_or_tuple, tuple):
        return tuple(
            _elem_or_tuple_to_variable(e) for e in elem_or_tuple
        )
    return from_numpy(elem_or_tuple).float()


def _filter_batch(np_batch):
    for k, v in np_batch.items():
        if v.dtype == np.bool:
            yield k, v.astype(int)
        else:
            yield k, v


def np_to_pytorch_batch(np_batch):
    return {
        k: _elem_or_tuple_to_variable(x)
        for k, x in _filter_batch(np_batch)
        if x.dtype != np.dtype('O')  # ignore object (e.g. dictionaries)
    }
