import importlib
import os
import sys

import numpy as np

from dgl.backend import *
from dgl.nn import *

from . import backend_unittest

mod = importlib.import_module(".%s" % backend_name, __name__)
thismod = sys.modules[__name__]

for api in backend_unittest.__dict__.keys():
    if api.startswith("__"):
        continue
    elif callable(mod.__dict__[api]):
        # Tensor APIs used in unit tests MUST be supported across all backends
        globals()[api] = mod.__dict__[api]

# Tensor creation with default dtype and context

_zeros = zeros
_ones = ones
_randn = randn
_tensor = tensor
_arange = arange
_full = full
_full_1d = full_1d
_softmax = softmax
_default_context_str = os.getenv("DGLTESTDEV", "cpu")
_context_dict = {
    "cpu": cpu(),
    "gpu": cuda(),
}
_default_context = _context_dict[_default_context_str]


def ctx():
    return _default_context


def gpu_ctx():
    return _default_context_str == "gpu"


def zeros(shape, dtype=float32, ctx=_default_context):
    return _zeros(shape, dtype, ctx)


def ones(shape, dtype=float32, ctx=_default_context):
    return _ones(shape, dtype, ctx)


def randn(shape):
    return copy_to(_randn(shape), _default_context)


def tensor(data, dtype=None):
    return copy_to(_tensor(data, dtype), _default_context)


def arange(start, stop, dtype=int64, ctx=None):
    return _arange(
        start, stop, dtype, ctx if ctx is not None else _default_context
    )


def full(shape, fill_value, dtype, ctx=_default_context):
    return _full(shape, fill_value, dtype, ctx)


def full_1d(length, fill_value, dtype, ctx=_default_context):
    return _full_1d(length, fill_value, dtype, ctx)


def softmax(x, dim):
    return _softmax(x, dim)