r"""This file is allowed to initialize CUDA context when imported."""

import functools
import torch
import torch.cuda
from torch.testing._internal.common_utils import TEST_NUMBA
import inspect
import contextlib


TEST_CUDA = torch.cuda.is_available()
TEST_MULTIGPU = TEST_CUDA and torch.cuda.device_count() >= 2
CUDA_DEVICE = torch.device("cuda:0") if TEST_CUDA else None
# note: if ROCm is targeted, TEST_CUDNN is code for TEST_MIOPEN
TEST_CUDNN = TEST_CUDA and torch.backends.cudnn.is_acceptable(torch.tensor(1., device=CUDA_DEVICE))
TEST_CUDNN_VERSION = torch.backends.cudnn.version() if TEST_CUDNN else 0

TEST_MAGMA = TEST_CUDA
if TEST_CUDA:
    torch.ones(1).cuda()  # has_magma shows up after cuda is initialized
    TEST_MAGMA = torch.cuda.has_magma

if TEST_NUMBA:
    import numba.cuda
    TEST_NUMBA_CUDA = numba.cuda.is_available()
else:
    TEST_NUMBA_CUDA = False

# Used below in `initialize_cuda_context_rng` to ensure that CUDA context and
# RNG have been initialized.
__cuda_ctx_rng_initialized = False


# after this call, CUDA context and RNG must have been initialized on each GPU
def initialize_cuda_context_rng():
    global __cuda_ctx_rng_initialized
    assert TEST_CUDA, 'CUDA must be available when calling initialize_cuda_context_rng'
    if not __cuda_ctx_rng_initialized:
        # initialize cuda context and rng for memory tests
        for i in range(torch.cuda.device_count()):
            torch.randn(1, device="cuda:{}".format(i))
        __cuda_ctx_rng_initialized = True


# Test whether hardware TF32 math mode enabled. It is enabled only on:
# - CUDA >= 11
# - arch >= Ampere
def tf32_is_not_fp32():
    if not torch.cuda.is_available() or torch.version.cuda is None:
        return False
    if torch.cuda.get_device_properties(torch.cuda.current_device()).major < 8:
        return False
    if int(torch.version.cuda.split('.')[0]) < 11:
        return False
    return True


@contextlib.contextmanager
def tf32_off():
    old_allow_tf32_matmul = torch.backends.cuda.matmul.allow_tf32
    try:
        torch.backends.cuda.matmul.allow_tf32 = False
        with torch.backends.cudnn.flags(enabled=None, benchmark=None, deterministic=None, allow_tf32=False):
            yield 
    finally:
        torch.backends.cuda.matmul.allow_tf32 = old_allow_tf32_matmul


@contextlib.contextmanager
def tf32_on(self, tf32_precision=1e-5):
    old_allow_tf32_matmul = torch.backends.cuda.matmul.allow_tf32
    old_precison = self.precision
    try:
        torch.backends.cuda.matmul.allow_tf32 = True
        self.precision = tf32_precision
        with torch.backends.cudnn.flags(enabled=None, benchmark=None, deterministic=None, allow_tf32=True):
            yield 
    finally:
        torch.backends.cuda.matmul.allow_tf32 = old_allow_tf32_matmul
        self.precision = old_precison


# This is a wrapper that wraps a test to run this test twice, one with
# allow_tf32=True, another with allow_tf32=False. When running with
# allow_tf32=True, it will use reduced precision as pecified by the
# argument. For example:
#    @dtypes(torch.float32, torch.float64, torch.complex64, torch.complex128)
#    @tf32_on_and_off(0.005)
#    def test_matmul(self, device, dtype):
#        a = ...; b = ...;
#        c = torch.matmul(a, b)
#        self.assertEqual(c, expected)
# In the above example, when testing torch.float32 and torch.complex64 on CUDA
# on a CUDA >= 11 build on an >=Ampere architecture, the matmul will be running at
# TF32 mode and TF32 mode off, and on TF32 mode, the assertEqual will use reduced
# precision to check values.
def tf32_on_and_off(tf32_precision=1e-5):
    def with_tf32_disabled(self, function_call):
        with tf32_off():
            function_call()

    def with_tf32_enabled(self, function_call):
        with tf32_on(self, tf32_precision):
            function_call()

    def wrapper(f):
        nargs = len(inspect.signature(f).parameters)
        if nargs == 2:
            @functools.wraps(f)
            def wrapped(self, device):
                if self.device_type == 'cuda' and tf32_is_not_fp32():
                    with_tf32_disabled(self, lambda: f(self, device))
                    with_tf32_enabled(self, lambda: f(self, device))
                else:
                    f(self, device)
        else:
            assert nargs == 3, "this decorator only support function with signature (self, device) or (self, device, dtype)"

            @functools.wraps(f)
            def wrapped(self, device, dtype):
                if self.device_type == 'cuda' and dtype in {torch.float32, torch.complex64} and tf32_is_not_fp32():
                    with_tf32_disabled(self, lambda: f(self, device, dtype))
                    with_tf32_enabled(self, lambda: f(self, device, dtype))
                else:
                    f(self, device, dtype)

        return wrapped
    return wrapper


# This is a wrapper that wraps a test to run it with TF32 turned off.
# This wrapper is designed to be used when a test uses matmul or convolutions
# but the purpose of that test is not testing matmul or convolutions.
# Disabling TF32 will enforce torch.float tensors to be always computed
# at full precision.
def with_tf32_off(f):
    @functools.wraps(f)
    def wrapped(*args, **kwargs):
        with tf32_off():
            return f(*args, **kwargs)

    return wrapped


def _get_torch_cuda_version():
    if torch.version.cuda is None:
        return [0, 0]
    cuda_version = str(torch.version.cuda)
    return [int(x) for x in cuda_version.split(".")]
