# Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import threading
from concurrent.futures.process import _ExecutorManagerThread
from pathlib import Path
from unittest.mock import Mock

import pytest
import torch.distributed

import lightning.fabric
from lightning.fabric.accelerators import XLAAccelerator
from lightning.fabric.strategies.launchers.subprocess_script import _ChildProcessObserver
from lightning.fabric.utilities.distributed import _destroy_dist_connection


@pytest.fixture(autouse=True)
def preserve_global_rank_variable():
    """Ensures that the rank_zero_only.rank global variable gets reset in each test."""
    from lightning_utilities.core.rank_zero import rank_zero_only as rank_zero_only_utilities

    from lightning.fabric.utilities.rank_zero import rank_zero_only as rank_zero_only_fabric

    functions = (rank_zero_only_fabric, rank_zero_only_utilities)
    ranks = [getattr(fn, "rank", None) for fn in functions]
    yield
    for fn, rank in zip(functions, ranks):
        if rank is not None:
            setattr(fn, "rank", rank)


@pytest.fixture(autouse=True)
def restore_env_variables():
    """Ensures that environment variables set during the test do not leak out."""
    env_backup = os.environ.copy()
    yield
    leaked_vars = os.environ.keys() - env_backup.keys()
    # restore environment as it was before running the test
    os.environ.clear()
    os.environ.update(env_backup)
    # these are currently known leakers - ideally these would not be allowed
    # TODO(fabric): this list can be trimmed, maybe PL's too after moving tests
    allowlist = {
        "CUDA_DEVICE_ORDER",
        "LOCAL_RANK",
        "NODE_RANK",
        "WORLD_SIZE",
        "MASTER_ADDR",
        "MASTER_PORT",
        "PL_GLOBAL_SEED",
        "PL_SEED_WORKERS",
        "RANK",  # set by DeepSpeed
        "CUDA_MODULE_LOADING",  # leaked by PyTorch
        "CRC32C_SW_MODE",  # set by tensorboardX
        "OMP_NUM_THREADS",  # set by our launchers
        # set by torchdynamo
        "TRITON_CACHE_DIR",
        "TRITON_PTXAS_PATH",
        "TORCHINDUCTOR_CACHE_DIR",
    }
    leaked_vars.difference_update(allowlist)
    assert not leaked_vars, f"test is leaking environment variable(s): {set(leaked_vars)}"


@pytest.fixture(autouse=True)
def teardown_process_group():
    """Ensures that the distributed process group gets closed before the next test runs."""
    yield
    _destroy_dist_connection()


@pytest.fixture(autouse=True)
def thread_police_duuu_daaa_duuu_daaa():
    """Attempts to stop left-over threads to avoid test interactions."""
    active_threads_before = set(threading.enumerate())
    yield
    active_threads_after = set(threading.enumerate())

    if XLAAccelerator.is_available():
        # Ignore the check when running XLA tests for now
        return

    for thread in active_threads_after - active_threads_before:
        stop = getattr(thread, "stop", None) or getattr(thread, "exit", None)
        if thread.daemon and callable(stop):
            # A daemon thread would anyway be stopped at the end of a program
            # We do it preemptively here to reduce the risk of interactions with other tests that run after
            stop()
            assert not thread.is_alive()
        elif isinstance(thread, _ChildProcessObserver):
            thread.join(timeout=10)
        elif (
            thread.name == "QueueFeederThread"  # tensorboardX
            or thread.name == "QueueManagerThread"  # torch.compile
            or "(_read_thread)" in thread.name  # torch.compile
            or thread.name == "subproc_worker_timer"  # torch.compile subprocess pool watchdog
        ):
            thread.join(timeout=20)
        elif type(thread).__name__ == "_DummyThread":
            # threads not created by the `threading` module (e.g. native NCCL/torch.compile threads
            # that call into Python) surface as `_DummyThread` and can't be joined or stopped
            continue
        elif (
            sys.version_info >= (3, 9)
            and isinstance(thread, _ExecutorManagerThread)
            or "ThreadPoolExecutor-" in thread.name
            or thread.name == "InductorSubproc"  # torch.compile
        ):
            # probably `torch.compile`, can't narrow it down further
            continue
        else:
            raise AssertionError(f"Test left zombie thread: {thread}")


@pytest.fixture(autouse=True)
def reset_in_fabric_backward():
    """Ensures that the wrappers.in_fabric_backward global variable gets reset after each test."""
    import lightning.fabric.wrappers as wrappers

    assert hasattr(wrappers, "_in_fabric_backward")
    yield
    wrappers._in_fabric_backward = False


@pytest.fixture
def reset_deterministic_algorithm():
    """Ensures that torch determinism settings are reset before the next test runs."""
    yield
    os.environ.pop("CUBLAS_WORKSPACE_CONFIG", None)
    torch.use_deterministic_algorithms(False)


@pytest.fixture
def reset_cudnn_benchmark():
    """Ensures that the `torch.backends.cudnn.benchmark` setting gets reset before the next test runs."""
    yield
    torch.backends.cudnn.benchmark = False


def mock_xla_available(monkeypatch: pytest.MonkeyPatch, value: bool = True) -> None:
    monkeypatch.setattr(lightning.fabric.accelerators.xla, "_XLA_AVAILABLE", value)
    monkeypatch.setattr(lightning.fabric.plugins.environments.xla, "_XLA_AVAILABLE", value)
    monkeypatch.setattr(lightning.fabric.plugins.precision.xla, "_XLA_AVAILABLE", value)
    monkeypatch.setattr(lightning.fabric.plugins.io.xla, "_XLA_AVAILABLE", value)
    monkeypatch.setattr(lightning.fabric.strategies.single_xla, "_XLA_AVAILABLE", value)
    monkeypatch.setattr(lightning.fabric.strategies.xla_fsdp, "_XLA_AVAILABLE", value)
    monkeypatch.setattr(lightning.fabric.strategies.launchers.xla, "_XLA_AVAILABLE", value)
    monkeypatch.setitem(sys.modules, "torch_xla", Mock())
    monkeypatch.setitem(sys.modules, "torch_xla.core.xla_model", Mock())
    monkeypatch.setitem(sys.modules, "torch_xla.experimental", Mock())
    monkeypatch.setitem(sys.modules, "torch_xla.distributed.fsdp.wrap", Mock())


@pytest.fixture
def xla_available(monkeypatch: pytest.MonkeyPatch) -> None:
    mock_xla_available(monkeypatch)


def mock_tpu_available(monkeypatch: pytest.MonkeyPatch, value: bool = True) -> None:
    mock_xla_available(monkeypatch, value)
    monkeypatch.setattr(lightning.fabric.accelerators.xla.XLAAccelerator, "is_available", lambda: value)
    monkeypatch.setattr(lightning.fabric.accelerators.xla.XLAAccelerator, "auto_device_count", lambda *_: 8)


@pytest.fixture
def tpu_available(monkeypatch: pytest.MonkeyPatch) -> None:
    mock_tpu_available(monkeypatch)


@pytest.fixture
def caplog(caplog):
    """Workaround for https://github.com/pytest-dev/pytest/issues/3697.

    Setting ``filterwarnings`` with pytest breaks ``caplog`` when ``not logger.propagate``.

    """
    import logging

    lightning_logger = logging.getLogger("lightning.fabric")
    propagate = lightning_logger.propagate
    lightning_logger.propagate = True
    yield caplog
    lightning_logger.propagate = propagate


@pytest.fixture(autouse=True)
def leave_no_artifacts_behind():
    """Checks that no artifacts are left behind after the test."""
    tests_root = Path(__file__).parent.parent
    # Ignore the __pycache__ directories
    files_before = {p for p in tests_root.rglob("*") if "__pycache__" not in p.parts}
    yield
    files_after = {p for p in tests_root.rglob("*") if "__pycache__" not in p.parts}
    difference = files_after - files_before
    difference = {str(f.relative_to(tests_root)) for f in difference}
    # ignore the .coverage files
    difference = {f for f in difference if not f.endswith(".coverage")}
    assert not difference, f"Test left artifacts behind: {difference}"


def pytest_collection_modifyitems(items: list[pytest.Function], config: pytest.Config) -> None:
    """An adaptation of `tests/tests_pytorch/conftest.py::pytest_collection_modifyitems`"""
    initial_size = len(items)
    conditions = []
    filtered, skipped = 0, 0

    options = {
        "standalone": "PL_RUN_STANDALONE_TESTS",
        "min_cuda_gpus": "RUN_ONLY_CUDA_TESTS",
        "tpu": "PL_RUN_TPU_TESTS",
    }
    if os.getenv(options["standalone"], "0") == "1" and os.getenv(options["min_cuda_gpus"], "0") == "1":
        # special case: we don't have a CPU job for standalone tests, so we shouldn't run only cuda tests.
        # by deleting the key, we avoid filtering out the CPU tests
        del options["min_cuda_gpus"]

    for kwarg, env_var in options.items():
        # this will compute the intersection of all tests selected per environment variable
        if os.getenv(env_var, "0") != "1":
            continue
        conditions.append(env_var)
        for i, test in reversed(list(enumerate(items))):  # loop in reverse, since we are going to pop items
            already_skipped = any(marker.name == "skip" for marker in test.own_markers)
            if already_skipped:
                # the test was going to be skipped anyway, filter it out
                items.pop(i)
                skipped += 1
                continue
            has_runif_with_kwarg = any(
                marker.name == "skipif" and marker.kwargs.get(kwarg) for marker in test.own_markers
            )
            if not has_runif_with_kwarg:
                # the test has `@RunIf(kwarg=True)`, filter it out
                items.pop(i)
                filtered += 1

    if config.option.verbose >= 0 and (filtered or skipped):
        writer = config.get_terminal_writer()
        writer.write(
            f"\nThe number of tests has been filtered from {initial_size} to {initial_size - filtered} after the"
            f" filters {conditions}.\n{skipped} tests are marked as unconditional skips.\nIn total, {len(items)} tests"
            " will run.\n",
            flush=True,
            bold=True,
            purple=True,  # oh yeah, branded pytest messages
        )

    # error out on our deprecation warnings - ensures the code and tests are kept up-to-date
    deprecation_error = pytest.mark.filterwarnings(
        "error::lightning.fabric.utilities.rank_zero.LightningDeprecationWarning",
    )
    for item in items:
        item.add_marker(deprecation_error)
