import inspect
import logging
import os
import re
import shutil
import sys
import tempfile
import unittest
from distutils.util import strtobool
from io import StringIO
from pathlib import Path

from .file_utils import (
    _datasets_available,
    _faiss_available,
    _flax_available,
    _sentencepiece_available,
    _tf_available,
    _tokenizers_available,
    _torch_available,
    _torch_tpu_available,
)
from .integrations import _has_optuna, _has_ray


SMALL_MODEL_IDENTIFIER = "julien-c/bert-xsmall-dummy"
DUMMY_UNKWOWN_IDENTIFIER = "julien-c/dummy-unknown"
DUMMY_DIFF_TOKENIZER_IDENTIFIER = "julien-c/dummy-diff-tokenizer"
# Used to test Auto{Config, Model, Tokenizer} model_type detection.


def parse_flag_from_env(key, default=False):
    try:
        value = os.environ[key]
    except KeyError:
        # KEY isn't set, default to `default`.
        _value = default
    else:
        # KEY is set, convert it to True or False.
        try:
            _value = strtobool(value)
        except ValueError:
            # More values are supported, but let's keep the message simple.
            raise ValueError("If set, {} must be yes or no.".format(key))
    return _value


def parse_int_from_env(key, default=None):
    try:
        value = os.environ[key]
    except KeyError:
        _value = default
    else:
        try:
            _value = int(value)
        except ValueError:
            raise ValueError("If set, {} must be a int.".format(key))
    return _value


_run_slow_tests = parse_flag_from_env("RUN_SLOW", default=False)
_run_pt_tf_cross_tests = parse_flag_from_env("RUN_PT_TF_CROSS_TESTS", default=False)
_run_custom_tokenizers = parse_flag_from_env("RUN_CUSTOM_TOKENIZERS", default=False)
_run_pipeline_tests = parse_flag_from_env("RUN_PIPELINE_TESTS", default=False)
_tf_gpu_memory_limit = parse_int_from_env("TF_GPU_MEMORY_LIMIT", default=None)


def is_pt_tf_cross_test(test_case):
    """
    Decorator marking a test as a test that control interactions between PyTorch and TensorFlow.

    PT+TF tests are skipped by default and we can run only them by setting RUN_PT_TF_CROSS_TESTS environment variable
    to a truthy value and selecting the is_pt_tf_cross_test pytest mark.

    """
    if not _run_pt_tf_cross_tests or not _torch_available or not _tf_available:
        return unittest.skip("test is PT+TF test")(test_case)
    else:
        try:
            import pytest  # We don't need a hard dependency on pytest in the main library
        except ImportError:
            return test_case
        else:
            return pytest.mark.is_pt_tf_cross_test()(test_case)


def is_pipeline_test(test_case):
    """
    Decorator marking a test as a pipeline test.

    Pipeline tests are skipped by default and we can run only them by setting RUN_PIPELINE_TEST environment variable to
    a truthy value and selecting the is_pipeline_test pytest mark.

    """
    if not _run_pipeline_tests:
        return unittest.skip("test is pipeline test")(test_case)
    else:
        try:
            import pytest  # We don't need a hard dependency on pytest in the main library
        except ImportError:
            return test_case
        else:
            return pytest.mark.is_pipeline_test()(test_case)


def slow(test_case):
    """
    Decorator marking a test as slow.

    Slow tests are skipped by default. Set the RUN_SLOW environment variable to a truthy value to run them.

    """
    if not _run_slow_tests:
        return unittest.skip("test is slow")(test_case)
    else:
        return test_case


def custom_tokenizers(test_case):
    """
    Decorator marking a test for a custom tokenizer.

    Custom tokenizers require additional dependencies, and are skipped by default. Set the RUN_CUSTOM_TOKENIZERS
    environment variable to a truthy value to run them.
    """
    if not _run_custom_tokenizers:
        return unittest.skip("test of custom tokenizers")(test_case)
    else:
        return test_case


def require_torch(test_case):
    """
    Decorator marking a test that requires PyTorch.

    These tests are skipped when PyTorch isn't installed.

    """
    if not _torch_available:
        return unittest.skip("test requires PyTorch")(test_case)
    else:
        return test_case


def require_tf(test_case):
    """
    Decorator marking a test that requires TensorFlow.

    These tests are skipped when TensorFlow isn't installed.

    """
    if not _tf_available:
        return unittest.skip("test requires TensorFlow")(test_case)
    else:
        return test_case


def require_flax(test_case):
    """
    Decorator marking a test that requires JAX & Flax

    These tests are skipped when one / both are not installed

    """
    if not _flax_available:
        test_case = unittest.skip("test requires JAX & Flax")(test_case)
    return test_case


def require_sentencepiece(test_case):
    """
    Decorator marking a test that requires SentencePiece.

    These tests are skipped when SentencePiece isn't installed.

    """
    if not _sentencepiece_available:
        return unittest.skip("test requires SentencePiece")(test_case)
    else:
        return test_case


def require_tokenizers(test_case):
    """
    Decorator marking a test that requires 🤗 Tokenizers.

    These tests are skipped when 🤗 Tokenizers isn't installed.

    """
    if not _tokenizers_available:
        return unittest.skip("test requires tokenizers")(test_case)
    else:
        return test_case


def require_torch_multigpu(test_case):
    """
    Decorator marking a test that requires a multi-GPU setup (in PyTorch).

    These tests are skipped on a machine without multiple GPUs.

    To run *only* the multigpu tests, assuming all test names contain multigpu: $ pytest -sv ./tests -k "multigpu"
    """
    if not _torch_available:
        return unittest.skip("test requires PyTorch")(test_case)

    import torch

    if torch.cuda.device_count() < 2:
        return unittest.skip("test requires multiple GPUs")(test_case)
    else:
        return test_case


def require_torch_non_multigpu(test_case):
    """
    Decorator marking a test that requires 0 or 1 GPU setup (in PyTorch).
    """
    if not _torch_available:
        return unittest.skip("test requires PyTorch")(test_case)

    import torch

    if torch.cuda.device_count() > 1:
        return unittest.skip("test requires 0 or 1 GPU")(test_case)
    else:
        return test_case


def require_torch_tpu(test_case):
    """
    Decorator marking a test that requires a TPU (in PyTorch).
    """
    if not _torch_tpu_available:
        return unittest.skip("test requires PyTorch TPU")
    else:
        return test_case


if _torch_available:
    # Set env var CUDA_VISIBLE_DEVICES="" to force cpu-mode
    import torch

    torch_device = "cuda" if torch.cuda.is_available() else "cpu"
else:
    torch_device = None


def require_torch_gpu(test_case):
    """Decorator marking a test that requires CUDA and PyTorch. """
    if torch_device != "cuda":
        return unittest.skip("test requires CUDA")(test_case)
    else:
        return test_case


def require_datasets(test_case):
    """Decorator marking a test that requires datasets."""

    if not _datasets_available:
        return unittest.skip("test requires `datasets`")(test_case)
    else:
        return test_case


def require_faiss(test_case):
    """Decorator marking a test that requires faiss."""
    if not _faiss_available:
        return unittest.skip("test requires `faiss`")(test_case)
    else:
        return test_case


def require_optuna(test_case):
    """
    Decorator marking a test that requires optuna.

    These tests are skipped when optuna isn't installed.

    """
    if not _has_optuna:
        return unittest.skip("test requires optuna")(test_case)
    else:
        return test_case


def require_ray(test_case):
    """
    Decorator marking a test that requires Ray/tune.

    These tests are skipped when Ray/tune isn't installed.

    """
    if not _has_ray:
        return unittest.skip("test requires Ray/tune")(test_case)
    else:
        return test_case


def get_tests_dir(append_path=None):
    """
    Args:
        append_path: optional path to append to the tests dir path

    Return:
        The full path to the `tests` dir, so that the tests can be invoked from anywhere. Optionally `append_path` is
        joined after the `tests` dir the former is provided.

    """
    # this function caller's __file__
    caller__file__ = inspect.stack()[1][1]
    tests_dir = os.path.abspath(os.path.dirname(caller__file__))
    if append_path:
        return os.path.join(tests_dir, append_path)
    else:
        return tests_dir


#
# Helper functions for dealing with testing text outputs
# The original code came from:
# https://github.com/fastai/fastai/blob/master/tests/utils/text.py

# When any function contains print() calls that get overwritten, like progress bars,
# a special care needs to be applied, since under pytest -s captured output (capsys
# or contextlib.redirect_stdout) contains any temporary printed strings, followed by
# \r's. This helper function ensures that the buffer will contain the same output
# with and without -s in pytest, by turning:
# foo bar\r tar mar\r final message
# into:
# final message
# it can handle a single string or a multiline buffer
def apply_print_resets(buf):
    return re.sub(r"^.*\r", "", buf, 0, re.M)


def assert_screenout(out, what):
    out_pr = apply_print_resets(out).lower()
    match_str = out_pr.find(what.lower())
    assert match_str != -1, f"expecting to find {what} in output: f{out_pr}"


class CaptureStd:
    """
    Context manager to capture:
        stdout, clean it up and make it available via obj.out stderr, and make it available via obj.err

        init arguments: - out - capture stdout: True/False, default True - err - capture stdout: True/False, default
        True

        Examples::

            with CaptureStdout() as cs:
                print("Secret message")
            print(f"captured: {cs.out}")

            import sys
            with CaptureStderr() as cs:
                print("Warning: ", file=sys.stderr)
            print(f"captured: {cs.err}")

            # to capture just one of the streams, but not the other
            with CaptureStd(err=False) as cs:
                print("Secret message")
            print(f"captured: {cs.out}")
            # but best use the stream-specific subclasses

    """

    def __init__(self, out=True, err=True):
        if out:
            self.out_buf = StringIO()
            self.out = "error: CaptureStd context is unfinished yet, called too early"
        else:
            self.out_buf = None
            self.out = "not capturing stdout"

        if err:
            self.err_buf = StringIO()
            self.err = "error: CaptureStd context is unfinished yet, called too early"
        else:
            self.err_buf = None
            self.err = "not capturing stderr"

    def __enter__(self):
        if self.out_buf:
            self.out_old = sys.stdout
            sys.stdout = self.out_buf

        if self.err_buf:
            self.err_old = sys.stderr
            sys.stderr = self.err_buf

        return self

    def __exit__(self, *exc):
        if self.out_buf:
            sys.stdout = self.out_old
            self.out = apply_print_resets(self.out_buf.getvalue())

        if self.err_buf:
            sys.stderr = self.err_old
            self.err = self.err_buf.getvalue()

    def __repr__(self):
        msg = ""
        if self.out_buf:
            msg += f"stdout: {self.out}\n"
        if self.err_buf:
            msg += f"stderr: {self.err}\n"
        return msg


# in tests it's the best to capture only the stream that's wanted, otherwise
# it's easy to miss things, so unless you need to capture both streams, use the
# subclasses below (less typing). Or alternatively, configure `CaptureStd` to
# disable the stream you don't need to test.


class CaptureStdout(CaptureStd):
    """ Same as CaptureStd but captures only stdout """

    def __init__(self):
        super().__init__(err=False)


class CaptureStderr(CaptureStd):
    """ Same as CaptureStd but captures only stderr """

    def __init__(self):
        super().__init__(out=False)


class CaptureLogger:
    """
    Context manager to capture `logging` streams

    Args:
    - logger: 'logging` logger object

    Results:
        The captured output is available via `self.out`

    Example::

        >>> from transformers import logging
        >>> from transformers.testing_utils import CaptureLogger

        >>> msg = "Testing 1, 2, 3"
        >>> logging.set_verbosity_info()
        >>> logger = logging.get_logger("transformers.tokenization_bart")
        >>> with CaptureLogger(logger) as cl:
        ...     logger.info(msg)
        >>> assert cl.out, msg+"\n"
    """

    def __init__(self, logger):
        self.logger = logger
        self.io = StringIO()
        self.sh = logging.StreamHandler(self.io)
        self.out = ""

    def __enter__(self):
        self.logger.addHandler(self.sh)
        return self

    def __exit__(self, *exc):
        self.logger.removeHandler(self.sh)
        self.out = self.io.getvalue()

    def __repr__(self):
        return f"captured: {self.out}\n"


class TestCasePlus(unittest.TestCase):
    """
    This class extends `unittest.TestCase` with additional features.

    Feature 1: A set of fully resolved important file and dir path accessors.

    In tests often we need to know where things are relative to the current test file, and it's not trivial since the
    test could be invoked from more than one directory or could reside in sub-directories with different depths. This
    class solves this problem by sorting out all the basic paths and provides easy accessors to them:

    * ``pathlib`` objects (all fully resolved):

       - ``test_file_path`` - the current test file path (=``__file__``)
       - ``test_file_dir`` - the directory containing the current test file
       - ``tests_dir`` - the directory of the ``tests`` test suite
       - ``examples_dir`` - the directory of the ``examples`` test suite
       - ``repo_root_dir`` - the directory of the repository
       - ``src_dir`` - the directory of ``src`` (i.e. where the ``transformers`` sub-dir resides)

    * stringified paths---same as above but these return paths as strings, rather than ``pathlib`` objects:

       - ``test_file_path_str``
       - ``test_file_dir_str``
       - ``tests_dir_str``
       - ``examples_dir_str``
       - ``repo_root_dir_str``
       - ``src_dir_str``

    Feature 2: Flexible auto-removable temp dirs which are guaranteed to get removed at the end of test.

    In all the following scenarios the temp dir will be auto-removed at the end of test, unless `after=False`.

    # 1. create a unique temp dir, `tmp_dir` will contain the path to the created temp dir

    ::

        def test_whatever(self):
            tmp_dir = self.get_auto_remove_tmp_dir()

    # 2. create a temp dir of my choice and delete it at the end - useful for debug when you want to # monitor a
    specific directory

    ::

        def test_whatever(self):
            tmp_dir = self.get_auto_remove_tmp_dir(tmp_dir="./tmp/run/test")

    # 3. create a temp dir of my choice and do not delete it at the end - useful for when you want # to look at the
    temp results

    ::
        def test_whatever(self):
            tmp_dir = self.get_auto_remove_tmp_dir(tmp_dir="./tmp/run/test", after=False)

    # 4. create a temp dir of my choice and ensure to delete it right away - useful for when you # disabled deletion in
    the previous test run and want to make sure the that tmp dir is empty # before the new test is run

    ::

        def test_whatever(self):
            tmp_dir = self.get_auto_remove_tmp_dir(tmp_dir="./tmp/run/test", before=True)

    Note 1: In order to run the equivalent of `rm -r` safely, only subdirs of the project repository checkout are
    allowed if an explicit `tmp_dir` is used, so that by mistake no `/tmp` or similar important part of the filesystem
    will get nuked. i.e. please always pass paths that start with `./`

    Note 2: Each test can register multiple temp dirs and they all will get auto-removed, unless requested otherwise.

    Feature 3: Get a copy of the ``os.environ`` object that sets up ``PYTHONPATH`` specific to the current test suite.
    This is useful for invoking external programs from the test suite - e.g. distributed training.


    ::
        def test_whatever(self):
            env = self.get_env()

    """

    def setUp(self):
        self.teardown_tmp_dirs = []

        # figure out the resolved paths for repo_root, tests, examples, etc.
        self._test_file_path = inspect.getfile(self.__class__)
        path = Path(self._test_file_path).resolve()
        self._test_file_dir = path.parents[0]
        for up in [1, 2, 3]:
            tmp_dir = path.parents[up]
            if (tmp_dir / "src").is_dir() and (tmp_dir / "tests").is_dir():
                break
        if tmp_dir:
            self._repo_root_dir = tmp_dir
        else:
            raise ValueError(f"can't figure out the root of the repo from {self._test_file_path}")
        self._tests_dir = self._repo_root_dir / "tests"
        self._examples_dir = self._repo_root_dir / "examples"
        self._src_dir = self._repo_root_dir / "src"

    @property
    def test_file_path(self):
        return self._test_file_path

    @property
    def test_file_path_str(self):
        return str(self._test_file_path)

    @property
    def test_file_dir(self):
        return self._test_file_dir

    @property
    def test_file_dir_str(self):
        return str(self._test_file_dir)

    @property
    def tests_dir(self):
        return self._tests_dir

    @property
    def tests_dir_str(self):
        return str(self._tests_dir)

    @property
    def examples_dir(self):
        return self._examples_dir

    @property
    def examples_dir_str(self):
        return str(self._examples_dir)

    @property
    def repo_root_dir(self):
        return self._repo_root_dir

    @property
    def repo_root_dir_str(self):
        return str(self._repo_root_dir)

    @property
    def src_dir(self):
        return self._src_dir

    @property
    def src_dir_str(self):
        return str(self._src_dir)

    def get_env(self):
        """
        Return a copy of the ``os.environ`` object that sets up ``PYTHONPATH`` correctly, depending on the test suite
        it's invoked from. This is useful for invoking external programs from the test suite - e.g. distributed
        training.

        It always inserts ``./src`` first, then ``./tests`` or ``./examples`` depending on the test suite type and
        finally the preset ``PYTHONPATH`` if any (all full resolved paths).

        """
        env = os.environ.copy()
        paths = [self.src_dir_str]
        if "/examples" in self.test_file_dir_str:
            paths.append(self.examples_dir_str)
        else:
            paths.append(self.tests_dir_str)
        paths.append(env.get("PYTHONPATH", ""))

        env["PYTHONPATH"] = ":".join(paths)
        return env

    def get_auto_remove_tmp_dir(self, tmp_dir=None, after=True, before=False):
        """
        Args:
            tmp_dir (:obj:`string`, `optional`):
                use this path, if None a unique path will be assigned
            before (:obj:`bool`, `optional`, defaults to :obj:`False`):
                if `True` and tmp dir already exists make sure to empty it right away
            after (:obj:`bool`, `optional`, defaults to :obj:`True`):
                delete the tmp dir at the end of the test

        Returns:
            tmp_dir(:obj:`string`): either the same value as passed via `tmp_dir` or the path to the auto-created tmp
            dir
        """
        if tmp_dir is not None:
            # using provided path
            path = Path(tmp_dir).resolve()

            # to avoid nuking parts of the filesystem, only relative paths are allowed
            if not tmp_dir.startswith("./"):
                raise ValueError(
                    f"`tmp_dir` can only be a relative path, i.e. `./some/path`, but received `{tmp_dir}`"
                )

            # ensure the dir is empty to start with
            if before is True and path.exists():
                shutil.rmtree(tmp_dir, ignore_errors=True)

            path.mkdir(parents=True, exist_ok=True)

        else:
            # using unique tmp dir (always empty, regardless of `before`)
            tmp_dir = tempfile.mkdtemp()

        if after is True:
            # register for deletion
            self.teardown_tmp_dirs.append(tmp_dir)

        return tmp_dir

    def tearDown(self):
        # remove registered temp dirs
        for path in self.teardown_tmp_dirs:
            shutil.rmtree(path, ignore_errors=True)
        self.teardown_tmp_dirs = []


def mockenv(**kwargs):
    """
    this is a convenience wrapper, that allows this:

    @mockenv(RUN_SLOW=True, USE_TF=False) def test_something(): run_slow = os.getenv("RUN_SLOW", False) use_tf =
    os.getenv("USE_TF", False)
    """
    return unittest.mock.patch.dict(os.environ, kwargs)


def pytest_terminal_summary_main(tr, id):
    """
    Generate multiple reports at the end of test suite run - each report goes into a dedicated file in the current
    directory. The report files are prefixed with the test suite name.

    This function emulates --duration and -rA pytest arguments.

    This function is to be called from `conftest.py` via `pytest_terminal_summary` wrapper that has to be defined
    there.

    Args:
    - tr: `terminalreporter` passed from `conftest.py`
    - id: unique id like `tests` or `examples` that will be incorporated into the final reports
      filenames - this is needed as some jobs have multiple runs of pytest, so we can't have them overwrite each other.

    NB: this functions taps into a private _pytest API and while unlikely, it could break should
    pytest do internal changes - also it calls default internal methods of terminalreporter which
    can be hijacked by various `pytest-` plugins and interfere.

    """
    from _pytest.config import create_terminal_writer

    if not len(id):
        id = "tests"

    config = tr.config
    orig_writer = config.get_terminal_writer()
    orig_tbstyle = config.option.tbstyle
    orig_reportchars = tr.reportchars

    report_files = dict(
        durations="durations",
        short_summary="short_summary",
        summary_errors="errors",
        summary_failures="failures",
        summary_warnings="warnings",
        summary_passes="passes",
        summary_stats="stats",
    )
    dir = "reports"
    Path(dir).mkdir(parents=True, exist_ok=True)
    report_files.update((k, f"{dir}/report_{id}_{v}.txt") for k, v in report_files.items())

    # custom durations report
    # note: there is no need to call pytest --durations=XX to get this separate report
    # adapted from https://github.com/pytest-dev/pytest/blob/897f151e/src/_pytest/runner.py#L66
    dlist = []
    for replist in tr.stats.values():
        for rep in replist:
            if hasattr(rep, "duration"):
                dlist.append(rep)
    if dlist:
        dlist.sort(key=lambda x: x.duration, reverse=True)
        with open(report_files["durations"], "w") as f:
            durations_min = 0.05  # sec
            f.write("slowest durations\n")
            for i, rep in enumerate(dlist):
                if rep.duration < durations_min:
                    f.write(f"{len(dlist)-i} durations < {durations_min} secs were omitted")
                    break
                f.write(f"{rep.duration:02.2f}s {rep.when:<8} {rep.nodeid}\n")

    # use ready-made report funcs, we are just hijacking the filehandle to log to a dedicated file each
    # adapted from https://github.com/pytest-dev/pytest/blob/897f151e/src/_pytest/terminal.py#L814
    # note: some pytest plugins may interfere by hijacking the default `terminalreporter` (e.g.
    # pytest-instafail does that)
    tr.reportchars = "wPpsxXEf"  # emulate -rA (used in summary_passes() and short_test_summary())
    config.option.tbstyle = "auto"
    with open(report_files["summary_failures"], "w") as f:
        tr._tw = create_terminal_writer(config, f)
        tr.summary_failures()

    with open(report_files["summary_errors"], "w") as f:
        tr._tw = create_terminal_writer(config, f)
        tr.summary_errors()

    with open(report_files["summary_warnings"], "w") as f:
        tr._tw = create_terminal_writer(config, f)
        tr.summary_warnings()  # normal warnings
        tr.summary_warnings()  # final warnings

    with open(report_files["summary_passes"], "w") as f:
        tr._tw = create_terminal_writer(config, f)
        tr.summary_passes()

    with open(report_files["short_summary"], "w") as f:
        tr._tw = create_terminal_writer(config, f)
        tr.short_test_summary()

    with open(report_files["summary_stats"], "w") as f:
        tr._tw = create_terminal_writer(config, f)
        tr.summary_stats()

    # restore:
    tr._tw = orig_writer
    tr.reportchars = orig_reportchars
    config.option.tbstyle = orig_tbstyle


# the following code deals with async io between processes

# adapted from https://stackoverflow.com/a/59041913/9201239
import asyncio  # noqa


class _RunOutput:
    def __init__(self, returncode, stdout, stderr):
        self.returncode = returncode
        self.stdout = stdout
        self.stderr = stderr


async def _read_stream(stream, callback):
    while True:
        line = await stream.readline()
        if line:
            callback(line)
        else:
            break


async def _stream_subprocess(cmd, env=None, stdin=None, timeout=None, quiet=False, echo=False) -> _RunOutput:
    if echo:
        print("\nRunning: ", " ".join(cmd))

    p = await asyncio.create_subprocess_exec(
        cmd[0],
        *cmd[1:],
        stdin=stdin,
        stdout=asyncio.subprocess.PIPE,
        stderr=asyncio.subprocess.PIPE,
        env=env,
    )

    # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
    # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
    #
    # If it starts hanging, will need to switch to the following code. The problem is that no data
    # will be seen until it's done and if it hangs for example there will be no debug info.
    # out, err = await p.communicate()
    # return _RunOutput(p.returncode, out, err)

    out = []
    err = []

    def tee(line, sink, pipe, label=""):
        line = line.decode("utf-8").rstrip()
        sink.append(line)
        if not quiet:
            print(label, line, file=pipe)

    # XXX: the timeout doesn't seem to make any difference here
    await asyncio.wait(
        [
            _read_stream(p.stdout, lambda l: tee(l, out, sys.stdout)),
            _read_stream(p.stderr, lambda l: tee(l, err, sys.stderr, label="stderr:")),
        ],
        timeout=timeout,
    )
    return _RunOutput(await p.wait(), out, err)


def execute_subprocess_async(cmd, env=None, stdin=None, timeout=180, quiet=False, echo=True) -> _RunOutput:

    loop = asyncio.get_event_loop()
    result = loop.run_until_complete(
        _stream_subprocess(cmd, env=env, stdin=stdin, timeout=timeout, quiet=quiet, echo=echo)
    )

    cmd_str = " ".join(cmd)
    if result.returncode > 0:
        raise RuntimeError(
            f"'{cmd_str}' failed with returncode {result.returncode} - see the `stderr:` messages from above for details."
        )
    if not result.stdout:
        raise RuntimeError(f"'{cmd_str}' produced no output.")

    return result
