# -*- coding: utf-8 -*-

import os
import pytest
from operator import add, mul
import subprocess
import sys
import time

from toolz import merge

import dask
from dask import delayed
from dask.base import (compute, tokenize, normalize_token, normalize_function,
                       visualize, persist, function_cache, is_dask_collection,
                       DaskMethodsMixin, optimize, unpack_collections,
                       named_schedulers, get_scheduler)
from dask.delayed import Delayed
from dask.utils import tmpdir, tmpfile, ignoring
from dask.utils_test import inc, dec
from dask.compatibility import long, unicode, PY2
from dask.diagnostics import Profiler


def import_or_none(path):
    with ignoring(BaseException):
        return pytest.importorskip(path)
    return None


tz = pytest.importorskip('toolz')
da = import_or_none('dask.array')
db = import_or_none('dask.bag')
dd = import_or_none('dask.dataframe')
np = import_or_none('numpy')
sp = import_or_none('scipy.sparse')
pd = import_or_none('pandas')


def f1(a, b, c=1):
    pass


def f2(a, b=1, c=2):
    pass


def f3(a):
    pass


def test_normalize_function():

    assert normalize_function(f2)

    assert normalize_function(lambda a: a)

    assert (normalize_function(tz.partial(f2, b=2)) ==
            normalize_function(tz.partial(f2, b=2)))

    assert (normalize_function(tz.partial(f2, b=2)) !=
            normalize_function(tz.partial(f2, b=3)))

    assert (normalize_function(tz.partial(f1, b=2)) !=
            normalize_function(tz.partial(f2, b=2)))

    assert (normalize_function(tz.compose(f2, f3)) ==
            normalize_function(tz.compose(f2, f3)))

    assert (normalize_function(tz.compose(f2, f3)) !=
            normalize_function(tz.compose(f2, f1)))

    assert normalize_function(tz.curry(f2)) == normalize_function(tz.curry(f2))
    assert normalize_function(tz.curry(f2)) != normalize_function(tz.curry(f1))
    assert (normalize_function(tz.curry(f2, b=1)) ==
            normalize_function(tz.curry(f2, b=1)))
    assert (normalize_function(tz.curry(f2, b=1)) !=
            normalize_function(tz.curry(f2, b=2)))


def test_tokenize():
    a = (1, 2, 3)
    assert isinstance(tokenize(a), (str, bytes))


@pytest.mark.skipif('not np')
def test_tokenize_numpy_array_consistent_on_values():
    assert (tokenize(np.random.RandomState(1234).random_sample(1000)) ==
            tokenize(np.random.RandomState(1234).random_sample(1000)))


@pytest.mark.skipif('not np')
def test_tokenize_numpy_array_supports_uneven_sizes():
    tokenize(np.random.random(7).astype(dtype='i2'))


@pytest.mark.skipif('not np')
def test_tokenize_discontiguous_numpy_array():
    tokenize(np.random.random(8)[::2])


@pytest.mark.skipif('not np')
def test_tokenize_numpy_datetime():
    tokenize(np.array(['2000-01-01T12:00:00'], dtype='M8[ns]'))


@pytest.mark.skipif('not np')
def test_tokenize_numpy_scalar():
    assert tokenize(np.array(1.0, dtype='f8')) == tokenize(np.array(1.0, dtype='f8'))
    assert (tokenize(np.array([(1, 2)], dtype=[('a', 'i4'), ('b', 'i8')])[0]) ==
            tokenize(np.array([(1, 2)], dtype=[('a', 'i4'), ('b', 'i8')])[0]))


@pytest.mark.skipif('not np')
def test_tokenize_numpy_array_on_object_dtype():
    assert (tokenize(np.array(['a', 'aa', 'aaa'], dtype=object)) ==
            tokenize(np.array(['a', 'aa', 'aaa'], dtype=object)))
    assert (tokenize(np.array(['a', None, 'aaa'], dtype=object)) ==
            tokenize(np.array(['a', None, 'aaa'], dtype=object)))
    assert (tokenize(np.array([(1, 'a'), (1, None), (1, 'aaa')], dtype=object)) ==
            tokenize(np.array([(1, 'a'), (1, None), (1, 'aaa')], dtype=object)))
    if PY2:
        assert (tokenize(np.array([unicode("Rebeca Alón", encoding="utf-8")], dtype=object)) ==
                tokenize(np.array([unicode("Rebeca Alón", encoding="utf-8")], dtype=object)))


@pytest.mark.skipif('not np')
def test_tokenize_numpy_memmap():
    with tmpfile('.npy') as fn:
        x = np.arange(5)
        np.save(fn, x)
        y = tokenize(np.load(fn, mmap_mode='r'))

    with tmpfile('.npy') as fn:
        x = np.arange(5)
        np.save(fn, x)
        z = tokenize(np.load(fn, mmap_mode='r'))

    assert y != z

    with tmpfile('.npy') as fn:
        x = np.random.normal(size=(10, 10))
        np.save(fn, x)
        mm = np.load(fn, mmap_mode='r')
        mm2 = np.load(fn, mmap_mode='r')
        a = tokenize(mm[0, :])
        b = tokenize(mm[1, :])
        c = tokenize(mm[0:3, :])
        d = tokenize(mm[:, 0])
        assert len(set([a, b, c, d])) == 4
        assert tokenize(mm) == tokenize(mm2)
        assert tokenize(mm[1, :]) == tokenize(mm2[1, :])


@pytest.mark.skipif('not np')
def test_tokenize_numpy_memmap_no_filename():
    # GH 1562:
    with tmpfile('.npy') as fn1, tmpfile('.npy') as fn2:
        x = np.arange(5)
        np.save(fn1, x)
        np.save(fn2, x)

        a = np.load(fn1, mmap_mode='r')
        b = a + a
        assert tokenize(b) == tokenize(b)


@pytest.mark.skipif('not np')
def test_tokenize_numpy_ufunc_consistent():
    assert tokenize(np.sin) == '02106e2c67daf452fb480d264e0dac21'
    assert tokenize(np.cos) == 'c99e52e912e4379882a9a4b387957a0b'

    # Make a ufunc that isn't in the numpy namespace. Similar to
    # any found in other packages.
    inc = np.frompyfunc(lambda x: x + 1, 1, 1)
    assert tokenize(inc) == tokenize(inc)


def test_tokenize_partial_func_args_kwargs_consistent():
    f = tz.partial(f3, f2, c=f1)
    res = normalize_token(f)
    sol = (b'cdask.tests.test_base\nf3\np0\n.',
           (b'cdask.tests.test_base\nf2\np0\n.',),
           (('c', b'cdask.tests.test_base\nf1\np0\n.'),))
    assert res == sol


def test_normalize_base():
    for i in [1, long(1), 1.1, '1', slice(1, 2, 3)]:
        assert normalize_token(i) is i


@pytest.mark.skipif('not pd')
def test_tokenize_pandas():
    a = pd.DataFrame({'x': [1, 2, 3], 'y': ['4', 'asd', None]}, index=[1, 2, 3])
    b = pd.DataFrame({'x': [1, 2, 3], 'y': ['4', 'asd', None]}, index=[1, 2, 3])

    assert tokenize(a) == tokenize(b)
    b.index.name = 'foo'
    assert tokenize(a) != tokenize(b)

    a = pd.DataFrame({'x': [1, 2, 3], 'y': ['a', 'b', 'a']})
    b = pd.DataFrame({'x': [1, 2, 3], 'y': ['a', 'b', 'a']})
    a['z'] = a.y.astype('category')
    assert tokenize(a) != tokenize(b)
    b['z'] = a.y.astype('category')
    assert tokenize(a) == tokenize(b)


def test_tokenize_kwargs():
    assert tokenize(5, x=1) == tokenize(5, x=1)
    assert tokenize(5) != tokenize(5, x=1)
    assert tokenize(5, x=1) != tokenize(5, x=2)
    assert tokenize(5, x=1) != tokenize(5, y=1)


def test_tokenize_same_repr():
    class Foo(object):

        def __init__(self, x):
            self.x = x

        def __repr__(self):
            return 'a foo'

    assert tokenize(Foo(1)) != tokenize(Foo(2))


def test_tokenize_method():
    class Foo(object):
        def __init__(self, x):
            self.x = x

        def __dask_tokenize__(self):
            return self.x

    a, b = Foo(1), Foo(2)
    assert tokenize(a) == tokenize(a)
    assert tokenize(a) != tokenize(b)

    # dispatch takes precedence
    before = tokenize(a)
    normalize_token.register(Foo, lambda self: self.x + 1)
    after = tokenize(a)
    assert before != after


@pytest.mark.skipif('not np')
def test_tokenize_sequences():
    assert tokenize([1]) != tokenize([2])
    assert tokenize([1]) != tokenize((1,))
    assert tokenize([1]) == tokenize([1])

    x = np.arange(2000)  # long enough to drop information in repr
    y = np.arange(2000)
    y[1000] = 0  # middle isn't printed in repr
    assert tokenize([x]) != tokenize([y])


def test_tokenize_dict():
    assert tokenize({'x': 1, 1: 'x'}) == tokenize({'x': 1, 1: 'x'})


def test_tokenize_set():
    assert tokenize({1, 2, 'x', (1, 'x')}) == tokenize({1, 2, 'x', (1, 'x')})


def test_tokenize_ordered_dict():
    with ignoring(ImportError):
        from collections import OrderedDict
        a = OrderedDict([('a', 1), ('b', 2)])
        b = OrderedDict([('a', 1), ('b', 2)])
        c = OrderedDict([('b', 2), ('a', 1)])

        assert tokenize(a) == tokenize(b)
        assert tokenize(a) != tokenize(c)


@pytest.mark.skipif('not np')
def test_tokenize_object_array_with_nans():
    a = np.array([u'foo', u'Jos\xe9', np.nan], dtype='O')
    assert tokenize(a) == tokenize(a)


@pytest.mark.parametrize('x', [1, True, 'a', b'a', 1.0, 1j, 1.0j,
                               [], (), {}, None, str, int])
def test_tokenize_base_types(x):
    assert tokenize(x) == tokenize(x), x


@pytest.mark.skipif('not np')
def test_tokenize_numpy_matrix():
    rng = np.random.RandomState(1234)
    a = np.asmatrix(rng.rand(100))
    b = a.copy()
    assert tokenize(a) == tokenize(b)

    b[:10] = 1
    assert tokenize(a) != tokenize(b)


@pytest.mark.skipif('not sp')
@pytest.mark.parametrize('cls_name',
                         ('dia', 'bsr', 'coo', 'csc', 'csr', 'dok', 'lil'))
def test_tokenize_dense_sparse_array(cls_name):
    rng = np.random.RandomState(1234)

    with pytest.warns(None):
        # ignore scipy.sparse.SparseEfficiencyWarning
        a = sp.rand(10, 10000, random_state=rng).asformat(cls_name)
    b = a.copy()

    assert tokenize(a) == tokenize(b)

    # modifying the data values
    if hasattr(b, 'data'):
        b.data[:10] = 1
    elif cls_name == 'dok':
        b[3, 3] = 1
    else:
        raise ValueError

    assert tokenize(a) != tokenize(b)

    # modifying the data indices
    with pytest.warns(None):
        b = a.copy().asformat('coo')
        b.row[:10] = np.arange(10)
        b = b.asformat(cls_name)
    assert tokenize(a) != tokenize(b)


def test_is_dask_collection():
    class DummyCollection(object):
        def __init__(self, dsk=None):
            self.dask = dsk

        def __dask_graph__(self):
            return self.dask

    x = delayed(1) + 2
    assert is_dask_collection(x)
    assert not is_dask_collection(2)
    assert is_dask_collection(DummyCollection({}))
    assert not is_dask_collection(DummyCollection())
    assert not is_dask_collection(DummyCollection)


try:
    import dataclasses
    # Avoid @dataclass decorator as Python < 3.7 fail to interpret the type hints
    ADataClass = dataclasses.make_dataclass('ADataClass', [('a', int)])
except ImportError:
    dataclasses = None


def test_unpack_collections():
    a = delayed(1) + 5
    b = a + 1
    c = a + 2

    def build(a, b, c, iterator):
        t = (a, b,                  # Top-level collections
                {'a': a,            # dict
                 a: b,              # collections as keys
                 'b': [1, 2, [b]],  # list
                 'c': 10,           # other builtins pass through unchanged
                 'd': (c, 2),       # tuple
                 'e': {a, 2, 3}},   # set
                iterator)           # Iterator

        if dataclasses is not None:
            t[2]['f'] = ADataClass(a=a)

        return t

    args = build(a, b, c, (i for i in [a, b, c]))

    collections, repack = unpack_collections(*args)
    assert len(collections) == 3

    # Replace collections with `'~a'` strings
    result = repack(['~a', '~b', '~c'])
    sol = build('~a', '~b', '~c', ['~a', '~b', '~c'])
    assert result == sol

    # traverse=False
    collections, repack = unpack_collections(*args, traverse=False)
    assert len(collections) == 2  # just a and b
    assert repack(collections) == args

    # No collections
    collections, repack = unpack_collections(1, 2, {'a': 3})
    assert not collections
    assert repack(collections) == (1, 2, {'a': 3})

    # Result that looks like a task
    def fail(*args):
        raise ValueError("Shouldn't have been called")

    collections, repack = unpack_collections(a, (fail, 1), [(fail, 2, 3)],
                                             traverse=False)
    repack(collections)  # Smoketest task literals
    repack([(fail, 1)])  # Smoketest results that look like tasks


class Tuple(DaskMethodsMixin):
    __slots__ = ('_dask', '_keys')
    __dask_scheduler__ = staticmethod(dask.threaded.get)

    def __init__(self, dsk, keys):
        self._dask = dsk
        self._keys = keys

    def __add__(self, other):
        if isinstance(other, Tuple):
            return Tuple(merge(self._dask, other._dask),
                         self._keys + other._keys)
        return NotImplemented

    def __dask_graph__(self):
        return self._dask

    def __dask_keys__(self):
        return self._keys

    def __dask_tokenize__(self):
        return self._keys

    def __dask_postcompute__(self):
        return tuple, ()

    def __dask_postpersist__(self):
        return Tuple, (self._keys,)


def test_custom_collection():
    dsk = {'a': 1, 'b': 2}
    dsk2 = {'c': (add, 'a', 'b'),
            'd': (add, 'c', 1)}
    dsk2.update(dsk)
    dsk3 = {'e': (add, 'a', 4),
            'f': (inc, 'e')}
    dsk3.update(dsk)

    x = Tuple(dsk, ['a', 'b'])
    y = Tuple(dsk2, ['c', 'd'])
    z = Tuple(dsk3, ['e', 'f'])

    # __slots__ defined on base mixin class propogates
    with pytest.raises(AttributeError):
        x.foo = 1

    # is_dask_collection
    assert is_dask_collection(x)

    # tokenize
    assert tokenize(x) == tokenize(x)
    assert tokenize(x) != tokenize(y)

    # compute
    assert x.compute() == (1, 2)
    assert dask.compute(x, [y, z]) == ((1, 2), [(3, 4), (5, 6)])
    t = x + y + z
    assert t.compute() == (1, 2, 3, 4, 5, 6)

    # persist
    t2 = t.persist()
    assert isinstance(t2, Tuple)
    assert t2._dask == dict(zip('abcdef', range(1, 7)))
    assert t2.compute() == (1, 2, 3, 4, 5, 6)
    x2, y2, z2 = dask.persist(x, y, z)
    t3 = x2 + y2 + z2
    assert t2._dask == t3._dask


@pytest.mark.skipif('not db')
def test_compute_no_opt():
    # Bag does `fuse` by default. Test that with `optimize_graph=False` that
    # doesn't get called. We check this by using a callback to track the keys
    # that are computed.
    from dask.callbacks import Callback
    b = db.from_sequence(range(100), npartitions=4)
    add1 = tz.partial(add, 1)
    mul2 = tz.partial(mul, 2)
    o = b.map(add1).map(mul2)
    # Check that with the kwarg, the optimization doesn't happen
    keys = []
    with Callback(pretask=lambda key, *args: keys.append(key)):
        o.compute(scheduler='single-threaded', optimize_graph=False)
    assert len([k for k in keys if '-mul-' in k[0]]) == 4
    assert len([k for k in keys if '-add-' in k[0]]) == 4
    # Check that without the kwarg, the optimization does happen
    keys = []
    with Callback(pretask=lambda key, *args: keys.append(key)):
        o.compute(scheduler='single-threaded')
    # Names of fused tasks have been merged, and the original key is an alias.
    # Otherwise, the lengths below would be 4 and 0.
    assert len([k for k in keys if '-mul-' in k[0]]) == 8
    assert len([k for k in keys if '-add-' in k[0]]) == 4
    assert len([k for k in keys if 'add-map-mul' in k[0]]) == 4  # See? Renamed


@pytest.mark.skipif('not da')
def test_compute_array():
    arr = np.arange(100).reshape((10, 10))
    darr = da.from_array(arr, chunks=(5, 5))
    darr1 = darr + 1
    darr2 = darr + 2
    out1, out2 = compute(darr1, darr2)
    assert np.allclose(out1, arr + 1)
    assert np.allclose(out2, arr + 2)


@pytest.mark.skipif('not da')
def test_persist_array():
    from dask.array.utils import assert_eq
    arr = np.arange(100).reshape((10, 10))
    x = da.from_array(arr, chunks=(5, 5))
    x = (x + 1) - x.mean(axis=0)
    y = x.persist()

    assert_eq(x, y)
    assert set(y.dask).issubset(x.dask)
    assert len(y.dask) == y.npartitions


@pytest.mark.skipif('not dd')
def test_compute_dataframe():
    df = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [5, 5, 3, 3]})
    ddf = dd.from_pandas(df, npartitions=2)
    ddf1 = ddf.a + 1
    ddf2 = ddf.a + ddf.b
    out1, out2 = compute(ddf1, ddf2)
    pd.util.testing.assert_series_equal(out1, df.a + 1)
    pd.util.testing.assert_series_equal(out2, df.a + df.b)


@pytest.mark.skipif('not dd or not da')
def test_compute_array_dataframe():
    arr = np.arange(100).reshape((10, 10))
    darr = da.from_array(arr, chunks=(5, 5)) + 1
    df = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [5, 5, 3, 3]})
    ddf = dd.from_pandas(df, npartitions=2).a + 2
    arr_out, df_out = compute(darr, ddf)
    assert np.allclose(arr_out, arr + 1)
    pd.util.testing.assert_series_equal(df_out, df.a + 2)


@pytest.mark.skipif('not da or not db')
def test_compute_array_bag():
    x = da.arange(5, chunks=2)
    b = db.from_sequence([1, 2, 3])

    pytest.raises(ValueError, lambda: compute(x, b))

    xx, bb = compute(x, b, scheduler='single-threaded')
    assert np.allclose(xx, np.arange(5))
    assert bb == [1, 2, 3]


@pytest.mark.skipif('not da')
def test_compute_with_literal():
    x = da.arange(5, chunks=2)
    y = 10

    xx, yy = compute(x, y)
    assert (xx == x.compute()).all()
    assert yy == y

    assert compute(5) == (5,)


def test_compute_nested():
    a = delayed(1) + 5
    b = a + 1
    c = a + 2
    assert (compute({'a': a, 'b': [1, 2, b]}, (c, 2)) ==
            ({'a': 6, 'b': [1, 2, 7]}, (8, 2)))

    res = compute([a, b], c, traverse=False)
    assert res[0][0] is a
    assert res[0][1] is b
    assert res[1] == 8


@pytest.mark.skipif('not da')
@pytest.mark.skipif(sys.flags.optimize,
                    reason="graphviz exception with Python -OO flag")
def test_visualize():
    pytest.importorskip('graphviz')
    with tmpdir() as d:
        x = da.arange(5, chunks=2)
        x.visualize(filename=os.path.join(d, 'mydask'))
        assert os.path.exists(os.path.join(d, 'mydask.png'))

        x.visualize(filename=os.path.join(d, 'mydask.pdf'))
        assert os.path.exists(os.path.join(d, 'mydask.pdf'))

        visualize(x, 1, 2, filename=os.path.join(d, 'mydask.png'))
        assert os.path.exists(os.path.join(d, 'mydask.png'))

        dsk = {'a': 1, 'b': (add, 'a', 2), 'c': (mul, 'a', 1)}
        visualize(x, dsk, filename=os.path.join(d, 'mydask.png'))
        assert os.path.exists(os.path.join(d, 'mydask.png'))

        x = Tuple(dsk, ['a', 'b', 'c'])
        visualize(x, filename=os.path.join(d, 'mydask.png'))
        assert os.path.exists(os.path.join(d, 'mydask.png'))


@pytest.mark.skipif('not da')
@pytest.mark.skipif(sys.flags.optimize,
                    reason="graphviz exception with Python -OO flag")
def test_visualize_order():
    pytest.importorskip('matplotlib')
    x = da.arange(5, chunks=2)
    with tmpfile(extension='dot') as fn:
        x.visualize(color='order', filename=fn, cmap='RdBu')
        with open(fn) as f:
            text = f.read()
        assert 'color="#' in text


def test_use_cloudpickle_to_tokenize_functions_in__main__():
    import sys
    from textwrap import dedent

    defn = dedent("""
    def inc():
        return x
    """)

    __main__ = sys.modules['__main__']
    exec(compile(defn, '<test>', 'exec'), __main__.__dict__)
    f = __main__.inc

    t = normalize_token(f)
    assert b'cloudpickle' in t


def inc_to_dec(dsk, keys):
    dsk = dict(dsk)
    for key in dsk:
        if dsk[key][0] == inc:
            dsk[key] = (dec,) + dsk[key][1:]
    return dsk


def test_optimizations_keyword():
    x = dask.delayed(inc)(1)
    assert x.compute() == 2

    with dask.config.set(optimizations=[inc_to_dec]):
        assert x.compute() == 0

    assert x.compute() == 2


def test_optimize():
    x = dask.delayed(inc)(1)
    y = dask.delayed(inc)(x)
    z = x + y

    x2, y2, z2, constant = optimize(x, y, z, 1)
    assert constant == 1

    # Same graphs for each
    dsk = dict(x2.dask)
    assert dict(y2.dask) == dsk
    assert dict(z2.dask) == dsk

    # Computationally equivalent
    assert dask.compute(x2, y2, z2) == dask.compute(x, y, z)

    # Applying optimizations before compute and during compute gives
    # same results. Shows optimizations are occurring.
    sols = dask.compute(x, y, z, optimizations=[inc_to_dec])
    x3, y3, z3 = optimize(x, y, z, optimizations=[inc_to_dec])
    assert dask.compute(x3, y3, z3) == sols

    # Optimize respects global optimizations as well
    with dask.config.set(optimizations=[inc_to_dec]):
        x4, y4, z4 = optimize(x, y, z)
    for a, b in zip([x3, y3, z3], [x4, y4, z4]):
        assert dict(a.dask) == dict(b.dask)


def test_optimize_nested():
    a = dask.delayed(inc)(1)
    b = dask.delayed(inc)(a)
    c = a + b

    result = optimize({'a': a, 'b': [1, 2, b]}, (c, 2))

    a2 = result[0]['a']
    b2 = result[0]['b'][2]
    c2 = result[1][0]

    assert isinstance(a2, Delayed)
    assert isinstance(b2, Delayed)
    assert isinstance(c2, Delayed)
    assert dict(a2.dask) == dict(b2.dask) == dict(c2.dask)
    assert compute(*result) == ({'a': 2, 'b': [1, 2, 3]}, (5, 2))

    res = optimize([a, b], c, traverse=False)
    assert res[0][0] is a
    assert res[0][1] is b
    assert res[1].compute() == 5


def test_default_imports():
    """
    Startup time: `import dask` should not import too many modules.
    """
    code = """if 1:
        import dask
        import sys

        print(sorted(sys.modules))
        """

    out = subprocess.check_output([sys.executable, '-c', code])
    modules = set(eval(out.decode()))
    assert 'dask' in modules
    blacklist = ['dask.array', 'dask.dataframe', 'numpy', 'pandas',
                 'partd', 's3fs', 'distributed']
    for mod in blacklist:
        assert mod not in modules


def test_persist_literals():
    assert persist(1, 2, 3) == (1, 2, 3)


def test_persist_nested():
    a = delayed(1) + 5
    b = a + 1
    c = a + 2
    result = persist({'a': a, 'b': [1, 2, b]}, (c, 2))
    assert isinstance(result[0]['a'], Delayed)
    assert isinstance(result[0]['b'][2], Delayed)
    assert isinstance(result[1][0], Delayed)
    assert compute(*result) == ({'a': 6, 'b': [1, 2, 7]}, (8, 2))

    res = persist([a, b], c, traverse=False)
    assert res[0][0] is a
    assert res[0][1] is b
    assert res[1].compute() == 8


def test_persist_delayed():
    x1 = delayed(1)
    x2 = delayed(inc)(x1)
    x3 = delayed(inc)(x2)

    xx, = persist(x3)
    assert isinstance(xx, Delayed)
    assert xx.key == x3.key
    assert len(xx.dask) == 1

    assert x3.compute() == xx.compute()


@pytest.mark.skipif('not da or not db')
def test_persist_array_bag():
    x = da.arange(5, chunks=2) + 1
    b = db.from_sequence([1, 2, 3]).map(inc)

    with pytest.raises(ValueError):
        persist(x, b)

    xx, bb = persist(x, b, scheduler='single-threaded')

    assert isinstance(xx, da.Array)
    assert isinstance(bb, db.Bag)

    assert xx.name == x.name
    assert bb.name == b.name
    assert len(xx.dask) == xx.npartitions < len(x.dask)
    assert len(bb.dask) == bb.npartitions < len(b.dask)

    assert np.allclose(x, xx)
    assert list(b) == list(bb)


def test_normalize_function_limited_size():
    for i in range(1000):
        normalize_function(lambda x: x)

    assert 50 < len(function_cache) < 600


def test_optimize_globals():
    da = pytest.importorskip('dask.array')
    db = pytest.importorskip('dask.bag')

    x = da.ones(10, chunks=(5,))

    def optimize_double(dsk, keys):
        return {k: (mul, 2, v) for k, v in dsk.items()}

    from dask.array.utils import assert_eq

    assert_eq(x + 1, np.ones(10) + 1)

    with dask.config.set(array_optimize=optimize_double):
        assert_eq(x + 1, (np.ones(10) * 2 + 1) * 2)

    assert_eq(x + 1, np.ones(10) + 1)

    b = db.range(10, npartitions=2)

    with dask.config.set(array_optimize=optimize_double):
        xx, bb = dask.compute(x + 1, b.map(inc), scheduler='single-threaded')
        assert_eq(xx, (np.ones(10) * 2 + 1) * 2)


def test_optimize_None():
    da = pytest.importorskip('dask.array')

    x = da.ones(10, chunks=(5,))
    y = x[:9][1:8][::2] + 1  # normally these slices would be fused

    def my_get(dsk, keys):
        assert dsk == dict(y.dask)  # but they aren't
        return dask.get(dsk, keys)

    with dask.config.set(array_optimize=None, scheduler=my_get):
        y.compute()


def test_scheduler_keyword():
    def schedule(dsk, keys, **kwargs):
        return [[123]]

    named_schedulers['foo'] = schedule

    x = delayed(inc)(1)

    try:
        assert x.compute() == 2
        assert x.compute(scheduler='foo') == 123

        with dask.config.set(scheduler='foo'):
            assert x.compute() == 123
        assert x.compute() == 2

        with dask.config.set(scheduler='foo'):
            assert x.compute(scheduler='threads') == 2
    finally:
        del named_schedulers['foo']


def test_raise_get_keyword():
    x = delayed(inc)(1)

    with pytest.raises(TypeError) as info:
        x.compute(get=dask.get)

    assert 'scheduler=' in str(info.value)


def test_get_scheduler():
    assert get_scheduler() is None
    assert get_scheduler(scheduler='threads') is dask.threaded.get
    assert get_scheduler(scheduler='sync') is dask.local.get_sync
    with dask.config.set(scheduler='threads'):
        assert get_scheduler(scheduler='threads') is dask.threaded.get
    assert get_scheduler() is None


def test_callable_scheduler():
    called = [False]

    def get(dsk, keys, *args, **kwargs):
        called[0] = True
        return dask.get(dsk, keys)

    assert delayed(lambda: 1)().compute(scheduler=get) == 1
    assert called[0]


@pytest.mark.parametrize('scheduler', ['threads', 'processes'])
def test_num_workers_config(scheduler):
    # Regression test for issue #4082

    @delayed
    def f(x):
        time.sleep(0.5)
        return x

    a = [f(i) for i in range(5)]
    num_workers = 3
    with dask.config.set(num_workers=num_workers), Profiler() as prof:
        a = compute(*a, scheduler=scheduler)

    workers = {i.worker_id for i in prof.results}

    assert len(workers) == num_workers
