content: import pytest
  pytest.importorskip('numpy')
  
  import numpy as np
  
  import dask.array as da
  from dask.array.utils import assert_eq, same_keys
  
  
  def test_percentile():
      d = da.ones((16,), chunks=(4,))
      assert_eq(da.percentile(d, [0, 50, 100]),
                np.array([1, 1, 1], dtype=d.dtype))
  
      x = np.array([0, 0, 5, 5, 5, 5, 20, 20])
      d = da.from_array(x, chunks=(3,))
      result = da.percentile(d, [0, 50, 100])
      assert_eq(da.percentile(d, [0, 50, 100]),
                np.array([0, 5, 20], dtype=result.dtype))
      assert same_keys(da.percentile(d, [0, 50, 100]),
                       da.percentile(d, [0, 50, 100]))
      assert not same_keys(da.percentile(d, [0, 50, 100]),
                           da.percentile(d, [0, 50]))
  
      x = np.array(['a', 'a', 'd', 'd', 'd', 'e'])
      d = da.from_array(x, chunks=(3,))
      assert_eq(da.percentile(d, [0, 50, 100]),
                np.array(['a', 'd', 'e'], dtype=x.dtype))
  
  
  @pytest.mark.skip
  def test_percentile_with_categoricals():
      try:
          import pandas as pd
      except ImportError:
          return
      x0 = pd.Categorical(['Alice', 'Bob', 'Charlie', 'Dennis', 'Alice', 'Alice'])
      x1 = pd.Categorical(['Alice', 'Bob', 'Charlie', 'Dennis', 'Alice', 'Alice'])
  
      dsk = {('x', 0): x0, ('x', 1): x1}
  
      x = da.Array(dsk, 'x', chunks=((6, 6),))
  
      p = da.percentile(x, [50])
      assert (p.compute().categories == x0.categories).all()
      assert (p.compute().codes == [0]).all()
      assert same_keys(da.percentile(x, [50]),
                       da.percentile(x, [50]))
  
  
  def test_percentiles_with_empty_arrays():
      x = da.ones(10, chunks=((5, 0, 5),))
      assert_eq(da.percentile(x, [10, 50, 90]), np.array([1, 1, 1], dtype=x.dtype))
  
  
  @pytest.mark.parametrize('q', [5, 5.0, np.int64(5), np.float64(5)])
  def test_percentiles_with_scaler_percentile(q):
      # Regression test to ensure da.percentile works with scalar percentiles
      # See #3020
      d = da.ones((16,), chunks=(4,))
      assert_eq(da.percentile(d, q), np.array([1], dtype=d.dtype))
  
  
  def test_unknown_chunk_sizes():
      x = da.random.random(1000, chunks=(100,))
      x._chunks = ((np.nan,) * 10,)
  
      result = da.percentile(x, 50).compute()
      assert 0.1 < result < 0.9
  
      a, b = da.percentile(x, [40, 60]).compute()
      assert 0.1 < a < 0.9
      assert 0.1 < b < 0.9
      assert a < b
  