code: import sys
  import operator
  import pytest
  import ctypes
  import gc
  
  import numpy as np
  from numpy.core._rational_tests import rational
  from numpy.core._multiarray_tests import create_custom_field_dtype
  from numpy.testing import (
      assert_, assert_equal, assert_array_equal, assert_raises, HAS_REFCOUNT)
  from numpy.compat import pickle
  from itertools import permutations
  
  def assert_dtype_equal(a, b):
      assert_equal(a, b)
      assert_equal(hash(a), hash(b),
                   "two equivalent types do not hash to the same value !")
  
  def assert_dtype_not_equal(a, b):
      assert_(a != b)
      assert_(hash(a) != hash(b),
              "two different types hash to the same value !")
  
  class TestBuiltin:
      @pytest.mark.parametrize('t', [int, float, complex, np.int32, str, object,
                                     np.compat.unicode])
      def test_run(self, t):
          """Only test hash runs at all."""
          dt = np.dtype(t)
          hash(dt)
  
      @pytest.mark.parametrize('t', [int, float])
      def test_dtype(self, t):
          # Make sure equivalent byte order char hash the same (e.g. < and = on
          # little endian)
          dt = np.dtype(t)
          dt2 = dt.newbyteorder("<")
          dt3 = dt.newbyteorder(">")
          if dt == dt2:
              assert_(dt.byteorder != dt2.byteorder, "bogus test")
              assert_dtype_equal(dt, dt2)
          else:
              assert_(dt.byteorder != dt3.byteorder, "bogus test")
              assert_dtype_equal(dt, dt3)
  
      def test_equivalent_dtype_hashing(self):
          # Make sure equivalent dtypes with different type num hash equal
          uintp = np.dtype(np.uintp)
          if uintp.itemsize == 4:
              left = uintp
              right = np.dtype(np.uint32)
          else:
              left = uintp
              right = np.dtype(np.ulonglong)
          assert_(left == right)
          assert_(hash(left) == hash(right))
  
      def test_invalid_types(self):
          # Make sure invalid type strings raise an error
  
          assert_raises(TypeError, np.dtype, 'O3')
          assert_raises(TypeError, np.dtype, 'O5')
          assert_raises(TypeError, np.dtype, 'O7')
          assert_raises(TypeError, np.dtype, 'b3')
          assert_raises(TypeError, np.dtype, 'h4')
          assert_raises(TypeError, np.dtype, 'I5')
          assert_raises(TypeError, np.dtype, 'e3')
          assert_raises(TypeError, np.dtype, 'f5')
  
          if np.dtype('g').itemsize == 8 or np.dtype('g').itemsize == 16:
              assert_raises(TypeError, np.dtype, 'g12')
          elif np.dtype('g').itemsize == 12:
              assert_raises(TypeError, np.dtype, 'g16')
  
          if np.dtype('l').itemsize == 8:
              assert_raises(TypeError, np.dtype, 'l4')
              assert_raises(TypeError, np.dtype, 'L4')
          else:
              assert_raises(TypeError, np.dtype, 'l8')
              assert_raises(TypeError, np.dtype, 'L8')
  
          if np.dtype('q').itemsize == 8:
              assert_raises(TypeError, np.dtype, 'q4')
              assert_raises(TypeError, np.dtype, 'Q4')
          else:
              assert_raises(TypeError, np.dtype, 'q8')
              assert_raises(TypeError, np.dtype, 'Q8')
  
      @pytest.mark.parametrize("dtype",
               ['Bool', 'Complex32', 'Complex64', 'Float16', 'Float32', 'Float64',
                'Int8', 'Int16', 'Int32', 'Int64', 'Object0', 'Timedelta64',
                'UInt8', 'UInt16', 'UInt32', 'UInt64', 'Void0',
                "Float128", "Complex128"])
      def test_numeric_style_types_are_invalid(self, dtype):
          with assert_raises(TypeError):
              np.dtype(dtype)
  
      @pytest.mark.parametrize(
          'value',
          ['m8', 'M8', 'datetime64', 'timedelta64',
           'i4, (2,3)f8, f4', 'a3, 3u8, (3,4)a10',
           '>f', '<f', '=f', '|f',
          ])
      def test_dtype_bytes_str_equivalence(self, value):
          bytes_value = value.encode('ascii')
          from_bytes = np.dtype(bytes_value)
          from_str = np.dtype(value)
          assert_dtype_equal(from_bytes, from_str)
  
      def test_dtype_from_bytes(self):
          # Empty bytes object
          assert_raises(TypeError, np.dtype, b'')
          # Byte order indicator, but no type
          assert_raises(TypeError, np.dtype, b'|')
  
          # Single character with ordinal < NPY_NTYPES returns
          # type by index into _builtin_descrs
          assert_dtype_equal(np.dtype(bytes([0])), np.dtype('bool'))
          assert_dtype_equal(np.dtype(bytes([17])), np.dtype(object))
  
          # Single character where value is a valid type code
          assert_dtype_equal(np.dtype(b'f'), np.dtype('float32'))
  
          # Bytes with non-ascii values raise errors
          assert_raises(TypeError, np.dtype, b'\xff')
          assert_raises(TypeError, np.dtype, b's\xff')
  
      def test_bad_param(self):
          # Can't give a size that's too small
          assert_raises(ValueError, np.dtype,
                          {'names':['f0', 'f1'],
                           'formats':['i4', 'i1'],
                           'offsets':[0, 4],
                           'itemsize':4})
          # If alignment is enabled, the alignment (4) must divide the itemsize
          assert_raises(ValueError, np.dtype,
                          {'names':['f0', 'f1'],
                           'formats':['i4', 'i1'],
                           'offsets':[0, 4],
                           'itemsize':9}, align=True)
          # If alignment is enabled, the individual fields must be aligned
          assert_raises(ValueError, np.dtype,
                          {'names':['f0', 'f1'],
                           'formats':['i1', 'f4'],
                           'offsets':[0, 2]}, align=True)
  
      def test_field_order_equality(self):
          x = np.dtype({'names': ['A', 'B'],
                        'formats': ['i4', 'f4'],
                        'offsets': [0, 4]})
          y = np.dtype({'names': ['B', 'A'],
                        'formats': ['f4', 'i4'],
                        'offsets': [4, 0]})
          assert_equal(x == y, False)
          # But it is currently an equivalent cast:
          assert np.can_cast(x, y, casting="equiv")
  
  
  class TestRecord:
      def test_equivalent_record(self):
          """Test whether equivalent record dtypes hash the same."""
          a = np.dtype([('yo', int)])
          b = np.dtype([('yo', int)])
          assert_dtype_equal(a, b)
  
      def test_different_names(self):
          # In theory, they may hash the same (collision) ?
          a = np.dtype([('yo', int)])
          b = np.dtype([('ye', int)])
          assert_dtype_not_equal(a, b)
  
      def test_different_titles(self):
          # In theory, they may hash the same (collision) ?
          a = np.dtype({'names': ['r', 'b'],
                        'formats': ['u1', 'u1'],
                        'titles': ['Red pixel', 'Blue pixel']})
          b = np.dtype({'names': ['r', 'b'],
                        'formats': ['u1', 'u1'],
                        'titles': ['RRed pixel', 'Blue pixel']})
          assert_dtype_not_equal(a, b)
  
      @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
      def test_refcount_dictionary_setting(self):
          names = ["name1"]
          formats = ["f8"]
          titles = ["t1"]
          offsets = [0]
          d = dict(names=names, formats=formats, titles=titles, offsets=offsets)
          refcounts = {k: sys.getrefcount(i) for k, i in d.items()}
          np.dtype(d)
          refcounts_new = {k: sys.getrefcount(i) for k, i in d.items()}
          assert refcounts == refcounts_new
  
      def test_mutate(self):
          # Mutating a dtype should reset the cached hash value
          a = np.dtype([('yo', int)])
          b = np.dtype([('yo', int)])
          c = np.dtype([('ye', int)])
          assert_dtype_equal(a, b)
          assert_dtype_not_equal(a, c)
          a.names = ['ye']
          assert_dtype_equal(a, c)
          assert_dtype_not_equal(a, b)
          state = b.__reduce__()[2]
          a.__setstate__(state)
          assert_dtype_equal(a, b)
          assert_dtype_not_equal(a, c)
  
      def test_not_lists(self):
          """Test if an appropriate exception is raised when passing bad values to
          the dtype constructor.
          """
          assert_raises(TypeError, np.dtype,
                        dict(names={'A', 'B'}, formats=['f8', 'i4']))
          assert_raises(TypeError, np.dtype,
                        dict(names=['A', 'B'], formats={'f8', 'i4'}))
  
      def test_aligned_size(self):
          # Check that structured dtypes get padded to an aligned size
          dt = np.dtype('i4, i1', align=True)
          assert_equal(dt.itemsize, 8)
          dt = np.dtype([('f0', 'i4'), ('f1', 'i1')], align=True)
          assert_equal(dt.itemsize, 8)
          dt = np.dtype({'names':['f0', 'f1'],
                         'formats':['i4', 'u1'],
                         'offsets':[0, 4]}, align=True)
          assert_equal(dt.itemsize, 8)
          dt = np.dtype({'f0': ('i4', 0), 'f1':('u1', 4)}, align=True)
          assert_equal(dt.itemsize, 8)
          # Nesting should preserve that alignment
          dt1 = np.dtype([('f0', 'i4'),
                         ('f1', [('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')]),
                         ('f2', 'i1')], align=True)
          assert_equal(dt1.itemsize, 20)
          dt2 = np.dtype({'names':['f0', 'f1', 'f2'],
                         'formats':['i4',
                                    [('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')],
                                    'i1'],
                         'offsets':[0, 4, 16]}, align=True)
          assert_equal(dt2.itemsize, 20)
          dt3 = np.dtype({'f0': ('i4', 0),
                         'f1': ([('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')], 4),
                         'f2': ('i1', 16)}, align=True)
          assert_equal(dt3.itemsize, 20)
          assert_equal(dt1, dt2)
          assert_equal(dt2, dt3)
          # Nesting should preserve packing
          dt1 = np.dtype([('f0', 'i4'),
                         ('f1', [('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')]),
                         ('f2', 'i1')], align=False)
          assert_equal(dt1.itemsize, 11)
          dt2 = np.dtype({'names':['f0', 'f1', 'f2'],
                         'formats':['i4',
                                    [('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')],
                                    'i1'],
                         'offsets':[0, 4, 10]}, align=False)
          assert_equal(dt2.itemsize, 11)
          dt3 = np.dtype({'f0': ('i4', 0),
                         'f1': ([('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')], 4),
                         'f2': ('i1', 10)}, align=False)
          assert_equal(dt3.itemsize, 11)
          assert_equal(dt1, dt2)
          assert_equal(dt2, dt3)
          # Array of subtype should preserve alignment
          dt1 = np.dtype([('a', '|i1'),
                          ('b', [('f0', '<i2'),
                          ('f1', '<f4')], 2)], align=True)
          assert_equal(dt1.descr, [('a', '|i1'), ('', '|V3'),
                                   ('b', [('f0', '<i2'), ('', '|V2'),
                                   ('f1', '<f4')], (2,))])
  
      def test_union_struct(self):
          # Should be able to create union dtypes
          dt = np.dtype({'names':['f0', 'f1', 'f2'], 'formats':['<u4', '<u2', '<u2'],
                          'offsets':[0, 0, 2]}, align=True)
          assert_equal(dt.itemsize, 4)
          a = np.array([3], dtype='<u4').view(dt)
          a['f1'] = 10
          a['f2'] = 36
          assert_equal(a['f0'], 10 + 36*256*256)
          # Should be able to specify fields out of order
          dt = np.dtype({'names':['f0', 'f1', 'f2'], 'formats':['<u4', '<u2', '<u2'],
                          'offsets':[4, 0, 2]}, align=True)
          assert_equal(dt.itemsize, 8)
          # field name should not matter: assignment is by position
          dt2 = np.dtype({'names':['f2', 'f0', 'f1'],
                          'formats':['<u4', '<u2', '<u2'],
                          'offsets':[4, 0, 2]}, align=True)
          vals = [(0, 1, 2), (3, -1, 4)]
          vals2 = [(0, 1, 2), (3, -1, 4)]
          a = np.array(vals, dt)
          b = np.array(vals2, dt2)
          assert_equal(a.astype(dt2), b)
          assert_equal(b.astype(dt), a)
          assert_equal(a.view(dt2), b)
          assert_equal(b.view(dt), a)
          # Should not be able to overlap objects with other types
          assert_raises(TypeError, np.dtype,
                  {'names':['f0', 'f1'],
                   'formats':['O', 'i1'],
                   'offsets':[0, 2]})
          assert_raises(TypeError, np.dtype,
                  {'names':['f0', 'f1'],
                   'formats':['i4', 'O'],
                   'offsets':[0, 3]})
          assert_raises(TypeError, np.dtype,
                  {'names':['f0', 'f1'],
                   'formats':[[('a', 'O')], 'i1'],
                   'offsets':[0, 2]})
          assert_raises(TypeError, np.dtype,
                  {'names':['f0', 'f1'],
                   'formats':['i4', [('a', 'O')]],
                   'offsets':[0, 3]})
          # Out of order should still be ok, however
          dt = np.dtype({'names':['f0', 'f1'],
                         'formats':['i1', 'O'],
                         'offsets':[np.dtype('intp').itemsize, 0]})
  
      @pytest.mark.parametrize(["obj", "dtype", "expected"],
          [([], ("(2)f4,"), np.empty((0, 2), dtype="f4")),
           (3, "(3)f4,", [3, 3, 3]),
           (np.float64(2), "(2)f4,", [2, 2]),
           ([((0, 1), (1, 2)), ((2,),)], '(2,2)f4', None),
           (["1", "2"], "(2)i,", None)])
      def test_subarray_list(self, obj, dtype, expected):
          dtype = np.dtype(dtype)
          res = np.array(obj, dtype=dtype)
  
          if expected is None:
              # iterate the 1-d list to fill the array
              expected = np.empty(len(obj), dtype=dtype)
              for i in range(len(expected)):
                  expected[i] = obj[i]
  
          assert_array_equal(res, expected)
  
      def test_comma_datetime(self):
          dt = np.dtype('M8[D],datetime64[Y],i8')
          assert_equal(dt, np.dtype([('f0', 'M8[D]'),
                                     ('f1', 'datetime64[Y]'),
                                     ('f2', 'i8')]))
  
      def test_from_dictproxy(self):
          # Tests for PR #5920
          dt = np.dtype({'names': ['a', 'b'], 'formats': ['i4', 'f4']})
          assert_dtype_equal(dt, np.dtype(dt.fields))
          dt2 = np.dtype((np.void, dt.fields))
          assert_equal(dt2.fields, dt.fields)
  
      def test_from_dict_with_zero_width_field(self):
          # Regression test for #6430 / #2196
          dt = np.dtype([('val1', np.float32, (0,)), ('val2', int)])
          dt2 = np.dtype({'names': ['val1', 'val2'],
                          'formats': [(np.float32, (0,)), int]})
  
          assert_dtype_equal(dt, dt2)
          assert_equal(dt.fields['val1'][0].itemsize, 0)
          assert_equal(dt.itemsize, dt.fields['val2'][0].itemsize)
  
      def test_bool_commastring(self):
          d = np.dtype('?,?,?')  # raises?
          assert_equal(len(d.names), 3)
          for n in d.names:
              assert_equal(d.fields[n][0], np.dtype('?'))
  
      def test_nonint_offsets(self):
          # gh-8059
          def make_dtype(off):
              return np.dtype({'names': ['A'], 'formats': ['i4'],
                               'offsets': [off]})
  
          assert_raises(TypeError, make_dtype, 'ASD')
          assert_raises(OverflowError, make_dtype, 2**70)
          assert_raises(TypeError, make_dtype, 2.3)
          assert_raises(ValueError, make_dtype, -10)
  
          # no errors here:
          dt = make_dtype(np.uint32(0))
          np.zeros(1, dtype=dt)[0].item()
  
      def test_fields_by_index(self):
          dt = np.dtype([('a', np.int8), ('b', np.float32, 3)])
          assert_dtype_equal(dt[0], np.dtype(np.int8))
          assert_dtype_equal(dt[1], np.dtype((np.float32, 3)))
          assert_dtype_equal(dt[-1], dt[1])
          assert_dtype_equal(dt[-2], dt[0])
          assert_raises(IndexError, lambda: dt[-3])
  
          assert_raises(TypeError, operator.getitem, dt, 3.0)
  
          assert_equal(dt[1], dt[np.int8(1)])
  
      @pytest.mark.parametrize('align_flag',[False, True])
      def test_multifield_index(self, align_flag):
          # indexing with a list produces subfields
          # the align flag should be preserved
          dt = np.dtype([
              (('title', 'col1'), '<U20'), ('A', '<f8'), ('B', '<f8')
          ], align=align_flag)
  
          dt_sub = dt[['B', 'col1']]
          assert_equal(
              dt_sub,
              np.dtype({
                  'names': ['B', 'col1'],
                  'formats': ['<f8', '<U20'],
                  'offsets': [88, 0],
                  'titles': [None, 'title'],
                  'itemsize': 96
              })
          )
          assert_equal(dt_sub.isalignedstruct, align_flag)
  
          dt_sub = dt[['B']]
          assert_equal(
              dt_sub,
              np.dtype({
                  'names': ['B'],
                  'formats': ['<f8'],
                  'offsets': [88],
                  'itemsize': 96
              })
          )
          assert_equal(dt_sub.isalignedstruct, align_flag)
  
          dt_sub = dt[[]]
          assert_equal(
              dt_sub,
              np.dtype({
                  'names': [],
                  'formats': [],
                  'offsets': [],
                  'itemsize': 96
              })
          )
          assert_equal(dt_sub.isalignedstruct, align_flag)
  
          assert_raises(TypeError, operator.getitem, dt, ())
          assert_raises(TypeError, operator.getitem, dt, [1, 2, 3])
          assert_raises(TypeError, operator.getitem, dt, ['col1', 2])
          assert_raises(KeyError, operator.getitem, dt, ['fake'])
          assert_raises(KeyError, operator.getitem, dt, ['title'])
          assert_raises(ValueError, operator.getitem, dt, ['col1', 'col1'])
  
      def test_partial_dict(self):
          # 'names' is missing
          assert_raises(ValueError, np.dtype,
                  {'formats': ['i4', 'i4'], 'f0': ('i4', 0), 'f1':('i4', 4)})
  
      def test_fieldless_views(self):
          a = np.zeros(2, dtype={'names':[], 'formats':[], 'offsets':[],
                                 'itemsize':8})
          assert_raises(ValueError, a.view, np.dtype([]))
  
          d = np.dtype((np.dtype([]), 10))
          assert_equal(d.shape, (10,))
          assert_equal(d.itemsize, 0)
          assert_equal(d.base, np.dtype([]))
  
          arr = np.fromiter((() for i in range(10)), [])
          assert_equal(arr.dtype, np.dtype([]))
          assert_raises(ValueError, np.frombuffer, b'', dtype=[])
          assert_equal(np.frombuffer(b'', dtype=[], count=2),
                       np.empty(2, dtype=[]))
  
          assert_raises(ValueError, np.dtype, ([], 'f8'))
          assert_raises(ValueError, np.zeros(1, dtype='i4').view, [])
  
          assert_equal(np.zeros(2, dtype=[]) == np.zeros(2, dtype=[]),
                       np.ones(2, dtype=bool))
  
          assert_equal(np.zeros((1, 2), dtype=[]) == a,
                       np.ones((1, 2), dtype=bool))
  
  
  class TestSubarray:
      def test_single_subarray(self):
          a = np.dtype((int, (2)))
          b = np.dtype((int, (2,)))
          assert_dtype_equal(a, b)
  
          assert_equal(type(a.subdtype[1]), tuple)
          assert_equal(type(b.subdtype[1]), tuple)
  
      def test_equivalent_record(self):
          """Test whether equivalent subarray dtypes hash the same."""
          a = np.dtype((int, (2, 3)))
          b = np.dtype((int, (2, 3)))
          assert_dtype_equal(a, b)
  
      def test_nonequivalent_record(self):
          """Test whether different subarray dtypes hash differently."""
          a = np.dtype((int, (2, 3)))
          b = np.dtype((int, (3, 2)))
          assert_dtype_not_equal(a, b)
  
          a = np.dtype((int, (2, 3)))
          b = np.dtype((int, (2, 2)))
          assert_dtype_not_equal(a, b)
  
          a = np.dtype((int, (1, 2, 3)))
          b = np.dtype((int, (1, 2)))
          assert_dtype_not_equal(a, b)
  
      def test_shape_equal(self):
          """Test some data types that are equal"""
          assert_dtype_equal(np.dtype('f8'), np.dtype(('f8', tuple())))
          # FutureWarning during deprecation period; after it is passed this
          # should instead check that "(1)f8" == "1f8" == ("f8", 1).
          with pytest.warns(FutureWarning):
              assert_dtype_equal(np.dtype('f8'), np.dtype(('f8', 1)))
          assert_dtype_equal(np.dtype((int, 2)), np.dtype((int, (2,))))
          assert_dtype_equal(np.dtype(('<f4', (3, 2))), np.dtype(('<f4', (3, 2))))
          d = ([('a', 'f4', (1, 2)), ('b', 'f8', (3, 1))], (3, 2))
          assert_dtype_equal(np.dtype(d), np.dtype(d))
  
      def test_shape_simple(self):
          """Test some simple cases that shouldn't be equal"""
          assert_dtype_not_equal(np.dtype('f8'), np.dtype(('f8', (1,))))
          assert_dtype_not_equal(np.dtype(('f8', (1,))), np.dtype(('f8', (1, 1))))
          assert_dtype_not_equal(np.dtype(('f4', (3, 2))), np.dtype(('f4', (2, 3))))
  
      def test_shape_monster(self):
          """Test some more complicated cases that shouldn't be equal"""
          assert_dtype_not_equal(
              np.dtype(([('a', 'f4', (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),
              np.dtype(([('a', 'f4', (1, 2)), ('b', 'f8', (1, 3))], (2, 2))))
          assert_dtype_not_equal(
              np.dtype(([('a', 'f4', (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),
              np.dtype(([('a', 'f4', (2, 1)), ('b', 'i8', (1, 3))], (2, 2))))
          assert_dtype_not_equal(
              np.dtype(([('a', 'f4', (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),
              np.dtype(([('e', 'f8', (1, 3)), ('d', 'f4', (2, 1))], (2, 2))))
          assert_dtype_not_equal(
              np.dtype(([('a', [('a', 'i4', 6)], (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),
              np.dtype(([('a', [('a', 'u4', 6)], (2, 1)), ('b', 'f8', (1, 3))], (2, 2))))
  
      def test_shape_sequence(self):
          # Any sequence of integers should work as shape, but the result
          # should be a tuple (immutable) of base type integers.
          a = np.array([1, 2, 3], dtype=np.int16)
          l = [1, 2, 3]
          # Array gets converted
          dt = np.dtype([('a', 'f4', a)])
          assert_(isinstance(dt['a'].shape, tuple))
          assert_(isinstance(dt['a'].shape[0], int))
          # List gets converted
          dt = np.dtype([('a', 'f4', l)])
          assert_(isinstance(dt['a'].shape, tuple))
          #
  
          class IntLike:
              def __index__(self):
                  return 3
  
              def __int__(self):
                  # (a PyNumber_Check fails without __int__)
                  return 3
  
          dt = np.dtype([('a', 'f4', IntLike())])
          assert_(isinstance(dt['a'].shape, tuple))
          assert_(isinstance(dt['a'].shape[0], int))
          dt = np.dtype([('a', 'f4', (IntLike(),))])
          assert_(isinstance(dt['a'].shape, tuple))
          assert_(isinstance(dt['a'].shape[0], int))
  
      def test_shape_matches_ndim(self):
          dt = np.dtype([('a', 'f4', ())])
          assert_equal(dt['a'].shape, ())
          assert_equal(dt['a'].ndim, 0)
  
          dt = np.dtype([('a', 'f4')])
          assert_equal(dt['a'].shape, ())
          assert_equal(dt['a'].ndim, 0)
  
          dt = np.dtype([('a', 'f4', 4)])
          assert_equal(dt['a'].shape, (4,))
          assert_equal(dt['a'].ndim, 1)
  
          dt = np.dtype([('a', 'f4', (1, 2, 3))])
          assert_equal(dt['a'].shape, (1, 2, 3))
          assert_equal(dt['a'].ndim, 3)
  
      def test_shape_invalid(self):
          # Check that the shape is valid.
          max_int = np.iinfo(np.intc).max
          max_intp = np.iinfo(np.intp).max
          # Too large values (the datatype is part of this)
          assert_raises(ValueError, np.dtype, [('a', 'f4', max_int // 4 + 1)])
          assert_raises(ValueError, np.dtype, [('a', 'f4', max_int + 1)])
          assert_raises(ValueError, np.dtype, [('a', 'f4', (max_int, 2))])
          # Takes a different code path (fails earlier:
          assert_raises(ValueError, np.dtype, [('a', 'f4', max_intp + 1)])
          # Negative values
          assert_raises(ValueError, np.dtype, [('a', 'f4', -1)])
          assert_raises(ValueError, np.dtype, [('a', 'f4', (-1, -1))])
  
      def test_alignment(self):
          #Check that subarrays are aligned
          t1 = np.dtype('(1,)i4', align=True)
          t2 = np.dtype('2i4', align=True)
          assert_equal(t1.alignment, t2.alignment)
  
  
  def iter_struct_object_dtypes():
      """
      Iterates over a few complex dtypes and object pattern which
      fill the array with a given object (defaults to a singleton).
  
      Yields
      ------
      dtype : dtype
      pattern : tuple
          Structured tuple for use with `np.array`.
      count : int
          Number of objects stored in the dtype.
      singleton : object
          A singleton object. The returned pattern is constructed so that
          all objects inside the datatype are set to the singleton.
      """
      obj = object()
  
      dt = np.dtype([('b', 'O', (2, 3))])
      p = ([[obj] * 3] * 2,)
      yield pytest.param(dt, p, 6, obj, id="<subarray>")
  
      dt = np.dtype([('a', 'i4'), ('b', 'O', (2, 3))])
      p = (0, [[obj] * 3] * 2)
      yield pytest.param(dt, p, 6, obj, id="<subarray in field>")
  
      dt = np.dtype([('a', 'i4'),
                     ('b', [('ba', 'O'), ('bb', 'i1')], (2, 3))])
      p = (0, [[(obj, 0)] * 3] * 2)
      yield pytest.param(dt, p, 6, obj, id="<structured subarray 1>")
  
      dt = np.dtype([('a', 'i4'),
                     ('b', [('ba', 'O'), ('bb', 'O')], (2, 3))])
      p = (0, [[(obj, obj)] * 3] * 2)
      yield pytest.param(dt, p, 12, obj, id="<structured subarray 2>")
  
  
  @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
  class TestStructuredObjectRefcounting:
      """These tests cover various uses of complicated structured types which
      include objects and thus require reference counting.
      """
      @pytest.mark.parametrize(['dt', 'pat', 'count', 'singleton'],
                               iter_struct_object_dtypes())
      @pytest.mark.parametrize(["creation_func", "creation_obj"], [
          pytest.param(np.empty, None,
               # None is probably used for too many things
               marks=pytest.mark.skip("unreliable due to python's behaviour")),
          (np.ones, 1),
          (np.zeros, 0)])
      def test_structured_object_create_delete(self, dt, pat, count, singleton,
                                               creation_func, creation_obj):
          """Structured object reference counting in creation and deletion"""
          # The test assumes that 0, 1, and None are singletons.
          gc.collect()
          before = sys.getrefcount(creation_obj)
          arr = creation_func(3, dt)
  
          now = sys.getrefcount(creation_obj)
          assert now - before == count * 3
          del arr
          now = sys.getrefcount(creation_obj)
          assert now == before
  
      @pytest.mark.parametrize(['dt', 'pat', 'count', 'singleton'],
                               iter_struct_object_dtypes())
      def test_structured_object_item_setting(self, dt, pat, count, singleton):
          """Structured object reference counting for simple item setting"""
          one = 1
  
          gc.collect()
          before = sys.getrefcount(singleton)
          arr = np.array([pat] * 3, dt)
          assert sys.getrefcount(singleton) - before == count * 3
          # Fill with `1` and check that it was replaced correctly:
          before2 = sys.getrefcount(one)
          arr[...] = one
          after2 = sys.getrefcount(one)
          assert after2 - before2 == count * 3
          del arr
          gc.collect()
          assert sys.getrefcount(one) == before2
          assert sys.getrefcount(singleton) == before
  
      @pytest.mark.parametrize(['dt', 'pat', 'count', 'singleton'],
                               iter_struct_object_dtypes())
      @pytest.mark.parametrize(
          ['shape', 'index', 'items_changed'],
          [((3,), ([0, 2],), 2),
           ((3, 2), ([0, 2], slice(None)), 4),
           ((3, 2), ([0, 2], [1]), 2),
           ((3,), ([True, False, True]), 2)])
      def test_structured_object_indexing(self, shape, index, items_changed,
                                          dt, pat, count, singleton):
          """Structured object reference counting for advanced indexing."""
          zero = 0
          one = 1
  
          arr = np.zeros(shape, dt)
  
          gc.collect()
          before_zero = sys.getrefcount(zero)
          before_one = sys.getrefcount(one)
          # Test item getting:
          part = arr[index]
          after_zero = sys.getrefcount(zero)
          assert after_zero - before_zero == count * items_changed
          del part
          # Test item setting:
          arr[index] = one
          gc.collect()
          after_zero = sys.getrefcount(zero)
          after_one = sys.getrefcount(one)
          assert before_zero - after_zero == count * items_changed
          assert after_one - before_one == count * items_changed
  
      @pytest.mark.parametrize(['dt', 'pat', 'count', 'singleton'],
                               iter_struct_object_dtypes())
      def test_structured_object_take_and_repeat(self, dt, pat, count, singleton):
          """Structured object reference counting for specialized functions.
          The older functions such as take and repeat use different code paths
          then item setting (when writing this).
          """
          indices = [0, 1]
  
          arr = np.array([pat] * 3, dt)
          gc.collect()
          before = sys.getrefcount(singleton)
          res = arr.take(indices)
          after = sys.getrefcount(singleton)
          assert after - before == count * 2
          new = res.repeat(10)
          gc.collect()
          after_repeat = sys.getrefcount(singleton)
          assert after_repeat - after == count * 2 * 10
  
  
  class TestStructuredDtypeSparseFields:
      """Tests subarray fields which contain sparse dtypes so that
      not all memory is used by the dtype work. Such dtype's should
      leave the underlying memory unchanged.
      """
      dtype = np.dtype([('a', {'names':['aa', 'ab'], 'formats':['f', 'f'],
                               'offsets':[0, 4]}, (2, 3))])
      sparse_dtype = np.dtype([('a', {'names':['ab'], 'formats':['f'],
                                      'offsets':[4]}, (2, 3))])
  
      @pytest.mark.xfail(reason="inaccessible data is changed see gh-12686.")
      @pytest.mark.valgrind_error(reason="reads from uninitialized buffers.")
      def test_sparse_field_assignment(self):
          arr = np.zeros(3, self.dtype)
          sparse_arr = arr.view(self.sparse_dtype)
  
          sparse_arr[...] = np.finfo(np.float32).max
          # dtype is reduced when accessing the field, so shape is (3, 2, 3):
          assert_array_equal(arr["a"]["aa"], np.zeros((3, 2, 3)))
  
      def test_sparse_field_assignment_fancy(self):
          # Fancy assignment goes to the copyswap function for complex types:
          arr = np.zeros(3, self.dtype)
          sparse_arr = arr.view(self.sparse_dtype)
  
          sparse_arr[[0, 1, 2]] = np.finfo(np.float32).max
          # dtype is reduced when accessing the field, so shape is (3, 2, 3):
          assert_array_equal(arr["a"]["aa"], np.zeros((3, 2, 3)))
  
  
  class TestMonsterType:
      """Test deeply nested subtypes."""
  
      def test1(self):
          simple1 = np.dtype({'names': ['r', 'b'], 'formats': ['u1', 'u1'],
              'titles': ['Red pixel', 'Blue pixel']})
          a = np.dtype([('yo', int), ('ye', simple1),
              ('yi', np.dtype((int, (3, 2))))])
          b = np.dtype([('yo', int), ('ye', simple1),
              ('yi', np.dtype((int, (3, 2))))])
          assert_dtype_equal(a, b)
  
          c = np.dtype([('yo', int), ('ye', simple1),
              ('yi', np.dtype((a, (3, 2))))])
          d = np.dtype([('yo', int), ('ye', simple1),
              ('yi', np.dtype((a, (3, 2))))])
          assert_dtype_equal(c, d)
  
      def test_list_recursion(self):
          l = list()
          l.append(('f', l))
          with pytest.raises(RecursionError):
              np.dtype(l)
  
      def test_tuple_recursion(self):
          d = np.int32
          for i in range(100000):
              d = (d, (1,))
          with pytest.raises(RecursionError):
              np.dtype(d)
  
      def test_dict_recursion(self):
          d = dict(names=['self'], formats=[None], offsets=[0])
          d['formats'][0] = d
          with pytest.raises(RecursionError):
              np.dtype(d)
  
  
  class TestMetadata:
      def test_no_metadata(self):
          d = np.dtype(int)
          assert_(d.metadata is None)
  
      def test_metadata_takes_dict(self):
          d = np.dtype(int, metadata={'datum': 1})
          assert_(d.metadata == {'datum': 1})
  
      def test_metadata_rejects_nondict(self):
          assert_raises(TypeError, np.dtype, int, metadata='datum')
          assert_raises(TypeError, np.dtype, int, metadata=1)
          assert_raises(TypeError, np.dtype, int, metadata=None)
  
      def test_nested_metadata(self):
          d = np.dtype([('a', np.dtype(int, metadata={'datum': 1}))])
          assert_(d['a'].metadata == {'datum': 1})
  
      def test_base_metadata_copied(self):
          d = np.dtype((np.void, np.dtype('i4,i4', metadata={'datum': 1})))
          assert_(d.metadata == {'datum': 1})
  
  class TestString:
      def test_complex_dtype_str(self):
          dt = np.dtype([('top', [('tiles', ('>f4', (64, 64)), (1,)),
                                  ('rtile', '>f4', (64, 36))], (3,)),
                         ('bottom', [('bleft', ('>f4', (8, 64)), (1,)),
                                     ('bright', '>f4', (8, 36))])])
          assert_equal(str(dt),
                       "[('top', [('tiles', ('>f4', (64, 64)), (1,)), "
                       "('rtile', '>f4', (64, 36))], (3,)), "
                       "('bottom', [('bleft', ('>f4', (8, 64)), (1,)), "
                       "('bright', '>f4', (8, 36))])]")
  
          # If the sticky aligned flag is set to True, it makes the
          # str() function use a dict representation with an 'aligned' flag
          dt = np.dtype([('top', [('tiles', ('>f4', (64, 64)), (1,)),
                                  ('rtile', '>f4', (64, 36))],
                                  (3,)),
                         ('bottom', [('bleft', ('>f4', (8, 64)), (1,)),
                                     ('bright', '>f4', (8, 36))])],
                         align=True)
          assert_equal(str(dt),
                      "{'names':['top','bottom'], "
                       "'formats':[([('tiles', ('>f4', (64, 64)), (1,)), "
                                    "('rtile', '>f4', (64, 36))], (3,)),"
                                   "[('bleft', ('>f4', (8, 64)), (1,)), "
                                    "('bright', '>f4', (8, 36))]], "
                       "'offsets':[0,76800], "
                       "'itemsize':80000, "
                       "'aligned':True}")
          assert_equal(np.dtype(eval(str(dt))), dt)
  
          dt = np.dtype({'names': ['r', 'g', 'b'], 'formats': ['u1', 'u1', 'u1'],
                          'offsets': [0, 1, 2],
                          'titles': ['Red pixel', 'Green pixel', 'Blue pixel']})
          assert_equal(str(dt),
                      "[(('Red pixel', 'r'), 'u1'), "
                      "(('Green pixel', 'g'), 'u1'), "
                      "(('Blue pixel', 'b'), 'u1')]")
  
          dt = np.dtype({'names': ['rgba', 'r', 'g', 'b'],
                         'formats': ['<u4', 'u1', 'u1', 'u1'],
                         'offsets': [0, 0, 1, 2],
                         'titles': ['Color', 'Red pixel',
                                    'Green pixel', 'Blue pixel']})
          assert_equal(str(dt),
                      "{'names':['rgba','r','g','b'],"
                      " 'formats':['<u4','u1','u1','u1'],"
                      " 'offsets':[0,0,1,2],"
                      " 'titles':['Color','Red pixel',"
                                "'Green pixel','Blue pixel'],"
                      " 'itemsize':4}")
  
          dt = np.dtype({'names': ['r', 'b'], 'formats': ['u1', 'u1'],
                          'offsets': [0, 2],
                          'titles': ['Red pixel', 'Blue pixel']})
          assert_equal(str(dt),
                      "{'names':['r','b'],"
                      " 'formats':['u1','u1'],"
                      " 'offsets':[0,2],"
                      " 'titles':['Red pixel','Blue pixel'],"
                      " 'itemsize':3}")
  
          dt = np.dtype([('a', '<m8[D]'), ('b', '<M8[us]')])
          assert_equal(str(dt),
                      "[('a', '<m8[D]'), ('b', '<M8[us]')]")
  
      def test_repr_structured(self):
          dt = np.dtype([('top', [('tiles', ('>f4', (64, 64)), (1,)),
                                  ('rtile', '>f4', (64, 36))], (3,)),
                         ('bottom', [('bleft', ('>f4', (8, 64)), (1,)),
                                     ('bright', '>f4', (8, 36))])])
          assert_equal(repr(dt),
                       "dtype([('top', [('tiles', ('>f4', (64, 64)), (1,)), "
                       "('rtile', '>f4', (64, 36))], (3,)), "
                       "('bottom', [('bleft', ('>f4', (8, 64)), (1,)), "
                       "('bright', '>f4', (8, 36))])])")
  
          dt = np.dtype({'names': ['r', 'g', 'b'], 'formats': ['u1', 'u1', 'u1'],
                          'offsets': [0, 1, 2],
                          'titles': ['Red pixel', 'Green pixel', 'Blue pixel']},
                          align=True)
          assert_equal(repr(dt),
                      "dtype([(('Red pixel', 'r'), 'u1'), "
                      "(('Green pixel', 'g'), 'u1'), "
                      "(('Blue pixel', 'b'), 'u1')], align=True)")
  
      def test_repr_structured_not_packed(self):
          dt = np.dtype({'names': ['rgba', 'r', 'g', 'b'],
                         'formats': ['<u4', 'u1', 'u1', 'u1'],
                         'offsets': [0, 0, 1, 2],
                         'titles': ['Color', 'Red pixel',
                                    'Green pixel', 'Blue pixel']}, align=True)
          assert_equal(repr(dt),
                      "dtype({'names':['rgba','r','g','b'],"
                      " 'formats':['<u4','u1','u1','u1'],"
                      " 'offsets':[0,0,1,2],"
                      " 'titles':['Color','Red pixel',"
                                "'Green pixel','Blue pixel'],"
                      " 'itemsize':4}, align=True)")
  
          dt = np.dtype({'names': ['r', 'b'], 'formats': ['u1', 'u1'],
                          'offsets': [0, 2],
                          'titles': ['Red pixel', 'Blue pixel'],
                          'itemsize': 4})
          assert_equal(repr(dt),
                      "dtype({'names':['r','b'], "
                      "'formats':['u1','u1'], "
                      "'offsets':[0,2], "
                      "'titles':['Red pixel','Blue pixel'], "
                      "'itemsize':4})")
  
      def test_repr_structured_datetime(self):
          dt = np.dtype([('a', '<M8[D]'), ('b', '<m8[us]')])
          assert_equal(repr(dt),
                      "dtype([('a', '<M8[D]'), ('b', '<m8[us]')])")
  
      def test_repr_str_subarray(self):
          dt = np.dtype(('<i2', (1,)))
          assert_equal(repr(dt), "dtype(('<i2', (1,)))")
          assert_equal(str(dt), "('<i2', (1,))")
  
      def test_base_dtype_with_object_type(self):
          # Issue gh-2798, should not error.
          np.array(['a'], dtype="O").astype(("O", [("name", "O")]))
  
      def test_empty_string_to_object(self):
          # Pull request #4722
          np.array(["", ""]).astype(object)
  
      def test_void_subclass_unsized(self):
          dt = np.dtype(np.record)
          assert_equal(repr(dt), "dtype('V')")
          assert_equal(str(dt), '|V0')
          assert_equal(dt.name, 'record')
  
      def test_void_subclass_sized(self):
          dt = np.dtype((np.record, 2))
          assert_equal(repr(dt), "dtype('V2')")
          assert_equal(str(dt), '|V2')
          assert_equal(dt.name, 'record16')
  
      def test_void_subclass_fields(self):
          dt = np.dtype((np.record, [('a', '<u2')]))
          assert_equal(repr(dt), "dtype((numpy.record, [('a', '<u2')]))")
          assert_equal(str(dt), "(numpy.record, [('a', '<u2')])")
          assert_equal(dt.name, 'record16')
  
  
  class TestDtypeAttributeDeletion:
  
      def test_dtype_non_writable_attributes_deletion(self):
          dt = np.dtype(np.double)
          attr = ["subdtype", "descr", "str", "name", "base", "shape",
                  "isbuiltin", "isnative", "isalignedstruct", "fields",
                  "metadata", "hasobject"]
  
          for s in attr:
              assert_raises(AttributeError, delattr, dt, s)
  
      def test_dtype_writable_attributes_deletion(self):
          dt = np.dtype(np.double)
          attr = ["names"]
          for s in attr:
              assert_raises(AttributeError, delattr, dt, s)
  
  
  class TestDtypeAttributes:
      def test_descr_has_trailing_void(self):
          # see gh-6359
          dtype = np.dtype({
              'names': ['A', 'B'],
              'formats': ['f4', 'f4'],
              'offsets': [0, 8],
              'itemsize': 16})
          new_dtype = np.dtype(dtype.descr)
          assert_equal(new_dtype.itemsize, 16)
  
      def test_name_dtype_subclass(self):
          # Ticket #4357
          class user_def_subcls(np.void):
              pass
          assert_equal(np.dtype(user_def_subcls).name, 'user_def_subcls')
  
  
  class TestPickling:
  
      def check_pickling(self, dtype):
          for proto in range(pickle.HIGHEST_PROTOCOL + 1):
              pickled = pickle.loads(pickle.dumps(dtype, proto))
              assert_equal(pickled, dtype)
              assert_equal(pickled.descr, dtype.descr)
              if dtype.metadata is not None:
                  assert_equal(pickled.metadata, dtype.metadata)
              # Check the reconstructed dtype is functional
              x = np.zeros(3, dtype=dtype)
              y = np.zeros(3, dtype=pickled)
              assert_equal(x, y)
              assert_equal(x[0], y[0])
  
      @pytest.mark.parametrize('t', [int, float, complex, np.int32, str, object,
                                     np.compat.unicode, bool])
      def test_builtin(self, t):
          self.check_pickling(np.dtype(t))
  
      def test_structured(self):
          dt = np.dtype(([('a', '>f4', (2, 1)), ('b', '<f8', (1, 3))], (2, 2)))
          self.check_pickling(dt)
  
      def test_structured_aligned(self):
          dt = np.dtype('i4, i1', align=True)
          self.check_pickling(dt)
  
      def test_structured_unaligned(self):
          dt = np.dtype('i4, i1', align=False)
          self.check_pickling(dt)
  
      def test_structured_padded(self):
          dt = np.dtype({
              'names': ['A', 'B'],
              'formats': ['f4', 'f4'],
              'offsets': [0, 8],
              'itemsize': 16})
          self.check_pickling(dt)
  
      def test_structured_titles(self):
          dt = np.dtype({'names': ['r', 'b'],
                         'formats': ['u1', 'u1'],
                         'titles': ['Red pixel', 'Blue pixel']})
          self.check_pickling(dt)
  
      @pytest.mark.parametrize('base', ['m8', 'M8'])
      @pytest.mark.parametrize('unit', ['', 'Y', 'M', 'W', 'D', 'h', 'm', 's',
                                        'ms', 'us', 'ns', 'ps', 'fs', 'as'])
      def test_datetime(self, base, unit):
          dt = np.dtype('%s[%s]' % (base, unit) if unit else base)
          self.check_pickling(dt)
          if unit:
              dt = np.dtype('%s[7%s]' % (base, unit))
              self.check_pickling(dt)
  
      def test_metadata(self):
          dt = np.dtype(int, metadata={'datum': 1})
          self.check_pickling(dt)
  
  
  def test_rational_dtype():
      # test for bug gh-5719
      a = np.array([1111], dtype=rational).astype
      assert_raises(OverflowError, a, 'int8')
  
      # test that dtype detection finds user-defined types
      x = rational(1)
      assert_equal(np.array([x,x]).dtype, np.dtype(rational))
  
  
  def test_dtypes_are_true():
      # test for gh-6294
      assert bool(np.dtype('f8'))
      assert bool(np.dtype('i8'))
      assert bool(np.dtype([('a', 'i8'), ('b', 'f4')]))
  
  
  def test_invalid_dtype_string():
      # test for gh-10440
      assert_raises(TypeError, np.dtype, 'f8,i8,[f8,i8]')
      assert_raises(TypeError, np.dtype, u'Fl\xfcgel')
  
  
  def test_keyword_argument():
      # test for https://github.com/numpy/numpy/pull/16574#issuecomment-642660971
      assert np.dtype(dtype=np.float64) == np.dtype(np.float64)
  
  
  class TestFromDTypeAttribute:
      def test_simple(self):
          class dt:
              dtype = "f8"
  
          assert np.dtype(dt) == np.float64
          assert np.dtype(dt()) == np.float64
  
      def test_recursion(self):
          class dt:
              pass
  
          dt.dtype = dt
          with pytest.raises(RecursionError):
              np.dtype(dt)
  
          dt_instance = dt()
          dt_instance.dtype = dt
          with pytest.raises(RecursionError):
              np.dtype(dt_instance)
  
      def test_void_subtype(self):
          class dt(np.void):
              # This code path is fully untested before, so it is unclear
              # what this should be useful for. Note that if np.void is used
              # numpy will think we are deallocating a base type [1.17, 2019-02].
              dtype = np.dtype("f,f")
              pass
  
          np.dtype(dt)
          np.dtype(dt(1))
  
      def test_void_subtype_recursion(self):
          class dt(np.void):
              pass
  
          dt.dtype = dt
  
          with pytest.raises(RecursionError):
              np.dtype(dt)
  
          with pytest.raises(RecursionError):
              np.dtype(dt(1))
  
  
  class TestDTypeClasses:
      @pytest.mark.parametrize("dtype", list(np.typecodes['All']) + [rational])
      def test_basic_dtypes_subclass_properties(self, dtype):
          # Note: Except for the isinstance and type checks, these attributes
          #       are considered currently private and may change.
          dtype = np.dtype(dtype)
          assert isinstance(dtype, np.dtype)
          assert type(dtype) is not np.dtype
          assert type(dtype).__name__ == f"dtype[{dtype.type.__name__}]"
          assert type(dtype).__module__ == "numpy"
          assert not type(dtype)._abstract
  
          # the flexible dtypes and datetime/timedelta have additional parameters
          # which are more than just storage information, these would need to be
          # given when creating a dtype:
          parametric = (np.void, np.str_, np.bytes_, np.datetime64, np.timedelta64)
          if dtype.type not in parametric:
              assert not type(dtype)._parametric
              assert type(dtype)() is dtype
          else:
              assert type(dtype)._parametric
              with assert_raises(TypeError):
                  type(dtype)()
  
      def test_dtype_superclass(self):
          assert type(np.dtype) is not type
          assert isinstance(np.dtype, type)
  
          assert type(np.dtype).__name__ == "_DTypeMeta"
          assert type(np.dtype).__module__ == "numpy"
          assert np.dtype._abstract
  
  
  class TestFromCTypes:
  
      @staticmethod
      def check(ctype, dtype):
          dtype = np.dtype(dtype)
          assert_equal(np.dtype(ctype), dtype)
          assert_equal(np.dtype(ctype()), dtype)
  
      def test_array(self):
          c8 = ctypes.c_uint8
          self.check(     3 * c8,  (np.uint8, (3,)))
          self.check(     1 * c8,  (np.uint8, (1,)))
          self.check(     0 * c8,  (np.uint8, (0,)))
          self.check(1 * (3 * c8), ((np.uint8, (3,)), (1,)))
          self.check(3 * (1 * c8), ((np.uint8, (1,)), (3,)))
  
      def test_padded_structure(self):
          class PaddedStruct(ctypes.Structure):
              _fields_ = [
                  ('a', ctypes.c_uint8),
                  ('b', ctypes.c_uint16)
              ]
          expected = np.dtype([
              ('a', np.uint8),
              ('b', np.uint16)
          ], align=True)
          self.check(PaddedStruct, expected)
  
      def test_bit_fields(self):
          class BitfieldStruct(ctypes.Structure):
              _fields_ = [
                  ('a', ctypes.c_uint8, 7),
                  ('b', ctypes.c_uint8, 1)
              ]
          assert_raises(TypeError, np.dtype, BitfieldStruct)
          assert_raises(TypeError, np.dtype, BitfieldStruct())
  
      def test_pointer(self):
          p_uint8 = ctypes.POINTER(ctypes.c_uint8)
          assert_raises(TypeError, np.dtype, p_uint8)
  
      def test_void_pointer(self):
          self.check(ctypes.c_void_p, np.uintp)
  
      def test_union(self):
          class Union(ctypes.Union):
              _fields_ = [
                  ('a', ctypes.c_uint8),
                  ('b', ctypes.c_uint16),
              ]
          expected = np.dtype(dict(
              names=['a', 'b'],
              formats=[np.uint8, np.uint16],
              offsets=[0, 0],
              itemsize=2
          ))
          self.check(Union, expected)
  
      def test_union_with_struct_packed(self):
          class Struct(ctypes.Structure):
              _pack_ = 1
              _fields_ = [
                  ('one', ctypes.c_uint8),
                  ('two', ctypes.c_uint32)
              ]
  
          class Union(ctypes.Union):
              _fields_ = [
                  ('a', ctypes.c_uint8),
                  ('b', ctypes.c_uint16),
                  ('c', ctypes.c_uint32),
                  ('d', Struct),
              ]
          expected = np.dtype(dict(
              names=['a', 'b', 'c', 'd'],
              formats=['u1', np.uint16, np.uint32, [('one', 'u1'), ('two', np.uint32)]],
              offsets=[0, 0, 0, 0],
              itemsize=ctypes.sizeof(Union)
          ))
          self.check(Union, expected)
  
      def test_union_packed(self):
          class Struct(ctypes.Structure):
              _fields_ = [
                  ('one', ctypes.c_uint8),
                  ('two', ctypes.c_uint32)
              ]
              _pack_ = 1
          class Union(ctypes.Union):
              _pack_ = 1
              _fields_ = [
                  ('a', ctypes.c_uint8),
                  ('b', ctypes.c_uint16),
                  ('c', ctypes.c_uint32),
                  ('d', Struct),
              ]
          expected = np.dtype(dict(
              names=['a', 'b', 'c', 'd'],
              formats=['u1', np.uint16, np.uint32, [('one', 'u1'), ('two', np.uint32)]],
              offsets=[0, 0, 0, 0],
              itemsize=ctypes.sizeof(Union)
          ))
          self.check(Union, expected)
  
      def test_packed_structure(self):
          class PackedStructure(ctypes.Structure):
              _pack_ = 1
              _fields_ = [
                  ('a', ctypes.c_uint8),
                  ('b', ctypes.c_uint16)
              ]
          expected = np.dtype([
              ('a', np.uint8),
              ('b', np.uint16)
          ])
          self.check(PackedStructure, expected)
  
      def test_large_packed_structure(self):
          class PackedStructure(ctypes.Structure):
              _pack_ = 2
              _fields_ = [
                  ('a', ctypes.c_uint8),
                  ('b', ctypes.c_uint16),
                  ('c', ctypes.c_uint8),
                  ('d', ctypes.c_uint16),
                  ('e', ctypes.c_uint32),
                  ('f', ctypes.c_uint32),
                  ('g', ctypes.c_uint8)
                  ]
          expected = np.dtype(dict(
              formats=[np.uint8, np.uint16, np.uint8, np.uint16, np.uint32, np.uint32, np.uint8 ],
              offsets=[0, 2, 4, 6, 8, 12, 16],
              names=['a', 'b', 'c', 'd', 'e', 'f', 'g'],
              itemsize=18))
          self.check(PackedStructure, expected)
  
      def test_big_endian_structure_packed(self):
          class BigEndStruct(ctypes.BigEndianStructure):
              _fields_ = [
                  ('one', ctypes.c_uint8),
                  ('two', ctypes.c_uint32)
              ]
              _pack_ = 1
          expected = np.dtype([('one', 'u1'), ('two', '>u4')])
          self.check(BigEndStruct, expected)
  
      def test_little_endian_structure_packed(self):
          class LittleEndStruct(ctypes.LittleEndianStructure):
              _fields_ = [
                  ('one', ctypes.c_uint8),
                  ('two', ctypes.c_uint32)
              ]
              _pack_ = 1
          expected = np.dtype([('one', 'u1'), ('two', '<u4')])
          self.check(LittleEndStruct, expected)
  
      def test_little_endian_structure(self):
          class PaddedStruct(ctypes.LittleEndianStructure):
              _fields_ = [
                  ('a', ctypes.c_uint8),
                  ('b', ctypes.c_uint16)
              ]
          expected = np.dtype([
              ('a', '<B'),
              ('b', '<H')
          ], align=True)
          self.check(PaddedStruct, expected)
  
      def test_big_endian_structure(self):
          class PaddedStruct(ctypes.BigEndianStructure):
              _fields_ = [
                  ('a', ctypes.c_uint8),
                  ('b', ctypes.c_uint16)
              ]
          expected = np.dtype([
              ('a', '>B'),
              ('b', '>H')
          ], align=True)
          self.check(PaddedStruct, expected)
  
      def test_simple_endian_types(self):
          self.check(ctypes.c_uint16.__ctype_le__, np.dtype('<u2'))
          self.check(ctypes.c_uint16.__ctype_be__, np.dtype('>u2'))
          self.check(ctypes.c_uint8.__ctype_le__, np.dtype('u1'))
          self.check(ctypes.c_uint8.__ctype_be__, np.dtype('u1'))
  
      all_types = set(np.typecodes['All'])
      all_pairs = permutations(all_types, 2)
  
      @pytest.mark.parametrize("pair", all_pairs)
      def test_pairs(self, pair):
          """
          Check that np.dtype('x,y') matches [np.dtype('x'), np.dtype('y')]
          Example: np.dtype('d,I') -> dtype([('f0', '<f8'), ('f1', '<u4')])
          """
          # gh-5645: check that np.dtype('i,L') can be used
          pair_type = np.dtype('{},{}'.format(*pair))
          expected = np.dtype([('f0', pair[0]), ('f1', pair[1])])
          assert_equal(pair_type, expected)
  
  
  class TestUserDType:
      @pytest.mark.leaks_references(reason="dynamically creates custom dtype.")
      def test_custom_structured_dtype(self):
          class mytype:
              pass
  
          blueprint = np.dtype([("field", object)])
          dt = create_custom_field_dtype(blueprint, mytype, 0)
          assert dt.type == mytype
          # We cannot (currently) *create* this dtype with `np.dtype` because
          # mytype does not inherit from `np.generic`.  This seems like an
          # unnecessary restriction, but one that has been around forever:
          assert np.dtype(mytype) == np.dtype("O")
  
      def test_custom_structured_dtype_errors(self):
          class mytype:
              pass
  
          blueprint = np.dtype([("field", object)])
  
          with pytest.raises(ValueError):
              # Tests what happens if fields are unset during creation
              # which is currently rejected due to the containing object
              # (see PyArray_RegisterDataType).
              create_custom_field_dtype(blueprint, mytype, 1)
  
          with pytest.raises(RuntimeError):
              # Tests that a dtype must have its type field set up to np.dtype
              # or in this case a builtin instance.
              create_custom_field_dtype(blueprint, mytype, 2)
  
apis: numpy.ones numpy.testing.assert_equal numpy.dtype numpy.float64 numpy.testing.assert_array_equal numpy.uint32 numpy.core._rational_tests.rational numpy.can_cast numpy.int8 numpy.zeros numpy.compat.pickle.dumps numpy.finfo numpy.core._multiarray_tests.create_custom_field_dtype numpy.testing.assert_raises numpy.empty numpy.iinfo numpy.testing.assert_ numpy.array numpy.frombuffer