Drop Python 2 support

docs/requirements.txt

@@ -2,5 +2,4 @@ contextlib2
 multipledispatch
 numpy>=1.7
 opt_einsum>=2.3.2
-six>=1.10.0
 unification

examples/discrete_hmm.py

@@ -1,5 +1,3 @@
-from __future__ import absolute_import, division, print_function
-
 import argparse
 from collections import OrderedDict
 

examples/kalman_filter.py

@@ -1,5 +1,3 @@
-from __future__ import absolute_import, division, print_function
-
 import argparse
 
 import torch

examples/minipyro.py

@@ -1,5 +1,3 @@
-from __future__ import absolute_import, division, print_function
-
 import argparse
 
 import torch

examples/pcfg.py

@@ -1,5 +1,3 @@
-from __future__ import absolute_import, division, print_function
-
 import argparse
 import math
 from collections import OrderedDict

examples/slds.py

@@ -1,5 +1,3 @@
-from __future__ import absolute_import, division, print_function
-
 import argparse
 
 import torch

examples/ss_vae_delayed.py

@@ -1,5 +1,3 @@
-from __future__ import absolute_import, division, print_function
-
 import argparse
 from collections import OrderedDict
 

examples/vae.py

@@ -1,5 +1,3 @@
-from __future__ import absolute_import, division, print_function
-
 import argparse
 import os
 from collections import OrderedDict

funsor/__init__.py

@@ -1,5 +1,3 @@
-from __future__ import absolute_import, division, print_function
-
 from funsor.domains import Domain, bint, find_domain, reals
 from funsor.integrate import Integrate
 from funsor.interpreter import reinterpret

funsor/adjoint.py

@@ -1,5 +1,3 @@
-from __future__ import absolute_import, division, print_function
-
 from collections import defaultdict
 
 import torch

funsor/affine.py

@@ -1,5 +1,3 @@
-from __future__ import absolute_import, division, print_function
-
 from collections import OrderedDict
 
 import funsor.ops as ops

funsor/contract.py

@@ -1,5 +1,3 @@
-from __future__ import absolute_import, division, print_function
-
 import functools
 from collections import OrderedDict
 

funsor/delta.py

@@ -1,9 +1,5 @@
-from __future__ import absolute_import, division, print_function
-
 from collections import OrderedDict
 
-from six import add_metaclass
-
 import funsor.ops as ops
 import funsor.terms
 from funsor.domains import Domain, reals
@@ -38,8 +34,7 @@ def __call__(cls, name, point, log_density=0):
         return super(DeltaMeta, cls).__call__(name, point, log_density)
 
 
-@add_metaclass(DeltaMeta)
-class Delta(Funsor):
+class Delta(Funsor, metaclass=DeltaMeta):
     """
     Normalized delta distribution binding a single variable.
 

funsor/distributions.py

@@ -1,12 +1,9 @@
-from __future__ import absolute_import, division, print_function
-
 import math
 from collections import OrderedDict
 
 import pyro.distributions as dist
 import torch
 from pyro.distributions.util import broadcast_shape
-from six import add_metaclass
 
 import funsor.delta
 import funsor.ops as ops
@@ -55,8 +52,7 @@ def __call__(cls, *args, **kwargs):
             return super(DistributionMeta, cls).__call__(*args)
 
 
-@add_metaclass(DistributionMeta)
-class Distribution(Funsor):
+class Distribution(Funsor, metaclass=DistributionMeta):
     """
     Funsor backed by a PyTorch distribution object.
     """

funsor/domains.py

@@ -1,15 +1,12 @@
-from __future__ import absolute_import, division, print_function
-
 import operator
 from collections import namedtuple
+from functools import reduce
 
+import torch
 from pyro.distributions.util import broadcast_shape
-from six import integer_types
-from six.moves import reduce
 
 import funsor.ops as ops
 from funsor.util import lazy_property
-import torch
 
 
 class Domain(namedtuple('Domain', ['shape', 'dtype'])):
@@ -21,8 +18,8 @@ def __new__(cls, shape, dtype):
         assert isinstance(shape, tuple)
         if torch._C._get_tracing_state():
             shape = tuple(map(int, shape))
-        assert all(isinstance(size, integer_types) for size in shape), shape
-        if isinstance(dtype, integer_types):
+        assert all(isinstance(size, int) for size in shape), shape
+        if isinstance(dtype, int):
             assert not shape
         elif isinstance(dtype, str):
             assert dtype == 'real'
@@ -32,7 +29,7 @@ def __new__(cls, shape, dtype):
 
     def __repr__(self):
         shape = tuple(self.shape)
-        if isinstance(self.dtype, integer_types):
+        if isinstance(self.dtype, int):
             if not shape:
                 return 'bint({})'.format(self.dtype)
             return 'bint({}, {})'.format(self.dtype, shape)
@@ -41,7 +38,7 @@ def __repr__(self):
         return 'reals{}'.format(shape)
 
     def __iter__(self):
-        if isinstance(self.dtype, integer_types) and not self.shape:
+        if isinstance(self.dtype, int) and not self.shape:
             from funsor.terms import Number
             return (Number(i, self.dtype) for i in range(self.dtype))
         raise NotImplementedError
@@ -69,7 +66,7 @@ def bint(size):
     """
     if torch._C._get_tracing_state():
         size = int(size)
-    assert isinstance(size, integer_types) and size >= 0
+    assert isinstance(size, int) and size >= 0
     return Domain((), size)
 
 

funsor/einsum.py

@@ -1,10 +1,7 @@
-from __future__ import absolute_import, division, print_function
-
 from collections import OrderedDict
+from functools import reduce
 
 import torch
-from six import integer_types
-from six.moves import reduce
 
 import funsor.ops as ops
 from funsor.contract import Contract
@@ -16,7 +13,7 @@
 
 
 def _make_base_lhs(prod_op, arg, reduced_vars, normalized=False):
-    if not all(isinstance(d.dtype, integer_types) for d in arg.inputs.values()):
+    if not all(isinstance(d.dtype, int) for d in arg.inputs.values()):
         raise NotImplementedError("TODO implement continuous base lhss")
 
     if prod_op not in (ops.add, ops.mul):

funsor/gaussian.py

@@ -1,13 +1,10 @@
-from __future__ import absolute_import, division, print_function
-
 import math
 import warnings
 from collections import OrderedDict, defaultdict
+from functools import reduce
 
 import torch
 from pyro.distributions.util import broadcast_shape
-from six import add_metaclass, integer_types
-from six.moves import reduce
 
 import funsor.ops as ops
 from funsor.delta import Delta
@@ -140,7 +137,7 @@ def _parse_slices(index, value):
     for pos, i in reversed(list(enumerate(index))):
         if isinstance(i, slice):
             start_stops.append((i.start, i.stop))
-        elif isinstance(i, integer_types):
+        elif isinstance(i, int):
             start_stops.append((i, i + 1))
             value = value.unsqueeze(pos - len(index))
         else:
@@ -288,8 +285,7 @@ def __call__(cls, loc, precision, inputs):
         return super(GaussianMeta, cls).__call__(loc, precision, inputs)
 
 
-@add_metaclass(GaussianMeta)
-class Gaussian(Funsor):
+class Gaussian(Funsor, metaclass=GaussianMeta):
     """
     Funsor representing a batched joint Gaussian distribution as a log-density
     function.
@@ -316,7 +312,7 @@ def __init__(self, loc, precision, inputs):
 
         # Compute total shape of all bint inputs.
         batch_shape = tuple(d.dtype for d in inputs.values()
-                            if isinstance(d.dtype, integer_types))
+                            if isinstance(d.dtype, int))
         if not torch._C._get_tracing_state():
             assert _issubshape(loc.shape, batch_shape + (dim,))
             assert _issubshape(precision.shape, batch_shape + (dim, dim))

funsor/integrate.py

@@ -1,5 +1,3 @@
-from __future__ import absolute_import, division, print_function
-
 import functools
 from collections import OrderedDict
 

funsor/interpreter.py

@@ -1,11 +1,10 @@
-from __future__ import absolute_import, division, print_function
-
 import functools
 import inspect
 import os
 import re
 import types
 from collections import OrderedDict
+from functools import singledispatch
 
 import numpy
 import torch
@@ -14,7 +13,6 @@
 from funsor.domains import Domain
 from funsor.ops import Op
 from funsor.registry import KeyedRegistry
-from funsor.six import singledispatch
 
 _ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 _DEBUG = int(os.environ.get("FUNSOR_DEBUG", 0))

funsor/joint.py

@@ -1,11 +1,7 @@
-from __future__ import absolute_import, division, print_function
-
 import functools
 import math
 from collections import OrderedDict
-
-from six import add_metaclass
-from six.moves import reduce
+from functools import reduce
 
 import funsor.interpreter as interpreter
 import funsor.ops as ops
@@ -43,8 +39,7 @@ def __call__(cls, deltas=(), discrete=0, gaussian=0):
         return super(JointMeta, cls).__call__(deltas, discrete, gaussian)
 
 
-@add_metaclass(JointMeta)
-class Joint(Funsor):
+class Joint(Funsor, metaclass=JointMeta):
     """
     Normal form for a joint log probability density funsor.
 

funsor/minipyro.py

@@ -11,8 +11,6 @@
 An accompanying example that makes use of this implementation can be
 found at examples/minipyro.py.
 """
-from __future__ import absolute_import, division, print_function
-
 import functools
 import warnings
 import weakref

funsor/montecarlo.py

@@ -1,5 +1,3 @@
-from __future__ import absolute_import, division, print_function
-
 from collections import OrderedDict
 
 from contextlib2 import contextmanager

funsor/numpy.py

@@ -1,10 +1,7 @@
-from __future__ import absolute_import, division, print_function
-
 from collections import OrderedDict
 
 import numpy as np
 from multipledispatch import dispatch
-from six import add_metaclass, integer_types
 
 import funsor.ops as ops
 from funsor.domains import Domain, bint, find_domain
@@ -24,7 +21,7 @@ def align_array(new_inputs, x):
     """
     assert isinstance(new_inputs, OrderedDict)
     assert isinstance(x, (Number, Array))
-    assert all(isinstance(d.dtype, integer_types) for d in x.inputs.values())
+    assert all(isinstance(d.dtype, int) for d in x.inputs.values())
 
     data = x.data
     if isinstance(x, Number):
@@ -77,8 +74,7 @@ def __call__(cls, data, inputs=None, dtype="real"):
         return super(ArrayMeta, cls).__call__(data, inputs, dtype)
 
 
-@add_metaclass(ArrayMeta)
-class Array(Funsor):
+class Array(Funsor, metaclass=ArrayMeta):
     """
     Funsor backed by a numpy ndarray.
 
@@ -88,7 +84,7 @@ class Array(Funsor):
     def __init__(self, data, inputs=None, dtype="real"):
         assert isinstance(data, np.ndarray) or np.isscalar(data)
         assert isinstance(inputs, tuple)
-        assert all(isinstance(d.dtype, integer_types) for k, d in inputs)
+        assert all(isinstance(d.dtype, int) for k, d in inputs)
         inputs = OrderedDict(inputs)
         output = Domain(data.shape[len(inputs):], dtype)
         fresh = frozenset(inputs.keys())
@@ -282,7 +278,7 @@ def materialize(x):
         return x
     subs = []
     for name, domain in x.inputs.items():
-        if not isinstance(domain.dtype, integer_types):
+        if not isinstance(domain.dtype, int):
             raise ValueError('materialize() requires integer free variables but found '
                              '"{}" of domain {}'.format(name, domain))
         assert not domain.shape

funsor/ops.py

@@ -1,11 +1,8 @@
-from __future__ import absolute_import, division, print_function
-
 import operator
 from numbers import Number
 
 import numpy as np
 from multipledispatch import Dispatcher
-from six import add_metaclass
 
 _builtin_abs = abs
 _builtin_max = max
@@ -84,8 +81,7 @@ def __call__(cls, offset):
             return instance
 
 
-@add_metaclass(GetitemMeta)
-class GetitemOp(Op):
+class GetitemOp(Op, metaclass=GetitemMeta):
     """
     Op encoding an index into one dime, e.g. ``x[:,:,:,y]`` for offset of 3.
     """

funsor/optimizer.py

@@ -1,9 +1,7 @@
-from __future__ import absolute_import, division, print_function
-
 import collections
+from functools import reduce
 
 from opt_einsum.paths import greedy
-from six.moves import reduce
 
 import funsor.ops as ops
 from funsor.contract import Contract, contractor
@@ -21,7 +19,7 @@
 class Finitary(Funsor):
     """
     Lazy finitary operation. Used internally in the optimizer.
-    Finitary(op, operands) == six.moves.reduce(op, operands)
+    Finitary(op, operands) == functools.reduce(op, operands)
     """
     def __init__(self, op, operands):
         assert callable(op)

funsor/pattern.py

@@ -1,5 +1,3 @@
-from __future__ import absolute_import, division, print_function
-
 import functools
 
 import unification.match