Set black target version to Python 3.6

.travis.yml

@@ -17,7 +17,6 @@ python:
 
 stages:
   - lint
-  # - doc
   - test
 
 env:
@@ -49,11 +48,8 @@ jobs:
     - stage: lint
       script:
         - pip install black
-        - black --check theano/ setup.py
-        - flake8 theano/ setup.py
-    # - &doctest
-    #   stage: doc
-    #   env: DOC=1
+        - black -t py36 --check theano/ setup.py
+        - flake8
     - &normaltest
       stage: test
       env: FAST_COMPILE=1 FLOAT32=1 PART="theano --ignore=theano/tensor/nnet --ignore=theano/tensor/signal"

.travis/travis_install.sh

@@ -2,18 +2,8 @@
 
 set -e
 
-if [[ $DOC == "1" ]]; then
-# this is a hack to deal with the fact that the docs and flake8 config are all set up
-# for old versions
-conda create --yes -q -n pyenv python=3.6 numpy=1.13.1
-conda activate pyenv
-conda install --yes -q mkl numpy=1.13.1 scipy=0.19.1 pip flake8=3.5 six=1.11.0 pep8=1.7.1 pyflakes=1.6.0 mkl-service graphviz pytest
-python -m pip install pydot-ng
-else
 conda create --yes -q -n pyenv python=$TRAVIS_PYTHON_VERSION
 conda activate pyenv
 conda install --yes -q mkl numpy scipy pip flake8 six pep8 pyflakes sphinx mkl-service graphviz pytest  # libgfortran
 python -m pip install -q pydot-ng sphinx_rtd_theme
-fi
-
 python -m pip install --no-deps --upgrade -e .

theano/compile/builders.py

@@ -262,7 +262,7 @@ class OpFromGraph(gof.Op):
         rop_overrides="default",
         connection_pattern=None,
         name=None,
-        **kwargs
+        **kwargs,
     ):
         if not isinstance(outputs, list):
             raise TypeError("outputs must be list, got %s" % type(outputs))

theano/compile/sharedvalue.py

@@ -283,7 +283,7 @@ def shared(value, name=None, strict=False, allow_downcast=None, **kwargs):
                     name=name,
                     strict=strict,
                     allow_downcast=allow_downcast,
-                    **kwargs
+                    **kwargs,
                 )
                 utils.add_tag_trace(var)
                 return var

theano/compile/tests/test_misc.py

@@ -15,7 +15,7 @@ class NNet(object):
         n_hidden=1,
         n_output=1,
         lr=1e-3,
-        **kw
+        **kw,
     ):
         super(NNet, self).__init__(**kw)
 

theano/gof/opt.py

@@ -2382,7 +2382,7 @@ def out2in(*local_opts, **kwargs):
         local_opts,
         order="out_to_in",
         failure_callback=TopoOptimizer.warn_inplace,
-        **kwargs
+        **kwargs,
     )
     if name:
         ret.__name__ = name
@@ -2405,7 +2405,7 @@ def in2out(*local_opts, **kwargs):
         local_opts,
         order="in_to_out",
         failure_callback=TopoOptimizer.warn_inplace,
-        **kwargs
+        **kwargs,
     )
     if name:
         ret.__name__ = name

theano/gpuarray/__init__.py

@@ -112,7 +112,7 @@ def init_dev(dev, name=None, preallocate=None):
             dev,
             sched=config.gpuarray.sched,
             single_stream=config.gpuarray.single_stream,
-            **args
+            **args,
         )
         os.environ["THEANO_GPU_IS_ALREADY_ACTIVE"] = "Yes"
         theano_gpu_is_already_active = True

theano/gpuarray/opt.py

@@ -257,7 +257,7 @@ def register_inplace(*tags, **kwargs):
             "fast_run",
             "inplace",
             "gpuarray",
-            *tags
+            *tags,
         )
         return local_opt
 
@@ -1667,7 +1667,7 @@ def local_gpu_crossentropycategorical1hotgrad(op, context_name, inputs, outputs)
     idx0 = theano.tensor.arange(shape_i(coding, 0))
     z = GpuAlloc(context_name, memset_0=True)(
         as_gpuarray_variable(np.zeros((), dtype=coding.dtype), context_name),
-        *[shape_i(coding, i) for i in xrange(coding.ndim)]
+        *[shape_i(coding, i) for i in xrange(coding.ndim)],
     )
     gcoding = tensor.set_subtensor(
         z[idx0, one_of_n], gpu_neg(gpu_true_div(gy, coding[idx0, one_of_n]))

theano/ifelse.py

@@ -145,7 +145,7 @@ class IfElse(Op):
             new_outs = new_ifelse(
                 node.inputs[0],
                 *(new_ts_inputs + new_fs_inputs),
-                **dict(return_list=True)
+                **dict(return_list=True),
             )
         else:
             new_outs = []

theano/printing.py

@@ -683,14 +683,10 @@ if use_ascii:
     )
 else:
 
-    special = dict(middle_dot=u"\u00B7", big_sigma=u"\u03A3")
+    special = dict(middle_dot="\u00B7", big_sigma="\u03A3")
 
     greek = dict(
-        alpha=u"\u03B1",
-        beta=u"\u03B2",
-        gamma=u"\u03B3",
-        delta=u"\u03B4",
-        epsilon=u"\u03B5",
+        alpha="\u03B1", beta="\u03B2", gamma="\u03B3", delta="\u03B4", epsilon="\u03B5",
     )
 
 

theano/sandbox/rng_mrg.py

@@ -933,7 +933,7 @@ class MRG_RandomStreams(object):
             node_rstate,
             *mrg_uniform.new(node_rstate, ndim, dtype, size),
             size=size,
-            nstreams=orig_nstreams
+            nstreams=orig_nstreams,
         )
         # Add a reference to distinguish from other shared variables
         node_rstate.tag.is_rng = True
@@ -967,7 +967,7 @@ class MRG_RandomStreams(object):
         ndim=None,
         dtype="int64",
         nstreams=None,
-        **kwargs
+        **kwargs,
     ):
         # TODO : need description for parameter and return
         """
@@ -1030,7 +1030,7 @@ class MRG_RandomStreams(object):
         ndim=None,
         dtype="int64",
         nstreams=None,
-        **kwargs
+        **kwargs,
     ):
         """
         Sample `size` times from a multinomial distribution defined by
@@ -1114,7 +1114,7 @@ class MRG_RandomStreams(object):
         ndim=None,
         dtype="int64",
         nstreams=None,
-        **kwargs
+        **kwargs,
     ):
         warnings.warn(
             "MRG_RandomStreams.multinomial_wo_replacement() is "
@@ -1130,7 +1130,7 @@ class MRG_RandomStreams(object):
             dtype=dtype,
             nstreams=nstreams,
             ndim=ndim,
-            **kwargs
+            **kwargs,
         )
 
     def normal(
@@ -1142,7 +1142,7 @@ class MRG_RandomStreams(object):
         dtype=None,
         nstreams=None,
         truncate=False,
-        **kwargs
+        **kwargs,
     ):
         """
         Sample a tensor of values from a normal distribution.
@@ -1197,7 +1197,7 @@ class MRG_RandomStreams(object):
             ndim=1,
             dtype=dtype,
             nstreams=nstreams,
-            **kwargs
+            **kwargs,
         )
 
         # box-muller transform
@@ -1228,7 +1228,7 @@ class MRG_RandomStreams(object):
                 ndim=1,
                 dtype=dtype,
                 nstreams=nstreams,
-                **kwargs
+                **kwargs,
             )
             r_fix = tensor.sqrt(-2.0 * tensor.log(u_fix))
             z0_fixed = r_fix[: to_fix0.size] * cos_theta[to_fix0]
@@ -1292,7 +1292,7 @@ class MRG_RandomStreams(object):
             ndim=ndim,
             dtype=dtype,
             nstreams=nstreams,
-            **kwargs
+            **kwargs,
         )
 
 

theano/scalar/basic.py

@@ -4371,7 +4371,7 @@ class Composite(ScalarOp):
                 izip(("i%i" % i for i in xrange(len(inames))), inames),
                 izip(("o%i" % i for i in xrange(len(onames))), onames),
             ),
-            **sub
+            **sub,
         )
         d["nodename"] = nodename
         if "id" not in sub:

theano/scan_module/scan_opt.py

@@ -651,7 +651,7 @@ class PushOutSeqScan(gof.Optimizer):
             # Do not call make_node for test_value
             nw_node = nwScan(
                 *(node.inputs[:1] + nw_outer + node.inputs[1:]),
-                **dict(return_list=True)
+                **dict(return_list=True),
             )[0].owner
 
             fgraph.replace_all_validate_remove(

theano/scan_module/scan_utils.py

@@ -1335,7 +1335,7 @@ class scan_args(object):
             n_shared_outs=len(self.outer_in_shared),
             n_mit_mot_outs=sum(len(s) for s in self.mit_mot_out_slices),
             mit_mot_out_slices=self.mit_mot_out_slices,
-            **self.other_info
+            **self.other_info,
         )
     )
 

theano/tensor/basic.py

@@ -7194,7 +7194,7 @@ class Choose(Op):
             if isinstance(choice.type, TensorType):
                 choice = choice.dimshuffle(
                     0,
-                    *(("x",) * (out_ndim - choice_ndim) + tuple(range(1, choice.ndim)))
+                    *(("x",) * (out_ndim - choice_ndim) + tuple(range(1, choice.ndim))),
                 )
                 choice_ndim = choice.ndim - 1
                 choice_bcast = choice.broadcastable[1:]

theano/tensor/nnet/__init__.py

@@ -73,7 +73,7 @@ def conv2d(
     filter_dilation=(1, 1),
     num_groups=1,
     unshared=False,
-    **kwargs
+    **kwargs,
 ):
     """
     This function will build the symbolic graph for convolving a mini-batch of a

theano/tensor/nnet/conv.py

@@ -48,7 +48,7 @@ def conv2d(
     filter_shape=None,
     border_mode="valid",
     subsample=(1, 1),
-    **kargs
+    **kargs,
 ):
     """
     Deprecated, old conv2d interface.
@@ -174,7 +174,7 @@ def conv2d(
         kshp=kshp,
         nkern=nkern,
         bsize=bsize,
-        **kargs
+        **kargs,
     )
 
     return op(input, filters)

theano/tensor/nnet/tests/test_abstract_conv.py

@@ -884,7 +884,7 @@ class BaseTestConv2d(BaseTestConv):
         conv_fn=conv.conv2d,
         conv_op=conv.AbstractConv2d,
         ref=conv2d_corr,
-        **kwargs
+        **kwargs,
     ):
         super().run_fwd(
             inputs_shape=inputs_shape,
@@ -892,7 +892,7 @@ class BaseTestConv2d(BaseTestConv):
             conv_fn=conv_fn,
             conv_op=conv_op,
             ref=ref,
-            **kwargs
+            **kwargs,
         )
 
     def run_gradweight(
@@ -902,7 +902,7 @@ class BaseTestConv2d(BaseTestConv):
         output_shape,
         gradWeights_fn=conv.AbstractConv2d_gradWeights,
         ref=conv2d_corr_gw,
-        **kwargs
+        **kwargs,
     ):
         super().run_gradweight(
             inputs_shape=inputs_shape,
@@ -910,7 +910,7 @@ class BaseTestConv2d(BaseTestConv):
             output_shape=output_shape,
             gradWeights_fn=gradWeights_fn,
             ref=ref,
-            **kwargs
+            **kwargs,
         )
 
     def run_gradinput(
@@ -920,7 +920,7 @@ class BaseTestConv2d(BaseTestConv):
         output_shape,
         gradInputs_fn=conv.AbstractConv2d_gradInputs,
         ref=conv2d_corr_gi,
-        **kwargs
+        **kwargs,
     ):
         super().run_gradinput(
             inputs_shape=inputs_shape,
@@ -928,7 +928,7 @@ class BaseTestConv2d(BaseTestConv):
             output_shape=output_shape,
             gradInputs_fn=gradInputs_fn,
             ref=ref,
-            **kwargs
+            **kwargs,
         )
 
 
@@ -1244,7 +1244,7 @@ class BaseTestConv3d(BaseTestConv):
         conv_fn=conv.conv3d,
         conv_op=conv.AbstractConv3d,
         ref=conv3d_corr,
-        **kwargs
+        **kwargs,
     ):
         super().run_fwd(
             inputs_shape=inputs_shape,
@@ -1252,7 +1252,7 @@ class BaseTestConv3d(BaseTestConv):
             conv_fn=conv_fn,
             conv_op=conv_op,
             ref=ref,
-            **kwargs
+            **kwargs,
         )
 
     def run_gradweight(
@@ -1262,7 +1262,7 @@ class BaseTestConv3d(BaseTestConv):
         output_shape,
         gradWeights_fn=conv.AbstractConv3d_gradWeights,
         ref=conv3d_corr_gw,
-        **kwargs
+        **kwargs,
     ):
         super().run_gradweight(
             inputs_shape=inputs_shape,
@@ -1270,7 +1270,7 @@ class BaseTestConv3d(BaseTestConv):
             output_shape=output_shape,
             gradWeights_fn=gradWeights_fn,
             ref=ref,
-            **kwargs
+            **kwargs,
         )
 
     def run_gradinput(
@@ -1280,7 +1280,7 @@ class BaseTestConv3d(BaseTestConv):
         output_shape,
         gradInputs_fn=conv.AbstractConv3d_gradInputs,
         ref=conv3d_corr_gi,
-        **kwargs
+        **kwargs,
     ):
         super().run_gradinput(
             inputs_shape=inputs_shape,
@@ -1288,7 +1288,7 @@ class BaseTestConv3d(BaseTestConv):
             output_shape=output_shape,
             gradInputs_fn=gradInputs_fn,
             ref=ref,
-            **kwargs
+            **kwargs,
         )
 
 

theano/tensor/opt.py

@@ -152,7 +152,7 @@ def broadcast_like(value, template, fgraph, dtype=None):
                 i
                 for i in xrange(rval.ndim)
                 if rval.broadcastable[i] and not template.broadcastable[i]
-            ]
+            ],
         )
     assert rval.type.dtype == dtype
 
@@ -2760,7 +2760,7 @@ def local_upcast_elemwise_constant_inputs(node):
                             new_inputs.append(
                                 T.alloc(
                                     T.cast(cval_i, output_dtype),
-                                    *[shape_i(d)(i) for d in xrange(i.ndim)]
+                                    *[shape_i(d)(i) for d in xrange(i.ndim)],
                                 )
                             )
                             # print >> sys.stderr, "AAA",
@@ -6223,7 +6223,7 @@ def local_opt_alloc(node):
                             shapes[i]
                             for i in xrange(len(shapes))
                             if i not in node.op.axis
-                        ]
+                        ],
                     )
                 ]
             except NotScalarConstantError:

theano/tensor/signal/conv.py

@@ -25,7 +25,7 @@ def conv2d(
     filter_shape=None,
     border_mode="valid",
     subsample=(1, 1),
-    **kargs
+    **kargs,
 ):
     """
     signal.conv.conv2d performs a basic 2D convolution of the input with the
@@ -105,7 +105,7 @@ def conv2d(
         kshp=kshp,
         nkern=nkern,
         bsize=bsize,
-        **kargs
+        **kargs,
     )
 
     output = op(input4D, filters4D)

theano/tensor/subtensor.py

@@ -1524,7 +1524,7 @@ class IncSubtensor(Op):
             sub=sub,
             idx_list=self.idx_list,
             view_ndim=view_ndim,
-            **helper_args
+            **helper_args,
         )
 
         # Make a view on the output, as we will write into it.

theano/tensor/tests/test_basic.py

@@ -872,7 +872,7 @@ TestAddBroadcast = makeBroadcastTester(
             randuint32(2, 3),
         ),
         four_inputs_broadcast=(rand(2, 3), rand(1, 3), rand(2, 1), rand(1, 1)),
-        **_good_broadcast_binary_normal
+        **_good_broadcast_binary_normal,
     ),
     bad_build=_bad_build_broadcast_binary_normal,
     bad_runtime=_bad_runtime_broadcast_binary_normal,
@@ -986,14 +986,14 @@ TestMulBroadcast = makeBroadcastTester(
     good=dict(
         three_inputs_same_shapes=(rand(2, 3), rand(2, 3), rand(2, 3)),
         four_inputs_broadcast=(rand(2, 3), rand(1, 3), rand(2, 1), rand(1, 1)),
-        **_good_broadcast_binary_normal
+        **_good_broadcast_binary_normal,
     ),
     bad_build=_bad_build_broadcast_binary_normal,
     bad_runtime=_bad_runtime_broadcast_binary_normal,
     grad=dict(
         three_inputs_same_shapes=(rand(2, 3), rand(2, 3), rand(2, 3)),
         four_inputs_broadcast=(rand(2, 3), rand(1, 3), rand(2, 1), rand(1, 1)),
-        **_grad_broadcast_binary_normal
+        **_grad_broadcast_binary_normal,
     ),
 )
 

theano/tensor/var.py

@@ -646,7 +646,7 @@ class _tensor_py_operators(object):
                     self,
                     *theano.tensor.subtensor.Subtensor.collapse(
                         args, lambda entry: isinstance(entry, Variable)
-                    )
+                    ),
                 )
 
     def take(self, indices, axis=None, mode="raise"):