rerun newest black on entire repo

examples/air/air.py

@@ -57,7 +57,6 @@ def __init__(
         likelihood_sd=0.3,
         use_cuda=False,
     ):
-
         super().__init__()
 
         self.num_steps = num_steps
@@ -127,7 +126,6 @@ def __init__(
             self.cuda()
 
     def prior(self, n, **kwargs):
-
         state = ModelState(
             x=torch.zeros(n, self.x_size, self.x_size, **self.options),
             z_pres=torch.ones(n, self.z_pres_size, **self.options),
@@ -145,7 +143,6 @@ def prior(self, n, **kwargs):
         return (z_where, z_pres), state.x
 
     def prior_step(self, t, n, prev, z_pres_prior_p=default_z_pres_prior_p):
-
         # Sample presence indicators.
         z_pres = pyro.sample(
             "z_pres_{}".format(t),
@@ -263,7 +260,6 @@ def guide(self, data, batch_size, **kwargs):
             return z_where, z_pres
 
     def guide_step(self, t, n, prev, inputs):
-
         rnn_input = torch.cat(
             (inputs["embed"], prev.z_where, prev.z_what, prev.z_pres), 1
         )

examples/air/main.py

@@ -128,7 +128,6 @@ def load_data():
 
 
 def main(**kwargs):
-
     args = argparse.Namespace(**kwargs)
 
     if "save" in args:
@@ -229,7 +228,6 @@ def per_param_optim_args(param_name):
     examples_to_viz = X[5:10]
 
     for i in range(1, args.num_steps + 1):
-
         loss = svi.step(
             X, batch_size=args.batch_size, z_pres_prior_p=partial(z_pres_prior_p, i)
         )

examples/air/modules.py

@@ -50,7 +50,7 @@ def __init__(
         layers = []
         in_sizes = [in_size] + out_sizes[0:-1]
         sizes = list(zip(in_sizes, out_sizes))
-        for (i, o) in sizes[0:-1]:
+        for i, o in sizes[0:-1]:
             layers.append(nn.Linear(i, o))
             layers.append(non_linear_layer())
         layers.append(nn.Linear(sizes[-1][0], sizes[-1][1]))

examples/contrib/funsor/hmm.py

@@ -178,6 +178,7 @@ def model_0(sequences, lengths, args, batch_size=None, include_prior=True):
 # and randomly subsample data to size batch_size.  To add jit support we
 # silence some warnings and try to avoid dynamic program structure.
 
+
 # Note that this is the "HMM" model in reference [1] (with the difference that
 # in [1] the probabilities probs_x and probs_y are not MAP-regularized with
 # Dirichlet and Beta distributions for any of the models)

examples/contrib/mue/FactorMuE.py

@@ -60,7 +60,6 @@ def generate_data(small_test, include_stop, device):
 
 
 def main(args):
-
     # Load dataset.
     if args.cpu_data or not args.cuda:
         device = torch.device("cpu")

examples/contrib/mue/ProfileHMM.py

@@ -64,7 +64,6 @@ def generate_data(small_test, include_stop, device):
 
 
 def main(args):
-
     pyro.set_rng_seed(args.rng_seed)
 
     # Load dataset.

examples/contrib/oed/ab_test.py

@@ -92,7 +92,6 @@ def true_ape(ns):
 
 
 def main(num_vi_steps, num_bo_steps, seed):
-
     pyro.set_rng_seed(seed)
     pyro.clear_param_store()
 

examples/cvae/baseline.py

@@ -48,7 +48,6 @@ def train(
     early_stop_patience,
     model_path,
 ):
-
     # Train baseline
     baseline_net = BaselineNet(500, 500)
     baseline_net.to(device)

examples/cvae/cvae.py

@@ -70,7 +70,6 @@ def model(self, xs, ys=None):
         pyro.module("generation_net", self)
         batch_size = xs.shape[0]
         with pyro.plate("data"):
-
             # Prior network uses the baseline predictions as initial guess.
             # This is the generative process with recurrent connection
             with torch.no_grad():
@@ -130,7 +129,6 @@ def train(
     model_path,
     pre_trained_baseline_net,
 ):
-
     # clear param store
     pyro.clear_param_store()
 

examples/cvae/util.py

@@ -47,7 +47,6 @@ def visualize(
     num_samples,
     image_path=None,
 ):
-
     # Load sample random data
     datasets, _, dataset_sizes = get_data(
         num_quadrant_inputs=num_quadrant_inputs, batch_size=num_images
@@ -121,7 +120,6 @@ def generate_table(
     num_particles,
     col_name,
 ):
-
     # Load sample random data
     datasets, dataloaders, dataset_sizes = get_data(
         num_quadrant_inputs=num_quadrant_inputs, batch_size=32

examples/dmm.py

@@ -208,7 +208,6 @@ def model(
         mini_batch_seq_lengths,
         annealing_factor=1.0,
     ):
-
         # this is the number of time steps we need to process in the mini-batch
         T_max = mini_batch.size(1)
 
@@ -269,7 +268,6 @@ def guide(
         mini_batch_seq_lengths,
         annealing_factor=1.0,
     ):
-
         # this is the number of time steps we need to process in the mini-batch
         T_max = mini_batch.size(1)
         # register all PyTorch (sub)modules with pyro

examples/einsum.py

@@ -43,7 +43,6 @@ def jit_prob(equation, *operands, **kwargs):
     """
     key = "prob", equation, kwargs["plates"]
     if key not in _CACHE:
-
         # This simply wraps einsum for jit compilation.
         def _einsum(*operands):
             return einsum(equation, *operands, **kwargs)
@@ -61,7 +60,6 @@ def jit_logprob(equation, *operands, **kwargs):
     """
     key = "logprob", equation, kwargs["plates"]
     if key not in _CACHE:
-
         # This simply wraps einsum for jit compilation.
         def _einsum(*operands):
             return einsum(
@@ -81,7 +79,6 @@ def jit_gradient(equation, *operands, **kwargs):
     """
     key = "gradient", equation, kwargs["plates"]
     if key not in _CACHE:
-
         # This wraps einsum for jit compilation, but we will call backward on the result.
         def _einsum(*operands):
             return einsum(
@@ -114,7 +111,6 @@ def _jit_adjoint(equation, *operands, **kwargs):
     backend = kwargs.pop("backend", "pyro.ops.einsum.torch_sample")
     key = backend, equation, tuple(x.shape for x in operands), kwargs["plates"]
     if key not in _CACHE:
-
         # This wraps a complete adjoint algorithm call.
         @ignore_jit_warnings()
         def _forward_backward(*operands):

examples/hmm.py

@@ -167,6 +167,7 @@ def model_0(sequences, lengths, args, batch_size=None, include_prior=True):
 # and randomly subsample data to size batch_size.  To add jit support we
 # silence some warnings and try to avoid dynamic program structure.
 
+
 # Note that this is the "HMM" model in reference [1] (with the difference that
 # in [1] the probabilities probs_x and probs_y are not MAP-regularized with
 # Dirichlet and Beta distributions for any of the models)

examples/mixed_hmm/experiment.py

@@ -35,7 +35,6 @@ def _size(tensor):
 
 
 def run_expt(args):
-
     data_dir = args["folder"]
     dataset = "seal"  # args["dataset"]
     seed = args["seed"]
@@ -79,7 +78,6 @@ def run_expt(args):
             schedule_step_loss = True
 
         for t in range(timesteps):
-
             optimizer.zero_grad()
             loss = loss_fn(model, guide)
             loss.backward()
@@ -166,7 +164,6 @@ def closure():
 
 
 if __name__ == "__main__":
-
     parser = argparse.ArgumentParser()
     parser.add_argument("-g", "--group", default="none", type=str)
     parser.add_argument("-i", "--individual", default="none", type=str)

examples/mixed_hmm/model.py

@@ -48,7 +48,6 @@ def guide_generic(config):
 
     N_c = config["sizes"]["group"]
     with pyro.plate("group", N_c, dim=-1):
-
         if config["group"]["random"] == "continuous":
             pyro.sample(
                 "eps_g",
@@ -59,7 +58,6 @@ def guide_generic(config):
         with pyro.plate("individual", N_s, dim=-2), poutine.mask(
             mask=config["individual"]["mask"]
         ):
-
             # individual-level random effects
             if config["individual"]["random"] == "continuous":
                 pyro.sample(
@@ -158,7 +156,6 @@ def model_generic(config):
 
     N_c = config["sizes"]["group"]
     with pyro.plate("group", N_c, dim=-1):
-
         # group-level random effects
         if config["group"]["random"] == "discrete":
             # group-level discrete effect
@@ -179,7 +176,6 @@ def model_generic(config):
         with pyro.plate("individual", N_s, dim=-2), poutine.mask(
             mask=config["individual"]["mask"]
         ):
-
             # individual-level random effects
             if config["individual"]["random"] == "discrete":
                 # individual-level discrete effect

examples/mixed_hmm/seal_data.py

@@ -18,7 +18,6 @@ def download_seal_data(filename):
 
 
 def prepare_seal(filename, random_effects):
-
     if not os.path.exists(filename):
         download_seal_data(filename)
 

examples/rsa/hyperbole.py

@@ -202,7 +202,6 @@ def test_truth():
 
 
 def main(args):
-
     # test_truth()
 
     pragmatic_marginal = pragmatic_listener(args.price)

examples/rsa/schelling_false.py

@@ -67,7 +67,6 @@ def bob(preference, depth):
 
 
 def main(args):
-
     # Here Alice and Bob slightly prefer one location over the other a priori
     shared_preference = torch.tensor([args.preference])
 

examples/rsa/search_inference.py

@@ -168,7 +168,6 @@ def sample_escape(tr, site):
         )
 
     def _fn(*args, **kwargs):
-
         for i in range(int(1e6)):
             assert (
                 not queue.empty()

examples/rsa/semantic_parsing.py

@@ -325,7 +325,6 @@ def literal_listener_raw(utterance, qud):
 
 
 def main(args):
-
     mll = Marginal(literal_listener_raw, num_samples=args.num_samples)
 
     def is_any_qud(world):

examples/vae/ss_vae_M2.py

@@ -51,7 +51,6 @@ def __init__(
         use_cuda=False,
         aux_loss_multiplier=None,
     ):
-
         super().__init__()
 
         # initialize the class with all arguments provided to the constructor
@@ -68,7 +67,6 @@ def __init__(
         self.setup_networks()
 
     def setup_networks(self):
-
         z_dim = self.z_dim
         hidden_sizes = self.hidden_layers
 
@@ -127,7 +125,6 @@ def model(self, xs, ys=None):
         batch_size = xs.size(0)
         options = dict(dtype=xs.dtype, device=xs.device)
         with pyro.plate("data"):
-
             # sample the handwriting style from the constant prior distribution
             prior_loc = torch.zeros(batch_size, self.z_dim, **options)
             prior_scale = torch.ones(batch_size, self.z_dim, **options)
@@ -167,7 +164,6 @@ def guide(self, xs, ys=None):
         """
         # inform Pyro that the variables in the batch of xs, ys are conditionally independent
         with pyro.plate("data"):
-
             # if the class label (the digit) is not supervised, sample
             # (and score) the digit with the variational distribution
             # q(y|x) = categorical(alpha(x))
@@ -245,7 +241,6 @@ def run_inference_for_epoch(data_loaders, losses, periodic_interval_batches):
     # count the number of supervised batches seen in this epoch
     ctr_sup = 0
     for i in range(batches_per_epoch):
-
         # whether this batch is supervised or not
         is_supervised = (i % periodic_interval_batches == 1) and ctr_sup < sup_batches
 
@@ -277,7 +272,7 @@ def get_accuracy(data_loader, classifier_fn, batch_size):
     predictions, actuals = [], []
 
     # use the appropriate data loader
-    for (xs, ys) in data_loader:
+    for xs, ys in data_loader:
         # use classification function to compute all predictions for each batch
         predictions.append(classifier_fn(xs))
         actuals.append(ys)
@@ -370,7 +365,6 @@ def main(args):
 
         # run inference for a certain number of epochs
         for i in range(0, args.num_epochs):
-
             # get the losses for an epoch
             epoch_losses_sup, epoch_losses_unsup = run_inference_for_epoch(
                 data_loaders, losses, periodic_interval_batches

examples/vae/utils/custom_mlp.py

@@ -168,13 +168,11 @@ def __init__(
                     else output_activation
                 )
         else:
-
             # we're going to have a bunch of separate layers we can spit out (a tuple of outputs)
             out_layers = []
 
             # multiple outputs? handle separately
             for out_ix, out_size in enumerate(output_size):
-
                 # for a single output object, we create a linear layer and some weights
                 split_layer = []
 

examples/vae/utils/mnist_cached.py

@@ -150,7 +150,6 @@ def target_transform(y):
         ], "invalid train/test option values"
 
         if mode in ["sup", "unsup", "valid"]:
-
             # transform the training data if transformations are provided
             if transform is not None:
                 self.data = transform(self.data.float())

pyro/contrib/funsor/handlers/enum_messenger.py

@@ -234,10 +234,10 @@ def queue(
     :param num_samples: optional number of extended traces for extend_fn to return
     :returns: stochastic function decorated with poutine logic
     """
+
     # TODO rewrite this to use purpose-built trace/replay handlers
     def wrapper(wrapped):
         def _fn(*args, **kwargs):
-
             for i in range(max_tries):
                 assert (
                     not queue.empty()

pyro/contrib/funsor/handlers/named_messenger.py

@@ -59,7 +59,6 @@ def __exit__(self, *args, **kwargs):
 
     @staticmethod  # only depends on the global _DIM_STACK state, not self
     def _pyro_to_data(msg):
-
         (funsor_value,) = msg["args"]
         name_to_dim = msg["kwargs"].setdefault("name_to_dim", OrderedDict())
         dim_type = msg["kwargs"].setdefault("dim_type", DimType.LOCAL)
@@ -82,7 +81,6 @@ def _pyro_to_data(msg):
 
     @staticmethod  # only depends on the global _DIM_STACK state, not self
     def _pyro_to_funsor(msg):
-
         if len(msg["args"]) == 2:
             raw_value, output = msg["args"]
         else:

pyro/contrib/funsor/handlers/runtime.py

@@ -181,7 +181,6 @@ def _genvalue(self, key, value_request):
         )
 
     def allocate(self, key_to_value_request):
-
         # step 1: split into fresh and non-fresh
         key_to_value = OrderedDict()
         for key, value_request in tuple(key_to_value_request.items()):