gurudesh/pytracebugs huggingface dataset:
# Buggy code
def _compute_grid(self, inputs):
    if inputs.ndimension() == 1:
        inputs = inputs.unsqueeze(1)

    interp_indices, interp_values = Interpolation().interpolate(self.grid, inputs)
    return interp_indices, interp_values


# Traceback (AttributeError)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5     for x_batch, y_batch in test_loader:
----> 6         preds = model(x_batch)
7         means = torch.cat([means, preds.mean.cpu()])
8 means = means[1:]

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/models/abstract_variational_gp.py in __call__(self, inputs, **kwargs)
20             inputs = inputs.unsqueeze(-1)
21
---> 22         return self.variational_strategy(inputs)

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/variational/variational_strategy.py in __call__(self, x)
228                 self._memoize_cache = dict()
229
--> 230         return super(VariationalStrategy, self).__call__(x)

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/module.py in __call__(self, *inputs, **kwargs)
20
21     def __call__(self, *inputs, **kwargs):
---> 22         outputs = self.forward(*inputs, **kwargs)
23         if isinstance(outputs, list):
24             return [_validate_module_outputs(output) for output in outputs]

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/variational/variational_strategy.py in forward(self, x)
214                     induc_induc_covar.inv_matmul(induc_data_covar)
215                 )
--> 216                 data_covariance = data_data_covar + neg_induc_data_data_covar
217             predictive_covar = PsdSumLazyTensor(predictive_covar, data_covariance)
218

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/lazy/sum_lazy_tensor.py in __add__(self, other)
75             return SumLazyTensor(*(list(self.lazy_tensors) + [other]))
76         else:
---> 77             raise AttributeError("other must be a LazyTensor")
78
79     def diag(self):

AttributeError: other must be a LazyTensor

# Fixed code
def _compute_grid(self, inputs):
    n_data, n_dimensions = inputs.size(-2), inputs.size(-1)
    batch_shape = inputs.shape[:-2]

    inputs = inputs.reshape(-1, n_dimensions)
    interp_indices, interp_values = Interpolation().interpolate(self.grid, inputs)
    interp_indices = interp_indices.view(*batch_shape, n_data, -1)
    interp_values = interp_values.view(*batch_shape, n_data, -1)

    if (interp_indices.dim() - 2) != len(self._variational_distribution.batch_shape):
        batch_shape = _mul_broadcast_shape(
            interp_indices.shape[:-2], self._variational_distribution.batch_shape
        )
        interp_indices = interp_indices.expand(*batch_shape, *interp_indices.shape[-2:])
        interp_values = interp_values.expand(*batch_shape, *interp_values.shape[-2:])
    return interp_indices, interp_values
# Buggy code
def prior_distribution(self):
    """
    The :func:`~gpytorch.variational.VariationalStrategy.prior_distribution` method determines how to compute the
    GP prior distribution of the inducing points, e.g. :math:`p(u) \sim N(\mu(X_u), K(X_u, X_u))`. Most commonly,
    this is done simply by calling the user defined GP prior on the inducing point data directly.
    """
    out = self.model.forward(self.inducing_points)
    res = MultivariateNormal(out.mean, out.lazy_covariance_matrix.add_jitter())
    return res


# Traceback (AttributeError)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5     for x_batch, y_batch in test_loader:
----> 6         preds = model(x_batch)
7         means = torch.cat([means, preds.mean.cpu()])
8 means = means[1:]

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/models/abstract_variational_gp.py in __call__(self, inputs, **kwargs)
20             inputs = inputs.unsqueeze(-1)
21
---> 22         return self.variational_strategy(inputs)

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/variational/variational_strategy.py in __call__(self, x)
228                 self._memoize_cache = dict()
229
--> 230         return super(VariationalStrategy, self).__call__(x)

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/module.py in __call__(self, *inputs, **kwargs)
20
21     def __call__(self, *inputs, **kwargs):
---> 22         outputs = self.forward(*inputs, **kwargs)
23         if isinstance(outputs, list):
24             return [_validate_module_outputs(output) for output in outputs]

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/variational/variational_strategy.py in forward(self, x)
214                     induc_induc_covar.inv_matmul(induc_data_covar)
215                 )
--> 216                 data_covariance = data_data_covar + neg_induc_data_data_covar
217             predictive_covar = PsdSumLazyTensor(predictive_covar, data_covariance)
218

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/lazy/sum_lazy_tensor.py in __add__(self, other)
75             return SumLazyTensor(*(list(self.lazy_tensors) + [other]))
76         else:
---> 77             raise AttributeError("other must be a LazyTensor")
78
79     def diag(self):

AttributeError: other must be a LazyTensor

# Fixed code
def prior_distribution(self):
    zeros = torch.zeros_like(self.variational_distribution.mean)
    ones = torch.ones_like(zeros)
    res = MultivariateNormal(zeros, DiagLazyTensor(ones))
    return res
# Buggy code
def covar_trace(self):
    variational_covar = (
        self.variational_distribution.variational_distribution.covariance_matrix
    )
    prior_covar = self.prior_distribution.covariance_matrix
    batch_shape = prior_covar.shape[:-2]
    return (variational_covar * prior_covar).view(*batch_shape, -1).sum(-1)


# Traceback (AttributeError)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5     for x_batch, y_batch in test_loader:
----> 6         preds = model(x_batch)
7         means = torch.cat([means, preds.mean.cpu()])
8 means = means[1:]

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/models/abstract_variational_gp.py in __call__(self, inputs, **kwargs)
20             inputs = inputs.unsqueeze(-1)
21
---> 22         return self.variational_strategy(inputs)

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/variational/variational_strategy.py in __call__(self, x)
228                 self._memoize_cache = dict()
229
--> 230         return super(VariationalStrategy, self).__call__(x)

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/module.py in __call__(self, *inputs, **kwargs)
20
21     def __call__(self, *inputs, **kwargs):
---> 22         outputs = self.forward(*inputs, **kwargs)
23         if isinstance(outputs, list):
24             return [_validate_module_outputs(output) for output in outputs]

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/variational/variational_strategy.py in forward(self, x)
214                     induc_induc_covar.inv_matmul(induc_data_covar)
215                 )
--> 216                 data_covariance = data_data_covar + neg_induc_data_data_covar
217             predictive_covar = PsdSumLazyTensor(predictive_covar, data_covariance)
218

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/lazy/sum_lazy_tensor.py in __add__(self, other)
75             return SumLazyTensor(*(list(self.lazy_tensors) + [other]))
76         else:
---> 77             raise AttributeError("other must be a LazyTensor")
78
79     def diag(self):

AttributeError: other must be a LazyTensor

# Fixed code
def covar_trace(self):
    variational_covar = self.variational_distribution.covariance_matrix
    prior_covar = self.prior_distribution.covariance_matrix
    batch_shape = prior_covar.shape[:-2]
    return (variational_covar * prior_covar).view(*batch_shape, -1).sum(-1)
# Buggy code
def mean_diff_inv_quad(self):
    prior_mean = self.prior_distribution.mean
    prior_covar = self.prior_distribution.lazy_covariance_matrix
    variational_mean = self.variational_distribution.variational_distribution.mean
    return prior_covar.inv_quad(variational_mean - prior_mean)


# Traceback (AttributeError)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5     for x_batch, y_batch in test_loader:
----> 6         preds = model(x_batch)
7         means = torch.cat([means, preds.mean.cpu()])
8 means = means[1:]

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/models/abstract_variational_gp.py in __call__(self, inputs, **kwargs)
20             inputs = inputs.unsqueeze(-1)
21
---> 22         return self.variational_strategy(inputs)

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/variational/variational_strategy.py in __call__(self, x)
228                 self._memoize_cache = dict()
229
--> 230         return super(VariationalStrategy, self).__call__(x)

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/module.py in __call__(self, *inputs, **kwargs)
20
21     def __call__(self, *inputs, **kwargs):
---> 22         outputs = self.forward(*inputs, **kwargs)
23         if isinstance(outputs, list):
24             return [_validate_module_outputs(output) for output in outputs]

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/variational/variational_strategy.py in forward(self, x)
214                     induc_induc_covar.inv_matmul(induc_data_covar)
215                 )
--> 216                 data_covariance = data_data_covar + neg_induc_data_data_covar
217             predictive_covar = PsdSumLazyTensor(predictive_covar, data_covariance)
218

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/lazy/sum_lazy_tensor.py in __add__(self, other)
75             return SumLazyTensor(*(list(self.lazy_tensors) + [other]))
76         else:
---> 77             raise AttributeError("other must be a LazyTensor")
78
79     def diag(self):

AttributeError: other must be a LazyTensor

# Fixed code
def mean_diff_inv_quad(self):
    prior_mean = self.prior_distribution.mean
    prior_covar = self.prior_distribution.lazy_covariance_matrix
    variational_mean = self.variational_distribution.mean
    return prior_covar.inv_quad(variational_mean - prior_mean)
# Buggy code
def _sparse_left_interp_t(self, left_interp_indices_tensor, left_interp_values_tensor):
    if hasattr(self, "_sparse_left_interp_t_memo"):
        if torch.equal(
            self._left_interp_indices_memo, left_interp_indices_tensor
        ) and torch.equal(self._left_interp_values_memo, left_interp_values_tensor):
            return self._sparse_left_interp_t_memo

    left_interp_t = sparse.make_sparse_from_indices_and_values(
        left_interp_indices_tensor,
        left_interp_values_tensor,
        self.base_lazy_tensor.size()[-1],
    )
    self._left_interp_indices_memo = left_interp_indices_tensor
    self._left_interp_values_memo = left_interp_values_tensor
    self._sparse_left_interp_t_memo = left_interp_t
    return self._sparse_left_interp_t_memo


# Traceback (RuntimeError)
(py37) vdhiman@dwarf:~/wrk/BayesCBF_ws/BayesCBF$ python tests/test_interpolated_lazy_tensor.py
Traceback (most recent call last):
File "tests/test_interpolated_lazy_tensor.py", line 30, in <module>
test_interpolated_lazy_tensor()
File "tests/test_interpolated_lazy_tensor.py", line 7, in test_interpolated_lazy_tensor
res = itplzt @ torch.eye(3)
File "/home/vdhiman/wrk/gpytorch/gpytorch/lazy/lazy_tensor.py", line 1731, in __matmul__
return self.matmul(other)
File "/home/vdhiman/wrk/gpytorch/gpytorch/lazy/interpolated_lazy_tensor.py", line 393, in matmul
right_interp_res = left_t_interp(self.right_interp_indices, self.right_interp_values, tensor, base_size)
File "/home/vdhiman/wrk/gpytorch/gpytorch/utils/interpolation.py", line 202, in left_t_interp
values = rhs.unsqueeze(-2) * interp_values.unsqueeze(-1)
RuntimeError: The size of tensor a (3) must match the size of tensor b (2) at non-singleton dimension 0

# Fixed code
def _sparse_left_interp_t(self, left_interp_indices_tensor, left_interp_values_tensor):
    if hasattr(self, "_sparse_left_interp_t_memo"):
        if torch.equal(
            self._left_interp_indices_memo, left_interp_indices_tensor
        ) and torch.equal(self._left_interp_values_memo, left_interp_values_tensor):
            return self._sparse_left_interp_t_memo

    left_interp_t = sparse.make_sparse_from_indices_and_values(
        left_interp_indices_tensor,
        left_interp_values_tensor,
        self.base_lazy_tensor.size()[-2],
    )
    self._left_interp_indices_memo = left_interp_indices_tensor
    self._left_interp_values_memo = left_interp_values_tensor
    self._sparse_left_interp_t_memo = left_interp_t
    return self._sparse_left_interp_t_memo
# Buggy code
def __init__(self, representation_tree, preconditioner=None, has_left=False):
    self.representation_tree = representation_tree
    self.preconditioner = preconditioner
    self.has_left = has_left


# Traceback (RuntimeError)
$ python test_1D_grid_gp_regression.py
E.
======================================================================
ERROR: test_grid_gp_mean_abs_error (__main__.TestGridGPRegression)
----------------------------------------------------------------------
Traceback (most recent call last):
File "test_1D_grid_gp_regression.py", line 87, in test_grid_gp_mean_abs_error
loss.backward()
File "/usr/local/lib/python3.6/dist-packages/torch/tensor.py", line 102, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/usr/local/lib/python3.6/dist-packages/torch/autograd/__init__.py", line 90, in backward
allow_unreachable=True)  # allow_unreachable flag
File "/home/gpytorch/gpytorch/functions/_matmul.py", line 34, in backward
rhs = self.saved_tensors[0]
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation

----------------------------------------------------------------------
Ran 2 tests in 0.088s

FAILED (errors=1)

# Fixed code
def __init__(self, representation_tree, has_left=False):
    self.representation_tree = representation_tree
    self.has_left = has_left
# Buggy code
def __init__(
    self,
    active_dims=None,
    batch_size=1,
    period_length_prior=None,
    eps=1e-6,
    param_transform=softplus,
    inv_param_transform=None,
    **kwargs,
):
    period_length_prior = _deprecate_kwarg(
        kwargs, "log_period_length_prior", "period_length_prior", period_length_prior
    )
    super(CosineKernel, self).__init__(
        active_dims=active_dims,
        param_transform=param_transform,
        inv_param_transform=inv_param_transform,
    )
    self.eps = eps
    self.register_parameter(
        name="raw_period_length",
        parameter=torch.nn.Parameter(torch.zeros(batch_size, 1, 1)),
    )
    if period_length_prior is not None:
        self.register_prior(
            "period_length_prior",
            period_length_prior,
            lambda: self.period_length,
            lambda v: self._set_period_length(v),
        )


# Traceback (TypeError)
import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579, in __setattr__
object.__setattr__(self, name, value)
File ".../gpytorch/gpytorch/likelihoods/gaussian_likelihood.py", line 63, in noise
self.noise_covar.initialize(value)
TypeError: initialize() takes 1 positional argument but 2 were given

# Fixed code
def __init__(
    self,
    active_dims=None,
    batch_size=1,
    period_length_prior=None,
    eps=1e-6,
    param_transform=softplus,
    inv_param_transform=None,
    **kwargs,
):
    super(CosineKernel, self).__init__(
        active_dims=active_dims,
        param_transform=param_transform,
        inv_param_transform=inv_param_transform,
    )
    self.eps = eps
    self.register_parameter(
        name="raw_period_length",
        parameter=torch.nn.Parameter(torch.zeros(batch_size, 1, 1)),
    )
    if period_length_prior is not None:
        self.register_prior(
            "period_length_prior",
            period_length_prior,
            lambda: self.period_length,
            lambda v: self._set_period_length(v),
        )
# Buggy code
def __init__(
    self,
    nu=2.5,
    ard_num_dims=None,
    batch_size=1,
    active_dims=None,
    lengthscale_prior=None,
    param_transform=softplus,
    inv_param_transform=None,
    eps=1e-6,
    **kwargs,
):
    _deprecate_kwarg(
        kwargs, "log_lengthscale_prior", "lengthscale_prior", lengthscale_prior
    )
    if nu not in {0.5, 1.5, 2.5}:
        raise RuntimeError("nu expected to be 0.5, 1.5, or 2.5")
    super(MaternKernel, self).__init__(
        has_lengthscale=True,
        ard_num_dims=ard_num_dims,
        batch_size=batch_size,
        active_dims=active_dims,
        lengthscale_prior=lengthscale_prior,
        param_transform=param_transform,
        inv_param_transform=inv_param_transform,
        eps=eps,
    )
    self.nu = nu


# Traceback (TypeError)
import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579, in __setattr__
object.__setattr__(self, name, value)
File ".../gpytorch/gpytorch/likelihoods/gaussian_likelihood.py", line 63, in noise
self.noise_covar.initialize(value)
TypeError: initialize() takes 1 positional argument but 2 were given

# Fixed code
def __init__(
    self,
    nu=2.5,
    ard_num_dims=None,
    batch_size=1,
    active_dims=None,
    lengthscale_prior=None,
    param_transform=softplus,
    inv_param_transform=None,
    eps=1e-6,
    **kwargs,
):
    if nu not in {0.5, 1.5, 2.5}:
        raise RuntimeError("nu expected to be 0.5, 1.5, or 2.5")
    super(MaternKernel, self).__init__(
        has_lengthscale=True,
        ard_num_dims=ard_num_dims,
        batch_size=batch_size,
        active_dims=active_dims,
        lengthscale_prior=lengthscale_prior,
        param_transform=param_transform,
        inv_param_transform=inv_param_transform,
        eps=eps,
    )
    self.nu = nu
# Buggy code
def __init__(
    self,
    active_dims=None,
    batch_size=1,
    lengthscale_prior=None,
    period_length_prior=None,
    param_transform=softplus,
    inv_param_transform=None,
    eps=1e-6,
    **kwargs,
):
    lengthscale_prior = _deprecate_kwarg(
        kwargs, "log_lengthscale_prior", "lengthscale_prior", lengthscale_prior
    )
    period_length_prior = _deprecate_kwarg(
        kwargs, "log_period_length_prior", "period_length_prior", period_length_prior
    )
    super(PeriodicKernel, self).__init__(
        has_lengthscale=True,
        active_dims=active_dims,
        batch_size=batch_size,
        lengthscale_prior=lengthscale_prior,
        param_transform=param_transform,
        inv_param_transform=inv_param_transform,
        eps=eps,
    )
    self.register_parameter(
        name="raw_period_length",
        parameter=torch.nn.Parameter(torch.zeros(batch_size, 1, 1)),
    )
    if period_length_prior is not None:
        self.register_prior(
            "period_length_prior",
            period_length_prior,
            lambda: self.period_length,
            lambda v: self._set_period_length(v),
        )


# Traceback (TypeError)
import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579, in __setattr__
object.__setattr__(self, name, value)
File ".../gpytorch/gpytorch/likelihoods/gaussian_likelihood.py", line 63, in noise
self.noise_covar.initialize(value)
TypeError: initialize() takes 1 positional argument but 2 were given

# Fixed code
def __init__(
    self,
    active_dims=None,
    batch_size=1,
    lengthscale_prior=None,
    period_length_prior=None,
    param_transform=softplus,
    inv_param_transform=None,
    eps=1e-6,
    **kwargs,
):
    super(PeriodicKernel, self).__init__(
        has_lengthscale=True,
        active_dims=active_dims,
        batch_size=batch_size,
        lengthscale_prior=lengthscale_prior,
        param_transform=param_transform,
        inv_param_transform=inv_param_transform,
        eps=eps,
    )
    self.register_parameter(
        name="raw_period_length",
        parameter=torch.nn.Parameter(torch.zeros(batch_size, 1, 1)),
    )
    if period_length_prior is not None:
        self.register_prior(
            "period_length_prior",
            period_length_prior,
            lambda: self.period_length,
            lambda v: self._set_period_length(v),
        )
# Buggy code
def __init__(
    self,
    ard_num_dims=None,
    batch_size=1,
    active_dims=None,
    lengthscale_prior=None,
    param_transform=softplus,
    inv_param_transform=None,
    eps=1e-6,
    **kwargs,
):
    _deprecate_kwarg(
        kwargs, "log_lengthscale_prior", "lengthscale_prior", lengthscale_prior
    )
    super(RBFKernel, self).__init__(
        has_lengthscale=True,
        ard_num_dims=ard_num_dims,
        batch_size=batch_size,
        active_dims=active_dims,
        lengthscale_prior=lengthscale_prior,
        param_transform=param_transform,
        inv_param_transform=inv_param_transform,
        eps=eps,
    )


# Traceback (TypeError)
import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579, in __setattr__
object.__setattr__(self, name, value)
File ".../gpytorch/gpytorch/likelihoods/gaussian_likelihood.py", line 63, in noise
self.noise_covar.initialize(value)
TypeError: initialize() takes 1 positional argument but 2 were given

# Fixed code
def __init__(
    self,
    ard_num_dims=None,
    batch_size=1,
    active_dims=None,
    lengthscale_prior=None,
    param_transform=softplus,
    inv_param_transform=None,
    eps=1e-6,
    **kwargs,
):
    super(RBFKernel, self).__init__(
        has_lengthscale=True,
        ard_num_dims=ard_num_dims,
        batch_size=batch_size,
        active_dims=active_dims,
        lengthscale_prior=lengthscale_prior,
        param_transform=param_transform,
        inv_param_transform=inv_param_transform,
        eps=eps,
    )
# Buggy code
def __init__(
    self,
    base_kernel,
    batch_size=1,
    outputscale_prior=None,
    param_transform=softplus,
    inv_param_transform=None,
    **kwargs,
):
    outputscale_prior = _deprecate_kwarg(
        kwargs, "log_outputscale_prior", "outputscale_prior", outputscale_prior
    )
    super(ScaleKernel, self).__init__(has_lengthscale=False, batch_size=batch_size)
    self.base_kernel = base_kernel
    self._param_transform = param_transform
    self._inv_param_transform = _get_inv_param_transform(
        param_transform, inv_param_transform
    )
    self.register_parameter(
        name="raw_outputscale", parameter=torch.nn.Parameter(torch.zeros(batch_size))
    )
    if outputscale_prior is not None:
        self.register_prior(
            "outputscale_prior",
            outputscale_prior,
            lambda: self.outputscale,
            lambda v: self._set_outputscale(v),
        )


# Traceback (TypeError)
import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579, in __setattr__
object.__setattr__(self, name, value)
File ".../gpytorch/gpytorch/likelihoods/gaussian_likelihood.py", line 63, in noise
self.noise_covar.initialize(value)
TypeError: initialize() takes 1 positional argument but 2 were given

# Fixed code
def __init__(
    self,
    base_kernel,
    batch_size=1,
    outputscale_prior=None,
    param_transform=softplus,
    inv_param_transform=None,
    **kwargs,
):
    super(ScaleKernel, self).__init__(has_lengthscale=False, batch_size=batch_size)
    self.base_kernel = base_kernel
    self._param_transform = param_transform
    self._inv_param_transform = _get_inv_param_transform(
        param_transform, inv_param_transform
    )
    self.register_parameter(
        name="raw_outputscale", parameter=torch.nn.Parameter(torch.zeros(batch_size))
    )
    if outputscale_prior is not None:
        self.register_prior(
            "outputscale_prior",
            outputscale_prior,
            lambda: self.outputscale,
            lambda v: self._set_outputscale(v),
        )
# Buggy code
def __init__(
    self,
    noise_prior=None,
    batch_size=1,
    param_transform=softplus,
    inv_param_transform=None,
    **kwargs,
):
    noise_prior = _deprecate_kwarg(
        kwargs, "log_noise_prior", "noise_prior", noise_prior
    )
    noise_covar = HomoskedasticNoise(
        noise_prior=noise_prior,
        batch_size=batch_size,
        param_transform=param_transform,
        inv_param_transform=inv_param_transform,
    )
    super().__init__(noise_covar=noise_covar)


# Traceback (TypeError)
import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579, in __setattr__
object.__setattr__(self, name, value)
File ".../gpytorch/gpytorch/likelihoods/gaussian_likelihood.py", line 63, in noise
self.noise_covar.initialize(value)
TypeError: initialize() takes 1 positional argument but 2 were given

# Fixed code
def __init__(
    self,
    noise_prior=None,
    batch_size=1,
    param_transform=softplus,
    inv_param_transform=None,
    **kwargs,
):
    noise_covar = HomoskedasticNoise(
        noise_prior=noise_prior,
        batch_size=batch_size,
        param_transform=param_transform,
        inv_param_transform=inv_param_transform,
    )
    super().__init__(noise_covar=noise_covar)
# Buggy code
def noise(self, value):
    self.noise_covar.initialize(value)


# Traceback (TypeError)
import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579, in __setattr__
object.__setattr__(self, name, value)
File ".../gpytorch/gpytorch/likelihoods/gaussian_likelihood.py", line 63, in noise
self.noise_covar.initialize(value)
TypeError: initialize() takes 1 positional argument but 2 were given

# Fixed code
def noise(self, value):
    self.noise_covar.initialize(noise=value)
# Buggy code
def __getattr__(self, name):
    try:
        return super().__getattr__(name)
    except AttributeError as e:
        from .utils.log_deprecation import LOG_DEPRECATION_MSG, MODULES_WITH_LOG_PARAMS

        if (
            any(isinstance(self, mod_type) for mod_type in MODULES_WITH_LOG_PARAMS)
            and "log_" in name
        ):
            base_name = name.split("log_")[1]  # e.g. log_lengthscale -> lengthscale
            raw_name = "raw_" + base_name
            warnings.warn(
                LOG_DEPRECATION_MSG.format(log_name=name, name=raw_name),
                DeprecationWarning,
            )
            return (
                super().__getattribute__(base_name).log()
            )  # Get real param value and transform to log
        else:
            try:
                return super().__getattribute__(name)
            except AttributeError:
                raise e


# Traceback (TypeError)
import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579, in __setattr__
object.__setattr__(self, name, value)
File ".../gpytorch/gpytorch/likelihoods/gaussian_likelihood.py", line 63, in noise
self.noise_covar.initialize(value)
TypeError: initialize() takes 1 positional argument but 2 were given

# Fixed code
def __getattr__(self, name):
    try:
        return super().__getattr__(name)
    except AttributeError as e:
        try:
            return super().__getattribute__(name)
        except AttributeError:
            raise e
# Buggy code
def play_mv_by_mvid(cls, mvid):
    mv_model = ControllerApi.api.get_mv_detail(mvid)
    if not ControllerApi.api.is_response_ok(mv_model):
        return

    url_high = mv_model["url_high"]
    clipboard = QApplication.clipboard()
    clipboard.setText(url_high)

    if platform.system() == "Linux":
        ControllerApi.player.pause()
        ControllerApi.notify_widget.show_message(
            "通知", "正在尝试调用VLC视频播放器播放MV"
        )
        subprocess.Popen(["vlc", url_high, "--play-and-exit", "-f"])
    elif platform.system().lower() == "Darwin".lower():
        ControllerApi.player.pause()
        cls.view.ui.STATUS_BAR.showMessage("准备调用 QuickTime Player 播放mv", 4000)
        subprocess.Popen(["open", "-a", "QuickTime Player", url_high])
    else:
        cls.view.ui.STATUS_BAR.showMessage("程序已经将视频的播放地址复制到剪切板", 5000)


# Traceback (FileNotFoundError)
Traceback (most recent call last):
File "../feeluown/controller_api.py", line 46, in play_mv_by_mvid
subprocess.Popen(['vlc', url_high, '--play-and-exit', '-f'])
File "/usr/lib/python3.5/subprocess.py", line 950, in __init__
restore_signals, start_new_session)
File "/usr/lib/python3.5/subprocess.py", line 1544, in _execute_child
raise child_exception_type(errno_num, err_msg)
FileNotFoundError: [Errno 2] 没有那个文件或目录: 'vlc'
/usr/bin/FeelUOwn: 行 6: 10302 已放弃               (核心已转储)python /usr/share/feeluown-git/feeluown/main.py

# Fixed code
def play_mv_by_mvid(cls, mvid):
    mv_model = ControllerApi.api.get_mv_detail(mvid)
    if not ControllerApi.api.is_response_ok(mv_model):
        return

    url_high = mv_model["url_high"]
    clipboard = QApplication.clipboard()
    clipboard.setText(url_high)

    cls.view.ui.STATUS_BAR.showMessage("程序已经将视频的播放地址复制到剪切板", 5000)
    if platform.system() == "Linux":
        ControllerApi.player.pause()
        ControllerApi.notify_widget.show_message(
            "通知", "正在尝试调用VLC视频播放器播放MV"
        )
        try:
            subprocess.Popen(["vlc", url_high, "--play-and-exit", "-f"])
        except Exception as e:
            LOG.error("call vlc player failed")
    elif platform.system().lower() == "Darwin".lower():
        ControllerApi.player.pause()
        ControllerApi.notify_widget.show_message(
            "通知", "准备调用 QuickTime Player 播放mv"
        )
        try:
            subprocess.Popen(["open", "-a", "QuickTime Player", url_high])
        except Exception as e:
            LOG.error("call quicktime player failed")
# Buggy code
def catch_error(exc):
    """Return true if exception exc should be caught"""
    for exception_class in (
        rpath.SkipFileException,
        rpath.RPathException,
        librsync.librsyncError,
        C.UnknownFileTypeError,
        zlib.error,
    ):
        if isinstance(exc, exception_class):
            return 1
    if (
        isinstance(exc, EnvironmentError)
        # the invalid mode shows up in backups of /proc for some reason
        and (
            "invalid mode: rb" in str(exc)
            or "Not a gzipped file" in str(exc)
            or exc.errno
            in (
                errno.EPERM,
                errno.ENOENT,
                errno.EACCES,
                errno.EBUSY,
                errno.EEXIST,
                errno.ENOTDIR,
                errno.EILSEQ,
                errno.ENAMETOOLONG,
                errno.EINTR,
                errno.ESTALE,
                errno.ENOTEMPTY,
                errno.EIO,
                errno.ETXTBSY,
                errno.ESRCH,
                errno.EINVAL,
                errno.EDEADLOCK,
                errno.EDEADLK,
                errno.EOPNOTSUPP,
                errno.ETIMEDOUT,
            )
        )
    ):
        return 1
    return 0


# Traceback (AttributeError)
$ python3
Python 3.7.7 (default, Mar 10 2020, 15:43:27)
[Clang 10.0.0 (clang-1000.11.45.5)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
import errno
errno.EDEADLOCK
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: module 'errno' has no attribute 'EDEADLOCK'
errno.EDEADLK
11

# Fixed code
def catch_error(exc):
    """Return true if exception exc should be caught"""
    for exception_class in (
        rpath.SkipFileException,
        rpath.RPathException,
        librsync.librsyncError,
        C.UnknownFileTypeError,
        zlib.error,
    ):
        if isinstance(exc, exception_class):
            return 1
    if (
        isinstance(exc, EnvironmentError)
        # the invalid mode shows up in backups of /proc for some reason
        and (
            "invalid mode: rb" in str(exc)
            or "Not a gzipped file" in str(exc)
            or exc.errno in _robust_errno_list
        )
    ):
        return 1
    return 0
# Buggy code
def rorp_eq(src_rorp, dest_rorp):
    """Compare hardlinked for equality

    Return false if dest_rorp is linked differently, which can happen
    if dest is linked more than source, or if it is represented by a
    different inode.

    """
    if (
        not src_rorp.isreg()
        or not dest_rorp.isreg()
        or src_rorp.getnumlinks() == dest_rorp.getnumlinks() == 1
    ):
        return 1  # Hard links don't apply

    if src_rorp.getnumlinks() < dest_rorp.getnumlinks():
        return 0
    src_key = get_inode_key(src_rorp)
    index, remaining, dest_key, digest = _inode_index[src_key]
    if dest_key == "NA":
        # Allow this to be ok for first comparison, but not any
        # subsequent ones
        _inode_index[src_key] = (index, remaining, None, None)
        return 1
    try:
        return dest_key == get_inode_key(dest_rorp)
    except KeyError:
        return 0  # Inode key might be missing if the metadata file is corrupt


# Traceback (AssertionError)
======================================================================
FAIL: test_session (__main__.HashTest)
Run actual sessions and make sure proper hashes recorded
----------------------------------------------------------------------
Traceback (most recent call last):
File "./testing/hashtest.py", line 95, in test_session
assert hashlist == hashlist1, (hashlist1, hashlist)
AssertionError: ([None, '943a702d06f34599aee1f8da8ef9f7296031d699', '943a702d06f34599aee1f8da8ef9f7296031d699', 'eab21fb1a18b408909bae552b847f6b13f370f62', 'eab21fb1a18b408909bae552b847f6b13f370f62'], [None, '943a702d06f34599aee1f8da8ef9f7296031d699', None, 'eab21fb1a18b408909bae552b847f6b13f370f62', None])

# Fixed code
def rorp_eq(src_rorp, dest_rorp):
    """Compare hardlinked for equality

    Return false if dest_rorp is linked differently, which can happen
    if dest is linked more than source, or if it is represented by a
    different inode.

    """
    if (
        not src_rorp.isreg()
        or not dest_rorp.isreg()
        or src_rorp.getnumlinks() == dest_rorp.getnumlinks() == 1
    ):
        return 1  # Hard links don't apply

    """The sha1 of linked files is only stored in the metadata of the first
    linked file on the dest side.  If the first linked file on the src side is
    deleted, then the sha1 will also be deleted on the dest side, so we test for this
    & report not equal so that another sha1 will be stored with the next linked
    file on the dest side"""
    if not islinked(src_rorp) and not dest_rorp.has_sha1():
        return 0
    if src_rorp.getnumlinks() != dest_rorp.getnumlinks():
        return 0
    src_key = get_inode_key(src_rorp)
    index, remaining, dest_key, digest = _inode_index[src_key]
    if dest_key == "NA":
        # Allow this to be ok for first comparison, but not any
        # subsequent ones
        _inode_index[src_key] = (index, remaining, None, None)
        return 1
    try:
        return dest_key == get_inode_key(dest_rorp)
    except KeyError:
        return 0  # Inode key might be missing if the metadata file is corrupt
# Buggy code
def compare_hash(cls, repo_iter):
    """Like above, but also compare sha1 sums of any regular files"""

    def hashes_changed(src_rp, mir_rorp):
        """Return 0 if their data hashes same, 1 otherwise"""
        if not mir_rorp.has_sha1():
            log.Log(
                "Warning: Metadata file has no digest for %s, "
                "unable to compare." % (mir_rorp.get_safeindexpath(),),
                2,
            )
            return 0
        elif (
            src_rp.getsize() == mir_rorp.getsize()
            and hash.compute_sha1(src_rp) == mir_rorp.get_sha1()
        ):
            return 0
        return 1

    src_iter = cls.get_source_select()
    for src_rp, mir_rorp in rorpiter.Collate2Iters(src_iter, repo_iter):
        report = get_basic_report(src_rp, mir_rorp, hashes_changed)
        if report:
            yield report
        else:
            log_success(src_rp, mir_rorp)


# Traceback (AssertionError)
======================================================================
FAIL: test_session (__main__.HashTest)
Run actual sessions and make sure proper hashes recorded
----------------------------------------------------------------------
Traceback (most recent call last):
File "./testing/hashtest.py", line 95, in test_session
assert hashlist == hashlist1, (hashlist1, hashlist)
AssertionError: ([None, '943a702d06f34599aee1f8da8ef9f7296031d699', '943a702d06f34599aee1f8da8ef9f7296031d699', 'eab21fb1a18b408909bae552b847f6b13f370f62', 'eab21fb1a18b408909bae552b847f6b13f370f62'], [None, '943a702d06f34599aee1f8da8ef9f7296031d699', None, 'eab21fb1a18b408909bae552b847f6b13f370f62', None])

# Fixed code
def compare_hash(cls, repo_iter):
    """Like above, but also compare sha1 sums of any regular files"""

    def hashes_changed(src_rp, mir_rorp):
        """Return 0 if their data hashes same, 1 otherwise"""
        verify_sha1 = get_hash(mir_rorp)
        if not verify_sha1:
            log.Log(
                "Warning: Metadata file has no digest for %s, "
                "unable to compare." % (mir_rorp.get_safeindexpath(),),
                2,
            )
            return 0
        elif (
            src_rp.getsize() == mir_rorp.getsize()
            and hash.compute_sha1(src_rp) == verify_sha1
        ):
            return 0
        return 1

    src_iter = cls.get_source_select()
    for src_rp, mir_rorp in rorpiter.Collate2Iters(src_iter, repo_iter):
        report = get_basic_report(src_rp, mir_rorp, hashes_changed)
        if report:
            yield report
        else:
            log_success(src_rp, mir_rorp)
# Buggy code
def hashes_changed(src_rp, mir_rorp):
    """Return 0 if their data hashes same, 1 otherwise"""
    if not mir_rorp.has_sha1():
        log.Log(
            "Warning: Metadata file has no digest for %s, "
            "unable to compare." % (mir_rorp.get_safeindexpath(),),
            2,
        )
        return 0
    elif (
        src_rp.getsize() == mir_rorp.getsize()
        and hash.compute_sha1(src_rp) == mir_rorp.get_sha1()
    ):
        return 0
    return 1


# Traceback (AssertionError)
======================================================================
FAIL: test_session (__main__.HashTest)
Run actual sessions and make sure proper hashes recorded
----------------------------------------------------------------------
Traceback (most recent call last):
File "./testing/hashtest.py", line 95, in test_session
assert hashlist == hashlist1, (hashlist1, hashlist)
AssertionError: ([None, '943a702d06f34599aee1f8da8ef9f7296031d699', '943a702d06f34599aee1f8da8ef9f7296031d699', 'eab21fb1a18b408909bae552b847f6b13f370f62', 'eab21fb1a18b408909bae552b847f6b13f370f62'], [None, '943a702d06f34599aee1f8da8ef9f7296031d699', None, 'eab21fb1a18b408909bae552b847f6b13f370f62', None])

# Fixed code
def hashes_changed(src_rp, mir_rorp):
    """Return 0 if their data hashes same, 1 otherwise"""
    verify_sha1 = get_hash(mir_rorp)
    if not verify_sha1:
        log.Log(
            "Warning: Metadata file has no digest for %s, "
            "unable to compare." % (mir_rorp.get_safeindexpath(),),
            2,
        )
        return 0
    elif (
        src_rp.getsize() == mir_rorp.getsize()
        and hash.compute_sha1(src_rp) == verify_sha1
    ):
        return 0
    return 1
# Buggy code
def start_process(self, index, diff_rorp):
    """Start processing directory"""
    self.base_rp, inc_prefix = longname.get_mirror_inc_rps(
        self.CCPP.get_rorps(index), self.basis_root_rp, self.inc_root_rp
    )
    self.base_rp.setdata()
    assert diff_rorp.isdir() or self.base_rp.isdir(), (
        "Either %s or %s must be a directory"
        % (repr(diff_rorp.path), repr(self.base_rp.path))
    )
    if diff_rorp.isdir():
        inc = increment.Increment(diff_rorp, self.base_rp, inc_prefix)
        if inc and inc.isreg():
            inc.fsync_with_dir()  # must write inc before rp changed
        self.base_rp.setdata()  # in case written by increment above
        self.prepare_dir(diff_rorp, self.base_rp)
    elif self.set_dir_replacement(diff_rorp, self.base_rp):
        inc = increment.Increment(self.dir_replacement, self.base_rp, inc_prefix)
        if inc:
            self.CCPP.set_inc(index, inc)
            self.CCPP.flag_success(index)


# Traceback (AttributeError)
Exception 'RORPath instance has no attribute 'path'' raised of class '<type 'exceptions.AttributeError'>':
File "rdiff_backup\Main.pyc", line 304, in error_check_Main
File "rdiff_backup\Main.pyc", line 324, in Main
File "rdiff_backup\Main.pyc", line 280, in take_action
File "rdiff_backup\Main.pyc", line 343, in Backup
File "rdiff_backup\backup.pyc", line 51, in Mirror_and_increment
File "rdiff_backup\backup.pyc", line 243, in patch_and_increment
File "rdiff_backup\rorpiter.pyc", line 284, in __call__
File "rdiff_backup\backup.pyc", line 718, in start_process

Traceback (most recent call last):
File "rdiff-backup", line 30, in <module>
File "rdiff_backup\Main.pyc", line 304, in error_check_Main
File "rdiff_backup\Main.pyc", line 324, in Main
File "rdiff_backup\Main.pyc", line 280, in take_action
File "rdiff_backup\Main.pyc", line 343, in Backup
File "rdiff_backup\backup.pyc", line 51, in Mirror_and_increment
File "rdiff_backup\backup.pyc", line 243, in patch_and_increment
File "rdiff_backup\rorpiter.pyc", line 284, in __call__
File "rdiff_backup\backup.pyc", line 718, in start_process
AttributeError: RORPath instance has no attribute 'path'

# Fixed code
def start_process(self, index, diff_rorp):
    """Start processing directory"""
    self.base_rp, inc_prefix = longname.get_mirror_inc_rps(
        self.CCPP.get_rorps(index), self.basis_root_rp, self.inc_root_rp
    )
    self.base_rp.setdata()
    assert diff_rorp.isdir() or self.base_rp.isdir(), (
        "Either %s or %s must be a directory"
        % (repr(diff_rorp.get_safeindexpath()), repr(self.base_rp.get_safepath()))
    )
    if diff_rorp.isdir():
        inc = increment.Increment(diff_rorp, self.base_rp, inc_prefix)
        if inc and inc.isreg():
            inc.fsync_with_dir()  # must write inc before rp changed
        self.base_rp.setdata()  # in case written by increment above
        self.prepare_dir(diff_rorp, self.base_rp)
    elif self.set_dir_replacement(diff_rorp, self.base_rp):
        inc = increment.Increment(self.dir_replacement, self.base_rp, inc_prefix)
        if inc:
            self.CCPP.set_inc(index, inc)
            self.CCPP.flag_success(index)
# Buggy code
def parse_json_main_index(out_dir: str = OUTPUT_DIR) -> Iterator[Link]:
    """parse an archive index json file and return the list of links"""

    index_path = os.path.join(out_dir, JSON_INDEX_FILENAME)
    if os.path.exists(index_path):
        with open(index_path, "r", encoding="utf-8") as f:
            links = pyjson.load(f)["links"]
            for link_json in links:
                yield Link.from_json(link_json)

    return ()


# Traceback (KeyError)
Traceback (most recent call last):
File "/home/USERNAME/.local/bin/archivebox", line 8, in <module>
sys.exit(main())
File "/home/USERNAME/.local/lib/python3.7/site-packages/archivebox/cli/__init__.py", line 126, in main
pwd=pwd or OUTPUT_DIR,
File "/home/USERNAME/.local/lib/python3.7/site-packages/archivebox/cli/__init__.py", line 62, in run_subcommand
module.main(args=subcommand_args, stdin=stdin, pwd=pwd)    # type: ignore
File "/home/USERNAME/.local/lib/python3.7/site-packages/archivebox/cli/archivebox_init.py", line 34, in main
out_dir=pwd or OUTPUT_DIR,
File "/home/USERNAME/.local/lib/python3.7/site-packages/archivebox/util.py", line 108, in typechecked_function
return func(*args, **kwargs)
File "/home/USERNAME/.local/lib/python3.7/site-packages/archivebox/main.py", line 316, in init
for link in load_main_index(out_dir=out_dir, warn=False)
File "/home/USERNAME/.local/lib/python3.7/site-packages/archivebox/util.py", line 108, in typechecked_function
return func(*args, **kwargs)
File "/home/USERNAME/.local/lib/python3.7/site-packages/archivebox/index/__init__.py", line 250, in load_main_index
all_links = list(parse_json_main_index(out_dir))
File "/home/USERNAME/.local/lib/python3.7/site-packages/archivebox/index/json.py", line 52, in parse_json_main_index
yield Link.from_json(link_json)
File "/home/USERNAME/.local/lib/python3.7/site-packages/archivebox/index/schema.py", line 203, in from_json
cast_result = ArchiveResult.from_json(json_result)
File "/home/USERNAME/.local/lib/python3.7/site-packages/archivebox/index/schema.py", line 62, in from_json
info['start_ts'] = parse_date(info['start_ts'])
KeyError: 'start_ts'

# Fixed code
def parse_json_main_index(out_dir: str = OUTPUT_DIR) -> Iterator[Link]:
    """parse an archive index json file and return the list of links"""

    index_path = os.path.join(out_dir, JSON_INDEX_FILENAME)
    if os.path.exists(index_path):
        with open(index_path, "r", encoding="utf-8") as f:
            links = pyjson.load(f)["links"]
            for link_json in links:
                try:
                    yield Link.from_json(link_json)
                except KeyError:
                    detail_index_path = (
                        Path(OUTPUT_DIR) / ARCHIVE_DIR_NAME / link_json["timestamp"]
                    )
                    yield parse_json_link_details(str(detail_index_path))

    return ()