gurudesh/pytracebugs huggingface dataset:
# Buggy code
def _setup_x(adata, layer, use_raw):
    if use_raw and layer:
        logging.warning("use_raw and layer were both passed in. Defaulting to use_raw.")

    # checking layers
    if use_raw:
        if adata.raw is None:
            raise ValueError("use_raw is True but adata.raw is None")
        logger.info("Using data from adata.raw.X")
        x_loc = "X"
        x_key = "None"
        x = adata.raw.X
    elif layer is not None:
        assert layer in adata.layers.keys(), (
            "{} is not a valid key in adata.layers".format(layer)
        )
        logger.info('Using data from adata.layers["{}"]'.format(layer))
        x_loc = "layers"
        x_key = layer
        x = adata.layers[x_key]
    else:
        logger.info("Using data from adata.X")
        x_loc = "X"
        x_key = "None"
        x = adata.X

    if _check_nonnegative_integers(x) is False:
        logger_data_loc = (
            "adata.X" if layer is None else "adata.layers[{}]".format(layer)
        )
        warnings.warn(
            "{} does not contain unnormalized count data. Are you sure this is what you want?".format(
                logger_data_loc
            )
        )

    return x_loc, x_key


# Traceback (AttributeError)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-125-eb1e0fcfe1d1> in <module>
1 de = model.differential_expression(
----> 2     groupby="leiden",
3 )
4 de.head()

~/anaconda3/lib/python3.7/site-packages/scvi/core/models/rnamixin.py in differential_expression(self, adata, groupby, group1, group2, idx1, idx2, mode, delta, batch_size, all_stats, batch_correction, batchid1, batchid2, **kwargs)
179         adata = self._validate_anndata(adata)
180
--> 181         col_names = _get_var_names_from_setup_anndata(adata)
182         model_fn = partial(
183             self.get_normalized_expression,

~/anaconda3/lib/python3.7/site-packages/scvi/model/_utils.py in _get_var_names_from_setup_anndata(adata)
119         adata.var_names
120         if adata.uns["_scvi"]["use_raw"] is False
--> 121         else adata.raw.var_names
122     )
123

AttributeError: 'NoneType' object has no attribute 'var_names'

# Fixed code
def _setup_x(adata, layer):
    if layer is not None:
        assert layer in adata.layers.keys(), (
            "{} is not a valid key in adata.layers".format(layer)
        )
        logger.info('Using data from adata.layers["{}"]'.format(layer))
        x_loc = "layers"
        x_key = layer
        x = adata.layers[x_key]
    else:
        logger.info("Using data from adata.X")
        x_loc = "X"
        x_key = "None"
        x = adata.X

    if _check_nonnegative_integers(x) is False:
        logger_data_loc = (
            "adata.X" if layer is None else "adata.layers[{}]".format(layer)
        )
        warnings.warn(
            "{} does not contain unnormalized count data. Are you sure this is what you want?".format(
                logger_data_loc
            )
        )

    return x_loc, x_key
# Buggy code
def _setup_library_size(adata, batch_key, layer, use_raw):
    # computes the library size per batch
    logger.info("Computing library size prior per batch")
    local_l_mean_key = "_scvi_local_l_mean"
    local_l_var_key = "_scvi_local_l_var"
    _compute_library_size_batch(
        adata,
        batch_key=batch_key,
        local_l_mean_key=local_l_mean_key,
        local_l_var_key=local_l_var_key,
        layer=layer,
        use_raw=use_raw,
    )
    return local_l_mean_key, local_l_var_key


# Traceback (AttributeError)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-125-eb1e0fcfe1d1> in <module>
1 de = model.differential_expression(
----> 2     groupby="leiden",
3 )
4 de.head()

~/anaconda3/lib/python3.7/site-packages/scvi/core/models/rnamixin.py in differential_expression(self, adata, groupby, group1, group2, idx1, idx2, mode, delta, batch_size, all_stats, batch_correction, batchid1, batchid2, **kwargs)
179         adata = self._validate_anndata(adata)
180
--> 181         col_names = _get_var_names_from_setup_anndata(adata)
182         model_fn = partial(
183             self.get_normalized_expression,

~/anaconda3/lib/python3.7/site-packages/scvi/model/_utils.py in _get_var_names_from_setup_anndata(adata)
119         adata.var_names
120         if adata.uns["_scvi"]["use_raw"] is False
--> 121         else adata.raw.var_names
122     )
123

AttributeError: 'NoneType' object has no attribute 'var_names'

# Fixed code
def _setup_library_size(adata, batch_key, layer):
    # computes the library size per batch
    logger.info("Computing library size prior per batch")
    local_l_mean_key = "_scvi_local_l_mean"
    local_l_var_key = "_scvi_local_l_var"
    _compute_library_size_batch(
        adata,
        batch_key=batch_key,
        local_l_mean_key=local_l_mean_key,
        local_l_var_key=local_l_var_key,
        layer=layer,
    )
    return local_l_mean_key, local_l_var_key
# Buggy code
def _get_var_names_from_setup_anndata(adata):
    """Gets var names by checking if using raw."""
    var_names = (
        adata.var_names
        if adata.uns["_scvi"]["use_raw"] is False
        else adata.raw.var_names
    )

    return var_names


# Traceback (AttributeError)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-125-eb1e0fcfe1d1> in <module>
1 de = model.differential_expression(
----> 2     groupby="leiden",
3 )
4 de.head()

~/anaconda3/lib/python3.7/site-packages/scvi/core/models/rnamixin.py in differential_expression(self, adata, groupby, group1, group2, idx1, idx2, mode, delta, batch_size, all_stats, batch_correction, batchid1, batchid2, **kwargs)
179         adata = self._validate_anndata(adata)
180
--> 181         col_names = _get_var_names_from_setup_anndata(adata)
182         model_fn = partial(
183             self.get_normalized_expression,

~/anaconda3/lib/python3.7/site-packages/scvi/model/_utils.py in _get_var_names_from_setup_anndata(adata)
119         adata.var_names
120         if adata.uns["_scvi"]["use_raw"] is False
--> 121         else adata.raw.var_names
122     )
123

AttributeError: 'NoneType' object has no attribute 'var_names'

# Fixed code
def _get_var_names_from_setup_anndata(adata):
    """Gets var names by checking if using raw."""
    var_names = adata.var_names
    return var_names
# Buggy code
def history(self):
    """Returns computed metrics during training."""
    if self.is_trained_ is False:
        return {}
    else:
        return {
            "unsupervised_trainer_history": self._unsupervised_trainer.history,
            "semisupervised_trainer_history": self.trainer.history,
        }


# Traceback (AttributeError)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-125-eb1e0fcfe1d1> in <module>
1 de = model.differential_expression(
----> 2     groupby="leiden",
3 )
4 de.head()

~/anaconda3/lib/python3.7/site-packages/scvi/core/models/rnamixin.py in differential_expression(self, adata, groupby, group1, group2, idx1, idx2, mode, delta, batch_size, all_stats, batch_correction, batchid1, batchid2, **kwargs)
179         adata = self._validate_anndata(adata)
180
--> 181         col_names = _get_var_names_from_setup_anndata(adata)
182         model_fn = partial(
183             self.get_normalized_expression,

~/anaconda3/lib/python3.7/site-packages/scvi/model/_utils.py in _get_var_names_from_setup_anndata(adata)
119         adata.var_names
120         if adata.uns["_scvi"]["use_raw"] is False
--> 121         else adata.raw.var_names
122     )
123

AttributeError: 'NoneType' object has no attribute 'var_names'

# Fixed code
def history(self):
    """Returns computed metrics during training."""
    return {
        "unsupervised_trainer_history": self.unsupervised_history_,
        "semisupervised_trainer_history": self.semisupervised_history_,
    }
# Buggy code
def _setup_extra_categorical_covs(
    adata: anndata.AnnData,
    categorical_covariate_keys: List[str],
    category_dict: Dict[str, List[str]] = None,
):
    """
    Setup obsm df for extra categorical covariates.

    Parameters
    ----------
    adata
        AnnData to setup
    categorical_covariate_keys
        List of keys in adata.obs with categorical data
    category_dict
        Optional dictionary with keys being keys of categorical data in obs
        and values being precomputed categories for each obs vector

    """
    for key in categorical_covariate_keys:
        _assert_key_in_obs(adata, key)

    cat_loc = "obsm"
    cat_key = "_scvi_extra_categoricals"

    one_hots = []

    categories = {}
    for key in categorical_covariate_keys:
        cat = adata.obs[key]
        if category_dict is not None:
            possible_cats = category_dict[key]
            cat = cat.astype(CategoricalDtype(categories=possible_cats))
        else:
            categories[key] = cat.astype("category").cat.categories

        one_hot_rep = pd.get_dummies(cat, prefix=key)
        one_hots.append(one_hot_rep)

    adata.obsm[cat_key] = pd.concat(one_hots, axis=1)

    store_cats = categories if category_dict is None else category_dict
    adata.uns["_scvi"]["extra_categorical_mappings"] = store_cats
    return cat_loc, cat_key


# Traceback (NotImplementedError)
NotImplementedError: Failed to write value for uns/_scvi/categorical_mappings/_scvi_batch/mapping, since a writer for type <class 'pandas.core.indexes.base.Index'> has not been implemented yet.

Above error raised while writing key 'uns/_scvi/categorical_mappings/_scvi_batch/mapping' of <class 'h5py._hl.files.File'> from /.

# Fixed code
def _setup_extra_categorical_covs(
    adata: anndata.AnnData,
    categorical_covariate_keys: List[str],
    category_dict: Dict[str, List[str]] = None,
):
    """
    Setup obsm df for extra categorical covariates.

    Parameters
    ----------
    adata
        AnnData to setup
    categorical_covariate_keys
        List of keys in adata.obs with categorical data
    category_dict
        Optional dictionary with keys being keys of categorical data in obs
        and values being precomputed categories for each obs vector

    """
    for key in categorical_covariate_keys:
        _assert_key_in_obs(adata, key)

    cat_loc = "obsm"
    cat_key = "_scvi_extra_categoricals"

    one_hots = []

    categories = {}
    for key in categorical_covariate_keys:
        cat = adata.obs[key]
        if category_dict is not None:
            possible_cats = category_dict[key]
            cat = cat.astype(CategoricalDtype(categories=possible_cats))
        else:
            categories[key] = cat.astype("category").cat.categories.to_numpy(copy=True)

        one_hot_rep = pd.get_dummies(cat, prefix=key)
        one_hots.append(one_hot_rep)

    adata.obsm[cat_key] = pd.concat(one_hots, axis=1)

    store_cats = categories if category_dict is None else category_dict
    adata.uns["_scvi"]["extra_categorical_mappings"] = store_cats
    return cat_loc, cat_key
# Buggy code
def update_cells(self, subset_cells):
    new_n_cells = (
        len(subset_cells)
        if subset_cells.dtype is not np.dtype("bool")
        else subset_cells.sum()
    )
    print("Downsampling from %i to %i cells" % (len(self), new_n_cells))
    for attr_name in ["_X", "labels", "batch_indices", "local_means", "local_vars"]:
        setattr(self, attr_name, getattr(self, attr_name)[subset_cells])
    self.library_size_batch()


# Traceback (MemoryError)
---------------------------------------------------------------------------
MemoryError                               Traceback (most recent call last)
<ipython-input-3-138de299bfb8> in <module>
1 # load
2 dataset = AnnDataset('data/dataset.h5ad',
----> 3                      save_path=path)

~/anaconda3/envs/pyro/lib/python3.6/site-packages/scvi/dataset/anndata.py in __init__(self, filename, save_path, url, new_n_genes, subset_genes)
36         self.url = url
37
---> 38         data, gene_names = self.download_and_preprocess()
39
40         super().__init__(*GeneExpressionDataset.get_attributes_from_matrix(data),

~/anaconda3/envs/pyro/lib/python3.6/site-packages/scvi/dataset/dataset.py in download_and_preprocess(self)
61     def download_and_preprocess(self):
62         self.download()
---> 63         return self.preprocess()
64
65     def collate_fn(self, batch):

~/anaconda3/envs/pyro/lib/python3.6/site-packages/scvi/dataset/anndata.py in preprocess(self)
52             data = ad.X.copy()  # Dense
53         else:
---> 54             data = ad.X.toarray()  # Sparse
55         select = data.sum(axis=1) > 0  # Take out cells that doesn't express any gene
56         data = data[select, :]

~/anaconda3/envs/pyro/lib/python3.6/site-packages/scipy/sparse/compressed.py in toarray(self, order, out)
960         if out is None and order is None:
961             order = self._swap('cf')[0]
--> 962         out = self._process_toarray_args(order, out)
963         if not (out.flags.c_contiguous or out.flags.f_contiguous):
964             raise ValueError('Output array must be C or F contiguous')

~/anaconda3/envs/pyro/lib/python3.6/site-packages/scipy/sparse/base.py in _process_toarray_args(self, order, out)
1185             return out
1186         else:
-> 1187             return np.zeros(self.shape, dtype=self.dtype, order=order)
1188
1189

MemoryError:

# Fixed code
def update_cells(self, subset_cells):
    new_n_cells = (
        len(subset_cells)
        if subset_cells.dtype is not np.dtype("bool")
        else subset_cells.sum()
    )
    print("Downsampling from %i to %i cells" % (len(self), new_n_cells))
    for attr_name in [
        "_X",
        "labels",
        "batch_indices",
        "local_means",
        "local_vars",
        "x_coord",
        "y_coord",
    ]:
        if getattr(self, attr_name) is not None:
            setattr(self, attr_name, getattr(self, attr_name)[subset_cells])
    self.library_size_batch()
# Buggy code
def download(datapath):
    model_name = "pretrained_transformers"
    mdir = os.path.join(get_model_dir(datapath), model_name)
    version = "v2.0"
    if not built(mdir, version):
        opt = {"datapath": datapath}
        fnames = ["pretrained_transformers.tgz"]
        download_models(opt, fnames, model_name, version=version, use_model_type=False)


# Traceback (ModuleNotFoundError)
model = torch.load('/Downloads/polyranker/model', map_location=lambda cpu, _: cpu)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/edinan/anaconda3/lib/python3.6/site-packages/torch/serialization.py", line 368, in load
return _load(f, map_location, pickle_module)
File "/Users/edinan/anaconda3/lib/python3.6/site-packages/torch/serialization.py", line 542, in _load
result = unpickler.load()
ModuleNotFoundError: No module named 'apex'

# Fixed code
def download(datapath):
    model_name = "pretrained_transformers"
    mdir = os.path.join(get_model_dir(datapath), model_name)
    version = "v3.0"
    if not built(mdir, version):
        opt = {"datapath": datapath}
        fnames = ["pretrained_transformers.tgz"]
        download_models(opt, fnames, model_name, version=version, use_model_type=False)
# Buggy code
def _aggregate(self, arg, *args, **kwargs):
    _axis = kwargs.pop("_axis", 0)
    kwargs.pop("_level", None)

    if isinstance(arg, str):
        kwargs.pop("is_transform", None)
        return self._string_function(arg, *args, **kwargs)

    # Dictionaries have complex behavior because they can be renamed here.
    elif isinstance(arg, dict):
        return self._default_to_pandas("agg", arg, *args, **kwargs)
    elif is_list_like(arg) or callable(arg):
        kwargs.pop("is_transform", None)
        return self.apply(arg, axis=_axis, args=args, **kwargs)
    else:
        raise TypeError("type {} is not callable".format(type(arg)))


# Traceback (TypeError)
Traceback (most recent call last):
File "agg_test2.py", line 13, in <module>
df1 = df.agg(new_col=('col2', max))
File "/localdisk/gashiman/modin/modin/pandas/base.py", line 504, in aggregate
return self.apply(func, axis=axis, args=args, **kwargs)
File "/localdisk/gashiman/modin/modin/pandas/dataframe.py", line 289, in apply
query_compiler = super(DataFrame, self).apply(
File "/localdisk/gashiman/modin/modin/pandas/base.py", line 716, in apply
raise TypeError("{} object is not callable".format(type(func)))
TypeError: <class 'NoneType'> object is not callable

# Fixed code
def _aggregate(self, func, *args, **kwargs):
    _axis = kwargs.pop("_axis", 0)
    kwargs.pop("_level", None)

    if isinstance(func, str):
        kwargs.pop("is_transform", None)
        return self._string_function(func, *args, **kwargs)

    # Dictionaries have complex behavior because they can be renamed here.
    elif func is None or isinstance(func, dict):
        return self._default_to_pandas("agg", func, *args, **kwargs)
    elif is_list_like(func) or callable(func):
        kwargs.pop("is_transform", None)
        return self.apply(func, axis=_axis, args=args, **kwargs)
    else:
        raise TypeError("type {} is not callable".format(type(func)))
# Buggy code
def unique(self):
    return self._query_compiler.unique().to_numpy().squeeze()


# Traceback (TypeError)
===PANDAS===
0    green
dtype: object
<class 'pandas.core.series.Series'>
['green']
<class 'numpy.ndarray'>
1

===MODIN===
0    green
dtype: object
<class 'modin.pandas.series.Series'>
green
<class 'numpy.ndarray'>
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-1-c4f0aa247643> in <module>
19 print(su)
20 print(type(su))
---> 21 print(len(su))

TypeError: len() of unsized object

# Fixed code
def unique(self):
    return self.__constructor__(query_compiler=self._query_compiler.unique()).to_numpy()
# Buggy code
def reduce_fn(df, **kwargs):
    df.dropna(axis=1, inplace=True, how="any")
    return build_applyier(reduce_apply_fn, axis=axis)(df)


# Traceback (AssertionError)
Traceback (most recent call last):
File "../TESTS/t2.py", line 108, in <module>
df_equals(md_df.mean(axis=1), pd_df.mean(axis=1))
File "/localdisk/dchigare/repos/modin_bp/modin/pandas/test/utils.py", line 520, in df_equals
assert_series_equal(df1, df2, check_dtype=False, check_series_type=False)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/_testing.py", line 1401, in assert_series_equal
index_values=np.asarray(left.index),
File "pandas/_libs/testing.pyx", line 67, in pandas._libs.testing.assert_almost_equal
File "pandas/_libs/testing.pyx", line 182, in pandas._libs.testing.assert_almost_equal
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/_testing.py", line 1036, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Series are different

Series values are different (100.0 %)
[index]: [0, 1, 2, 3]
[left]:  [nan, nan, nan, nan]
[right]: [-2.5, -2.1333333333333333, 6.033333333333334, 8.0]

# Fixed code
def reduce_fn(df, **kwargs):
    sum_cols = df["sum"] if axis else df.loc["sum"]
    count_cols = df["count"] if axis else df.loc["count"]

    if not isinstance(sum_cols, pandas.Series):
        # If we got `NaN` as the result of the sum in any axis partition,
        # then we must consider the whole sum as `NaN`, so setting `skipna=False`
        sum_cols = sum_cols.sum(axis=axis, skipna=False)
        count_cols = count_cols.sum(axis=axis, skipna=False)
    return sum_cols / count_cols
# Buggy code
def applyier(df, **kwargs):
    result = df.apply(func, **applyier_kwargs)
    return result.set_axis(df.axes[axis ^ 1], axis=0)


# Traceback (AssertionError)
Traceback (most recent call last):
File "../TESTS/t2.py", line 108, in <module>
df_equals(md_df.mean(axis=1), pd_df.mean(axis=1))
File "/localdisk/dchigare/repos/modin_bp/modin/pandas/test/utils.py", line 520, in df_equals
assert_series_equal(df1, df2, check_dtype=False, check_series_type=False)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/_testing.py", line 1401, in assert_series_equal
index_values=np.asarray(left.index),
File "pandas/_libs/testing.pyx", line 67, in pandas._libs.testing.assert_almost_equal
File "pandas/_libs/testing.pyx", line 182, in pandas._libs.testing.assert_almost_equal
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/_testing.py", line 1036, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Series are different

Series values are different (100.0 %)
[index]: [0, 1, 2, 3]
[left]:  [nan, nan, nan, nan]
[right]: [-2.5, -2.1333333333333333, 6.033333333333334, 8.0]

# Fixed code
def applyier(df, other):
    concated = pandas.concat([df, other], axis=1, copy=False)
    result = concated.pivot_table(
        index=index,
        values=values if len(values) > 0 else None,
        columns=columns,
        aggfunc=aggfunc,
        fill_value=fill_value,
        margins=margins,
        dropna=dropna,
        margins_name=margins_name,
        observed=observed,
    )

    # in that case Pandas transposes the result of `pivot_table`,
    # transposing it back to be consistent with column axis values along
    # different partitions
    if len(index) == 0 and len(columns) > 0:
        result = result.T

    return result
# Buggy code
def make_reindexer(do_reindex: bool, frame_idx: int):
    # the order of the frames must match the order of the indexes
    if not do_reindex:
        return lambda df: df

    if is_duplicates:
        assert indexers != []

        return lambda df: df._reindex_with_indexers(
            {0: [joined_index, indexers[frame_idx]]},
            copy=True,
            allow_dups=True,
        )

    return lambda df: df.reindex(joined_index, axis=axis)


# Traceback (TypeError)
Traceback (most recent call last):
File "test_outer.py", line 13, in <module>
md_df["b"] = pd.Series(np.zeros(len(md_df))) # TypeError
File "/localdisk/dchigare/repos/modin_bp/modin/pandas/dataframe.py", line 1978, in __setitem__
join="left",
File "/localdisk/dchigare/repos/modin_bp/modin/backends/pandas/query_compiler.py", line 303, in concat
new_modin_frame = self._modin_frame._concat(axis, other_modin_frame, join, sort)
File "/localdisk/dchigare/repos/modin_bp/modin/engines/base/frame/data.py", line 1850, in _concat
axis ^ 1, others, how, sort, force_repartition=True
File "/localdisk/dchigare/repos/modin_bp/modin/engines/base/frame/data.py", line 1701, in _copartition
joined_index = self._join_index_objects(axis, index_other_obj, how, sort)
File "/localdisk/dchigare/repos/modin_bp/modin/engines/base/frame/data.py", line 980, in _join_index_objects
joined_obj = merge_index(joined_obj, obj)
File "/localdisk/dchigare/repos/modin_bp/modin/engines/base/frame/data.py", line 974, in merge_index
return obj1.join(obj2, how=how, sort=sort)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/indexes/datetimelike.py", line 893, in join
sort=sort,
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 3483, in join
return this.join(other, how=how, return_indexers=return_indexers)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 3494, in join
other, how=how, return_indexers=return_indexers
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 3815, in _join_monotonic
join_index, lidx, ridx = self._left_indexer(sv, ov)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 257, in _left_indexer
return libjoin.left_join_indexer(left, right)
File "pandas/_libs/join.pyx", line 357, in pandas._libs.join.left_join_indexer
TypeError: '<' not supported between instances of 'Timestamp' and 'int'

# Fixed code
def make_reindexer(do_reindex: bool, frame_idx: int):
    # the order of the frames must match the order of the indexes
    if not do_reindex:
        return lambda df: df

    if need_indexers:
        assert indexers is not None

        return lambda df: df._reindex_with_indexers(
            {0: [joined_index, indexers[frame_idx]]},
            copy=True,
            allow_dups=True,
        )

    return lambda df: df.reindex(joined_index, axis=axis)
# Buggy code
def setitem(self, axis, key, value):
    """Set the column defined by `key` to the `value` provided.

    Args:
        key: The column name to set.
        value: The value to set the column to.

    Returns:
         A new QueryCompiler
    """
    if axis == 1 or not isinstance(value, type(self)):
        return super().setitem(axis=axis, key=key, value=value)

    return self._setitem(axis, key, value)


# Traceback (TypeError)
Traceback (most recent call last):
File "test_outer.py", line 13, in <module>
md_df["b"] = pd.Series(np.zeros(len(md_df))) # TypeError
File "/localdisk/dchigare/repos/modin_bp/modin/pandas/dataframe.py", line 1978, in __setitem__
join="left",
File "/localdisk/dchigare/repos/modin_bp/modin/backends/pandas/query_compiler.py", line 303, in concat
new_modin_frame = self._modin_frame._concat(axis, other_modin_frame, join, sort)
File "/localdisk/dchigare/repos/modin_bp/modin/engines/base/frame/data.py", line 1850, in _concat
axis ^ 1, others, how, sort, force_repartition=True
File "/localdisk/dchigare/repos/modin_bp/modin/engines/base/frame/data.py", line 1701, in _copartition
joined_index = self._join_index_objects(axis, index_other_obj, how, sort)
File "/localdisk/dchigare/repos/modin_bp/modin/engines/base/frame/data.py", line 980, in _join_index_objects
joined_obj = merge_index(joined_obj, obj)
File "/localdisk/dchigare/repos/modin_bp/modin/engines/base/frame/data.py", line 974, in merge_index
return obj1.join(obj2, how=how, sort=sort)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/indexes/datetimelike.py", line 893, in join
sort=sort,
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 3483, in join
return this.join(other, how=how, return_indexers=return_indexers)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 3494, in join
other, how=how, return_indexers=return_indexers
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 3815, in _join_monotonic
join_index, lidx, ridx = self._left_indexer(sv, ov)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 257, in _left_indexer
return libjoin.left_join_indexer(left, right)
File "pandas/_libs/join.pyx", line 357, in pandas._libs.join.left_join_indexer
TypeError: '<' not supported between instances of 'Timestamp' and 'int'

# Fixed code
def setitem(self, axis, key, value):
    """Set the column defined by `key` to the `value` provided.

    Args:
        key: The column name to set.
        value: The value to set the column to.

    Returns:
         A new QueryCompiler
    """
    if axis == 1 or not isinstance(value, type(self)):
        return super().setitem(axis=axis, key=key, value=value)

    try:
        result = self._setitem(axis, key, value)
    # OmniSci engine does not yet support cases when `value` is not a subframe of `self`.
    except NotImplementedError:
        result = super().setitem(axis=axis, key=key, value=value)
    return result
# Buggy code
def map_func(df, *args, **kwargs):
    return df.squeeze(axis=1).value_counts(**kwargs)


# Traceback (Exception)
Traceback (most recent call last):
File "test.py", line 15, in <module>
print(f"modin:\n{modin_df.sum(min_count=1)}")
File "modin\pandas\base.py", line 3512, in __str__
return repr(self)
File "modin\pandas\series.py", line 307, in __repr__
temp_df = self._build_repr_df(num_rows, num_cols)
File "modin\pandas\base.py", line 108, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "modin\backends\pandas\query_compiler.py", line 191, in to_pandas
return self._modin_frame.to_pandas()
File "modin\engines\base\frame\data.py", line 1801, in to_pandas
"Internal and external indices do not match.",
File "modin\error_message.py", line 54, in catch_bugs_and_request_email
" caused this error.\n{}".format(extra_log)
Exception: Internal Error. Please email bug_reports@modin.org with the traceback and command that caused this error.
Internal and external indices do not match.

# Fixed code
def map_func(df, *args, **kwargs):
    return df.squeeze(axis=1).value_counts(**kwargs).to_frame()
# Buggy code
def applyier(df, other):
    concated = pandas.concat([df, other], axis=1, copy=False)
    result = concated.pivot_table(
        index=index,
        values=values if len(values) > 0 else None,
        columns=columns,
        aggfunc=aggfunc,
        fill_value=fill_value,
        margins=margins,
        dropna=dropna,
        margins_name=margins_name,
        observed=observed,
    )

    # in that case Pandas transposes the result of `pivot_table`,
    # transposing it back to be consistent with column axis values along
    # different partitions
    if len(index) == 0 and len(columns) > 0:
        result = result.T

    return result


# Traceback (Exception)
Traceback (most recent call last):
File "test.py", line 15, in <module>
print(f"modin:\n{modin_df.sum(min_count=1)}")
File "modin\pandas\base.py", line 3512, in __str__
return repr(self)
File "modin\pandas\series.py", line 307, in __repr__
temp_df = self._build_repr_df(num_rows, num_cols)
File "modin\pandas\base.py", line 108, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "modin\backends\pandas\query_compiler.py", line 191, in to_pandas
return self._modin_frame.to_pandas()
File "modin\engines\base\frame\data.py", line 1801, in to_pandas
"Internal and external indices do not match.",
File "modin\error_message.py", line 54, in catch_bugs_and_request_email
" caused this error.\n{}".format(extra_log)
Exception: Internal Error. Please email bug_reports@modin.org with the traceback and command that caused this error.
Internal and external indices do not match.

# Fixed code
def applyier(df, **kwargs):
    result = df.apply(func, **applyier_kwargs)
    return result.set_axis(df.axes[axis ^ 1], axis=0)
# Buggy code
def caller(df, *args, **kwargs):
    df = df.copy()
    func(df, *args, **kwargs)
    return df


# Traceback (Exception)
Traceback (most recent call last):
File "test.py", line 15, in <module>
print(f"modin:\n{modin_df.sum(min_count=1)}")
File "modin\pandas\base.py", line 3512, in __str__
return repr(self)
File "modin\pandas\series.py", line 307, in __repr__
temp_df = self._build_repr_df(num_rows, num_cols)
File "modin\pandas\base.py", line 108, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "modin\backends\pandas\query_compiler.py", line 191, in to_pandas
return self._modin_frame.to_pandas()
File "modin\engines\base\frame\data.py", line 1801, in to_pandas
"Internal and external indices do not match.",
File "modin\error_message.py", line 54, in catch_bugs_and_request_email
" caused this error.\n{}".format(extra_log)
Exception: Internal Error. Please email bug_reports@modin.org with the traceback and command that caused this error.
Internal and external indices do not match.

# Fixed code
def caller(self, *args, **kwargs):
    # If `numeric_only` is None then we don't know what columns/indices will
    # be dropped at the result of reduction function, and so can't preserve labels
    preserve_index = kwargs.get("numeric_only", None) is not None
    return applier.register(*funcs, preserve_index=preserve_index)(
        self, *args, **kwargs
    )
# Buggy code
def call(cls, fold_function, **call_kwds):
    def caller(query_compiler, *args, **kwargs):
        return query_compiler.__constructor__(
            query_compiler._modin_frame._fold(
                call_kwds.get("axis") if "axis" in call_kwds else kwargs.get("axis"),
                lambda x: fold_function(x, *args, **kwargs),
            )
        )

    return caller


# Traceback (Exception)
Traceback (most recent call last):
File "test.py", line 15, in <module>
print(f"modin:\n{modin_df.sum(min_count=1)}")
File "modin\pandas\base.py", line 3512, in __str__
return repr(self)
File "modin\pandas\series.py", line 307, in __repr__
temp_df = self._build_repr_df(num_rows, num_cols)
File "modin\pandas\base.py", line 108, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "modin\backends\pandas\query_compiler.py", line 191, in to_pandas
return self._modin_frame.to_pandas()
File "modin\engines\base\frame\data.py", line 1801, in to_pandas
"Internal and external indices do not match.",
File "modin\error_message.py", line 54, in catch_bugs_and_request_email
" caused this error.\n{}".format(extra_log)
Exception: Internal Error. Please email bug_reports@modin.org with the traceback and command that caused this error.
Internal and external indices do not match.

# Fixed code
def call(cls, fold_function, **call_kwds):
    def caller(query_compiler, *args, **kwargs):
        axis = call_kwds.get("axis", kwargs.get("axis"))
        return query_compiler.__constructor__(
            query_compiler._modin_frame._fold(
                cls.validate_axis(axis),
                lambda x: fold_function(x, *args, **kwargs),
            )
        )

    return caller
# Buggy code
def caller(query_compiler, *args, **kwargs):
    return query_compiler.__constructor__(
        query_compiler._modin_frame._fold(
            call_kwds.get("axis") if "axis" in call_kwds else kwargs.get("axis"),
            lambda x: fold_function(x, *args, **kwargs),
        )
    )


# Traceback (Exception)
Traceback (most recent call last):
File "test.py", line 15, in <module>
print(f"modin:\n{modin_df.sum(min_count=1)}")
File "modin\pandas\base.py", line 3512, in __str__
return repr(self)
File "modin\pandas\series.py", line 307, in __repr__
temp_df = self._build_repr_df(num_rows, num_cols)
File "modin\pandas\base.py", line 108, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "modin\backends\pandas\query_compiler.py", line 191, in to_pandas
return self._modin_frame.to_pandas()
File "modin\engines\base\frame\data.py", line 1801, in to_pandas
"Internal and external indices do not match.",
File "modin\error_message.py", line 54, in catch_bugs_and_request_email
" caused this error.\n{}".format(extra_log)
Exception: Internal Error. Please email bug_reports@modin.org with the traceback and command that caused this error.
Internal and external indices do not match.

# Fixed code
def caller(query_compiler, *args, **kwargs):
    axis = call_kwds.get("axis", kwargs.get("axis"))
    return query_compiler.__constructor__(
        query_compiler._modin_frame._fold(
            cls.validate_axis(axis),
            lambda x: fold_function(x, *args, **kwargs),
        )
    )
# Buggy code
def call(cls, map_function, reduce_function, **call_kwds):
    def caller(query_compiler, *args, **kwargs):
        preserve_index = call_kwds.pop("preserve_index", True)
        return query_compiler.__constructor__(
            query_compiler._modin_frame._map_reduce(
                call_kwds.get("axis") if "axis" in call_kwds else kwargs.get("axis"),
                lambda x: map_function(x, *args, **kwargs),
                lambda y: reduce_function(y, *args, **kwargs),
                preserve_index=preserve_index,
            )
        )

    return caller


# Traceback (Exception)
Traceback (most recent call last):
File "test.py", line 15, in <module>
print(f"modin:\n{modin_df.sum(min_count=1)}")
File "modin\pandas\base.py", line 3512, in __str__
return repr(self)
File "modin\pandas\series.py", line 307, in __repr__
temp_df = self._build_repr_df(num_rows, num_cols)
File "modin\pandas\base.py", line 108, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "modin\backends\pandas\query_compiler.py", line 191, in to_pandas
return self._modin_frame.to_pandas()
File "modin\engines\base\frame\data.py", line 1801, in to_pandas
"Internal and external indices do not match.",
File "modin\error_message.py", line 54, in catch_bugs_and_request_email
" caused this error.\n{}".format(extra_log)
Exception: Internal Error. Please email bug_reports@modin.org with the traceback and command that caused this error.
Internal and external indices do not match.

# Fixed code
def call(cls, map_function, reduce_function, **call_kwds):
    def caller(query_compiler, *args, **kwargs):
        preserve_index = call_kwds.pop("preserve_index", True)
        axis = call_kwds.get("axis", kwargs.get("axis"))
        return query_compiler.__constructor__(
            query_compiler._modin_frame._map_reduce(
                cls.validate_axis(axis),
                lambda x: map_function(x, *args, **kwargs),
                lambda y: reduce_function(y, *args, **kwargs),
                preserve_index=preserve_index,
            )
        )

    return caller
# Buggy code
def caller(query_compiler, *args, **kwargs):
    preserve_index = call_kwds.pop("preserve_index", True)
    return query_compiler.__constructor__(
        query_compiler._modin_frame._map_reduce(
            call_kwds.get("axis") if "axis" in call_kwds else kwargs.get("axis"),
            lambda x: map_function(x, *args, **kwargs),
            lambda y: reduce_function(y, *args, **kwargs),
            preserve_index=preserve_index,
        )
    )


# Traceback (Exception)
Traceback (most recent call last):
File "test.py", line 15, in <module>
print(f"modin:\n{modin_df.sum(min_count=1)}")
File "modin\pandas\base.py", line 3512, in __str__
return repr(self)
File "modin\pandas\series.py", line 307, in __repr__
temp_df = self._build_repr_df(num_rows, num_cols)
File "modin\pandas\base.py", line 108, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "modin\backends\pandas\query_compiler.py", line 191, in to_pandas
return self._modin_frame.to_pandas()
File "modin\engines\base\frame\data.py", line 1801, in to_pandas
"Internal and external indices do not match.",
File "modin\error_message.py", line 54, in catch_bugs_and_request_email
" caused this error.\n{}".format(extra_log)
Exception: Internal Error. Please email bug_reports@modin.org with the traceback and command that caused this error.
Internal and external indices do not match.

# Fixed code
def caller(query_compiler, *args, **kwargs):
    preserve_index = call_kwds.pop("preserve_index", True)
    axis = call_kwds.get("axis", kwargs.get("axis"))
    return query_compiler.__constructor__(
        query_compiler._modin_frame._map_reduce(
            cls.validate_axis(axis),
            lambda x: map_function(x, *args, **kwargs),
            lambda y: reduce_function(y, *args, **kwargs),
            preserve_index=preserve_index,
        )
    )
# Buggy code
def register(cls, map_function, reduce_function, **kwargs):
    return cls.call(map_function, reduce_function, **kwargs)


# Traceback (Exception)
Traceback (most recent call last):
File "test.py", line 15, in <module>
print(f"modin:\n{modin_df.sum(min_count=1)}")
File "modin\pandas\base.py", line 3512, in __str__
return repr(self)
File "modin\pandas\series.py", line 307, in __repr__
temp_df = self._build_repr_df(num_rows, num_cols)
File "modin\pandas\base.py", line 108, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "modin\backends\pandas\query_compiler.py", line 191, in to_pandas
return self._modin_frame.to_pandas()
File "modin\engines\base\frame\data.py", line 1801, in to_pandas
"Internal and external indices do not match.",
File "modin\error_message.py", line 54, in catch_bugs_and_request_email
" caused this error.\n{}".format(extra_log)
Exception: Internal Error. Please email bug_reports@modin.org with the traceback and command that caused this error.
Internal and external indices do not match.

# Fixed code
def register(cls, map_function, reduce_function=None, **kwargs):
    if reduce_function is None:
        reduce_function = map_function
    return cls.call(map_function, reduce_function, **kwargs)
# Buggy code
def call(cls, reduction_function, **call_kwds):
    def caller(query_compiler, *args, **kwargs):
        return query_compiler.__constructor__(
            query_compiler._modin_frame._fold_reduce(
                call_kwds.get("axis") if "axis" in call_kwds else kwargs.get("axis"),
                lambda x: reduction_function(x, *args, **kwargs),
            )
        )

    return caller


# Traceback (Exception)
Traceback (most recent call last):
File "test.py", line 15, in <module>
print(f"modin:\n{modin_df.sum(min_count=1)}")
File "modin\pandas\base.py", line 3512, in __str__
return repr(self)
File "modin\pandas\series.py", line 307, in __repr__
temp_df = self._build_repr_df(num_rows, num_cols)
File "modin\pandas\base.py", line 108, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "modin\backends\pandas\query_compiler.py", line 191, in to_pandas
return self._modin_frame.to_pandas()
File "modin\engines\base\frame\data.py", line 1801, in to_pandas
"Internal and external indices do not match.",
File "modin\error_message.py", line 54, in catch_bugs_and_request_email
" caused this error.\n{}".format(extra_log)
Exception: Internal Error. Please email bug_reports@modin.org with the traceback and command that caused this error.
Internal and external indices do not match.

# Fixed code
def call(cls, reduction_function, **call_kwds):
    def caller(query_compiler, *args, **kwargs):
        preserve_index = call_kwds.pop("preserve_index", True)
        axis = call_kwds.get("axis", kwargs.get("axis"))
        return query_compiler.__constructor__(
            query_compiler._modin_frame._fold_reduce(
                cls.validate_axis(axis),
                lambda x: reduction_function(x, *args, **kwargs),
                preserve_index=preserve_index,
            )
        )

    return caller
# Buggy code
def caller(query_compiler, *args, **kwargs):
    return query_compiler.__constructor__(
        query_compiler._modin_frame._fold_reduce(
            call_kwds.get("axis") if "axis" in call_kwds else kwargs.get("axis"),
            lambda x: reduction_function(x, *args, **kwargs),
        )
    )


# Traceback (Exception)
Traceback (most recent call last):
File "test.py", line 15, in <module>
print(f"modin:\n{modin_df.sum(min_count=1)}")
File "modin\pandas\base.py", line 3512, in __str__
return repr(self)
File "modin\pandas\series.py", line 307, in __repr__
temp_df = self._build_repr_df(num_rows, num_cols)
File "modin\pandas\base.py", line 108, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "modin\backends\pandas\query_compiler.py", line 191, in to_pandas
return self._modin_frame.to_pandas()
File "modin\engines\base\frame\data.py", line 1801, in to_pandas
"Internal and external indices do not match.",
File "modin\error_message.py", line 54, in catch_bugs_and_request_email
" caused this error.\n{}".format(extra_log)
Exception: Internal Error. Please email bug_reports@modin.org with the traceback and command that caused this error.
Internal and external indices do not match.

# Fixed code
def caller(query_compiler, *args, **kwargs):
    preserve_index = call_kwds.pop("preserve_index", True)
    axis = call_kwds.get("axis", kwargs.get("axis"))
    return query_compiler.__constructor__(
        query_compiler._modin_frame._fold_reduce(
            cls.validate_axis(axis),
            lambda x: reduction_function(x, *args, **kwargs),
            preserve_index=preserve_index,
        )
    )
# Buggy code
def _compute_map_reduce_metadata(self, axis, new_parts):
    if axis == 0:
        columns = self.columns
        index = ["__reduced__"]
        new_lengths = [1]
        new_widths = self._column_widths
        new_dtypes = self._dtypes
    else:
        columns = ["__reduced__"]
        index = self.index
        new_lengths = self._row_lengths
        new_widths = [1]
        if self._dtypes is not None:
            new_dtypes = pandas.Series(
                np.full(1, find_common_type(self.dtypes.values)),
                index=["__reduced__"],
            )
        else:
            new_dtypes = self._dtypes
    return self.__constructor__(
        new_parts,
        index,
        columns,
        new_lengths,
        new_widths,
        new_dtypes,
        validate_axes="reduced",
    )


# Traceback (Exception)
Traceback (most recent call last):
File "test.py", line 15, in <module>
print(f"modin:\n{modin_df.sum(min_count=1)}")
File "modin\pandas\base.py", line 3512, in __str__
return repr(self)
File "modin\pandas\series.py", line 307, in __repr__
temp_df = self._build_repr_df(num_rows, num_cols)
File "modin\pandas\base.py", line 108, in _build_repr_df
return self.iloc[indexer]._query_compiler.to_pandas()
File "modin\backends\pandas\query_compiler.py", line 191, in to_pandas
return self._modin_frame.to_pandas()
File "modin\engines\base\frame\data.py", line 1801, in to_pandas
"Internal and external indices do not match.",
File "modin\error_message.py", line 54, in catch_bugs_and_request_email
" caused this error.\n{}".format(extra_log)
Exception: Internal Error. Please email bug_reports@modin.org with the traceback and command that caused this error.
Internal and external indices do not match.

# Fixed code
def _compute_map_reduce_metadata(self, axis, new_parts, preserve_index=True):
    """
    Computes metadata for the result of reduce function.

    Parameters
    ----------
    axis: int,
        The axis on which reduce function was applied
    new_parts: numpy 2D array
        Partitions with the result of applied function
    preserve_index: boolean
        The flag to preserve labels for the reduced axis.

    Returns
    -------
    BasePandasFrame
        Pandas series containing the reduced data.
    """
    new_axes, new_axes_lengths = [0, 0], [0, 0]

    new_axes[axis] = ["__reduced__"]
    new_axes[axis ^ 1] = (
        self.axes[axis ^ 1]
        if preserve_index
        else self._compute_axis_labels(axis ^ 1, new_parts)
    )

    new_axes_lengths[axis] = [1]
    new_axes_lengths[axis ^ 1] = (
        self._axes_lengths[axis ^ 1] if preserve_index else None
    )

    if (axis == 0 or self._dtypes is None) and preserve_index:
        new_dtypes = self._dtypes
    elif preserve_index:
        new_dtypes = pandas.Series(
            [find_common_type(self.dtypes.values)], index=new_axes[axis]
        )
    else:
        new_dtypes = None

    return self.__constructor__(
        new_parts,
        *new_axes,
        *new_axes_lengths,
        new_dtypes,
        validate_axes="reduced",
    )