gurudesh/pytracebugs huggingface dataset:
# Buggy code
def should_stop_early(cfg: DictConfig, valid_loss: float) -> bool:
    # skip check if no validation was done in the current epoch
    if valid_loss is None:
        return False
    if cfg.checkpoint.patience <= 0:
        return False

    def is_better(a, b):
        return a > b if cfg.checkpoint.maximize_best_checkpoint_metric else a < b

    prev_best = getattr(should_stop_early, "best", None)
    if prev_best is None or is_better(valid_loss, prev_best):
        should_stop_early.best = valid_loss
        should_stop_early.num_runs = 0
        return False
    else:
        should_stop_early.num_runs += 1
        if should_stop_early.num_runs >= cfg.checkpoint.patience:
            logger.info(
                "early stop since valid performance hasn't improved for last {} runs".format(
                    cfg.checkpoint.patience
                )
            )
            return True
        else:
            return False


# Traceback (TypeError)
/experiments/falva/tools/fairseq/fairseq/models/fairseq_model.py:280: UserWarning: FairseqModel is deprecated, please use FairseqEncoderDecoderModel or BaseFairseqModel instead
for key in self.keys
Traceback (most recent call last):
File "/home/falva/anaconda3/envs/mtl4ts/bin/fairseq-generate", line 11, in <module>
load_entry_point('fairseq', 'console_scripts', 'fairseq-generate')()
File "/experiments/falva/tools/fairseq/fairseq_cli/generate.py", line 190, in cli_main
main(args)
File "/experiments/falva/tools/fairseq/fairseq_cli/generate.py", line 47, in main
task=task,
File "/experiments/falva/tools/fairseq/fairseq/checkpoint_utils.py", line 167, in load_model_ensemble
ensemble, args, _task = load_model_ensemble_and_task(filenames, arg_overrides, task)
File "/experiments/falva/tools/fairseq/fairseq/checkpoint_utils.py", line 186, in load_model_ensemble_and_task
model.load_state_dict(state['model'], strict=True, args=args)
TypeError: load_state_dict() got an unexpected keyword argument 'args'

# Fixed code
def should_stop_early(cfg: DictConfig, valid_loss: float) -> bool:
    # skip check if no validation was done in the current epoch
    if valid_loss is None:
        return False
    if cfg.checkpoint.patience <= 0:
        return False

    def is_better(a, b):
        return a > b if cfg.checkpoint.maximize_best_checkpoint_metric else a < b

    prev_best = getattr(should_stop_early, "best", None)
    if prev_best is None or is_better(valid_loss, prev_best):
        should_stop_early.best = valid_loss
        should_stop_early.num_runs = 0
        return False
    else:
        should_stop_early.num_runs += 1
        if should_stop_early.num_runs >= cfg.checkpoint.patience:
            logger.info(
                "early stop since valid performance hasn't improved for last {} runs".format(
                    cfg.checkpoint.patience
                )
            )
            return True
        else:
            return False
# Buggy code
def get_valid_stats(
    cfg: DictConfig, trainer: Trainer, stats: Dict[str, Any]
) -> Dict[str, Any]:
    stats["num_updates"] = trainer.get_num_updates()
    if hasattr(checkpoint_utils.save_checkpoint, "best"):
        key = "best_{0}".format(cfg.checkpoint.best_checkpoint_metric)
        best_function = max if cfg.checkpoint.maximize_best_checkpoint_metric else min
        stats[key] = best_function(
            checkpoint_utils.save_checkpoint.best,
            stats[cfg.checkpoint.best_checkpoint_metric],
        )
    return stats


# Traceback (TypeError)
/experiments/falva/tools/fairseq/fairseq/models/fairseq_model.py:280: UserWarning: FairseqModel is deprecated, please use FairseqEncoderDecoderModel or BaseFairseqModel instead
for key in self.keys
Traceback (most recent call last):
File "/home/falva/anaconda3/envs/mtl4ts/bin/fairseq-generate", line 11, in <module>
load_entry_point('fairseq', 'console_scripts', 'fairseq-generate')()
File "/experiments/falva/tools/fairseq/fairseq_cli/generate.py", line 190, in cli_main
main(args)
File "/experiments/falva/tools/fairseq/fairseq_cli/generate.py", line 47, in main
task=task,
File "/experiments/falva/tools/fairseq/fairseq/checkpoint_utils.py", line 167, in load_model_ensemble
ensemble, args, _task = load_model_ensemble_and_task(filenames, arg_overrides, task)
File "/experiments/falva/tools/fairseq/fairseq/checkpoint_utils.py", line 186, in load_model_ensemble_and_task
model.load_state_dict(state['model'], strict=True, args=args)
TypeError: load_state_dict() got an unexpected keyword argument 'args'

# Fixed code
def get_valid_stats(
    cfg: DictConfig, trainer: Trainer, stats: Dict[str, Any]
) -> Dict[str, Any]:
    stats["num_updates"] = trainer.get_num_updates()
    if hasattr(checkpoint_utils.save_checkpoint, "best"):
        key = "best_{0}".format(cfg.checkpoint.best_checkpoint_metric)
        best_function = max if cfg.checkpoint.maximize_best_checkpoint_metric else min
        stats[key] = best_function(
            checkpoint_utils.save_checkpoint.best,
            stats[cfg.checkpoint.best_checkpoint_metric],
        )
    return stats
# Buggy code
def reset_parameters(self):
    if self.qkv_same_dim:
        # Empirically observed the convergence to be much better with
        # the scaled initialization
        nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))
        nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2))
        nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))
    else:
        nn.init.xavier_uniform_(self.k_proj.weight)
        nn.init.xavier_uniform_(self.v_proj.weight)
        nn.init.xavier_uniform_(self.q_proj.weight)

    nn.init.xavier_uniform_(self.out_proj.weight)
    nn.init.constant_(self.out_proj.bias, 0.0)
    if self.bias_k is not None:
        nn.init.xavier_normal_(self.bias_k)
    if self.bias_v is not None:
        nn.init.xavier_normal_(self.bias_v)


# Traceback (AttributeError)
Traceback (most recent call last):
File "xxx/fairseq/fairseq/modules/multihead_attention.py", line 56, in __init__
self.reset_parameters()
File "xxx/fairseq/fairseq/modules/multihead_attention.py", line 82, in reset_parameters
nn.init.constant_(self.out_proj.bias, 0.)
File "xxx/torch/nn/init.py", line 120, in constant_
return _no_grad_fill_(tensor, val)
File "xxx/torch/nn/init.py", line 24, in _no_grad_fill_
return tensor.fill_(val)
AttributeError: 'NoneType' object has no attribute 'fill_'

# Fixed code
def reset_parameters(self):
    if self.qkv_same_dim:
        # Empirically observed the convergence to be much better with
        # the scaled initialization
        nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))
        nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2))
        nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))
    else:
        nn.init.xavier_uniform_(self.k_proj.weight)
        nn.init.xavier_uniform_(self.v_proj.weight)
        nn.init.xavier_uniform_(self.q_proj.weight)

    nn.init.xavier_uniform_(self.out_proj.weight)
    if self.out_proj.bias is not None:
        nn.init.constant_(self.out_proj.bias, 0.0)
    if self.bias_k is not None:
        nn.init.xavier_normal_(self.bias_k)
    if self.bias_v is not None:
        nn.init.xavier_normal_(self.bias_v)
# Buggy code
def __init__(self, *args, **kwargs):
    super().__init__(*args, **kwargs)


# Traceback (AttributeError)
Traceback (most recent call last):
File "xxx/fairseq/fairseq/modules/multihead_attention.py", line 56, in __init__
self.reset_parameters()
File "xxx/fairseq/fairseq/modules/multihead_attention.py", line 82, in reset_parameters
nn.init.constant_(self.out_proj.bias, 0.)
File "xxx/torch/nn/init.py", line 120, in constant_
return _no_grad_fill_(tensor, val)
File "xxx/torch/nn/init.py", line 24, in _no_grad_fill_
return tensor.fill_(val)
AttributeError: 'NoneType' object has no attribute 'fill_'

# Fixed code
def __init__(self, *args, **kwargs):
    super().__init__(*args, **kwargs)
    self._heartbeat_timeout = heartbeat_timeout
    if self._heartbeat_timeout > 0:
        self._heartbeat = threading.Event()
        self._heartbeat_thread = threading.Thread(
            target=self._check_heartbeat,
            args=(os.getpid(),),
            daemon=True,
        )
        self._heartbeat_thread.start()
    else:
        self._heartbeat = None
# Buggy code
def forward(self, *inputs, **kwargs):
    return self.module(*inputs, **kwargs)


# Traceback (AttributeError)
Traceback (most recent call last):
File "xxx/fairseq/fairseq/modules/multihead_attention.py", line 56, in __init__
self.reset_parameters()
File "xxx/fairseq/fairseq/modules/multihead_attention.py", line 82, in reset_parameters
nn.init.constant_(self.out_proj.bias, 0.)
File "xxx/torch/nn/init.py", line 120, in constant_
return _no_grad_fill_(tensor, val)
File "xxx/torch/nn/init.py", line 24, in _no_grad_fill_
return tensor.fill_(val)
AttributeError: 'NoneType' object has no attribute 'fill_'

# Fixed code
def forward(self, *args, **kwargs):
    if self._heartbeat is not None:
        self._heartbeat.set()
    return super().forward(*args, **kwargs)
# Buggy code
def execute(self):
    try:
        logger.debug("Passive hunter is attempting to get server certificate")
        addr = (str(self.event.host), self.event.port)
        cert = ssl.get_server_certificate(addr)
    except ssl.SSLError:
        # If the server doesn't offer SSL on this port we won't get a certificate
        return
    c = cert.strip(ssl.PEM_HEADER).strip(ssl.PEM_FOOTER)
    certdata = base64.decodebytes(c)
    emails = re.findall(email_pattern, certdata)
    for email in emails:
        self.publish_event(CertificateEmail(email=email))


# Traceback (TypeError)
2020-04-29 15:18:23,356 DEBUG kube_hunter.core.events.handler expected bytes-like object, not str
Traceback (most recent call last):
File "/usr/lib/python3.6/base64.py", line 510, in _input_type_check
m = memoryview(s)
TypeError: memoryview: a bytes-like object is required, not 'str'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/mac/kube-hunter/kube_hunter/core/events/handler.py", line 137, in worker
hook.execute()
File "/mac/kube-hunter/kube_hunter/modules/hunting/certificates.py", line 43, in execute
certdata = base64.decodebytes(c)
File "/usr/lib/python3.6/base64.py", line 545, in decodebytes
_input_type_check(s)
File "/usr/lib/python3.6/base64.py", line 513, in _input_type_check
raise TypeError(msg) from err
TypeError: expected bytes-like object, not str

# Fixed code
def execute(self):
    try:
        logger.debug("Passive hunter is attempting to get server certificate")
        addr = (str(self.event.host), self.event.port)
        cert = ssl.get_server_certificate(addr)
    except ssl.SSLError:
        # If the server doesn't offer SSL on this port we won't get a certificate
        return
    self.examine_certificate(cert)
# Buggy code
def extra_attributes(self):
    attributes = {}
    for listener in self.listeners:
        attributes.update(listener)

    return attributes


# Traceback (TypeError)
import anyio
listener = await anyio.create_tcp_listener(local_port=33333)
listener.extra(anyio.abc.SocketAttribute.local_address)
Traceback (most recent call last):
File ".../lib/python3.8/concurrent/futures/_base.py", line 439, in result
return self.__get_result()
File ".../lib/python3.8/concurrent/futures/_base.py", line 388, in __get_result
raise self._exception
File ".../lib/python3.8/asyncio/__main__.py", line 34, in callback
coro = func()
File "<console>", line 1, in <module>
File ".../python3.8/site-packages/anyio/_core/_typedattr.py", line 74, in extra
return self.extra_attributes[attribute]()
File ".../lib/python3.8/site-packages/anyio/streams/stapled.py", line 123, in extra_attributes
attributes.update(listener)
TypeError: 'SocketListener' object is not iterable

# Fixed code
def extra_attributes(self):
    attributes = {}
    for listener in self.listeners:
        attributes.update(listener.extra_attributes)

    return attributes
# Buggy code
def stats_history(runner):
    """Save current stats info to history for charts of report."""
    while True:
        stats = runner.stats
        r = {
            "time": datetime.datetime.now().strftime("%H:%M:%S"),
            "current_rps": stats.total.current_rps or 0,
            "current_fail_per_sec": stats.total.current_fail_per_sec or 0,
            "response_time_percentile_95": stats.total.get_current_response_time_percentile(
                0.95
            )
            or 0,
            "response_time_percentile_50": stats.total.get_current_response_time_percentile(
                0.5
            )
            or 0,
            "user_count": runner.user_count or 0,
        }
        stats.history.append(r)
        gevent.sleep(HISTORY_STATS_INTERVAL_SEC)


# Traceback (ValueError)
$ locust --worker --locustfile=main.py
[2020-08-20 11:02:50,637] C02TD0F6GTDX/INFO/locust.main: Starting Locust 1.2
Traceback (most recent call last):
File "src/gevent/greenlet.py", line 854, in gevent._gevent_cgreenlet.Greenlet.run
File "/usr/local/lib/python3.8/site-packages/locust/stats.py", line 766, in stats_history
'response_time_percentile_95': stats.total.get_current_response_time_percentile(0.95) or 0,
File "/usr/local/lib/python3.8/site-packages/locust/stats.py", line 553, in get_current_response_time_percentile
raise ValueError("StatsEntry.use_response_times_cache must be set to True if we should be able to calculate the _current_ response time percentile")
ValueError: StatsEntry.use_response_times_cache must be set to True if we should be able to calculate the _current_ response time percentile
2020-08-20T09:02:50Z <Greenlet at 0x10803f6a0: stats_history(<locust.runners.WorkerRunner object at 0x10806da60)> failed with ValueError

# Fixed code
def stats_history(runner):
    """Save current stats info to history for charts of report."""
    while True:
        stats = runner.stats
        if not stats.total.use_response_times_cache:
            break
        r = {
            "time": datetime.datetime.now().strftime("%H:%M:%S"),
            "current_rps": stats.total.current_rps or 0,
            "current_fail_per_sec": stats.total.current_fail_per_sec or 0,
            "response_time_percentile_95": stats.total.get_current_response_time_percentile(
                0.95
            )
            or 0,
            "response_time_percentile_50": stats.total.get_current_response_time_percentile(
                0.5
            )
            or 0,
            "user_count": runner.user_count or 0,
        }
        stats.history.append(r)
        gevent.sleep(HISTORY_STATS_INTERVAL_SEC)
# Buggy code
def fire(self, *, reverse=False, **kwargs):
    if reverse:
        handlers = reversed(self._handlers)
    else:
        handlers = self._handlers
    for handler in handlers:
        handler(**kwargs)


# Traceback (ZeroDivisionError)
[2020-07-03 03:37:18,086] instance-1/INFO/locust.main: Time limit reached. Stopping Locust.
Traceback (most recent call last):
File "src/gevent/greenlet.py", line 854, in gevent._gevent_cgreenlet.Greenlet.run
File "/home/camilojimenez/locust/venv/lib/python3.7/site-packages/locust/main.py", line 231, in timelimit_stop
runner.quit()
File "/home/camilojimenez/locust/venv/lib/python3.7/site-packages/locust/runners.py", line 294, in quit
self.stop()
File "/home/camilojimenez/locust/venv/lib/python3.7/site-packages/locust/runners.py", line 340, in stop
self.environment.events.test_stop.fire(environment=self.environment)
File "/home/camilojimenez/locust/venv/lib/python3.7/site-packages/locust/event.py", line 33, in fire
handler(**kwargs)
File "/home/camilojimenez/locust/locustfile.py", line 6, in on_test_stop
1/0
ZeroDivisionError: division by zero
2020-07-03T03:37:18Z <Greenlet at 0x7fcad134b9d8: timelimit_stop> failed with ZeroDivisionError

[2020-07-03 03:37:18,091] instance-1/CRITICAL/locust.main: Unhandled exception in greenlet: <Greenlet at 0x7fcad134b9d8: timelimit_stop>
Traceback (most recent call last):
File "src/gevent/greenlet.py", line 854, in gevent._gevent_cgreenlet.Greenlet.run
File "/home/camilojimenez/locust/venv/lib/python3.7/site-packages/locust/main.py", line 231, in timelimit_stop
runner.quit()
File "/home/camilojimenez/locust/venv/lib/python3.7/site-packages/locust/runners.py", line 294, in quit
self.stop()
File "/home/camilojimenez/locust/venv/lib/python3.7/site-packages/locust/runners.py", line 340, in stop
self.environment.events.test_stop.fire(environment=self.environment)
File "/home/camilojimenez/locust/venv/lib/python3.7/site-packages/locust/event.py", line 33, in fire
handler(**kwargs)
File "/home/camilojimenez/locust/locustfile.py", line 6, in on_test_stop
1/0
ZeroDivisionError: division by zero
Name                                                          # reqs      # fails     Avg     Min     Max  |  Median   req/s failures/s
--------------------------------------------------------------------------------------------------------------------------------------------
--------------------------------------------------------------------------------------------------------------------------------------------
Aggregated                                                         0     0(0.00%)       0       0       0  |       0    0.00    0.00

# Fixed code
def fire(self, *, reverse=False, **kwargs):
    if reverse:
        handlers = reversed(self._handlers)
    else:
        handlers = self._handlers
    for handler in handlers:
        try:
            handler(**kwargs)
        except Exception as e:
            logging.error("Uncaught exception in event handler: %s", e)
            unhandled_greenlet_exception = True
# Buggy code
def start_hatching(self, locust_count=None, hatch_rate=None, wait=False):
    self.hatching_greenlet = gevent.spawn(
        lambda: super(LocalLocustRunner, self).start_hatching(
            locust_count, hatch_rate, wait=wait
        )
    )
    self.greenlet = self.hatching_greenlet


# Traceback (RuntimeError)
Traceback (most recent call last):
File "gevent/pywsgi.py", line 964, in handle_one_response
self.run_application()
File "gevent/pywsgi.py", line 911, in run_application
self.result = self.application(self.environ, self.start_response)
File "flask/app.py", line 2463, in __call__
return self.wsgi_app(environ, start_response)
File "flask/app.py", line 2449, in wsgi_app
response = self.handle_exception(e)
File "flask/app.py", line 1866, in handle_exception
reraise(exc_type, exc_value, tb)
File "flask/_compat.py", line 39, in reraise
raise value
File "flask/app.py", line 2446, in wsgi_app
response = self.full_dispatch_request()
File "flask/app.py", line 1951, in full_dispatch_request
rv = self.handle_user_exception(e)
File "flask/app.py", line 1820, in handle_user_exception
reraise(exc_type, exc_value, tb)
File "flask/_compat.py", line 39, in reraise
raise value
File "flask/app.py", line 1949, in full_dispatch_request
rv = self.dispatch_request()
File "flask/app.py", line 1935, in dispatch_request
return self.view_functions[rule.endpoint](**req.view_args)
File "locust/web.py", line 85, in stop
runners.locust_runner.stop()
File "locust/runners.py", line 196, in stop
for locust_greenlet in self.locusts:
RuntimeError: Set changed size during iteration
2019-11-27T18:41:36Z
{'REMOTE_..., (hidden keys: 26)} failed with RuntimeError

# Fixed code
def start_hatching(self, locust_count=None, hatch_rate=None, wait=False):
    if hatch_rate > 100:
        logger.warning(
            "Your selected hatch rate is very high (>100), and this is known to sometimes cause issues. Do you really need to ramp up that fast?"
        )
    self.hatching_greenlet = gevent.spawn(
        lambda: super(LocalLocustRunner, self).start_hatching(
            locust_count, hatch_rate, wait=wait
        )
    )
    self.greenlet = self.hatching_greenlet
# Buggy code
def _python_apply_general(self, f: F, data: FrameOrSeriesUnion) -> FrameOrSeriesUnion:
    """
    Apply function f in python space

    Parameters
    ----------
    f : callable
        Function to apply
    data : Series or DataFrame
        Data to apply f to

    Returns
    -------
    Series or DataFrame
        data after applying f
    """
    keys, values, mutated = self.grouper.apply(f, data, self.axis)

    return self._wrap_applied_output(
        keys, values, not_indexed_same=mutated or self.mutated
    )


# Traceback (KeyError)
Empty DataFrame
Columns: []
Index: []
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/pandas/core/frame.py", line 2927, in __getitem__
indexer = self.columns.get_loc(key)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/indexes/base.py", line 2659, in get_lo
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas/_libs/index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 132, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1601, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1608, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'b'

# Fixed code
def _python_apply_general(self, f: F, data: FrameOrSeriesUnion) -> FrameOrSeriesUnion:
    """
    Apply function f in python space

    Parameters
    ----------
    f : callable
        Function to apply
    data : Series or DataFrame
        Data to apply f to

    Returns
    -------
    Series or DataFrame
        data after applying f
    """
    keys, values, mutated = self.grouper.apply(f, data, self.axis)

    return self._wrap_applied_output(
        data, keys, values, not_indexed_same=mutated or self.mutated
    )
# Buggy code
def _wrap_applied_output(self, keys, values, not_indexed_same: bool = False):
    raise AbstractMethodError(self)


# Traceback (KeyError)
Empty DataFrame
Columns: []
Index: []
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/pandas/core/frame.py", line 2927, in __getitem__
indexer = self.columns.get_loc(key)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/indexes/base.py", line 2659, in get_lo
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas/_libs/index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 132, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1601, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1608, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'b'

# Fixed code
def _wrap_applied_output(self, data, keys, values, not_indexed_same: bool = False):
    raise AbstractMethodError(self)
# Buggy code
def setup(self):
    N = 10**5
    na = np.arange(int(N / 2))
    self.left = np.concatenate([na[: int(N / 4)], na[: int(N / 4)]])
    self.right = np.concatenate([na, na])


# Traceback (IndexError)
In [9]: df1 = pd.DataFrame(np.random.randn(3,3), columns=['A', 'A', 'B1'])
...: df2 = pd.DataFrame(np.random.randn(3,3), columns=['A', 'A', 'B2'])

In [10]: pd.concat([df1, df2])

Traceback (most recent call last):
File "<ipython-input-10-f61a1ab4009e>", line 1, in <module>
pd.concat([df1, df2])
...
File "c:\users\vdbosscj\scipy\pandas-joris\pandas\core\index.py", line 765, in take
taken = self.view(np.ndarray).take(indexer)
IndexError: index 3 is out of bounds for axis 0 with size 3

# Fixed code
def setup(self, N):
    data = np.arange(N, dtype=float)
    data[40] = np.nan
    self.array = pd.array(data, dtype="Int64")
# Buggy code
def maybe_box_datetimelike(value: Scalar, dtype: Optional[Dtype] = None) -> Scalar:
    """
    Cast scalar to Timestamp or Timedelta if scalar is datetime-like
    and dtype is not object.

    Parameters
    ----------
    value : scalar
    dtype : Dtype, optional

    Returns
    -------
    scalar
    """
    if dtype == object:
        pass
    elif isinstance(value, (np.datetime64, datetime)):
        value = tslibs.Timestamp(value)
    elif isinstance(value, (np.timedelta64, timedelta)):
        value = tslibs.Timedelta(value)

    return value


# Traceback (AssertionError)
In [31]: import pandas as pd
...: import pandas._testing as tm
...:
...: td = pd.Timedelta(nanoseconds=500)
...: ser = pd.Series({"a": td})
...: expected = pd.Series(td, index=["a"], dtype="timedelta64[ns]")
...:
...: tm.assert_series_equal(ser, expected)
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-31-a9c6a6312101> in <module>
6 expected = pd.Series(td, index=["a"], dtype="timedelta64[ns]")
7
----> 8 tm.assert_series_equal(ser, expected)

[... skipping hidden 1 frame]

~/repos/pandas/pandas/_testing.py in assert_extension_array_equal(left, right, check_dtype, index_values, check_less_precise, check_exact, rtol, atol)
1243         # Avoid slow object-dtype comparisons
1244         # np.asarray for case where we have a np.MaskedArray
-> 1245         assert_numpy_array_equal(
1246             np.asarray(left.asi8), np.asarray(right.asi8), index_values=index_values
1247         )

[... skipping hidden 1 frame]

~/repos/pandas/pandas/_testing.py in _raise(left, right, err_msg)
1155             diff = diff * 100.0 / left.size
1156             msg = f"{obj} values are different ({np.round(diff, 5)} %)"
-> 1157             raise_assert_detail(obj, msg, left, right, index_values=index_values)
1158
1159         raise AssertionError(err_msg)

~/repos/pandas/pandas/_testing.py in raise_assert_detail(obj, message, left, right, diff, index_values)
1085         msg += f"\n[diff]: {diff}"
1086
-> 1087     raise AssertionError(msg)
1088
1089

AssertionError: numpy array are different

numpy array values are different (100.0 %)
[index]: [a]
[left]:  [500]
[right]: [0]

# Fixed code
def maybe_box_datetimelike(value: Scalar, dtype: Optional[Dtype] = None) -> Scalar:
    """
    Cast scalar to Timestamp or Timedelta if scalar is datetime-like
    and dtype is not object.

    Parameters
    ----------
    value : scalar
    dtype : Dtype, optional

    Returns
    -------
    scalar
    """
    if dtype == object:
        pass
    elif isinstance(value, (np.datetime64, datetime)):
        value = Timestamp(value)
    elif isinstance(value, (np.timedelta64, timedelta)):
        value = Timedelta(value)

    return value
# Buggy code
def drop(self, labels, errors: str_t = "raise"):
    """
    Make new Index with passed list of labels deleted.

    Parameters
    ----------
    labels : array-like
    errors : {'ignore', 'raise'}, default 'raise'
        If 'ignore', suppress error and existing labels are dropped.

    Returns
    -------
    dropped : Index

    Raises
    ------
    KeyError
        If not all of the labels are found in the selected axis
    """
    arr_dtype = "object" if self.dtype == "object" else None
    labels = com.index_labels_to_array(labels, dtype=arr_dtype)
    indexer = self.get_indexer(labels)
    mask = indexer == -1
    if mask.any():
        if errors != "ignore":
            raise KeyError(f"{labels[mask]} not found in axis")
        indexer = indexer[~mask]
    return self.delete(indexer)


# Traceback (pandas.errors.InvalidIndexError)
index = pd.Index(range(3)).repeat(2)

index.drop(1)
Traceback (most recent call last):
[...]
pandas.errors.InvalidIndexError: Reindexing only valid with uniquely valued Index objects

# Fixed code
def drop(self, labels, errors: str_t = "raise"):
    """
    Make new Index with passed list of labels deleted.

    Parameters
    ----------
    labels : array-like
    errors : {'ignore', 'raise'}, default 'raise'
        If 'ignore', suppress error and existing labels are dropped.

    Returns
    -------
    dropped : Index

    Raises
    ------
    KeyError
        If not all of the labels are found in the selected axis
    """
    arr_dtype = "object" if self.dtype == "object" else None
    labels = com.index_labels_to_array(labels, dtype=arr_dtype)
    indexer = self.get_indexer_for(labels)
    mask = indexer == -1
    if mask.any():
        if errors != "ignore":
            raise KeyError(f"{labels[mask]} not found in axis")
        indexer = indexer[~mask]
    return self.delete(indexer)
# Buggy code
def _convert_list_indexer(self, keyarr):
    """
    we are passed a list-like indexer. Return the
    indexer for matching intervals.
    """
    locs = self.get_indexer_for(keyarr)

    # we have missing values
    if (locs == -1).any():
        raise KeyError

    return locs


# Traceback (KeyError)
import numpy as np
import pandas as pd
pd.__version__
'0.25.0rc0+59.g0437f6899'

s=pd.Series(np.arange(5), pd.IntervalIndex.from_breaks(np.arange(6)))
s
(0, 1]    0
(1, 2]    1
(2, 3]    2
(3, 4]    3
(4, 5]    4
dtype: int32

s.loc[[4,5]]
(3, 4]    3
(4, 5]    4
dtype: int32

s.loc[[4,5,6]]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1409, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1816, in _getitem_axis
return self._getitem_iterable(key, axis=axis)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1118, in _getitem_iterable
keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1060, in _get_listlike_indexer
indexer, keyarr = ax._convert_listlike_indexer(key, kind=self.name)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexes\base.py", line 3239, in _convert_listlike_indexer
indexer = self._convert_list_indexer(keyarr, kind=kind)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexes\interval.py", line 626, in _convert_list_indexer
raise KeyError
KeyError

s.to_frame().loc[[4,5,6]]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1409, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1816, in _getitem_axis
return self._getitem_iterable(key, axis=axis)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1118, in _getitem_iterable
keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 1060, in _get_listlike_indexer
indexer, keyarr = ax._convert_listlike_indexer(key, kind=self.name)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexes\base.py", line 3239, in _convert_listlike_indexer
indexer = self._convert_list_indexer(keyarr, kind=kind)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexes\interval.py", line 626, in _convert_list_indexer
raise KeyError
KeyError


# Fixed code
def _convert_list_indexer(self, keyarr):
    """
    we are passed a list-like indexer. Return the
    indexer for matching intervals.
    """
    locs = self.get_indexer_for(keyarr)

    # we have missing values
    if (locs == -1).any():
        raise KeyError(keyarr[locs == -1].tolist())

    return locs
# Buggy code
def head(self, n=5):
    """
    Return first n rows of each group.

    Similar to ``.apply(lambda x: x.head(n))``, but it returns a subset of rows
    from the original DataFrame with original index and order preserved
    (``as_index`` flag is ignored).

    Does not work for negative values of `n`.

    Returns
    -------
    Series or DataFrame
    %(see_also)s
    Examples
    --------

    >>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
    ...                   columns=['A', 'B'])
    >>> df.groupby('A').head(1)
       A  B
    0  1  2
    2  5  6
    >>> df.groupby('A').head(-1)
    Empty DataFrame
    Columns: [A, B]
    Index: []
    """
    self._reset_group_selection()
    mask = self._cumcount_array() < n
    return self._selected_obj[mask]


# Traceback (IndexError)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jonas/Code/pandas/pandas/core/groupby.py", line 986, in head
in_head = self._cumcount_array() < n
File "/home/jonas/Code/pandas/pandas/core/groupby.py", line 1044, in _cumcount_array
cumcounts[indices] = values
IndexError: index 10 is out of bounds for axis 1 with size 10

# Fixed code
def head(self, n=5):
    """
    Return first n rows of each group.

    Similar to ``.apply(lambda x: x.head(n))``, but it returns a subset of rows
    from the original DataFrame with original index and order preserved
    (``as_index`` flag is ignored).

    Does not work for negative values of `n`.

    Returns
    -------
    Series or DataFrame
    %(see_also)s
    Examples
    --------

    >>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
    ...                   columns=['A', 'B'])
    >>> df.groupby('A').head(1)
       A  B
    0  1  2
    2  5  6
    >>> df.groupby('A').head(-1)
    Empty DataFrame
    Columns: [A, B]
    Index: []
    """
    self._reset_group_selection()
    mask = self._cumcount_array() < n
    if self.axis == 0:
        return self._selected_obj[mask]
    else:
        return self._selected_obj.iloc[:, mask]
# Buggy code
def tail(self, n=5):
    """
    Return last n rows of each group.

    Similar to ``.apply(lambda x: x.tail(n))``, but it returns a subset of rows
    from the original DataFrame with original index and order preserved
    (``as_index`` flag is ignored).

    Does not work for negative values of `n`.

    Returns
    -------
    Series or DataFrame
    %(see_also)s
    Examples
    --------

    >>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],
    ...                   columns=['A', 'B'])
    >>> df.groupby('A').tail(1)
       A  B
    1  a  2
    3  b  2
    >>> df.groupby('A').tail(-1)
    Empty DataFrame
    Columns: [A, B]
    Index: []
    """
    self._reset_group_selection()
    mask = self._cumcount_array(ascending=False) < n
    return self._selected_obj[mask]


# Traceback (IndexError)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jonas/Code/pandas/pandas/core/groupby.py", line 986, in head
in_head = self._cumcount_array() < n
File "/home/jonas/Code/pandas/pandas/core/groupby.py", line 1044, in _cumcount_array
cumcounts[indices] = values
IndexError: index 10 is out of bounds for axis 1 with size 10

# Fixed code
def tail(self, n=5):
    """
    Return last n rows of each group.

    Similar to ``.apply(lambda x: x.tail(n))``, but it returns a subset of rows
    from the original DataFrame with original index and order preserved
    (``as_index`` flag is ignored).

    Does not work for negative values of `n`.

    Returns
    -------
    Series or DataFrame
    %(see_also)s
    Examples
    --------

    >>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],
    ...                   columns=['A', 'B'])
    >>> df.groupby('A').tail(1)
       A  B
    1  a  2
    3  b  2
    >>> df.groupby('A').tail(-1)
    Empty DataFrame
    Columns: [A, B]
    Index: []
    """
    self._reset_group_selection()
    mask = self._cumcount_array(ascending=False) < n
    if self.axis == 0:
        return self._selected_obj[mask]
    else:
        return self._selected_obj.iloc[:, mask]
# Buggy code
def write(self, obj, **kwargs):
    super().write(obj, **kwargs)
    self.write_index("index", obj.index)
    self.write_array("values", obj.values)
    self.attrs.name = obj.name


# Traceback (TypeError)
In [1]: import pandas as pd
...:
...: def check_roundtrip(obj):
...:     with pd.HDFStore('test.h5', 'w') as store:
...:         store['obj'] = obj
...:         retrieved = store['obj']
...:         return obj.equals(retrieved)
...:
...: s = pd.Series([], dtype='datetime64[ns, UTC]')
...: t = pd.Series([0], dtype='datetime64[ns, UTC]')
...: df = pd.DataFrame({'A': s})

In [2]: print(check_roundtrip(s))
False

In [3]: print(check_roundtrip(t))
False

In [4]: print(check_roundtrip(df))

------------------------------------------------------------------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-4-57f1e9853bf6> in <module>()
----> 1 print(check_roundtrip(df))

<ipython-input-1-1d7fb84453e9> in check_roundtrip(obj)
4     with pd.HDFStore('test.h5', 'w') as store:
5         store['obj'] = obj
----> 6         retrieved = store['obj']
7         return obj.equals(retrieved)
8

/home/ashieh/.local/lib/python2.7/site-packages/pandas/io/pytables.pyc in __getitem__(self, key)
481
482     def __getitem__(self, key):
--> 483         return self.get(key)
484
485     def __setitem__(self, key, value):

/home/ashieh/.local/lib/python2.7/site-packages/pandas/io/pytables.pyc in get(self, key)
669         if group is None:
670             raise KeyError('No object named %s in the file' % key)
--> 671         return self._read_group(group)
672
673     def select(self, key, where=None, start=None, stop=None, columns=None,

/home/ashieh/.local/lib/python2.7/site-packages/pandas/io/pytables.pyc in _read_group(self, group, **kwargs)
1347         s = self._create_storer(group)
1348         s.infer_axes()
-> 1349         return s.read(**kwargs)
1350
1351

/home/ashieh/.local/lib/python2.7/site-packages/pandas/io/pytables.pyc in read(self, start, stop, **kwargs)
2902             blk_items = self.read_index('block%d_items' % i)
2903             values = self.read_array('block%d_values' % i,
-> 2904                                      start=_start, stop=_stop)
2905             blk = make_block(values,
2906                              placement=items.get_indexer(blk_items))

/home/ashieh/.local/lib/python2.7/site-packages/pandas/io/pytables.pyc in read_array(self, key, start, stop)
2464             if shape is not None:
2465                 # length 0 axis
-> 2466                 ret = np.empty(shape, dtype=dtype)
2467             else:
2468                 ret = node[start:stop]

TypeError: Invalid datetime unit in metadata string "[ns, UTC]"

# Fixed code
def write(self, obj, **kwargs):
    super().write(obj, **kwargs)
    self.write_index("index", obj.index)
    self.write_array("values", obj)
    self.attrs.name = obj.name
# Buggy code
def _validate_setitem_value(self, value):
    value = extract_array(value, extract_numpy=True)

    # require identical categories set
    if isinstance(value, Categorical):
        if not is_dtype_equal(self, value):
            raise ValueError(
                "Cannot set a Categorical with another, without identical categories"
            )
        new_codes = self._validate_listlike(value)
        value = Categorical.from_codes(new_codes, dtype=self.dtype)

    rvalue = value if is_list_like(value) else [value]

    from pandas import Index

    to_add = Index(rvalue).difference(self.categories)

    # no assignments of values not in categories, but it's always ok to set
    # something to np.nan
    if len(to_add) and not isna(to_add).all():
        raise ValueError(
            "Cannot setitem on a Categorical with a new "
            "category, set the categories first"
        )

    return self._unbox_listlike(rvalue)


# Traceback (ValueError)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-5-4d3f2e8a3cfd> in <module>()
----> 1 c[0] = (1, 2)

/Users/taugspurger/miniconda3/envs/pandas-0.19.2/lib/python3.5/site-packages/pandas/core/categorical.py in __setitem__(self, key, value)
1652         # something to np.nan
1653         if len(to_add) and not isnull(to_add).all():
-> 1654             raise ValueError("Cannot setitem on a Categorical with a new "
1655                              "category, set the categories first")
1656

ValueError: Cannot setitem on a Categorical with a new category, set the categories first

# Fixed code
def _validate_setitem_value(self, value):
    value = extract_array(value, extract_numpy=True)

    # require identical categories set
    if isinstance(value, Categorical):
        if not is_dtype_equal(self, value):
            raise ValueError(
                "Cannot set a Categorical with another, without identical categories"
            )
        new_codes = self._validate_listlike(value)
        value = Categorical.from_codes(new_codes, dtype=self.dtype)

    # wrap scalars and hashable-listlikes in list
    rvalue = value if not is_hashable(value) else [value]

    from pandas import Index

    to_add = Index(rvalue).difference(self.categories)

    # no assignments of values not in categories, but it's always ok to set
    # something to np.nan
    if len(to_add) and not isna(to_add).all():
        raise ValueError(
            "Cannot setitem on a Categorical with a new "
            "category, set the categories first"
        )

    return self._unbox_listlike(rvalue)
# Buggy code
def __rmatmul__(self, other):
    """
    Matrix multiplication using binary `@` operator in Python>=3.5.
    """
    return self.T.dot(np.transpose(other)).T


# Traceback (ValueError)
# a.__matmul__ is called
... a @ df
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
ValueError: shapes (10,4) and (5,3) not aligned: 4 (dim 1) != 5 (dim 0)

# df.__rmatmul__ is called
... a.tolist() @ df
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/mingli/GitHub/open/pandas/pandas/core/frame.py", line 901, in __rmatmul__
return self.T.dot(np.transpose(other)).T
File "/Users/mingli/GitHub/open/pandas/pandas/core/frame.py", line 879, in dot
r=rvals.shape))
ValueError: Dot product shape mismatch, (3, 5) vs (4, 10)

# Fixed code
def __rmatmul__(self, other):
    """
    Matrix multiplication using binary `@` operator in Python>=3.5.
    """
    try:
        return self.T.dot(np.transpose(other)).T
    except ValueError as err:
        if "shape mismatch" not in str(err):
            raise
        # GH#21581 give exception message for original shapes
        msg = f"shapes {np.shape(other)} and {self.shape} not aligned"
        raise ValueError(msg) from err
# Buggy code
def __init__(
    self,
    axis: Index,
    groupings: Sequence["grouper.Grouping"],
    sort: bool = True,
    group_keys: bool = True,
    mutated: bool = False,
    indexer: Optional[np.ndarray] = None,
):
    assert isinstance(axis, Index), axis

    self._filter_empty_groups = self.compressed = len(groupings) != 1
    self.axis = axis
    self._groupings: List[grouper.Grouping] = list(groupings)
    self.sort = sort
    self.group_keys = group_keys
    self.mutated = mutated
    self.indexer = indexer


# Traceback (AttributeError)
In [1]: import pandas as pd
...: df = pd.DataFrame({"A": [0, 0, 1, None], "B": [10, 2, 10, None]})
...: gb = df.groupby('A', dropna=False)

In [2]: gb.dropna
Out[2]: False

In [3]: gb.grouper.dropna
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-4d8081f9c78e> in <module>
----> 1 gb.grouper.dropna

AttributeError: 'BaseGrouper' object has no attribute 'dropna'

# Fixed code
def __init__(
    self,
    axis: Index,
    groupings: Sequence["grouper.Grouping"],
    sort: bool = True,
    group_keys: bool = True,
    mutated: bool = False,
    indexer: Optional[np.ndarray] = None,
    dropna: bool = True,
):
    assert isinstance(axis, Index), axis

    self._filter_empty_groups = self.compressed = len(groupings) != 1
    self.axis = axis
    self._groupings: List[grouper.Grouping] = list(groupings)
    self.sort = sort
    self.group_keys = group_keys
    self.mutated = mutated
    self.indexer = indexer
    self.dropna = dropna
# Buggy code
def _get_cell_string_value(self, cell) -> str:
    """
    Find and decode OpenDocument text:s tags that represent
    a run length encoded sequence of space characters.
    """
    from odf.element import Element, Text
    from odf.namespaces import TEXTNS
    from odf.text import P, S

    text_p = P().qname
    text_s = S().qname

    p = cell.childNodes[0]

    value = []
    if p.qname == text_p:
        for k, fragment in enumerate(p.childNodes):
            if isinstance(fragment, Text):
                value.append(fragment.data)
            elif isinstance(fragment, Element):
                if fragment.qname == text_s:
                    spaces = int(fragment.attributes.get((TEXTNS, "c"), 1))
                value.append(" " * spaces)
    return "".join(value)


# Traceback (UnboundLocalError)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/michael/.local/share/virtualenvs/merge-csv-NFbvYFrS/lib/python3.8/site-packages/pandas/util/_decorators.py", line 296, in wrapper
return func(*args, **kwargs)
File "/home/michael/.local/share/virtualenvs/merge-csv-NFbvYFrS/lib/python3.8/site-packages/pandas/io/excel/_base.py", line 311, in read_excel
return io.parse(
File "/home/michael/.local/share/virtualenvs/merge-csv-NFbvYFrS/lib/python3.8/site-packages/pandas/io/excel/_base.py", line 906, in parse
return self._reader.parse(
File "/home/michael/.local/share/virtualenvs/merge-csv-NFbvYFrS/lib/python3.8/site-packages/pandas/io/excel/_base.py", line 443, in parse
data = self.get_sheet_data(sheet, convert_float)
File "/home/michael/.local/share/virtualenvs/merge-csv-NFbvYFrS/lib/python3.8/site-packages/pandas/io/excel/_odfreader.py", line 91, in get_sheet_data
value = self._get_cell_value(sheet_cell, convert_float)
File "/home/michael/.local/share/virtualenvs/merge-csv-NFbvYFrS/lib/python3.8/site-packages/pandas/io/excel/_odfreader.py", line 175, in _get_cell_value
return self._get_cell_string_value(cell)
File "/home/michael/.local/share/virtualenvs/merge-csv-NFbvYFrS/lib/python3.8/site-packages/pandas/io/excel/_odfreader.py", line 211, in _get_cell_string_value
value.append(" " * spaces)
UnboundLocalError: local variable 'spaces' referenced before assignment

# Fixed code
def _get_cell_string_value(self, cell) -> str:
    """
    Find and decode OpenDocument text:s tags that represent
    a run length encoded sequence of space characters.
    """
    from odf.element import Element
    from odf.namespaces import TEXTNS
    from odf.text import S

    text_s = S().qname

    value = []

    for fragment in cell.childNodes:
        if isinstance(fragment, Element):
            if fragment.qname == text_s:
                spaces = int(fragment.attributes.get((TEXTNS, "c"), 1))
                value.append(" " * spaces)
            else:
                # recursive impl needed in case of nested fragments
                # with multiple spaces
                # https://github.com/pandas-dev/pandas/pull/36175#discussion_r484639704
                value.append(self._get_cell_string_value(fragment))
        else:
            value.append(str(fragment))
    return "".join(value)