benjamintli/cosqa-llm-filtered-hard-negatives huggingface dataset:
Query: move cursor down line python

Relevant code:
def move_up(lines=1, file=sys.stdout):
    """ Move the cursor up a number of lines.

        Esc[ValueA:
        Moves the cursor up by the specified number of lines without changing
        columns. If the cursor is already on the top line, ANSI.SYS ignores
        this sequence.
    """
    move.up(lines).write(file=file)

Similar but not relevant code:
def cursor_up(self, count=1):
        """ (for multiline edit). Move cursor to the previous line.  """
        original_column = self.preferred_column or self.document.cursor_position_col
        self.cursor_position += self.document.get_cursor_up_position(
            count=count, preferred_column=original_column)

        # Remember the original column for the next up/down movement.
        self.preferred_column = original_column

Similar but not relevant code:
def go_to_line(self, line):
        """
        Moves the text cursor to given line.

        :param line: Line to go to.
        :type line: int
        :return: Method success.
        :rtype: bool
        """

        cursor = self.textCursor()
        cursor.setPosition(self.document().findBlockByNumber(line - 1).position())
        self.setTextCursor(cursor)
        return True

Similar but not relevant code:
def select_up(self):
        """move cursor up"""
        r, c = self._index
        self._select_index(r-1, c)
Query: python, turn a string into a dict

Relevant code:
def string_to_identity(identity_str):
    """Parse string into Identity dictionary."""
    m = _identity_regexp.match(identity_str)
    result = m.groupdict()
    log.debug('parsed identity: %s', result)
    return {k: v for k, v in result.items() if v}

Similar but not relevant code:
def str_dict(some_dict):
    """Convert dict of ascii str/unicode to dict of str, if necessary"""
    return {str(k): str(v) for k, v in some_dict.items()}

Similar but not relevant code:
def stringify_dict_contents(dct):
    """Turn dict keys and values into native strings."""
    return {
        str_if_nested_or_str(k): str_if_nested_or_str(v)
        for k, v in dct.items()
    }

Similar but not relevant code:
def clean_dict_keys(d):
    """Convert all keys of the dict 'd' to (ascii-)strings.

    :Raises: UnicodeEncodeError
    """
    new_d = {}
    for (k, v) in d.iteritems():
        new_d[str(k)] = v
    return new_d
Query: python2 url parse query to dict

Relevant code:
def get_url_args(url):
    """ Returns a dictionary from a URL params """
    url_data = urllib.parse.urlparse(url)
    arg_dict = urllib.parse.parse_qs(url_data.query)
    return arg_dict

Similar but not relevant code:
def create_search_url(self):
        """ Generates (urlencoded) query string from stored key-values tuples

        :returns: A string containing all arguments in a url-encoded format
        """

        url = '?'
        for key, value in self.arguments.items():
            url += '%s=%s&' % (quote_plus(key), quote_plus(value))
        self.url = url[:-1]
        return self.url

Similar but not relevant code:
def dict_to_querystring(dictionary):
    """Converts a dict to a querystring suitable to be appended to a URL."""
    s = u""
    for d in dictionary.keys():
        s = unicode.format(u"{0}{1}={2}&", s, d, dictionary[d])
    return s[:-1]
Query: mxnet ndarray to python list

Relevant code:
def _parse_array(self, tensor_proto):
        """Grab data in TensorProto and convert to numpy array."""
        try:
            from onnx.numpy_helper import to_array
        except ImportError as e:
            raise ImportError("Unable to import onnx which is required {}".format(e))
        np_array = to_array(tensor_proto).reshape(tuple(tensor_proto.dims))
        return mx.nd.array(np_array)

Similar but not relevant code:
def A(*a):
    """convert iterable object into numpy array"""
    return np.array(a[0]) if len(a)==1 else [np.array(o) for o in a]

Similar but not relevant code:
def astensor(array: TensorLike) -> BKTensor:
    """Covert numpy array to tensorflow tensor"""
    tensor = tf.convert_to_tensor(value=array, dtype=CTYPE)
    return tensor

Similar but not relevant code:
def segments_to_numpy(segments):
    """given a list of 4-element tuples, transforms it into a numpy array"""
    segments = numpy.array(segments, dtype=SEGMENT_DATATYPE, ndmin=2)  # each segment in a row
    segments = segments if SEGMENTS_DIRECTION == 0 else numpy.transpose(segments)
    return segments

Similar but not relevant code:
def _matrix3_to_dcm_array(self, m):
        """
        Converts Matrix3 in an array
        :param m: Matrix3
        :returns: 3x3 array
        """
        assert(isinstance(m, Matrix3))
        return np.array([[m.a.x, m.a.y, m.a.z],
                         [m.b.x, m.b.y, m.b.z],
                         [m.c.x, m.c.y, m.c.z]])
Query: python3 2 list to dictionary

Relevant code:
def list2dict(list_of_options):
    """Transforms a list of 2 element tuples to a dictionary"""
    d = {}
    for key, value in list_of_options:
        d[key] = value
    return d

Similar but not relevant code:
def multidict_to_dict(d):
    """
    Turns a werkzeug.MultiDict or django.MultiValueDict into a dict with
    list values
    :param d: a MultiDict or MultiValueDict instance
    :return: a dict instance
    """
    return dict((k, v[0] if len(v) == 1 else v) for k, v in iterlists(d))
Query: name 'python' is not defined tensorflow

Relevant code:
def unit_ball_L2(shape):
  """A tensorflow variable tranfomed to be constrained in a L2 unit ball.

  EXPERIMENTAL: Do not use for adverserial examples if you need to be confident
  they are strong attacks. We are not yet confident in this code.
  """
  x = tf.Variable(tf.zeros(shape))
  return constrain_L2(x)

Similar but not relevant code:
def tf2():
  """Provide the root module of a TF-2.0 API for use within TensorBoard.

  Returns:
    The root module of a TF-2.0 API, if available.

  Raises:
    ImportError: if a TF-2.0 API is not available.
  """
  # Import the `tf` compat API from this file and check if it's already TF 2.0.
  if tf.__version__.startswith('2.'):
    return tf
  elif hasattr(tf, 'compat') and hasattr(tf.compat, 'v2'):
    # As a fallback, try `tensorflow.compat.v2` if it's defined.
    return tf.compat.v2
  raise ImportError('cannot import tensorflow 2.0 API')

Similar but not relevant code:
def astensor(array: TensorLike) -> BKTensor:
    """Covert numpy array to tensorflow tensor"""
    tensor = tf.convert_to_tensor(value=array, dtype=CTYPE)
    return tensor

Similar but not relevant code:
def fcast(value: float) -> TensorLike:
    """Cast to float tensor"""
    newvalue = tf.cast(value, FTYPE)
    if DEVICE == 'gpu':
        newvalue = newvalue.gpu()  # Why is this needed?  # pragma: no cover
    return newvalue

Similar but not relevant code:
def get_tensor_device(self, tensor_name):
    """The device of a tensor.

    Note that only tf tensors have device assignments.

    Args:
      tensor_name: a string, name of a tensor in the graph.

    Returns:
      a string or None, representing the device name.
    """
    tensor = self._name_to_tensor(tensor_name)
    if isinstance(tensor, tf.Tensor):
      return tensor.device
    else:  # mtf.Tensor
      return None

Similar but not relevant code:
def revnet_164_cifar():
  """Tiny hparams suitable for CIFAR/etc."""
  hparams = revnet_cifar_base()
  hparams.bottleneck = True
  hparams.num_channels = [16, 32, 64]
  hparams.num_layers_per_block = [8, 8, 8]
  return hparams
Query: natural log of array in python

Relevant code:
def to_distribution_values(self, values):
        """
        Returns numpy array of natural logarithms of ``values``.
        """
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            # avoid RuntimeWarning: divide by zero encountered in log
            return numpy.log(values)

Similar but not relevant code:
def log_normalize(data):
    """Perform log transform log(x + 1).
    
    Parameters
    ----------
    data : array_like
    
    """
    if sp.issparse(data):
        data = data.copy()
        data.data = np.log2(data.data + 1)
        return data

    return np.log2(data.astype(np.float64) + 1)
Query: python3 datetime to integer timestamp

Relevant code:
def dt_to_ts(value):
    """ If value is a datetime, convert to timestamp """
    if not isinstance(value, datetime):
        return value
    return calendar.timegm(value.utctimetuple()) + value.microsecond / 1000000.0

Similar but not relevant code:
def timestamp_to_datetime(timestamp):
    """Convert an ARF timestamp to a datetime.datetime object (naive local time)"""
    from datetime import datetime, timedelta
    obj = datetime.fromtimestamp(timestamp[0])
    return obj + timedelta(microseconds=int(timestamp[1]))
Query: negative log likelihood python code tobit regression

Relevant code:
def log_loss(preds, labels):
    """Logarithmic loss with non-necessarily-binary labels."""
    log_likelihood = np.sum(labels * np.log(preds)) / len(preds)
    return -log_likelihood

Similar but not relevant code:
def cric__lasso():
    """ Lasso Regression
    """
    model = sklearn.linear_model.LogisticRegression(penalty="l1", C=0.002)

    # we want to explain the raw probability outputs of the trees
    model.predict = lambda X: model.predict_proba(X)[:,1]
    
    return model

Similar but not relevant code:
def LogBinomialCoef(n, k):
    """Computes the log of the binomial coefficient.

    http://math.stackexchange.com/questions/64716/
    approximating-the-logarithm-of-the-binomial-coefficient

    n: number of trials
    k: number of successes

    Returns: float
    """
    return n * log(n) - k * log(k) - (n - k) * log(n - k)

Similar but not relevant code:
def tree_predict(x, root, proba=False, regression=False):
    """Predicts a probabilities/value/label for the sample x.
    """

    if isinstance(root, Leaf):
        if proba:
            return root.probabilities
        elif regression:
            return root.mean
        else:
            return root.most_frequent

    if root.question.match(x):
        return tree_predict(x, root.true_branch, proba=proba, regression=regression)
    else:
        return tree_predict(x, root.false_branch, proba=proba, regression=regression)

Similar but not relevant code:
def lognorm(x, mu, sigma=1.0):
    """ Log-normal function from scipy """
    return stats.lognorm(sigma, scale=mu).pdf(x)
Query: new array object that looks at the same data in python

Relevant code:
def full_like(array, value, dtype=None):
    """ Create a shared memory array with the same shape and type as a given array, filled with `value`.
    """
    shared = empty_like(array, dtype)
    shared[:] = value
    return shared

Similar but not relevant code:
def layer_with(self, sample: np.ndarray, value: int) -> np.ndarray:
        """Create an identical 2d array where the second row is filled with value"""
        b = np.full((2, len(sample)), value, dtype=float)
        b[0] = sample
        return b

Similar but not relevant code:
def torecarray(*args, **kwargs):
    """
    Convenient shorthand for ``toarray(*args, **kwargs).view(np.recarray)``.

    """

    import numpy as np
    return toarray(*args, **kwargs).view(np.recarray)
Query: python3 get function locals

Relevant code:
def caller_locals():
    """Get the local variables in the caller's frame."""
    import inspect
    frame = inspect.currentframe()
    try:
        return frame.f_back.f_back.f_locals
    finally:
        del frame

Similar but not relevant code:
def get_caller_module():
    """
    Returns the name of the caller's module as a string.

    >>> get_caller_module()
    '__main__'
    """
    stack = inspect.stack()
    assert len(stack) > 1
    caller = stack[2][0]
    return caller.f_globals['__name__']

Similar but not relevant code:
def getFunction(self):
        """Called by remote workers. Useful to populate main module globals()
        for interactive shells. Retrieves the serialized function."""
        return functionFactory(
            self.code,
            self.name,
            self.defaults,
            self.globals,
            self.imports,
        )

Similar but not relevant code:
def get_function(function_name):
    """
    Given a Python function name, return the function it refers to.
    """
    module, basename = str(function_name).rsplit('.', 1)
    try:
        return getattr(__import__(module, fromlist=[basename]), basename)
    except (ImportError, AttributeError):
        raise FunctionNotFound(function_name)
Query: normalize vector python numpy

Relevant code:
def v_normalize(v):
    """
    Normalizes the given vector.
    
    The vector given may have any number of dimensions.
    """
    vmag = v_magnitude(v)
    return [ v[i]/vmag  for i in range(len(v)) ]

Similar but not relevant code:
def _normalize_abmn(abmn):
    """return a normalized version of abmn
    """
    abmn_2d = np.atleast_2d(abmn)
    abmn_normalized = np.hstack((
        np.sort(abmn_2d[:, 0:2], axis=1),
        np.sort(abmn_2d[:, 2:4], axis=1),
    ))
    return abmn_normalized
Query: python3 how to print out sqlite table names

Relevant code:
def get_table_names(connection):
	"""
	Return a list of the table names in the database.
	"""
	cursor = connection.cursor()
	cursor.execute("SELECT name FROM sqlite_master WHERE type == 'table'")
	return [name for (name,) in cursor]

Similar but not relevant code:
def get_table_columns(dbconn, tablename):
    """
    Return a list of tuples specifying the column name and type
    """
    cur = dbconn.cursor()
    cur.execute("PRAGMA table_info('%s');" % tablename)
    info = cur.fetchall()
    cols = [(i[1], i[2]) for i in info]
    return cols

Similar but not relevant code:
def get_table(ports):
    """
    This function returns a pretty table used to display the port results.

    :param ports: list of found ports
    :return: the table to display
    """
    table = PrettyTable(["Name", "Port", "Protocol", "Description"])
    table.align["Name"] = "l"
    table.align["Description"] = "l"
    table.padding_width = 1

    for port in ports:
        table.add_row(port)

    return table
Query: np array from list python

Relevant code:
def _to_array(value):
    """As a convenience, turn Python lists and tuples into NumPy arrays."""
    if isinstance(value, (tuple, list)):
        return array(value)
    elif isinstance(value, (float, int)):
        return np.float64(value)
    else:
        return value

Similar but not relevant code:
def array(self):
        """
        The underlying array of shape (N, L, I)
        """
        return numpy.array([self[sid].array for sid in sorted(self)])

Similar but not relevant code:
def to_np(*args):
    """ convert GPU arras to numpy and return them"""
    if len(args) > 1:
        return (cp.asnumpy(x) for x in args)
    else:
        return cp.asnumpy(args[0])
Query: python3 inspect get local variable

Relevant code:
def getvariable(name):
    """Get the value of a local variable somewhere in the call stack."""
    import inspect
    fr = inspect.currentframe()
    try:
        while fr:
            fr = fr.f_back
            vars = fr.f_locals
            if name in vars:
                return vars[name]
    except:
        pass
    return None

Similar but not relevant code:
def vars_(self):
        """ Returns symbol instances corresponding to variables
        of the current scope.
        """
        return [x for x in self[self.current_scope].values() if x.class_ == CLASS.var]

Similar but not relevant code:
def _get_var_from_string(item):
    """ Get resource variable. """
    modname, varname = _split_mod_var_names(item)
    if modname:
        mod = __import__(modname, globals(), locals(), [varname], -1)
        return getattr(mod, varname)
    else:
        return globals()[varname]

Similar but not relevant code:
def get_var(self, name):
        """ Returns the variable set with the given name.
        """
        for var in self.vars:
            if var.name == name:
                return var
        else:
            raise ValueError

Similar but not relevant code:
def get_caller_module():
    """
    Returns the name of the caller's module as a string.

    >>> get_caller_module()
    '__main__'
    """
    stack = inspect.stack()
    assert len(stack) > 1
    caller = stack[2][0]
    return caller.f_globals['__name__']
Query: number of bins in histogram python

Relevant code:
def _histplot_bins(column, bins=100):
    """Helper to get bins for histplot."""
    col_min = np.min(column)
    col_max = np.max(column)
    return range(col_min, col_max + 2, max((col_max - col_min) // bins, 1))

Similar but not relevant code:
def inverseHistogram(hist, bin_range):
    """sample data from given histogram and min, max values within range

    Returns:
        np.array: data that would create the same histogram as given
    """
    data = hist.astype(float) / np.min(hist[np.nonzero(hist)])
    new_data = np.empty(shape=np.sum(data, dtype=int))
    i = 0
    xvals = np.linspace(bin_range[0], bin_range[1], len(data))
    for d, x in zip(data, xvals):
        new_data[i:i + d] = x
        i += int(d)
    return new_data

Similar but not relevant code:
def get_bin_edges_from_axis(axis) -> np.ndarray:
    """ Get bin edges from a ROOT hist axis.

    Note:
        Doesn't include over- or underflow bins!

    Args:
        axis (ROOT.TAxis): Axis from which the bin edges should be extracted.
    Returns:
        Array containing the bin edges.
    """
    # Don't include over- or underflow bins
    bins = range(1, axis.GetNbins() + 1)
    # Bin edges
    bin_edges = np.empty(len(bins) + 1)
    bin_edges[:-1] = [axis.GetBinLowEdge(i) for i in bins]
    bin_edges[-1] = axis.GetBinUpEdge(axis.GetNbins())

    return bin_edges
Query: python3 merge two ranges

Relevant code:
def merge(self, other):
        """
        Merge this range object with another (ranges need not overlap or abut).

        :returns: a new Range object representing the interval containing both
                  ranges.
        """
        newstart = min(self._start, other.start)
        newend = max(self._end, other.end)
        return Range(newstart, newend)

Similar but not relevant code:
def __or__(self, other):
        """Return the union of two RangeSets as a new RangeSet.

        (I.e. all elements that are in either set.)
        """
        if not isinstance(other, set):
            return NotImplemented
        return self.union(other)

Similar but not relevant code:
def _merge_maps(m1, m2):
    """merge two Mapping objects, keeping the type of the first mapping"""
    return type(m1)(chain(m1.items(), m2.items()))

Similar but not relevant code:
def merge(database=None, directory=None, verbose=None):
    """Merge migrations into one."""
    router = get_router(directory, database, verbose)
    router.merge()

Similar but not relevant code:
def add_range(self, sequence, begin, end):
    """Add a read_range primitive"""
    sequence.parser_tree = parsing.Range(self.value(begin).strip("'"),
                                         self.value(end).strip("'"))
    return True
Query: numerically solve exponential equations in python

Relevant code:
def algo_exp(x, m, t, b):
    """mono-exponential curve."""
    return m*np.exp(-t*x)+b

Similar but not relevant code:
def logx_linear(x, a, b):
    """logx linear

    Parameters
    ----------
    x: int
    a: float
    b: float

    Returns
    -------
    float
        a * np.log(x) + b
    """
    x = np.log(x)
    return a*x + b

Similar but not relevant code:
def Exponential(x, a, tau, y0):
    """Exponential function

    Inputs:
    -------
        ``x``: independent variable
        ``a``: scaling factor
        ``tau``: time constant
        ``y0``: additive constant

    Formula:
    --------
        ``a*exp(x/tau)+y0``
    """
    return np.exp(x / tau) * a + y0

Similar but not relevant code:
def log(x):
    """
    Natural logarithm
    """
    if isinstance(x, UncertainFunction):
        mcpts = np.log(x._mcpts)
        return UncertainFunction(mcpts)
    else:
        return np.log(x)

Similar but not relevant code:
def eval(e, amplitude, e_0, alpha, beta):
        """One dimenional log parabola model function"""

        ee = e / e_0
        eeponent = -alpha - beta * np.log(ee)
        return amplitude * ee ** eeponent
Query: python3 move a byte object to string

Relevant code:
def to_str(obj):
    """Attempts to convert given object to a string object
    """
    if not isinstance(obj, str) and PY3 and isinstance(obj, bytes):
        obj = obj.decode('utf-8')
    return obj if isinstance(obj, string_types) else str(obj)

Similar but not relevant code:
def stn(s, length, encoding, errors):
    """Convert a string to a null-terminated bytes object.
    """
    s = s.encode(encoding, errors)
    return s[:length] + (length - len(s)) * NUL

Similar but not relevant code:
def str2bytes(x):
  """Convert input argument to bytes"""
  if type(x) is bytes:
    return x
  elif type(x) is str:
    return bytes([ ord(i) for i in x ])
  else:
    return str2bytes(str(x))

Similar but not relevant code:
def u2b(string):
    """ unicode to bytes"""
    if ((PY2 and isinstance(string, unicode)) or
        ((not PY2) and isinstance(string, str))):
        return string.encode('utf-8')
    return string
Query: numpy array to string python

Relevant code:
def _array2cstr(arr):
    """ Serializes a numpy array to a compressed base64 string """
    out = StringIO()
    np.save(out, arr)
    return b64encode(out.getvalue())

Similar but not relevant code:
def to_np(*args):
    """ convert GPU arras to numpy and return them"""
    if len(args) > 1:
        return (cp.asnumpy(x) for x in args)
    else:
        return cp.asnumpy(args[0])
Query: python3 numpy generate onehot vector

Relevant code:
def one_hot(x, size, dtype=np.float32):
  """Make a n+1 dim one-hot array from n dim int-categorical array."""
  return np.array(x[..., np.newaxis] == np.arange(size), dtype)

Similar but not relevant code:
def mask_and_flatten(self):
        """Return a vector of the masked data.

        Returns
        -------
        np.ndarray, tuple of indices (np.ndarray), tuple of the mask shape
        """
        self._check_for_mask()

        return self.get_data(smoothed=True, masked=True, safe_copy=False)[self.get_mask_indices()],\
               self.get_mask_indices(), self.mask.shape

Similar but not relevant code:
def generate_matrices(dim = 40):
  """
  Generates the matrices that positive and negative samples are multiplied
  with.  The matrix for positive samples is randomly drawn from a uniform
  distribution, with elements in [-1, 1].  The matrix for negative examples
  is the sum of the positive matrix with a matrix drawn from a normal
  distribution with mean 0 variance 1.
  """
  positive = numpy.random.uniform(-1, 1, (dim, dim))
  negative = positive + numpy.random.normal(0, 1, (dim, dim))
  return positive, negative

Similar but not relevant code:
def _unique_rows_numpy(a):
    """return unique rows"""
    a = np.ascontiguousarray(a)
    unique_a = np.unique(a.view([('', a.dtype)] * a.shape[1]))
    return unique_a.view(a.dtype).reshape((unique_a.shape[0], a.shape[1]))