benjamintli/cosqa-llm-filtered-hard-negatives huggingface dataset:
Query: if a string contains any of a list python

Relevant code:
def any_contains_any(strings, candidates):
    """Whether any of the strings contains any of the candidates."""
    for string in strings:
        for c in candidates:
            if c in string:
                return True

Similar but not relevant code:
def all_strings(arr):
        """
        Ensures that the argument is a list that either is empty or contains only strings
        :param arr: list
        :return:
        """
        if not isinstance([], list):
            raise TypeError("non-list value found where list is expected")
        return all(isinstance(x, str) for x in arr)

Similar but not relevant code:
def is_list_of_ipachars(obj):
    """
    Return ``True`` if the given object is a list of IPAChar objects.

    :param object obj: the object to test
    :rtype: bool
    """
    if isinstance(obj, list):
        for e in obj:
            if not isinstance(e, IPAChar):
                return False
        return True
    return False

Similar but not relevant code:
def list_of(cls):
    """
    Returns a function that checks that each element in a
    list is of a specific type.
    """
    return lambda l: isinstance(l, list) and all(isinstance(x, cls) for x in l)
Query: if file changes then python

Relevant code:
def example_write_file_to_disk_if_changed():
    """ Try to remove all comments from a file, and save it if changes were made. """
    my_file = FileAsObj('/tmp/example_file.txt')
    my_file.rm(my_file.egrep('^#'))
    if my_file.changed:
        my_file.save()

Similar but not relevant code:
def writefile(openedfile, newcontents):
    """Set the contents of a file."""
    openedfile.seek(0)
    openedfile.truncate()
    openedfile.write(newcontents)

Similar but not relevant code:
def copyFile(input, output, replace=None):
    """Copy a file whole from input to output."""

    _found = findFile(output)
    if not _found or (_found and replace):
        shutil.copy2(input, output)
Query: if matches a set of strings python

Relevant code:
def any_contains_any(strings, candidates):
    """Whether any of the strings contains any of the candidates."""
    for string in strings:
        for c in candidates:
            if c in string:
                return True

Similar but not relevant code:
def not_matching_list(self):
        """
        Return a list of string which don't match the
        given regex.
        """

        pre_result = comp(self.regex)

        return [x for x in self.data if not pre_result.search(str(x))]

Similar but not relevant code:
def __is__(cls, s):
        """Test if string matches this argument's format."""
        return s.startswith(cls.delims()[0]) and s.endswith(cls.delims()[1])
Query: python table column row slicing

Relevant code:
def getcolslice(self, blc, trc, inc=[], startrow=0, nrow=-1, rowincr=1):
        """Get a slice from a table column holding arrays.
        (see :func:`table.getcolslice`)"""
        return self._table.getcolslice(self._column, blc, trc, inc, startrow, nrow, rowincr)

Similar but not relevant code:
def column_names(self, table):
      """An iterable of column names, for a particular table or
      view."""

      table_info = self.execute(
        u'PRAGMA table_info(%s)' % quote(table))
      return (column['name'] for column in table_info)

Similar but not relevant code:
def _idx_col2rowm(d):
    """Generate indexes to change from col-major to row-major ordering"""
    if 0 == len(d):
        return 1
    if 1 == len(d):
        return np.arange(d[0])
    # order='F' indicates column-major ordering
    idx = np.array(np.arange(np.prod(d))).reshape(d, order='F').T
    return idx.flatten(order='F')

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 _shape(self, df):
        """
        Calculate table chape considering index levels.
        """

        row, col = df.shape
        return row + df.columns.nlevels, col + df.index.nlevels

Similar but not relevant code:
def _columns_for_table(table_name):
    """
    Return all of the columns registered for a given table.

    Parameters
    ----------
    table_name : str

    Returns
    -------
    columns : dict of column wrappers
        Keys will be column names.

    """
    return {cname: col
            for (tname, cname), col in _COLUMNS.items()
            if tname == table_name}
Query: if request args get flask python

Relevant code:
def get_path_from_query_string(req):
    """Gets path from query string

    Args:
        req (flask.request): Request object from Flask

    Returns:
        path (str): Value of "path" parameter from query string

    Raises:
        exceptions.UserError: If "path" is not found in query string
    """
    if req.args.get('path') is None:
        raise exceptions.UserError('Path not found in query string')
    return req.args.get('path')

Similar but not relevant code:
def run_web(self, flask, host='127.0.0.1', port=5000, **options):
        # type: (Zsl, str, int, **Any)->None
        """Alias for Flask.run"""
        return flask.run(
            host=flask.config.get('FLASK_HOST', host),
            port=flask.config.get('FLASK_PORT', port),
            debug=flask.config.get('DEBUG', False),
            **options
        )

Similar but not relevant code:
def initialize_api(flask_app):
    """Initialize an API."""
    if not flask_restplus:
        return

    api = flask_restplus.Api(version="1.0", title="My Example API")
    api.add_resource(HelloWorld, "/hello")

    blueprint = flask.Blueprint("api", __name__, url_prefix="/api")
    api.init_app(blueprint)
    flask_app.register_blueprint(blueprint)

Similar but not relevant code:
def handleFlaskPostRequest(flaskRequest, endpoint):
    """
    Handles the specified flask request for one of the POST URLS
    Invokes the specified endpoint to generate a response.
    """
    if flaskRequest.method == "POST":
        return handleHttpPost(flaskRequest, endpoint)
    elif flaskRequest.method == "OPTIONS":
        return handleHttpOptions()
    else:
        raise exceptions.MethodNotAllowedException()

Similar but not relevant code:
def getFlaskResponse(responseString, httpStatus=200):
    """
    Returns a Flask response object for the specified data and HTTP status.
    """
    return flask.Response(responseString, status=httpStatus, mimetype=MIMETYPE)

Similar but not relevant code:
def init_app(self, app):
        """Initialize Flask application."""
        app.config.from_pyfile('{0}.cfg'.format(app.name), silent=True)
Query: python tell all threads to end

Relevant code:
def finish():
    """Print warning about interrupt and empty the job queue."""
    out.warn("Interrupted!")
    for t in threads:
        t.stop()
    jobs.clear()
    out.warn("Waiting for download threads to finish.")

Similar but not relevant code:
def remove_stopped_threads (self):
        """Remove the stopped threads from the internal thread list."""
        self.threads = [t for t in self.threads if t.is_alive()]
Query: python tell scalar or vector

Relevant code:
def is_scalar(value):
    """Test if the given value is a scalar.

    This function also works with memory mapped array values, in contrast to the numpy is_scalar method.

    Args:
        value: the value to test for being a scalar value

    Returns:
        boolean: if the given value is a scalar or not
    """
    return np.isscalar(value) or (isinstance(value, np.ndarray) and (len(np.squeeze(value).shape) == 0))

Similar but not relevant code:
def normalized(vector):
    """
    Get unit vector for a given one.

    :param vector:
        Numpy vector as coordinates in Cartesian space, or an array of such.
    :returns:
        Numpy array of the same shape and structure where all vectors are
        normalized. That is, each coordinate component is divided by its
        vector's length.
    """
    length = numpy.sum(vector * vector, axis=-1)
    length = numpy.sqrt(length.reshape(length.shape + (1, )))
    return vector / length
Query: if set is in subset python

Relevant code:
def issubset(self, other):
        """Report whether another set contains this RangeSet."""
        self._binary_sanity_check(other)
        return set.issubset(self, other)

Similar but not relevant code:
def issuperset(self, items):
        """Return whether this collection contains all items.

        >>> Unique(['spam', 'eggs']).issuperset(['spam', 'spam', 'spam'])
        True
        """
        return all(_compat.map(self._seen.__contains__, items))

Similar but not relevant code:
def is_any_type_set(sett: Set[Type]) -> bool:
    """
    Helper method to check if a set of types is the {AnyObject} singleton

    :param sett:
    :return:
    """
    return len(sett) == 1 and is_any_type(min(sett))
Query: python tensor squeeze specific dimension

Relevant code:
def _squeeze(x, axis):
  """A version of squeeze that works with dynamic axis."""
  x = tf.convert_to_tensor(value=x, name='x')
  if axis is None:
    return tf.squeeze(x, axis=None)
  axis = tf.convert_to_tensor(value=axis, name='axis', dtype=tf.int32)
  axis += tf.zeros([1], dtype=axis.dtype)  # Make axis at least 1d.
  keep_axis, _ = tf.compat.v1.setdiff1d(tf.range(0, tf.rank(x)), axis)
  return tf.reshape(x, tf.gather(tf.shape(input=x), keep_axis))

Similar but not relevant code:
def transformer_tpu_1b():
  """Hparams for machine translation with ~1.1B parameters."""
  hparams = transformer_tpu()
  hparams.hidden_size = 2048
  hparams.filter_size = 8192
  hparams.num_hidden_layers = 8
  # smaller batch size to avoid OOM
  hparams.batch_size = 1024
  hparams.activation_dtype = "bfloat16"
  hparams.weight_dtype = "bfloat16"
  # maximize number of parameters relative to computation by not sharing.
  hparams.shared_embedding_and_softmax_weights = False
  return hparams

Similar but not relevant code:
def encode_to_shape(inputs, shape, scope):
  """Encode the given tensor to given image shape."""
  with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
    w, h = shape[1], shape[2]
    x = inputs
    x = tfl.flatten(x)
    x = tfl.dense(x, w * h, activation=None, name="enc_dense")
    x = tf.reshape(x, (-1, w, h, 1))
    return x

Similar but not 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 forget_xy(t):
  """Ignore sizes of dimensions (1, 2) of a 4d tensor in shape inference.

  This allows using smaller input sizes, which create an invalid graph at higher
  layers (for example because a spatial dimension becomes smaller than a conv
  filter) when we only use early parts of it.
  """
  shape = (t.shape[0], None, None, t.shape[3])
  return tf.placeholder_with_default(t, shape)
Query: python tensorflow argmax axis

Relevant code:
def argmax(attrs, inputs, proto_obj):
    """Returns indices of the maximum values along an axis"""
    axis = attrs.get('axis', 0)
    keepdims = attrs.get('keepdims', 1)
    argmax_op = symbol.argmax(inputs[0], axis=axis, keepdims=keepdims)
    # onnx argmax operator always expects int64 as output type
    cast_attrs = {'dtype': 'int64'}
    return 'cast', cast_attrs, argmax_op

Similar but not relevant code:
def Max(a, axis, keep_dims):
    """
    Max reduction op.
    """
    return np.amax(a, axis=axis if not isinstance(axis, np.ndarray) else tuple(axis),
                   keepdims=keep_dims),

Similar but not relevant code:
def _get_example_length(example):
  """Returns the maximum length between the example inputs and targets."""
  length = tf.maximum(tf.shape(example[0])[0], tf.shape(example[1])[0])
  return length

Similar but not relevant code:
def argmax(l,f=None):
    """http://stackoverflow.com/questions/5098580/implementing-argmax-in-python"""
    if f:
        l = [f(i) for i in l]
    return max(enumerate(l), key=lambda x:x[1])[0]
Query: python tensorflow estimator get prediction on data

Relevant code:
def predict(self, X):
        """
        Apply transforms to the data, and predict with the final estimator

        Parameters
        ----------
        X : iterable
            Data to predict on. Must fulfill input requirements of first step
            of the pipeline.

        Returns
        -------
        yp : array-like
            Predicted transformed target
        """
        Xt, _, _ = self._transform(X)
        return self._final_estimator.predict(Xt)

Similar but not relevant code:
def NeuralNetLearner(dataset, sizes):
   """Layered feed-forward network."""

   activations = map(lambda n: [0.0 for i in range(n)], sizes)
   weights = []

   def predict(example):
      unimplemented()

   return predict

Similar but not relevant code:
def output_scores(self, name=None):
        """ Returns: N x #class scores, summed to one for each box."""
        return tf.nn.softmax(self.label_logits, name=name)

Similar but not relevant code:
def predict(self, X):
        """Predict the class for X.

        The predicted class for each sample in X is returned.

        Parameters
        ----------
        X : List of ndarrays, one for each training example.
            Each training example's shape is (string1_len,
            string2_len, n_features), where string1_len and
            string2_len are the length of the two training strings and
            n_features the number of features.

        Returns
        -------
        y : iterable of shape = [n_samples]
            The predicted classes.

        """
        return [self.classes[prediction.argmax()] for prediction in self.predict_proba(X)]

Similar but not relevant code:
def token_accuracy(labels, outputs):
  """Compute tokenwise (elementwise) accuracy.

  Args:
    labels: ground-truth labels, shape=(batch, seq_length)
    outputs: predicted tokens, shape=(batch, seq_length)
  Returns:
    Two ops, one for getting the current average accuracy and another for
    updating the running average estimate.
  """
  weights = tf.to_float(tf.not_equal(labels, 0))
  return tf.metrics.accuracy(labels, outputs, weights=weights)
Query: python tensorflow list gpus

Relevant code:
def available_gpus():
  """List of GPU device names detected by TensorFlow."""
  local_device_protos = device_lib.list_local_devices()
  return [x.name for x in local_device_protos if x.device_type == 'GPU']

Similar but not relevant code:
def batch_tensor(self, name):
        """ A buffer of a given value in a 'flat' (minibatch-indexed) format """
        if name in self.transition_tensors:
            return tensor_util.merge_first_two_dims(self.transition_tensors[name])
        else:
            return self.rollout_tensors[name]

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 get_weights_from_kmodel(kmodel):
        """
        Convert kmodel's weights to bigdl format.
        We are supposing the order is the same as the execution order.
        :param kmodel: keras model
        :return: list of ndarray
        """
        layers_with_weights = [layer for layer in kmodel.layers if layer.weights]
        bweights = []
        for klayer in layers_with_weights:
            # bws would be [weights, bias] or [weights]
            bws = WeightsConverter.get_bigdl_weights_from_klayer(klayer)
            for w in bws:
                bweights.append(w)
        return bweights
Query: python tensorflow unequal dimensions

Relevant code:
def flatten_all_but_last(a):
  """Flatten all dimensions of a except the last."""
  ret = tf.reshape(a, [-1, tf.shape(a)[-1]])
  if not tf.executing_eagerly():
    ret.set_shape([None] + a.get_shape().as_list()[-1:])
  return ret

Similar but not relevant code:
def forget_xy(t):
  """Ignore sizes of dimensions (1, 2) of a 4d tensor in shape inference.

  This allows using smaller input sizes, which create an invalid graph at higher
  layers (for example because a spatial dimension becomes smaller than a conv
  filter) when we only use early parts of it.
  """
  shape = (t.shape[0], None, None, t.shape[3])
  return tf.placeholder_with_default(t, shape)

Similar but not relevant code:
def rank(tensor: BKTensor) -> int:
    """Return the number of dimensions of a tensor"""
    if isinstance(tensor, np.ndarray):
        return len(tensor.shape)

    return len(tensor[0].size())

Similar but not relevant code:
def batch_tensor(self, name):
        """ A buffer of a given value in a 'flat' (minibatch-indexed) format """
        if name in self.transition_tensors:
            return tensor_util.merge_first_two_dims(self.transition_tensors[name])
        else:
            return self.rollout_tensors[name]

Similar but not 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 top_1_tpu(inputs):
  """find max and argmax over the last dimension.

  Works well on TPU

  Args:
    inputs: A tensor with shape [..., depth]

  Returns:
    values: a Tensor with shape [...]
    indices: a Tensor with shape [...]
  """
  inputs_max = tf.reduce_max(inputs, axis=-1, keepdims=True)
  mask = tf.to_int32(tf.equal(inputs_max, inputs))
  index = tf.range(tf.shape(inputs)[-1]) * mask
  return tf.squeeze(inputs_max, -1), tf.reduce_max(index, axis=-1)
Query: index number in a series python

Relevant code:
def series_index(self, series):
        """
        Return the integer index of *series* in this sequence.
        """
        for idx, s in enumerate(self):
            if series is s:
                return idx
        raise ValueError('series not in chart data object')

Similar but not relevant code:
def getRowCurrentIndex(self):
        """ Returns the index of column 0 of the current item in the underlying model.
            See also the notes at the top of this module on current item vs selected item(s).
        """
        curIndex = self.currentIndex()
        col0Index = curIndex.sibling(curIndex.row(), 0)
        return col0Index

Similar but not relevant code:
def __getitem__(self, index):
    """Get the item at the given index.

    Index is a tuple of (row, col)
    """
    row, col = index
    return self.rows[row][col]
Query: python test if file in folder

Relevant code:
def contained_in(filename, directory):
    """Test if a file is located within the given directory."""
    filename = os.path.normcase(os.path.abspath(filename))
    directory = os.path.normcase(os.path.abspath(directory))
    return os.path.commonprefix([filename, directory]) == directory

Similar but not relevant code:
def is_valid_folder(parser, arg):
    """Check if arg is a valid file that already exists on the file system."""
    arg = os.path.abspath(arg)
    if not os.path.isdir(arg):
        parser.error("The folder %s does not exist!" % arg)
    else:
        return arg
Query: index value of cell python

Relevant code:
def get_cell(self, index):
        """
        For a single index and return the value

        :param index: index value
        :return: value
        """
        i = sorted_index(self._index, index) if self._sort else self._index.index(index)
        return self._data[i]

Similar but not relevant code:
def getRowCurrentIndex(self):
        """ Returns the index of column 0 of the current item in the underlying model.
            See also the notes at the top of this module on current item vs selected item(s).
        """
        curIndex = self.currentIndex()
        col0Index = curIndex.sibling(curIndex.row(), 0)
        return col0Index
Query: init a python list of certain size

Relevant code:
def __init__(self, capacity=10):
        """
        Initialize python List with capacity of 10 or user given input.
        Python List type is a dynamic array, so we have to restrict its
        dynamic nature to make it work like a static array.
        """
        super().__init__()
        self._array = [None] * capacity
        self._front = 0
        self._rear = 0

Similar but not relevant code:
def chunk_list(l, n):
    """Return `n` size lists from a given list `l`"""
    return [l[i:i + n] for i in range(0, len(l), n)]

Similar but not relevant code:
def batch(items, size):
    """Batches a list into a list of lists, with sub-lists sized by a specified
    batch size."""
    return [items[x:x + size] for x in xrange(0, len(items), size)]

Similar but not relevant code:
def _fill_array_from_list(the_list, the_array):
        """Fill an `array` from a `list`"""
        for i, val in enumerate(the_list):
            the_array[i] = val
        return the_array

Similar but not relevant code:
def ensure_list(iterable: Iterable[A]) -> List[A]:
    """
    An Iterable may be a list or a generator.
    This ensures we get a list without making an unnecessary copy.
    """
    if isinstance(iterable, list):
        return iterable
    else:
        return list(iterable)
Query: python three labels matthews correlation coefficient

Relevant code:
def mcc(y, z):
    """Matthews correlation coefficient
    """
    tp, tn, fp, fn = contingency_table(y, z)
    return (tp * tn - fp * fn) / K.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))

Similar but not relevant code:
def cor(y_true, y_pred):
    """Compute Pearson correlation coefficient.
    """
    y_true, y_pred = _mask_nan(y_true, y_pred)
    return np.corrcoef(y_true, y_pred)[0, 1]

Similar but not relevant code:
def sample_correlations(self):
        """Returns an `ExpMatrix` containing all pairwise sample correlations.

        Returns
        -------
        `ExpMatrix`
            The sample correlation matrix.

        """
        C = np.corrcoef(self.X.T)
        corr_matrix = ExpMatrix(genes=self.samples, samples=self.samples, X=C)
        return corr_matrix

Similar but not relevant code:
def cov_to_correlation(cov):
    """Compute the correlation matrix given the covariance matrix.

    Parameters
    ----------
    cov : `~numpy.ndarray`
        N x N matrix of covariances among N parameters.

    Returns
    -------
    corr : `~numpy.ndarray`
        N x N matrix of correlations among N parameters.
    """
    err = np.sqrt(np.diag(cov))
    errinv = np.ones_like(err) * np.nan
    m = np.isfinite(err) & (err != 0)
    errinv[m] = 1. / err[m]
    corr = np.array(cov)
    return corr * np.outer(errinv, errinv)

Similar but not relevant code:
def show_correlation_matrix(self, correlation_matrix):
        """Shows the given correlation matrix as image

        :param correlation_matrix: Correlation matrix of features
        """
        cr_plot.create_correlation_matrix_plot(
            correlation_matrix, self.title, self.headers_to_test
        )
        pyplot.show()
Query: python time is between start end

Relevant code:
def timespan(start_time):
    """Return time in milliseconds from start_time"""

    timespan = datetime.datetime.now() - start_time
    timespan_ms = timespan.total_seconds() * 1000
    return timespan_ms

Similar but not relevant code:
def stopwatch_now():
    """Get a timevalue for interval comparisons

    When possible it is a monotonic clock to prevent backwards time issues.
    """
    if six.PY2:
        now = time.time()
    else:
        now = time.monotonic()
    return now

Similar but not relevant code:
def run_time() -> timedelta:
    """

    :return:
    """

    delta = start_time if start_time else datetime.utcnow()
    return datetime.utcnow() - delta