benjamintli/cosqa-llm-filtered-hard-negatives huggingface dataset:
Query: does hash in python guarantee that uniqueness for string

Relevant code:
def _string_hash(s):
    """String hash (djb2) with consistency between py2/py3 and persistency between runs (unlike `hash`)."""
    h = 5381
    for c in s:
        h = h * 33 + ord(c)
    return h

Similar but not relevant code:
def check_hash_key(query_on, key):
    """Only allows == against query_on.hash_key"""
    return (
        isinstance(key, BaseCondition) and
        (key.operation == "==") and
        (key.column is query_on.hash_key)
    )

Similar but not relevant code:
def _hash_the_file(hasher, filename):
    """Helper function for creating hash functions.

    See implementation of :func:`dtoolcore.filehasher.shasum`
    for more usage details.
    """
    BUF_SIZE = 65536
    with open(filename, 'rb') as f:
        buf = f.read(BUF_SIZE)
        while len(buf) > 0:
            hasher.update(buf)
            buf = f.read(BUF_SIZE)
    return hasher

Similar but not relevant code:
def update_hash_from_str(hsh, str_input):
    """
    Convert a str to object supporting buffer API and update a hash with it.
    """
    byte_input = str(str_input).encode("UTF-8")
    hsh.update(byte_input)

Similar but not relevant code:
def __hash__(self):
        """Return ``hash(self)``."""
        return hash((type(self), self.domain, self.range, self.partition))
Query: python get propery by name

Relevant code:
def get_property_by_name(pif, name):
    """Get a property by name"""
    return next((x for x in pif.properties if x.name == name), None)

Similar but not relevant code:
def get_qualified_name(_object):
    """Return the Fully Qualified Name from an instance or class."""
    module = _object.__module__
    if hasattr(_object, '__name__'):
        _class = _object.__name__

    else:
        _class = _object.__class__.__name__

    return module + '.' + _class

Similar but not relevant code:
def class_name(obj):
    """
    Get the name of an object, including the module name if available.
    """

    name = obj.__name__
    module = getattr(obj, '__module__')

    if module:
        name = f'{module}.{name}'
    return name
Query: does python allow for missing function args like r

Relevant code:
def call_with_context(func, context, *args):
    """
    Check if given function has more arguments than given. Call it with context
    as last argument or without it.
    """
    return make_context_aware(func, len(args))(*args + (context,))

Similar but not relevant code:
def _correct_args(func, kwargs):
    """
        Convert a dictionary of arguments including __argv into a list
        for passing to the function.
    """
    args = inspect.getargspec(func)[0]
    return [kwargs[arg] for arg in args] + kwargs['__args']

Similar but not relevant code:
def arguments_as_dict(cls, *args, **kwargs):
        """
        Generate the arguments dictionary provided to :py:meth:`generate_name` and :py:meth:`calculate_total_steps`.

        This makes it possible to fetch arguments by name regardless of
        whether they were passed as positional or keyword arguments.  Unnamed
        positional arguments are provided as a tuple under the key ``pos``.
        """
        all_args = (None, ) + args
        return inspect.getcallargs(cls.run, *all_args, **kwargs)
Query: does tensorflow work with language other than python

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 mtf_unitransformer_all_layers_tiny():
  """Test out all the layers on local CPU."""
  hparams = mtf_unitransformer_tiny()
  hparams.moe_num_experts = 4
  hparams.moe_expert_x = 4
  hparams.moe_expert_y = 4
  hparams.moe_hidden_size = 512
  hparams.layers = ["self_att", "local_self_att", "moe_1d", "moe_2d", "drd"]
  return hparams

Similar but not relevant code:
def get_example_features(example):
  """Returns the non-sequence features from the provided example."""
  return (example.features.feature if isinstance(example, tf.train.Example)
          else example.context.feature)
Query: dot product using for loop in python

Relevant code:
def dot(self, w):
        """Return the dotproduct between self and another vector."""

        return sum([x * y for x, y in zip(self, w)])

Similar but not relevant code:
def kdot(x, y, K=2):
    """Algorithm 5.10. Dot product algorithm in K-fold working precision,
    K >= 3.
    """
    xx = x.reshape(-1, x.shape[-1])
    yy = y.reshape(y.shape[0], -1)

    xx = numpy.ascontiguousarray(xx)
    yy = numpy.ascontiguousarray(yy)

    r = _accupy.kdot_helper(xx, yy).reshape((-1,) + x.shape[:-1] + y.shape[1:])
    return ksum(r, K - 1)
Query: python get shape of list of lists

Relevant code:
def get_list_dimensions(_list):
    """
    Takes a nested list and returns the size of each dimension followed
    by the element type in the list
    """
    if isinstance(_list, list) or isinstance(_list, tuple):
        return [len(_list)] + get_list_dimensions(_list[0])
    return []

Similar but not relevant code:
def getTuple(self):
        """ Returns the shape of the region as (x, y, w, h) """
        return (self.x, self.y, self.w, self.h)

Similar but not relevant code:
def shape(self):
        """Compute the shape of the dataset as (rows, cols)."""
        if not self.data:
            return (0, 0)
        return (len(self.data), len(self.dimensions))

Similar but not relevant code:
def out_shape_from_array(arr):
    """Get the output shape from an array."""
    arr = np.asarray(arr)
    if arr.ndim == 1:
        return arr.shape
    else:
        return (arr.shape[1],)

Similar but not relevant code:
def get_shape(self):
		"""
		Return a tuple of this array's dimensions.  This is done by
		querying the Dim children.  Note that once it has been
		created, it is also possible to examine an Array object's
		.array attribute directly, and doing that is much faster.
		"""
		return tuple(int(c.pcdata) for c in self.getElementsByTagName(ligolw.Dim.tagName))[::-1]
Query: python get ssl wrapped socket

Relevant code:
def enable_ssl(self, *args, **kwargs):
        """
        Transforms the regular socket.socket to an ssl.SSLSocket for secure
        connections. Any arguments are passed to ssl.wrap_socket:
        http://docs.python.org/dev/library/ssl.html#ssl.wrap_socket
        """
        if self.handshake_sent:
            raise SSLError('can only enable SSL before handshake')

        self.secure = True
        self.sock = ssl.wrap_socket(self.sock, *args, **kwargs)

Similar but not relevant code:
def is_connected(self):
        """
        Return true if the socket managed by this connection is connected

        :rtype: bool
        """
        try:
            return self.socket is not None and self.socket.getsockname()[1] != 0 and BaseTransport.is_connected(self)
        except socket.error:
            return False

Similar but not relevant code:
def connect(self):
        """Connects to the given host"""
        self.socket = socket.create_connection(self.address, self.timeout)

Similar but not relevant code:
def connected_socket(address, timeout=3):
    """ yields a connected socket """
    sock = socket.create_connection(address, timeout)
    yield sock
    sock.close()
Query: draw a line in python

Relevant code:
def hline(self, x, y, width, color):
        """Draw a horizontal line up to a given length."""
        self.rect(x, y, width, 1, color, fill=True)

Similar but not relevant code:
def straight_line_show(title, length=100, linestyle="=", pad=0):
        """Print a formatted straight line.
        """
        print(StrTemplate.straight_line(
            title=title, length=length, linestyle=linestyle, pad=pad))
Query: python get stacktrace from sys

Relevant code:
def _extract_traceback(start):
    """
    SNAGGED FROM traceback.py

    RETURN list OF dicts DESCRIBING THE STACK TRACE
    """
    tb = sys.exc_info()[2]
    for i in range(start):
        tb = tb.tb_next
    return _parse_traceback(tb)

Similar but not relevant code:
def __run(self):
    """Hacked run function, which installs the trace."""
    sys.settrace(self.globaltrace)
    self.__run_backup()
    self.run = self.__run_backup
Query: drawing image with python in latex

Relevant code:
def print_display_png(o):
    """
    A function to display sympy expression using display style LaTeX in PNG.
    """
    s = latex(o, mode='plain')
    s = s.strip('$')
    # As matplotlib does not support display style, dvipng backend is
    # used here.
    png = latex_to_png('$$%s$$' % s, backend='dvipng')
    return png

Similar but not relevant code:
def draw_image(self, ax, image):
        """Process a matplotlib image object and call renderer.draw_image"""
        self.renderer.draw_image(imdata=utils.image_to_base64(image),
                                 extent=image.get_extent(),
                                 coordinates="data",
                                 style={"alpha": image.get_alpha(),
                                        "zorder": image.get_zorder()},
                                 mplobj=image)

Similar but not relevant code:
def add_text_to_image(fname, txt, opFilename):
    """ convert an image by adding text """
    ft = ImageFont.load("T://user//dev//src//python//_AS_LIB//timR24.pil")
    #wh = ft.getsize(txt)
    print("Adding text ", txt, " to ", fname, " pixels wide to file " , opFilename)
    im = Image.open(fname)
    draw = ImageDraw.Draw(im)
    draw.text((0, 0), txt, fill=(0, 0, 0), font=ft)
    del draw  
    im.save(opFilename)

Similar but not relevant code:
def _convert(self, image, output=None):
        """Private method for converting a single PNG image to a PDF."""
        with Image.open(image) as im:
            width, height = im.size

            co = CanvasObjects()
            co.add(CanvasImg(image, 1.0, w=width, h=height))

            return WatermarkDraw(co, tempdir=self.tempdir, pagesize=(width, height)).write(output)
Query: python get text of response

Relevant code:
async def json_or_text(response):
    """Turns response into a properly formatted json or text object"""
    text = await response.text()
    if response.headers['Content-Type'] == 'application/json; charset=utf-8':
        return json.loads(text)
    return text

Similar but not relevant code:
def text_response(self, contents, code=200, headers={}):
        """shortcut to return simple plain/text messages in the response.

        :param contents: a string with the response contents
        :param code: the http status code
        :param headers: a dict with optional headers
        :returns: a :py:class:`flask.Response` with the ``text/plain`` **Content-Type** header.
        """
        return Response(contents, status=code, headers={
            'Content-Type': 'text/plain'
        })
Query: dropping columns with wild card in column name from python data frame

Relevant code:
def del_Unnamed(df):
    """
    Deletes all the unnamed columns

    :param df: pandas dataframe
    """
    cols_del=[c for c in df.columns if 'Unnamed' in c]
    return df.drop(cols_del,axis=1)

Similar but not relevant code:
def remove_rows_matching(df, column, match):
    """
    Return a ``DataFrame`` with rows where `column` values match `match` are removed.

    The selected `column` series of values from the supplied Pandas ``DataFrame`` is compared
    to `match`, and those rows that match are removed from the DataFrame.

    :param df: Pandas ``DataFrame``
    :param column: Column indexer
    :param match: ``str`` match target
    :return: Pandas ``DataFrame`` filtered
    """
    df = df.copy()
    mask = df[column].values != match
    return df.iloc[mask, :]

Similar but not relevant code:
def clean_colnames(df):
    """ Cleans the column names on a DataFrame
    Parameters:
    df - DataFrame
        The DataFrame to clean
    """
    col_list = []
    for index in range(_dutils.cols(df)):
        col_list.append(df.columns[index].strip().lower().replace(' ','_'))
    df.columns = col_list

Similar but not relevant code:
def normalize_column_names(df):
    r""" Clean up whitespace in column names. See better version at `pugnlp.clean_columns`

    >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['Hello World', 'not here'])
    >>> normalize_column_names(df)
    ['hello_world', 'not_here']
    """
    columns = df.columns if hasattr(df, 'columns') else df
    columns = [c.lower().replace(' ', '_') for c in columns]
    return columns
Query: python get the first day of current month

Relevant code:
def get_month_start(day=None):
    """Returns the first day of the given month."""
    day = add_timezone(day or datetime.date.today())
    return day.replace(day=1)

Similar but not relevant code:
def first_sunday(self, year, month):
        """Get the first sunday of a month."""
        date = datetime(year, month, 1, 0)
        days_until_sunday = 6 - date.weekday()

        return date + timedelta(days=days_until_sunday)

Similar but not relevant code:
def gday_of_year(self):
        """Return the number of days since January 1 of the given year."""
        return (self.date - dt.date(self.date.year, 1, 1)).days
Query: dynamically create documents python mongoegine

Relevant code:
def __call__(self, *args, **kwargs):
        """ Instanciates a new *Document* from this collection """
        kwargs["mongokat_collection"] = self
        return self.document_class(*args, **kwargs)

Similar but not relevant code:
def insert_one(self, mongo_collection, doc, mongo_db=None, **kwargs):
        """
        Inserts a single document into a mongo collection
        https://api.mongodb.com/python/current/api/pymongo/collection.html#pymongo.collection.Collection.insert_one
        """
        collection = self.get_collection(mongo_collection, mongo_db=mongo_db)

        return collection.insert_one(doc, **kwargs)

Similar but not relevant code:
def upsert_multi(db, collection, object, match_params=None):
        """
        Wrapper for pymongo.insert_many() and update_many()
        :param db: db connection
        :param collection: collection to update
        :param object: the modifications to apply
        :param match_params: a query that matches the documents to update
        :return: ids of inserted/updated document
        """
        if isinstance(object, list) and len(object) > 0:
            return str(db[collection].insert_many(object).inserted_ids)
        elif isinstance(object, dict):
            return str(db[collection].update_many(match_params, {"$set": object}, upsert=False).upserted_id)

Similar but not relevant code:
def upsert_single(db, collection, object, match_params=None):
        """
        Wrapper for pymongo.update_one()
        :param db: db connection
        :param collection: collection to update
        :param object: the modifications to apply
        :param match_params: a query that matches the documents to update
        :return: id of updated document
        """
        return str(db[collection].update_one(match_params, {"$set": object}, upsert=True).upserted_id)

Similar but not relevant code:
def remove(self, document_id, namespace, timestamp):
        """Removes documents from Solr

        The input is a python dictionary that represents a mongo document.
        """
        self.solr.delete(id=u(document_id),
                         commit=(self.auto_commit_interval == 0))

Similar but not relevant code:
def delete_collection(mongo_uri, database_name, collection_name):
    """
    Delete a mongo document collection using pymongo. Mongo daemon assumed to be running.

    Inputs: - mongo_uri: A MongoDB URI.
            - database_name: The mongo database name as a python string.
            - collection_name: The mongo collection as a python string.
    """
    client = pymongo.MongoClient(mongo_uri)

    db = client[database_name]

    db.drop_collection(collection_name)
Query: python get the last n from array

Relevant code:
def tail(self, n=10):
        """
        Get an SArray that contains the last n elements in the SArray.

        Parameters
        ----------
        n : int
            The number of elements to fetch

        Returns
        -------
        out : SArray
            A new SArray which contains the last n rows of the current SArray.
        """
        with cython_context():
            return SArray(_proxy=self.__proxy__.tail(n))

Similar but not relevant code:
def last(self):
        """Get the last object in file."""
        # End of file
        self.__file.seek(0, 2)

        # Get the last struct
        data = self.get(self.length - 1)

        return data
Query: early stopping use keras lstm in python

Relevant code:
def lmx_h1k_f64k():
  """HParams for training languagemodel_lm1b32k_packed.  880M Params."""
  hparams = lmx_base()
  hparams.hidden_size = 1024
  hparams.filter_size = 65536
  hparams.batch_size = 2048
  return hparams

Similar but not relevant code:
def transformer_ae_a3():
  """Set of hyperparameters."""
  hparams = transformer_ae_base()
  hparams.batch_size = 4096
  hparams.layer_prepostprocess_dropout = 0.3
  hparams.optimizer = "Adafactor"
  hparams.learning_rate = 0.25
  hparams.learning_rate_warmup_steps = 10000
  return hparams

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 super_lm_tpu_memtest():
  """Crazy set of hyperparameters to test memory optimizations.

  Quality will be very poor due to lack of attention layers.
  853M parameters
  This seems to run on TPU for languagemodel_lm1b8k_packed as of 2018-01-19.

  Returns:
    An hparams object.
  """
  hparams = super_lm_base()
  hparams.num_model_shards = 1
  hparams.layers = "ffn," * 8
  hparams.hidden_size = 4096
  hparams.filter_size = 12000
  hparams.batch_size = 512
  return hparams

Similar but not relevant code:
def local_accuracy(X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model):
    """ The how well do the features plus a constant base rate sum up to the model output.
    """

    X_train, X_test = to_array(X_train, X_test)

    # how many features to mask
    assert X_train.shape[1] == X_test.shape[1]

    # keep nkeep top features and re-train the model for each test explanation
    yp_test = trained_model.predict(X_test)

    return metric(yp_test, strip_list(attr_test).sum(1))

Similar but not relevant code:
def error_rate(predictions, labels):
  """Return the error rate based on dense predictions and 1-hot labels."""
  return 100.0 - (
      100.0 *
      np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1)) /
      predictions.shape[0])
Query: python get the stdout from external command

Relevant code:
def check_output(args, env=None, sp=subprocess):
    """Call an external binary and return its stdout."""
    log.debug('calling %s with env %s', args, env)
    output = sp.check_output(args=args, env=env)
    log.debug('output: %r', output)
    return output

Similar but not relevant code:
def __exit__(self, *args):
        """Redirect stdout back to the original stdout."""
        sys.stdout = self._orig
        self._devnull.close()

Similar but not relevant code:
def capture_stdout():
    """Intercept standard output in a with-context
    :return: cStringIO instance

    >>> with capture_stdout() as stdout:
            ...
        print stdout.getvalue()
    """
    stdout = sys.stdout
    sys.stdout = six.moves.cStringIO()
    try:
        yield sys.stdout
    finally:
        sys.stdout = stdout

Similar but not relevant code:
def redirect_stdout(new_stdout):
    """Redirect the stdout

    Args:
        new_stdout (io.StringIO): New stdout to use instead
    """
    old_stdout, sys.stdout = sys.stdout, new_stdout
    try:
        yield None
    finally:
        sys.stdout = old_stdout

Similar but not relevant code:
def redirect_std():
    """
    Connect stdin/stdout to controlling terminal even if the scripts input and output
    were redirected. This is useful in utilities based on termenu.
    """
    stdin = sys.stdin
    stdout = sys.stdout
    if not sys.stdin.isatty():
        sys.stdin = open_raw("/dev/tty", "r", 0)
    if not sys.stdout.isatty():
        sys.stdout = open_raw("/dev/tty", "w", 0)

    return stdin, stdout
Query: easiest way to create matrix in python

Relevant code:
def a2s(a):
    """
     convert 3,3 a matrix to 6 element "s" list  (see Tauxe 1998)
    """
    s = np.zeros((6,), 'f')  # make the a matrix
    for i in range(3):
        s[i] = a[i][i]
    s[3] = a[0][1]
    s[4] = a[1][2]
    s[5] = a[0][2]
    return s

Similar but not relevant code:
def ident():
    """
    This routine returns the 3x3 identity matrix.

    http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/ident_c.html

    :return: The 3x3 identity matrix.
    :rtype: 3x3-Element Array of floats
    """
    matrix = stypes.emptyDoubleMatrix()
    libspice.ident_c(matrix)
    return stypes.cMatrixToNumpy(matrix)

Similar but not relevant code:
def scipy_sparse_to_spmatrix(A):
    """Efficient conversion from scipy sparse matrix to cvxopt sparse matrix"""
    coo = A.tocoo()
    SP = spmatrix(coo.data.tolist(), coo.row.tolist(), coo.col.tolist(), size=A.shape)
    return SP

Similar but not relevant code:
def trans_from_matrix(matrix):
    """ Convert a vtk matrix to a numpy.ndarray """
    t = np.zeros((4, 4))
    for i in range(4):
        for j in range(4):
            t[i, j] = matrix.GetElement(i, j)
    return t

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]])