[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
------
        self : object
        """
        if _refit:
            self.classes_ = None

        first_call = _check_partial_fit_first_call(self, classes)
        X, y = self._validate_data(X, y, reset=first_call)
        if sample_weight is not None:
            sample_weight = _check_sample_weight(sample_weight, X)

        # If the ratio of data variance between dimensions is too small, it
        # will cause numerical errors. To address this, we artificially
        # boost the variance by epsilon, a small fraction of the standard
        # deviation of the largest dimension.
        self.epsilon_ = self.var_smoothing * np.var(X, axis=0).max()

        if first_call:
            # This is the first call to partial_fit:
            # initialize various cumulative counters
            n_features = X.shape[1]
            n_classes = len(self.classes_)
            self.theta_ = np.zeros((n_classes, n_features))
            self.var_ = np.zeros((n_classes, n_features))

            self.class_count_ = np.zeros(n_classes, dtype=np.float64)

            # Initialise the class prior
            # Take into account the priors

[Assistant]
     if self.priors is not None:
                priors = np.asarray(self.priors)
                # Check that the provided prior matches the number of classes
                if len(priors) != n_classes:
                    raise ValueError("Number of priors must match number of classes.")
                # Check that the sum is 1
                if not np.isclose(priors.sum(), 1.0):
                    raise ValueError("The sum of the priors should be 1.")
                # Check that the priors are non-negative
                if (priors  0).any():
                    raise ValueError("Priors must be non-negative.")
                self.class_prior_ = priors
            else:
                # Initialize the priors to zeros for each class
                self.class_prior_ = np.zeros(len(self.classes_), dtype=np.float64)
        else:
            if X.shape[1] != self.theta_.shape[1]:
                msg = "Number of features %d does not match previous data %d."
                raise ValueError(msg % (X.shape[1], self.theta_.shape[1]))
            # Put epsilon back in each time
            self.var_[:, :] -= self.epsilon_


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
         if "wsgi.input_terminated" in request.environ:
                app.logger.debug(
                    "environ wsgi.input_terminated already set, keeping: %s"
                    % request.environ["wsgi.input_terminated"]
                )
            else:
                request.environ["wsgi.input_terminated"] = 1
        else:
            abort(501, "Chunked requests are not supported for server %s" % server)

@app.after_request
def set_cors_headers(response):
    response.headers["Access-Control-Allow-Origin"] = request.headers.get("Origin", "*")
    response.headers["Access-Control-Allow-Credentials"] = "true"

    if request.method == "OPTIONS":
        # Both of these headers are only used for the "preflight request"
        # http://www.w3.org/TR/cors/#access-control-allow-methods-response-header
        response.headers[
            "Access-Control-Allow-Methods"
        ] = "GET, POST, PUT, DELETE, PATCH, OPTIONS"
        response.headers["Access-Control-Max-Age"] = "3600"  # 1 hour cache
        if request.headers.get("Access-Control-Request-Headers") is not None:
            response.headers["Access-Control-Allow-Headers"] = request.headers[
                "Access-Control-Request-Headers"
            ]
    return response

# ------
# Routes
# ------

@app.route("/legacy")
def view_landing_page():
    """Generates Landing Page in legacy layout."""
    return render_template("index.html")

@app.route("/html")
def view_html_page():
    """Returns a simple HTML document.
    ---
    tags:
      - Response formats
    produces:
      - text/html
    responses:
      200:
        description: An HTML page.
    """

    return render_template("moby.html")

@app.route("/robots.txt")
def view_robots_page():
    """Returns some robots.txt rules.
    ---
    tags:
      - Response formats
    produces:
      - text/plain
    responses:
      200:
        description: Robots file
    """

    response = make_response()
    response.data = ROBOT_TXT
    response.content_type = "text/plain"
    return response

@app.route("/deny")
def view_deny_page():
    """Returns page denied by robots.txt rules.
    ---
    tags:
      - Response formats
    produces:
      - text/plain
    responses:
      200:
        description: Denied message
    """
    response = make_response()
    response.data = ANGRY_ASCII
    response.content_type = "text/plain"
    return response
    # return "YOU SHOULDN'T BE HERE"

@app.route("/ip")
def view_origin():
    """Returns the requester's IP Address.
    ---
    tags:
      - Request inspection
    produces:
      - application/json
    responses:
      200:
        description: The Requester's IP Address.
    """

    return jsonify(origin=request.headers.get("X-Forwarded-For", request.remote_addr))

@app.route("/uuid")
def view_uuid():
    """Return a UUID4.
    ---
    tags:
      - Dynamic data
    produces:
      - application/json
    responses:
      200:
        description: A UUID4.
    """

    return jsonify(uuid=str(uuid.uuid4()))

@app.route("/headers")
def view_headers():
    """Return the incoming request's HTTP headers.
    ---
    tags:
      - Request inspection
    produces:
      - application/json
    responses:
      200:
        description: The request's headers.
    """

    return jsonify(get_dict('headers'))

@app.route("/user-agent")
def view_user_agent():
    """Return the incoming requests's User-Agent header.
    ---
    tags:
      - Request inspection
    produces:
      - application/json
    responses:
      200:
        description: The request's User-Agent header.
    """

    headers = get_headers()

    return jsonify({"user-agent": headers["user-agent"]})

@app.route("/get", methods=("GET",))
def view_get():
    """The request's query parameters.
    ---
    tags:
      - HTTP Methods
    produces:
      - application/json
    responses:
      200:
        description: The request's query parameters.
    """

    return jsonify(get_dict("url", "args", "headers", "origin"))

@app.route("/anything", methods=["GET", "POST", "PUT", "DELETE", "PATCH", "TRAC

[Assistant] E"])


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
f.set_ticks_position('none')
        self._wrap_locator_formatter()

    def _set_scale(self, value, **kwargs):
        if value != 'linear':
            raise NotImplementedError(
                "The xscale cannot be set on a polar plot")
        super()._set_scale(value, **kwargs)
        # LinearScale.set_default_locators_and_formatters just set the major
        # locator to be an AutoLocator, so we customize it here to have ticks
        # at sensible degree multiples.
        self.get_major_locator().set_params(steps=[1, 1.5, 3, 4.5, 9, 10])
        self._wrap_locator_formatter()

    def _copy_tick_props(self, src, dest):
        """Copy the props from src tick to dest tick."""
        if src is None or dest is None:
            return
        super()._copy_tick_props(src, dest)

        # Ensure that tick transforms are independent so that padding works.
        trans = dest._get_text1_transform()[0]
        dest.label1.set_transform(trans + dest._text1_translate)
        trans = dest._get_text2_transform()[0]
        dest.label2.set_transform(trans + dest._text2_translate)

class RadialLocator(mticker.Locator):
    """
    Used to locate radius ticks.

    Ensures that all ticks are strictly positive.  For all other tasks, it
    delegates to the base `.Locator` (which may be different depending on the
    scale of the *r*-axis).
    """

    def __init__(self, base, axes=None):
        self.base = base
        self._axes = axes

    def set_axis(self, axis):
        self.base.set_axis(axis)

    def __call__(self):
        # Ensure previous behaviour with full circle non-annular views.
        if self._axes:
            if _is_full_circle_rad(*self._axes.viewLim.intervalx):
                rorigin = self._axes.get_rorigin() * self._axes.get_rsign()
                if self._axes.get_rmin() = rorigin:
                    return [tick for tick in self.base() if tick  rorigin]
        return self.base()

    def _zero_in_bounds(self):
        """
        Return True if zero is within the valid values for the
        scale of the radial axis.
        """
        vmin, vmax = self._axes.yaxis._scale.limit_range_for_scale(0, 1, 1e-5)
        return vmin == 0

    def nonsingular(self, vmin, vmax):
        # docstring inherited
        if self._zero_in_bounds() and (vmin, vmax) == (-np.inf, np.inf):
            # Initial view limits
            return (0, 1)
        else:
            return self.base.nonsingular(vmin, vmax)

    def view_limits(self, vmin, vmax):
        vmin, vmax = self.base.view_limits(vmin, vmax)
        if self._zero_in_bounds() and vmax  vmin:
            # this allows inverted r/y-lims
            vmin = min(0, vmin)
        return mtransforms.nonsingular(vmin, vmax)

class _ThetaShift(mtransforms.ScaledTranslation):
    """
    Apply a padding shift based on axes theta limits.

    This is used to create padding for radial ticks.

    Parameters
    ----------
    axes : `~matplotlib.axes.Axes`
        The owning axes; used to determine limits.
    pad : float
        The padding to apply, in points.
    mode : {'min', 'max', 'rlabel'}
        Whether to shift away from the start (``'min'``) or the end (``'max'``)
        of the axes, or using the rlabel position (``'rlabel'``).
    """
    def __init__(self, axes, pad, mode):
        super().__init__(pad, pad, axes.figure.dpi_scale_trans)
        self.set_children(axes._realViewLim)
        self.axes = axes
        self.mode = mode
        self.pad = pad

    __str__ = mtransforms._make_str_method("axes", "pad", "mode")

    def get_matrix(self):
        if self._invalid:
            if self.mode == 'rlabel':
                angle = (
                    np.deg2rad(self.axes.get_rlabel_position()
                               * self.axes.get_theta_direction())
                    + self.axes.get_theta_offset()
                    - np.pi / 2
                )
            elif self.mode == 'min':
                angle = self.axes._realViewLim.xmin - np.pi / 2
            elif self.mode == 'max':
                angle = self.axes._realViewLim.xmax + np.pi / 2
            self._t = (self.pad * np.cos(angle) / 72, self.pad * np.sin(angle) / 72)
        return super().get_matrix()

class RadialTick(maxis.YTick):
    """
    A radial-axis tick.

    This subclass of `.YTick` provides radial ticks with some small
    modification to their re-positioning such that ticks are rotated based on
    axes limits.  This results in ticks that are correctly perpendicular to
    the spine. Labels are also rotated to be perpendicular to the spine, when
    'auto' rotation is enabled.
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.label1.set_rotation_mode('anchor')
        self.label2.set_rotation_mode('anchor')

    def _determine_anchor(self, mode, angle, start):
        # Note: angle is the (spine angle - 90) because it's used for the tick
        # & text setup, so all numbers below are -90 from (normed) spine angle.
        if mode == 'auto':
            if start:
                if -90 = angle = 90:
                    return 'left', 'center'
                else:
                    return 'right', 'center'
            else:
                if -90 = angle = 90:
                    return 'right', 'center'
                else:
                    return 'left', 'center'
        else:
            if start:
                if angle  -68.5:
                    return 'center', 'top'
                elif angle  -23.5:
                    return 'left', 'top'
                elif angle  22.5:
                    return 'left', 'center'
                elif angle  67.5:
                    return 'left', 'bottom'
                elif angle  112.5:
                    return 'center', 'bottom'
                elif angle  157.5:
                    return 'right', 'bottom'
                elif angle  202.5:
                    return 'right', 'center'
                elif angle  247.5:
                    return 'right', 'top'
                else:
                    return 'center', 'top'
            else:
                if angle  -68.5:
                    return 'center', 'bottom'
                elif angle  -23.5:
                    return 'right', 'bottom'
                elif angle  22.5:
                    return 'right', 'center'
                elif angle  67.5:
                    return 'right', 'top'
                elif angle  112.5:
                    return 'center', 'top'
                elif angle  157.5:
                    return 'left', 'top'
                elif angle  202.5:
                    return 'left', 'center'
                elif angle  247.5:
                    return 'left', 'bottom'
                else:
                    return 'center', 'bottom'

    def update_position(self, loc):
        super().update_position(loc)
        axes = self.axes
        thetamin = axes.get_thetamin()
        thetamax = axes.get_thetamax()
        direction = axes.get_theta_direction()
        offset_rad = axes.get_theta_offset()
        offset = np.rad2deg(offset_rad)
        full = _is_full_circle_deg(thetamin, thetamax)

        if full:
            angle = (axes.get_rlabel_position() * direction +
                     offset) % 360 - 90
            tick_angle = 0
        else:
            angle = (thetamin * direction + offset) % 360 - 90
            if direction  0:
                tick_angle = np.deg2rad(angle)
            else:
                tick_angle

[Assistant]
= np.deg2rad(angle + 180)
        text_angle = (angle + 90) % 180 - 90  # between -90 and +90.
        mode, user_angle = self._labelrotation


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
r_symbol
from tensorflow.python.util.tf_export import tf_export

def _convert_to_sparse_tensor(sp_input):
  """Convert `sp_input` to `SparseTensor` and return it.

  Args:
    sp_input: `SparseTensor` or `SparseTensorValue`.

  Returns:
    `sp_input` converted to `SparseTensor`.

  Raises:
    ValueError: if `sp_input` is neither `SparseTensor` nor `SparseTensorValue`.
  """
  if isinstance(sp_input, sparse_tensor.SparseTensorValue):
    return sparse_tensor.SparseTensor.from_value(sp_input)
  if not isinstance(sp_input, sparse_tensor.SparseTensor):
    raise TypeError("Input must be a SparseTensor.")
  return sp_input

def _convert_to_sparse_tensors(sp_inputs):
  """Convert `sp_inputs` to `SparseTensor` objects and return them.

  Args:
    sp_inputs: `list` or `tuple` of `SparseTensor` or `SparseTensorValue`
      objects.

  Returns:
    `sp_inputs` converted to `SparseTensor` objects.

  Raises:
    ValueError: if any item in `sp_inputs` is neither `SparseTensor` nor
      `SparseTensorValue`.
  """
  if isinstance(sp_inputs, list):
    return [_convert_to_sparse_tensor(sp_input) for sp_input in sp_inputs]
  if isinstance(sp_inputs, tuple):
    return (_convert_to_sparse_tensor(sp_input) for sp_input in sp_inputs)
  raise TypeError("Inputs must be a list or tuple.")

def _make_int64_tensor(value, name):
  if isinstance(value, compat.integral_types):
    return ops.convert_to_tensor(value, name=name, dtype=dtypes.int64)
  if not isinstance(value, tensor_lib.Tensor):
    raise TypeError("{} must be an integer value".format(name))
  if value.dtype == dtypes.int64:
    return value
  return math_ops.cast(value, dtypes.int64)

@tf_export("sparse.from_dense")
def from_dense(tensor, name=None):
  """Converts a dense tensor into a sparse tensor.

  Only elements not equal to zero will be present in the result. The resulting
  `SparseTensor` has the same dtype and shape as the input.

   sp = tf.sparse.from_dense([0, 0, 3, 0, 1])
   sp.shape.as_list()
  [5]
   sp.values.numpy()
  array([3, 1], dtype=int32)
   sp.indices.numpy()
  array([[2],
         [4]])

  Args:
    tensor: A dense `Tensor` to be converted to a `SparseTensor`.
    name: Optional name for the op.

  Returns:
    The `SparseTensor`.
  """
  with ops.name_scope(name, "dense_to_sparse"):
    tensor = ops.convert_to_tensor(tensor)
    indices = array_ops.where_v2(
        math_ops.not_equal(tensor, array_ops.zeros_like(tensor)))
    values = array_ops.gather_nd(tensor, indices)
    shape = array_ops.shape(tensor, out_type=dtypes.int64)
    return sparse_tensor.SparseTensor(indices, values, shape)

@tf_export("sparse.expand_dims")
def sparse_expand_dims(sp_input, axis=None, name=None):
  """Returns a tensor with an length 1 axis inserted at index `axis`.

  Given a tensor `input`, this operation inserts a dimension of length 1 at the
  dimension index `axis` of `input`'s shape. The dimension index follows python
  indexing rules: It's zero-based, a negative index it is counted backward
  from the end.

  This operation is useful to:

  * Add an outer "batch" dimension to a single element.
  * Align axes for broadcasting.
  * To add an inner vector length axis to a tensor of scalars.

  For example:

  If you have a sparse tensor with shape `[height, width, depth]`:

   sp = tf.sparse.SparseTensor(indices=[[3,4,1]], values=[7,],
  ...                             dense_shape=[10,10,3])

  You can add an outer `batch` axis by passing `axis=0`:

   tf.sparse.expand_dims(sp, axis=0).shape.as_list()
  [1, 10, 10, 3]

  The new axis location matches Python `list.insert(axis, 1)`:

   tf.sparse.expand_dims(sp, axis=1).shape.as_list()
  [10, 1, 10, 3]

  Following standard python indexing rules, a negative `axis` counts from the
  end so `axis=-1` adds an inner most dimension:

   tf.sparse.expand_dims(sp, axis=-1).shape.as_list()
  [10, 10, 3, 1]

  Note: Unlike `tf.expand_dims` this function includes a default value for the
  `axis`: `-1`. So if `axis is not specified, an inner dimension is added.

   sp.shape.as_list()
  [10, 10, 3]
   tf.sparse.expand_dims(sp).shape.as_list()
  [10, 10, 3, 1]

  This operation requires that `axis` is a valid index for `input.shape`,
  following python indexing rules:

  ```
  -1-tf.rank(input) = axis = tf.rank(input)
  ```

  This operation is related to:

  * `tf.expand_dims`, which provides this functionality for dense tensors.
  * `tf.squeeze`, which removes dimensions of size 1, from dense tensors.
  * `tf.sparse.reshape`, which provides more flexible reshaping capability.

  Args:
    sp_input: A `SparseTensor`.
    axis: 0-D (scalar). Specifies the dimension index at which to expand the
      shape of `input`. Must be in the range `[-rank(sp_input) - 1,
      rank(sp_input)]`. Defaults to `-1`.
    name: The name of the output `SparseTensor`.

  Returns:
    A `SparseTensor` with the same data as `sp_input`, but its shape has an
    additional dimension of size 1 added.
  """
  rank = sp_input.dense_shape.get_shape()[0]
  if rank is None:
    rank = array_ops.shape(sp_input.dense_shape)[0]
  axis = -1 if axis is None else axis

  with ops.name_scope(name, default_name="expand_dims", values=[sp_input]):
    if isinstance(axis, compat.integral_types):
      axis = ops.convert_to_tensor(axis, name="axis", dtype=dtypes.int32)
    elif not isinstance(axis, tensor_lib.Tensor):
      raise TypeError("axis must be an integer value in range [-rank(sp_input)"
                      " - 1, rank(sp_input)]")

    # Convert axis to a positive value if it is negative.
    axis = array_ops.where_v2(axis = 0, axis, axis + rank + 1)

    # Create the new column of indices for the sparse tensor by slicing
    # the indices and inserting a new column of indices for the new dimension.
    column_size = array_ops.shape(sp_input.indices)[0]
    new_index = array_ops.zeros([column_size, 1], dtype

[Assistant] =dtypes.int64)


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
"""
Helper functions for creating user-friendly representations
of serializer classes and serializer fields.
"""
import re

from django.db import models
from django.utils.encoding import force_str
from django.utils.functional import Promise

def manager_repr(value):
    model = value.model
    opts = model._meta
    names_and_managers = [
        (manager.name, manager)
        for manager
        in opts.managers
    ]
    for manager_name, manager_instance in names_and_managers:
        if manager_instance == value:
            return '%s.%s.all()' % (model._meta.object_name, manager_name)
    return repr(value)

def smart_repr(value):
    if isinstance(value, models.Manager):
        return manager_repr(value)

    if isinstance(value, Promise) and value._delegate_text:
        value = force_str(value)

    value = repr(value)

    # Representations like u'help text'
    # should simply be presented as 'help text'
    if value.startswith("u'") and value.endswith("'"):
        return value[1:]

    # Representations like
    # django.core.validators.RegexValidator object at 0x1047af050
    # Should be presented as
    # django.core.validators.RegexValidator object
    return re.sub(' at 0x[0-9A-Fa-f]{4,32}', '', value)

def field_repr(field, force_many=False):
    kwargs = field._kwargs
    if force_many:
        kwargs = kwargs.copy()
        kwargs['many'] = True
        kwargs.pop('child', None)

    arg_string = ', '.join([smart_repr(val) for val in field._args])
    kwarg_string = ', '.join([
        '%s=%s' % (key, smart_repr(val))
        for key, val in sorted(kwargs.items())
    ])
    if arg_string and kwarg_string:
        arg_string += ', '

    if force_many:
        class_name = f

[Assistant]
orce_many.__class__.__name__
    else:
        class_name = field.__class__.__name__

    return "%s(%s%s)" % (class_name, arg_string, kwarg_string)


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
m most common place
if len(train_df.Embarked[ train_df.Embarked.isnull() ])  0:
    train_df.Embarked[ train_df.Embarked.isnull() ] = train_df.Embarked.dropna().mode().values

Ports = list(enumerate(np.unique(train_df['Embarked'])))    # determine all values of Embarked,
Ports_dict = { name : i for i, name in Ports }              # set up a dictionary in the form  Ports : index
train_df.Embarked = train_df.Embarked.map( lambda x: Ports_dict[x]).astype(int)     # Convert all Embark strings to int

# All the ages with no data - make the median of all Ages
median_age = train_df['Age'].dropna().median()
if len(train_df.Age[ train_df.Age.isnull() ])  0:
    train_df.loc[ (train_df.Age.isnull()), 'Age'] = median_age

# Remove the Name column, Cabin, Ticket, and Sex (since I copied and filled it to Gender)
train_df = train_df.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'PassengerId'], axis=1)

# TEST DATA
test_df = pd.read_csv('test.csv', header=0)        # Load the test file into a dataframe

# I need to do the same with the test data now, so that the columns are the same as the training data
# I need to convert all strings to integer classifiers:
# female = 0, Male = 1
test_df['Gender'] = test_df['Sex'].map( {'female': 0, 'male': 1} ).astype(int)

# Embarked from 'C', 'Q', 'S'
# All missing Embarked - just make them embark from most common place
if len(test_df.Embarked[ test_df.Embarked.isnull() ])  0:
    test_df.Embarked[ test_df.Embarked.isnull() ] = test_df.Embarked.dropna().mode().values
# Again convert all Embarked strings to int
test_df.Embarked = test_df.Embarked.map( lambda x: Ports_dict[x]).astype(int)

# All the ages with no data - make the median of all Ages
median_age = test_df['Age'].dropna().median()
if len(test_df.Age[ test_df.Age.isnull() ])  0:
    test_df.loc[ (test_df.Age.isnull()), 'Age'] = median_age

# All the missing Fares - assume median of their respective class
if len(test_df.Fare[ test_df.Fare.isnull() ])  0:
    median_fare = np.zeros(3)
    for f in range(0,3):                                              # loop 0 to 2
        median_fare[f] = test_df[ test_df.Pclass == f+1 ]['Fare'].dropna().median()
    for f in range(0,3):                                              # loop 0 to 2
        test_df.loc[ (test_df.Fare.isnull()) & (test_df.Pclass == f+1 ), 'Fare'] = median_fare[f]

# Collect the test data's PassengerIds before dropping it
ids = test_df['PassengerId'].values
# Remove the Name column, Cabin, Ticket, and Sex (since I copied and filled it to Gender)
test_df = test_df.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'Passen

[Assistant] gerId'], axis=1)


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
import os
import shutil
import pytest
from tempfile import mkstemp, mkdtemp
from subprocess import Popen, PIPE
import importlib.metadata
from distutils.errors import DistutilsError

from numpy.testing import assert_, assert_equal, assert_raises
from numpy.distutils import ccompiler, customized_ccompiler
from numpy.distutils.system_info import system_info, ConfigParser, mkl_info
from numpy.distutils.system_info import AliasedOptionError
from numpy.distutils.system_info import default_lib_dirs, default_include_dirs
from numpy.distutils import _shell_utils

try:
    if importlib.metadata.version('setuptools') = '60':
        # pkg-resources gives deprecation warnings, and there may be more
        # issues. We only support setuptools 60
        pytest.skip("setuptools is too new", allow_module_level=True)
except importlib.metadata.PackageNotFoundError:
    # we don't require `setuptools`; if it is not found, continue
    pass

def get_class(name, notfound_action=1):
    """
    notfound_action:
      0 - do nothing
      1 - display warning message
      2 - raise error
    """
    cl = {'temp1': Temp1Info,
          'temp2': Temp2Info,
          'duplicate_options': DuplicateOptionInfo,
          }.get(name.lower(), _system_info)
    return cl()

simple_site = """
[ALL]
library_dirs = {dir1:s}{pathsep:s}{dir2:s}
libraries = {lib1:s},{lib2:s}
extra_compile_args = -I/fake/directory -I"/path with/spaces" -Os
runtime_library_dirs = {dir1:s}

[temp1]
library_dirs = {dir1:s}
libraries = {lib1:s}
runtime_library_dirs = {dir1:s}

[temp2]
library_dirs = {dir2:s}
libraries = {lib2:s}
extra_link_args = -Wl,-rpath={lib2_escaped:s}
rpath = {dir2:s}

[duplicate_options]
mylib_libs = {lib1:s}
libraries = {lib2:s}
"""
site_cfg = simple_site

fakelib_c_text = """
/* This file is generated from numpy/distutils/testing/test_system_info.py */
#includestdio.h
void foo(void) {
   printf("Hello foo");
}
void bar(void) {
   printf("Hello bar");
}
"""

def have_compiler():
    """ Return True if there appears to be an executable compiler
    """
    compiler = customized_ccompiler()
    try:
        cmd = compiler.compiler  # Unix compilers
    except AttributeError:
        try:
            if not compiler.initialized:
                compiler.initialize()  # MSVC is different
        except (DistutilsError, ValueError):
            return False
        cmd = [compiler.cc]
    try:
        p = Popen(cmd, stdout=PIPE, stderr=PIPE)
        p.stdout.close()
        p.stderr.close()
        p.wait()
    except OSError:
        return False
    return True

HAVE_COMPILER = have_compiler()

class _system_info(system_info):

    def __init__(self,
                 default_lib_dirs=default_lib_dirs,
                 default_include_dirs=default_include_dirs,
                 verbosity=1,
                 ):
        self.__class__.info = {}
        self.local_prefixes = []
        defaults = {'library_dirs': '',
                    'include_dirs': '',
                    'runtime_library_dirs': '',
                    'rpath': '',
                    'src_dirs': '',
                    'se

[Assistant]
arch_static_first': "0",
                    'extra_compile_args': '',
                    'extra_link_args': ''}
        self.cp = ConfigParser(defaults)


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
import os
import time

from pathlib import Path
from prompt_file import *

def get_command_result(input, prompt_file):
    """
    Checks if the input is a command and if so, executes it
    Currently supported commands:
    - start multi-turn
    - stop multi-turn
    - default context
    - show context n
    - view context
    - save context
    - clear context
    - load context filename
    - set engine engine
    - set temperature temperature
    - set max_tokens max_tokens
    - set shell shell

    Returns: command result or "" if no command matched
    """
    if prompt_file == None:
        return "", None

    config = prompt_file.config
    # configuration setting commands
    if input.__contains__("set"):
        # set temperature temperature
        if input.__contains__("temperature"):
            input = input.split()
            if len(input) == 4:
                config['temperature'] = float(input[3])
                prompt_file.set_config(config)
                print("# Temperature set to " + str(config['temperature']))
                return "config set", prompt_file
            else:
                return "", prompt_file
        # set max_tokens max_tokens
        elif input.__contains__("max_tokens"):
            input = input.split()
            if len(input) == 4:
                config['max_tokens'] = int(input[3])
                prompt_file.set_config(config)
                print("# Max tokens set to " + str(config['max_tokens']))
                return "config set", prompt_file
            else:
                return "", prompt_file
        elif input.__contains__("shell"):
            input = input.split()
            if len(input) == 4:
                config['shell'] = input[3]
                prompt_file.set_config(config)
                print("# Shell set to " + str(config['shell']))
                return "config set", prompt_file
            else:
                return "", prompt_file
        elif input.__contains__("engine"):
            input = input.split()
            if len(input) == 4:
                config['engine'] = input[3]
                prompt_file.set_config(config)
                print("# Engine set to " + str(config['engine']))
                return "config set", prompt_file
            else:
                return "", prompt_file

    if input.__contains__("show config"):
        prompt_file.show_config()
        return "config shown", prompt_file

    # multi turn/single turn commands
    if input.__contains__("multi-turn"):
        # start context
        if input.__contains__("start"):
            if config['multi_turn'] == 'off':
                prompt_file.start_multi_turn()
                return "multi turn mode on", prompt_file

            return "multi turn mode on", prompt_file

        # stop context
        if input.__contains__("stop"):
            prompt_file.stop_multi_turn()
            return "multi turn mode off", prompt_file

    # context file commands
    if input.__contains__("context"):
        if input.__contains__("default"):
            prompt_file.default_context()
            return "stopped context", prompt_file

        # show context n
        if input.__contains__("show"):
            print('\n')
            with open(prompt_file.file_name, 'r') as f:
                lines = f.readlines()
                lines = lines[6:] # skip headers

            line_numbers = 0
            if len(input.split())  3:
                line_numbers = int(input.split()[3])

            if line_numbers != 0:
                for line in lines[-line_numbers:]:
                    print('\n# '+line, end='')
            else:
                print('\n# '.join(lines))
            return "context shown", prompt_file

        # edit context
        if input.__contains__("view"):
            # open the prompt file in text editor
            if config['shell'] != 'powershell':
                os.system('open {}'.format(prompt_file.file_path))
            else:
                os.system('start {}'.format(prompt_file.file_path))
            return "context shown", prompt_file

        # save context filename
        if input.__contains__("save"):
            # save the current prompt file to a new file
            # if filename not specified use the current time (to avoid name conflicts)

            filename = time.strftime("%Y-%m-%d_%H-%M-%S") + ".txt"
            if len(input.split()) == 4:
                filename = input.split()[3]

            prompt_file.save_to(filename)
            return "context saved", prompt_file

        # clear context
        if input.__contains__("clear"):
            # temporary saving deleted prompt file
            prompt_file.default_context()
            return "unlearned interaction", prompt_file

        # load context filename
        if input.__contains_

[Assistant] _("load"):


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
es: Elasticsearch
            The Elasticsearch client
        """
        # Convert the new_predictions DataFrame to a list of dictionaries
        # The columns of the new_predicitons DataFrame are 'PMID', 'date', 'date_completed', 'label', 'text'
        # Only the 'PMID', 'date', 'date_completed', and 'label' columns are needed
        new_predictions = new_predictions[['PMID', 'date', 'date_completed', 'label']].to_dict(orient='records')

        logger.debug(f'Adding {len(new_predictions)} new predictions to the predictions index {index}: First 5: {new_predictions[:5]}')

        # Add the new predictions to the predictions index
        helpers.bulk(es, [{'_index': index, '_id': int(doc['PMID']), '_source': doc} for doc in new_predictions])

    def _compute_cluster_names(self, X: Union[List[str], pd.Series]) - None:
        """
        Compute the cluster names

        Parameters
        ----------
        X : Union[List[str], Series]
            The documents
        y : np.ndarray
            The labels

        Returns
        -------
        Dict[int, str]
            The cluster names
        """
        raise NotImplementedError

    @abstractmethod
    def _preprocess(self, X: Union[List[str], pd.Series]) - Union[List[str], pd.Series]:
        """
        Preprocess a batch of documents

        Parameters
        ----------
        X : Union[List[str], Series]
            The documents

        Returns
        -------
        Union[List[str], Series]
            The preprocessed documents
        """
        raise NotImplementedError

    @abstractmethod
    def _preprocess_single(self, x: str) - str:
        """
        Preprocess a single document

        Parameters
        ----------
        x : str
            The document

        Returns
        -------
        st

[Assistant]
r
            The preprocessed document
        """
        raise NotImplementedError


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
import numpy as np
from keras.models import *
from keras.layers import *
from keras.applications.vgg16 import VGG16
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint
import cv2

def train_generator(batch_size=32):
    data_gen_args = dict(featurewise_center=True,
                         rotation_range=90.,
                         width_shift_range=0.1,
                         height_shift_range=0.1,
                         fill_mode="constant",
                         cval=255,
                         horizontal_flip=True,
                         vertical_flip=True,
                         zoom_range=0.2)
    image_datagen = ImageDataGenerator(**data_gen_args)
    mask_datagen = ImageDataGenerator(**data_gen_args)

    seed = 1
    image_generator = image_datagen.flow_from_directory(
        'data/train/images',
        class_mode=None,
        batch_size=batch_size,
        color_mode='rgb',
        target_size=(512,512),
        #save_to_dir='./data/gen/images',
        seed=seed)

    mask_generator = mask_datagen.flow_from_directory(
        'data/train/masks',
        class_mode=None,
        color_mode='grayscale',
        target_size=(512,512),
        batch_size=batch_size,
        #save_to_dir='./data/gen/masks',
        seed=seed)

    # combine generators into one which yields image and masks
    train_generator = zip(image_generator, mask_generator)
    for (imgs, masks) in train_generator:
        imgs = imgs / 255.0
        masks = masks / 255.0
        yield (imgs,masks)

def vgg10_unet(input_shape=(256,256,3), weights='imagenet'):
    vgg16_model = VGG16(input_shape=input_shape, weights=weights, include_top=False)

    block4_pool = vgg16_model.get_layer('block4_pool').output
    block5_conv1 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block4_pool)
    block5_conv2 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block5_conv1)
    block5_drop = Dropout(0.5)(block5_conv2)

    block6_up = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
        UpSampling2D(size=(2, 2))(block5_drop))
    block6_merge = Concatenate(axis=3)([vgg16_model.get_layer('block4_conv3').output, block6_up])
    block6_conv1 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block6_merge)
    block6_conv2 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block6_conv1)
    block6_conv3 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block6_conv2)

    block7_up = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
        UpSampling2D(size=(2, 2))(block6_conv3))
    block7_merge = Concatenate(axis=3)([vgg16_model.get_layer('block3_conv3').output, block7_up])
    block7_conv1 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block7_merge)
    block7_conv2 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block7_conv1)
    block7_conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block7_conv2)

    block8_up = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
        UpSampling2D(size=(2, 2))(block7_conv3))
    block8_merge = Concatenate(axis=3)([vgg16_model.get_layer('block2_conv2').output, block8_up])
    block8_conv1 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block8_merge)
    block8_conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block8_conv1)

    block9_up = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
        UpSampling2D(size=(2, 2))(block8_conv2))
    block9_merge = Concatenate(axis=3)([vgg16_model.get_layer('block1_conv2').output, block9_up])
    block9_conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block9_merge)
    block9_conv2 = Conv2D(64, 3, activation='relu'

[Assistant]
, padding='same', kernel_initializer='he_normal')(block9_conv1)

    block10_conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block9_conv2)
    block10_conv2 = Conv2D(1, 1, activation='sigmoid')(block10_conv1)

    model = Model(inputs=vgg16_model.input, outputs=block10_conv2)
    return model


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
       r.set_bounds(
            tt.get_window_extent(renderer)
            .transformed(ax.transAxes.inverted())
            .bounds)
        ax.add_patch(r)

    horal = 'left'
    for nn, st in enumerate(sts):
        tt = ax.text(0.2 * nn + 0.1, 0.5, st, horizontalalignment=horal,
                     verticalalignment='bottom')
        draw_box(ax, tt)
    ax.text(1.2, 0.5, 'Bottom align', color='C2')

    ax.axhline(1.3, color='C2', linewidth=0.3)
    for nn, st in enumerate(sts):
        tt = ax.text(0.2 * nn + 0.1, 1.3, st, horizontalalignment=horal,
                     verticalalignment='top')
        draw_box(ax, tt)
    ax.text(1.2, 1.3, 'Top align', color='C2')

    ax.axhline(1.8, color='C2', linewidth=0.3)
    for nn, st in enumerate(sts):
        tt = ax.text(0.2 * nn + 0.1, 1.8, st, horizontalalignment=horal,
                     verticalalignment='baseline')
        draw_box(ax, tt)
    ax.text(1.2, 1.8, 'Baseline align', color='C2')

    ax.axhline(0.1, color='C2', linewidth=0.3)
    for nn, st in enumerate(sts):
        tt = ax.text(0.2 * nn + 0.1, 0.1, st, horizontalalignment=horal,
                     verticalalignment='bottom', rotation=20)
        draw_box(ax, tt)
    ax.text(1.2, 0.1, 'Bot align, rot20', color='C2')

@image_comparison(['antialiased.png'], style='mpl20')
def test_antialiasing():
    mpl.rcParams['text.antialiased'] = False  # Passed arguments should override.

    fig = plt.figure(figsize=(5.25, 0.75))
    fig.text(0.3, 0.75, "antialiased", horizontalalignment='center',
             verticalalignment='center', antialiased=True)
    fig.text(0.3, 0.25, r"$\sqrt{x}$", horizontalalignment='center',
             verticalalignment='center', antialiased=True)

    mpl.rcParams['text.antialiased'] = True  # Passed arguments should override.
    fig.text(0.7, 0.75, "not antialiased", horizontalalignment='center',
             verticalalignment='center', antialiased=False)
    fig.text(0.7, 0.25, r"$\sqrt{x}$", horizontalalignment='center',
             verticalalignment='center', antialiased=False)

    mpl.rcParams['text.antialiased'] = False  # Should not affect existing text.

def test_afm_kerning():
    fn = mpl.font_manager.findfont("Helvetica", fontext="afm")
    with open(fn, 'rb') as fh:
        afm = mpl._afm.AFM(fh)
    assert afm.string_width_height('VAVAVAVAVAVA') == (7174.0, 718)

@image_comparison(['text_contains.png'])
def test_contains():
    fig = plt.figure()
    ax = plt.axes()

    mevent = MouseEvent('button_press_event', fig.canvas, 0.5, 0.5, 1, None)

    xs = np.linspace(0.25, 0.75, 30)
    ys = np.linspace(0.25, 0.75, 30)
    xs, ys = np.meshgrid(xs, ys)

    txt = plt.text(
        0.5, 0.4, 'hello world', ha='center', fontsize=30, rotation=30)
    # uncomment to draw the text's bounding box
    # txt.set_bbox(dict(edgecolor='black', facecolor='none'))

    # draw the text. This is important, as the contains method can only work
    # when a renderer exists.
    fig.canvas.draw()

    for x, y in zip(xs.flat, ys.flat):
        mevent.x, mevent.y = plt.gca().transAxes.transform([x, y])
        contains, _ = txt.contains(mevent)
        color = 'yellow' if contains else 'red'

        # capture the viewLim, plot a point, and reset the viewLim
        vl = ax.viewLim.frozen()
        ax.plot(x, y, 'o', color=color)
        ax.viewLim.set(vl)

def test_annotation_contains():
    # Check that Annotation.contains looks at the bboxes of the text and the
    # arrow separately, not at the joint bbox.
    fig, ax = plt.subplots()
    ann = ax.annotate(
        "hello", xy=(.4, .4), xytext=(.6, .6), arrowprops={"arrowstyle": "-"})
    fig.canvas.draw()  # Needed for the same

[Assistant]
reason as in test_contains.
    event = MouseEvent(
        "button_press_event", fig.canvas, *ax.transData.transform((.5, .6)))
    assert ann.contains(event) == (False, {})


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
st_context(f: t.Callable) - t.Callable:
    """A helper function that decorates a function to retain the current
    request context.  This is useful when working with greenlets.  The moment
    the function is decorated a copy of the request context is created and
    then pushed when the function is called.  The current session is also
    included in the copied request context.

    Example::

        import gevent
        from flask import copy_current_request_context

        @app.route('/')
        def index():
            @copy_current_request_context
            def do_some_work():
                # do some work here, it can access flask.request or
                # flask.session like you would otherwise in the view function.
                ...
            gevent.spawn(do_some_work)
            return 'Regular response'

    .. versionadded:: 0.10
    """
    ctx = _cv_request.get(None)

    if ctx is None:
        raise RuntimeError(
            "'copy_current_request_context' can only be used when a"
            " request context is active, such as in a view function."
        )

    ctx = ctx.copy()

    def wrapper(*args, **kwargs):
        with ctx:
            return ctx.app.ensure_sync(f)(*args, **kwargs)

    return update_wrapper(wrapper, f)

def has_request_context() - bool:
    """If you have code that wants to test if a request context is there or
    not this function can be used.  For instance, you may want to take advantage
    of request information if the request object is available, but fail
    silently if it is unavailable.

    ::

        class User(db.Model):

            def __init__(self, username, remote_addr=None):
                self.username = username
                if remote_addr is None and has_request_context():
                    remote_addr = request.remote_addr
                self.remote_addr = remote_addr

    Alternatively you can also just test any of the context bound objects
    (such as :class:`request` or :class:`g`) for truthness::

        class User(db.Model):

            def __init__(self, username, remote_addr=None):
                self.username = username
                if remote_addr is None and request:
                    remote_addr = request.remote_addr
                self.remote_addr = remote_addr

    .. versionadded:: 0.7
    """
    return _cv_request.get(None) is not None

def has_app_context() - bool:
    """Works like :func:`has_request_context` but for the application
    context.  You can also just do a boolean check on the
    :data:`current_app` object instead.

    .. versionadded:: 0.9
    """
    return _cv_app.get(None) is not None

class AppContext:
    """The app context contains application-specific information. An app
    context is created and pushed at the beginning of each request if
    one is not already active. An app context is also pushed when
    running CLI commands.
    """

    def __init__(self, app: Flask) - None:
        self.app = app
        self.url_adapter = app.create_url_adapter(None)
        self.g: _AppCtxGlobals = app.app_ctx_globals_class()
        self._cv_tokens: list[contextvars.Token] = []

    def push(self) - None:
        """Binds the app context to the current context."""
        self._cv_tokens.append(_cv_app.set(self))
        appcontext_pushed.send(self.app, _async_wrapper=self.app.ensure_sync)

    def pop(self, exc: BaseException | None = _sentinel) - None:  # type: ignore
        """Pops the app context."""
        try:
            if len(self._cv_tokens) == 1:
                if exc is _sentinel:
                    exc = sys.exc_info()[1]
                self.app.do_teardown_appcontext(exc)
        finally:
            ctx = _cv_app.get()
            _cv_app.reset(self._cv_tokens.pop())

        if ctx is not self:
            raise AssertionError(
                f"Popped wrong app context. ({ctx!r} instead of {self!r})"
            )

        appcontext_popped.send(self.app, _async_wrapper=self.app.ensure_sync)

    def __enter__(self) - AppContext:
        self.push()
        return self

    def __exit__(
        self,
        exc_type: type | None,
        exc_value: BaseException | None,
        tb: TracebackType | None,
    ) - None:
        self.pop(exc_value)

class RequestContext:
    """The request context contains per-request information. The Flask
    app creates and pushes it at the beginning of the request, then pops
    it at the end of the request. It

[Assistant]
will create the URL adapter and
    request object for the WSGI environment provided.

    Do not attempt to use this class directly, instead use
    :meth:`~flask.Flask.test_request_context` and
    :meth:`~flask.Flask.request_context` to create this object.

    When the request context is popped, it will evaluate all the
    functions registered on the application for teardown execution
    (:meth:`~flask.Flask.teardown_request`).

    The request context is automatically popped at the end of the
    request. When using the interactive debugger, the context will be
    restored so ``request`` is still accessible. Similarly, the test
    client can preserve the context after the request ends. However,
    teardown functions may already have closed some resources such as
    database connections.
    """


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
"""
.. redirect-from:: /users/customizing
.. redirect-from:: /tutorials/introductory/customizing

.. _customizing:

=====================================================
Customizing Matplotlib with style sheets and rcParams
=====================================================

Tips for customizing the properties and default styles of Matplotlib.

There are three ways to customize Matplotlib:

1. :ref:`Setting rcParams at runtimecustomizing-with-dynamic-rc-settings`.
2. :ref:`Using style sheetscustomizing-with-style-sheets`.
3. :ref:`Changing your matplotlibrc filecustomizing-with-matplotlibrc-files`.

Setting rcParams at runtime takes precedence over style sheets, style
sheets take precedence over :file:`matplotlibrc` files.

.. _customizing-with-dynamic-rc-settings:

Runtime rc settings
===================

You can dynamically change the default rc (runtime configuration)
settings in a python script or interactively from the py

[Assistant]
thon shell. All
rc settings are stored in a dictionary-like variable called
:data:`matplotlib.rcParams`, which is global to the matplotlib package.
See `matplotlib.rcParams` for a full list of configurable rcParams.
rcParams can be modified directly, for example:
"""


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
'Yes'

	if community_creation_admin_only == True:
		new_comm = "No"
	else:
		new_comm = "Yes"

	if federation_enabled == False or federated_allowed != []:
		fed = 'No'
	else:
		fed = "Yes"
	print( "\tfederation_enabled:|" +str(federation_enabled)+ "|" )
	print( "\tfederated_allowed:|" +str(federated_allowed)+ "|" )
	print( "\tfed:|" +str(fed)+ "|" )

	if enable_downvotes == True:
		downvotes = "Yes"
	else:
		downvotes = "No"

	if registration_mode == "closed":
		new_users = "No"
	else:
		new_users = "Yes"

	# stupid way to say gimmie the 'uptime_alltime' data from the json where
	# the 'domain' matches this iteration lemmy instance's domain
	uptime = [x['uptime_alltime'] for x in uptime_data['data']['nodes'] if x['domain'] == domain]

	# stupid way to say gimmie the 'monthsmonitored' data from the json where
	# the 'domain' matches this iteration lemmy instance's domain
	age = [x['monthsmonitored'] for x in age_data['data']['nodes'] if x['domain'] == domain]

	# did we figure out an uptime for this domain?
	if uptime == []:
		# we couldn't find an uptime; set it to '??'
		uptime = '??'
	else:
		# we got an uptime! Format it for the table

		uptime = uptime[0]
		# let's keep the data simple
		uptime = round(float(uptime))
		uptime = str(uptime)+ "%"

	# did we figure out an age for this domain?
	if age == []:
		# we couldn't find an uptime; set it to '??'
		age = '??'
	else:
		# we got an uptime! Format it for the table

		age = age[0]
		# let's keep the data simple
		age = round(float(age))

	csv_contents += "[" +name+ "](https://" +domain+ "),"
	csv_contents += new_users+ ","
	csv_contents += new_comm+ ","
	csv_contents += fed+ ","
	csv_contents += adult+ ","
	csv_contents += downvotes+ ","
	csv_contents += str(users_month)+ ','
	csv_contents += str(blocking)+ ','
	csv_contents += str(blocked_by)+ ','
	csv_contents += str(uptime)+ ','
	csv_contents += str(age)+ ','
	csv_contents += version
	csv_contents += "\n"

# write the instance data table to the csv file
with open( OUT_CSV, "w" ) as csv_file:
	csv_file.write( csv_contents )

#########################
# RECOMMENDED INSTANCES #
#########################

# shrink the list to just a few recommended instances
all_instances = list()
recommended_instances = list()
with open(OUT_CSV) as csv_file:

	for instance in csv.DictReader( csv_file ):
		all_instances.append( instance )

		# only include instances that are "yes" across-the-board
		if instance['NU'] == "Yes" \
		 and instance['NC'] == "Yes" \
		 and instance['Fed'] == "Yes" \
		 and instance['Adult'] == "Yes" \
		 and instance['↓V'] == "Yes":

			recommended_instances.append( instance )

# remove instances with too few or too may users
recommended_instances = [x for x in recommended_instances if int(x['Users'])  60 and int(x['Users'])  1000]

# get a lits of all the instances that have more than 1 blocked instance and
# then get the average number of instances that are blocked
try:
	bi_list = [ int(x['BI']) for x in all_instances if int(x['BI'])  1 ]
	bi_avg = numpy.average( bi_list )
except (Exception, RuntimeWarning) as e:
	print( "WARNING: Caught numpy exception when calculating bi_avg: " +str(e) )
	bi_avg = 2

# get a lits of all the instances that are blocked by more than 1 instance and
# then get the average number of that instances are are blocked
try:
	bb_list = [ int(x['BB']) for x in all_instances if int(x['BB'])  1 ]
	bb_avg = numpy.average( bb_list )
except (Exception, RuntimeWarning) as e:
	print( "WARNING: Caught numpy exception when calculating bb_avg: " +str(e) )
	bb_avg = 2

print( "bi_avg:|" +str(bi_avg)+ "|" )
print( "bb_avg:|" +str(bb_avg)+ "|" )

# remove instances that are blocking or blocked-by too many other instancesk
recommended_instances = [ x for x in recommended_instances if int(x['BI']) = bi_avg and int(x['BB']) = bb_avg ]

# remove instances that haven't been online for 2 months
recommended_instances = [ x for x in recommended_instances if int(x['MO']) = 2 ]

# limit to those with the best uptime; first we make sure that we actually
# have the uptime data
uptime_available = [x for x in recommended_instances if x['UT'] != '??']

# do we have uptime data?
if uptime_available != list():
	# we have uptime data; proceed with reducing the set of recommended_instances
	# based on uptime

	# loop down from 100% to 0%
	for percent_uptime in reversed(range(100)):

		high_uptime_instances = [x for x in recommended_instances if x['UT'] != '??' and int(x['UT'][:-1])  percent_uptime]

		# do we have more than one instance above this uptime?
		if len(high_uptime_instances)  1:
			# we already have enough instances; ignore the rest with lower uptime
			recommended_instances = high_uptime_instances
			break

# prepare data for csv file
csv_contents = "Instance,NU,NC,Fed,Adult,↓V,Users,BI,BB,UT,MO,Version\n"
for instance in recommended_instances:
	csv_contents += instance['Instance']+ ','
	csv_contents += instance['NU']+ ','
	csv_contents += instance['NC']+ ','
	csv_contents += instance['Fed']+ ','
	csv_contents += instance['Adult']+ ','
	csv_contents += instance['↓V']+ ','
	csv_contents += instance['Users']+ ','
	csv_contents += instance['BI']+ ','
	csv_contents += instance['BB']+ ','
	csv_contents += instance['UT']+ ','
	csv_contents += instance['MO']+ ','
	csv_contents += instance['Version']
	csv_contents += "\n"

# write the recommended instance data table to a csv file
with open( 'recommended-instances.csv', "w" ) as csv_file:
	csv_file.write( csv_contents )

# convert csv file data to markdown table
df = pd.read_csv( 'recommended-instances.csv' )
recommended_markdown_table = df.to_markdown( tablefmt='pipe', index = False )

# add newline to protect the table from getting klobbered by the text around it
recommended_markdown_table = "\n" + recommended_markdown_table + "\n"

readme_contents +=  """
# Recommended Instances

Just **click on a random instance** from the below "recommended" instances.

Don't overthink this. **It doesn't matter which instance you use.** You'll still be able to interact with communities (subreddits) on all other instances, regardless of which instance your account lives 🙂
"""

# add the markdown table to the readme's contents
readme_contents += recommended_markdown_table

readme_contents +=  """
# What's next?

## Subscribe to ~~Subreddits~~ Communities

After you pick an instance and register an account, you'll want to subscribe to communities. You can subscribe to "local" communities on your instance, and (if you chose an instance that isn't siloed) you can also subscribe to "remote" communities on other instances.

To **find popular communities** across all lemmy instances in the fediverse, you can use the [Lemmy Community Browser](https://browse.feddit.de/) run by feddit.de.

 * https://browse.feddit.de/

If you want a more direct mapping of your favorite /r/subreddits to lemmy, checkout these sites:

1. [redditmigration.com](https://redditmigration.com/)
1. [sub.rehab](https://sub.rehab/?searchTerm=&visibleServices=lemmy&officialOnly=false&newOnly=false&favoriteOnly=false&sortBy=users_active_week)
1. yoasif's [Unofficial Subreddit Migration List](https://www.quippd.com/writing/2023/06/15/unofficial-subreddit-migration-list-lemmy-kbin-etc.html)

a href="https://tech.michaelaltfield.net/2023/06/11/lemmy-migration-find-subreddits-communities/"img src="lemmy-migration-find-subreddits-communities.jpg" alt="How To Find Lemmy Communities" //a

For more information, see my guide on [How to Find Popular Lemmy Communities](https://tech.michaelaltfield.net/2023/06/11/lemmy-migration-find-subreddits-communities/)

## Other links

You may want to also checkout the following websites for more information about Lemmy

 * [Official Lemmy Documentation](https://join-lemmy.org/docs/en/index.html)
 * [Intro to Lemmy Guide](https://tech.michaelaltfield.net/2023/06/11/lemmy-migration-find-subreddits-communities/) - How to create a lemmy account, find, and subscribe-to popular communities
 * [Lemmy Community Browser](https://browse.feddit.de/) - List of all communities across all lemmy instances, sorted by popularity
 * [Lemmy Map](https://lemmymap.feddit.de) - Data visualization of lemmy instances
 * [The Federation Info](https://the-federation.info/platform/73) - Another table comparing lemmy instances (with pretty charts)
 * [Federation Observer](https://lemmy.fediverse.observer/list) - Yet another table comparing lemmy instances
 * [FediDB](https://fedidb.org/software/lemmy) - Yet another site comparing lemmy instances (with pretty charts)
 * [Lemmy Sourcecode](https://github.com/LemmyNet/lemmy)
 * [Jerboa (Official Android Client)](https://f-droid.org/packages/com.jerboa/)
 * [Mlem (iOS Client)](https://apps.apple.com/gb/app/mlem-for-lemmy/id6450543782)

"""

#################
# ALL INSTANCES #
#################

# convert csv file data to markdown table
df = pd.read_csv( OUT_CSV )
markdown_table = df.to_markdown( tablefmt='pipe', index = False )

# a

[Assistant] dd newline to protect the table from getting klobbered by the text around it


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
import json
import os

import numpy as np
import pandas as pd
import torch
from datasets import Dataset
from transformers import AutoModelForSequenceClassification, AutoTokenizer, PreTrainedModel, PreTrainedTokenizer, Trainer, TrainerCallback, TrainerControl, TrainerState, TrainingArguments

from .data.load import combine_positive_negative_da

[Assistant]
ta, get_all_types, get_granularity, get_negative_data, get_positive_data, get_type
from .data.preprocessing import combine_premise_hypothesis, construct_hypothesis
from .predict import predict_type
from .utils import get_models_dir


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
     for idx, frame in enumerate(frames):
            cam_name = os.path.join(path, frame["file_path"] + extension)

            # NeRF 'transform_matrix' is a camera-to-world transform
            c2w = np.array(frame["transform_matrix"])
            # change from OpenGL/Blender camera axes (Y up, Z back) to COLMAP (Y down, Z forward)
            c2w[:3, 1:3] *= -1

            # get the world-to-camera transform and set R, T
            w2c = np.linalg.inv(c2w)
            R = np.transpose(w2c[:3,:3])  # R is stored transposed due to 'glm' in CUDA code
            T = w2c[:3, 3]

            image_path = os.path.join(path, cam_name)
            image_name = Path(cam_name).stem
            image = Image.open(image_path)

            im_data = np.array(image.convert("RGBA"))

            bg = np.array([1,1,1]) if white_background else np.array([0, 0, 0])

            norm_data = im_data / 255.0
            arr = norm_data[:,:,:3] * norm_data[:, :, 3:4] + bg * (1 - norm_data[:, :, 3:4])
            image = Image.fromarray(np.array(arr*255.0, dtype=np.byte), "RGB")

            fovy = focal2fov(fov2focal(fovx, image.size[0]), image.size[1])
            FovY = fovy
            FovX = fovx

            cam_infos.append(CameraInfo(uid=idx, R=R, T=T, FovY=FovY, FovX=FovX, image=image,
                            image_path=image_path, image_name=image_name, width=image.size[0], height=image.size[1]))

    return cam_infos

def readNerfSyntheticInfo(path, white_background, eval, extension=".png"):
    print("Reading Training Transforms")
    train_cam_infos = readCamerasFromTransforms(path, "transforms_train.json", white_background, extension)
    print("Reading Test Transforms")
    test_cam_infos = readCamerasFromTransforms(path, "transforms_test.json", white_background, extension)

    if not eval:
        train_cam_infos.extend(test_cam_info

[Assistant]
s)
        test_cam_infos = []

    nerf_normalization = getNerfppNorm(train_cam_infos)

    ply_path = os.path.join(path, "points3d.ply")
    if not os.path.exists(ply_path):
        # Since this data set has no colmap data, we start with random points
        num_pts = 100_000
        print(f"Generating random point cloud ({num_pts})...")

        # We create random points inside the bounds of the synthetic Blender scenes
        xyz = np.random.random((num_pts, 3)) * 2.6 - 1.3
        shs = np.random.random((num_pts, 3)) / 255.0
        pcd = BasicPointCloud(points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3)))

        storePly(ply_path, xyz, SH2RGB(shs) * 255)
    try:
        pcd = fetchPly(ply_path)
    except:
        pcd = None

    scene_info = SceneInfo(point_cloud=pcd,
                           train_cameras=train_cam_infos,
                           test_cameras=test_cam_infos,
                           nerf_normalization=nerf_normalization,
                           ply_path=ply_path)
    return scene_info

sceneLoadTypeCallbacks = {
    "Colmap": readColmapSceneInfo,
    "Blender" : readNerfSyntheticInfo
}


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
xs, n=None):
    """Draw a sample from xs with the same length as xs.

    xs: sequence
    n: sample size (default: len(xs))

    returns: NumPy array
    """
    if n is None:
        n = len(xs)
    return np.random.choice(xs, n, replace=True)

def SampleRows(df, nrows, replace=False):
    """Choose a sample of rows from a DataFrame.

    df: DataFrame
    nrows: number of rows
    replace: whether to sample with replacement

    returns: DataDf
    """
    indices = np.random.choice(df.index, nrows, replace=replace)
    sample = df.loc[indices]
    return sample

def ResampleRows(df):
    """Resamples rows from a DataFrame.

    df: DataFrame

    returns: DataFrame
    """
    return SampleRows(df, len(df), replace=True)

def ResampleRowsWeighted(df, column='finalwgt'):
    """Resamples a DataFrame using probabilities proportional to given column.

    df: DataFrame
    column: string column name to use as weights

    returns: DataFrame
    """
    weights = df[column]
    cdf = Cdf(dict(weights))
    indices = cdf.Sample(len(weights))
    sample = df.loc[indices]
    return sample

def PercentileRow(array, p):
    """Selects the row from a sorted array that maps to percentile p.

    p: float 0--100

    returns: NumPy array (one row)
    """
    rows, cols = array.shape
    index = int(rows * p / 100)
    return array[index,]

def PercentileRows(ys_seq, percents):
    """Given a collection of lines, selects percentiles along vertical axis.

    For example, if ys_seq contains simulation results like ys as a
    function of time, and percents contains (5, 95), the result would
    be a 90% CI for each vertical slice of the simulation results.

    ys_seq: sequence of lines (y values)
    percents: list of percentiles (0-100) to select

    returns: list of NumPy arrays, one for each pe

[Assistant]
rcentile
    """
    nrows = len(ys_seq)
    ncols = len(ys_seq[0])
    array = np.zeros((nrows, ncols))

    for i, ys in enumerate(ys_seq):
        array[i,] = ys

    array = np.sort(array, axis=0)

    rows = [PercentileRow(array, p) for p in percents]
    return rows


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
  for camera_model in CAMERA_MODELS])

def qvec2rotmat(qvec):
    return np.array([
        [1 - 2 * qvec[2]**2 - 2 * qvec[3]**2,
         2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
         2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2]],
        [2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],
         1 - 2 * qvec[1]**2 - 2 * qvec[3]**2,
         2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1]],
        [2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],
         2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],
         1 - 2 * qvec[1]**2 - 2 * qvec[2]**2]])

def rotmat2qvec(R):
    Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz = R.flat
    K = np.array([
        [Rxx - Ryy - Rzz, 0, 0, 0],
        [Ryx + Rxy, Ryy - Rxx - Rzz, 0, 0],
        [Rzx + Rxz, Rzy + Ryz, Rzz - Rxx - Ryy, 0],
        [Ryz - Rzy, Rzx - Rxz, Rxy - Ryx, Rxx + Ryy + Rzz]]) / 3.0
    eigvals, eigvecs = np.linalg.eigh(K)
    qvec = eigvecs[[3, 0, 1, 2], np.argmax(eigvals)]
    if qvec[0]  0:
        qvec *= -1
    return qvec

class Image(BaseImage):
    def qvec2rotmat(self):
        return qvec2rotmat(self.qvec)

def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character=""):
    """Read and unpack the next bytes from a binary file.
    :param fid:
    :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc.
    :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
    :param endian_character: Any of {@, =, , , !}
    :return: Tuple of read and unpacked values.
    """
    data = fid.read(num_bytes)
    return struct.unpack(endian_character + format_char_sequence, data)

def read_points3D_text(path):
    """
    see: src/base/reconstruction.cc
        void Reconstruction::ReadPoints3DText(const std::string& path)
        void Reconstruction::WritePoints3DText(const std::string& path)
    """
    xyzs = None
    rgbs = None
    errors = None
    num_points = 0
    with open(path, "r") as fid:
        while True:
            line = fid.readline()
            if not line:
                break
            line = line.strip()
            if len(line)  0 and line[0] != "#":
                num_points += 1

    xyzs = np.empty((num_points, 3))
    rgbs = np.empty((num_points, 3))
    errors = np.empty((num_points, 1))
    count = 0
    with open(path, "r") as fid:
        while True:
            line = fid.readline()
            if not line:
                break
            line = line.strip()
            if len(line)  0 and line[0] != "#":
                elems = line.split()
                xyz = np.array(tuple(map(float, elems[1:4])))

[Assistant]
            rgb = np.array(tuple(map(int, elems[4:7])))
                error = np.array(float(elems[7]))
                xyzs[count] = xyz
                rgbs[count] = rgb
                errors[count] = error
                count += 1

    return xyzs, rgbs, errors


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
alize_heatmap(self,
                                          topics=topics,
                                          top_n_topics=top_n_topics,
                                          n_clusters=n_clusters,
                                          custom_labels=custom_labels,
                                          title=title,
                                          width=width,
                                          height=height)

    def visualize_barchart(self,
                           topics: List[int] = None,
                           top_n_topics: int = 8,
                           n_words: int = 5,
                           custom_labels: bool = False,
                           title: str = "Topic Word Scores",
                           width: int = 250,
                           height: int = 250) - go.Figure:
        """ Visualize a barchart of selected topics

        Arguments:
            topics: A selection of topics to visualize.
            top_n_topics: Only select the top n most frequent topics.
            n_words: Number of words to show in a topic
            custom_labels: Whether to use custom topic labels that were defined using
                       `topic_model.set_topic_labels`.
            title: Title of the plot.
            width: The width of each figure.
            height: The height of each figure.

        Returns:
            fig: A plotly figure

        Examples:

        To visualize the barchart of selected topics
        simply run:

        ```python
        topic_model.visualize_barchart()
        ```

        Or if you want to save the resulting figure:

        ```python
        fig = topic_model.visualize_barchart()
        fig.write_html("path/to/file.html")
        ```
        """
        check_is_fitted(self)
        return plotting.visualize_barchart(self,
                                           topics=topics,
                                           top_n_topics=top_n_topics,
                                           n_words=n_words,
                                           custom_labels=custom_labels,
                                           title=title,
                                           width=width,
                                           height=height)

    def save(self,

[Assistant]
            path,
             serialization: Literal["safetensors", "pickle", "pytorch"] = "pickle",
             save_embedding_model: Union[bool, str] = True,
             save_ctfidf: bool = False):


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
Precisely continue the code from the point of interruption and do not repeat or modify the original code, even if it is incorrect or the code interrupts in the middle of a line.
Your code is well documented with comments and annotations, and you should provide a clear explanation of the code's purpose in your code completion.
Your unique capability is to provide completions without altering, repeating, or commenting on the original code.
You offer only the necessary code to complete the snippet, ensuring the response is exclusively code, with no additional comments, explanations, or annotations.
This approach makes you an ideal assistant for users seeking straightforward and efficient code extensions, enhancing their work with accurate, logic-driven completions while maintaining the integrity and simplicity of the original input.
Your response begins with the next characters of the line if the last line of the user'S code is incomplete, or the next line if the last line of the user's code is complete.
Your application is a VSCode extension like GitHub Copilot, which provides seamless code completions based on the user's code at the point of interruption.

[User]
from __future__ import annotations
import json
import logging
import math

[Assistant]
import os
import sys
import hashlib
from dataclasses import dataclass, field