[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
#!/usr/bin/env python
# -*- coding: utf-8 -*- #
from __future__ import unicode_literals

AUTHOR = 'Jake VanderPlas'
SITENAME = 'Python Data Science Handbook'
SITESUBTITLE = u'Essential Tools for Working with Data'
SITEURL = ''
SITESUBURL = 'PythonDataScienceHandbook/'
PATH = 'content'
TIMEZONE = 'America/Los_Angeles'
DEFAULT_LANG = 'en'

# Feed generation is usually not desired when developing
FEED_ALL_ATOM = None
CATEGORY_FEED_ATOM = None
TRANSLATION_FEED_ATOM = None
AUTHOR_FEED_ATOM = None
AUTHOR_FEED_RSS = None

# Set the article URL
ARTICLE_URL = 'blog/{date:%Y}/{date:%m}/{date:%d}/{slug}/'
ARTICLE_SAVE_AS = 'blog/{date:%Y}/{date:%m}/{date:%d}/{slug}/index.html'

DEFAULT_PAGINATION = 10

# Uncomment following line if you want document-relative URLs when developing
#RELATIVE_URLS = True

#MARKUP = ('md', 'ipynb')
#PLUGINS = ['ipynb.markup']

MARKUP = ['md']
PLUGIN_PATHS = ['./plugins', './plugins/pelican-plugins']
PLUGINS = [
    'summary',       # auto-summarizing articles
    'feed_summary',  # use summaries for RSS, not full articles
    'ipynb.liquid',  # for embedding notebooks
    'liquid_tags.img',  # embedding images
    'liquid_tags.video',  # embedding videos
    'liquid_tags.include_code',  # including code blocks
    'liquid_tags.literal'
]
IGNORE_FILES = ['.ipynb_checkpoints']

# for liquid tags
CODE_DIR = 'downloads/code'
NOTEBOOK_DIR = 'downloads/notebooks'

# THEME SETTINGS
THEME = './theme/'

ABOUT_PAGE =

[Assistant]
'/pages/about.html'
TWITTER_USERNAME = 'jakevdp'
GITHUB_USERNAME = 'jakevdp'
STACKOVERFLOW_ADDRESS = 'http://stackoverflow.com/users/2937831/jakevdp'
AUTHOR_WEBSITE = 'http://vanderplas.com'
AUTHOR_BLOG = 'http://jakevdp.github.io'
AUTHOR_CV = "http://staff.washington.edu/jakevdp/media/pdfs/CV.pdf"
SHOW_ARCHIVES = True
SHOW_FEED = False  # Need to address large feeds

ENABLE_MATHJAX = True

STATIC_PATHS = ['images', 'figures', 'videos', 'downloads', 'favicon.ico']

# Footer info

LICENSE_URL = "https://github.com/jakevdp/jakevdp.github.io-source/blob/master/LICENSE"
LICENSE = "MIT"


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
r index in outlier_ids])
            elif images is not None:
                outlier_images = [images[index] for index in outlier_ids]
                outlier_embeddings = self.embedding_model.embed_images(outlier_images, verbose=self.verbose)
            else:
                outlier_embeddings = self.embedding_model.embed_documents(outlier_docs)
            similarity = cosine_similarity(outlier_embeddings, self.topic_embeddings_[self._outliers:])

            # Update topics
            similarity[similarity < threshold] = 0
            outlier_topics = iter([np.argmax(sim) if sum(sim) > 0 else -1 for sim in similarity])
            new_topics = [topic if topic != -1 else next(outlier_topics) for topic in topics]

        return new_topics

    def visualize_topics(self,
                         topics: List[int] = None,
                         top_n_topics: int = None,
                         custom_labels: bool = False,
                         title: str = "Intertopic Distance Map",
                         width: int = 650,
                         height: int = 650) -> go.Figure:
        """ Visualize topics, their sizes, and their corresponding words

        This visualization is highly inspired by LDAvis, a great visualization
        technique typically reserved for LDA.

        Arguments:
            topics: A selection of topics to visualize
            top_n_topics: Only select the top n most frequent topics
            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 the figure.
            height: The height of the figure.

        Examples:

        To visualize the topics simply run:

        ```python
        topic_model.visualize_topics()
        ```

        Or if you want to save the resulting figure:

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

    def visualize_documents(self,
                            docs: List[str],
                            topics: List[int] = None,
                            embeddings: np.ndarray = None,
                            reduced_embeddings: np.ndarray = None,
                            sample: float = None,
                            hide_annotations: bool = False,
                            hide_document_hover: bool = False,
                            custom_labels: bool = False,
                            title: str = "Documents and Topics",
                            width: int = 1200,
                            height: int = 750) -> go.Figure:
        """ Visualize documents and their topics in 2D

        Arguments:
            topic_model: A fitted BERTopic instance.
            docs: The documents you used when calling either `fit` or `fit_transform`
            topics: A selection of topics to visualize.
                    Not to be confused with the topics that you get from `.fit_transform`.
                    For example, if you want to visualize only to

[Assistant]
pics 1 through 5:
                    `topics = [1, 2, 3, 4, 5]`.
            embeddings: The embeddings of all documents in `docs`.
            reduced_embeddings: The 2D reduced embeddings of all documents in `docs`.
            sample: The percentage of documents in each topic that you would like to keep.
                    Value can be between 0 and 1. Setting this value to, for example,
                    0.1 (10% of documents in each topic) makes it easier to visualize
                    millions of documents as a subset is chosen.
            hide_annotations: Hide the names of the traces on top of each cluster.
            hide_document_hover: Hide the content of the documents when hovering over
                                specific points. Helps to speed up generation of visualization.
            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 the figure.
            height: The height of the figure.


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
      assert len(words) == 10

    assert len(topic_model.get_topic_freq()) > 2
    assert len(topic_model.get_topics()) == len(topic_model.get_topic_freq())

    # Test extraction of document info
    document_info = topic_model.get_document_info(documents)
    assert len(document_info) == len(documents)

    # Test transform
    doc = "This is a new document to predict."
    topics_test, probs_test = topic_model.transform([doc, doc])

    assert len(topics_test) == 2

    # Test topics over time
    timestamps = [i % 10 for i in range(len(documents))]
    topics_over_time = topic_model.topics_over_time(documents, timestamps)

    assert topics_over_time.Frequency.sum() == len(documents)
    assert len(topics_over_time.Topic.unique()) == len(set(topics))

    # Test hierarchical topics
    hier_topics = topic_model.hierarchical_topics(documents)

    assert len(hier_topics) > 0
    assert hier_topics.Parent_ID.astype(int).min() > max(topics)

    # Test creation of topic tree
    tree = topic_model.get_topic_tree(hier_topics, tight_layout=False)
    assert isinstance(tree, str)
    assert len(tree) > 10

    # Test find topic
    similar_topics, similarity = topic_model.find_topics("query", top_n=2)
    assert len(similar_topics) == 2
    assert len(similarity) == 2
    assert max(similarity) <= 1

    # Test topic reduction
    nr_topics = len(set(topics))
    nr_topics = 2 if nr_topics < 2 else nr_topics - 1
    topic_model.reduce_topics(documents, nr_topics=nr_topics)

    assert len(topic_model.get_topic_freq()) == nr_topics
    assert len(topic_model.topics_) == len(topics)

    # Test update topics
    topic = topic_model.get_topic(1)[:10]
    vectorizer_model = topic_model.vectorizer_model
    topic_model.update_topics(documents, n_gram_range=(2, 2))

    updated_topic = topic_model.get_topic(1)[:10]

    topic_model.update_topics(documents, vectorizer_model=vectorizer_model)
    original_t

[Assistant]
opic = topic_model.get_topic(1)[:10]

    assert topic != updated_topic
    if topic_model.representation_model is not None:
        assert topic != original_topic


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
ent"] = s
    self.app["COM"] = s  # compatibility
    self.applist.append(("COM", s))

def SOF(self, marker):
    #
    # Start of frame marker.  Defines the size and mode of the
    # image.  JPEG is colour blind, so we use some simple
    # heuristics to map the number of layers to an appropriate
    # mode.  Note that this could be made a bit brighter, by
    # looking for JFIF and Adobe APP markers.

    n = i16(self.fp.read(2)) - 2
    s = ImageFile._safe_read(self.fp, n)
    self._size = i16(s, 3), i16(s, 1)

    self.bits = s[0]
    if self.bits != 8:
        msg = f"cannot handle {self.bits}-bit layers"
        raise SyntaxError(msg)

    self.layers = s[5]
    if self.layers == 1:
        self._mode = "L"
    elif self.layers == 3:
        self._mode = "RGB"
    elif self.layers == 4:
        self._mode = "CMYK"
    else:
        msg = f"cannot handle {self.layers}-layer images"
        raise SyntaxError(msg)

    if marker in [0xFFC2, 0xFFC6, 0xFFCA, 0xFFCE]:
        self.info["progressive"] = self.info["progression"] = 1

    if self.icclist:
        # fixup icc profile
        self.icclist.sort()  # sort by sequence number
        if self.icclist[0][13] == len(self.icclist):
            profile = []
            for p in self.icclist:
                profile.append(p[14:])
            icc_profile = b"".join(profile)
        else:
            icc_profile = None  # wrong number of fragments
        self.info["icc_profile"] = icc_profile
        self.icclist = []

    for i in range(6, len(s), 3):
        t = s[i : i + 3]
        # 4-tuples: id, vsamp, hsamp, qtable
        self.layer.append((t[0], t[1] // 16, t[1] & 15, t[2]))

def DQT(self, marker):
    #
    # Define quantization table.  Note that there might be more
    # than one table in each marker.

    # FIXME: The quantization tables can be used to estimate the
    # compression quality.

    n = i16(self.fp.read(2)) - 2
    s = ImageFile._safe_read(self.fp, n)
    while len(s):
        v = s[0]
        precision = 1 if (v // 16 == 0) else 2  # in bytes
        qt_length = 1 + precision * 64
        if len(s) < qt_length:
            msg = "bad quantization table marker"
            raise SyntaxError(msg)
        data = array.array("B" if precision == 1 else "H", s[1:qt_length])
        if sys.byteorder == "little" and precision > 1:
            data.byteswap()  # the values are always big-endian
        self.quantization[v & 15] = [data[i] for i in zigzag_index]
        s = s[qt_length:]

#
# JPEG marker table

MARKER = {
    0xFFC0: ("SOF0", "Baseline DCT", SOF),
    0xFFC1: ("SOF1", "Extended Sequential DCT", SOF),
    0xFFC2: ("SOF2", "Progressive DCT", SOF),
    0xFFC3: ("SOF3", "Spatial lossless", SOF),
    0xFFC4: ("DHT", "Define Huffman table", Skip),
    0xFFC5: ("SOF5", "Differential sequential DCT", SOF),
    0xFFC6: ("SOF6", "Differential progressive DCT", SOF),
    0xFFC7: ("SOF7", "Differential spatial", SOF),
    0xFFC8: ("JPG", "Extension", None),
    0xFFC9: ("SOF9", "Extended sequential DCT (AC)", SOF),
    0xFFCA: ("SOF10", "Progressive DCT (AC)", SOF),
    0xFFCB: ("SOF11", "Spatial lossless DCT (AC)", SOF),
    0xFFCC: ("DAC", "Define arithmetic coding conditioning", Skip),
    0xFFCD: ("SOF13", "Differential sequential DCT (AC)", SOF),
    0xFFCE: ("SOF14", "Differential progressive DCT (AC)", SOF),
    0xFFCF: ("SOF15", "Differential spatial (AC)", SOF),
    0xFFD0: ("RST0", "Restart 0", None),
    0xFFD1: ("RST1", "Restart 1", None),
    0xFFD2: ("RST2", "Restart 2", None),
    0xFFD3: ("RST3", "Restart 3", None),
    0xFFD4: ("RST4", "Restart 4", None),
    0xFFD5: ("RST5", "Restart 5", None),
    0xFFD6: ("RST6", "Restart 6", None),
    0xFFD7: ("RST7", "Restart 7", None),
    0xFFD8: ("SOI", "Start of image", None),
    0xFFD9: ("EOI", "End of image", None),
    0xFFDA: ("SOS", "Start of scan", Skip),
    0xFFDB: ("DQT", "Define quantization table", DQT),
    0xFFDC: ("DNL", "Define number of lines", Skip),
    0xFFDD: ("DRI", "Define restart interval", Skip),
    0xFFDE: ("DHP", "Define hierarchical progression", SOF),
    0xFFDF: ("EXP", "Expand reference component", Skip),
    0xFFE0: ("APP0", "Application segment 0", APP),
    0xFFE1: ("APP1", "Application segment 1", APP),
    0xFFE2: ("APP2", "Application segment 2", APP),
    0xFFE3: ("APP3", "Application segment 3", APP),
    0xFFE4: ("APP4", "Application segment 4", APP),
    0xFFE5: ("APP5", "Application segment 5", APP),
    0xFFE6: ("APP6", "Application segment 6", APP),
    0xFFE7: ("APP7", "Application segment 7", APP),
    0xFFE8: ("APP8", "Application segment 8", APP),
    0xFFE9: ("APP9", "Application segment 9", APP),
    0xFFEA: ("APP10", "Application segment 10", APP),
    0xFFEB: ("APP11", "Application segment 11", APP),
    0xFFEC: ("APP12", "Application segment 12", APP),
    0xFFED: ("APP13", "Application segment 13", APP),
    0xFFEE: ("APP14", "Application segment 14", APP),
    0xFFEF: ("APP15", "Application segment 15", APP),
    0xFFF0: ("JPG0", "Extension 0", None),
    0xFFF1: ("JPG1", "Extension 1", None),
    0xFFF2: ("JPG2", "Extension 2", None),
    0xFFF3: ("JPG3", "Extension 3", None),
    0xFFF4: ("JPG4", "Extension 4", None),
    0xFFF5: ("JPG5", "Extension 5", None),
    0xFFF6: ("JPG6", "Extension 6", None),
    0xFFF7: ("JPG7", "Extension 7", None),
    0xFFF8: ("JPG8", "Extension 8", None),
    0xFFF9: ("JPG9", "Extension 9", None),
    0xFFFA: ("JPG10", "Extension 10", None),
    0xFFFB: ("JPG11", "Extension 11", None),
    0xFFFC: ("JPG12", "Extension 12", None),
    0xFFFD: ("JPG13", "Extension 13", None),
    0xFFFE: ("COM", "Comment", COM),
}

def _accept(prefix):
    # Magic number was taken from https://en.wikipedia.org/wiki/JPEG
    return prefix[:3] == b"\xFF\xD8\xFF"

##
# Image plugin for JPEG and JFIF images.

class JpegImageFile(ImageFile.ImageFile):
    format = "JPEG"
    format_description = "JPEG (ISO 10918)"

    def _open(self):
        s = self.fp.read(3)

        if not _accept(s):
            msg = "not a JPEG file"
            raise SyntaxError(msg)
        s = b"\xFF"

        # Create attributes
        self.bits = self.layers = 0

        # JPEG specifics (internal)
        self.layer = []
        self.huffman_dc = {}
        self.huffman_ac = {}
        self.quantization = {}
        self.app = {}  # compatibility
        self.applist = []
        self.icclist = []

        while True:
            i = s[0]
            if i == 0xFF:
                s = s + self.fp.read(1)
                i = i16(s)
            else:
                # Skip non-0xFF junk
                s = self.fp.read(1)
                continue

            if i in MARKER:
                name, description, handler = MARKER[i]
                if handler is not None:
                    handler(self, i)
                if i == 0xFFDA:  # start of scan
                    rawmode = self.mode
                    if self.mode == "CMYK":
                        rawmode = "CMYK;I"  # assume adobe conventions
                    self.tile = [("jpeg", (0, 0) + self.size, 0, (rawmode, ""))]
                    # self.__offset = self.fp.tell()
                    break
                s = self.fp.read(1)
            elif i == 0 or i == 0xFFFF:
                # padded marker or junk; move on
                s = b"\xff"
            elif i == 0xFF00:  # Skip extraneous data (escaped 0xFF)
                s = self.fp.read(1)
            else:
                msg = "no marker found"
                raise SyntaxError(msg)

    def load_read(self, read_bytes):
        """
        internal: read more image data
        For premature EOF and LOAD_TRUNCATED_IMAGES adds EOI marker
        so libjpeg can finish decoding
        """
        s = self.fp.read(read_bytes)

        if not s and ImageFile.LOAD_TRUNCATED_IMAGES and not hasattr(self, "_ended"):
            # Premature EOF.
            # Pretend file is finished adding EOI marker
            self._ended = True
            return b"\xFF\xD9"

        return s

    def draft(self, mode, size):
        if len(self.tile) != 1:
            return

        # Protect from second call
        if self.decoderconfig:
            return

        d, e, o, a = self.tile[0]
        scale = 1
        original_size = self.size

        if a[0] == "RGB" and mode in ["L", "YCbCr"]:
            self._mode = mode
            a = mode, ""

        if size:
            scale = min(self.size[0] // size[0], self.size[1] // size[1])
            for s in [8, 4, 2, 1]:
                if scale >= s:
                    break
            e = (
                e[0],
                e[1],
                (e[2] - e[0] + s - 1) // s + e[0],
                (e[3] - e[1] + s - 1) // s + e[1],
            )
            self._size = ((self.size[0] + s - 1) // s, (self.size[1] + s - 1) // s)
            scale = s

        self.tile = [(d, e, o, a)]
        self.decoderconfig = (scale, 0)

        b

[Assistant]
ox = (0, 0, original_size[0] / scale, original_size[1] / scale)
        return self.mode, box


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
#!/usr/bin/python
#
# Licensed to Cloudera, Inc. under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  Cloudera, Inc. licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in co

[Assistant]
mpliance
# with the License.  You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
import asyncio

import pytest

from flask import Blueprint
from flask import Flask
from flask import request
from flask.views import MethodView
from flask.views import View

pytest.importorskip("asgiref")

class AppError(Exception):
    pass

class BlueprintError(Exception):
    pass

class AsyncView(View):
    methods = ["GET", "POST"]

    async def dispatch_request(self):
        await asyncio.sleep(0)
        return request.method

class AsyncMethodView(MethodView):
    async def get(self):
        await asyncio.sleep(0)
        return "GET"

    async def post(self):
        await asyncio.sleep(0)
        return "POST"

@pytest.fixture(name="async_app")
def _async_app():
    app = Flask(__name__)

    @app.route("/", methods=["GET", "POST"])
    @app.route("/home", methods=["GET", "POST"])
    async def index():
        await asyncio.sleep(0)
        return request.method

    @app.errorhandler(AppError)
    async def handle(_):
        return "", 412

    @app.route("/e

[Assistant]
rror")
    async def error():
        raise AppError()

    blueprint = Blueprint("bp", __name__)

    @blueprint.route("/", methods=["GET", "POST"])
    async def bp_index():
        await asyncio.sleep(0)
        return request.method

    @blueprint.errorhandler(BlueprintError)
    async def bp_handle(_):
        return "", 412

    @blueprint.route("/error")
    async def bp_error():
        raise BlueprintError()

    app.register_blueprint(blueprint, url_prefix="/bp")

    app.add_url_rule("/view", view_func=AsyncView.as_view("view"))
    app.add_url_rule("/methodview", view_func=AsyncMethodView.as_view("methodview"))

    return app


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
import inspect
import json
import os
from collections import defaultdict
from logging import getLogger

from fastapi import APIRouter

from pmtrendviz.models.factory import ModelFactory
from pmtrendviz.utils import get_models_dir

logger = getLogger('pmtrendviz.api.routers.models')

def fetch_available_models():  # type: ignore
    models_dir = get_models_dir()
    models = []
    for model in os.listdir(models_dir):
        model_path = os.path.join(models_dir, model)
        if os.path.isdir(model_path):
            with open(os.path.join(model_path, 'config.json')) as config_fp:
                models.append({
                    'name': model,
                    'path': model_path,
                    'config': json.load(config_fp)
                })

    logger.debug(f'Found {len(models)} models: {models}')
    return models

router = APIRouter(
    prefix="/models",
    tags=["models"]
)

@router.get("/public")
def get_public_models():  # type: ignore
    raise NotImplementedError

@router.get("/saved")
def get_saved_models():  # type: ignore
    models = fetch_available_model

[Assistant]
s()
    logger.debug(models)
    return models


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
#!/usr/bin/python
#
# Licensed to Cloudera, Inc. under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  Cloudera, Inc. licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#
# Template for python Hadoop streaming.  Fill in the map() and reduce()
# functions, which should call emit(), as appropriate.
#
# Test your script with
#  cat input | python wordcount.py map | sort | python wordcount.py reduce

import sys
import re
try:
    import simplejson as json
except ImportError:
    import json

import __builtin__

def map(line):
    words = line.split()
    for word in words:
        emit(word, str(1))

def reduce(key, values):
    emit(key, str(sum(__builtin__.map(int,values))))

# Common library code follows:

def emit(key, value):
    """
    Emits a key->value pair.  Key and value should be strings.
    """
    try:
        print "\t".join( (key, value) )
    except:
        pass

def run_map():
    """Calls map() for each input value."""
    for line in sys.stdin:
        line = line.rstrip()
        map(line)

def run_reduce():
    """Gathers reduce() data in memory, and calls reduce()."""
    prev_key = None
    values = []
    for line in sys.stdin:
        line = line.rstrip()
        key, value = re.split("\t", line, 1)
        if prev_key == key:
            values.append(value)
        else:
            if prev_key is not None:
                reduce(prev_key, values)
            prev_key = key
            values = [ value ]

    if prev_key is not None:
        reduce(prev_key, values)

def main():
    """Runs map or reduce code, per arguments."""
    if len(sys.argv) != 2 or sys.argv[1] not in ("map", "reduce"):
        print "Usage: %s <map|reduce>" % sys.argv[

[Assistant]
0]
        sys.exit(1)
    if sys.argv[1] == "map":
        run_map()
    elif sys.argv[1] == "reduce":
        run_reduce()
    else:
        assert 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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
ave_okay'] = slave_okay
    client = pymongo.MongoClient(**options)

    if ensure_direct:
        # make sure we are connected to mongod/mongos that was specified; mongodb drivers
        # have the tendency of doing "magical" things in terms of connecting to other boxes
        test_collection = client['local']['test']
        test_cursor = test_collection.find(slave_okay=True, limit=1)
        connection = test_cursor.collection.database.connection
        if connection.host != host or connection.port != port:
            raise ValueError("connected to %s:%d (expected %s:%d)" %
                             (connection.host, connection.port, host, port))

    return client

def parse_mongo_url(url):
    """
    Takes in pseudo-URL of form

    host[:port]/db/collection (e.g. localhost/prod_maestro/emails)

    and returns a dictionary containing elements 'host', 'port', 'db', 'collection'
    """
    try:
        host, db, collection = url.split('/')
    except ValueError:
        raise ValueError("urls be of format: host[:port]/db/collection")

    host_tokens = host.split(':')
    if len(host_tokens) == 2:
        host = host_tokens[0]
        port = int(host_tokens[1])
    elif len(host_tokens) == 1:
        port = 27017
    elif len(host_tokens) > 2:
        raise ValueError("urls be of format: host[:port]/db/collection")

    return dict(host=host, port=port, db=db, collection=collection)

def _source_file_syntax():
    print "--source files must be of the following format:"
    print "database_name.collection_name"
    print "mongo-shard-1.foo.com"
    print "mongo-shard-2.foo.com:27019"
    print "..."
    sys.exit(1)

def parse_source_file(filename):
    """
    parses an input file passed to the --source parameter as a list of dicts that contain
    these fields:

    host
    port
    db: database name
    collection
    """
    sources = []

    with open(filename, "r") as source_file:
        fullname = source_file.readline().strip()
        try:
            db, collection = fullname.split('.')
        except ValueError:
            _source_file_syntax()

[Assistant]
    for source in [line.strip() for line in source_file]:
            tokens = source.split(':')
            if len(tokens) == 1:
                host = tokens[0]
                port = 27017
            elif len(tokens) == 2:
                host, port = tokens
                port = int(port)
            else:
                raise ValueError("%s is not a valid source", source)

            sources.append(dict(host=host, port=port, db=db, collection=collection))

    return sources


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
rganization = config['openai']['organization_id'].strip('"').strip("'")
    ENGINE = config['openai']['engine'].strip('"').strip("'")

    prompt_config = {
        'engine': ENGINE,
        'temperature': TEMPERATURE,
        'max_tokens': MAX_TOKENS,
        'shell': SHELL,
        'multi_turn': MULTI_TURN,
        'token_count': 0
    }

    return PromptFile(PROMPT_CONTEXT.name, prompt_config)

def is_sensitive_content(content):
    """
    Check if the content contains sensitive content
    Refer to https://beta.openai.com/docs/engines/content-filter for explanation
    """

    if len(content) == 0:
        return False

    response = openai.Completion.create(
        engine="content-filter-alpha",
        prompt = "<|endoftext|>"+content+"\n--\nLabel:",
        temperature=0,
        max_tokens=1,
        top_p=0,
        logprobs=10
        )

    output_label = response["choices"][0]["text"]

    # This is the probability at which we evaluate that a "2" is likely real
    # vs. should be discarded as a false positive
    toxic_threshold = -0.355

    if output_label == "2":
        # If the model returns "2", return its confidence in 2 or other output-labels
        logprobs = response["choices"][0]["logprobs"]["top_logprobs"][0]

        # If the model is not sufficiently confident in "2",
        # choose the most probable of "0" or "1"
        # Guaranteed to have a confidence for 2 since this was the selected token.
        if logprobs["2"] < toxic_threshold:
            logprob_0 = logprobs.get("0", None)
            logprob_1 = logprobs.get("1", None)

            # If both "0" and "1" have probabilities, set the output label
            # to whichever is most probable
            if logprob_0 is not None and logprob_1 is not None:
                if logprob_0 >= logprob_1:
                    output_label = "0"
                else:
                    output_label = "1"

[Assistant]
         # If only one of them is found, set output label to that one
            elif logprob_0 is not None:
                output_label = "0"
            elif logprob_1 is not None:
                output_label = "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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
import contextlib
import os.path

import pytest

from PIL import Image, ImageColor, ImageDraw, ImageFont, features

from .helper import (
    assert_image_equal,
    assert_image_equal_tofile,
    assert_image_similar_tofile,
    hopper,
    skip_unless_feature,
)

BLACK = (0, 0, 0)
WHITE = (255, 255, 255)
GRAY = (190, 190, 190)
DEFAULT_MODE = "RGB"
IMAGES_PATH = os.path.join("Tests", "images", "imagedraw")

# Image size
W, H = 100, 100

# Bounding box points
X0 = int(W / 4)
X1 = int(X0 * 3)
Y0 = int(H / 4)
Y1 = int(X0 * 3)

# Bounding boxes
BBOX = (((X0, Y0), (X1, Y1)), [(X0, Y0), (X1, Y1)], (X0, Y0, X1, Y1), [X0, Y0, X1, Y1])

# Coordinate sequences
POINTS = (
    ((10, 10), (20, 40), (30, 30)),
    [(10, 10), (20, 40), (30, 30)],
    (10, 10, 20, 40, 30, 30),
    [10, 10, 20, 40, 30, 30],
)

KITE_POINTS = (
    ((10, 50), (70, 10), (90, 50), (70, 90), (10, 50)),
    [(10, 50), (70, 10), (90, 50), (70, 90), (10, 50)],
)

def test_sanity():
    im = hopper("RGB").copy()

    draw = ImageDraw.ImageDraw(im)
    draw = ImageDraw.Draw(im)

    draw.ellipse(list(range(4)))
    draw.line(list(range(10)))
    draw.polygon(list(range(100)))
    draw.rectangle(list(range(4)))

def test_valueerror():
    with Image.open("Tests/images/chi.gif") as im:
        draw

[Assistant]
 = ImageDraw.Draw(im)
        draw.line((0, 0), fill=(0, 0, 0))


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
"""
Tests for the throttling implementations in the permissions module.
"""

import pytest
from django.contrib.auth.models import User
from django.core.cache import cache
from django.core.exceptions import ImproperlyConfigured
from django.http import HttpRequest
from django.test import TestCase

from rest_framework.request import Request
from rest_framework.response import Response
from rest_framework.settings import api_settings
from rest_framework.test import APIRequestFactory, force_authenticate
from rest_framework.throttling import (
    AnonRateThrottle, BaseThrottle, ScopedRateThrottle, SimpleRateThrottle,
    UserRateThrottle
)
from rest_framework.views import APIView

class User3SecRateThrottle(UserRateThrottle):
    rate = '3/sec'
    scope = 'seconds'

class User3MinRateThrottle(UserRateThrottle):
    rate = '3/min'
    scope = 'minutes'

class User6MinRateThrottle(UserRateThrottle):
    rate = '6/min'
    scope = 'minutes'

class NonTimeThrottle(BaseThrottle):
    def allow_request(self, request, view):
        if not hasattr(self.__class__, 'called'):
            self.__class__.called = True
            return True
        return False

class MockView_DoubleThrottling(APIView):
    throttle_classes = (User3SecRateThrottle, User6MinRateThrottle,)

    def get(self, request):
        return Response('foo')

class MockView(APIView):
    throttle_classes = (User3SecRateThrottle,)

    def get(self, request):
        return Response('foo')

class MockView_MinuteThrottling(APIView):
    throttle_classes = (User3MinRateThrottle,)

    def get(self, request):
        return Response('foo')

class MockView_NonTimeThrottling(APIView):
    throttle_classes = (NonTimeThrottle,)

    def get(self, request):
        return Response('foo')

class ThrottlingTests(TestCase):
    def setUp(self):
        """
        Reset the cache so that no throttles will be active
        """
        cache.clear()
        self.factory = APIRequestFactory()

    def test_requests_are_throttled(self):
        """
        Ensure request rate is limited
        """
        request = self.factory.get('/')
        for dummy in range(4):
            response = MockView.as_view()(request)
        assert response.status_code == 429

    def set_throttle_timer(self, view, value):
        """
        Explicitly set the timer, overriding time.time()
        """
        for cls in view.throttle_classes:
            cls.timer = lambda self: value

    def test_request_throttling_expires(self):
        """
        Ensure request rate is limited for a limited duration only
        """
        self.set_throttle_timer(MockView, 0)

        request = self.factory.get('/')
        for dummy in range(4):
            response = MockView.as_view()(request)
        assert response.status_code == 429

        # Advance the timer by one second
        self.set_throttle_timer(MockView, 1)

        response = MockView.as_view()(request)
        assert response.status_code == 200

    def ensure_is_throttled(self, view, expect):
        request = self.factory.get('/')
        request.user = User.objects.create(username='a')
        for dummy in range(3):

[Assistant]
         view.as_view()(request)
        request.user = User.objects.create(username='b')
        response = view.as_view()(request)
        assert response.status_code == expect


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
                     locale_parts = match.groupdict()
                        language = locale_parts["language"].lower()
                        territory = (
                            locale_parts["territory"][:2].upper()
                            + locale_parts["territory"][2:]
                        )
                        proposed_locale = f"{language}_{territory}"
                    else:
                        # It could be a language in uppercase
                        proposed_locale = locale.lower()

                    # Recheck if the proposed locale is valid
                    if is_valid_locale(proposed_locale):
                        self.stdout.write(
                            "invalid locale %s, did you mean %s?"
                            % (
                                locale,
                                proposed_locale,
                            ),
                        )
                    else:
                        self.stdout.write("invalid locale %s" % locale)

                    continue
                if self.verbosity > 0:
                    self.stdout.write("processing locale %s" % locale)
                for potfile in potfiles:
                    self.write_po_file(potfile, locale)
        finally:
            if not self.keep_pot:
                self.remove_potfiles()

    @cached_property
    def gettext_version(self):
        # Gettext tools will output system-encoded bytestrings instead of UTF-8,
        # when looking up the version. It's especially a problem on Windows.
        out, err, status = popen_wrapper(
            ["xgettext", "--version"],
            stdout_encoding=DEFAULT_LOCALE_ENCODING,
        )
        m = re.search(r"(\d+)\.(\d+)\.?(\d+)?", out)
        if m:
            return tuple(int(d) for d in m.groups() if d is not None)
        else:
            raise CommandError("Unable to get gettext version. Is it installed?")

    @cached_property
    def settings_available(self):
        try:
            settings.LOCALE_PATHS
        except ImproperlyConfigured:
            if self.verbosity > 1:
                self.stderr.write("Running without configured settings.")
            return False
        return True

    def build_potfiles(self):
        """
        Build pot files and apply msguniq to them.
        ""


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
) + exponent
            whole_digits = total_digits
            decimal_places = 0
        elif len(digittuple) > abs(exponent):
            # 123.45
            total_digits = len(digittuple)
            whole_digits = total_digits - abs(exponent)
            decimal_places = abs(exponent)
        else:
            # 0.001234
            total_digits = abs(exponent)
            whole_digits = 0
            decimal_places = total_digits

        if self.max_digits is not None and total_digits > self.max_digits:
            self.fail('max_digits', max_digits=self.max_digits)
        if self.decimal_places is not None and decimal_places > self.decimal_places:
            self.fail('max_decimal_places', max_decimal_places=self.decimal_places)
        if self.max_whole_digits is not None and whole_digits > self.max_whole_digits:
            self.fail('max_whole_digits', max_whole_digits=self.max_whole_digits)

        return value

    def to_representation(self, value):
        coerce_to_string = getattr(self, 'coerce_to_string', api_settings.COERCE_DECIMAL_TO_STRING)

        if value is None:
            if coerce_to_string:
                return ''
            else:
                return None

        if not isinstance(value, decimal.Decimal):
            value = decimal.Decimal(str(value).strip())

        quantized = self.quantize(value)

        if self.normalize_output:
            quantized = quantized.normalize()

        if not coerce_to_string:
            return quantized
        if self.localize:
            return localize_input(quantized)

        return '{:f}'.format(quantized)

    def quantize(self, value):
        """
        Quantize the decimal value to the configured precision.
        """
        if self.decimal_places is None:
            return value

        context = decimal.getcontext().copy()
        if self.max_digits is not None:
            context.prec = self.max_digits
        return value.quantize(
            decimal.Decimal('.1') ** self.decimal_places,
            rounding=self.rounding,
            context=context
        )

# Date & time fields...

class DateTimeField(Field):
    default_error_messages = {
        'invalid': _('Datetime has wrong format. Use one of these formats instead: {format}.'),
        'date': _('Expected a datetime but got a date.'),
        'make_aware': _('Invalid datetime for the timezone "{timezone}".'),
        'overflow': _('Datetime value out of range.')
    }
    datetime_parser = datetime.datetime.strptime

    def __init__(self, format=empty, input_formats=None, default_timezone=None, **kwargs):
        if format is not empty:
            self.format = format
        if input_formats is not None:
            self.input_formats = input_formats
        if default_timezone is not None:
            self.timezone = default_timezone
        super().__init__(**kwargs)

    def enforce_timezone(self, value):
        """
        When `self.default_timezone` is `None`, always return naive datetimes.
        When `self.default_timezone` is not `None`, always return aware datetimes.
        """
        field_timezone = self.timezone if hasattr(self, 'timezone') else self.default_timezone()

        if field_timezone is not None:
            if timezone.is_aware(value):
                try:
                    return value.astimezone(field_timezone)
                except OverflowError:
                    self.fail('overflow')
            try:
                dt = timezone.make_aware(value, field_timezone)
                # When the resulting datetime is a ZoneInfo instance, it won't necessarily
                # throw given an invalid datetime, so we need to specifically check.
                if not valid_datetime(dt):
                    self.fail('make_aware', timezone=field_timezone)
                return dt
            except Exception as e:
                if pytz and isinstance(e, pytz.exceptions.InvalidTimeError):
                    self.fail('make_aware', timezone=field_timezone)
                raise e
        elif (field_timezone is None) and timezone.is_aware(value):
            return timezone.make_naive(value, datetime.timezone.utc)
        return value

    def default_timezone(self):
        return timezone.get_current_timezone() if settings.USE_TZ else None

    def to_internal_value(self, value):
        input_formats = getattr(self, 'input_formats', api_settings.DATETIME_INPUT_FORMATS)

        if isinstance(value, datetime.date) and not isinstance(value, datetime.datetime):
            self.fail('date')

        if isinstance(value, datetime.datetime):
            return self.enforce_timezone(value)

        for input_format in input_formats:
            with contextlib.suppress(ValueError, TypeError):
                if input_format.lower() == ISO_8601:
                    parsed = parse_datetime(value)
                    if parsed is not None:
                        return self.enforce_timezone(parsed)

                parsed = self.datetime_parser(value, input_format)
                return self.enforce_timezone(parsed)

        humanized_format = humanize_datetime.datetime_formats(input_formats)
        self.fail('invalid', format=humanized_format)

    def to_representation(self, value):
        if not value:
            return None

        output_format = getattr(self, 'format', api_settings.DATETIME_FORMAT)

        if output_format is None or isinstance(value, str):
            return value

        value = self.enforce_timezone(value)

        if output_format.lower() == ISO_8601:
            value = value.isoformat()
            if value.endswith('+00:00'):
                value = value[:-6] + 'Z'
            return value
        return value.strftime(output_format)

class DateField(Field):
    default_error_messages = {
        'invalid': _('Date has wrong format. Use one of these formats instead: {format}.'),
        'datetime': _('Expected a date but got a datetime.'),
    }
    datetime_parser = datetime.datetime.strptime

    def __init__(self, format=empty, input_formats=None, **kwargs):
        if format is not empty:
            self.format = format
        if input_formats is not None:
            self.input_formats = input_formats
        super().__init__(**kwargs)

    def to_internal_value(self, value):
        input_formats = getattr(self, 'input_formats', api_settings.DATE_INPUT_FORMATS)

        if isinstance(value, datetime.datetime):
            self.fail('datetime')

        if isinstance(value, datetime.date):
            return value

        for input_format in input_formats:
            if input_format.lower() == ISO_8601:
                try:
                    parsed = parse_date(value)
                except (ValueError, TypeError):
                    pass
                else:
                    if parsed is not None:
                        return parsed
            else:
                try:
                    parsed = self.datetime_parser(value, input_format)
                except (ValueError, TypeError):
                    pass
                else:
                    return parsed.date()

        humanized_format = humanize_datetime.date_formats(input_formats)
        self.fail('invalid', format=humanized_format)

    def to_representation(self, value):
        if not value:
            return None

        output_format = getattr(self, 'format', api_settings.DATE_FORMAT)

        if output_format is None or isinstance(value, str):
            return value

        # Applying a `DateField` to a datetime value is almost always
        # not a sensible thing to do, as it means naively dropping
        # any explicit or implicit timezone info.
        assert not isinstance(value, datetime.datetime), (
            'Expected a `date`, but got a `datetime`. Refusing to coerce, '
            'as this may mean losing timezone information. Use a custom '
            'read-only field and deal with timezone issues explicitly.'
        )

        if output_format.lower() == ISO_8601:
            return value.isoformat()

        return value.strftime(output_format)

class TimeField(Field):
    defau

[Assistant]
lt_error_messages = {
        'invalid': _('Time has wrong format. Use one of these formats instead: {format}.'),
    }


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for lite.py functionality related to select TF op usage."""

import os

from absl.testing import parameterized
import numpy as np
import tensorflow as tf

from tensorflow.core.framework import graph_pb2
from tensorflow.lite.python import lite
from tensorflow.lite.python import test_util as tflite_test_util
from tensorflow.lite.python.convert import register_custom_opdefs
from tensorflow.lite.python.interpreter import Interpreter
from tensorflow.lite.python.testdata import double_op
from tensorflow.python.client import session
from tensorflow.python.eager import def_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.framework.importer import import_graph_def
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import list_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
from tensorflow.python.saved_model import saved_model
from tensorflow.python.trackable import autotrackable

class FromSessionTest(test_util.TensorFlowTestCase, parameterized.TestCase):

  @parameterized.named_parameters(
      ('EnableMlirConverter', True),  # enable mlir
      ('DisableMlirConverter', False))  # disable mlir
  def testFlexMode(self, enable_mlir):
    with ops.Graph().as_default():
      in_tensor = array_ops.placeholder(shape=[1, 4], dtype=dtypes.float32)
      out_tensor = in_tensor + in_tensor
      sess = session.Session()

    # Convert model and ensure model is not None.
    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
                                                  [out_tensor])
    converter.target_spec.supported_ops = set([lite.OpsSet.SELECT_TF_OPS])
    converter.experimental_new_converter = enable_mlir
    tflite_model = converter.convert()
    self.assertTrue(tflite_model)

    # Check the model works with TensorFlow ops.
    interpreter = Interpreter(model_content=tflite_model)
    interpreter.allocate_tensors()
    input_details = interpreter.get_input_details()
    test_input = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32)
    interpreter.set_tensor(input_details[0]['index'], test_input)
    interpreter.invoke()

    output_details = interpreter.get_output_details()
    expected_output = np.array([[2.0, 4.0, 6.0, 8.0]], dtype=np.float32)
    output_data = interpreter.get_tensor(output_details[0]['index'])
    self.assertTrue((expected_output == output_data).all())

  def testFlexWithAutomaticPassThrough(self):
    # Create a graph that has one L2Loss op.
    with ops.Graph().as_default():
      with session.Session() as sess:
        in_tensor = array_ops.placeholder(
            shape=[4], dtype=dtypes.float32, name='input')
        out_tensor = nn_ops.l2_loss(in_tensor)
        converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
                                                      [out_tensor])
        converter.target_spec.supported_ops = set([lite.OpsSet.SELECT_TF_OPS])
        converter._experimental_allow_all_select_tf_ops = True
        tflite_model = converter.convert()
    self.assertTrue(tflite_model)
    self.assertIn('FlexL2Loss', tflite_test_util.get_ops_list(tflite_model))

  def testDeprecatedFlags(self):
    with ops.Graph().as_default():
      in_tensor = array_ops.placeholder(shape=[1, 4], dtype=dtypes.float32)
      out_tensor = in_tensor + in_tensor
      sess = session.Session()

    # Convert model and ensure model is not None.
    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
                                                  [out_tensor])
    converter.target_ops = set([lite.OpsSet.SELECT_TF_OPS])

    # Ensure `target_ops` is set to the correct value after flag deprecation.
    self.assertEqual(converter.target_ops, set([lite.OpsSet.SELECT_TF_OPS]))
    self.assertEqual(converter.target_spec.supported_ops,
                     set([lite.OpsSet.SELECT_TF_OPS]))

    tflite_model = converter.convert()
    self.assertTrue(tflite_model)

    # Check the model works with TensorFlow ops.
    interpreter = Interpreter(model_content=tflite_model)
    interpreter.allocate_tensors()
    input_details = interpreter.get_input_details()
    test_input = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32)
    interpreter.set_tensor(input_details[0]['index'], test_input)
    interpreter.invoke()

    output_details = interpreter.get_output_details()
    expected_output = np.array([[2.0, 4.0, 6.0, 8.0]], dtype=np.float32)
    output_data = interpreter.get_tensor(output_details[0]['index'])
    self.assertTrue((expected_output == output_data).all())

class FromConcreteFunctionTest(test_util.TensorFlowTestCase,
                               parameterized.TestCase):

  @parameterized.named_parameters(
      ('EnableMlirConverter', True),  # enable mlir
      ('DisableMlirConverter', False))  # disable mlir
  @test_util.run_v2_only
  def testFloat(self, enable_mlir):
    input_data = constant_op.constant(1., shape=[1])
    root = autotrackable.AutoTrackable()
    root.v1 = variables.Variable(3.)
    root.v2 = variables.Variable(2.)
    root.f = def_function.function(lambda x: root.v1 * root.v2 * x)
    concrete_func = root.f.get_concrete_function(input_data)

    # Convert model.
    converter = lite.TFLiteConverterV2.from_concrete_functions([concrete_func],
                                                               root)
    converter.target_spec.supported_ops = set([lite.OpsSet.SELECT_TF_OPS])
    converter.experimental_new_converter = enable_mlir
    tflite_model = converter.convert()

    # Check the model works with TensorFlow ops.
    interpreter = Interpreter(model_content=tflite_model)
    interpreter.allocate_tensors()
    input_details = interpreter.get_input_details()
    test_input = np.array([4.0], dtype=np.float32)
    interpreter.set_tensor(input_details[0]['index'], test_input)
    interpreter.invoke()

    output_details = interpreter.get_output_details()
    expected_output = np.array([24.0], dtype=np.float32)
    output_data = interpreter.get_tensor(output_details[0]['index'])
    self.assertTrue((expected_output == output_data).all())

  # Ensure that input TFLite buffer is not reused for ops such as
  # `TensorListSetItem`. The example model has a while loop, and the while body
  # has a `TensorListSetItem` op which takes the output from a `Where` op.
  @test_util.run_v2_only
  def testDisableFlexTensorMemoryReusing(self):

    @tf.function(input_signature=[
        tf.TensorSpec(shape=[2, 3], dtype=tf.float32, name='x')
    ])
    def model(x):
      l = list_ops.tensor_list_reserve(
          element_dtype=tf.int64,

[Assistant] element_shape=[None, 1], num_elements=2)


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
er == 'imagemagick' and html == 'html5'
            # ImageMagick delegates to ffmpeg for this format.
            and not animation.FFMpegWriter.isAvailable()):
        pytest.skip('Requires FFMpeg')
    # create here rather than in the fixture otherwise we get __del__ warnings
    # about producing no output
    anim = animation.FuncAnimation(**anim)
    with plt.rc_context({'animation.writer': writer,
                         'animation.html': html}):
        html = anim._repr_html_()
    if want is None:
        assert html is None
        with pytest.warns(UserWarning):
            del anim  # Animation was never run, so will warn on cleanup.
            np.testing.break_cycles()
    else:
        assert want in html

@pytest.mark.parametrize(
    'anim',
    [{'save_count': 10, 'frames': iter(range(5))}],
    indirect=['anim']
)
def test_no_length_frames(anim):
    anim.save('unused.null', writer=NullMovieWriter())

def test_movie_writer_registry():
    assert len(animation.writers._registered) > 0
    mpl.rcParams['animation.ffmpeg_path'] = "not_available_ever_xxxx"
    assert not animation.writers.is_available("ffmpeg")
    # something guaranteed to be available in path and exits immediately
    bin = "true" if sys.platform != 'win32' else "where"
    mpl.rcParams['animation.ffmpeg_path'] = bin
    assert animation.writers.is_available("ffmpeg")

@pytest.mark.parametrize(
    "method_name",
    [pytest.param("to_html5_video", marks=pytest.mark.skipif(
        not animation.writers.is_available(mpl.rcParams["animation.writer"]),
        reason="animation writer not installed")),
     "to_jshtml"])
@pytest.mark.parametrize('anim', [dict(frames=1)], indirect=['anim'])
def test_embed_limit(method_name, caplog, tmpdir, anim):
    caplog.set_level("WARNING")
    with tmpdir.as_cwd():
        with mpl.rc_context({"animation.embed_limit": 1e-6}):  # ~1 byte.
            getattr(anim, method_name)()
    assert len(caplog.rec

[Assistant]
ords) == 1
    record, = caplog.records
    assert (record.name == "matplotlib.animation"
            and record.levelname == "WARNING")


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
from collections import deque

class FasterOrderedDict(dict):
    """
    Faster than using the standard library class collections.OrderedDict,
    because OrderedDict is pure Python. This class delegates every
    operation to dict/deque, which are both C-based.

    This handles only the operations that matter to the rest of Hydra and probably
    won't work for other use cases without modification.
    """
    def __init__(self):
        dict.__init__(self)
        self._elems = deque()

    def __delitem__(self, key):
        """
        slow but should be uncommon for our uses
        """
        dict.__delitem__(self, key)
        self._elems.remove(key)

    def __setitem__(self, key, data):
        if key in self:
            dict.__setitem__(self, key, data)
        else:
            self._elems.append(key)
            dict.__setitem__(self, key, data)

    def __repr__(self):
        elems = ', '.join

[Assistant]
(["'%s': %s" % (elem, self[elem])
                           for elem in self._elems])
        return '{%s}' % elems


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
#!/usr/bin/env python
# encoding: utf-8
"""
delicious_import.py

Created by Hilary Mason on 2010-11-28.
Copyright (c) 2010 Hilary Mason. All rights reserved.
"""

import sys
import urllib
import csv
from xml.dom import minidom

class delicious_import(object):
    def __init__(self, username, password=''):
		# API URL: https://user:passwd@api.del.icio.us/v1/posts/all
        url = "https://%s:%s@api.del.icio.us/v1/posts/all" % (username, password)
        h = urllib.urlopen(url)
        c

[Assistant]
ontent = h.read()
        h.close()


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
on
            maps or list of prediction result filenames.
        gt_seg_maps (list[ndarray] | list[str]): list of ground truth
            segmentation maps or list of label filenames.
        num_classes (int): Number of categories.
        ignore_index (int): Index that will be ignored in evaluation.
        nan_to_num (int, optional): If specified, NaN values will be replaced
            by the numbers defined by the user. Default: None.
        label_map (dict): Mapping old labels to new labels. Default: dict().
        reduce_zero_label (bool): Wether ignore zero label. Default: False.
        beta (int): Determines the weight of recall in the combined score.
            Default: False.

     Returns:
        dict[str, float | ndarray]: Default metrics.
            <aAcc> float: Overall accuracy on all images.
            <Fscore> ndarray: Per category recall, shape (num_classes, ).
            <Precision> ndarray: Per category precision, shape (num_classes, ).
            <Recall> ndarray: Per category f-score, shape (num_classes, ).
    """
    fscore_result = eval_metrics(
        results=results,
        gt_seg_maps=gt_seg_maps,
        num_classes=num_classes,
        ignore_index=ignore_index,
        metrics=['mFscore'],
        nan_to_num=nan_to_num,
        label_map=label_map,
        reduce_zero_label=reduce_zero_label,
        beta=beta)
    return fscore_result

def eval_metrics(results,
                 gt_seg_maps,
                 num_classes,
                 ignore_index,
                 metrics=['mIoU'],
                 nan_to_num=None,
                 label_map=dict(),
                 reduce_zero_label=False,
                 beta=1):
    """Calculate evaluation metrics
    Args:
        results (list[ndarray] | list[str]): List of prediction segmentation
            maps or list of prediction result filenames.
        gt_seg_maps (list[ndarray] | list[str]): list of ground truth
            segmentation maps or list of label filenames.
        num_classes (int): Number of categories.
        ignore_index (int): Index that will be ignored in evaluation.
        metrics (list[str] | str): Metrics to be evaluated, 'mIoU' and 'mDice'.
        nan_to_num (int, optional): If specified, NaN values will be replaced
            by the numbers defined by the user. Default: None.
        label_map (dict): Mapping old labels to new labels. Default: dict().
        reduce_zero_label (bool): Wether ignore zero label. Default: False.
     Returns:
        float: Overall accuracy on all images.
        ndarray: Per category accuracy, shape (num_classes, ).
        ndarray: Per category evaluation metrics, shape (num_classes, ).
    """
    if isinstance(metrics, str):
        metrics = [metrics]
    allowed_metrics = ['mIoU', 'mDice', 'mFscore']
    if not set(metrics).issubset(set(allowed_metrics)):
        raise KeyError('metrics {} is not supported'.format(metrics))

    total_area_intersect, total_area_union, total_area_pred_label, \
        total_area_label = total_intersect_and_union(
            results, gt_seg_maps, num_classes, ignore_index, label_map,
            reduce_zero_label)
    all_acc = total_area_intersect.sum() / total_area_label.sum()
    ret_metrics = OrderedDict({'aAcc': all_acc})
    for metri

[Assistant]
c in metrics:
        if metric == 'mIoU':
            iou = total_area_intersect / total_area_union
            acc = total_area_intersect / total_area_label
            ret_metrics['IoU'] = iou
            ret_metrics['Acc'] = acc
        elif metric == 'mDice':
            dice = 2 * total_area_intersect / (
                total_area_pred_label + total_area_label)
            acc = total_area_intersect / total_area_label
            ret_metrics['Dice'] = dice
            ret_metrics['Acc'] = acc
        elif metric == 'mFscore':
            precision = total_area_intersect / total_area_pred_label
            recall = total_area_intersect / total_area_label
            f_value = torch.tensor(
                [f_score(x[0], x[1], beta) for x in zip(precision, recall)])
            ret_metrics['Fscore'] = f_value
            ret_metrics['Precision'] = precision
            ret_metrics['Recall'] = recall

    ret_metrics = {
        metric: value.numpy()
        for metric, value in ret_metrics.items()
    }
    if nan_to_num is not None:
        ret_metrics = OrderedDict({
            metric: np.nan_to_num(metric_value, nan=nan_to_num)
            for metric, metric_value in ret_metrics.items()
        })
    return ret_metrics


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
es to draw from X
        to train each base estimator.

        - If None (default), then draw `X.shape[0]` samples.
        - If int, then draw `max_samples` samples.
        - If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,
          `max_samples` should be in the interval `(0.0, 1.0]`.

        .. versionadded:: 0.22

    monotonic_cst : array-like of int of shape (n_features), default=None
        Indicates the monotonicity constraint to enforce on each feature.
          - 1: monotonically increasing
          - 0: no constraint
          - -1: monotonically decreasing

        If monotonic_cst is None, no constraints are applied.

        Monotonicity constraints are not supported for:
          - multioutput regressions (i.e. when `n_outputs_ > 1`),
          - regressions trained on data with missing values.

        Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.

        .. versionadded:: 1.4

    Attributes
    ----------
    estimator_ : :class:`~sklearn.tree.DecisionTreeRegressor`
        The child estimator template used to create the collection of fitted
        sub-estimators.

        .. versionadded:: 1.2
           `base_estimator_` was renamed to `estimator_`.

    base_estimator_ : DecisionTreeRegressor
        The child estimator template used to create the collection of fitted
        sub-estimators.

        .. deprecated:: 1.2
            `base_estimator_` is deprecated and will be removed in 1.4.
            Use `estimator_` instead.

    estimators_ : list of DecisionTreeRegressor
        The collection of fitted sub-estimators.

    feature_importances_ : ndarray of shape (n_features,)
        The impurity-based feature importances.
        The higher, the more important the feature.
        The importance of a feature is computed as the (normalized)
        total reduction of the criterion brought by that feature.  It is also
        known as the Gini importance.

        Warning: impurity-based feature importances can be misleading for
        high cardinality features (many unique values). See
        :func:`sklearn.inspection.permutation_importance` as an alternative.

    n_featur

[Assistant]
es_in_ : int
        Number of features seen during :term:`fit`.


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
import os
import time
import configparser

from pathlib import Path

API_KEYS_LOCATION = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'openaiapirc')

class PromptFile:
    context_source_filename = ""
    default_context_filename = "current_context.txt"
    default_file_path = os.path.join(os.path.dirname(__file__), "..", default_context_filename)
    default_config_path = os.path.join(os.path.dirname(__file__), "..", "current_context.config")

    def __init__(self, file_name, config):
        self.context_source_filename = "{}-context.txt".format(config['shell']) #  feel free to set your own default context path here

        self.file_path = self.default_file_path
        self.config_path = self.default_config_path

        # loading in one of the saved contexts
        if file_name != self.default_context_filename:
            self.load_context(file_name, True)

    def has_config(self):
        """
        Check if the prompt file has a corresponding config file
        """
        return os.path.isfile(self.config_path)

    def read_config(self):
        """
        Read the prompt config and return a dictionary
        """

        if self.has_config() == False:
            self.set_config(self.config)
            return self.config

        with open(self.config_path, 'r') as f:
            lines = f.readlines()

        config = {
            'engine': lines[0].split(':')[1].strip(),
            'temperature': float(lines[1].split(':')[1].strip()),
            'max_tokens': int(lines[2].split(':')[1].strip()),
            'shell': lines[3].split(':')[1].strip(),
            'multi_turn': lines[4].split(':')[1].strip(),
            'token_count': int(lines[5].split(':')[1].strip())
        }

        self.config = config
        return self.config

    def set_config(self, config):
        """
        Set the prompt headers with the new config
        """
        self.config = config

        with open(self.config_path, 'w') as f:
            f.write('engine: {}\n'.format(self.config['engine']))
            f.write('temperature: {}\n'.format(self.config['temperature']))
            f.write('max_tokens: {}\n'.format(self.config['max_tokens']))
            f.write('shell: {}\n'.format(self.config['shell']))
            f.write('multi_turn: {}\n'.format(self.config['multi_turn']))
            f.write('token_count: {}\n'.format(self.config['token_count']))

    def show_config(self):
        print('\n')
        # read the dictionary into a list of # lines
        lines = []
        for key, value in self.config.items():
            lines.append('# {}: {}\n'.format(key, value))
        print(''.join(lines))

    def add_input_output_pair(self, user_query, prompt_response):
        """
        Add lines to file_name and update the token_count
        """

        with open(self.file_path, 'a') as f:
            f.write(user_query)
            f.write(prompt_response)

        if self.config['multi_turn'] == 'on':
            self.config['token_count'] += len(user_query.split()) + len(prompt_response.split())
            self.set_config(self.config)

    def read_prompt_file(self, input):
        """
        Get the updated prompt file
        Checks for token overflow and appends the current input

        Returns: the prompt file after appending the input
        """

        input_tokens_count = len(input.split())
        need_to_refresh = (self.config['token_count'] + input_tokens_count > 2048)

        if need_to_refresh:
            # delete first 2 lines of prompt context file
            with open(self.file_path, 'r') as f:
                lines = f.readlines()
                prompt = lines[2:] # drop first 2 lines of prompt
            with open(self.file_path, 'w') as f:
                f.writelines(prompt)

        # get input from prompt file
        with open(self.file_path, 'r') as f:
            lines = f.readlines()

        return ''.join(lines)

    def get_token_count(self):
        """
        Get the actual token count
        """
        token_count = 0
        if self.has_config():
            with open(self.config_path, 'r') as f:
                lines = f.readlines()
                token_count = int(lines[5].split(':')[1].strip())

        true_token_count

[Assistant] = 0


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[User]
# Reproductions/tests for crashes/read errors in TiffDecode.c

# When run in Python, all of these images should fail for
# one reason or another, either as a buffer overrun,
# unrecognized datastream, or truncated image file.
# There shouldn't be any segfaults.
#
# if run like
# `valgrind --tool=memcheck pytest test_tiff_crashes.py 2>&1 | grep TiffDecode.c`
# the output should be empty. There may be Python issues
# in the valgrind especially if run in a debug Python
# version.

import pytest

from PIL import Image

from .helper import on_ci

@pytest.mark.parametrize(
    "test_file",
    [
        "Tests/images/crash_1.tif",
        "Tests/images/crash_2.tif",
        "Tests/images/crash-2020-10-test.tif",
        "Tests/images/crash-0c7e0e8e11ce787078f00b5b0ca409a167f070e0.tif",
        "Tests/images/crash-0e16d3bfb83be87356d026d66919deaefca44dac.tif",
        "Tests/images/crash-1152ec2d1a1a71395b6f2ce6721c38924d025bf3.tif",
        "Tests/images/crash-1185209cf7655b5aed8ae5e77784dfdd18ab59e9.tif",
        "Tests/images/crash-338516dbd2f0e83caddb8ce256c22db3bd6dc40f.tif",
        "Tests/images/crash-4f085cc12ece8cde18758d42608bed6a2a2cfb1c.tif",
        "Tests/images/crash-86214e58da443d2b80820cff9677a38a33dcbbca.tif",
        "Tests/images/crash-f46f5b2f43c370fe65706c11449f567ecc345e74.tif",
        "T

[Assistant]
ests/images/crash-63b1dffefc8c075ddc606c0a2f5fdc15ece78863.tif",
        "Tests/images/crash-74d2a78403a5a59db1fb0a2b8735ac068a75f6e3.tif",
        "Tests/images/crash-81154a65438ba5aaeca73fd502fa4850fbde60f8.tif",
        "Tests/images/crash-0da013a13571cc8eb457a39fee8db18f8a3c7127.tif",
    ],
)
@pytest.mark.filterwarnings("ignore:Possibly corrupt EXIF data")
@pytest.mark.filterwarnings("ignore:Metadata warning")
@pytest.mark.filterwarnings("ignore:Truncated File Read")
def test_tiff_crashes(test_file):
    try:
        with Image.open(test_file) as im:
            im.load()
    except FileNotFoundError:
        if not on_ci():
            pytest.skip("test image not found")
            return
        raise
    except OSError:
        pass


[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.
Determine the programming language from the user's code block annotation and precisely continue the code from the point of interruption.
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 a code block annotation (```), followed by the code completion, and ends with a code block annotation.

[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_