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

[User]
cache__")

        # clean the generated pxi files
        for pxifile in _pxifiles:
            pxifile_replaced = pxifile.replace(".pxi.in", ".pxi")
            self._clean_me.append(pxifile_replaced)

        self._clean_trees.append(d for d in ("build", "dist") if os.path.exists(d))

    def finalize_options(self):
        pass

    def run(self):
        for clean_me in self._clean_me:
            try:
                os.unlink(clean_me)
            except OSError:
                pass
        for clean_tree in self._clean_trees:
            try:
                shutil.rmtree(clean_tree)
            except OSError:
                pass

# we need to inherit from the versioneer
# class as it encodes the version info
sdist_class = cmdclass["sdist"]

class CheckSDist(sdist_class):
    """Custom sdist that ensures Cython has compiled all pyx files to c."""

    _pyxfiles = [
        "pandas/_libs/arrays.pyx",
        "pandas/_libs/lib.pyx",
        "pandas/_libs/hashtable.pyx",
        "pandas/_libs/tslib.pyx",
        "pandas/_libs/index.pyx",
        "pandas/_libs/internals.pyx",
        "pandas/_libs/algos.pyx",
        "pandas/_libs/join.pyx",
        "pandas/_libs/indexing.pyx",
        "pandas/_libs/interval.pyx",
        "pandas/_libs/hashing.pyx",
        "pandas/_libs/missing.pyx",
        "pandas/_libs/testing.pyx",
        "pandas/_libs/sparse.pyx",
        "pandas/_libs/ops.pyx",
        "pandas/_libs/parsers.pyx",
        "pandas/_libs/tslibs/base.pyx",
        "pandas/_libs/tslibs/ccalendar.pyx",
        "pandas/_libs/tslibs/dtypes.pyx",
        "pandas/_libs/tslibs/period.pyx",
        "pandas/_libs/tslibs/strptime.pyx",
        "pandas/_libs/tslibs/np_datetime.pyx",
        "pandas/_libs/tslibs/timedeltas.pyx",
        "pandas/_libs/tslibs/timestamps.pyx",
        "pandas/_libs/tslibs/timezones.pyx",
        "pandas/_libs/tslibs/conversion.pyx",
        "pandas/_libs/tslibs/fields.pyx",
        "pandas/_libs/tslibs/offsets.pyx",
        "pandas/_libs/tslibs/parsing.pyx",
        "pandas/_libs/tslibs/tzconversion.pyx",
        "pandas/_libs/tslibs/vectorized.pyx",
        "pandas/_libs/window/indexers.pyx",
        "pandas/_libs/writers.pyx",
        "pandas/_libs/sas.pyx",
        "pandas/_libs/byteswap.pyx",
    ]

    _cpp_pyxfiles = [
        "pandas/_libs/window/aggregations.pyx",
    ]

    def initialize_options(self):
        sdist_class.initialize_options(self)

    def run(self):
        if "cython" in cmdclass:
            self.run_command("cython")
        else:
            # If we are not running cython then
            # compile the extensions correctly
            pyx_files = [(self._pyxfiles, "c"), (self._cpp_pyxfiles, "cpp")]

            for pyxfiles, extension in pyx_files:
                for pyxfile in pyxfiles:
                    sourcefile = pyxfile[:-3] + extension
                    msg = (
                        f"{extension}-source file '{sourcefile}' not found.\n"
                        "Run 'setup.py cython' before sdist."
                    )
                    assert os.path.isfile(sourcefile), msg
        sdist_class.run(self)

class CheckingBuildExt(build_ext):
    """
    Subclass build_ext to get clearer report if Cython is necessary.
    """

    def check_cython_extensions(self, extensions):
        for ext in extensions:
            for src in ext.sources:
                if not os.path.exists(src):
                    print(f"{ext.name}: - [{ext.sources}]")
                    raise Exception(
                        f"""Cython-generated file '{src}' not found.
                Cython is required to compile pandas from a development branch.
                Please install Cython or download a release package of pandas.
                """
                    )

    def build_extensions(self):
        self.check_cython_extensions(self.extensions)
        build_ext.build_extensions(self)

class CythonCommand(build_ext):
    """
    Custom command subclassed from Cython.Distutils.build_ext
    to compile pyx-c, and stop there. All this does is override the
    C-compile method build_extension() with a no-op.
    """

    def build_extension(self, ext):
        pass

class DummyBuildSrc(Command):
    """numpy's build_src command interferes with Cython's build_ext."""

    user_options = []

    def initialize_options(self):
        self.py_modules_dict = {}

    def finalize_options(self):
        pass

    def run(self):
        pass

cmdclass["clean"] = CleanCommand
cmdclass["build_ext"] = CheckingBuildExt

if _CYTHON_INSTALLED:
    suffix = ".pyx"
    cmdclass["cython"] = CythonCommand
else:
    suffix = ".c"
    cmdclass["build_src"] = DummyBuildSrc

# ----------------------------------------------------------------------
# Preparation of compiler arguments

debugging_symbols_requested = "--with-debugging-symbols" in sys.argv
if debugging_symbols_requested:
    sys.argv.remove("--with-debugging-symbols")

if sys.byteorder == "big":
    endian_macro = [("__BIG_ENDIAN__", "1")]
else:
    endian_macro = [("__LITTLE_ENDIAN__", "1")]

extra_compile_args = []
extra_link_args = []
if is_platform_windows():
    if debugging_symbols_requested:
        extra_compile_args.append("/Z7")
        extra_link_args.append("/DEBUG")
else:
    # PANDAS_CI=1 is set in CI
    if os.environ.get("PANDAS_CI", "0") == "1":
        extra_compile_args.append("-Werror")
    if debugging_symbols_requested:
        extra_compile_args.append("-g3")
        extra_compile_args.append("-UNDEBUG")
        extra_compile_args.append("-O0")

# Build for at least macOS 10.9 when compiling on a 10.9 system or above,
# overriding CPython distuitls behaviour which is to target the version that
# python was built for. This may be overridden by setting
# MACOSX_DEPLOYMENT_TARGET before calling setup.py
if is_platform_mac():
    if "MACOSX_DEPLOYMENT_TARGET" not in os.environ:
        current_system = platform.mac_ver()[0]
        python_target = get_config_vars().get(
            "MACOSX_DEPLOYMENT_TARGET", current_system
        )
        target_macos_version = "10.9"
        parsed_macos_version = parse_version(target_macos_version)
        if (
            parse_version(str(python_target))
             parsed_macos_version
            = parse_version(current_system)
        ):
            os.environ["MACOSX_DEPLOYMENT_TARGET"] = target_macos_version

    if sys.version_info[:2] == (3, 8):  # GH 33239
        extra_compile_args.append("-Wno-error=deprecated-declarations")

    # https://github.com/pandas-de

[Assistant]
v/pandas/issues/35559
    extra_compile_args.append("-Wno-error=unreachable-code")


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

[User]
"""This script generates the papers.md file from the papers.yml file."""
from pathlib import Path

import yaml

data_file = "papers.yml"
papers_header = Path("stylesheets") / "papers_header.txt"
output_file = "papers.md"

# Load YAML file:
with open(data_file, "r") as stream:
    papers = yaml.load(stream, Loader=yaml.SafeLoader)["papers"]

# Load header:
with open(papers_header, "r") as stream:
    header = stream.read()

with open(output_file, "w") as f:
    f.write(header)

    # First, we sort the papers by date.
    # This is in the format of "2022-03-15"
    papers = sorted(papers, key=lambda paper: paper["date"], reverse=True)

    snippets = []
    for paper in papers:
        title = paper["title"]
        authors = (
            ", ".join(paper["authors"]).replace("(", "sup").replace(")", "/sup")
        )
        affiliations = ", ".join(
            f"sup{num}/sup{affil}" for num, affil in paper["affiliations"].items()
        )
        link = paper["link"]

[Assistant]
      abstract = paper["abstract"]
        image_file = paper["image"]

        # Begin:
        paper_snippet = f"""

figure markdown
![](images/{image_file}){{ width="500"}}
figcaption
!-- Large font: --
h2
a href="{link}"{title}/a
/h2
/figcaption
/figure

center
{authors}

small{affiliations}/small
/center

**Abstract:** {abstract}\n\n
"""
        snippets.append(paper_snippet)

    f.write("\n\n---\n\n".join(snippets))


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

[User]
s.
        """
        changes = self.get_changes([self.author_name_null], [self.author_name_default])
        # Right number/type of migrations?
        self.assertNumberMigrations(changes, "testapp", 1)
        self.assertOperationTypes(changes, "testapp", 0, ["AlterField"])
        self.assertOperationAttributes(
            changes, "testapp", 0, 0, name="name", preserve_default=True
        )
        self.assertOperationFieldAttributes(
            changes, "testapp", 0, 0, default="Ada Lovelace"
        )

    @mock.patch(
        "django.db.migrations.questioner.MigrationQuestioner.ask_not_null_alteration",
        side_effect=AssertionError("Should not have prompted for not null alteration"),
    )
    def test_alter_field_to_not_null_with_db_default(self, mocked_ask_method):
        changes = self.get_changes(
            [self.author_name_null], [self.author_name_db_default]
        )
        self.assertNumberMigrations(changes, "testapp", 1)
        self.assertOperationTypes(changes, "testapp", 0, ["AlterField"])
        self.assertOperationAttributes(
            changes, "testapp", 0, 0, name="name", preserve_default=True
        )
        self.assertOperationFieldAttributes(
            changes, "testapp", 0, 0, db_default=models.Value("Ada Lovelace")
        )

    @mock.patch(
        "django.db.migrations.questioner.MigrationQuestioner.ask_not_null_alteration",
        return_value=models.NOT_PROVIDED,
    )
    def test_alter_field_to_not_null_without_default(self, mocked_ask_method):
        """
        #23609 - Tests autodetection of nullable to non-nullable alterations.
        """
        changes = self.get_changes([self.author_name_null], [self.author_name])
        self.assertEqual(mocked_ask_method.call_count, 1)
        # Right number/type of migrations?
        self.assertNumberMigrations(changes, "testapp", 1)
        self.assertOperationTypes(changes, "testapp", 0, ["AlterField"])
        self.assertOperationAttributes(
            changes, "testapp", 0, 0, name="name", preserve_default=True
        )
        self.assertOperationFieldAttributes(
            changes, "testapp", 0, 0, default=models.NOT_PROVIDED
        )

    @mock.patch(
        "django.db.migrations.questioner.MigrationQuestioner.ask_not_null_alteration",
        return_value="Some Name",
    )
    def test_alter_field_to_not_null_oneoff_default(self, mocked_ask_method):
        """
        #23609 - Tests autodetection of nullable to non-nullable alterations.
        """
        changes = self.get_changes([self.author_name_null], [self.author_name])
        self.assertEqual(mocked_ask_method.call_count, 1)
        # Right number/type of migrations?
        self.assertNumberMigrations(changes, "testapp", 1)
        self.assertOperationTypes(changes, "testapp", 0, ["AlterField"])
        self.assertOperationAttributes(
            changes, "testapp", 0, 0, name="name", preserve_default=False
        )
        self.assertOperationFieldAttributes(
            changes, "testapp", 0, 0, default="Some Name"
        )

    def test_rename_field(self):
        """Tests autodetection of renamed fields."""
        changes = self.get_changes(
            [self.author_name],
            [self.author_name_renamed],
            MigrationQuestioner({"ask_rename": True}),
        )
        # Right number/type of migrations?
        self.assertNumberMigrations(changes, "testapp", 1)
        self.assertOperationTypes(changes, "testapp", 0, ["RenameField"])
        self.assertOperationAttributes(
            changes, "testapp", 0, 0, old_name="name", new_name="names"
        )

    def test_rename_field_foreign_key_to_field(self):
        before = [
            ModelState(
                "app",
                "Foo",
                [
                    ("id", models.AutoField(primary_key=True)),
                    ("field", models.IntegerField(unique=True)),
                ],
            ),
            ModelState(
                "app",
                "Bar",
                [
                    ("id", models.AutoField(primary_key=True)),
                    (
                        "foo",
                        models.ForeignKey("app.Foo", models.CASCADE, to_field="field"),
                    ),
                ],
            ),
        ]
        after = [
            ModelState(
                "app",
                "Foo",
                [
                    ("id", models.AutoField(primary_key=True)),
                    ("renamed_field", models.IntegerField(unique=True)),
                ],
            ),
            ModelState(
                "app",
                "Bar",
                [
                    ("id", models.AutoField(primary_key=True)),
                    (
                        "foo",
                        models.ForeignKey(
                            "app.Foo", models.CASCADE, to_field="renamed_field"
                        ),
                    ),
                ],
            ),
        ]
        changes = self.get_changes(
            before, after, MigrationQuestioner({"ask_rename": True})
        )
        # Right number/type of migrations?
        self.assertNumberMigrations(changes, "app", 1)
        self.assertOperationTypes(changes, "app", 0, ["RenameField"])
        self.assertOperationAttributes(
            changes, "app", 0, 0, old_name="field", new_name="renamed_field"
        )

    def test_foreign_object_from_to_fields_list(self):
        author_state = ModelState(
            "app",
            "Author",
            [("id", models.AutoField(primary_key=True))],
        )
        book_state = ModelState(
            "app",
            "Book",
            [
                ("id", models.AutoField(primary_key=True)),
                ("name", models.CharField()),
                ("author_id", models.IntegerField()),
                (
                    "author",
                    models.ForeignObject(
                        "app.Author",
                        models.CASCADE,
                        from_fields=["author_id"],
                        to_fields=["id"],
                    ),
                ),
            ],

[Assistant]
   )
        book_state_copy = copy.deepcopy(book_state)
        changes = self.get_changes(
            [author_state, book_state],
            [author_state, book_state_copy],
        )
        self.assertEqual(changes, {})


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

[User]
#! /usr/bin/env python

# dependencies to handle:
# - gevent
# - pymongo
# - apt: python-dev
# - apt: libevent-dev

import copier
from copy_state_db import CopyStateDB
import multiprocessing
import oplog_applier
import os
import os.path
from pymongo.read_preferences import ReadPreference
import string
import sys
import utils

log = utils.get_logger(__name__)

PARENT_PROCESS_NAME = 'parent process'

#
# child processes
#

def die(msg):
    log.error(msg)
    sys.exit(1)

def ensure_empty_dest(dest):
    client = utils.mongo_connect(dest['host'], dest['port'],
                                 ensure_direct=True,
                                 max_pool_size=1,
                                 read_preference=ReadPreference.PRIMARY)
    collection = client[dest['db']][dest['collection']]
    if collection.count()  0:
        die("destination must be empty!")

def copy_collection_parent(sources, dest, state_db, args):
    """
    drive the collection copying process by delegating work to a pool of worker processes
    """

    # ensure state db has rows for each source/dest pair
    for source in sources:
        state_db.add_source_and_dest(source, dest)

    # space-pad all process names so that tabular output formats line up
    process_names = {repr(source): "%s:%d" % (source['host'], source['port'])
                     for source in sources}
    process_names['parent'] = PARENT_PROCESS_NAME
    max_process_name_len = max(len(name) for name in process_names.itervalues())
    for key in process_names:
        process_names[key] = string.ljust(process_names[key], max_process_name_len)

    multiprocessing.current_process().name = process_names['parent']

    # -----------------------------------------------------------------------
    # perform initial copy, if it hasn't been done yet
    # --------------

[Assistant]
---------------------------------------------------------
    in_initial_copy = len(state_db.select_by_state(CopyStateDB.STATE_INITIAL_COPY))
    if in_initial_copy and in_initial_copy  len(sources):
        die("prior attempt at initial copy failed; rerun with --restart")
    if in_initial_copy  0:
        ensure_empty_dest(dest)

        # each worker process copies one shard
        processes = []
        for source in sources:
            name = process_names[repr(source)]
            process = multiprocessing.Process(target=copier.copy_collection,
                                              name=name,
                                              kwargs=dict(source=source,
                                                          dest=dest,
                                                          state_path=state_db._path,
                                                          percent=args.percent))
            process.start()
            processes.append(process)

        # wait for all workers to finish
        utils.wait_for_processes(processes)


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

[User]
    except ModuleNotFoundError:
        return False

    return spec is not None

def repo_dir(name):
    return os.path.join(script_path, dir_repos, name)

def run_pip(command, desc=None, live=default_command_live):
    if args.skip_install:
        return

    index_url_line = f' --index-url {index_url}' if index_url != '' else ''
    return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)

def check_run_python(code: str) - bool:
    result = subprocess.run([python, "-c", code], capture_output=True, shell=False)
    return result.returncode == 0

def git_fix_workspace(dir, name):
    run(f'"{git}" -C "{dir}" fetch --refetch --no-auto-gc', f"Fetching all contents for {name}", f"Couldn't fetch {name}", live=True)
    run(f'"{git}" -C "{dir}" gc --aggressive --prune=now', f"Pruning {name}", f"Couldn't prune {name}", live=True)
    return

def run_git(dir, name, command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live, autofix=True):
    try:
        return run(f'"{git}" -C "{dir}" {command}', desc=desc, errdesc=errdesc, custom_env=custom_env, live=live)
    except RuntimeError:
        if not auto

[Assistant]
fix:
            raise

    print(f"{errdesc}, attempting autofix...")
    git_fix_workspace(dir, name)

    return run(f'"{git}" -C "{dir}" {command}', desc=desc, errdesc=errdesc, custom_env=custom_env, live=live)


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

[User]
#
# The Python Imaging Library.
# $Id$
#
# standard image operations
#
# History:
# 2001-10-20 fl   Created
# 2001-10-23 fl   Added autocontrast operator
# 2001-12-18 fl   Added Kevin's fit operator
# 2004-03-14 fl   Fixed potential division by zero in equalize
# 2005-05-05 fl   Fixed equalize for low number of values
#
# Copyright (c) 2001-2004 by Secret Labs AB
# Copyright (c) 2001-2004 by Fredrik Lundh
#
# See the README file for information on usage and redistribution.
#

import functools
import operator
import re

from . import ExifTags, Image, ImagePalette

#
# helpers

def _border(border):
    if isinstance(border, tuple):
        if len(border) == 2:
            left, top = right, bottom = border
        elif len(border) == 4:
            left, top, right, bottom = border
    else:
        left = top = right = bottom = border
    return left, top, right, bottom

def _color(color, mode):
    if isinstance(color, str):
        from . import ImageColor

        color = ImageColor.getcolor(color, mode)
    return color

def _lut(image, lut):
    if image.mode == "P":
        # FIXME: apply to lookup table, not image data
        msg = "mode P support coming soon"
        raise NotImplementedError(msg)
    elif image.mode in ("L", "RGB"):
        if image.mode == "RGB" and len(lut) == 256:
            lut = lut + lut + lut
        return image.point(lut)
    else:
        msg = f"not supported for mode {image.mode}"
        raise OSError(msg)

#
# actions

def autocontrast(image, cutoff=0, ignore=None, mask=None, preserve_tone=False):
    """
    Maximize (normalize) image contrast. This function calculates a
    histogram of the input image (or mask region), removes ``cutoff`` percent of the
    lightest and darkest pixels from the histogram, and remaps the image
    so that the darkest pixel becomes black (0), and the lightest
    becomes white (255).

    :param image: The image to process.
    :param cutoff: The percent to cut off from the histogram on the low and
                   high ends. Either a tuple of (low, high), or a single
                   number for both.
    :param ignore: The background pixel value (use None for no background).
    :param mask: Histogram used in contrast operation is computed using pixels
                 within the mask. If no mask is given the entire image is used
                 for histogram computation.
    :param preserve_tone: Preserve image tone in Photoshop-like style autocontrast.

                          .. versionadded:: 8.2.0

    :return: An image.
    """
    if preserve_tone:
        histogram = image.convert("L").histogram(mask)
    else:
        histogram = image.histogram(mask)

    lut = []
    for layer in range(0, len(histogram), 256):
        h = histogram[layer : layer + 256]
        if ignore is not None:
            # get rid of outliers
            try:
                h[ignore] = 0
            except TypeError:
                # assume sequence
                for ix in ignore:
                    h[ix] = 0
        if cutoff:
            # cut off pixels from both ends of the histogram
            if not isinstance(cutoff, tuple):
                cutoff = (cutoff, cutoff)
            # get number of pixels
            n = 0
            for ix in range(256):
                n = n + h[ix]
            # remove cutoff% pixels from the low end
            cut = n * cutoff[0] // 100
            for lo in range(256):
                if cut  h[lo]:
                    cut = cut - h[lo]
                    h[lo] = 0
                else:
                    h[lo] -= cut
                    cut = 0
                if cut = 0:

[Assistant]
                 break
            # remove cutoff% samples from the high end
            cut = n * cutoff[1] // 100
            for hi in range(255, -1, -1):
                if cut  h[hi]:
                    cut = cut - h[hi]
                    h[hi] = 0
                else:
                    h[hi] -= cut
                    cut = 0
                if cut = 0:
                    break
        # find lowest/highest samples after preprocessing
        for lo in range(256):
            if h[lo]:
                break
        for hi in range(255, -1, -1):
            if h[hi]:
                break
        if hi = lo:
            # don't bother
            lut.extend(list(range(256)))
        else:
            scale = 255.0 / (hi - lo)
            offset = -lo * scale
            for ix in range(256):
                ix = int(ix * scale + offset)
                if ix  0:
                    ix = 0
                elif ix  255:
                    ix = 255
                lut.append(ix)
    return _lut(image, lut)


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

[User]
_increasing:
            if isna(values).any():
                raise ValueError(f"Merge keys contain null values on {side} side")
            raise ValueError(f"{side} keys must be sorted")

        if isinstance(values, ArrowExtensionArray):
            values = values._maybe_convert_datelike_array()

        if needs_i8_conversion(values.dtype):
            values = values.view("i8")

        elif isinstance(values, BaseMaskedArray):
            # we've verified above that no nulls exist
            values = values._data
        elif isinstance(values, ExtensionArray):
            values = values.to_numpy()

        # error: Incompatible return value type (got "Union[ExtensionArray,
        # Any, ndarray[Any, Any], ndarray[Any, dtype[Any]], Index, Series]",
        # expected "ndarray[Any, Any]")
        return values  # type: ignore[return-value]

    def _get_join_indexers(self) - tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
        """return the join indexers"""

        # values to compare
        left_values = (
            self.left.index._values if self.left_index else self.left_join_keys[-1]
        )
        right_values = (
            self.right.index._values if self.right_index else self.right_join_keys[-1]
        )

        # _maybe_require_matching_dtypes already checked for dtype matching
        assert left_values.dtype == right_values.dtype

        tolerance = self.tolerance
        if tolerance is not None:
            # TODO: can we reuse a tolerance-conversion function from
            #  e.g. TimedeltaIndex?
            if needs_i8_conversion(left_values.dtype):
                tolerance = Timedelta(tolerance)
                # TODO: we have no test cases with PeriodDtype here; probably
                #  need to adjust tolerance for that case.
                if left_values.dtype.kind in "mM":
                    # Make sure the i8 representation for tolerance
                    #  matches that for left_values/right_values.
                    lvs = ensure_wrapped_if_datetimelike(left_values)
                    tolerance = tolerance.as_unit(lvs.unit)

                tolerance = tolerance._value

        # initial type conversion as needed
        left_values = self._convert_values_for_libjoin(left_values, "left")
        right_values = self._convert_values_for_libjoin(right_values, "right")

        # a "by" parameter requires special handling
        if self.left_by is not None:
            # remove 'on' parameter from values if one existed
            if self.left_index and self.right_index:
                left_join_keys = self.left_join_keys
                right_join_keys = self.right_join_keys
            else:
                left_join_keys = self.left_join_keys[0:-1]
                right_join_keys = self.right_join_keys[0:-1]

            mapped = [
                _factorize_keys(
                    left_join_keys[n],
                    right_join_keys[n],
                    sort=False,
                    how="left",
                )
                for n in range(len(left_join_keys))
            ]

            if len(left_join_keys) == 1:
                left_by_values = mapped[0][0]
                right_by_values = mapped[0][1]
            else:
                arrs = [np.concatenate(m[:2]) for m in mapped]
                shape = tuple(m[2] for m in mapped)
                group_index = get_group_index(
                    arrs, shape=shape, sort=False, xnull=False
                )
                left_len = len(left_join_keys[0])
                left_by_values = group_index[:left_len]
                right_by_values = group_index[left_len:]

            left_by_values = ensure_int64(left_by_values)
            right_by_values = ensure_int64(right_by_values)

            # choose appropriate function by type
            func = _asof_by_function(self.direction)
            return func(
                left_values,
                right_values,
                left_by_values,
                right_by_values,
                self.allow_exact_matches,
                tolerance,
            )
        else:
            # choose appropriate function by type
            func = _asof_by_function(self.direction)
            return func(
                left_values,
                right_values,
                None,
                None,
                self.allow_exact_matches,
                tolerance,
                False,
            )

def _get_multiindex_indexer(
    join_keys: list[ArrayLike], index: MultiIndex, sort: bool
) - tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
    # left & right join labels and num. of levels at each location
    mapped = (
        _factorize_keys(index.levels[n]._values, join_keys[n], sort=sort)
        for n in range(index.nlevels)
    )
    zipped = zip(*mapped)
    rcodes, lcodes, shape = (list(x) for x in zipped)
    if sort:
        rcodes = list(map(np.take, rcodes, index.codes))
    else:
        i8copy = lambda a: a.astype("i8", subok=False, copy=True)
        rcodes = list(map(i8copy, index.codes))

    # fix right labels if there were any nulls
    for i, join_key in enumerate(join_keys):
        mask = index.codes[i] == -1
        if mask.any():
            # check if there already was any nulls at this location
            # if there was, it is factorized to `shape[i] - 1`
            a = join_key[lcodes[i] == shape[i] - 1]
            if a.size == 0 or not a[0] != a[0]:
                shape[i] += 1

            rcodes[i][mask] = shape[i] - 1

    # get flat i8 join keys
    lkey, rkey = _get_join_keys(lcodes, rcodes, tuple(shape), sort)
    return lkey, rkey

def _get_empty_indexer() - tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
    """Return empty join indexers."""
    return (
        np.array([], dtype=np.intp),
        np.array([], dtype=np.intp),
    )

def _get_no_sort_one_missing_indexer(
    n: int, left_missing: bool
) - tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
    """
    Return join indexers where all of one side is selected without sorting
    and none of the other side is selected.

    Parameters
    ----------
    n : int
        Length of indexers to create.
    left_missing : bool
        If True, the left indexer will contain only -1's.
        If False, the right indexer will contain only -1's.

    Returns
    -------
    np.ndarray[np.intp]
        Left indexer
    np.ndarray[np.intp]
        Right indexer
    """
    idx = np.arange(n, dtype=np.intp)
    idx_missing = np.full(shape=n, fill_value=-1, dtype=np.intp)
    if left_missing:
        return idx_missing, idx
    return idx, idx_missing

def _left_join_on_index(
    left_ax: Index, right_ax: Index, join_keys: list[ArrayLike], sort: bool = False
) - tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp]]:
    if isinstance(right_ax, MultiIndex):
        lkey, rkey = _get_multiindex_indexer(join_keys, right_ax, sort=sort)
    else:
        # error: Incompatible types in assignment (expression has type
        # "Union[Union[ExtensionArray, ndarray[Any, Any]], Index, Series]",
        # variable has type "ndarray[Any, dtype[signedinteger[Any]]]")
        lkey = join_keys[0]  # type: ignore[assignment]
        # error: Incompatible types in assignment (expression has type "Index",
        # variable has type "ndarray[Any, dtype[signedinteger[Any]]]")
        rkey = right_ax._values  # type: ignore[assignment]

    left_key, right_key, count = _factorize_keys(lkey, rkey, sort=sort)
    left_indexer, right_indexer = libjoin.left_outer_join(

[Assistant]
      left_key, right_key, count, sort=sort
    )

    if sort or len(left_ax) != len(left_indexer):
        # if asked to sort or there are 1-to-many matches
        join_index = left_ax.take(left_indexer)
        return join_index, left_indexer, right_indexer

    # left frame preserves order & length of its index
    return left_ax, None, right_indexer


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

[User]
#!/usr/bin/env python3
"""
A script to create C code-coverage reports based on the output of
valgrind's callgrind tool.

"""
import os
import re
import sys
from xml.sax.saxutils import quoteattr, escape

try:
    import pygments
    if tuple([int(x) for x in pygments.__version__.split('.')])  (0, 11):
        raise ImportError()
    from pygments import highlight
    from pygments.lexers import CLexer
    from pygments.formatters import HtmlFormatter
    has_pygments = True
except ImportError:
    print("This script requires pygments 0.11 or greater to generate HTML")
    has_pygments = False

class FunctionHtmlFormatter(HtmlFormatter):
    """Custom HTML formatter to insert extra information with the lines."""
    def __init__(self, lines, **kwargs):
        HtmlFormatter.__init__(self, **kwargs)
        self.lines = lines

    def wrap(self, source, outfile):
        for i, (c, t) in enumerate(HtmlFormatter.wrap(self, source, outfile)):
            as_functions = self.lines.get(i-1, None)
            if as_functions is not None:
                yield 0, ('div title=%s style="background: #ccffcc"[%2d]' %
                          (quoteattr('as ' + ', '.join(as_functions)),
                           len(as_functions)))
            else:
                yield 0, '    '
            yield c, t
            if as_functions is not None:
                yield 0, '/div'

class SourceFile:
    def __init__(self, path):
        self.path = path
        self.lines = {}

    def mark_line(self, lineno, as_func=None):
        line = self.lines.setdefault(lineno, set())
        if as_func is not None:
            as_func = as_func.split("'", 1)[0]
            line.add(as_func)

    def write_text(self, fd):
        source = open(self.path, "r")
        for i, line in enumerate(source):
            if i + 1 in self.lines:
                fd.write(" ")
            else:
                fd.write("! ")
            fd.write(line)
        source.close()

    def write_html(self, fd):
        source = open(self.path, 'r')
        code = source.read()
        lexer = CLexer()
        formatter = FunctionHtmlFormatter(
            self.lines,
            full=True,
            linenos='inline')
        fd.write(highlight(code, lexer, formatter))
        source.close()

class SourceFiles:
    def __init__(self):
        self.files = {}
        self.prefix = None

    def get_file(self, path):
        if path not in self.files:
            self.files[path] = SourceFile(path)
            if self.prefix is None:
                self.prefix = path
            else:
                self.prefix = os.path.commonprefix([self.prefix, path])
        return self.files[path]

    def clean_path(self, path):
        path = path[len(self.prefix):]
        return re.sub(r"[^A-Za-z0-9\.]", '_', path)

    def write_text(self, root):
        for path, source in self.files.items():
            fd = open(os.path.join(root, self.clean_path(path)), "w")
            source.write_text(fd)
            fd.close()

    def write_html(self, root):
        for path, source in self.files.items():
            fd = open(os.path.join(root, self.clean_path(path) + ".html"), "w")
            source.write_html(fd)
            fd.close()

        fd = open(os.path.join(root, 'index.html'), 'w')
        fd.write("html")
        paths = sorted(self.files.keys())
        for path in paths:
            fd.write('pa href="%s.html"%s/a/p' %
                     (self.clean_path(path), escape(path[len(self.prefix):])))
        fd.write("/html")
        fd.close()

def collect_stats(files, fd, pattern):
    # TODO: Handle compressed callgrind files
    line_regexs = [
        re.compile(r"(?Plineno[0-9]+)(\s[0-9]+)+"),
        re.compile(r"((jump)|(jcnd))=([0-9]+)\s(?Plineno[0-9]+)")
        ]

    current_file = None
    current_function = N

[Assistant] one


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

[User]
x_t = (
                    expand_dims(sigma_t / sigma_s, dims) * x
                    - expand_dims(alpha_t * phi_1, dims) * model_s
            )
            if return_intermediate:
                return x_t, {'model_s': model_s}
            else:
                return x_t
        else:
            phi_1 = torch.expm1(h)
            if model_s is None:
                model_s = self.model_fn(x, s)
            x_t = (
                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
                    - expand_dims(sigma_t * phi_1, dims) * model_s
            )
            if return_intermediate:
                return x_t, {'model_s': model_s}
            else:
                return x_t

    def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False,
                                            solver_type='dpm_solver'):
        """
        Singlestep solver DPM-Solver-2 from time `s` to time `t`.
        Args:
            x: A pytorch tensor. The initial value at time `s`.
            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
            r1: A `float`. The hyperparameter of the second-order solver.
            model_s: A pytorch tensor. The model function evaluated at time `s`.
                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
            return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
        Returns:
            x_t: A pytorch tensor. The approximated solution at time `t`.
        """
        if solver_type not in ['dpm_solver', 'taylor']:
            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
        if r1 is None:
            r1 = 0.5
        ns = self.noise_schedule
        dims = x.dim()
        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
        h = lambda_t - lambda_s
        lambda_s1 = lambda_s + r1 * h
        s1 = ns.inverse_lambda(lambda_s1)
        log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(
            s1), ns.marginal_log_mean_coeff(t)
        sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
        alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)

        if self.predict_x0:
            phi_11 = torch.expm1(-r1 * h)
            phi_1 = torch.expm1(-h)

            if model_s is None:
                model_s = self.model_fn(x, s)
            x_s1 = (
                    expand_dims(sigma_s1 / sigma_s, dims) * x
                    - expand_dims(alpha_s1 * phi_11, dims) * model_s
            )
            model_s1 = self.model_fn(x_s1, s1)
            if solver_type == 'dpm_solver':
                x_t = (
                        expand_dims(sigma_t / sigma_s, dims) * x
                        - expand_dims(alpha_t * phi_1, dims) * model_s
                        - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
                )
            elif solver_type == 'taylor':
                x_t = (
                        expand_dims(sigma_t / sigma_s, dims) * x
                        - expand_dims(alpha_t * phi_1, dims) * model_s
                        + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (
                                    model_s1 - model_s)
                )
        else:
            phi_11 = torch.expm1(r1 * h)
            phi_1 = torch.expm1(h)

            if model_s is None:
                model_s = self.model_fn(x, s)
            x_s1 = (
                    expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
                    - expand_dims(sigma_s1 * phi_11, dims) * model_s
            )
            model_s1 = self.model_fn(x_s1, s1)
            if solver_type == 'dpm_solver':
                x_t = (
                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
                        - expand_dims(sigma_t * phi_1, dims) * model_s
                        - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
                )
            elif solver_type == 'taylor':
                x_t = (
                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
                        - expand_dims(sigma_t * phi_1, dims) * model_s
                        - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
                )
        if return_intermediate:
            return x_t, {'model_s': model_s, 'model_s1': model_s1}
        else:
            return x_t

    def singlestep_dpm_solver_third_update(self, x, s, t, r1=1. / 3., r2=2. / 3., model_s=None, model_s1=None,
                                           return_intermediate=False, solver_type='dpm_solver'):
        """
        Singlestep solver DPM-Solver-3 from time `s` to time `t`.
        Args:
            x: A pytorch tensor. The initial value at time `s`.
            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
            r1: A `float`. The hyperparameter of the third-order solver.
            r2: A `float`. The hyperparameter of the third-order solver.
            model_s: A pytorch tensor. The model function evaluated at time `s`.
                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
            model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
                If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
        Returns:
            x_t: A pytorch tensor. The approximated solution at time `t`.
        """
        if solver_type not in ['dpm_solver', 'taylor']:
            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
        if r1 is None:
            r1 = 1. / 3.
        if r2 is None:
            r2 = 2. / 3.
        ns = self.noise_schedule
        dims = x.dim()
        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
        h = lambda_t - lambda_s
        lambda_s1 = lambda_s + r1 * h
        lambda_s2 = lambda_s + r2 * h
        s1 = ns.inverse_lambda(lambda_s1)
        s2 = ns.inverse_lambda(lambda_s2)
        log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(
            s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
        sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(
            s2), ns.marginal_std(t)
        alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)

        if self.predict_x0:
            phi_11 = torch.expm1(-r1 * h)
            phi_12 = torch.expm1(-r2 * h)
            phi_1 = torch.expm1(-h)
            phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
            phi_2 = phi_1 / h + 1.
            phi_3 = phi_2 / h - 0.5

            if model_s is None:
                model_s = self.mode

[Assistant]
l_fn(x, s)
            if model_s1 is None:
                x_s1 = (
                        expand_dims(sigma_s1 / sigma_s, dims) * x
                        - expand_dims(alpha_s1 * phi_11, dims) * model_s
                )
                model_s1 = self.model_fn(x_s1, s1)
            x_s2 = (
                    expand_dims(sigma_s2 / sigma_s, dims) * x
                    - expand_dims(alpha_s2 * phi_12, dims) * model_s
                    + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
            )
            model_s2 = self.model_fn(x_s2, s2)
            if solver_type == 'dpm_solver':
                x_t = (
                        expand_dims(sigma_t / sigma_s, dims) * x
                        - expand_dims(alpha_t * phi_1, dims) * model_s
                        + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
                )
            elif solver_type == 'taylor':
                D1_0 = (1. / r1) * (model_s1 - model_s)
                D1_1 = (1. / r2) * (model_s2 - model_s)
                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
                x_t = (
                        expand_dims(sigma_t / sigma_s, dims) * x
                        - expand_dims(alpha_t * phi_1, dims) * model_s
                        + expand_dims(alpha_t * phi_2, dims) * D1
                        - expand_dims(alpha_t * phi_3, dims) * D2
                )


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

[User]
temperature=0.12
                )

             # complete the prompt
             result = client.complete(request, model=model_name)
        """
        response = self._post_request("complete", request, model)
        return CompletionResponse.from_json(response)

    def tokenize(
        self,
        request: TokenizationRequest,
        model: str,
    ) - TokenizationResponse:
        """Tokenizes the given prompt for the given model.

        Parameters:
            request (TokenizationRequest, required):
                Parameters for the requested tokenization.

            model (string, required):
                Name of model to use. A model name refers to a model architecture (number of parameters among others).
                Always the latest version of model is used.

        Examples:
             request = TokenizationRequest(
                    prompt="hello", token_ids=True, tokens=True
                )
             response = client.tokenize(request, model=model_name)
        """
        response = self._post_request(
            "tokenize",
            request,
            model,
        )
        return TokenizationResponse.from_json(response)

    def detokenize(
        self,
        request: DetokenizationRequest,
        model: str,
    ) - DetokenizationResponse:
        """Detokenizes the given prompt for the given model.

        Parameters:
            request (DetokenizationRequest, required):
                Parameters for the requested detokenization.

            model (string, required):
                Name of model to use. A model name refers to a model architecture (number of parameters among others).
                Always the latest version of model is used.

        Examples:
             request = DetokenizationRequest(token_ids=[2, 3, 4])
             response = client.detokenize(request, model=model_name)
        """
        response = sel

[Assistant]
f._post_request(
            "detokenize",
            request,
            model,
        )
        return DetokenizationResponse.from_json(response)


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

[User]
elf.all_negative_prompts]

        self.main_prompt = self.all_prompts[0]
        self.main_negative_prompt = self.all_negative_prompts[0]

    def cached_params(self, required_prompts, steps, extra_network_data, hires_steps=None, use_old_scheduling=False):
        """Returns parameters that invalidate the cond cache if changed"""

        return (
            required_prompts,
            steps,
            hires_steps,
            use_old_scheduling,
            opts.CLIP_stop_at_last_layers,
            shared.sd_model.sd_checkpoint_info,
            extra_network_data,
            opts.sdxl_crop_left,
            opts.sdxl_crop_top,
            self.width,
            self.height,
        )

    def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data, hires_steps=None):
        """
        Returns the result of calling function(shared.sd_model, required_prompts, steps)
        using a cache to store the result if the same arguments have been used before.

        cache is an array containing two elements. The first element is a tuple
        representing the previously used arguments, or None if no arguments
        have been used before. The second element is where the previously
        computed result is stored.

        caches is a list with items described above.
        """

        if shared.opts.use_old_scheduling:
            old_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(required_prompts, steps, hires_steps, False)
            new_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(required_prompts, steps, hires_steps, True)
            if old_schedules != new_schedules:
                self.extra_generation_params["Old prompt editing timelines"] = True

        cached_params = self.cached_params(required_prompts, steps, extra_network_data, hires_steps, shared.opts.use_old_scheduling)

        for cache in caches:
            if cache[0] is not None and cached_params == cache[0]:
                return cache[1]

        cache = caches[0]

        with devices.autocast():
            cache[1] = function(shared.sd_model, required_prompts, steps, hires_steps, shared.opts.use_old_scheduling)

        cache[0] = cached_params
        return cache[1]

    def setup_conds(self):
        prompts = prompt_parser.SdConditioning(self.prompts, width=self.width, height=self.height)
        negative_prompts = prompt_parser.SdConditioning(self.negative_prompts, width=self.width, height=self.height, is_negative_prompt=True)

        sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
        total_steps = sampler_config.total_steps(self.steps) if sampler_config else self.steps
        self.step_multiplier = total_steps // self.steps
        self.firstpass_steps = total_steps

        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, total_steps, [self.cached_uc], self.extra_network_data)
        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, total_steps, [self.cached_c], self.extra_network_data)

    def get_conds(self):
        return self.c, self.uc

    def parse_extra_network_prompts(self):
        self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)

    def save_samples(self) - bool:
        """Returns whether generated images need to be written to disk"""
        return opts.samples_save and not self.do_not_save_samples and (opts.save_incomplete_images or not state.interrupted and not state.skipped)

class Processed:
    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
        self.images = images_list
        self.prompt = p.prompt
        self.negative_prompt = p.negative_prompt
        self.seed = seed
        self.subseed = subseed
        self.subseed_strength = p.subseed_strength
        self.info = info
        self.comments = "".join(f"{comment}\n" for comment in p.comments)
        self.width = p.width
        self.height = p.height
        self.sampler_name = p.sampler_name
        self.cfg_scale = p.cfg_scale
        self.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
        self.steps = p.steps
        self.batch_size = p.batch_size
        self.restore_faces = p.restore_faces
        self.face_restoration_model = opts.face_restoration_model if p.restore_faces else None
        self.sd_model_name = p.sd_model_name
        self.sd_model_hash = p.sd_model_hash
        self.sd_vae_name = p.sd_vae_name
        self.sd_vae_hash = p.sd_vae_hash
        self.seed_resize_from_w = p.seed_resize_from_w
        self.seed_resize_from_h = p.seed_resize_from_h
        self.den

[Assistant]
oising_strength = getattr(p, 'denoising_strength', None)
        self.extra_generation_params = p.extra_generation_params
        self.index_of_first_image = index_of_first_image
        self.styles = p.styles
        self.job_timestamp = state.job_timestamp
        self.clip_skip = opts.CLIP_stop_at_last_layers
        self.token_merging_ratio = p.token_merging_ratio
        self.token_merging_ratio_hr = p.token_merging_ratio_hr

        self.eta = p.eta
        self.ddim_discretize = p.ddim_discretize
        self.s_churn = p.s_churn
        self.s_tmin = p.s_tmin
        self.s_tmax = p.s_tmax
        self.s_noise = p.s_noise
        self.s_min_uncond = p.s_min_uncond
        self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
        self.prompt = self.prompt if not isinstance(self.prompt, list) else self.prompt[0]
        self.negative_prompt = self.negative_prompt if not isinstance(self.negative_prompt, list) else self.negative_prompt[0]
        self.seed = int(self.seed if not isinstance(self.seed, list) else self.seed[0]) if self.seed is not None else -1
        self.subseed = int(self.subseed if not isinstance(self.subseed, list) else self.subseed[0]) if self.subseed is not None else -1
        self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning

        self.all_prompts = all_prompts or p.all_prompts or [self.prompt]
        self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]
        self.all_seeds = all_seeds or p.all_seeds or [self.seed]
        self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
        self.infotexts = infotexts or [info]


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

[User]
# Authors: Lars Buitinck
#          Dan Blanchard dblanchard@ets.org
# License: BSD 3 clause

from array import array
from collections.abc import Iterable, Mapping
from numbers import Number
from operator import itemgetter

import numpy as np
import scipy.sparse as sp

from ..base import BaseEstimator, TransformerMixin, _fit_context
from ..utils import check_array
from ..utils.validation import check_is_fitted

class DictVectorizer(TransformerMixin, BaseEstimator):
    """Transforms lists of feature-value mappings to vectors.

    This transformer turns lists of mappings (dict-like objects) of feature
    names to feature values into Numpy arrays or scipy.sparse matrices for use
    with scikit-learn estimators.

    When feature values are strings, this transformer will do a binary one-hot
    (aka one-of-K) coding: one boolean-valued feature is constructed for each
    of the possible string values that the feature can take on. For instance,
    a feature "f" that can take on the values "ham" and "spam" will become two
    features in the output, one signifying "f=ham", the other "f=spam".

    If a feature value is a sequence or set of

[Assistant]
strings, this transformer
    will iterate over the values and will count the occurrences of each string
    value.


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

[User]
ta_put_form or raw_data_patch_form

        response_headers = dict(sorted(response.items()))
        renderer_content_type = ''
        if renderer:
            renderer_content_type = '%s' % renderer.media_type
            if renderer.charset:
                renderer_content_type += ' ;%s' % renderer.charset
        response_headers['Content-Type'] = renderer_content_type

        if getattr(view, 'paginator', None) and view.paginator.display_page_controls:
            paginator = view.paginator
        else:
            paginator = None

        csrf_cookie_name = settings.CSRF_COOKIE_NAME
        csrf_header_name = settings.CSRF_HEADER_NAME
        if csrf_header_name.startswith('HTTP_'):
            csrf_header_name = csrf_header_name[5:]
        csrf_header_name = csrf_header_name.replace('_', '-')

        return {
            'content': self.get_content(renderer, data, accepted_media_type, renderer_context),
            'code_style': pygments_css(self.code_style),
            'view': view,
            'request': request,
            'response': response,
            'user': request.user,
            'description': self.get_description(view, response.status_code),
            'name': self.get_name(view),
            'version': VERSION,
            'paginator': paginator,
            'breadcrumblist': self.get_breadcrumbs(request),
            'allowed_methods': view.allowed_methods,
            'available_formats': [renderer_cls.format for renderer_cls in view.renderer_classes],
            'response_headers': response_headers,

            'put_form': self.get_rendered_html_form(data, view, 'PUT', request),
            'post_form': self.get_rendered_html_form(data, view, 'POST', request),
            'delete_form': self.get_rendered_html_form(data, view, 'DELETE', request),
            'options_form': self.get_rendered_html_form(data, view, 'OPTIONS', request),

            'extra_actions': self.get_extra_actions(view, response.status_code),

            'filter_form': self.get_filter_form(data, view, request),

            'raw_data_put_form': raw_data_put_form,
            'raw_data_post_form': raw_data_post_form,
            'raw_data_patch_form': raw_data_patch_form,
            'raw_data_put_or_patch_form': raw_data_put_or_patch_form,

            'display_edit_forms': bool(response.status_code != 403),

            'api_settings': api_settings,
            'csrf_cookie_name': csrf_cookie_name,
            'csrf_header_name': csrf_header_name
        }

    def render(self, data, accepted_media_type=None, renderer_context=None):
        """
        Render the HTML for the browsable API representation.
        """
        self.accepted_media_type = accepted_media_type or ''
        self.renderer_context = renderer_context or {}

        template = loader.get_template(self.template)
        context = self.get_context(data, accepted_media_type, renderer_context)
        ret = template.render(context, request=renderer_context['request'])

        # Munge DELETE Response code to allow us to return content
        # (Do this *after* we've rendered the template so that we include
        # the normal deletion response code in the output)
        response = renderer_context['response']
        if response.status_code == status.HTTP_204_NO_CONTENT:
            response.status_code = status.HTTP_200_OK

        return ret

class AdminRenderer(BrowsableAPIRenderer):
    template = 'rest_framework/admin.html'
    format = 'admin'

    def render(self, data, accepted_media_type=None, renderer_context=None):
        s

[Assistant]
elf.accepted_media_type = accepted_media_type or ''
        self.renderer_context = renderer_context or {}


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

[User]
import platform
import unittest

import numpy as np
import pandas as pd
import sympy

from .. import PySRRegressor, sympy2torch

# Need to initialize Julia before importing torch...

def _import_torch():
    if platform.system() == "Darwin":
        # Import PyJulia, then Torch
        from ..julia_helpers import init_julia

        init_julia()

        import torch
    else:
        # Import Torch, then PyJulia
        # https://github.com/pytorch/pytorch/issues/78829
        import torch
    return torch

class TestTorch(unittest.TestCase):
    def setUp(self):
        np.random.seed(0)

    def test_sympy2torch(self):
        torch = _import_torch()
        x, y, z = sympy.symbols("x y z")
        cosx = 1.0 * sympy.cos(x) + y

        X = torch.tensor(np.random.randn(1000, 3))
        true = 1.0 * torch.cos(X[:, 0]) + X[:, 1]
        torch_module = sympy2torch(cosx, [x, y, z])
        self.assertTrue(
            np.all(np.isclose(torch_module(X).detach().numpy(), true.detach().numpy()))
        )

    def test_pipeline_pandas(self):
        torch = _import_torch()
        X = pd.DataFrame(np.random.randn(100, 10))
        y = np.ones(X.shape[0])
        model = PySRRegressor(
            progress=False,
            max_evals=10000,
            model_selection="accuracy",
            extra_sympy_mappings={},
            output_torch_format=True,
        )
        model.fit(X, y)

        equations = pd.DataFrame(
            {
                "Equation": ["1.0", "cos(x1)", "square(cos(x1))"],
                "Loss": [1.0, 0.1, 1e-5],
                "Complexity": [1, 2, 3],
            }
        )

        equations["Complexity Loss Equation".split(" ")].to_csv(
            "equation_file.csv.bkup"
        )

        m

[Assistant]
odel.refresh(checkpoint_file="equation_file.csv")
        tformat = model.pytorch()
        self.assertEqual(str(tformat), "_SingleSymPyModule(expression=cos(x1)**2)")

        np.testing.assert_almost_equal(
            tformat(torch.tensor(X.values)).detach().numpy(),
            np.square(np.cos(X.values[:, 1])),  # Selection 1st feature
            decimal=3,
        )


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

[User]
# Copyright (c) OpenMMLab. All rights reserved.
import numbers
from math import cos, pi

import annotator.

[Assistant] uniformer.mmcv as mmcv


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

[User]
import numpy as np
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier
from ipywidgets import interact

def visualize_tree(estimator, X, y, boundaries=True,
                   xlim=None, ylim=None, ax=None):
    ax = ax or plt.gca()

    # Plot the training points
    ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap='viridis',
               clim=(y.min(), y.max()), zorder=3)
    ax.axis('tight')
    ax.axis('off')
    if xlim is None:
        xlim = ax.get_xlim()
    if ylim is None:
        ylim = ax.get_ylim()

    # fit the estimator
    estimator.fit(X, y)
    xx, yy = np.meshgrid(np.linspace(*xlim, num=200),
                         np.linspace(*ylim, num=200))
    Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    n_classes = len(np.unique(y))
    Z = Z.reshape(xx.shape)
    contours = ax.contourf(xx, yy, Z, alpha=0.3,
                           levels=np.arange(n_classes + 1) - 0.5,
                           cmap='viridis', clim=(y.min(), y.max()),
                           zorder=1)

    ax.set(xlim=xlim, ylim=ylim)

    # Plot the decision boundaries
    def plot_boundaries(i, xlim, ylim):
        if i = 0:
            tree = estimator.tree_

            if tree.feature[i] == 0:
                ax.plot([tree.threshold[i], tree.threshold[i]], ylim, '-k', zorder=2)
                plot_boundaries(tree.children_left[i],
                                [xlim[0], tree.threshold[i]], ylim)
                plot_boundaries(tree.children_right[i],
                                [tree.threshold[i], xlim[1]], ylim)

            elif tree.feature[i] == 1:
                ax.plot(xlim, [tree.threshold[i], tree.threshold[i]], '-k', zorder=2)
                plot_boundaries(tree.children_left[i], xlim,
                                [ylim[0], tree.threshold[i]])
                plot_boundaries(tree.children_right[i], xlim,
                                [tree.threshold[i], ylim[1]])

    if boundaries:
        plot_boundaries(0, xlim, ylim)

def plot_tree_interactive(X, y):
    def interactive_tree(depth=5):
        clf = DecisionTreeClassifier(max_depth=depth, random_state=0)
        visualize_tree(clf, X, y)

    return interact(interactive_tree, depth=[1, 5])

def randomized_tree_interactive(X, y):
    N = int(0.75 * X.shape[0])

    xlim = (X[:, 0].min(), X[:, 0].max())
    ylim = (X[:, 1].min(), X[:, 1].max())

    def fit_randomized_tree(random_state=0):
        clf = DecisionTreeClassifier(max_depth=15)

[Assistant]
 i = np.arange(len(y))
        rng = np.random.RandomState(random_state)
        rng.shuffle(i)
        visualize_tree(clf, X[i[:N]], y[i[:N]], boundaries=False,
                       xlim=xlim, ylim=ylim)

    interact(fit_randomized_tree, random_state=[0, 100]);


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

[User]
     [0, 1, 2, 3, 4, 5, 6],
                    ]

    @classmethod
    def Colors(cls):
        """Returns the list of colors.
        """
        return cls.colors

    @classmethod
    def ColorGenerator(cls, n):
        """Returns an iterator of color strings.

        n: how many colors will be used
        """
        for i in cls.which_colors[n]:
            yield cls.colors[i]
        raise StopIteration('Ran out of colors in _Brewer.ColorGenerator')

    @classmethod
    def InitializeIter(cls, num):
        """Initializes the color iterator with the given number of colors."""
        cls.color_iter = cls.ColorGenerator(num)

    @classmethod
    def ClearIter(cls):
        """Sets the color iterator to None."""
        cls.color_iter = None

    @classmethod
    def GetIter(cls):
        """Gets the color iterator."""
        if cls.color_iter is None:
            cls.InitializeIter(7)

        return cls.color_iter

def PrePlot(num=None, rows=None, cols=None):
    """Takes hints about what's coming.

    num: number of lines that will be plotted
    rows: number of rows of subplots
    cols: number of columns of subplots
    """
    if num:
        _Brewer.InitializeIter(num)

    if rows is None and cols is None:
        return

    if rows is not None and cols is None:
        cols = 1

    if cols is not None and rows is None:
        rows = 1

    # resize the image, depending on the number of rows and cols
    size_map = {(1, 1): (8, 6),
                (1, 2): (14, 6),
                (1, 3): (14, 6),
                (2, 2): (10, 10),
                (2, 3): (16, 10),
                (3, 1): (8, 10),
                }

    if (rows, cols) in size_map:
        fig = pyplot.gcf()
        fig.set_size_inches(*size_map[rows, cols])

    # create the first subplot
    if rows  1 or cols  1:
        pyplot.subplot(rows, cols, 1)
        global SUBPLOT_ROWS, SUBPLOT_COLS
        SUBPLOT_ROWS = rows
        SUBPLOT_COLS = cols

def SubPlot(plot_number, rows=None, cols=None):
    """Configures the number of subplots and changes the current plot.

    rows: int
    cols: int
    plot_number: int
    """
    rows = rows or SUBPLOT_ROWS
    cols = cols or SUBPLOT_COLS
    pyplot.subplot(rows, cols, plot_number)

def _Underride(d, **options):
    """Add key-value pairs to d only if key is not in d.

    If d is None, create a new dictionary.

    d: dictionary
    options: keyword args to add to d
    """
    if d is None:
        d = {}

    for key, val in options.items():
        d.setdefault(key, val)

    return d

def Clf():
    """Clears the figure and any hints that have been set."""
    global LOC
    LOC = None
    _Brewer.ClearIter()
    pyplot.clf()
    fig = pyplot.gcf()
    fig.set_size_inches(8, 6)

def Figure(**options):
    """Sets options for the current figure."""
    _Underride(options, figsize=(6, 8))
    pyplot.figure(**options)

def _UnderrideColor(options):
    if 'color' in options:
        return options

    color_iter = _Brewer.GetIter()

    if color_iter:
        try:
            options['color'] = next(color_iter)
        except StopIteration:
            # TODO: reconsider whether this should warn
            # warnings.warn('Warning: Brewer ran out of colors.')
            _Brewer.ClearIter()
    return options

def Plot(obj, ys=None, style='', **options):
    """Plots a line.

    Args:
      obj: sequence of x values, or Series, or anything with Render()
      ys: sequence of y values
      style: style string passed along to pyplot.plot
      options: keyword args passed to pyplot.plot
    """
    options = _UnderrideColor(options)
    label = getattr(obj, 'label', '_nolegend_')
    options = _Underride(options, linewidth=3, alpha=0.8, label=label)

    xs = obj
    if ys is None:
        if hasattr(obj, 'Render'):
            xs, ys = obj.Render()
        if isinstance(obj, pandas.Series):
            ys = obj.values
            xs = obj.index

    if ys is None:
        pyplot.plot(xs, style, **options)
    else:
        pyplot.plot(xs, ys, style, **options)

def FillBetween(xs, y1, y2=None, where=None, **options):
    """Plots a line.

    Args:
      xs: sequence of x values
      y1: sequence of y values
      y2: sequence of y values
      where: sequence of boolean
      options: keyword args passed to pyplot.fill_between
    """
    options = _UnderrideColor(options)
    options = _Underride(options, linewidth=0, alpha=0.5)
    pyplot.fill_between(xs, y1, y2, where, **options)

def Bar(xs, ys, **options):
    """Plots a line.

    Args:
      xs: sequence of x values
      ys: sequence of y values
      options: keyword args passed to pyplot.bar
    """
    options = _UnderrideColor(options)
    options = _Underride(options, linewidth=0, alpha=0.6)
    pyplot.bar(xs, ys, **options)

def Scatter(xs, ys=None, **options):
    """Makes a scatter plot.

    xs: x values
    ys: y values
    options: options passed to pyplot.scatter
    """
    options = _Underride(options, color='blue', alpha=0.2,
                        s=30, edgecolors='none')

    if ys is None and isinstance(xs, pandas.Series):
        ys = xs.values
        xs = xs.index

    pyplot.scatter(xs, ys, **options)

def HexBin(xs, ys, **options):
    """Makes a scatter plot.

    xs: x values
    ys: y values
    options: options passed to pyplot.scatter
    """
    options = _Underride(options, cmap=matplotlib.cm.Blues)
    pyplot.hexbin(xs, ys, **options)

def Pdf(pdf, **options):
    """Plots a Pdf, Pmf, or Hist as a line.

    Args:
      pdf: Pdf, Pmf, or Hist object
      options: keyword args passed to pyplot.plot
    """
    low, high = options.pop('low', None), options.pop('high', None)
    n = options.pop('n', 101)
    xs, ps = pdf.Render(low=low, high=high, n=n)
    options = _Underride(options, label=pdf.label)
    Plot(xs, ps, **options)

def Pdfs(pdfs, **options):
    """Plots a sequence of PDFs.

    Options are passed along for all PDFs.  If you want different
    options for each pdf, make multiple calls to Pdf.

    Args:
      pdfs: sequence of PDF objects
      options: keyword args passed to pyplot.plot
    """
    for pdf in pdfs:
        Pdf(pdf, **options)

def Hist(hist, **options):
    """Plots a Pmf or Hist with a bar plot.

    The default width of the bars is based on the minimum difference
    between values in the Hist.  If that's too small, you can override
    it by providing a width keyword argument, in the same units
    as the values.

    Args:
      hist: Hist or Pmf object
      options: keyword args passed to pyplot.bar
    """
    # find the minimum distance between adjacent values
    xs, ys = hist.Render()

    if 'width' not in options:
        try:
            options['width'] = 0.9 * np.diff(xs).min()
        except TypeError:
            warnings.warn("Hist: Can't compute bar width automatically."
                            "Check for non-numeric types in Hist."
                            "Or try providing width option."
                            )

    options = _Underride(options, label=hist.label)
    options = _Underride(options, align='center')
    if options['align'] == 'left':
        options['align'] = 'edge'
    elif options['align'] == 'right':
        options['align'] = 'edge'
        options['width'] *= -1

    Bar(xs, ys, **options)

def Hists(hists, **options):
    """Plots two histograms as interleaved bar plots.

    Options are passed along for all PMFs.  If you want different
    options for each pmf, make multiple calls to Pmf.

    Args:
      hists: list of two Hist or Pmf objects
      options: keyword args passed to pyplot.plot
    """
    for hist in hists:
        Hist(hist, **options)

def Pmf(pmf, **options):
    """Plots a Pmf or Hist as a line.

    Args:
      pmf: Hist or Pmf object
      options: keyword args passed to pyplot.plot
    """
    xs, ys = pmf.Render()
    low, high = min(xs), max(xs)

    width = options.pop('width', None)
    if width is None:
        try:
            width = np.diff(xs).min()
        except TypeError:
            warnings.warn("Pmf: Can't compute bar width automatically."
                          "Check for non-numeric types in Pmf."

[Assistant] "Or try providing width option.")