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

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

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

    seed = 1
    image_generator = image_datagen.flow_fr

[Assistant]
om_directory(
        'data/train/images',
        class_mode=None,
        batch_size=batch_size,
        color_mode='rgb',
        target_size=(512,512),
        #save_to_dir='./data/gen/images',
        seed=seed)


[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 csv
import pylab as pl

from sklearn import datasets
from sklearn.decompo

[Assistant] sition import PCA


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

[User]
from dataclasses import asdict, dataclass
from enum import Enum
from typing import (
    Any,
    Dict,
    List,
    Mapping,
    Optional,
    Sequence,
    Tuple,
)
from aleph_alpha_client.prompt import Prompt

@dataclass(frozen=True)
class EmbeddingRequest:
    """
    Embeds a text and returns vectors that can be used for downstream tasks (e.g. semantic similarity) and models (e.g. classifiers).

    Parameters:
        prompt
            The text and/or image(s) to be embedded.

        layers
            A list of layer indices from which to return embeddings.

            * Index 0 corresponds to the word embeddings used as input to the first transformer layer
            * Index 1 corresponds to the hidden state as output by the first transformer layer, index 2 to the output of the second layer etc.
            * Index -1 corresponds to the last transformer layer (not the language modelling head), index -2 to the second last layer etc.

        pooling
            Pooling operation to use.
            Pooling operations include:

            * mean: aggregate token embeddings across the sequence dimension using an average
            * max: aggregate token embeddings across the sequence dimension using a maximum
            * last_token: just use the last token
            * abs_max: aggregate token embeddings across the sequence dimension using a maximum of absolute values

        type
            Type of the embedding (e.g. symmetric or asymmetric)

        tokens
            Flag indicating whether the tokenized prompt is to be returned (True) or not (False)

        normalize
            Return normalized embeddings. This can be used to save on additional compute when applying a cosine similarity metric.

            Note that at the moment this parameter does not yet have any effect. This will change as soon as the
            corresponding feature is available in the backend

        contextual_control_threshold (float, default None)
            If set to None, attention control parameters only apply to those tokens that have
            explicitly b

[Assistant]
een set in the request.
            If set to a non-None value, we apply the control parameters to similar tokens as well.
            Controls that have been applied to one token will then be applied to all other tokens
            that have at least the similarity score defined by this parameter.
            The similarity score is the cosine similarity of token embeddings.


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

[User]
import json
import os
import shutil

import numpy as np
import pandas as pd
import urllib3
from tqdm import tqdm

from ..utils import get_data_dir, get_labels
from .preprocessing import get_granularity, get_type, stringify_context

def download_data(target_dir: str = None) - None:
    """
    Download the Ultra-Fine Entity Typing (ACL 2018) data from the web.
    For more information, see https://www.cs.utexas.edu/~eunsol/html_pages/open_entity.html

    Parameters
    ----------
    target_dir : str, optional
        The directory to download the data to. The default is 'data'.
    """
    DATA_URL = 'http://nlp.cs.washington.edu/entity_type/data/ultrafine_acl18.tar.gz'

    if target_dir is None:
        target_dir = get_data_dir()

    print(f'Downloading the data from {DATA_URL} to {target_dir}...')

    # Create the data directory if it does not exist
    os.makedirs(target_dir, exist_ok=True)

    # Download the data
    http = urllib3.PoolManager()
    with http.request('GET', DATA_URL, preload_content=False) as r, open('dat

[Assistant]
a/ultrafine_acl18.tar.gz', 'wb') as out_file:
        shutil.copyfileobj(r, out_file)

    # Extract the data
    print('Extracting the data...')
    shutil.unpack_archive('data/ultrafine_acl18.tar.gz', 'data')

    # Remove the tar.gz file
    os.remove('data/ultrafine_acl18.tar.gz')

    # Move the files form the 'data/release/ontonotes/' directory to the 'data' directory
    for filename in os.listdir('data/release/ontonotes/'):
        shutil.move(f'data/release/ontonotes/{filename}', f'data/{filename}')

    # Remove the 'data/release/' directory
    shutil.rmtree('data/release')


[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]
l.set_topic_labels`.
            title: Title of the plot.
            width: The width of the figure.
            height: The height of the figure.

        Examples:

        To visualize the topics simply run:

        ```python
        topic_model.visualize_documents(docs)
        ```

        Do note that this re-calculates the embeddings and reduces them to 2D.
        The advised and prefered pipeline for using this function is as follows:

        ```python
        from sklearn.datasets import fetch_20newsgroups
        from sentence_transformers import SentenceTransformer
        from bertopic import BERTopic
        from umap import UMAP

        # Prepare embeddings
        docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']
        sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
        embeddings = sentence_model.encode(docs, show_progress_bar=False)

        # Train BERTopic
        topic_model = BERTopic().fit(docs, embeddings)

        # Reduce dimensionality of embeddings, this step is optional
        # reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)

        # Run the visualization with the original embeddings
        topic_model.visualize_documents(docs, embeddings=embeddings)

        # Or, if you have reduced the original embeddings already:
        topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)
        ```

        Or if you want to save the resulting figure:

        ```python
        fig = topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)
        fig.write_html("path/to/file.html")
        ```

        iframe src="../getting_started/visualization/documents.html"
        style="width:1000px; height: 800px; border: 0px;""/iframe
        """
        check_is_fitted(self)
        return plotting.visualize_documents(self,
                                            docs=docs,
                                            topics=topics,
                                            embeddings=embeddings,
                                            reduced_embeddings=reduced_embeddings,
                                            sample=sample,
                                            hide_annotations=hide_annotations,
                                            hide_document_hover=hide_document_hover,
                                            custom_labels=custom_labels,
                                            title=title,
                                            width=width,
                                            height=height)

    def visualize_hierarchical_documents(self,
                                         docs: List[str],
                                         hierarchical_topics: pd.DataFrame,
                                         topics: List[int] = None,
                                         embeddings: np.ndarray = None,
                                         reduced_embeddings: np.ndarray = None,
                                         sample: Union[float, int] = None,
                                         hide_annotations: bool = False,
                                         hide_document_hover: bool = True,
                                         nr_levels: int = 10,
                                         level_scale: str = 'linear',
                                         custom_labels: bool = False,
                                         title: str = "bHierarchical Documents and Topics/b",
                                         width: int = 1200,
                                         height: int = 750) - go.Figure:
        """ Visualize documents and their topics in 2D at different levels of hierarchy

        Arguments:
            docs: The documents you used when calling either `fit` or `fit_transform`
            hierarchical_topics: A dataframe that contains a hierarchy of topics
                                represented by their parents and their children
            topics: A selection of topics to visualize.
                    Not to be confused with the topics that you get from `.fit_transform`.
                    For example, if you want to visualize only topics 1 through 5:
                    `topics = [1, 2, 3, 4, 5]`.
            embeddings: The embeddings of all documents in `docs`.
            reduced_embeddings: The 2D reduced embeddings of all documents in `docs`.
            sample: The percentage of documents in each topic that you would like to keep.
                    Value can be between 0 and 1. Setting this value to, for example,
                    0.1 (10% of documents in each topic) makes it easier to visualize
                    millions of documents as a subset is chosen.
            hide_annotations: Hide the names of the traces on top of each cluster.
            hide_document_hover: Hide the content of the documents when hovering over
                                specific points. Helps to speed up generation of visualizations.
            nr_levels: The number of levels to be visualized in the hierarchy. First, the distances
                    in `hierarchical_topics.Distance` are split in `nr_levels` lists of distances with
                    equal length. Then, for each list of distances, the merged topics are selected that
                    have a distance less or equal to the maximum distance of the selected list of distances.
                    NOTE: To get all possible merged steps, make sure that `nr_levels` is equal to
                    the length of `hierarchical_topics`.
            level_scale: Whether to apply a linear or logarithmic ('log') scale levels of the distance
                         vector. Linear scaling will perform an equal number of merges at each level
                         while logarithmic scaling will perform more mergers in earlier levels to
                         provide more resolution at higher levels (this can be used for when the number
                         of topics is large).
            custom_labels: Whether to use custom topic labels that were defined using
                           `topic_model.set_topic_labels`.
                           NOTE: Custom labels are only generated for the original
                           un-merged topics.
            title: Title of the plot.
            width: The width of the figure.
            height: The height of the figure.

        Examples:

        To visualize the topics simply run:

        ```python
        topic_model.visualize_hierarchical_documents(docs, hierarchical_topics)
        ```

        Do note that this re-calculates the embeddings and reduces them to 2D.
        The advised and prefered pipeline for using this function is as follows:

        ```python
        from sklearn.datasets import fetch_20newsgroups
        from sentence_transformers import SentenceTransformer
        from bertopic import BERTopic
        from umap import UMAP

        # Prepare embeddings
        docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']
        sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
        embeddings = sentence_model.encode(docs, show_progress_bar=False)

        # Train BERTopic and extract hierarchical topics
        topic_model = BERTopic().fit(docs, embeddings)
        hierarchical_topics = topic_model.hierarchical_topics(docs)

        # Reduce dimensionality of embeddings, this step is optional
        # reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)

        # Run the visualization with the original embeddings
        topic_model.visualize_hierarchical_documents(docs, hierarchical_topics, embeddings=embeddings)

        # Or, if you have reduced the original embeddings already:
        topic_model.visualize_hierarchical_documents(docs, hierarchical_topics, reduced_embeddings=reduced_embeddings)
        ```

        Or if you want to save the resulting figure:

        ```python
        fig = topic_model.visualize_hierarchical_documents(docs, hierarchical_topics, reduced_embeddings=reduced_embeddings)
        fig.write_html("path/to/file.html")
        ```

        iframe src="../getting_started/visualization/hierarchical_documents.html"
        style="width:1000px; height: 770px; border: 0px;""/iframe
        """
        check_is_fitted(self)
        return plotting.visualize_hierarchical_documents(self,
                                                         docs=docs,
                                                         hierarchical_topics=hierarchical_topics,
                                                         topics=topics,
                                                         embeddings=embeddings,
                                                         reduced_embeddings=reduced_embeddings,
                                                         sample=sample,
                                                         hide_annotations=hide_annotations,
                                                         hide_document_hover=hide_document_hover,

[Assistant]
                nr_levels=nr_levels,
                                                         level_scale=level_scale,
                                                         custom_labels=custom_labels,
                                                         title=title,
                                                         width=width,
                                                         height=height)


[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]
t_init(self):
        # Smoke test for bug where warm_start=True is set at init
        y = self.X[:, 0]
        regressor = PySRRegressor(warm_start=True, max_evals=10)
        regressor.fit(self.X, y)

    def test_noisy(self):
        y = self.X[:, [0, 1]] ** 2 + self.rstate.randn(self.X.shape[0], 1) * 0.05
        model = PySRRegressor(
            # Test that passing a single operator works:
            unary_operators="sq(x) = x^2",
            binary_operators="plus",
            extra_sympy_mappings={"sq": lambda x: x**2},
            **self.default_test_kwargs,
            procs=0,
            denoise=True,
            early_stop_condition="stop_if(loss, complexity) = loss  0.05 && complexity == 2",
        )
        # We expect in this case that the "best"
        # equation should be the right one:
        model.set_params(model_selection="best")
        # Also try without a temp equation file:
        model.set_params(temp_equation_file=False)
        model.fit(self.X, y)
        self.assertLessEqual(model.get_best()[1]["loss"], 1e-2)
        self.assertLessEqual(model.get_best()[1]["loss"], 1e-2)

    def test_pandas_resample_with_nested_constraints(self):
        X = pd.DataFrame(
            {
                "T": self.rstate.randn(500),
                "x": self.rstate.randn(500),
                "unused_feature": self.rstate.randn(500),
            }
        )
        true_fn = lambda x: np.array(x["T"] + x["x"] ** 2 + 1.323837)
        y = true_fn(X)
        noise = self.rstate.randn(500) * 0.01
        y = y + noise
        # We also test y as a pandas array:
        y = pd.Series(y)
        # Resampled array is a different order of features:
        Xresampled = pd.DataFrame(
            {
                "unused_feature": self.rstate.randn(100),
                "x": self.rstate.randn(100),
                "T": self.rstate.randn(100),
            }
        )
        model = PySRRegressor(
            unary_operators=[],
            binary_operators=["+", "*", "/", "-"],
            **self.default_test_kwargs,
            denoise=True,
            nested_constraints={"/": {"+": 1, "-": 1}, "+": {"*": 4}},
            early_stop_condition="stop_if(loss, complexity) = loss  1e-3 && complexity == 7",
        )
        model.fit(X, y, Xresampled=Xresampled)
        self.assertNotIn("unused_feature", model.latex())
        self.assertIn("T", model.latex())
        self.assertIn("x", model.latex())
        self.assertLessEqual(model.get_best()["loss"], 1e-1)
        fn = model.get_best()["lambda_format"]
        X2 = pd.DataFrame(
            {
                "T": self.rstate.randn(100),
                "unused_feature": self.rstate.randn(100),
                "x": self.rstate.randn(100),
            }
        )
        self.assertLess(np.average((fn(X2) - true_fn(X2)) ** 2), 1e-1)
        self.assertLess(np.average((model.predict(X2) - true_fn(X2)) ** 2), 1e-1)

    def test_high_dim_selection_early_stop(self):
        X = pd.DataFrame({f"k{i}": self.rstate.randn(10000) for i in range(10)})
        Xresampled = pd.DataFrame({f"k{i}": self.rstate.randn(100) for i in range(10)})
        y = X["k7"] ** 2 + np.cos(X["k9"]) * 3

        model = PySRRegressor(
            unary_operators=["cos"],
            select_k_features=3,
            early_stop_condition=1e-4,  # Stop once most accurate equation is 1e-4 MSE
            maxsize=12,
            **self.default_test_kwargs,
        )
        model.set_params(model_selection="accuracy")
        model.fit(X, y, Xresampled=Xresampled)
        self.assertLess(np.average((model.predict(X) - y) ** 2), 1e-4)
        # Again, but with numpy arrays:
        model.fit(X.values, y.values, Xresampled=Xresampled.values)
        self.assertLess(np.average((model.predict(X.values) - y.values) ** 2), 1e-4)

    def test_load_model(self):
        """See if we can load a ran model from the equation file."""
        csv_file_data = """
        Complexity,Loss,Equation
        1,0.19951081,"1.9762075"
        3,0.12717344,"(f0 + 1.4724599)"
        4,0.104823045,"pow_abs(2.2683423, cos(f3))\""""
        # Strip the indents:
        csv_file_data = "\n".join([l.strip() for l in csv_file_data.split("\n")])

        for from_backup in [False, True]:
            rand_dir = Path(tempfile.mkdtemp())
            equation_filename = str(rand_dir / "equation.csv")
            with open(equation_filename + (".bkup" if from_backup else ""), "w") as f:
                f.write(csv_file_data)
            model = PySRRegressor.from_file(
                equation_filename,
                n_features_in=5,
                feature_names_in=["f0", "f1", "f2", "f3", "f4"],
                binary_operators=["+", "*", "/", "-", "^"],
                unary_operators=["cos"],
            )
            X = self.rstate.rand(100, 5)
            y_truth = 2.2683423 ** np.cos(X[:, 3])
            y_test = model.predict(X, 2)

            np.testing.assert_allclose(y_truth, y_test)

    def test_load_model_simple(self):
        # Test that we can simply load a model from its equation file.
        y = self.X[:, [0, 1]] ** 2
        model = PySRRegressor(
            # Test that passing a single operator works:
            unary_operators="sq(x) = x^2",
            binary_operators="plus",
            extra_sympy_mappings={"sq": lambda x: x**2},
            **self.default_test_kwargs,
            procs=0,
            denoise=True,
            early_stop_condition="stop_if(loss, complexity) = loss  0.05 && complexity == 2",
        )
        rand_dir = Path(tempfile.mkdtemp())
        equation_file = rand_dir / "equations.csv"
        model.set_params(temp_equation_file=False)
        model.set_params(equation_file=equation_file)
        model.fit(self.X, y)

        # lambda functions are removed from the pickling, so we need
        # to pass it during the loading:
        model2 = PySRRegressor.from_file(
            model.equation_file_, extra_sympy_mappings={"sq": lambda x: x**2}
        )

        np.testing.assert_allclose(model.predict(self.X), model2.predict(self.X))

        # Try again, but using only the pickle file:
        for file_to_delete in [str(equation_file), str(equation_file) + ".bkup"]:
            if os.path.exists(file_to_delete):
                os.remove(file_to_delete)

        pickle_file = rand_dir / "equations.pkl"
        model3 = PySRRegressor.from_file(
            model.equation_file_, extra_sympy_mappings={"sq": lambda x: x**2}
        )
        np.testing.assert_allclose(model.predict(self.X), model3.predict(self.X))

def manually_create_model(equations, feature_names=None):
    if feature_names is None:
        feature_names = ["x0", "x1"]

    model = PySRRegressor(
        progress=False,
        niterations=1,
        extra_sympy_mappings={},
        output_jax_format=False,
        model_selection="accuracy",
        equation_file="equation_file.csv",
    )

    # Set up internal parameters as if it had been fitted:
    if isinstance(equations, list):
        # Multi-output.
        model.equation_file_ = "equation_file.csv"
        model.nout_ = len(equations)
        model.selection_mask_ = None
        model.feature_names_in_ = np.array(feature_names, dtype=object)
        for i in range(model.nout_):
            equations[i]["complexity loss equation".split(" ")].to_csv(
                f"equation_file.csv.out{i+1}.bkup"
            )
    else:
        model.equation_file_ = "equation_file.csv"
        model.nout_ = 1
        model.selection_mask_ = None
        model.feature_names_in_ = np.array(feature_names, dtype=object)
        equations["complexity loss equation".split(" ")].to_csv(
            "equation_file.csv.bkup"
        )

    model.refresh()

    return model

class TestBest(unittest.TestCase):
    def setUp(self):
        self.rstate = np.random.RandomState(0)
        self.X = self.rstate.randn(10, 2)
        self.y = np.cos(self.X[:, 0]) ** 2
        equations = pd.DataFrame(
            {
                "equation": ["1.0", "cos(x0)", "square(cos(x0))"],
                "loss": [1.0, 0.1, 1e-5],
                "complexity": [1, 2, 3],
            }
        )
        self.model = manually_create_model(equations)
        self.equations_ = self.model.equations_

    def test_best(self):
        self.assertEqual(self.model.sympy(), sympy.cos(sympy.Symbol("x0")) ** 2)

    def test_index_selection(self):
        self.assertEqual(self.model.sympy(-1), sympy.cos(sympy.Symbo

[Assistant]
l("x0")) ** 2)
        self.assertEqual(self.model.sympy(2), sympy.cos(sympy.Symbol("x0")) ** 2)
        self.assertEqual(self.model.sympy(1), sympy.cos(sympy.Symbol("x0")))
        self.assertEqual(self.model.sympy(0), 1.0)


[System]
You are AutocompleteGPT, a useful AI autocomplete tool that provides code completions based on the user's code.
You are a precision-focused tool for code autocompletion, adept in languages like Python, JavaScript, C++, and SQL.
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]
    arr = np.repeat([0.0, 1.0], n_points)  # binary
    assert_almost_equal(matthews_corrcoef(arr, arr), 1.0)
    arr = np.repeat([0.0, 1.0, 2.0], n_points)  # multiclass
    assert_almost_equal(matthews_corrcoef(arr, arr), 1.0)

    y_true, y_pred = random_ys(n_points)
    assert_almost_equal(matthews_corrcoef(y_true, y_true), 1.0)
    assert_almost_equal(matthews_corrcoef(y_true, y_pred), mcc_safe(y_true, y_pred))

def test_precision_recall_f1_score_multiclass():
    # Test Precision Recall and F1 Score for multiclass classification task
    y_true, y_pred, _ = make_prediction(binary=False)

    # compute scores with default labels introspection
    p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None)
    assert_array_almost_equal(p, [0.83, 0.33, 0.42], 2)
    assert_array_almost_equal(r, [0.79, 0.09, 0.90], 2)
    assert_array_almost_equal(f, [0.81, 0.15, 0.57], 2)
    assert_array_equal(s, [24, 31, 20])

    # averaging tests
    ps = precision_score(y_true, y_pred, pos_label=1, average="micro")
    assert_array_almost_equal(ps, 0.53, 2)

    rs = recall_score(y_true, y_pred, average="micro")
    assert_array_almost_equal(rs, 0.53, 2)

    fs = f1_score(y_true, y_pred, average="micro")
    assert_array_almost_equal(fs, 0.53, 2)

    ps = precision_score(y_true, y_pred, average="macro")
    assert_array_almost_equal(ps, 0.53, 2)

    rs = recall_score(y_true, y_pred, average="macro")
    assert_array_almost_equal(rs, 0.60, 2)

    fs = f1_score(y_true, y_pred, average="macro")
    assert_array_almost_equal(fs, 0.51, 2)

    ps = precision_score(y_true, y_pred, average="weighted")
    assert_array_almost_equal(ps, 0.51, 2)

    rs = recall_score(y_true, y_pred, average="weighted")
    assert_array_almost_equal(rs, 0.53, 2)

    fs = f1_score(y_true, y_pred, average="weighted")
    assert_array_almost_equal(fs, 0.47, 2)

    with pytest.raises(ValueError):
        precision_score(y_true, y_pred, average="samples")
    with pytest.raises(ValueError):
        recall_score(y_true, y_pred, average="samples")
    with pytest.raises(ValueError):
        f1_score(y_true, y_pred, average="samples")
    with pytest.raises(ValueError):
        fbeta_score(y_true, y_pred, average="samples", beta=0.5)

    # same prediction but with and explicit label ordering
    p, r, f, s = precision_recall_fscore_support(
        y_true, y_pred, labels=[0, 2, 1], average=None
    )
    assert_array_almost_equal(p, [0.83, 0.41, 0.33], 2)
    assert_array_almost_equal(r, [0.79, 0.90, 0.10], 2)
    assert_array_almost_equal(f, [0.81, 0.57, 0.15], 2)
    assert_array_equal(s, [24, 20, 31])

@pytest.mark.parametrize("average", ["samples", "micro", "macro", "weighted", None])
def test_precision_refcall_f1_score_multilabel_unordered_labels(average):
    # test that labels need not be sorted in the multilabel case
    y_true = np.array([[1, 1, 0, 0]])
    y_pred = np.array([[0, 0, 1, 1]])
    p, r, f, s = precision_recall_fscore_support(
        y_true, y_pred, labels=[3, 0, 1, 2], warn_for=[], average=average
    )
    assert_array_equal(p, 0)
    assert_array_equal(r, 0)
    assert_array_equal(f, 0)
    if average is None:
        assert_array_equal(s, [0, 1, 1, 0])

def test_precision_recall_f1_score_binary_averaged():
    y_true = np.array([0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1])
    y_pred = np.array([1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1])

    # compute scores with default labels introspection
    ps, rs, fs, _ = precision_recall_fscore_support(y_true, y_pred, average=None)
    p, r, f, _ = precision_recall_fscore_support(y_true, y_pred, average="macro")
    assert p == np.mean(ps)
    assert r == np.mean(rs)
    assert f == np.mean(fs)
    p, r, f, _ = precision_recall_fscore_support(y_true, y_pred, average="weighted")
    support = np.bincount(y_true)
    assert p == np.average(ps, weights=support)
    assert r == np.average(rs, weights=support)
    assert f == np.average(fs, weights=support)

def test_zero_precision_recall():
    # Check that pathological cases do not bring NaNs

    old_error_settings = np.seterr(all="raise")

    try:
        y_true = np.array([0, 1, 2, 0, 1, 2])
        y_pred = np.array([2, 0, 1, 1, 2, 0])

        assert_almost_equal(precision_score(y_true, y_pred, average="macro"), 0.0, 2)
        assert_almost_equal(recall_score(y_true, y_pred, average="macro"), 0.0, 2)
        assert_almost_equal(f1_score(y_true, y_pred, average="macro"), 0.0, 2)

    finally:
        np.seterr(**old_error_settings)

def test_confusion_matrix_multiclass_subset_labels():
    # Test confusion matrix - multi-class case with subset of labels
    y_true, y_pred, _ = make_prediction(binary=False)

    # compute confusion matrix with only first two labels considered
    cm = confusion_matrix(y_true, y_pred, labels=[0, 1])
    assert_array_equal(cm, [[19, 4], [4, 3]])

    # compute confusion matrix with explicit label ordering for only subset
    # of labels
    cm = confusion_matrix(y_true, y_pred, labels=[2, 1])
    assert_array_equal(cm, [[18, 2], [24, 3]])

    # a label not in y_true should result in zeros for that row/column
    extra_label = np.max(y_true) + 1
    cm = confusion_matrix(y_true, y_pred, labels=[2, extra_label])
    assert_array_equal(cm, [[18, 0], [0, 0]])

@pytest.mark.parametrize(
    "labels, err_msg",
    [
        ([], "'labels' should contains at least one label."),
        ([3, 4], "At least one label specified must be in y_true"),
    ],
    ids=["empty list", "unknown labels"],
)
def test_confusion_matrix_error(labels, err_msg):
    y_true, y_pred, _ = make_prediction(binary=False)
    with pytest.raises(ValueError, match=err_msg):
        confusion_matrix(y_true, y_pred, labels=labels)

@pytest.mark.parametrize(
    "labels", (None, [0, 1], [0, 1, 2]), ids=["None", "binary", "multiclass"]
)
def test_confusion_matrix_on_zero_length_input(labels):
    expected_n_classes = len(labels) if labels else 0
    expected = np.zeros((expected_n_classes, expected_n_classes), dtype=int)
    cm = confusion_matrix([], [], labels=labels)
    assert_array_equal(cm, expected)

def test_confusion_matrix_dtype():
    y = [0, 1, 1]
    weight = np.ones(len(y))
    # confusion_matrix returns int64 by default
    cm = confusion_matrix(y, y)
    assert cm.dtype == np.int64
    # The dtype of confusion_matrix is always 64 bit
    for dtype in [np.bool_, np.int32, np.uint64]:
        cm = confusion_matrix(y, y, sample_weight=weight.astype(dtype, copy=False))
        assert cm.dtype == np.int64
    for dtype in [np.float32, np.float64, None, object]:
        cm = confusion_matrix(y, y, sample_weight=weight.astype(dtype, copy=False))
        assert cm.dtype == np.float64

    # np.iinfo(np.uint32).max should be accumulated correctly
    weight = np.full(len(y), 4294967295, dtype=np.uint32)
    cm = confusion_matrix(y, y, sample_weight=weight)
    assert cm[0, 0] == 4294967295
    assert cm[1, 1] == 8589934590

    # np.iinfo(np.int64).max should cause an overflow
    weight = np.full(len(y), 9223372036854775807, dtype=np.int64)
    cm = confusion_matrix(y, y, sample_weight=weight)
    assert cm[0, 0] == 9223372036854775807
    assert cm[1, 1] == -2

@pytest.mark.parametrize("dtype", ["Int64", "Float64", "boolean"])
def test_confusion_matrix_pandas_nullable(dtype):
    """Checks that confusion_matrix works with pandas nullable dtypes.

    Non-regression test for gh-25635.
    """
    pd = pytest.importorskip("pandas")

    y_ndarray = np.array([1, 0, 0, 1, 0, 1, 1, 0, 1])
    y_true = pd.Series(y_ndarray, dtype=dtype)
    y_predicted = pd.Series([0, 0, 1, 1, 0, 1, 1, 1, 1], dtype="int64")

    output = confusion_matrix(y_true, y_predicted)
    expected_output = confusion_matrix(y_ndarray, y_predicted)

    assert_array_equal(output, expected_output)

def test_classification_report_multiclass():
    # Test performance report
    iris = datasets.load_iris()
    y_true, y_pred, _ = make_prediction(dataset=iris, binary=False)

    # print classification report with class names
    expected_report = """\
              precision    recall  f1-score   support

      setosa       0.83      0.79      0.81        24
  versicolor       0.33      0.10      0.15        31
   virginica       0.42      0.90      0.57        20

    accuracy                           0.53        75
   macro avg       0.53      0.60      0.51        75
weighted avg       0.51      0.53      0.47        75
"""
    report = classificatio

[Assistant]
n_report(
        y_true,
        y_pred,
        labels=np.arange(len(iris.target_names)),
        target_names=iris.target_names,
    )
    assert report == expected_report


[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]
t inside a strategy scope and within a
  `tf.distribute.Strategy.run` function. The `ReplicaContext` object has an
  `all_reduce` method for aggregating across all replicas.

Note that we provide a default version of `tf.distribute.Strategy` that is
used when no other strategy is in scope, that provides the same API with
reasonable default behavior.
"""
# pylint: enable=line-too-long

import collections
import contextlib
import copy
import enum  # pylint: disable=g-bad-import-order
import functools
import threading
import weakref

import six

from tensorflow.python import tf2
from tensorflow.python.autograph.core import ag_ctx as autograph_ctx
from tensorflow.python.autograph.impl import api as autograph
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.distribute import collective_util
from tensorflow.python.distribute import device_util
from tensorflow.python.distribute import numpy_dataset
from tensorflow.python.distribute import reduce_util
from tensorflow.python.eager import context as eager_context
from tensorflow.python.eager import def_function
from tensorflow.python.eager import monitoring
from tensorflow.python.eager import tape
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import indexed_slices
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor as tensor_lib
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import custom_gradient
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import ref_variable
from tensorflow.python.ops import summary_ops_v2
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variable_v1
from tensorflow.python.platform import tf_logging
from tensorflow.python.trackable import base as trackable
from tensorflow.python.types import distribute as ds_types
from tensorflow.python.util import deprecation
from tensorflow.python.util import nest
from tensorflow.python.util import tf_contextlib
from tensorflow.python.util.deprecation import deprecated
from tensorflow.python.util.tf_export import tf_export
from tensorflow.tools.docs import doc_controls

# ------------------------------------------------------------------------------
# Context tracking whether in a strategy.update() or .update_non_slot() call.

_update_replica_id = threading.local()

def get_update_replica_id():
  """Get the current device if in a `tf.distribute.Strategy.update()` call."""
  try:
    return _update_replica_id.current
  except AttributeError:
    return None

class UpdateContext(object):
  """Context manager when you are in `update()` or `update_non_slot()`."""

  __slots__ = ["_replica_id", "_old_replica_id"]

  def __init__(self, replica_id):
    self._replica_id = replica_id
    self._old_replica_id = None

  def __enter__(self):
    self._old_replica_id = get_update_replica_id()
    _update_replica_id.current = self._replica_id

  def __exit__(self, exception_type, exception_value, traceback):
    del exception_type, exception_value, traceback
    _update_replica_id.current = self._old_replica_id

# ------------------------------------------------------------------------------
# Internal API for validating the current thread mode

def _require_cross_replica_or_default_context_extended(extended,
                                                       error_message=None):
  """Verify in cross-replica context."""
  context = _get_per_thread_mode()
  cross_replica = context.cross_replica_context
  if cross_replica is not None and cross_replica.extended is extended:
    return
  if context is _get_default_replica_mode():
    return
  strategy = extended._container_strategy()  # pylint: disable=protected-access
  # We have an error to report, figure out the right message.
  if context.strategy is not strategy:
    _wrong_strategy_scope(strategy, context)
  assert cross_replica is None
  if not error_message:
    error_message = ("Method requires being in cross-replica context, use "
                     "get_replica_context().merge_call()")
  raise RuntimeError(error_message)

def _wrong_strategy_scope(strategy, context):
  # Figure out the right error message.
  if not has_strategy():
    raise RuntimeError(
        'Need to be inside "with strategy.scope()" for %s' %
        (strategy,))
  else:
    raise RuntimeError(
        "Mixing different tf.distribute.Strategy objects: %s is not %s" %
        (context.strategy, strategy))

def require_replica_context(replica_ctx):
  """Verify in `replica_ctx` replica context."""
  context = _get_per_thread_mode()
  if context.replica_context is replica_ctx: return
  # We have an error to report, figure out the right message.
  if context.replica_context is None:
    raise RuntimeError("Need to be inside `call_for_each_replica()`")
  if context.strategy is replica_ctx.strategy:
    # Two different ReplicaContexts with the same tf.distribute.Strategy.
    raise RuntimeError("Mismatching ReplicaContext.")
  raise RuntimeError(
      "Mismatching tf.distribute.Strategy objects: %s is not %s." %
      (context.strategy, replica_ctx.str

[Assistant] ategy))


[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 copies all notebooks from the book into the website directory, and
creates pages which wrap them and link together.
"""
import os
import nbformat
import shutil

PAGEFILE = """title: {title}
url:
save_as: {htmlfile}
Template: {template}

{{% notebook notebooks/{notebook_file} cells[{cells}] %}}
"""

INTRO_TEXT = """This website contains the full text of the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook) in the form of

[Assistant]
 Jupyter notebooks.

The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT).

If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!
"""


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

[User]
from __future__ import annotations

import os
import typing as t
from collections import defaultdict
from functools import update_wrapper

from .. import typing as ft
from .scaffold import _endpoint_from_view_func
from .scaffold import _sentinel
from .scaffold import Scaffold
from .scaffold import setupmethod

if t.TYPE_CHECKING:  # pragma: no cover
    from .app import App

DeferredSetupFunction = t.Callable[["BlueprintSetupState"], t.Callable]
T_after_request = t.TypeVar("T_after_request", bound=ft.AfterRequestCallable)
T_before_request = t.TypeVar("T_before_request", bound=ft.BeforeRequestCallable)
T_error_handler = t.TypeVar("T_error_handler", bound=ft.ErrorHandlerCallable)
T_teardown = t.TypeVar("T_teardown", bound=ft.TeardownCallable)
T_template_context_processor = t.TypeVar(
    "T_template_context_processor", bound=ft.TemplateContextProcessorCallable
)
T_template_filter = t.TypeVar("T_template_filter", bound=ft.TemplateFilterCallable)
T_template_global = t.TypeVar("T_template_global", bound=ft.TemplateGlobalCallable)
T_template_test = t.TypeVar("T_template_test", bound=ft.TemplateTestCallable)
T_url_defaults = t.TypeVar("T_url_defaults", bound=ft.URLDefaultCallable)
T_url_value_preprocessor = t.TypeVar(
    "T_url_value_preprocessor", bound=ft.URLValuePreprocessorCallable
)

class BlueprintSetupState:
    """Temporary holder object for registering a blueprint with the
    application.  An instance of this class is created by the
    :meth:`~flask.Blueprint.make_setup_state` method and later passed
    to all register callback functions.
    """

    def __init__(
        self,
        blueprint: Blueprint,
        app: App,
        options: t.Any,
        first_registration: bool,
    ) - None:
        #: a reference to the current application
        self.app = app

        #: a reference to the blueprint that created this setup state.
        self.blueprint = blueprint

        #: a dictionary with all options that were passed to the
        #: :meth:`~flask.Flask.register_blueprint` method.
        self.options = options

        #: as blueprints can be registered multiple times with the
        #: application and not everything wants to be registered
        #: multiple times on it, this attribute can be used to figure
        #: out if the blueprint was registered in the past already.
        self.first_registration = first_registration

        subdomain = self.options.get("subdomain")
        if subdomain is None:
            subdomain = self.blueprint.subdomain

        #: The subdomain that the blueprint should be active for, ``None``
        #: otherwise.
        self.subdomain = subdomain

        url_prefix = self.options.get("url_prefix")
        if url_prefix is None:
            url_prefix = self.blueprint.url_prefix
        #: The prefix that should be used for all URLs defined on the
        #: blueprint.
        self.url_prefix = url_prefix

        self.name = self.options.get("name", blueprint.name)
        self.name_prefix = self.options.get("name_prefix", "")

        #: A dictionary with URL defaults that is added to each and every
        #: URL that was defined with the blueprint.
        self.url_defaults = dict(self.blueprint.url_values_defaults)
        self.url_defaults.update(self.options.get("url_defaults", ()))

    def add_url_rule(
        self,
        rule: str,
        endpoint: str | None = None,
        view_func: t.Callable | None = None,
        **options: t.Any,
    ) - None:
        """A helper method to register a rule (and optionally a view function)
        to the application.  The endpoint is automatically prefixed with the
        blueprint's name.
        """
        if self.url_prefix is not None:
            if rule:
                rule = "/".join((self.url_prefix.rstrip("/"), rule.lstrip("/")))
            else:
                rule = self.url_prefix
        options.setdefault("subdomain", self.subdomain)
        if endpoint is None:
            endpoint = _endpoint_from_view_func(view_func)  # type: ignore
        defaults = self.url_defaults
        if "defaults" in options:
            defaults = dict(defaults, **options.pop("defaults"))

        self.app.add_url_rule(
            rule,
            f"{self.name_prefix}.{self.name}.{endpoint}".lstrip("."),
            view_func,

[Assistant]
 defaults=defaults,
            **options,
        )


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

[User]
import os
import shutil
import time
from typing import Tuple

import numpy as np
import pandas as pd
import pytest
from sklearn.pipeline import Pipeline

from pmtrendviz.models.factory import ModelFactory
from pmtrendviz.models.manager import SimpleSklearnPipelineManager
from pmtrendviz.train.data import sample_training_data
from pmtrendviz.utils import get_es_client, get_models_dir

@pytest.mark.usefixtures('manage_pytest_index', 'manage_prediction_indices')
class TestSimpleSklearnPipelineManager:
    sample_data: pd.DataFrame = pd.DataFrame()
    n_samples: int = 1000
    index: str = 'pytest'
    model_name: str = 'pytest-model'
    model = 'tfidf_truncatedsvd_kmeans'
    stop_words = 'english'
    max_df: float = 0.999
    min_df: float = 0.001
    n_components: int = 100
    n_clusters: int = 69
    ngram_range: Tuple[int, int] = (1, 1)
    random_state: int = 42
    include_title: bool = True
    include_abstract: bool = True
    include_keywords_major: bool = False
    include_keywords_minor: bool = False

    @classmethod
    def setup_class(cls) - None:
        # Clear the test model directory
        shutil.rmtree(os.path.join(get_models_dir(), cls.model_name), ignore_errors=True)

        # Sample some data
        cls.sample_data = sample_training_data(cls.index, random_state=42, n_samples=cls.n_samples, method='forward')['text']

    @classmethod
    def teardown_class(cls) - None:
        # Clear the test model directory
        shutil.rmtree(os.path.join(get_models_dir(), cls.model_name), ignore_errors=True)

    def teardown_method(self) - None:
        # Clear the test model directory
        shutil.rmtree(os.path.join(get_models_dir(), self.model_name), ignore_errors=True)

    def test_sample_data(self) - None:
        assert len(self.sample_data) == self.n_samples

    def test_create_default_simple_sklearn_pipeline_manager(self) - None:
        manager = ModelFactory.create(
            self.model,
            self.include_title,
            self.include_abstract,
            self.include_keywords_major,
            self.include_keywords_minor,
            stop_words=self.stop_words,
            max_df=self.max_df,
            min_df=self.min_df,
            n_components=self.n_components,
            n_clusters=self.n_clusters,
            ngram_range=self.ngram_range,
            random_state=None)

        assert manager is not None
        assert isinstance(manager.model, Pipeline)
        assert manager.random_state is None
        assert manager.preprocessing_args == {
            'include_title': True,
            'include_abstract': True,
            'include_keywords_major': False,
            'include_keywords_minor': False,
        }

        assert (manager.cluster_names.columns == ['name']).all()
        assert len(manager.cluster_names) == 0

    def test_create_custom_simple_sklearn_pipeline_manager(self) - None:
        manager = ModelFactory.create(
            self.model,
            self.include_title,
            self.include_abstract,
            True,
            self.include_keywords_minor,
            stop_words=self.stop_words,
            max_df=self.max_df,
            min_df=self.min_df,
            n_components=self.n_components,
            n_clusters=self.n_clusters,
            ngram_range=self.ngram_range,
            random_state=42)

        assert manager.random_state == 42
        assert manager.preprocessing_args == {
            'include_title': True,
            'include_abstract': True,
            'include_keywords_major': True,
            'include_keywords_minor': False,
        }

    def test_set_name(self) - None:
        manager = ModelFactory.create(
            self.model,
            self.include_title,
            self.include_abstract,
            self.include_keywords_major,
            self.include_keywords_minor,
            stop_words=self.stop_words,
            max_df=self.max_df,
            min_df=self.min_df,
            n_components=self.n_components,
            n_clusters=self.n_clusters,
            ngram_range=self.ngram_range,
            random_state=self.random_state)
        manager.set_name(self.model_name)

        assert manager._name == self.model_name

    def test_construct_predictions_index_name(self) - None:
        manager = ModelFactory.create(
            self.model,
            self.include_title,
            self.include_abstract,
            self.include_keywords_major,
            self.include_keywords_minor,
            stop_words=self.stop_words,
            max_df=self.max_df,
            min_df=self.min_df,
            n_components=self.n_components,
            n_clusters=self.n_clusters,
            ngram_range=self.ngram_range,
            random_state=self.random_state)
        manager.set_name(self.model_name)
        predictions_index_name = manager._construct_predictions_index_name(self.index)
        predictions_file_name = manager._construct_predictions_file_name(self.index)

        assert manager._name == self.model_name
        assert predictions_index_name == f'{self.index}_{manager._name}_predictions'
        assert predictions_file_name == f'{self.index}_predictions.json'

    def test_fit(self) - None:
        manager = ModelFactory.create(
            self.model,
            self.include_title,
            self.include_abstract,
            self.include_keywords_major,
            self.include_keywords_minor,
            stop_words=self.stop_words,
            max_df=self.max_df,
            min_df=self.min_df,
            n_components=self.n_components,
            n_clusters=self.n_clusters,
            ngram_range=self.ngram_range,
            random_state=self.random_state)

        manager.fit(self.sample_data)

        assert manager.model['clustering_kmeans'].labels_.shape == (self.n_samples,)
        assert len(manager.cluster_names) == self.n_clusters

    def test_predict(self) - None:
        manager = ModelFactory.create(
            self.model,
            self.include_title,
            self.include_abstract,
            self.include_keywords_major,
            self.include_keywords_minor,
            stop_words=self.stop_words,
            max_df=self.max_df,
            min_df=self.min_df,
            n_components=self.n_components,
            n_clusters=self.n_clusters,
            ngram_range=self.ngram_range,
            random_state=self.random_state)

        manager.fit(self.sample_data)
        predictions = manager.predict(self.sample_data)

        assert isinstance(predictions, np.ndarray)
        assert

[Assistant] predictions.shape == (self.n_samples,)


[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]
type_str', 'y', 'y_type', and 'original_y_str' columns
    df.drop(['annot_id', 'y_type_str', 'y', 'y_type', 'original_y_str'], axis=1, inplace=True, errors='ignore')

    # Convert the 'left_context_token' and 'right_context_token' columns into a string
    df['left_context_token'] = df['left_context_token'].apply(stringify_context)
    df['right_context_token'] = df['right_context_token'].apply(stringify_context)

    # Reconstruct the sentence from the 'left_context_token', 'mention_span', and 'right_context_token' columns
    df['sentence'] = df['left_context_token'] + ' ' + df['mention_span'] + ' ' + df['right_context_token']

    # Drop the 'left_context_token' and 'right_context_token' columns
    df.drop(['left_context_token', 'right_context_token'], axis=1, inplace=True, errors='ignore')

    # For each instance and for each type in the 'y_str' column, create a new row
    if explode:
        df = df.explode('y_str')

    # Rename the 'y_str' column to 'full_type'
    df.rename(columns={'y_str': 'full_type'}, inplace=True)

    # Reset the index
    df.reset_index(drop=True, inplace=True)

    # Calculate the granularity of the type
    if explode:
        df['granularity'] = df['full_type'].apply(get_granularity)

    # Add the 'label' column
    labels = get_labels()
    df['label'] = labels['ENTAILMENT']
    df['label'] = df['label'].astype(int)

    return df

def get_all_types(granularity: int = -1) - pd.DataFrame:
    """
    Get all the types in the training data.

    Parameters
    ----------
    granularity : int, optional
        The granularity of the type, measured from the root of the ontology, by default -1 (finest granularity)

    Returns
    -------
    all_types : pd.DataFrame
        A DataFrame containing all the full types and the types at the specified granularity.
    """
    all_types_path = os.path.join(get_data_dir(), 'derived', 'all_types.csv')
    os.makedirs(os.path.dirname(all_types_path), exist_ok=True)

    if os.path.exists(all_types_path):
        # If the file exists, load the types from the file
        all_types = pd.read_csv(all_types_path)
    else:
        # If the file doesn't exist, take the types from the training data and save them
        train_data = get_positive_data('augmented_train.json', explode=True)
        all_types = pd.DataFrame(train_data['full_type'].unique(), columns=['full_type'])

        # Save the types to a file
        all_types.to_csv(all_types_path, index=False)

    # Get the types at the specified granularity
    all_types['type'] = all_types['full_type'].apply(lambda x: get_type(x, granularity=granularity))

    return all_types

def get_ambiguity_index(filename: str = 'augmented_train.json') - dict:
    """
    Get the ambiguity index (a dictionary that maps each entity to a dictionary of types and their counts).

    Parameters
    ----------
    filename : str, optional
        The name of the json file, by default 'augmented_

[Assistant]
train.json'

    Returns
    -------
    ambiguity_index : dict
        The ambiguity index.
    """


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

[User]
f infer_params(state_dict):
    # this code is copied from https://github.com/victorca25/iNNfer
    scale2x = 0
    scalemin = 6
    n_uplayer = 0
    plus = False

    for block in list(state_dict):
        parts = block.split(".")
        n_parts = len(parts)
        if n_parts == 5 and parts[2] == "sub":
            nb = int(parts[3])
        elif n_parts == 3:
            part_num = int(parts[1])
            if (part_num  scalemin
                and parts[0] == "model"
                and parts[2] == "weight"):
                scale2x += 1
            if part_num  n_uplayer:
                n_uplayer = part_num
                out_nc = state_dict[block].shape[0]
        if not plus and "conv1x1" in block:
            plus = True

    nf = state_dict["model.0.weight"].shape[0]
    in_nc = state_dict["model.0.weight"].shape[1]
    out_nc = out_nc
    scale = 2 ** scale2x

    return in_nc, out_nc, nf, nb, plus, scale

class UpscalerESRGAN(Upscaler):
    def __init__(self, dirname):
        self.name = "ESRGAN"
        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth"
        self.model_name = "ESRGAN_4x"
        self.scalers = []
        self.user_path = dirname
        super().__init__()
        model_paths = self.find_models(ext_filter=[".pt", ".pth"])
        scalers = []
        if len(model_paths) == 0:
            scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
            scalers.append(scaler_data)
        for file in model_paths:
            if file.startswith("http"):
                name = self.model_name
            else:
                name = modelloader.friendly_name(file)

            scaler_data = UpscalerData(name, file, self, 4)
            self.scalers.append(scaler_data)

    def do_upscale(self, img, selected_model):
        try:
            model = self.load_model(selected_model)
        except Exception as e:
            print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
            return img
        model.to(devices.device_esrgan)
        img = esrgan_upscale(model, img)
        return img

    def load_model(self, path: str):
        if path.startswith("http"):
            # TODO: this doesn't use `path` at all?
            filename = modelloader.load_file_from_url(
                url=self.model_url,
                model_dir=self.model_download_path,
                file_name=f"{self.model_name}.pth",
            )
        else:
            filename = path

        state_

[Assistant] dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)


[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]
     cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(xc, t, context=cc)
        elif self.conditioning_key == 'hybrid-adm':
            assert c_adm is not None
            xc = torch.cat([x] + c_concat, dim=1)
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(xc, t, context=cc, y=c_adm)
        elif self.conditioning_key == 'crossattn-adm':
            assert c_adm is not None
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(x, t, context=cc, y=c_adm)
        elif self.conditioning_key == 'adm':
            cc = c_crossattn[0]
            out = self.diffusion_model(x, t, y=cc)
        else:
            raise NotImplementedError()

        return out

class LatentUpscaleDiffusion(LatentDiffusion):
    def __init__(self, *args, low_scale_config, low_scale_key="LR", noise_level_key=None, **kwargs):
        super().__init__(*args, **kwargs)
        # assumes that neither the cond_stage nor the low_scale_model contain trainable params
        assert not self.cond_stage_trainable
        self.instantiate_low_stage(low_scale_config)
        self.low_scale_key = low_scale_key
        self.noise_level_key = noise_level_key

    def instantiate_low_stage(self, config):
        model = instantiate_from_config(config)
        self.low_scale_model = model.eval()
        self.low_scale_model.train = disabled_train
        for param in self.low_scale_model.parameters():
            param.requires_grad = False

    @torch.no_grad()
    def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False):
        if not log_mode:
            z, c = super().get_input(batch, k, force_c_encode=True, bs=bs)
        else:
            z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
                                                  force_c_encode=True, return_original_cond=True, bs=bs)
        x_low = batch[self.low_scale_key][:bs]
        x_low = rearrange(x_low, 'b h w c - b c h w')
        x_low = x_low.to(memory_format=torch.contiguous_format).float()
        zx, noise_level = self.low_scale_model(x_low)
        if self.noise_level_key is not None:
            # get noise level from batch instead, e.g. when extracting a custom noise level for bsr
            raise NotImplementedError('TODO')

        all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level}
        if log_mode:
            # TODO: maybe disable if too expensive
            x_low_rec = self.low_scale_model.decode(zx)
            return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level
        return z, all_conds

    @torch.no_grad()
    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
                   plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True,
                   unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True,
                   **kwargs):
        ema_scope = self.ema_scope if use_ema_scope else nullcontext
        use_ddim = ddim_steps is not None

        log = dict()
        z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input(batch, self.first_stage_key, bs=N,
                                                                          log_mode=True)
        N = min(x.shape[0], N)
        n_row = min(x.shape[0], n_row)
        log["inputs"] = x
        log["reconstruction"] = xrec
        log["x_lr"] = x_low
        log[f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}"] = x_low_rec
        if self.model.conditioning_key is not None:
            if hasattr(self.cond_stage_model, "decode"):
                xc = self.cond_stage_model.decode(c)
                log["conditioning"] = xc
            elif self.cond_stage_key in ["caption", "txt"]:
                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[

[Assistant]
2] // 25)
                log["conditioning"] = xc
            elif self.cond_stage_key in ['class_label', 'cls']:
                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
                log['conditioning'] = xc
            elif isimage(xc):
                log["conditioning"] = xc
            if ismap(xc):
                log["original_conditioning"] = self.to_rgb(xc)


[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
################################################################################
# File:    generate_readme.py
# Version: 0.4
# Purpose: Creates the README.md file for this repo
# Authors: Michael Altfield michael@michaelaltfield.net
# Created: 2023-06-06
# Updated: 2023-08-04
################################################################################

################################################################################
#                                   IMPORTS                                    #
################################################################################

import json, csv, numpy, datetime, warnings
import pandas as pd

################################################################################
#                                  SETTINGS                                    #
################################################################################

LEMMY_STATS_CRAWLER_FILENAME = 'lemmy-stats-crawler.json'
LEMMY_STATS_CRAWLER_FILEPATH = 'lemmy-stats-crawler/' + LEMMY_STATS_CRAWLER_FILENAME

UPTIME_FILENAME = 'uptime.json'
AGE_FILENAME = 'age.json'

OUT_CSV = 'awesome-lemmy-instances.csv'

################################################################################
#                                  FUNCTIONS                                   #
################################################################################

# do some checks on text that we get back from the instance's API because it
# might break markdown or do something nasty
def sanitize_text( text ):

	# markdown table columns are delimited by pipes
	text = text.replace( '|', '' )

	# newlines
	text = text.replace( "\r", '' )
	text = text.replace( "\n", '' )

	# commas fuck with our CSV file; replace it with this homoglyph (0x201A)
	text = text.replace( ",", '‚' )

	return text

################################################################################
#                                  MAIN BODY                                   #
################################################################################

# catch runtime warnings from numpy on 'nan' errors when calculating averages
warnings.filterwarnings("error")

##################
# CHECK SETTINGS #
##############


[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 functools
import gc
import gevent
import logging
import pymongo
from pymongo.errors import AutoReconnect, ConnectionFailure, OperationFailure, TimeoutError
import signal
import sys

loggers = {}
def get_logger(name):
    """
    get a logger object with reasonable defaults for formatting

    @param name used to identify the logger (though not currently useful for anything)
    """
    global loggers
    if name in loggers:
        return loggers[name]

    logger = logging.getLogger(name)
    logger.setLevel(logging.DEBUG)
    sh = logging.StreamHandler()
    sh.setFormatter(logging.Formatter("%(asctime)s [%(levelname)s:%(processName)s] %(message)s",
                                      "%m-%d %H:%M:%S"))
    logger.addHandler(sh)

    loggers[name] = logger
    return logger

log = get_logger("utils")

def mongo_connect(host, port, ensure_direct=False, secondary_only=False, max_pool_size=1,
                  socketTimeoutMS=None, w=0, read_preference=None, document_class=dict,
                  replicaSet=None, slave_okay=None):
    """
    Same as MongoClient.connect, except that we are paranoid and ensure that cursors
    # point to what we intended to point them to. Also sets reasonable defaults for our
    needs.

    @param host            host to connect to
    @param port            port to connect to
    @param ensure_direct   do safety checks to ensure we are connected to specified mongo instance

    most other keyword arguments mirror those for pymongo.MongoClient
    """
    options = dict(
        host=host,
        port=port,
        socketTimeoutMS=socketTimeoutMS,
        use_greenlets=True,
        max_pool_size=max_pool_size,
        w=1,
        document_class=document_class)
    if replicaSet is not None:
        options['replicaSet'] = replicaSet
    if read_preference is not None:
        options['read_preference'] = read_preference
    if slave_okay is not None:
        options['slave_okay'] = slave_okay
    client

[Assistant] = pymongo.MongoClient(**options)


[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 pytest

import pandas as pd
import pandas._testing as tm
from pandas.api.extensions import ExtensionArray
from pandas.core.internals.blocks import EABackedBlock

class BaseConstructorsTests:
    def test_from_sequence_from_cls(self, data):
        result = type(data)._from_sequence(data, dtype=data.dtype)
        tm.assert_extension_array_equal(result, data)

        data = data[:0]
        result = type(data)._from_sequence(data, dtype=data.dtype)
        tm.assert_extension_array_equal(result, data)

    def test_array_from_scalars(self, data):
        scalars = [data[0], data[1], data[2]]
        result = data._from_sequence(scalars)
        assert isinstance(result, type(data))

    def test_series_constructor(self, data):
        result = pd.Series(data, copy=False)
        assert result.dtype == data.dtype
        assert len(result) == len(data)
        if hasattr(res

[Assistant]
ult._mgr, "blocks"):
            assert isinstance(result._mgr.blocks[0], EABackedBlock)
        assert result._mgr.array is data

        # Series[EA] is unboxed / boxed correctly
        result2 = pd.Series(result)
        assert result2.dtype == data.dtype
        if hasattr(result._mgr, "blocks"):
            assert isinstance(result2._mgr.blocks[0], EABackedBlock)


[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]
# -*- coding: utf-8 -*-

"""
httpbin.filters
~~~~~~~~~~~~~~~

This module provides response filter decorators.
"""

import gzip as gzip2
import zlib

import brotli as _brotli

from six import BytesIO
from decimal import Decimal
from time import time as now

from decorator import decorator
from flask import Flask, Response

app = Flask(__name__)

@decorator
def x_runtime(f, *args, **kwargs):
    """X-Runtime Flask Response Decorator."""

    _t0 = now()
    r = f(*args, **kwargs)
    _t1 = now()
    r.headers['X-Runtime'] = '{0}s'.format(Decimal(str(_t1 - _t0)))

    return r

@decorator
def gzip(f, *args, **kwargs):
    """GZip Flask Response Decorator."""

    data = f(*args, **kwargs)

    if isinstance(data, Response):
        content = data.data
    else:
        content = data

    gzip_buffer = BytesIO()
    gzip_file = gzip2.GzipFile(
        mode='wb',
        compresslevel=4,
        fileobj=gzip_buffer
    )
    gzip_file.write(content)
    gzip_file.close()

    gzip_data = gzip_buffer.getvalue()

    if isinstance(data, Response):
        data.data = gzip_data
        data.headers['Content-Encoding'] = 'gzip'
        data.headers['Content-Length'] = str(len(data.data))

        return data

    return gzip_data

@decorator
def deflate(f, *args, **kwargs):
    """Deflate Flask Response Decorator."""

    data = f(*args, **kwargs)

    if isinstance(data, Response):
        content = data.data
    else:
        content = data

    deflater = zlib.compressobj()
    deflated_data = deflater.compress(content)
    deflated_data += deflater.flush()

    if isinstance(data, Response):
        data.data = deflated_data
        data.headers['Content-Encoding'] = 'deflate'
        data.headers['Content-Length'] = str(len(data.data))

        ret

[Assistant]
urn data

    return deflated_data