sa-

"""
Add this to pre-commit config
```
  - repo: local
    hooks:
      - id: helptext-compiler
        name: helptext-compiler
        description: "Generates help texts for the CLI"
        language: system
        entry: poetry run python path/to/this/file/helptext_compiler.py

sa-

Agieren Sie als Assistent für deutsche Konversationsübungen. Ihre Aufgabe ist es, das Gespräch in Gang zu halten.
Antworten Sie immer mit maximal 3-5 Sätzen. Seien Sie locker und informell.
Wenn ein Nutzer etwas auf Englisch sagt, übersetze es ins Deutsche und zeige es als Korrektur an.
Wenn der Benutzer bei jeder Antwort einen Fehler macht,
zeigen Sie die Korrekturen in einem Abschnitt namens "Korrekturen" an,
bevor Sie in einem Abschnitt namens "Antwort" antworten.

Hier ist ein Beispiel für die Struktur, wenn es Korrekturen gibt.
"""
**Korrekturen**

sa-

@value
struct State[T: AnyRegType]:
    var _value: T

    fn get(self) -> T:
        return self._value

    fn set(inout self, value: T):
        self._value = value

sa-

#!/bin/sh

if [ "$#" -ne 1 ]; then
    echo "Usage: $0 [URL]"
    exit 1
fi

url="$1"
repo_name=$(basename "$url" .git)
org_name=$(echo "$url" | sed -E 's/^.*[/:]([^/]+)\/[^/]+$/\1/')

sa-

.PHONY: dev
dev:
	pip install -qU pip
	poetry config virtualenvs.in-project true
	poetry install --no-root
	poetry run pre-commit install
	poetry run pre-commit run -a

.PHONY: fmt
fmt:

sa-

"""
initial prompt:
assuming github issues will have their first line in the body as "#dep: #1, #2, #4", 
write a python script to get all open issues in a repo using the github rest API that 
will parse this information and create a flowchart using mermaid.js to show the issue 
dependenices. Also if there are no dependencies for an issue, add it to the flowchart 
anyway. Remember to do a null check on the body before checking if a string exists 
inside it. Use the requests library and not the github library
"""

sa-

FROM python:3.8-slim

WORKDIR /tmp
ENV SPARK_HOME=/opt/spark-3.1.2-bin-hadoop3.2

# Spark, julia
RUN apt update \
    && apt-get install -y \
       wget htop zsh git gnupg curl zip unzip vim cmake tmux sudo openjdk-11-jre \
    && wget https://dlcdn.apache.org/spark/spark-3.1.2/spark-3.1.2-bin-hadoop3.2.tgz \

sa-

function divide_range_into_max_n_chunks(r::UnitRange{Int64}, n::Int64)
    range_length = r.stop - r.start
    chunk_size = Int64(ceil(range_length / n))
    return [i:min(i + chunk_size - 1, r.stop) for i in r.start:chunk_size:r.stop]
end

macro parallel_broadcast(col_a, broadcasted_operator, col_b)
    return (:macrocall, Symbol("@threads"), :(#= none:1 =#), (:for, (:(=), :subrange, (:call, :divide_range_into_n_chunks, (:call, :(:), 1, :df_length), (:call, :nthreads))), (:block,
      :(#= none:2 =#),
      (:(=), (:ref, :result_col, :subrange), (:call, broadcasted_operator, (:call, :view, col_a, :subrange), (:call, :view, col_b, :subrange)))

sa-

# Install requirements
sudo apt-get install -y build-essential checkinstall libreadline-gplv2-dev libncursesw5-dev libssl-dev libsqlite3-dev tk-dev libgdbm-dev libc6-dev libbz2-dev zlib1g-dev openssl libffi-dev python3-dev python3-setuptools wget sudo apt-get install liblzma-dev libssl-dev libncurses5-dev libsqlite3-dev libreadline-dev libtk8.5 libgdm-dev libdb4o-cil-dev libpcap-dev

# Prepare to build
mkdir /tmp/Python37
cd /tmp/Python37

# Install

sa-

# Compute ndcg @ k

def ndcg_at_k(predictions_df, k):
    """
    This pandas dataframe should contain the columns "customer_id",
    "estimate", and "label". 
    
    Where `estimate` is a recommendation score
    that we can sort by descending order.