Your task: Combine multiple texts into one detailed document. Include every piece of information from each source. The goal is to avoid repetition while being thorough and exhaustive.
Essential steps:
Warnings:
| import fire | |
| import gin | |
| from absl import flags | |
| from absl import app | |
| flags.DEFINE_multi_string( | |
| 'gin_file', None, 'List of paths to the config files.') | |
| flags.DEFINE_multi_string( | |
| 'gin_param', None, 'Newline separated list of Gin parameter bindings.') |
| import requests | |
| import json | |
| class GistDictionary: | |
| def __init__(self, github_token, base_gist_id): | |
| self.github_token = github_token | |
| self.base_gist_id = base_gist_id | |
| self.headers = { | |
| "Authorization": f"token {github_token}", | |
| "Accept": "application/vnd.github.v3+json", |
| import random | |
| from sympy import Eq, Symbol, solve | |
| from transitions import Machine | |
| class WordProblemStateMachine: | |
| def __init__(self): | |
| self.global_state = {} |
| import nltk | |
| import random | |
| class ProductionRule: | |
| def __init__(self, production): | |
| self.production = production | |
| def get_lhs(self): | |
| return self.production.lhs() |
| import networkx as nx | |
| from itertools import product | |
| """ | |
| When we compare this code with Airflow, the strengths of your code lie in its simplicity, lightweight nature, and the ability to easily integrate with existing Python code: | |
| Simplicity: This code provides a simple and straightforward way to model and work with DAGs without needing to go through the process of setting up and configuring a comprehensive system like Airflow. For smaller teams or projects with less complexity, this can be an advantage. | |
| Lightweight and easy to incorporate: Your code is a compact, single-file solution that can be easily integrated into an existing Python project without having to set up an entire Airflow environment. When your primary focus is on creating task dependencies with parameter combinations, rather than scheduling and monitoring, your code is easier to incorporate. | |
| Focused on task generation: Your code emphasizes creating a Cartesian product of tasks associated with nodes' parameters. It is geared towards tackling |
| import numpy as np | |
| class PythagoreanTriples: | |
| def __init__(self): | |
| self.m = 2 | |
| self.n = 1 | |
| def __iter__(self): | |
| return self | |
| def __next__(self): |
| #The following code is the result of taking the code for gpt-j-6b and changing the model name and config name to gpt-neox along with concating the gpt-neox code from pytorch | |
| # our job is to modify it such that it will be correct for gpt-neox, since right now it is just a copy of gpt-j-6b (with names replaced) | |
| from functools import partial | |
| from typing import Optional, Tuple | |
| import flax.linen as nn | |
| import jax | |
| import jax.numpy as jnp | |
| import numpy as np | |
| from flax.core.frozen_dict import FrozenDict, freeze, unfreeze |
| import multiprocessing | |
| import tensorflow as tf | |
| import seqio | |
| import numpy as np | |
| import tqdm | |
| from transformers import AutoTokenizer | |
| import jax | |
| tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") | |
| tokenizer.pad_token = tokenizer.eos_token |
| import inspect | |
| from contextlib import ContextDecorator | |
| class VariableCapture(ContextDecorator): | |
| def __enter__(self): | |
| frame = inspect.currentframe().f_back | |
| self.keys = {k for k,v in frame.f_locals.items() if k != 'self'} | |
| return self | |
| def __exit__(self, exc_type, exc_value, traceback): |