Aunsiels

with Stm32.USB; use Stm32.USB;

procedure USBComm is
    C : Character;
begin

    loop
        C := Read;
        Send("Received : " & C & ". ");
    end loop;

Aunsiels

#include "hal.h"
#include "ch.h"
#include "sccb.h"
#include "chprintf.h"
#include <stdlib.h>
#include "usb_serial.h"

#define DELAY             2000
#define SCCB_UNINIT       0
#define SCCB_READY        1

Aunsiels

import os
import spacy
import inflect


_plural_engine = inflect.engine()
_nlp = spacy.load('en_core_web_sm')

class StatementMaker(object):

Aunsiels

def process_marked(marked, doc):
    res = []
    pos = 2
    done = False
    forbidden = None
    auxil = doc[1].text
    start_index = 2
    if doc[2].dep_ == "neg":
        auxil += " not"
        start_index = 3

Aunsiels

# An implementation of the gaussian naive bayes with missing values
# It has more or less the same interface as a sklearn classifier

import collections 
import numpy as np
import math


def get_prior(y, y_unique):
    y_counts = collections.Counter(y)

Aunsiels

import logging

from quasimodo.content_comparator import ContentComparator
from quasimodo.only_subject_submodule import get_subjects_in_all_forms


def get_parsing_tree(subjects):
    root = Node()
    for subject in subjects:
        root.add(subject)

Aunsiels

{"schemaVersion":1,"label":"Coverage Report","message":"99%","color":"brightgreen","namedLogo":"python"}

Aunsiels

Aunsiels

from peft import PeftModel
from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer
import torch

base_model = "/gpfswork/rech/zxg/ucq31xw/llama/models--decapoda-research--llama-7b-hf/snapshots/5f98eefcc80e437ef68d457ad7bf167c2c6a1348/"
model_str = "experiments/checkpoint-150/"

tokenizer = LlamaTokenizer.from_pretrained(base_model)

model = LlamaForCausalLM.from_pretrained(

Aunsiels

# Script to merge several scalars from different experiments.
# Experiments that follow the same pattern <name>_epoch<X>_part<Y> will be merged into a single directory <name>.
# The _part<Y> is optional
# If the experiment name contains no epoch and no part, it will be replicated several times
# (= max number of epochs in the experiments)
# The program takes as input the original runs directory and the wanted output merged runs directory.
# How to use:
# python tensorboard_merger.py source target
# Example:
# python tensorboard_merger.python /tmp/runs /tmp/runs_merged