proger

//
// Generated by LLVM NVPTX Back-End
// See scan3 in https://github.com/proger/nanokitchen/blob/main/triscan.py
//

.version 8.1
.target sm_86
.address_size 64

	// .globl	scan3_0d1d2d

proger

import sys
import requests
import json

context = """[INST] They are planning to host a party next weekend. [/INST] Вони планують провести вечірку наступного вікенду.
[INST] I enjoy swimming in the ocean and feeling the salty breeze. [/INST] Мені подобається плавати в океані та відчувати солоний вітер.
[INST]"""

# docker run --gpus all -p 8000:8000 -e HF_HOME=/hf -e CUDA_VISIBLE_DEVICES=1 -v ~/.cache/huggingface:/hf ghcr.io/mistralai/mistral-src/vllm:latest --host 0.0.0.0 --model mistralai/Mistral-7B-v0.1
def prompt(input, url="http://localhost:8000/v1/completions"):

proger

import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
import gzip                                                                                                    
import numpy as np                                                                                             

torch.set_float32_matmul_precision('high')


def read(filename):                                    

proger

import gzip
import numpy as np
from sklearn.cluster import MiniBatchKMeans

def read(filename):
    with gzip.open(filename, 'rb') as file:
        compressed_data = file.read()

    data = np.frombuffer(compressed_data, dtype=np.float32)
    return data

proger

"this module sets phasors to stun using gradient descent"
import torch
import torch.nn as nn
import torch.nn.utils.parametrize as parametrize

tau = 6.28
stun = 0.25*tau

class Cyclic(nn.Module):
    def forward(self, x):

proger

This patch makes robomaster_sim work with Coppelia 4.6 

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 03c1ab6..be6e2cf 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -11,7 +11,7 @@ IF (WIN32)
 
 ELSE()
     set(CMAKE_CXX_STANDARD 17)

proger

"accumulate repeating characters: convolution frontend, RNN backend"

import collections
from itertools import islice

def conv(iterable, n=2):
    "1d convolution"
    it = iter(iterable)
    window = collections.deque(islice(it, n-1), maxlen=n)
    for x in it:

proger

import torch
import torch.nn as nn
import torch.nn.functional as F


def forward_rnn(forget, input, output, hidden, T, x):
    outputs = []

    for t in range(T):
        u = input(x[:, t, :])

proger

import torch
import torch.nn as nn


def forward_rnn(forget, input, output, hidden, T, x):
    outputs = []

    for t in range(T):
        hidden = (forget(hidden) + input(x[:, t, :])).relu()
        outputs.append(output(hidden))

proger

import torch
import torch.nn as nn
import math


def scan0(f, x):
    y = [torch.zeros_like(x[..., 0])]
    for i in range(0, x.size(-1)):
        y.append(f*y[..., i-1] + x[..., i])
    return torch.stack(y, dim=-1)