Andrew Me veonua

## Docker
FROM depositame/xform_8bit_pytorch

RUN apt-get -y install git-lfs
RUN mkdir /model
WORKDIR /model/
RUN git lfs clone https://huggingface.co/eachadea/vicuna-13b .
RUN rm -rf ./git

RUN pip3 install fschat
RUN pip3 install git+https://github.com/huggingface/transformers

## samsung dishwasher protocol
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## vectorize_float.py
import numpy as np

def vectorize(val:float):
    INT   = 5
    FRACT = 2
    r = np.zeros((INT+FRACT, 10))
    ff = f'%{FRACT+INT+1}.{FRACT}f'
    s = ff%val
    i = -1
    for c in s:

## rotate3d.py
def resize_f(img, bo, _begin, _end):
    k = 8
    return tf.image.resize_with_pad(img, int(4032/k), int(3024 /k), method=tf.image.ResizeMethod.AREA) #, bo, _begin, _end


def random_rotate(image):
    r = tf.random.uniform([3])*.3 # numpy.random.rand((3)) * 0.3
    transform = tf.compat.v1.py_func( rotate3d_transform, [image, r[0],r[1],r[2]], tf.float32 )
    return tfa.image.transform(image, transforms=tf.reshape(transform,[-1])[:8], interpolation='BILINEAR')


## ragna.py
import collections
IslandInfo = collections.namedtuple('IslandInfo', 'area height_sum')


c_clabel = -1

colors = np.zeros_like(padded).astype(int)
heights = list()
area = list()
associations = list()

## fixLayout.kt
import com.uchuhimo.collections.BiMap
import com.uchuhimo.collections.toBiMap

val eng = "~!@#$%^&*()QWERTYUIOP{}|ASDFGHJKL:\"ZXCVBNM<>?`qwertyuiop[]asdfghjkl;'zxcvbnm,./"
val ru = "Ё!\"№;%:?*()ЙЦУКЕНГШЩЗХЪ/ФЫВАПРОЛДЖЭЯЧСМИТЬБЮ,ёйцукенгшщзхъфывапролджэячсмитьбю."

val biMap: BiMap<Char, Char> by lazy {
    eng.zip(ru).toMap().toBiMap()
}

## load_video.py
import subprocess as sp

f_width = 1024
f_height = 576
f_channels = 1
f_batch_size = 1

command = [ "ffmpeg",
            '-i', '/home/veon/Downloads/BybanenBareFront.1024x576p50.h264.Nrk.mp4',
            '-f', 'image2pipe',

## k_autoencoder.ipyb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {

## kcaptcha_loader.py
import matplotlib.pyplot as plt
import tensorflow as tf
from IPython.display import Image

filename = "/home/veon/temp/kcaptcha/generate/*.png"
filename_queue = tf.train.string_input_producer(tf.train.match_filenames_once(filename))

reader = tf.WholeFileReader()
key, value = reader.read(filename_queue)

## Log.java
package com.example;

import com.logentries.logger.AndroidLogger;

import static android.util.Log.getStackTraceString;

/**
 * Logentries Proxy
 */
public class Log {
	FROM depositame/xform_8bit_pytorch

	RUN apt-get -y install git-lfs
	RUN mkdir /model
	WORKDIR /model/
	RUN git lfs clone https://huggingface.co/eachadea/vicuna-13b .
	RUN rm -rf ./git

	RUN pip3 install fschat
	RUN pip3 install git+https://github.com/huggingface/transformers
	005d550f2a13313c0000011700161830
	0055550f2a13313c0000011700161830
	closed
	---

	0055550f2a13313c0000011700161838
	005d550f2a13313c0000011700161838
	opened

	005d550f2a13313c0000011700161830
	import numpy as np

	def vectorize(val:float):
	INT = 5
	FRACT = 2
	r = np.zeros((INT+FRACT, 10))
	ff = f'%{FRACT+INT+1}.{FRACT}f'
	s = ff%val
	i = -1
	for c in s:
	def resize_f(img, bo, _begin, _end):
	k = 8
	return tf.image.resize_with_pad(img, int(4032/k), int(3024 /k), method=tf.image.ResizeMethod.AREA) #, bo, _begin, _end


	def random_rotate(image):
	r = tf.random.uniform([3]).3 # numpy.random.rand((3)) 0.3
	transform = tf.compat.v1.py_func( rotate3d_transform, [image, r[0],r[1],r[2]], tf.float32 )
	return tfa.image.transform(image, transforms=tf.reshape(transform,[-1])[:8], interpolation='BILINEAR')
	import collections
	IslandInfo = collections.namedtuple('IslandInfo', 'area height_sum')


	c_clabel = -1

	colors = np.zeros_like(padded).astype(int)
	heights = list()
	area = list()
	associations = list()
	import com.uchuhimo.collections.BiMap
	import com.uchuhimo.collections.toBiMap

	val eng = "~!@#$%^&*()QWERTYUIOP{}\|ASDFGHJKL:\"ZXCVBNM<>?`qwertyuiop[]asdfghjkl;'zxcvbnm,./"
	val ru = "Ё!\"№;%:?*()ЙЦУКЕНГШЩЗХЪ/ФЫВАПРОЛДЖЭЯЧСМИТЬБЮ,ёйцукенгшщзхъфывапролджэячсмитьбю."

	val biMap: BiMap<Char, Char> by lazy {
	eng.zip(ru).toMap().toBiMap()
	}
	import subprocess as sp

	f_width = 1024
	f_height = 576
	f_channels = 1
	f_batch_size = 1

	command = [ "ffmpeg",
	'-i', '/home/veon/Downloads/BybanenBareFront.1024x576p50.h264.Nrk.mp4',
	'-f', 'image2pipe',
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	import matplotlib.pyplot as plt
	import tensorflow as tf
	from IPython.display import Image

	filename = "/home/veon/temp/kcaptcha/generate/*.png"
	filename_queue = tf.train.string_input_producer(tf.train.match_filenames_once(filename))

	reader = tf.WholeFileReader()
	key, value = reader.read(filename_queue)
	package com.example;

	import com.logentries.logger.AndroidLogger;

	import static android.util.Log.getStackTraceString;

	/**
	* Logentries Proxy
	*/
	public class Log {