游刃 Elfsong

## movie_studio.py
import os
import time
from multiprocessing import Process

import cocos
import cv2
import numpy as np
import pyglet
from PIL import ImageGrab
from cocos.actions import CallFunc

## pinyin
# coding: utf-8
import ast
import clr
import json
import os
import re
from StoryTelling.tts import append_pronunciation
from StoryTelling.tts.tools import OnlineTts
from Speech.Cognitive.Services import TimeLine

## average_split.py
def avsplit1(s, n):
    fn = len(s)//n
    rn = len(s)%n
    ar = [fn+1]*rn+ [fn]*(n-rn)
    si = [i*(fn+1) if i<rn else (rn*(fn+1)+(i-rn)*fn) for i in range(n)]
    sr = [s[si[i]:si[i]+ar[i]] for i in range(n)]
    return sr

def avsplit2(s, n):
    fn = len(s)//n

## V_Byte_V1.py
def vbyte_encode(num):

    # out_bytes stores a list of output bytes encoding the number
    out_bytes = []

    ###
    # Your answer BEGINS HERE
    ###
    while num >= 128:
        out_bytes = [num % 128] + out_bytes

## split_story_dialogue.py
import json
import os
import re
import jieba

ROOT_PATH = "/Users/elfsong/PycharmProjects/BERT_demo"

RESOURCE_PATH = os.path.join(ROOT_PATH, "Test text")


## combinations pair
(_,_,_) -> (0,_,_) = remove all three pitchs
(_,_,_) -> (a,b,c) = a + b == 3 or a + c == 3 retrive all three notes or octaves


## server.java
// TODO: FILE_BYTES_REQUEST

for (int initial_position = 0; initial_position < qq.fileDescriptor.fileSize; ) {
  long offset = 0;
  long remained = qq.fileDescriptor.fileSize - initial_position;

  if (remained > blockSize) {
    offset = blockSize;
  } else {
    offset = remained;

## esim.py
"""
Implementation of ESIM(Enhanced LSTM for Natural Language Inference)
https://arxiv.org/abs/1609.06038
"""
import numpy as np
from keras.layers import *
from keras.activations import softmax
from keras.models import Model

def StaticEmbedding(embedding_matrix):

## git_stat.sh
git log --format='%aN' | sort -u | while read name; do echo -en "$name\t"; git log --author="$name" --pretty=tformat: --since ==2020–05-02 --until=2018-06-11 --numstat | awk '{ add += $1; subs += $2; loc += $1 - $2 } END { printf "added lines: %s, removed lines: %s, total lines: %s\n", add, subs, loc }' -; done
	import os
	import time
	from multiprocessing import Process

	import cocos
	import cv2
	import numpy as np
	import pyglet
	from PIL import ImageGrab
	from cocos.actions import CallFunc
	# coding: utf-8
	import ast
	import clr
	import json
	import os
	import re
	from StoryTelling.tts import append_pronunciation
	from StoryTelling.tts.tools import OnlineTts
	from Speech.Cognitive.Services import TimeLine
	def avsplit1(s, n):
	fn = len(s)//n
	rn = len(s)%n
	ar = [fn+1]rn+ [fn](n-rn)
	si = [i(fn+1) if i<rn else (rn(fn+1)+(i-rn)*fn) for i in range(n)]
	sr = [s[si[i]:si[i]+ar[i]] for i in range(n)]
	return sr

	def avsplit2(s, n):
	fn = len(s)//n
	def vbyte_encode(num):

	# out_bytes stores a list of output bytes encoding the number
	out_bytes = []

	###
	# Your answer BEGINS HERE
	###
	while num >= 128:
	out_bytes = [num % 128] + out_bytes
	(_,_,_) -> (0,_,_) = remove all three pitchs
	(_,_,_) -> (a,b,c) = a + b == 3 or a + c == 3 retrive all three notes or octaves
	// TODO: FILE_BYTES_REQUEST

	for (int initial_position = 0; initial_position < qq.fileDescriptor.fileSize; ) {
	long offset = 0;
	long remained = qq.fileDescriptor.fileSize - initial_position;

	if (remained > blockSize) {
	offset = blockSize;
	} else {
	offset = remained;
	"""
	Implementation of ESIM(Enhanced LSTM for Natural Language Inference)
	https://arxiv.org/abs/1609.06038
	"""
	import numpy as np
	from keras.layers import *
	from keras.activations import softmax
	from keras.models import Model

	def StaticEmbedding(embedding_matrix):