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#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <xmmintrin.h>
#include <mmintrin.h>
#include "matrix.h"
#include "z_order.h"

using namespace nda;

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static uint32_t *
fast_bsearch_u32(uint32_t *base, size_t nmemb, uint32_t key) {
    while (nmemb) {
        size_t half = nmemb >> 1;
        if (base[half] < key) {
            base += nmemb - half;
        }
        nmemb = half;
    }
    return base;

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// Compile and run with:
//
//   gcc -O3 -march=native fast-strlen.c -lpthread -o fast-strlen
//       && ./fast-strlen
//
// Use gcc because clang is too smart and optimizes away parts of the
// benchmark. Results on Xeon(R) CPU E5-2650 v4 @ 2.20GHz with gcc
// 9.4.0:
//
//   Scanning 10 times over 4.00GB...

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// Notes:
//
// * unsigned int vs. int: makes a small difference for clang but
//   probably not for gcc.
// * best tiling appears to be 256x256x256.
//
// 12.31 for two 8192 matrices
//
//
#include <assert.h>

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// Notes:
//
// * unsigned int vs. int: makes a small difference for clang but
//   probably not for gcc.
// * best tiling appears to be 256x256x256.
//
// 12.31 for two 8192 matrices
#include <assert.h>
#include <math.h>
#include <pthread.h>

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# Usage:
#     python cas.py simplify "(x+y)**30*(2*x)+123*z**8"
#     python cas.py solve "3*x**2 == -x**2 + 5*x"
import math, sys
from ast import *
from functools import reduce
from collections import Counter, defaultdict

def parse_mv(mv):
    def pow(n, e):

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from ast import *
from pygraphviz import AGraph
from re import sub

COLOR_KWD = '#a020f0'
COLOR_VAR = '#6c71c4'
COLOR_STR = '#8b2252'

def colorize(s, col):
    return '<font color="%s">%s</font>' % (col, s)

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<nav class="navbar navbar-default navbar-fixed-top navbar-inverse">
  <div class="container-fluid">
    <!-- Brand and toggle get grouped for better mobile display -->
    <div class="navbar-header">
      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#bs-example-navbar-collapse-1">
        <span class="sr-only">Toggle navigation</span>
        <span class="icon-bar"></span>
        <span class="icon-bar"></span>
        <span class="icon-bar"></span>
      </button>

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from observations import ptb
from time import sleep, time
from torch.nn import *
from torch.optim import *
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from torch_xla.core.xla_model import (get_ordinal,
                                      is_master_ordinal,
                                      master_print,
                                      xla_device,

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"""
Character-based language model in TensorFlow
============================================
A character-based language model written in TensorFlow and trained on
the Penn Treebank dataset. The model can either be trained using TFs
model.fit() method (auto) or using a custom training loop
(manual). Results should not differ.

Usage:
    char_lm_tf.py [options] ( manual | auto )