Deepayan137

import spacy
import neuralcoref
import re
nlp = spacy.load('en')
neuralcoref.add_to_pipe(nlp, conv_dict={'Jack Porter': ['man', 'CEO'], 'Cognizer':['company', 'organization']})

history = ""
while True:
  text = input("Enter your text.\n")
  if text != "exit":

Deepayan137

import torch
from torch.utils.data import Dataset
import numpy as np
import pdb

class DummyDataset(Dataset):
    def __init__(self, prob, vocab_size=None,
                nSamples=None, max_len=None):
        self.prob = prob
        if not vocab_size: vocab_size = 10

Deepayan137

import torch
from torch.utils.data import Dataset
import numpy as np

class DummyDataset(Dataset):
    def __init__(self, **kwargs):
        self.prob = kwargs['prob']
        self.vocab_size = kwargs['vocab_size']
        self.nSamples = kwargs['nSamples']
        self.src_data = np.random.choice(self.vocab_size, 

Deepayan137

'STLR scheduler from https://arxiv.org/abs/1801.06146'

class STLR(_LRScheduler):
	def __init__(self, optimizer, T_max, last_epoch=-1, ratio=32):
		self.T_max = T_max
		self.cut =  np.floor(T_max*0.1)
		self.ratio = ratio
		super(STLR, self).__init__(optimizer, last_epoch)

	def get_lr(self):

Deepayan137

from collections import Counter, defaultdict
from textdistance import levenshtein as lev
import numpy as np
import pdb
from tqdm import *
def CharMajVoting(words):

    def most_frequent(list_):
        counter = Counter(list_)
        return counter.most_common()[0][0]

Deepayan137

def CharMajVoting(words):

    def most_frequent(list_):
        counter = Counter(list_)
        return counter.most_common()[0][0]
    dict_ = defaultdict(list)
    lengths = [len(word) for word in words]
    common_length = most_frequent(lengths)
    for word in words:
        for i in range(len(word)):

Deepayan137

import re
import sys
import os
import tempfile
import subprocess
import pdb
import pandas as pd
import numpy as np
from collections import defaultdict
from ocr.baselines.base_config import *

Deepayan137

import Levenshtein as lev

def cer(prediction, target):
    sum_edit_dists = lev.distance(prediction, target)
    sum_gt_lengths = sum(map(len, target))
    fraction = sum_edit_dists/sum_gt_lengths
    percent = fraction*100
    return (100.0-percent)

Deepayan137

import pandas as pd
from collections import defaultdict
import pdb
import numpy as np

def row(dict_ques):
        def dosomething(marks):
                marks = list(marks)
                total = min(np.sum(marks), 304)
                return total

Deepayan137

import cv2
import os
import sys 
from tqdm import *
def convert(**kwargs):
	dirname = kwargs['dir_']
	savedir = kwargs['save']
	image_paths = list(map(lambda f: dirname +'/'+f , os.listdir(dirname)))
	for path in tqdm(image_paths):
		image = cv2.imread(path)