eveem

t = int(input())

for r in range(1, t + 1):
    n = int(input())
    m = []
    for _ in range(n):
        m.append(input())

    fin_by = set()

eveem

t = int(input())

for r in range(1, t + 1):
    s = input()
    k = int(input())
    mapper = {}
    target = set()
    level = {f"{i}{i}": 0 for i in s}

    for _ in range(k):

eveem

t = int(input())

for r in range(1, t + 1):
    s = input()
    n = len(s)
    m = float("inf")

    for i in range(26):
        counter = 0
        curr = chr(ord("A") + i)

eveem

test_images[['image', 'annotated_predictions']].explore()

eveem

# load model
model = tc.load_model('./pokemon_5000.model')

# read images
test_images = tc.SFrame({'image':[tc.Image('dataset/pokemon_test1.jpg'), 
                                  tc.Image('dataset/pokemon_test2.jpeg'),
                                  tc.Image('dataset/pokemon_test3.jpg'),
                                  tc.Image('dataset/pokemon_test4.jpeg')]})

# predict

eveem

import turicreate as tc

starter_images = tc.SFrame({'image':[tc.Image('dataset/pokemon.png')], 'label':['pokemon']})

model = tc.one_shot_object_detector.create(starter_images, 'label', max_iterations=5000)
model.save('pokemon_5000.model')

eveem

f = open('ach_output.txt', 'w')

t = int(input())

for i in range(t):
    n = int(input())
    s = input()
    a = s.count('A')
    b = s.count('B')

eveem

f = open('output.txt', 'w')
t = int(input())

for a in range(t):
    n = int(input())
    i = input()
    o = input()

    result = []
    for b in range(n):

eveem

model = Sequential()
model.add(Bidirectional(LSTM(128), input_shape=(SEQUENCE_LEN, len(words))))
model.add(Dense(len(words)))
model.add(Activation("softmax"))
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

model.fit_generator(
  generator(sentences, next_words, BATCH_SIZE),
  steps_per_epoch=int(len(sentences) / BATCH_SIZE) + 1,
  epochs=EPOCHS,

eveem

word_indices = dict((c, i) for i, c in enumerate(words))
indices_word = dict((i, c) for i, c in enumerate(words))

for i in range(0, len(text_in_words) - SEQUENCE_LEN):
  if (len(set(text_in_words[i : i + SEQUENCE_LEN + 1]).intersection(ignored_words)) == 0):
    sentences.append(text_in_words[i : i + SEQUENCE_LEN])
    next_words.append(text_in_words[i + SEQUENCE_LEN])