import math, re, os
import tensorflow as tf
import numpy as np
from matplotlib import pyplot as plt
from kaggle_datasets import KaggleDatasets
from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix
print("Tensorflow version " + tf.__version__)
AUTO = tf.data.experimental.AUTOTUNE

try:
    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()  # TPU detection. No parameters necessary if TPU_NAME environment variable is set. On Kaggle this is always the case.
    print('Running on TPU ', tpu.master())
except ValueError:
    tpu = None

if tpu:
    tf.config.experimental_connect_to_cluster(tpu)
    tf.tpu.experimental.initialize_tpu_system(tpu)
    strategy = tf.distribute.experimental.TPUStrategy(tpu)
else:
    strategy = tf.distribute.get_strategy() # default distribution strategy in Tensorflow. Works on CPU and single GPU.

print("REPLICAS: ", strategy.num_replicas_in_sync)

GCS_DS_PATH = KaggleDatasets().get_gcs_path() # you can list the bucket with "!gsutil ls $GCS_DS_PATH"


IMAGE_SIZE = [512, 512] # At this size, a GPU will run out of memory. Use the TPU.
                        # For GPU training, please select 224 x 224 px image size.
EPOCHS = 12
BATCH_SIZE = 16 * strategy.num_replicas_in_sync

GCS_PATH_SELECT = { # available image sizes
    192: GCS_DS_PATH + '/tfrecords-jpeg-192x192',
    224: GCS_DS_PATH + '/tfrecords-jpeg-224x224',
    331: GCS_DS_PATH + '/tfrecords-jpeg-331x331',
    512: GCS_DS_PATH + '/tfrecords-jpeg-512x512'
}
GCS_PATH = GCS_PATH_SELECT[IMAGE_SIZE[0]]

TRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec')
VALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')
TEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec') # predictions on this dataset should be submitted for the competition

CLASSES = ['pink primrose',    'hard-leaved pocket orchid', 'canterbury bells', 'sweet pea',     'wild geranium',     'tiger lily',           'moon orchid',              'bird of paradise', 'monkshood',        'globe thistle',         # 00 - 09
           'snapdragon',       "colt's foot",               'king protea',      'spear thistle', 'yellow iris',       'globe-flower',         'purple coneflower',        'peruvian lily',    'balloon flower',   'giant white arum lily', # 10 - 19
           'fire lily',        'pincushion flower',         'fritillary',       'red ginger',    'grape hyacinth',    'corn poppy',           'prince of wales feathers', 'stemless gentian', 'artichoke',        'sweet william',         # 20 - 29
           'carnation',        'garden phlox',              'love in the mist', 'cosmos',        'alpine sea holly',  'ruby-lipped cattleya', 'cape flower',              'great masterwort', 'siam tulip',       'lenten rose',           # 30 - 39
           'barberton daisy',  'daffodil',                  'sword lily',       'poinsettia',    'bolero deep blue',  'wallflower',           'marigold',                 'buttercup',        'daisy',            'common dandelion',      # 40 - 49
           'petunia',          'wild pansy',                'primula',          'sunflower',     'lilac hibiscus',    'bishop of llandaff',   'gaura',                    'geranium',         'orange dahlia',    'pink-yellow dahlia',    # 50 - 59
           'cautleya spicata', 'japanese anemone',          'black-eyed susan', 'silverbush',    'californian poppy', 'osteospermum',         'spring crocus',            'iris',             'windflower',       'tree poppy',            # 60 - 69
           'gazania',          'azalea',                    'water lily',       'rose',          'thorn apple',       'morning glory',        'passion flower',           'lotus',            'toad lily',        'anthurium',             # 70 - 79
           'frangipani',       'clematis',                  'hibiscus',         'columbine',     'desert-rose',       'tree mallow',          'magnolia',                 'cyclamen ',        'watercress',       'canna lily',            # 80 - 89
           'hippeastrum ',     'bee balm',                  'pink quill',       'foxglove',      'bougainvillea',     'camellia',             'mallow',                   'mexican petunia',  'bromelia',         'blanket flower',        # 90 - 99
           'trumpet creeper',  'blackberry lily',           'common tulip',     'wild rose']                                                                                                                                               # 100 - 102


def decode_image(image_data):
    image = tf.image.decode_jpeg(image_data, channels=3)
    image = tf.cast(image, tf.float32) / 255.0  # convert image to floats in [0, 1] range
    image = tf.reshape(image, [*IMAGE_SIZE, 3]) # explicit size needed for TPU
    return image

def read_labeled_tfrecord(example):
    LABELED_TFREC_FORMAT = {
        "image": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring
        "class": tf.io.FixedLenFeature([], tf.int64),  # shape [] means single element
    }
    example = tf.io.parse_single_example(example, LABELED_TFREC_FORMAT)
    image = decode_image(example['image'])
    label = tf.cast(example['class'], tf.int32)
    return image, label # returns a dataset of (image, label) pairs

def read_unlabeled_tfrecord(example):
    UNLABELED_TFREC_FORMAT = {
        "image": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring
        "id": tf.io.FixedLenFeature([], tf.string),  # shape [] means single element
        # class is missing, this competitions's challenge is to predict flower classes for the test dataset
    }
    example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT)
    image = decode_image(example['image'])
    idnum = example['id']
    return image, idnum # returns a dataset of image(s)

def load_dataset(filenames, labeled=True, ordered=False):
    # Read from TFRecords. For optimal performance, reading from multiple files at once and
    # disregarding data order. Order does not matter since we will be shuffling the data anyway.

    ignore_order = tf.data.Options()
    if not ordered:
        ignore_order.experimental_deterministic = False # disable order, increase speed

    dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO) # automatically interleaves reads from multiple files
    dataset = dataset.with_options(ignore_order) # uses data as soon as it streams in, rather than in its original order
    dataset = dataset.map(read_labeled_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls=AUTO)
    # returns a dataset of (image, label) pairs if labeled=True or (image, id) pairs if labeled=False
    return dataset

def data_augment(image, label):
    # data augmentation. Thanks to the dataset.prefetch(AUTO) statement in the next function (below),
    # this happens essentially for free on TPU. Data pipeline code is executed on the "CPU" part
    # of the TPU while the TPU itself is computing gradients.
    image = tf.image.random_flip_left_right(image)
    #image = tf.image.random_saturation(image, 0, 2)
    return image, label   

def get_training_dataset():
    dataset = load_dataset(TRAINING_FILENAMES, labeled=True)
    dataset = dataset.map(data_augment, num_parallel_calls=AUTO)
    dataset = dataset.repeat() # the training dataset must repeat for several epochs
    dataset = dataset.shuffle(2048)
    dataset = dataset.batch(BATCH_SIZE)
    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)
    return dataset

def get_validation_dataset(ordered=False):
    dataset = load_dataset(VALIDATION_FILENAMES, labeled=True, ordered=ordered)
    dataset = dataset.batch(BATCH_SIZE)
    dataset = dataset.cache()
    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)
    return dataset

def get_test_dataset(ordered=False):
    dataset = load_dataset(TEST_FILENAMES, labeled=False, ordered=ordered)
    dataset = dataset.batch(BATCH_SIZE)
    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)
    return dataset

def count_data_items(filenames):
    # the number of data items is written in the name of the .tfrec files, i.e. flowers00-230.tfrec = 230 data items
    n = [int(re.compile(r"-([0-9]*)\.").search(filename).group(1)) for filename in filenames]
    return np.sum(n)

NUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)
NUM_VALIDATION_IMAGES = count_data_items(VALIDATION_FILENAMES)
NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)
STEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE
print(STEPS_PER_EPOCH)
print('Dataset: {} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))

with strategy.scope():
    #pretrained_model = tf.keras.applications.DenseNet201(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])
    #pretrained_model = tf.keras.applications.Xception(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])
    pretrained_model = tf.keras.applications.VGG19(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])
    pretrained_model.trainable = True # False = transfer learning, True = fine-tuning
    
    for i in pretrained_model.layers[:-5]:
        print(i.name)
        i.trainable = False
    
    model = tf.keras.Sequential([
        pretrained_model,
        tf.keras.layers.GlobalAveragePooling2D(),
        tf.keras.layers.Dense(len(CLASSES), activation='softmax')
    ])
        
model.compile(
    optimizer='adam',
    loss = 'sparse_categorical_crossentropy',
    metrics=['sparse_categorical_accuracy']
)
model.summary()
EPOCHS = 25

history = model.fit(get_training_dataset(), steps_per_epoch=STEPS_PER_EPOCH, epochs=EPOCHS, validation_data=get_validation_dataset())

test_ds = get_test_dataset(ordered=True) # since we are splitting the dataset and iterating separately on images and ids, order matters.

print('Computing predictions...')
test_images_ds = test_ds.map(lambda image, idnum: image)
probabilities = model.predict(test_images_ds)
predictions = np.argmax(probabilities, axis=-1)
print(predictions)

print('Generating submission.csv file...')
test_ids_ds = test_ds.map(lambda image, idnum: idnum).unbatch()
test_ids = next(iter(test_ids_ds.batch(NUM_TEST_IMAGES))).numpy().astype('U') # all in one batch
np.savetxt('submission.csv', np.rec.fromarrays([test_ids, predictions]), fmt=['%s', '%d'], delimiter=',', header='id,label', comments='')