import os, random, requests
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers, Model
import sentencepiece as spm

# =========================
# 설정
# =========================
TOKENIZER_PATH = "bpe.model"
DATA_PATH = "shuffled_corpus.txt"
MAX_LEN = 128
EMBED_DIM = 384
LATENT_DIM = 384
BATCH_SIZE = 512
EPOCHS = 1
SHUFFLE_BUFFER = 200000
LEARNING_RATE = 1e-4
TEMPERATURE = 0.05
DROPOUT_AUG = 0.1
EMBED_DROPOUT = 0.1

def download_file(url, save_path):
    if os.path.exists(save_path):
        print(f"exists: {save_path}")
        return
    print(f"Downloading {save_path} ...")
    r = requests.get(url, stream=True)
    r.raise_for_status()
    with open(save_path, "wb") as f:
        for chunk in r.iter_content(8192*2):
            if not chunk:
                break
            f.write(chunk)
    print(f"✅ {save_path} saved")

download_file(
    "https://huggingface.co/datasets/OpenLab-NLP/ko-corpus/resolve/main/bpe.model?download=true",
    TOKENIZER_PATH
)
download_file(
    "https://huggingface.co/datasets/OpenLab-NLP/ko-corpus/resolve/main/shuffled_corpus%20(1).txt?download=true",
    DATA_PATH
)

sp = spm.SentencePieceProcessor()
sp.load(TOKENIZER_PATH)
pad_id = sp.piece_to_id("<pad>") if sp.piece_to_id("<pad>") != -1 else 0
vocab_size = sp.get_piece_size()

# Python-side encoder for small utility
def encode_sentence_py(s: str):
    ids = sp.encode(s, out_type=int)[:MAX_LEN]
    if len(ids) < MAX_LEN:
        ids = ids + [pad_id] * (MAX_LEN - len(ids))
    else:
        ids = ids[:MAX_LEN]
    return np.array(ids, dtype=np.int32)

def tf_encode(line):
    # line: tf.Tensor (tf.string)
    def _encode_py(s_tensor):
        # s_tensor는 tf.Tensor -> numpy bytes
        s = s_tensor.numpy().decode("utf-8")
        return encode_sentence_py(s)
    
    # tf.py_function은 tf.Tensor -> tf.int32
    ids = tf.py_function(func=_encode_py, inp=[line], Tout=tf.int32)
    ids.set_shape([MAX_LEN])
    return ids

def token_dropout(tokens, drop_prob=DROPOUT_AUG):
    # tokens: (MAX_LEN,) int32
    rnd = tf.random.uniform(tf.shape(tokens), 0, 1)
    keep_mask = rnd > drop_prob
    return tf.where(keep_mask, tokens, tf.cast(pad_id, tf.int32))

def make_views(tokens):
    v1 = token_dropout(tokens)
    v2 = token_dropout(tokens)
    return v1, v2

ds = tf.data.TextLineDataset(DATA_PATH)
ds = ds.map(lambda x: tf.strings.strip(x), num_parallel_calls=tf.data.AUTOTUNE)
ds = ds.filter(lambda x: tf.not_equal(x, ""))

# encode
ds = ds.map(tf_encode, num_parallel_calls=tf.data.AUTOTUNE)

# shuffle, repeat, create views, batch
ds = ds.shuffle(SHUFFLE_BUFFER)
ds = ds.repeat()
ds = ds.map(lambda t: make_views(t), num_parallel_calls=tf.data.AUTOTUNE)
ds = ds.batch(BATCH_SIZE, drop_remainder=True)  # (BATCH, MAX_LEN) for v1 and v2
# model.fit expects (inputs, labels)
ds = ds.map(lambda v1, v2: ((v1, v2), tf.zeros([BATCH_SIZE], dtype=tf.float32)), num_parallel_calls=tf.data.AUTOTUNE)
ds = ds.prefetch(tf.data.AUTOTUNE)

class DynamicConv(layers.Layer):
    def __init__(self, k=7):
        super().__init__()
        assert k % 2 == 1
        self.k = k
        self.generator = layers.Dense(k)
    def call(self, x):
        B = tf.shape(x)[0]
        L = tf.shape(x)[1]
        D = tf.shape(x)[2]
        kernels = self.generator(x)  # (B,L,k)
        kernels = tf.nn.softmax(kernels, axis=-1)
        pad = (self.k - 1) // 2
        x_pad = tf.pad(x, [[0,0],[pad,pad],[0,0]])
        x_pad_4d = tf.expand_dims(x_pad, axis=1)
        patches = tf.image.extract_patches(
            images=x_pad_4d,
            sizes=[1,1,self.k,1],
            strides=[1,1,1,1],
            rates=[1,1,1,1],
            padding='VALID'
        )  # (B,1,L,k*D)
        patches = tf.reshape(patches, [B, L, self.k, D])
        kernels_exp = tf.expand_dims(kernels, axis=-1)
        out = tf.reduce_sum(patches * kernels_exp, axis=2)
        return out

class EncoderBlock(layers.Layer):
    def __init__(self, embed_dim=EMBED_DIM, ff_dim=1152, num_conv_layers=2, dropout_rate=EMBED_DROPOUT):
        super().__init__()
        self.fc1 = layers.Dense(ff_dim)
        self.fc2 = layers.Dense(embed_dim)
        self.blocks = [DynamicConv(k=7) for _ in range(num_conv_layers)]
        self.ln = layers.LayerNormalization(epsilon=1e-5)
        self.ln1 = layers.LayerNormalization(epsilon=1e-5)
        self.ln2 = layers.LayerNormalization(epsilon=1e-5)
        self.dropout = layers.Dropout(dropout_rate)
    def call(self, x, training=None):
        x_norm = self.ln(x)
        out = x_norm
        for block in self.blocks:
            out = block(out)
        out = self.dropout(out, training=training)
        x = x_norm + self.ln1(out)
        v = out
        h = self.fc1(v)
        g, v_split = tf.split(h, 2, axis=-1)
        h = tf.nn.silu(g) * v_split
        h = self.fc2(h)
        h = self.dropout(h, training=training)
        x = x + self.ln2(h)
        return x

class L2NormLayer(layers.Layer):
    def __init__(self, axis=1, epsilon=1e-10, **kwargs):
        super().__init__(**kwargs)
        self.axis = axis
        self.epsilon = epsilon
    def call(self, inputs):
        return tf.math.l2_normalize(inputs, axis=self.axis, epsilon=self.epsilon)

class SentenceEncoder(Model):
    def __init__(self, vocab_size, embed_dim=EMBED_DIM, latent_dim=LATENT_DIM, max_len=MAX_LEN, pad_id=pad_id, dropout_rate=EMBED_DROPOUT):
        super().__init__()
        self.pad_id = pad_id
        self.embed = layers.Embedding(vocab_size, embed_dim)
        self.pos_embed = layers.Embedding(input_dim=max_len, output_dim=embed_dim)
        self.dropout = layers.Dropout(dropout_rate)
        self.blocks = [EncoderBlock() for _ in range(2)]
        self.attn_pool = layers.Dense(1)
        self.ln_f = layers.LayerNormalization(epsilon=1e-5, dtype=tf.float32)
        self.latent = layers.Dense(latent_dim, activation=None)
        self.l2norm = L2NormLayer(axis=1)
    def call(self, x, training=None):
        positions = tf.range(tf.shape(x)[1])[tf.newaxis, :]
        x_embed = self.embed(x) + self.pos_embed(positions)
        x_embed = self.dropout(x_embed, training=training)
        mask = tf.cast(tf.not_equal(x, self.pad_id), tf.float32)
        h = x_embed
        for block in self.blocks:
            h = block(h, training=training)
        h = self.ln_f(h)
        scores = self.attn_pool(h)
        scores = tf.where(tf.equal(mask[..., tf.newaxis], 0), -1e9, scores)
        scores = tf.nn.softmax(scores, axis=1)
        pooled = tf.reduce_sum(h * scores, axis=1)
        latent = self.latent(pooled)
        return self.l2norm(latent)  # (B, D)

encoder = SentenceEncoder(vocab_size=vocab_size)

# =========================
# Wrapper model for model.fit
# takes (v1, v2) and returns concat([z1, z2]) shape (2B, D)
# =========================
input1 = layers.Input(shape=(MAX_LEN,), dtype=tf.int32, name="view1")
input2 = layers.Input(shape=(MAX_LEN,), dtype=tf.int32, name="view2")
z1 = encoder(input1)
z2 = encoder(input2)
out = layers.Concatenate(axis=0)([z1, z2])  # (2B, D)
model = Model(inputs=[input1, input2], outputs=out)

# =========================
# NT-Xent loss as Keras loss (ignores y_true)
# =========================
def nt_xent_loss(y_true, y_pred):
    # y_pred: (2N, D) normalized
    z = y_pred
    z = tf.cast(z, tf.float32)
    sim = tf.matmul(z, z, transpose_b=True)  # (2N, 2N)
    sim = sim / TEMPERATURE
    # large negative on diagonal to avoid trivial argmax
    diag = tf.eye(tf.shape(sim)[0])
    sim = sim - diag * 1e9
    N2 = tf.shape(sim)[0]
    N = N2 // 2
    # positive index for i: if i < N => i+N, else i-N
    labels_pos = tf.concat([tf.range(N, N2), tf.range(0, N)], axis=0)
    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels_pos, logits=sim)
    return tf.reduce_mean(loss)

optimizer = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)
model.compile(optimizer=optimizer, loss=nt_xent_loss)

model.summary()


steps_per_epoch = 36757266 // 512

#steps_per_epoch = 1000000 // BATCH_SIZE

model.fit(ds, epochs=EPOCHS, steps_per_epoch=steps_per_epoch)

# 저장
encoder.save_weights("encoder_fit.weights.h5")
print("Training finished and weights saved.")