import numpy as np
import keras
from nltk.translate.bleu_score import corpus_bleu

# Configuration
batch_size = 64
epochs = 350
latent_dim = 256
num_samples = 50000
data_path = "fra.txt"

# Preparing data
input_texts = []
target_texts = []
input_characters = set()
target_characters = set()
with open(data_path, "r", encoding="utf-8") as f:
    lines = f.read().split("\n")
for line in lines[: min(num_samples, len(lines) - 1)]:
    input_text, target_text, _ = line.split("\t")
    target_text = "\t" + target_text + "\n"
    input_texts.append(input_text)
    target_texts.append(target_text)
    for char in input_text:
        if char not in input_characters:
            input_characters.add(char)
    for char in target_text:
        if char not in target_characters:
            target_characters.add(char)

input_characters = sorted(list(input_characters))
target_characters = sorted(list(target_characters))
num_encoder_tokens = len(input_characters)
num_decoder_tokens = len(target_characters)
max_encoder_seq_length = max([len(txt) for txt in input_texts])
max_decoder_seq_length = max([len(txt) for txt in target_texts])

print("Number of samples:", len(input_texts))
print("Number of unique input tokens:", num_encoder_tokens)
print("Number of unique output tokens:", num_decoder_tokens)
print("Max sequence length for inputs:", max_encoder_seq_length)
print("Max sequence length for outputs:", max_decoder_seq_length)

input_token_index = dict([(char, i) for i, char in enumerate(input_characters)])
target_token_index = dict([(char, i) for i, char in enumerate(target_characters)])

encoder_input_data = np.zeros(
    (len(input_texts), max_encoder_seq_length, num_encoder_tokens),
    dtype="float32",
)
decoder_input_data = np.zeros(
    (len(input_texts), max_decoder_seq_length, num_decoder_tokens),
    dtype="float32",
)
decoder_target_data = np.zeros(
    (len(input_texts), max_decoder_seq_length, num_decoder_tokens),
    dtype="float32",
)

for i, (input_text, target_text) in enumerate(zip(input_texts, target_texts)):
    for t, char in enumerate(input_text):
        encoder_input_data[i, t, input_token_index[char]] = 1.0
    encoder_input_data[i, t + 1 :, input_token_index[" "]] = 1.0
    for t, char in enumerate(target_text):
        decoder_input_data[i, t, target_token_index[char]] = 1.0
        if t > 0:
            decoder_target_data[i, t - 1, target_token_index[char]] = 1.0
    decoder_input_data[i, t + 1 :, target_token_index[" "]] = 1.0
    decoder_target_data[i, t:, target_token_index[" "]] = 1.0

# Creating model
encoder_inputs = keras.Input(shape=(None, num_encoder_tokens))
encoder = keras.layers.LSTM(latent_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
encoder_states = [state_h, state_c]

decoder_inputs = keras.Input(shape=(None, num_decoder_tokens))
decoder_lstm = keras.layers.LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states)
decoder_dense = keras.layers.Dense(num_decoder_tokens, activation="softmax")
decoder_outputs = decoder_dense(decoder_outputs)

model = keras.Model([encoder_inputs, decoder_inputs], decoder_outputs)

# Creating the training model
model.compile(
    optimizer="rmsprop", loss="categorical_crossentropy", metrics=["accuracy"]
)
model.fit(
    [encoder_input_data, decoder_input_data],
    decoder_target_data,
    batch_size=batch_size,
    epochs=epochs,
    validation_split=0.2,
)
model.save("s2s_model.keras")

# Sampling
model = keras.models.load_model("s2s_model.keras")

encoder_inputs = model.input[0]
encoder_outputs, state_h_enc, state_c_enc = model.layers[2].output
encoder_states = [state_h_enc, state_c_enc]
encoder_model = keras.Model(encoder_inputs, encoder_states)

decoder_inputs = model.input[1]
decoder_state_input_h = keras.Input(shape=(latent_dim,))
decoder_state_input_c = keras.Input(shape=(latent_dim,))
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
decoder_lstm = model.layers[3]
decoder_outputs, state_h_dec, state_c_dec = decoder_lstm(
    decoder_inputs, initial_state=decoder_states_inputs
)
decoder_states = [state_h_dec, state_c_dec]
decoder_dense = model.layers[4]
decoder_outputs = decoder_dense(decoder_outputs)
decoder_model = keras.Model(
    [decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states
)

reverse_input_char_index = dict((i, char) for char, i in input_token_index.items())
reverse_target_char_index = dict((i, char) for char, i in target_token_index.items())

def decode_sequences(input_seqs):
    states_values = encoder_model.predict(input_seqs, verbose=0)
    target_seqs = np.zeros((len(input_seqs), 1, num_decoder_tokens))
    target_seqs[:, 0, target_token_index["\t"]] = 1.0
    decoded_sentences = [""] * len(input_seqs)
    stop_conditions = np.zeros(len(input_seqs), dtype=bool)

    while not np.all(stop_conditions):
        output_tokens, h, c = decoder_model.predict(
            [target_seqs] + states_values, verbose=0
        )

        sampled_token_indices = np.argmax(output_tokens[:, -1, :], axis=1)
        sampled_chars = [
            reverse_target_char_index[idx] for idx in sampled_token_indices
        ]

        for i, char in enumerate(sampled_chars):
            decoded_sentences[i] += char
            if char == "\n" or len(decoded_sentences[i]) > max_decoder_seq_length:
                stop_conditions[i] = True

        target_seqs = np.zeros((len(input_seqs), 1, num_decoder_tokens))
        for i, token_index in enumerate(sampled_token_indices):
            target_seqs[i, 0, token_index] = 1.0

        states_values = [h, c]

    return decoded_sentences

# BLEU score evaluation
def calculate_bleu_score(input_texts, target_texts, num_samples=500):
    input_seqs = np.zeros(
        (num_samples, max_encoder_seq_length, num_encoder_tokens), dtype="float32"
    )
    for i, input_text in enumerate(input_texts[:num_samples]):
        for t, char in enumerate(input_text):
            input_seqs[i, t, input_token_index[char]] = 1.0
        input_seqs[i, t + 1 :, input_token_index[" "]] = 1.0

    decoded_sentences = decode_sequences(input_seqs)

    references = [[list(text.strip())] for text in target_texts[:num_samples]]
    hypotheses = [list(text.strip()) for text in decoded_sentences]
    bleu = corpus_bleu(references, hypotheses)
    print("BLEU Score:", bleu)

    print("\nExample Translations:")
    for i in range(10):
        print("Input:", input_texts[i])
        print("Target:", target_texts[i])
        print("Translation:", decoded_sentences[i])
        print()

calculate_bleu_score(input_texts, target_texts)