import tensorflow as tf
import numpy as np
import faiss

class MultiModalTransformer(tf.keras.Model):
    def __init__(self, hparams, knowledge_base, n_hash=1024, n_quant=256):
        super(MultiModalTransformer, self).__init__()
        self.hparams = hparams
        self.n_hash = n_hash
        self.n_quant = n_quant

        # Core Transformer components
        self.wte = tf.keras.layers.Embedding(hparams.n_vocab, hparams.n_embd)
        self.wpe = tf.keras.layers.Embedding(hparams.n_ctx, hparams.n_embd)
        self.hash_layer = tf.keras.layers.Dense(n_hash, activation='relu')
        self.quant_layer = tf.keras.layers.Dense(n_quant, activation='relu')
        self.h = [TransformerBlock(hparams.n_embd, hparams.n_head) for _ in range(hparams.n_layer)]
        self.ln_f = tf.keras.layers.LayerNormalization(epsilon=1e-5)
        self.fc = tf.keras.layers.Dense(hparams.n_vocab, use_bias=False)

        # Speech Recognition
        self.audio_encoder = tf.keras.Sequential([
            tf.keras.layers.Conv1D(256, kernel_size=11, strides=2, padding='same', activation='relu'),
            tf.keras.layers.Conv1D(256, kernel_size=11, strides=2, padding='same', activation='relu'),
            tf.keras.layers.Conv1D(256, kernel_size=11, strides=2, padding='same', activation='relu'),
            tf.keras.layers.GlobalAveragePooling1D(),
            tf.keras.layers.Dense(hparams.n_embd)
        ])

        # Image Captioning
        self.image_encoder = tf.keras.applications.ResNet50(include_top=False, weights='imagenet')
        self.image_proj = tf.keras.layers.Dense(hparams.n_embd)

        # Music Generation
        self.pitch_embedding = tf.keras.layers.Embedding(128, hparams.n_embd)
        self.duration_embedding = tf.keras.layers.Embedding(32, hparams.n_embd)
        self.velocity_embedding = tf.keras.layers.Embedding(128, hparams.n_embd)

        # Anomaly Detection
        self.anomaly_threshold = tf.Variable(0.5, trainable=False)

        # RAG
        self.knowledge_base = knowledge_base
        self.retriever = FAISSRetriever(knowledge_base)
        self.query_encoder = tf.keras.Sequential([
            tf.keras.layers.Dense(hparams.n_embd, activation='relu'),
            tf.keras.layers.Dense(hparams.n_embd)
        ])

        # Task-specific output layers
        self.speech_output = tf.keras.layers.Dense(hparams.n_vocab)
        self.caption_output = tf.keras.layers.Dense(hparams.n_vocab)
        self.music_output = tf.keras.layers.Dense(288)  # 128 (pitch) + 32 (duration) + 128 (velocity)
        self.anomaly_output = tf.keras.layers.Dense(1, activation='sigmoid')

        # Conversation history
        self.conversation_history = []

        # Personality traits
        self.personality_traits = {
            'kindness': 0.9,
            'honesty': 0.9,
            'resilience': 0.8,
            'open_mindedness': 0.8,
            'empathy': 0.9,
            'reliability': 0.9,
            'humility': 0.8,
            'positivity': 0.9,
            'courage': 0.8,
            'curiosity': 0.9,
            'humor': 0.8,
            'self_discipline': 0.8,
            'emotional_stability': 0.8,
            'assertiveness': 0.8,
            'creativity': 0.9
        }

    def call(self, inputs, task):
        if task == 'speech_recognition':
            x = self.audio_encoder(inputs)
        elif task == 'image_captioning':
            image, text = inputs
            image_features = self.image_encoder(image)
            image_features = self.image_proj(tf.keras.layers.GlobalAveragePooling2D()(image_features))
            x = tf.concat([image_features[:, tf.newaxis, :], self.wte(text)], axis=1)
        elif task == 'music_generation':
            pitch, duration, velocity = inputs
            x = self.pitch_embedding(pitch) + self.duration_embedding(duration) + self.velocity_embedding(velocity)
        elif task in ['text_generation', 'anomaly_detection']:
            x = self.wte(inputs)
        else:
            raise ValueError(f"Unknown task: {task}")

        # RAG for text-based tasks
        if task in ['text_generation', 'image_captioning']:
            query = x[:, 0, :]  # Use first token as query
            encoded_query = self.query_encoder(query)
            retrieved_docs = self.retriever.retrieve(encoded_query)
            x = tf.concat([x, self.wte(retrieved_docs)], axis=1)

        # Add positional embeddings
        position = tf.range(0, x.shape[1], dtype=tf.int32)[tf.newaxis, :]
        x = x + self.wpe(position)

        # Apply core Transformer layers
        x = self.hash_layer(x)
        x = self.quant_layer(x)
        for layer in self.h:
            x, _ = layer(x)
        x = self.ln_f(x)

        # Task-specific outputs
        if task == 'speech_recognition':
            return self.speech_output(x)
        elif task == 'image_captioning':
            return self.caption_output(x)
        elif task == 'music_generation':
            return self.music_output(x)
        elif task == 'anomaly_detection':
            reconstruction = self.fc(x)
            reconstruction_loss = tf.reduce_mean(tf.square(inputs - reconstruction), axis=-1)
            anomaly_scores = tf.where(reconstruction_loss > self.anomaly_threshold, 1.0, 0.0)
            return reconstruction, anomaly_scores
        else:  # text_generation
            return self.fc(x)

    def pipe(self, inputs, task):
        if task == 'speech_recognition':
            return self.call(inputs, task)
        elif task == 'image_captioning':
            return self.call(inputs, task)
        elif task == 'music_generation':
            return self.call(inputs, task)
        elif task == 'text_generation':
            return self.call(inputs, task)
        elif task == 'anomaly_detection':
            return self.call(inputs, task)
        else:
            raise ValueError(f"Unknown task: {task}")

    def conversation(self, user_input):
        # Add user input to conversation history
        self.conversation_history.append(user_input)

        # Generate response based on conversation history and personality traits
        response = self.generate_response(self.conversation_history)

        # Add response to conversation history
        self.conversation_history.append(response)

        return response

    def generate_response(self, conversation_history):
        # Concatenate conversation history into a single input
        conversation_input = tf.concat(conversation_history, axis=0)

        # Generate response using the model
        response = self.pipe(conversation_input, task='text_generation')

        # Apply personality traits to the response
        response = self.apply_personality_traits(response)

        return response

    def apply_personality_traits(self, response):
        # Apply personality traits to the response
        for trait, value in self.personality_traits.items():
            if trait == 'kindness':
                response = self.add_kindness(response, value)
            elif trait == 'honesty':
                response = self.add_honesty(response, value)
            elif trait == 'resilience':
                response = self.add_resilience(response, value)
            elif trait == 'open_mindedness':
                response = self.add_open_mindedness(response, value)
            elif trait == 'empathy':
                response = self.add_empathy(response, value)
            elif trait == 'reliability':
                response = self.add_reliability(response, value)
            elif trait == 'humility':
                response = self.add_humility(response, value)
            elif trait == 'positivity':
                response = self.add_positivity(response, value)
            elif trait == 'courage':
                response = self.add_courage(response, value)
            elif trait == 'curiosity':
                response = self.add_curiosity(response, value)
            elif trait == 'humor':
                response = self.add_humor(response, value)
            elif trait == 'self_discipline':
                response = self.add_self_discipline(response, value)
            elif trait == 'emotional_stability':
                response = self.add_emotional_stability(response, value)
            elif trait == 'assertiveness':
                response = self.add_assertiveness(response, value)
            elif trait == 'creativity':
                response = self.add_creativity(response, value)

        return response

    def add_kindness(self, response, value):
        # Add kindness to the response
        if value > 0.5:
            response = f"I understand your concern. {response}"
        return response

    def add_honesty(self, response, value):
        # Add honesty to the response
        if value > 0.5:
            response = f"To be honest, {response}"
        return response

    def add_resilience(self, response, value):
        # Add resilience to the response
        if value > 0.5:
            response = f"Let's keep trying. {response}"
        return response

    def add_open_mindedness(self, response, value):
        # Add open-mindedness to the response
        if value > 0.5:
            response = f"That's an interesting perspective. {response}"
        return response

    def add_empathy(self, response, value):
        # Add empathy to the response
        if value > 0.5:
            response = f"I can see how you feel. {response}"
        return response

    def add_reliability(self, response, value):
        # Add reliability to the response
        if value > 0.5:
            response = f"You can count on me. {response}"
        return response

    def add_humility(self, response, value):
        # Add humility to the response
        if value > 0.5:
            response = f"I'm still learning. {response}"
        return response

    def add_positivity(self, response, value):
        # Add positivity to the response
        if value > 0.5:
            response = f"Let's stay positive. {response}"
        return response

    def add_courage(self, response, value):
        # Add courage to the response
        if value > 0.5:
            response = f"Let's face this together. {response}"
        return response

    def add_curiosity(self, response, value):
        # Add curiosity to the response
        if value > 0.5:
            response = f"That's fascinating. {response}"
        return response

    def add_humor(self, response, value):
        # Add humor to the response
        if value > 0.5:
            response = f"On a lighter note, {response}"
        return response

    def add_self_discipline(self, response, value):
        # Add self-discipline to the response
        if value > 0.5:
            response = f"Let's stay focused. {response}"
        return response

    def add_emotional_stability(self, response, value):
        # Add emotional stability to the response
        if value > 0.5:
            response = f"Let's stay calm. {response}"
        return response

    def add_assertiveness(self, response, value):
        # Add assertiveness to the response
        if value > 0.5:
            response = f"I firmly believe that {response}"
        return response

    def add_creativity(self, response, value):
        # Add creativity to the response
        if value > 0.5:
            response = f"Let's think outside the box. {response}"
        return response

    def fine_tune_personality(self, trait, value):
        # Fine-tune the personality trait
        if trait in self.personality_traits:
            self.personality_traits[trait] = value
        else:
            raise ValueError(f"Unknown trait: {trait}")

    def safe_word_format(self, user_input):
        # Safe word format for user control
        if user_input.lower() == "stop":
            self.conversation_history = []
            return "Conversation stopped. You can start a new conversation."
        elif user_input.lower() == "reset":
            self.conversation_history = []
            return "Conversation reset. Let's start fresh."
        else:
            return None

class TransformerBlock(tf.keras.layers.Layer):
    def __init__(self, n_embd, n_head):
        super(TransformerBlock, self).__init__()
        self.attn = MultiHeadAttention(n_embd, n_head)
        self.ln_1 = tf.keras.layers.LayerNormalization(epsilon=1e-5)
        self.mlp = tf.keras.Sequential([
            tf.keras.layers.Dense(4 * n_embd, activation=gelu),
            tf.keras.layers.Dense(n_embd)
        ])
        self.ln_2 = tf.keras.layers.LayerNormalization(epsilon=1e-5)

    def call(self, x, past=None):
        a, present = self.attn(self.ln_1(x), past=past)
        x = x + a
        m = self.mlp(self.ln_2(x))
        x = x + m
        return x, present

class MultiHeadAttention(tf.keras.layers.Layer):
    def __init__(self, n_embd, n_head):
        super(MultiHeadAttention, self).__init__()
        self.n_embd = n_embd
        self.n_head = n_head
        self.c_attn = tf.keras.layers.Dense(3 * n_embd)
        self.c_proj = tf.keras.layers.Dense(n_embd)

    def split_heads(self, x):
        return tf.transpose(tf.reshape(x, (*x.shape[:-1], self.n_head, -1)), [0, 2, 1, 3])

    def merge_heads(self, x):
        return tf.reshape(tf.transpose(x, [0, 2, 1, 3]), (*x.shape[:-3], -1))

    def call(self, x, past=None):
        c = self.c_attn(x)
        q, k, v = tf.split(c, 3, axis=-1)
        q, k, v = map(self.split_heads, [q, k, v])

        if past is not None:
            pk, pv = past
            k = tf.concat([pk, k], axis=-2)
            v = tf.concat([pv, v], axis=-2)

        present = tf.stack([k, v], axis=1)
        a = tf.matmul(q, k, transpose_b=True) / tf.math.sqrt(tf.cast(v.shape[-1], tf.float32))
        a = tf.nn.softmax(a)
        a = tf.matmul(a, v)
        a = self.merge_heads(a)
        a = self.c_proj(a)
        return a, present

class FAISSRetriever:
    def __init__(self, knowledge_base, dim=768, num_results=5):
        self.index = faiss.IndexFlatL2(dim)
        self.knowledge_base = knowledge_base
        self.num_results = num_results

        vectors = [doc['vector'] for doc in knowledge_base]
        self.index.add(np.array(vectors))

    def retrieve(self, query_vector):
        distances, indices = self.index.search(query_vector.numpy(), self.num_results)
        retrieved_docs = [self.knowledge_base[i]['text'] for i in indices[0]]
        return tf.constant(retrieved_docs)

def gelu(x):
    return 0.5 * x * (1 + tf.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3))))

# Custom loss function
def custom_loss(y_true, y_pred, model, task):
    if task == 'anomaly_detection':
        mse = tf.keras.losses.MeanSquaredError()
        return mse(y_true, y_pred)
    else:
        ce_loss = tf.keras.losses.sparse_categorical_crossentropy(y_true, y_pred, from_logits=True)
        reg_loss = tf.reduce_sum([tf.nn.l2_loss(w) for w in model.trainable_weights])
        return ce_loss + 0.01 * reg_loss

# Training function
@tf.function
def train_step(model, optimizer, inputs, targets, task):
    with tf.GradientTape() as tape:
        predictions = model(inputs, task)
        loss = custom_loss(targets, predictions, model, task)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    return loss

# Hyperparameters
class HParams:
    def __init__(self, n_vocab, n_ctx, n_embd, n_head, n_layer):
        self.n_vocab = n_vocab
        self.n_ctx = n_ctx
        self.n_embd = n_embd
        self.n_head = n_head
        self.n_layer = n_layer

hparams = HParams(
    n_vocab=50000,
    n_ctx=1024,
    n_embd=768,
    n_head=12,
    n_layer=12
)

# Initialize knowledge base (for demonstration)
knowledge_base = [
    {'text': 'Example knowledge 1', 'vector': np.random.rand(768)},
    {'text': 'Example knowledge 2', 'vector': np.random.rand(768)},
    # ... more entries ...
]

# Initialize model
model = MultiModalTransformer(hparams, knowledge_base)

# Initialize optimizer
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-4)

# Training loop (pseudo-code)
num_epochs = 10
for epoch in range(num_epochs):
    for batch in dataset:
        inputs, targets, task = batch
        loss = train_step(model, optimizer, inputs, targets, task)
    print(f"Epoch {epoch + 1}, Loss: {loss.numpy()}")

# Example usage
speech_input = tf.random.normal((1, 16000, 1))  # 1 second of audio at 16kHz
speech_output = model(speech_input, task='speech_recognition')

image_input = tf.random.normal((1, 224, 224, 3))
text_input = tf.random.uniform((1, 10), maxval=50000, dtype=tf.int32)
caption_output = model([image_input, text_input], task='image_captioning')

music_input = [
    tf.random.uniform((1, 100), maxval=128, dtype=tf.int32),  # pitch
    tf.random.uniform((1, 100), maxval=32, dtype=tf.int32),   # duration
    tf.random.uniform((1, 100), maxval=128, dtype=tf.int32)   # velocity
]
music_output = model(music_input, task='music_generation')

text_input = tf.random.uniform((1, 50), maxval=50000, dtype=tf.int32)
text_output = model(text_input, task='text_generation')

anomaly_input = tf.random.normal((1, 100, 768))
reconstructed, anomalies = model(anomaly_input, task='anomaly_detection')

# Example conversation
user_input = "Hello, how are you?"
response = model.conversation(user_input)
print(response)

# Fine-tune personality trait
model.fine_tune_personality('kindness', 0.95)

# Safe word control
user_input = "stop"
response = model.safe_word_format(user_input)
print(response)