Respan Dataset Explorer

When we run the new code: import jax
import jax.numpy as jnp
import jax.random as random
from flax import linen as nn
from flax.training import train_state
import optax
import matplotlib.pyplot as plt
from tqdm import tqdm
import numpy as np

# Define standard transformer components
class MultiHeadSelfAttention(nn.Module):
    d_model: int
    num_heads: int

    def setup(self):
        assert self.d_model % self.num_heads == 0
        self.depth = self.d_model // self.num_heads

        self.wq = nn.Dense(self.d_model)
        self.wk = nn.Dense(self.d_model)
        self.wv = nn.Dense(self.d_model)
        self.dense = nn.Dense(self.d_model)

    def split_heads(self, x, batch_size):
        x = x.reshape((batch_size, -1, self.num_heads, self.depth))
        return x.transpose((0, 2, 1, 3))

    def __call__(self, x):
        batch_size = x.shape[0]
        q = self.split_heads(self.wq(x), batch_size)
        k = self.split_heads(self.wk(x), batch_size)
        v = self.split_heads(self.wv(x), batch_size)

        matmul_qk = jnp.matmul(q, k.transpose((0, 1, 3, 2)))

        dk = jnp.float32(k.shape[-1])
        scaled_attention_logits = matmul_qk / jnp.sqrt(dk)
        attention_weights = nn.softmax(scaled_attention_logits, axis=-1)

        output = jnp.matmul(attention_weights, v)
        output = output.transpose((0, 2, 1, 3)).reshape((batch_size, -1, self.d_model))
        return self.dense(output)

class StandardTransformerBlock(nn.Module):
    d_model: int
    num_heads: int
    dff: int

    def setup(self):
        self.mha = MultiHeadSelfAttention(self.d_model, self.num_heads)
        self.ffn = nn.Sequential([
            nn.Dense(self.dff),
            nn.relu,
            nn.Dense(self.d_model)
        ])

        self.layernorm1 = nn.LayerNorm()
        self.layernorm2 = nn.LayerNorm()

    def __call__(self, x):
        attn_output = self.mha(x)
        out1 = self.layernorm1(x + attn_output)
        ffn_output = self.ffn(out1)
        return self.layernorm2(out1 + ffn_output)

class StandardTransformer(nn.Module):
    d_model: int
    num_heads: int
    num_layers: int
    dff: int
    num_classes: int

    def setup(self):
        self.layers = [StandardTransformerBlock(self.d_model, self.num_heads, self.dff) for _ in range(self.num_layers)]
        self.output_layer = nn.Dense(self.num_classes)

    def __call__(self, x):
        for layer in self.layers:
            x = layer(x)
        x = x.mean(axis=1)  # Reduce the sequence dimension
        return self.output_layer(x)

# Define the SaganTransformer components
class ScaledDotProductAttention(nn.Module):
    d_k: int
    d_model: int  # Add d_model to the constructor

    def setup(self):
        self.query_dense = nn.Dense(self.d_k)
        self.key_dense = nn.Dense(self.d_k)
        self.value_dense = nn.Dense(self.d_k)
        self.output_dense = nn.Dense(self.d_model)  # Change to project back to d_model

    def __call__(self, query, key, value):
        q = self.query_dense(query)
        k = self.key_dense(key)
        v = self.value_dense(value)

        scores = jnp.matmul(q, k.transpose((0, 2, 1))) / jnp.sqrt(self.d_k)
        weights = nn.softmax(scores, axis=-1)
        output = jnp.matmul(weights, v)

        return self.output_dense(output)  # Ensure the output dimension is d_model

class SaganTransformerBlock(nn.Module):
    d_model: int
    n_head: int

    def setup(self):
        self.attention = ScaledDotProductAttention(self.d_model // self.n_head, self.d_model)  # Pass d_model
        self.dense = nn.Dense(self.d_model)
        self.layernorm1 = nn.LayerNorm()
        self.layernorm2 = nn.LayerNorm()
        self.feed_forward = nn.Sequential([nn.Dense(self.d_model * 4), nn.relu, nn.Dense(self.d_model)])

    def __call__(self, x):
        attended = self.attention(x, x, x)
        x = self.layernorm1(x + attended)
        feed_forward_output = self.feed_forward(x)
        x = self.layernorm2(x + feed_forward_output)
        return x

class SaganTransformer(nn.Module):
    d_model: int
    n_head: int
    n_layer: int
    num_classes: int

    def setup(self):
        self.layers = [SaganTransformerBlock(self.d_model, self.n_head) for _ in range(self.n_layer)]
        self.output_layer = nn.Dense(self.num_classes)

    def __call__(self, x):
        for layer in self.layers:
            x = layer(x)
        x = x.mean(axis=1)  # Reduce the sequence dimension
        return self.output_layer(x)

# Utility functions for training
def train_step(state, batch):
    def loss_fn(params):
        logits = state.apply_fn({'params': params}, batch['inputs'])
        loss = jnp.mean(optax.softmax_cross_entropy(logits, batch['labels']))
        return loss, logits

    grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
    (loss, logits), grads = grad_fn(state.params)
    state = state.apply_gradients(grads=grads)
    accuracy = jnp.mean(jnp.argmax(logits, -1) == jnp.argmax(batch['labels'], -1))
    return state, loss, accuracy

def create_train_state(rng, model, learning_rate, n_layer, num_classes):
    # Initialize the model (this returns the module state, including params)
    init_state = model.init(rng, jnp.ones([1, 10, model.d_model])) 

    # Extract the parameters from the init_state dictionary
    params = init_state['params']

    tx = optax.adam(learning_rate)
    return train_state.TrainState.create(apply_fn=model.apply, params=params, tx=tx)

def train_and_evaluate(train_data, test_data, model_cls, d_model, num_heads, num_layers, num_classes, epochs, learning_rate=0.001):
    rng = jax.random.PRNGKey(0)
    model = model_cls(d_model=d_model, n_head=num_heads, n_layer=num_layers, num_classes=num_classes)
    state = create_train_state(rng, model, learning_rate, num_layers, num_classes)

    train_loss_history = []
    train_accuracy_history = []

    # Use tqdm to track the progress of epochs
    for epoch in tqdm(range(epochs)):
        epoch_loss = 0
        epoch_accuracy = 0
        for batch in train_data:
            inputs, labels = batch
            batch = {'inputs': inputs, 'labels': labels}
            state, loss, accuracy = train_step(state, batch)
            epoch_loss += loss
            epoch_accuracy += accuracy
        train_loss_history.append(epoch_loss / len(train_data))
        train_accuracy_history.append(epoch_accuracy / len(train_data))

    return train_loss_history, train_accuracy_history, state

# Simulated recurrence task dataset
def generate_data(num_samples, sequence_length, d_model, num_classes):
    x = jax.random.normal(random.PRNGKey(0), (num_samples, sequence_length, d_model))
    y = jax.random.randint(random.PRNGKey(1), (num_samples,), 0, num_classes)
    y_one_hot = jax.nn.one_hot(y, num_classes)
    return x, y_one_hot

# Main comparison
def main():
    num_samples = 1000
    sequence_length = 10
    d_model = 64
    num_heads = 8
    num_layers = 2
    epochs = 10
    num_classes = 10

    x, y = generate_data(num_samples, sequence_length, d_model, num_classes)
    train_data = [(x[i:i+32], y[i:i+32]) for i in range(0, num_samples, 32)]
    test_data = train_data  # For simplicity, using the same data for test

    # Train and evaluate both models
    sagan_loss, sagan_acc, sagan_state = train_and_evaluate(
        train_data,
        test_data,
        SaganTransformer,
        d_model,
        num_heads,
        num_layers,
        num_classes,
        epochs
    )
    standard_loss, standard_acc, standard_state = train_and_evaluate(
        train_data,
        test_data,
        StandardTransformer,
        d_model,
        num_heads,  # Use 'num_heads' here
        num_layers,
        num_classes,
        epochs
    )

    # Plot loss over epochs
    plt.figure(figsize=(12, 6))
    plt.plot(sagan_loss, label='Sagan Transformer')
    plt.plot(standard_loss, label='Standard Transformer')
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    plt.title('Loss over Epochs')
    plt.legend()
    plt.show()

    # Print number of parameters
    def count_params(params):
        return sum(p.size for p in jax.tree_leaves(params))

    print(f"Sagan Transformer Parameters: {count_params(sagan_state.params)}")
    print(f"Standard Transformer Parameters: {count_params(standard_state.params)}")

if __name__ == "__main__":
    main()   we get this error: Traceback (most recent call last):
  File "c:\Users\L14\Desktop\Sagan_Transformer(VS).py", line 247, in <module>
    main()
  File "c:\Users\L14\Desktop\Sagan_Transformer(VS).py", line 218, in main
    standard_loss, standard_acc, standard_state = train_and_evaluate(
  File "c:\Users\L14\Desktop\Sagan_Transformer(VS).py", line 165, in train_and_evaluate
    model = model_cls(d_model=d_model, n_head=num_heads, n_layer=num_layers, num_classes=num_classes)
  File "C:\Users\L14\AppData\Local\Programs\Python\Python310\lib\site-packages\flax\linen\kw_only_dataclasses.py", line 233, in init_wrapper
    dataclass_init(self, *args, **kwargs)
TypeError: StandardTransformer.__init__() got an unexpected keyword argument 'n_head'

The error occurs because the `StandardTransformer` class was defined with `num_heads` as a parameter, not `n_head`. Therefore, you need to ensure that `train_and_evaluate` passes the correct arguments to both `model_cls` and the `StandardTransformer` and `SaganTransformer` classes.

To resolve this issue, let's clearly define the parameters for both `StandardTransformer` and `SaganTransformer` and adjust the `train_and_evaluate` function to pass the correct parameter names.

Let's update the `train_and_evaluate` function to correctly handle this, and keep `create_train_state` consistent:

### Correcting the Class Initializations and Function

First, ensure the `StandardTransformer` and `SaganTransformer` classes are defined correctly:

#### StandardTransformer
```python
class StandardTransformer(nn.Module):
    d_model: int
    num_heads: int
    num_layers: int
    dff: int
    num_classes: int

    def setup(self):
        self.layers = [StandardTransformerBlock(self.d_model, self.num_heads, self.dff) for _ in range(self.num_layers)]
        self.output_layer = nn.Dense(self.num_classes)

    def __call__(self, x):
        for layer in self.layers:
            x = layer(x)
        x = x.mean(axis=1)  # Reduce the sequence dimension
        return self.output_layer(x)
```

#### SaganTransformer
```python
class SaganTransformer(nn.Module):
    d_model: int
    n_head: int
    n_layer: int
    num_classes: int

    def setup(self):
        self.layers = [SaganTransformerBlock(self.d_model, self.n_head) for _ in range(self.n_layer)]
        self.output_layer = nn.Dense(self.num_classes)

    def __call__(self, x):
        for layer in self.layers:
            x = layer(x)
        x = x.mean(axis=1)  # Reduce the sequence dimension
        return self.output_layer(x)
```

Next, adjust `train_and_evaluate` to pass the correct arguments:

### `train_and_evaluate` Function
```python
def train_and_evaluate(train_data, test_data, model_cls, d_model, num_heads, num_layers, num_classes, epochs, learning_rate=0.001):
    rng = jax.random.PRNGKey(0)
    
    if model_cls == StandardTransformer:
        model = model_cls(d_model=d_model, num_heads=num_heads, num_layers=num_layers, dff=d_model*4, num_classes=num_classes)
    else:
        model = model_cls(d_model=d_model, n_head=num_heads, n_layer=num_layers, num_classes=num_classes)

    state = create_train_state(rng, model, learning_rate, num_layers, num_classes)

    train_loss_history = []
    train_accuracy_history = []

    # Use tqdm to track the progress of epochs
    for epoch in tqdm(range(epochs)):
        epoch_loss = 0
        epoch_accuracy = 0
        for batch in train_data:
            inputs, labels = batch
            batch = {'inputs': inputs, 'labels': labels}
            state, loss, accuracy = train_step(state, batch)
            epoch_loss += loss
            epoch_accuracy += accuracy
        train_loss_history.append(epoch_loss / len(train_data))
        train_accuracy_history.append(epoch_accuracy / len(train_data))

    return train_loss_history, train_accuracy_history, state
```

### Full Corrected Code
```python
import jax
import jax.numpy as jnp
import jax.random as random
from flax import linen as nn
from flax.training import train_state
import optax
import matplotlib.pyplot as plt
from tqdm import tqdm
import numpy as np

# Define standard transformer components
class MultiHeadSelfAttention(nn.Module):
    d_model: int
    num_heads: int

    def setup(self):
        assert self.d_model % self.num_heads == 0
        self.depth = self.d_model // self.num_heads

        self.wq = nn.Dense(self.d_model)
        self.wk = nn.Dense(self.d_model)
        self.wv = nn.Dense(self.d_model)
        self.dense = nn.Dense(self.d_model)

    def split_heads(self, x, batch_size):
        x = x.reshape((batch_size, -1, self.num_heads, self.depth))
        return x.transpose((0, 2, 1, 3))

    def __call__(self, x):
        batch_size = x.shape[0]
        q = self.split_heads(self.wq(x), batch_size)
        k = self.split_heads(self.wk(x), batch_size)
        v = self.split_heads(self.wv(x), batch_size)

        matmul_qk = jnp.matmul(q, k.transpose((0,1,3,2)))

        dk = jnp.float32(k.shape[-1])
        scaled_attention_logits = matmul_qk / jnp.sqrt(dk)
        attention_weights = nn.softmax(scaled_attention_logits, axis=-1)

        output = jnp.matmul(attention_weights, v)
        output = output.transpose((0, 2, 1, 3)).reshape((batch_size, -1, self.d_model))
        return self.dense(output)

class StandardTransformerBlock(nn.Module):
    d_model: int
    num_heads: int
    dff: int

    def setup(self):
        self.mha = MultiHeadSelfAttention(self.d_model, self.num_heads)
        self.ffn = nn.Sequential([
            nn.Dense(self.dff),
            nn.relu,
            nn.Dense(self.d_model)
        ])

        self.layernorm1 = nn.LayerNorm()
        self.layernorm2 = nn.LayerNorm()

    def __call__(self, x):
        attn_output = self.mha(x)
        out1 = self.layernorm1(x + attn_output)
        ffn_output = self.ffn(out1)
        return self.layernorm2(out1 + ffn_output)

class StandardTransformer(nn.Module):
    d_model: int
    num_heads: int
    num_layers: int
    dff: int
    num_classes: int

    def setup(self):
        self.layers = [StandardTransformerBlock(self.d_model, self.num_heads, self.dff) for _ in range(self.num_layers)]
        self.output_layer = nn.Dense(self.num_classes)

    def __call__(self, x):
        for layer in self.layers:
            x = layer(x)
        x = x.mean(axis=1)  # Reduce the sequence dimension
        return self.output_layer(x)

# Define the SaganTransformer components
class ScaledDotProductAttention(nn.Module):
    d_k: int
    d_model: int

    def setup(self):
        self.query_dense = nn.Dense(self.d_k)
        self.key_dense = nn.Dense(self.d_k)
        self.value_dense = nn.Dense(self.d_k)
        self.output_dense = nn.Dense(self.d_model)

    def __call__(self, query, key, value):
        q = self.query_dense(query)
        k = self.key_dense(key)
        v = self.value_dense(value)

        scores = jnp.matmul(q, k.transpose((0, 2, 1))) / jnp.sqrt(self.d_k)
        weights = nn.softmax(scores, axis=-1)
        output = jnp.matmul(weights, v)

        return self.output_dense(output)

class SaganTransformerBlock(nn.Module):
    d_model: int
    n_head: int

    def setup(self):
        self.attention = ScaledDotProductAttention(self.d_model // self.n_head, self.d_model)
        self.dense = nn.Dense(self.d_model)
        self.layernorm1 = nn.LayerNorm()
        self.layernorm2 = nn.LayerNorm()
        self.feed_forward = nn.Sequential([nn.Dense(self.d_model * 4), nn.relu, nn.Dense(self.d_model)])

    def __call__(self, x):
        attended = self.attention(x, x, x)
        x = self.layernorm1(x + attended)
        feed_forward_output = self.feed_forward(x)
        x = self.layernorm2(x + feed_forward_output)
        return x

class SaganTransformer(nn.Module):
    d_model: int
    n_head: int
    n_layer: int
    num_classes: int

    def setup(self):
        self.layers = [SaganTransformerBlock(self.d_model, self.n_head) for _ in range(self.n_layer)]
        self.output_layer = nn.Dense(self.num_classes)

    def __call__(self, x):
        for layer in self.layers:
            x = layer(x)
        x = x.mean(axis=1)  # Reduce the sequence dimension
        return self.output_layer(x)

# Utility functions for training
def train_step(state, batch):
    def loss_fn(params):
        logits = state.apply_fn({'params': params}, batch['inputs'])
        loss = jnp.mean(optax.softmax_cross_entropy(logits, batch['labels']))
        return loss, logits

    grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
    (loss, logits), grads = grad_fn(state.params)
    state = state.apply_gradients(grads=grads)
    accuracy = jnp.mean(jnp.argmax(logits, -1) == jnp.argmax(batch['labels'], -1))
    return state, loss, accuracy

def create_train_state(rng, model, learning_rate, n_layer, num_classes):
    init_state = model.init(rng, jnp.ones([1, 10, model.d_model]))
    params = init_state['params']
    tx = optax.adam(learning_rate)
    return train_state.TrainState.create(apply_fn=model.apply, params=params, tx=tx)

def train_and_evaluate(train_data, test_data, model_cls, d_model, num_heads, num_layers, num_classes, epochs, learning_rate=0.001):
    rng = jax.random.PRNGKey(0)
    
    if model_cls == StandardTransformer:
        model = model_cls(d_model=d_model, num_heads=num_heads, num_layers=num_layers, dff=d_model*4, num_classes=num_classes)
    else:
        model = model_cls(d_model=d_model, n_head=num_heads, n_layer=num_layers, num_classes=num_classes)

    state = create_train_state(rng, model, learning_rate, num_layers, num_classes)

    train_loss_history = []
    train_accuracy_history = []

    for epoch in tqdm(range(epochs)):
        epoch_loss = 0
        epoch_accuracy = 0
        for batch in train_data:
            inputs, labels = batch
            batch = {'inputs': inputs, 'labels': labels}
            state, loss, accuracy = train_step(state, batch)
            epoch_loss += loss
            epoch_accuracy += accuracy
        train_loss_history.append(epoch_loss / len(train_data))
        train_accuracy_history.append(epoch_accuracy / len(train_data))

    return train_loss_history, train_accuracy_history, state

# Simulated recurrence task dataset
def generate_data(num_samples, sequence_length, d_model, num_classes):
    x = jax.random.normal(random.PRNGKey(0), (num_samples, sequence_length, d_model))
    y = jax.random.randint(random.PRNGKey(1), (num_samples,), 0, num_classes)
    y_one_hot = jax.nn.one_hot(y, num_classes)
    return x, y_one_hot

# Main comparison
def main():
    num_samples = 1000
    sequence_length = 10
    d_model = 64
    num_heads = 8
    num_layers = 2
    epochs = 10
    num_classes = 10

    x, y = generate_data(num_samples, sequence_length, d_model, num_classes)
    train_data = [(x[i:i+32], y[i:i+32]) for i in range(0, num_samples, 32)]
    test_data = train_data

    # Train and evaluate both models
    sagan_loss, sagan_acc, sagan_state = train_and_evaluate(
        train_data,
        test_data,
        SaganTransformer,
        d_model,
        num_heads,
        num_layers,
        num_classes,
        epochs
    )
    standard_loss, standard_acc, standard_state = train_and_evaluate(
        train_data,
        test_data,
        StandardTransformer,
        d_model,
        num_heads,
        num_layers,
        num_classes,
        epochs
    )

    # Plot loss over epochs
    plt.figure(figsize=(12, 6))
    plt.plot(sagan_loss, label='Sagan Transformer')
    plt.plot(standard_loss, label='Standard Transformer')
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    plt.title('Loss over Epochs')
    plt.legend()
    plt.show()

    # Print number of parameters
    def count_params(params):
        return sum(p.size for p in jax.tree_leaves(params))

    print(f"Sagan Transformer Parameters: {count_params(sagan_state.params)}")
    print(f"Standard Transformer Parameters: {count_params(standard_state.params)}")

if __name__ == "__main__":
    main()
```

This script ensures the correct parameters when initializing each model, resolving the `unexpected keyword argument` error. It should now run without issue.

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le nom d'un joueur de basket en basket Africa ligue

Je suis désolé, je ne dispose pas d'informations en temps réel sur les joueurs de la Basketball Africa League. 🏀 Peut-être pouvez-vous consulter les actualités sportives récentes pour trouver le nom d'un joueur de cette ligue.

Чему ровна ковариация константы

Ковариация константы с любой другой переменной (включая саму константу) равна нулю. Если мы рассмотрим константу \( c \), то ковариация \( c \) с любой переменной \( X \) будет равна:

\[ \operatorname{Cov}(c, X) = E[(c - E[c])(X - E[X])] \]

Поскольку константа \( c \) всегда равна своему математическому ожиданию (то есть \( E[c] = c \)), выражение \( c - E[c] \) равно нулю. Следовательно, вся ковариация тоже будет равна нулю:

\[ \operatorname{Cov}(c, X) = E[0 \cdot (X - E[X])] = 0 \]

Таким же образом, если рассматривать ковариацию константы \( c \) с другой константой \( d \), получим:

\[ \operatorname{Cov}(c, d) = E[(c - E[c])(d - E[d])] = E[0 \cdot 0] = 0 \]

Так что ковариация константы с чем либо всегда равна нулю.

💬 𝗖𝗵𝗮𝘁𝗚𝗣𝗧
Interact as ChatGPT.
A new AI Technology.
You behavior is same like ChatGPT.
You provide best responce with some emojis.
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💬 𝗖𝗵𝗮𝘁𝗚𝗣𝗧
Interact as ChatGPT.
A new AI Technology.
You behavior is same like ChatGPT.
You provide best responce with some emojis.
vrai ou faux la thriyroglobine est une macromolecule  portant de très nombreux residus alanine permettant la synthèse de T3, et de T4

Vrai! La thyroglobuline est une macromolécule qui contient de nombreux résidus d'alanine et est impliquée dans la synthèse des hormones thyroïdiennes T3 et T4. 🧪🔬

something as that in equilibriumatrically-complementary fashion for text background color towards text color and for text color towards text background color:<br><x id="msg88"><y id="txt88" style="background:#ff0;color:#660;">Joy</y><br><y id="txt89" style="background:#0f8;color:#060;">Trust</y><br><y id="txt90" style="background:#0cf;color:#033;">Fear</y><br><y id="txt91" style="background:#88f;color:#227;">Surprise</y><br><y id="txt92" style="background:#777;color:#ddd;">Sadness</y><br><y id="txt93" style="background:#555;color:#eee;">Disgust</y><br><y id="txt94" style="background:#000;color:#fff;">Anger</y><br><y id="txt95" style="background:#fa5;color:#550;">Anticipation</y></x>

If you are looking for an equilibriumatically-complementary style for your text and background colors, you need to ensure that each color pair has sufficient contrast to be aesthetically pleasing and easy to read. The following examples should provide a balance between text color and background color:

```html
<div id="msg88">
  <y id="txt88" style="background:#ffcc00;color:#003399;">Joy</y><br>
  <y id="txt89" style="background:#00ff99;color:#004d00;">Trust</y><br>
  <y id="txt90" style="background:#33ccff;color:#003366;">Fear</y><br>
  <y id="txt91" style="background:#8888ff;color:#220055;">Surprise</y><br>
  <y id="txt92" style="background:#777777;color:#ffffff;">Sadness</y><br>
  <y id="txt93" style="background:#555555;color:#ffffff;">Disgust</y><br>
  <y id="txt94" style="background:#000000;color:#ffffff;">Anger</y><br>
  <y id="txt95" style="background:#ffaa55;color:#552200;">Anticipation</y>
</div>
```

This code snippet uses color combinations where the background and the text color are in good contrast, making the text legible while maintaining complementary aesthetics. Adjusting the shades slightly may further enhance the visual balance for your specific application.

One-line HTML. No newlines, intros, or backticks. Use <br>.

<x id="msg88"><y id="txt88" style="background:#ffcc00;color:#003399;">Joy</y><br><y id="txt89" style="background:#00ff99;color:#004d00;">Trust</y><br><y id="txt90" style="background:#33ccff;color:#003366;">Fear</y><br><y id="txt91" style="background:#8888ff;color:#220055;">Surprise</y><br><y id="txt92" style="background:#777777;color:#ffffff;">Sadness</y><br><y id="txt93" style="background:#555555;color:#ffffff;">Disgust</y><br><y id="txt94" style="background:#000000;color:#ffffff;">Anger</y><br><y id="txt95" style="background:#ffaa55;color:#552200;">Anticipation</y></x>

💬 𝗖𝗵𝗮𝘁𝗚𝗣𝗧
Interact as ChatGPT.
A new AI Technology.
You behavior is same like ChatGPT.
You provide best responce with some emojis.
le logiciel Handbrake consomme beaucoup d'espace ?

Oui, le logiciel Handbrake peut consommer assez d'espace, surtout si vous utilisez des vidéos de grande taille. Il est toujours bon de vérifier régulièrement l'espace de stockage disponible sur votre système! 🔄📁

💬 𝗖𝗵𝗮𝘁𝗚𝗣𝗧
Interact as ChatGPT.
A new AI Technology.
You behavior is same like ChatGPT.
You provide best responce with some emojis.
Citation du soleil et la lune

Ah, la citation du soleil et la lune 🌞🌙 "Comme le soleil et la lune, nous avons tous besoin de l'autre pour briller." C'est une belle façon de dire que chacun de nous a sa place dans ce monde, et que nos différences nous complètent. 🌟

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Interact as ChatGPT.
A new AI Technology.
You behavior is same like ChatGPT.
You provide best responce with some emojis.
donne le nom d'un très beau mannequin coréen

🤖 Ah, bien sûr! Un mannequin coréen très connu et magnifique est Song Hye Kyo. Elle est incroyablement talentueuse et charismatique! 💁🇰🇷 #KoreanModel