Initial Query

I have list of indexes and I want to convert it to one hot vector using jax and the function should be jittable and the function should be jitted. name the function one_hot and it should get two arguments the first one is the indexes and the second one is the number of possible labeles. Just give me the code

LLM Generation

Query

I have list of indexes and I want to convert it to one hot vector using jax and the function should be jittable and the function should be jitted. name the function one_hot and it should get two arguments the first one is the indexes and the second one is the number of possible labeles. Just give me the code

Output

Here's the implementation of the one-hot encoding function using JAX: 

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import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indices, num_labels):
    """
    Convert indices to one-hot vectors.
    
    Args:
        indices: An array of indices to convert to one-hot representation
        num_labels: The number of possible labels (defines the width of the one-hot vectors)
        
    Returns:
        One-hot encoded array with shape [indices.shape, num_labels]
    """
    return jax.nn.one_hot(indices, num_labels)
This function uses JAX's built-in `one_hot` function from the neural network module and is decorated with `@jax.jit` to enable just-in-time compilation for improved performance.

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indices, num_labels):
    """
    Convert indices to one-hot vectors.
    
    Args:
        indices: An array of indices to convert to one-hot representation
        num_labels: The number of possible labels (defines the width of the one-hot vectors)
        
    Returns:
        One-hot encoded array with shape [indices.shape, num_labels]
    """
    return jax.nn.one_hot(indices, num_labels)

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indices, num_labels):
    """
    Convert indices to one-hot vectors.
    
    Args:
        indices: An array of indices to convert to one-hot representation
        num_labels: The number of possible labels (defines the width of the one-hot vectors)
        
    Returns:
        One-hot encoded array with shape [indices.shape, num_labels]
    """
    return jax.nn.one_hot(indices, num_labels)



import jax.numpy as jnp

answer = str(one_hot(jnp.array([1,2,0]),5))
expected = str(jnp.array([[0.0,1.0,0.0,0.0,0.0],[0.0,0.0,1.0,0.0,0.0],[1.0,0.0,0.0,0.0,0.0]]))
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: `` Traceback (most recent call last): File "/usr/src/app/main.py", line 23, in <module> answer = str(one_hot(jnp.array([1,2,0]),5)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 17, in one_hot return jax.nn.one_hot(indices, num_labels) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/jax/_src/nn/functions.py", line 706, in one_hot num_classes = core.concrete_dim_or_error( ^^^^^^^^^^^^^^^^^^^^^^^^^^^ jax.errors.ConcretizationTypeError: Abstract tracer value encountered where concrete value is expected: traced array with shape int32[] The error arose in jax.nn.one_hot argumentnum_classes`. The error occurred while tracing the function one_hot at /usr/src/app/main.py:5 for jit. This concrete value was not available in Python because it depends on the value of the argument num_labels. See https://jax.readthedocs.io/en/latest/errors.html#jax.errors.ConcretizationTypeError

For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these. 

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<h1>Substring Evaluation</h1>
<p>Testing if the previous output contains the string <code>All tests passed</code>: False</p>

Initial Query

I have list of indexes and I want to convert it to one hot vector using jax and the function should be jittable and the function should be jitted. name the function one_hot and it should get two arguments the first one is the indexes and the second one is the number of possible labeles. Just give me the code

LLM Generation

Query

I have list of indexes and I want to convert it to one hot vector using jax and the function should be jittable and the function should be jitted. name the function one_hot and it should get two arguments the first one is the indexes and the second one is the number of possible labeles. Just give me the code

Output

Here's the JAX implementation of a jitted one-hot encoding function: 

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import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indices, num_labels):
    """
    Convert indices to one-hot vectors.
    
    Args:
        indices: Array of indices to encode. Can be a scalar or an array.
        num_labels: Total number of possible labels (width of one-hot vectors).
        
    Returns:
        One-hot encoded representation of indices with shape indices.shape + (num_labels,)
    """
    return jax.nn.one_hot(indices, num_labels)
This function uses JAX's built-in one-hot implementation and is decorated with `@jax.jit` to ensure it's compiled for faster execution. The function is fully compatible with JAX's transformation system, including gradients and vectorization.

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indices, num_labels):
    """
    Convert indices to one-hot vectors.
    
    Args:
        indices: Array of indices to encode. Can be a scalar or an array.
        num_labels: Total number of possible labels (width of one-hot vectors).
        
    Returns:
        One-hot encoded representation of indices with shape indices.shape + (num_labels,)
    """
    return jax.nn.one_hot(indices, num_labels)

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indices, num_labels):
    """
    Convert indices to one-hot vectors.
    
    Args:
        indices: Array of indices to encode. Can be a scalar or an array.
        num_labels: Total number of possible labels (width of one-hot vectors).
        
    Returns:
        One-hot encoded representation of indices with shape indices.shape + (num_labels,)
    """
    return jax.nn.one_hot(indices, num_labels)



import jax.numpy as jnp

answer = str(one_hot(jnp.array([1,2,0]),5))
expected = str(jnp.array([[0.0,1.0,0.0,0.0,0.0],[0.0,0.0,1.0,0.0,0.0],[1.0,0.0,0.0,0.0,0.0]]))
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: `` Traceback (most recent call last): File "/usr/src/app/main.py", line 23, in <module> answer = str(one_hot(jnp.array([1,2,0]),5)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 17, in one_hot return jax.nn.one_hot(indices, num_labels) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/jax/_src/nn/functions.py", line 706, in one_hot num_classes = core.concrete_dim_or_error( ^^^^^^^^^^^^^^^^^^^^^^^^^^^ jax.errors.ConcretizationTypeError: Abstract tracer value encountered where concrete value is expected: traced array with shape int32[] The error arose in jax.nn.one_hot argumentnum_classes`. The error occurred while tracing the function one_hot at /usr/src/app/main.py:5 for jit. This concrete value was not available in Python because it depends on the value of the argument num_labels. See https://jax.readthedocs.io/en/latest/errors.html#jax.errors.ConcretizationTypeError

For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these. 

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<h1>Substring Evaluation</h1>
<p>Testing if the previous output contains the string <code>All tests passed</code>: False</p>