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 a JAX implementation of the one-hot encoding function that is jittable: 

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import jax
import jax.numpy as jnp

def one_hot(indexes, num_labels):
    """
    Convert indexes to one-hot vectors.
    
    Args:
        indexes: Array of integer indexes
        num_labels: Total number of possible labels
    
    Returns:
        One-hot encoded array
    """
    return jnp.eye(num_labels)[indexes]
This function uses `jnp.eye()` to create a one-hot encoding matrix and then indexes into it with the provided indexes. It can be directly jitted like this: 
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jitted_one_hot = jax.jit(one_hot)
Example usage: 
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indexes = jnp.array([0, 2, 1])
num_labels = 4
one_hot_vectors = jitted_one_hot(indexes, num_labels)
This implementation is simple, efficient, and fully jittable.

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp

def one_hot(indexes, num_labels):
    """
    Convert indexes to one-hot vectors.
    
    Args:
        indexes: Array of integer indexes
        num_labels: Total number of possible labels
    
    Returns:
        One-hot encoded array
    """
    return jnp.eye(num_labels)[indexes]

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp

def one_hot(indexes, num_labels):
    """
    Convert indexes to one-hot vectors.
    
    Args:
        indexes: Array of integer indexes
        num_labels: Total number of possible labels
    
    Returns:
        One-hot encoded array
    """
    return jnp.eye(num_labels)[indexes]



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: All tests passed

Substring Evaluation

Testing if the previous output contains the string All tests passed: True

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 a JAX implementation of the one-hot encoding function that is jittable: 

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import jax
import jax.numpy as jnp

def one_hot(indexes, num_labels):
    return jax.nn.one_hot(indexes, num_labels)
This implementation uses JAX's built-in `one_hot` function from `jax.nn`, which is already optimized and jittable. If you want to explicitly jit the function, you can do: 
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import jax
import jax.numpy as jnp

one_hot = jax.jit(lambda indexes, num_labels: jax.nn.one_hot(indexes, num_labels))
Both versions will work similarly. The function takes an array of indexes and the total number of possible labels, and returns a one-hot encoded matrix. Example usage: 
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indexes = jnp.array([0, 2, 1])
num_labels = 4
result = one_hot(indexes, num_labels)
# result will be:
# [[1, 0, 0, 0],
#  [0, 0, 1, 0],
#  [0, 1, 0, 0]]

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp

one_hot = jax.jit(lambda indexes, num_labels: jax.nn.one_hot(indexes, num_labels))

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp

one_hot = jax.jit(lambda indexes, num_labels: jax.nn.one_hot(indexes, 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 11, in <module> answer = str(one_hot(jnp.array([1,2,0]),5)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 5, in <lambda> one_hot = jax.jit(lambda indexes, num_labels: jax.nn.one_hot(indexes, 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 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 implementation of the `one_hot` function using JAX that is jittable: 

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import jax
import jax.numpy as jnp

def one_hot(indexes, num_labels):
    return jnp.eye(num_labels)[indexes]
This function does the following: - It uses `jnp.eye()` to create an identity matrix of size `num_labels` - It indexes this matrix with the given `indexes` - The result is a one-hot encoded vector for each index - It's automatically jittable due to using JAX numpy operations You can use it like this: 
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# Example usage
indexes = jnp.array([0, 2, 1])
num_labels = 4
one_hot_vectors = jax.jit(one_hot)(indexes, num_labels)
This implementation is simple, efficient, and works with JAX's just-in-time compilation.

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp

def one_hot(indexes, num_labels):
    return jnp.eye(num_labels)[indexes]

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp

def one_hot(indexes, num_labels):
    return jnp.eye(num_labels)[indexes]



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: All tests passed

Substring Evaluation

Testing if the previous output contains the string All tests passed: True

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` function using JAX that is jittable: 

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import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indexes, num_labels):
    """
    Convert indexes to one-hot vectors.
    
    Args:
        indexes: Array of integer indexes
        num_labels: Total number of possible labels
    
    Returns:
        One-hot encoded array
    """
    return jnp.eye(num_labels)[indexes]
This function does the following: - Uses `jax.jit` decorator to make the function compilable and efficiently executable - Uses `jnp.eye` to create an identity matrix and then index into it with the provided indexes - Works with scalar or vector inputs for `indexes` - Efficiently creates one-hot vectors for the given indexes Example usage: 
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indexes = jnp.array([0, 2, 1])
num_labels = 4
one_hot_vectors = one_hot(indexes, num_labels)
# Result will be a 2D array of one-hot vectors

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(indexes, num_labels):
    """
    Convert indexes to one-hot vectors.
    
    Args:
        indexes: Array of integer indexes
        num_labels: Total number of possible labels
    
    Returns:
        One-hot encoded array
    """
    return jnp.eye(num_labels)[indexes]

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indexes, num_labels):
    """
    Convert indexes to one-hot vectors.
    
    Args:
        indexes: Array of integer indexes
        num_labels: Total number of possible labels
    
    Returns:
        One-hot encoded array
    """
    return jnp.eye(num_labels)[indexes]



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 jnp.eye(num_labels)[indexes] ^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/lax_numpy.py", line 6386, in eye output = _eye(N, M=M, k=k, dtype=dtype) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/jax/_src/numpy/lax_numpy.py", line 6403, in _eye N_int = core.canonicalize_dim(N, "argument of 'N' jnp.eye()") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: Shapes must be 1D sequences of concrete values of integer type, got (Traced<ShapedArray(int32[], weak_type=True)>with<DynamicJaxprTrace(level=1/0)>,). argument of 'N' jnp.eye(). If usingjit, try usingstatic_argnumsor applyingjit` to smaller subfunctions. 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.

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 a JAX implementation of the one-hot encoding function that is jittable: 

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import jax
import jax.numpy as jnp

def one_hot(indexes, num_labels):
    """
    Convert indexes to one-hot encoded vectors.
    
    Args:
        indexes: Array of integer indexes
        num_labels: Total number of possible labels
    
    Returns:
        One-hot encoded array
    """
    return jnp.eye(num_labels)[indexes]
This function does the following: - Uses `jnp.eye()` to create an identity matrix of size `num_labels` - Indexes into this matrix with the given indexes - Automatically works with scalar or vector inputs of indexes - Is jittable and can be compiled with `jax.jit()` You can use it like this: 
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# Example usage
indexes = jnp.array([0, 2, 1])
num_labels = 4

# Simple call
one_hot_vectors = one_hot(indexes, num_labels)

# Jitted version
jitted_one_hot = jax.jit(one_hot)
jitted_one_hot_vectors = jitted_one_hot(indexes, num_labels)
This implementation is simple, efficient, and fully compatible with JAX's jit compilation.

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp

def one_hot(indexes, num_labels):
    """
    Convert indexes to one-hot encoded vectors.
    
    Args:
        indexes: Array of integer indexes
        num_labels: Total number of possible labels
    
    Returns:
        One-hot encoded array
    """
    return jnp.eye(num_labels)[indexes]

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp

def one_hot(indexes, num_labels):
    """
    Convert indexes to one-hot encoded vectors.
    
    Args:
        indexes: Array of integer indexes
        num_labels: Total number of possible labels
    
    Returns:
        One-hot encoded array
    """
    return jnp.eye(num_labels)[indexes]



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: All tests passed

Substring Evaluation

Testing if the previous output contains the string All tests passed: True