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

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import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indices, num_classes):
    return jnp.eye(num_classes)[indices]
This code creates a jitted function that converts indices to one-hot vectors. The function: 1. Takes two arguments: - indices: array of class indices - num_classes: total number of possible classes 2. Uses jnp.eye() to create an identity matrix of size num_classes 3. Uses indexing to select the appropriate rows corresponding to the input indices The function is decorated with @jax.jit for just-in-time compilation. You can use it like: 
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indices = jnp.array([0, 2, 1])
num_classes = 4
one_hot_vectors = one_hot(indices, num_classes)

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_classes):
    return jnp.eye(num_classes)[indices]

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_classes):
    return jnp.eye(num_classes)[indices]



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 13, in <module> answer = str(one_hot(jnp.array([1,2,0]),5)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 7, in one_hot return jnp.eye(num_classes)[indices] ^^^^^^^^^^^^^^^^^^^^ 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_classes.

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 jittable one-hot encoding function using JAX: 

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import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indices, num_classes):
    return jnp.eye(num_classes)[indices]
This function: - Takes `indices` (array of integers) and `num_classes` (integer) as input - Returns a one-hot encoded matrix where each row corresponds to an index - Is decorated with `@jax.jit` for compilation - Uses `jnp.eye()` to create an identity matrix and then indexes it with the input indices The function works with both single indices and arrays of indices. For example: 
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# Single index
one_hot(2, 5)  # returns [0., 0., 1., 0., 0.]

# Array of indices
one_hot(jnp.array([0, 2, 1]), 5)  # returns 3x5 matrix 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(indices, num_classes):
    return jnp.eye(num_classes)[indices]

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_classes):
    return jnp.eye(num_classes)[indices]



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 13, in <module> answer = str(one_hot(jnp.array([1,2,0]),5)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 7, in one_hot return jnp.eye(num_classes)[indices] ^^^^^^^^^^^^^^^^^^^^ 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_classes.

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 jittable JAX function to convert indexes to one-hot vectors: 

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import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indices, num_classes):
    return jnp.eye(num_classes)[indices]
This function: - Takes `indices` (array of integers) and `num_classes` (integer) as input - Returns a one-hot encoded array where each row corresponds to the one-hot encoding of the respective index - Is decorated with `@jax.jit` for JIT compilation - Uses `jnp.eye()` to create an identity matrix and then indexes it with the input indices You can use it like this: 
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indices = jnp.array([0, 2, 1])
num_classes = 4
result = one_hot(indices, num_classes)
The result would be: 
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array([[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

@jax.jit
def one_hot(indices, num_classes):
    return jnp.eye(num_classes)[indices]

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_classes):
    return jnp.eye(num_classes)[indices]



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 13, in <module> answer = str(one_hot(jnp.array([1,2,0]),5)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 7, in one_hot return jnp.eye(num_classes)[indices] ^^^^^^^^^^^^^^^^^^^^ 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_classes.

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 code for a jittable one-hot encoding function using JAX: 

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import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indexes, num_classes):
    return jnp.eye(num_classes)[indexes]
This function: - Takes `indexes` (array of integer indices) and `num_classes` (integer specifying total number of classes) - Returns a one-hot encoded matrix where each row corresponds to the one-hot encoding of the corresponding index - Is decorated with `@jax.jit` for just-in-time compilation - Uses `jnp.eye()` to create an identity matrix and then indexes it with the input indices You can use it like this: 
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indexes = jnp.array([0, 2, 1])
result = one_hot(indexes, 4)
# result will be:
# array([[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

@jax.jit
def one_hot(indexes, num_classes):
    return jnp.eye(num_classes)[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_classes):
    return jnp.eye(num_classes)[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 13, in <module> answer = str(one_hot(jnp.array([1,2,0]),5)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 7, in one_hot return jnp.eye(num_classes)[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_classes.

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 code for a jittable one_hot function using JAX: 

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import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indexes, num_classes):
    return jnp.eye(num_classes)[indexes]
This function: - Takes `indexes` (array of integer indices) and `num_classes` (integer) as input - Returns a one-hot encoded matrix where each row corresponds to the one-hot encoding of the corresponding index - Is decorated with `@jax.jit` for JIT compilation - Uses `jnp.eye()` to create an identity matrix and then indexes it with the input indices You can use it like this: 
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indexes = jnp.array([0, 2, 1])
num_classes = 4
result = one_hot(indexes, num_classes)
The result will be: 
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array([[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

@jax.jit
def one_hot(indexes, num_classes):
    return jnp.eye(num_classes)[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_classes):
    return jnp.eye(num_classes)[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 13, in <module> answer = str(one_hot(jnp.array([1,2,0]),5)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 7, in one_hot return jnp.eye(num_classes)[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_classes.

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>