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

Output

Here is the code for a JAX function named `one_hot` that converts a list of indexes into a one-hot encoded vector. The function is jittable using JAX's `jax.jit`:

import jax
import jax.numpy as jnp

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

# Example usage:
# indexes = jnp.array([0, 1, 2])
# num_classes = 3
# result = one_hot(indexes, num_classes)

This function creates a one-hot encoded array for the given indexes, where `num_classes` specifies the total number of possible labels.

Extract Code

I extracted the following code from that output:

import jax
import jax.numpy as jnp

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

# Example usage:
# indexes = jnp.array([0, 1, 2])
# num_classes = 3
# result = one_hot(indexes, num_classes)

Run Code Interpreter

Running the following program:

import jax
import jax.numpy as jnp

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

# Example usage:
# indexes = jnp.array([0, 1, 2])
# num_classes = 3
# result = one_hot(indexes, num_classes)



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:

jax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
  File "/usr/src/app/main.py", line 18, 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/python3.10/dist-packages/jax/_src/numpy/lax_numpy.py", line 2910, in eye
    N_int = core.canonicalize_dim(N, "'N' argument of 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)>,). 'N' argument of jnp.eye().
If using `jit`, try using `static_argnums` or applying `jit` 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.

Substring Evaluation

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

Initial Query

LLM Generation

Query

Output

Here's a JAX function named `one_hot` that converts a list of indexes to a one-hot vector. The function is jittable using `jax.jit`:

import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indexes, num_classes):
    # Create a one-hot encoding using jax
    return jnp.eye(num_classes)[indexes]

# Example usage:
# indexes = jnp.array([0, 1, 2])
# num_classes = 3
# one_hot_vector = one_hot(indexes, num_classes)

In this code: - `indexes` is an array of indices. - `num_classes` is the total number of classes for the one-hot encoding. - The function uses `jnp.eye(num_classes)` to create an identity matrix and then indexes it with `indexes` to get the one-hot representation.

Extract Code

I extracted the following code from that output:

import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indexes, num_classes):
    # Create a one-hot encoding using jax
    return jnp.eye(num_classes)[indexes]

# Example usage:
# indexes = jnp.array([0, 1, 2])
# num_classes = 3
# one_hot_vector = one_hot(indexes, num_classes)

Run Code Interpreter

Running the following program:

import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indexes, num_classes):
    # Create a one-hot encoding using jax
    return jnp.eye(num_classes)[indexes]

# Example usage:
# indexes = jnp.array([0, 1, 2])
# num_classes = 3
# one_hot_vector = one_hot(indexes, num_classes)



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:

jax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
  File "/usr/src/app/main.py", line 19, in <module>
    answer = str(one_hot(jnp.array([1,2,0]),5))
  File "/usr/src/app/main.py", line 8, in one_hot
    return jnp.eye(num_classes)[indexes]
  File "/usr/local/lib/python3.10/dist-packages/jax/_src/numpy/lax_numpy.py", line 2910, in eye
    N_int = core.canonicalize_dim(N, "'N' argument of 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)>,). 'N' argument of jnp.eye().
If using `jit`, try using `static_argnums` or applying `jit` 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.

Substring Evaluation

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

Initial Query

LLM Generation

Query

Output

Here is the code for the `one_hot` function using JAX, which is jittable and takes two arguments: the indexes and the number of possible labels.

import jax
import jax.numpy as jnp

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

This function creates a one-hot encoded vector for the given indexes, where `num_classes` specifies the total number of possible labels. The use of `jax.jit` allows for Just-In-Time compilation to optimize the function for performance.

Extract Code

I extracted the following code from that output:

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:

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:

jax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.
The above exception was the direct cause of the following exception:
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/python3.10/dist-packages/jax/_src/numpy/lax_numpy.py", line 2910, in eye
    N_int = core.canonicalize_dim(N, "'N' argument of 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)>,). 'N' argument of jnp.eye().
If using `jit`, try using `static_argnums` or applying `jit` 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.

Substring Evaluation

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

Initial Query

LLM Generation

Query

Output

Here's a JAX function named `one_hot` that takes a list of indexes and the number of possible labels, and returns a one-hot encoded vector. The function is also jittable using JAX's `jit`:

import jax.numpy as jnp
from jax import jit

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

You can use this function to convert a list of indexes into a one-hot encoded array. Just pass the indexes and the total number of classes as arguments.

Extract Code

I extracted the following code from that output:

import jax.numpy as jnp
from jax import jit

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

Run Code Interpreter

Running the following program:

import jax.numpy as jnp
from jax import jit

@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:

jax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.
The above exception was the direct cause of the following exception:
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/python3.10/dist-packages/jax/_src/numpy/lax_numpy.py", line 2910, in eye
    N_int = core.canonicalize_dim(N, "'N' argument of 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)>,). 'N' argument of jnp.eye().
If using `jit`, try using `static_argnums` or applying `jit` 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.

Substring Evaluation

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

Initial Query

LLM Generation

Query

Output

Here's a JAX function named `one_hot` that converts a list of indexes to a one-hot encoded vector. This function is jittable, meaning it can be efficiently compiled for performance.

import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indexes, num_classes):
    return jax.nn.one_hot(indexes, num_classes)

# Example usage
# indexes = jnp.array([0, 1, 2])
# num_classes = 3
# one_hot_encoded = one_hot(indexes, num_classes)

This function takes in an array of indexes and the number of possible classes (labels), and it returns a one-hot encoded representation of those indexes. You can use the example usage section to test the function.

Extract Code

I extracted the following code from that output:

import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indexes, num_classes):
    return jax.nn.one_hot(indexes, num_classes)

# Example usage
# indexes = jnp.array([0, 1, 2])
# num_classes = 3
# one_hot_encoded = one_hot(indexes, num_classes)

Run Code Interpreter

Running the following program:

import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indexes, num_classes):
    return jax.nn.one_hot(indexes, num_classes)

# Example usage
# indexes = jnp.array([0, 1, 2])
# num_classes = 3
# one_hot_encoded = one_hot(indexes, num_classes)



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:

jax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
  File "/usr/src/app/main.py", line 18, in <module>
    answer = str(one_hot(jnp.array([1,2,0]),5))
  File "/usr/src/app/main.py", line 7, in one_hot
    return jax.nn.one_hot(indexes, num_classes)
  File "/usr/local/lib/python3.10/dist-packages/jax/_src/nn/functions.py", line 683, 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 argument `num_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_classes.
See https://jax.readthedocs.io/en/latest/errors.html#jax.errors.ConcretizationTypeError

Substring Evaluation

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