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's a Jax implementation of a jittable `one_hot` function that converts a list of indexes to one-hot vectors:

import jax
import jax.numpy as jnp

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

This function does the following: 1. It uses the `@jax.jit` decorator to ensure the function is jitted (just-in-time compiled). 2. The function takes two arguments: - `indexes`: A 1D array or list of integer indexes. - `num_classes`: The total number of possible classes (i.e., the size of each one-hot vector). 3. It uses `jnp.eye(num_classes)` to create an identity matrix of size `num_classes x num_classes`. 4. It then uses advanced indexing `[indexes]` to select the rows corresponding to the given indexes, effectively creating the one-hot vectors. This function is efficient and fully compatible with Jax's transformations. You can use it like this:

# Example usage
indexes = jnp.array([0, 2, 1, 3])
num_classes = 4
result = one_hot(indexes, num_classes)
print(result)

This will output a 2D array where each row is a one-hot vector corresponding to the input indexes.

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 jittable JAX function that converts a list of indexes to one-hot vectors:

import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indexes, num_labels):
    return jax.nn.one_hot(jnp.array(indexes), num_labels)

This function does the following: 1. It uses the `@jax.jit` decorator to ensure the function is JIT-compiled for faster execution. 2. The function takes two arguments: - `indexes`: A list or array of integer indexes. - `num_labels`: The total number of possible labels (i.e., the length of each one-hot vector). 3. Inside the function, we first convert the `indexes` to a JAX array using `jnp.array()`. 4. We then use JAX's built-in `jax.nn.one_hot()` function to convert the indexes to one-hot vectors. This function is already optimized for JAX and is jittable. 5. The function returns the resulting one-hot vectors. You can use this function like this:

# Example usage
indexes = [0, 2, 1, 3]
num_labels = 4
result = one_hot(indexes, num_labels)
print(result)

This will output a 2D array where each row is a one-hot vector corresponding to the input indexes.

Extract Code

I extracted the following code from that output:

import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indexes, num_labels):
    return jax.nn.one_hot(jnp.array(indexes), num_labels)

Run Code Interpreter

Running the following program:

import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indexes, num_labels):
    return jax.nn.one_hot(jnp.array(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:

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 jax.nn.one_hot(jnp.array(indexes), num_labels)
  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_labels.
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

Initial Query

LLM Generation

Query

Output

Here's a jittable JAX function that converts a list of indexes to one-hot vectors:

import jax
import jax.numpy as jnp

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

This function does the following: 1. It uses the `@jax.jit` decorator to ensure the function is JIT-compiled. 2. The function takes two arguments: - `indexes`: A 1D array or list of integer indexes. - `num_classes`: The total number of possible classes (i.e., the size of each one-hot vector). 3. It uses `jnp.eye(num_classes)` to create an identity matrix of size `num_classes x num_classes`. 4. It then indexes this identity matrix with the provided `indexes`, effectively selecting the appropriate rows to create the one-hot vectors. This function is efficient and fully compatible with JAX's JIT compilation. You can use it like this:

indexes = jnp.array([0, 2, 1, 4])
num_classes = 5
one_hot_vectors = one_hot(indexes, num_classes)

The result `one_hot_vectors` will be a 2D array where each row is a one-hot vector corresponding to the input indexes.

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 11, in <module>
    answer = str(one_hot(jnp.array([1,2,0]),5))
  File "/usr/src/app/main.py", line 6, 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:4 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 jittable JAX function that converts a list of indexes to one-hot vectors:

import jax
import jax.numpy as jnp

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

This function does the following: 1. It's decorated with `@jax.jit` to ensure it's JIT-compiled for faster execution. 2. It takes two arguments: - `indexes`: A list or array of index values. - `num_classes`: The total number of possible classes (i.e., the length of each one-hot vector). 3. Inside the function: - We first convert the `indexes` to a JAX array using `jnp.array()`. - We then use JAX's built-in `jax.nn.one_hot()` function to convert these indexes to one-hot vectors. 4. The function returns the resulting one-hot vectors. You can use this function like this:

# Example usage
indexes = [0, 2, 1, 3]
num_classes = 4
result = one_hot(indexes, num_classes)
print(result)

This will output a 2D array where each row is a one-hot vector corresponding to the input indexes.

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(jnp.array(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(jnp.array(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 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 jax.nn.one_hot(jnp.array(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

Initial Query

LLM Generation

Query

Output

Here's a jittable JAX function that converts a list of indexes to one-hot vectors:

import jax
import jax.numpy as jnp

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

This function does the following: 1. It's decorated with `@jax.jit` to ensure it's JIT-compiled for faster execution. 2. It takes two arguments: - `indexes`: The list or array of indexes to be converted to one-hot vectors. - `num_classes`: The total number of possible classes (i.e., the length of each one-hot vector). 3. Inside the function: - We convert the input `indexes` to a JAX array using `jnp.array()`. - We use JAX's built-in `jax.nn.one_hot()` function to convert the indexes to one-hot vectors. 4. The function returns the resulting one-hot vectors. You can use this function like this:

# Example usage
indexes = [0, 2, 1, 3]
num_classes = 4
result = one_hot(indexes, num_classes)
print(result)

This will output a 2D array where each row is a one-hot vector corresponding to the input indexes.

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(jnp.array(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(jnp.array(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 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 jax.nn.one_hot(jnp.array(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