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

Sure, here is a JAX implementation of a `one_hot` function that converts a list of indices to a one-hot encoded matrix. The function is also jitted for optimized performance: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
import jax
import jax.numpy as jnp

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

# Example usage:
indices = jnp.array([0, 1, 2, 3])
num_classes = 4
one_hot_encoded = one_hot(indices, num_classes)
print(one_hot_encoded)
This code defines the `one_hot` function that takes a list of indices and the number of possible labels as arguments. The function uses `jax.nn.one_hot` to convert the indices into a one-hot encoded matrix. The `@jax.jit` decorator is used to compile the function with JAX's Just-In-Time (JIT) compilation for better performance.

Extract Code

I extracted the following code from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
import jax
import jax.numpy as jnp

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

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

Run Code Interpreter

Running the following program: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
import jax
import jax.numpy as jnp

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

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



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 12, in <module> one_hot_encoded = one_hot(indices, num_classes) File "/usr/src/app/main.py", line 7, in one_hot return jax.nn.one_hot(indices, 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

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

Sure, here's a code snippet in JAX that defines a `one_hot` function which can be JIT-compiled: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
import jax
import jax.numpy as jnp

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

# Example usage:
indexes = jnp.array([0, 1, 2, 4])
num_labels = 5
one_hot_vectors = one_hot(indexes, num_labels)
print(one_hot_vectors)
This code defines a `one_hot` function that takes two arguments: 1. `indexes`: A list or array of indices. 2. `num_labels`: The number of possible labels. The `jax.nn.one_hot` function is used to create the one-hot encoded vectors, and the `@jax.jit` decorator is used to JIT-compile the `one_hot` function for improved performance.

Extract Code

I extracted the following code from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
import jax
import jax.numpy as jnp

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

# Example usage:
indexes = jnp.array([0, 1, 2, 4])
num_labels = 5
one_hot_vectors = one_hot(indexes, num_labels)
print(one_hot_vectors)

Run Code Interpreter

Running the following program: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
import jax
import jax.numpy as jnp

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

# Example usage:
indexes = jnp.array([0, 1, 2, 4])
num_labels = 5
one_hot_vectors = one_hot(indexes, num_labels)
print(one_hot_vectors)



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 12, in <module> one_hot_vectors = one_hot(indexes, num_labels) File "/usr/src/app/main.py", line 7, in one_hot return jax.nn.one_hot(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

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

Sure, here's the code to create a jittable function in JAX that converts a list of indexes to a one-hot encoded vector: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
import jax
import jax.numpy as jnp

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

# Example usage
indexes = jnp.array([0, 1, 2])
num_labels = 3
one_hot_vector = one_hot(indexes, num_labels)
print(one_hot_vector)
This code defines a function `one_hot` that takes in a list of indexes and the number of possible labels, and returns the one-hot encoded vectors. The function is decorated with `@jax.jit` to ensure it is JIT-compiled for performance.

Extract Code

I extracted the following code from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
import jax
import jax.numpy as jnp

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

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

Run Code Interpreter

Running the following program: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
import jax
import jax.numpy as jnp

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

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



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 12, in <module> one_hot_vector = one_hot(indexes, num_labels) File "/usr/src/app/main.py", line 7, in one_hot return jax.nn.one_hot(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

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

Certainly! Below is a JAX implementation of a function `one_hot` that converts a list of indexes to a one-hot encoded vector. This function is jittable and jitted using JAX's `jax.jit`. 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
import jax
import jax.numpy as jnp

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

# Example usage:
# indices = jnp.array([0, 1, 2])
# num_classes = 4
# one_hot_vector = one_hot(indices, num_classes)
# print(one_hot_vector)
This code defines the `one_hot` function which takes `indices` and `num_classes` as arguments and returns the one-hot encoded vectors. The function is decorated with `@jax.jit` to ensure it is jitted for better performance. Remember, you can uncomment the example usage to test the function.

Extract Code

I extracted the following code from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
import jax
import jax.numpy as jnp

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

# Example usage:
# indices = jnp.array([0, 1, 2])
# num_classes = 4
# one_hot_vector = one_hot(indices, num_classes)
# print(one_hot_vector)

Run Code Interpreter

Running the following program: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
import jax
import jax.numpy as jnp

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

# Example usage:
# indices = jnp.array([0, 1, 2])
# num_classes = 4
# one_hot_vector = one_hot(indices, num_classes)
# print(one_hot_vector)



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 7, in one_hot return jax.nn.one_hot(indices, 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

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

Sure, here is the code to create a JAX function that converts a list of indexes to a one-hot vector. The function is also jitted for performance: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
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 = 4
result = one_hot(indexes, num_classes)
print(result)
In this code: - `jax.nn.one_hot` is used to create the one-hot encoded vectors. - The `@jax.jit` decorator is used to compile the function with JAX's just-in-time compilation for improved performance. - `indexes` is an array of indices that you want to convert to a one-hot representation. - `num_classes` is the total number of classes or labels. You can call this `one_hot` function with your list of indices and the number of possible labels to get the one-hot encoded vectors.

Extract Code

I extracted the following code from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
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 = 4
result = one_hot(indexes, num_classes)
print(result)

Run Code Interpreter

Running the following program: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
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 = 4
result = one_hot(indexes, num_classes)
print(result)



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 12, in <module> result = one_hot(indexes, num_classes) 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