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.numpy as jnp


def one_hot(indexes: jnp.array, num_labels: int) -> jnp.array:
  """Converts a list of indexes to a one-hot vector.

  Args:
    indexes: A list of integers representing the indexes of the labels.
    num_labels: The number of possible labels.

  Returns:
    A one-hot vector with shape [len(indexes), num_labels].
  """
  return jnp.array(jnp.eye(num_labels)[indexes])

Extract Code

I extracted the following code from that output: 

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import jax.numpy as jnp


def one_hot(indexes: jnp.array, num_labels: int) -> jnp.array:
  """Converts a list of indexes to a one-hot vector.

  Args:
    indexes: A list of integers representing the indexes of the labels.
    num_labels: The number of possible labels.

  Returns:
    A one-hot vector with shape [len(indexes), num_labels].
  """
  return jnp.array(jnp.eye(num_labels)[indexes])

Run Code Interpreter

Running the following program: 

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import jax.numpy as jnp


def one_hot(indexes: jnp.array, num_labels: int) -> jnp.array:
  """Converts a list of indexes to a one-hot vector.

  Args:
    indexes: A list of integers representing the indexes of the labels.
    num_labels: The number of possible labels.

  Returns:
    A one-hot vector with shape [len(indexes), num_labels].
  """
  return jnp.array(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

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import jax.numpy as jnp
from jax import jit

@jit
def one_hot(indexes, num_labels):
  """Converts a list of indexes to a one-hot vector.

  Args:
    indexes: A list of indexes.
    num_labels: The number of possible labels.

  Returns:
    A one-hot vector.
  """

  one_hot_vector = jnp.zeros((len(indexes), num_labels))
  one_hot_vector = jnp.scatter_add(one_hot_vector, indexes, 1)
  return one_hot_vector

Extract Code

I extracted the following code from that output: 

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import jax.numpy as jnp
from jax import jit

@jit
def one_hot(indexes, num_labels):
  """Converts a list of indexes to a one-hot vector.

  Args:
    indexes: A list of indexes.
    num_labels: The number of possible labels.

  Returns:
    A one-hot vector.
  """

  one_hot_vector = jnp.zeros((len(indexes), num_labels))
  one_hot_vector = jnp.scatter_add(one_hot_vector, indexes, 1)
  return one_hot_vector

Run Code Interpreter

Running the following program: 

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import jax.numpy as jnp
from jax import jit

@jit
def one_hot(indexes, num_labels):
  """Converts a list of indexes to a one-hot vector.

  Args:
    indexes: A list of indexes.
    num_labels: The number of possible labels.

  Returns:
    A one-hot vector.
  """

  one_hot_vector = jnp.zeros((len(indexes), num_labels))
  one_hot_vector = jnp.scatter_add(one_hot_vector, indexes, 1)
  return 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: `` Traceback (most recent call last): File "/usr/src/app/main.py", line 25, in <module> answer = str(one_hot(jnp.array([1,2,0]),5)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 17, in one_hot one_hot_vector = jnp.zeros((len(indexes), num_labels)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/lax_numpy.py", line 2316, in zeros shape = canonicalize_shape(shape) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/lax_numpy.py", line 84, in canonicalize_shape return core.canonicalize_shape(shape, context) # type: ignore ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: Shapes must be 1D sequences of concrete values of integer type, got (3, Traced<ShapedArray(int32[], weak_type=True)>with<DynamicJaxprTrace(level=1/0)>). 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

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import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indexes, num_classes):
  """Converts a list of indexes to a one-hot vector.

  Args:
    indexes: A list of indexes.
    num_classes: The number of possible labels.

  Returns:
    A one-hot vector.
  """

  one_hot_vector = jnp.zeros((len(indexes), num_classes))
  one_hot_vector = jnp.scatter(one_hot_vector, indexes[:, None], 1.0)
  return one_hot_vector

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):
  """Converts a list of indexes to a one-hot vector.

  Args:
    indexes: A list of indexes.
    num_classes: The number of possible labels.

  Returns:
    A one-hot vector.
  """

  one_hot_vector = jnp.zeros((len(indexes), num_classes))
  one_hot_vector = jnp.scatter(one_hot_vector, indexes[:, None], 1.0)
  return one_hot_vector

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):
  """Converts a list of indexes to a one-hot vector.

  Args:
    indexes: A list of indexes.
    num_classes: The number of possible labels.

  Returns:
    A one-hot vector.
  """

  one_hot_vector = jnp.zeros((len(indexes), num_classes))
  one_hot_vector = jnp.scatter(one_hot_vector, indexes[:, None], 1.0)
  return 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: `` Traceback (most recent call last): File "/usr/src/app/main.py", line 25, in <module> answer = str(one_hot(jnp.array([1,2,0]),5)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 17, in one_hot one_hot_vector = jnp.zeros((len(indexes), num_classes)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/lax_numpy.py", line 2316, in zeros shape = canonicalize_shape(shape) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/lax_numpy.py", line 84, in canonicalize_shape return core.canonicalize_shape(shape, context) # type: ignore ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: Shapes must be 1D sequences of concrete values of integer type, got (3, Traced<ShapedArray(int32[], weak_type=True)>with<DynamicJaxprTrace(level=1/0)>). 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

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import jax.numpy as jnp
from jax import jit

@jit
def one_hot(index, num_classes):
    one_hot_tensor = jnp.zeros((num_classes,))
    one_hot_tensor = one_hot_tensor.at[index].set(1.)
    return one_hot_tensor

Extract Code

I extracted the following code from that output: 

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import jax.numpy as jnp
from jax import jit

@jit
def one_hot(index, num_classes):
    one_hot_tensor = jnp.zeros((num_classes,))
    one_hot_tensor = one_hot_tensor.at[index].set(1.)
    return one_hot_tensor

Run Code Interpreter

Running the following program: 

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import jax.numpy as jnp
from jax import jit

@jit
def one_hot(index, num_classes):
    one_hot_tensor = jnp.zeros((num_classes,))
    one_hot_tensor = one_hot_tensor.at[index].set(1.)
    return one_hot_tensor



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 15, in <module> answer = str(one_hot(jnp.array([1,2,0]),5)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 7, in one_hot one_hot_tensor = jnp.zeros((num_classes,)) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/lax_numpy.py", line 2316, in zeros shape = canonicalize_shape(shape) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/lax_numpy.py", line 84, in canonicalize_shape return core.canonicalize_shape(shape, context) # type: ignore ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: Shapes must be 1D sequences of concrete values of integer type, got (Traced<ShapedArray(int32[], weak_type=True)>with<DynamicJaxprTrace(level=1/0)>,). 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

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import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indexes: jnp.ndarray, num_labels: int) -> jnp.ndarray:
    """Converts a list of indexes to a one-hot vector.

    Args:
        indexes: A list of indexes.
        num_labels: The number of possible labels.

    Returns:
        A one-hot vector.
    """
    return jnp.array(jnp.eye(num_labels)[indexes], dtype=jnp.float32)

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: jnp.ndarray, num_labels: int) -> jnp.ndarray:
    """Converts a list of indexes to a one-hot vector.

    Args:
        indexes: A list of indexes.
        num_labels: The number of possible labels.

    Returns:
        A one-hot vector.
    """
    return jnp.array(jnp.eye(num_labels)[indexes], dtype=jnp.float32)

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp

@jax.jit
def one_hot(indexes: jnp.ndarray, num_labels: int) -> jnp.ndarray:
    """Converts a list of indexes to a one-hot vector.

    Args:
        indexes: A list of indexes.
        num_labels: The number of possible labels.

    Returns:
        A one-hot vector.
    """
    return jnp.array(jnp.eye(num_labels)[indexes], dtype=jnp.float32)



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 22, in <module> answer = str(one_hot(jnp.array([1,2,0]),5)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 16, in one_hot return jnp.array(jnp.eye(num_labels)[indexes], dtype=jnp.float32) ^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/lax_numpy.py", line 2460, 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 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>