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 the `one_hot` function that takes in a list of indexes and the number of possible labels, and returns a one-hot vector representation using Jax. This function is jittable, meaning it can be compiled using Jax's just-in-time (JIT) compilation: 

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import jax.numpy as jnp
from jax import jit

@jit
def one_hot(indexes, num_labels):
    """
    Convert a list of indexes to a one-hot vector representation.
    
    Args:
        indexes (jnp.ndarray): A 1D array of integers representing the indexes.
        num_labels (int): The number of possible labels.
        
    Returns:
        jnp.ndarray: A 2D array of shape (len(indexes), num_labels) with one-hot vector representation.
    """
    one_hot_vectors = jnp.zeros((len(indexes), num_labels), dtype=jnp.float32)
    one_hot_vectors = one_hot_vectors.at[jnp.arange(len(indexes)), indexes].set(1)
    return one_hot_vectors
Here's how you can use this function: 
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import jax.numpy as jnp

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

one_hot_vectors = one_hot(indexes, num_labels)
print(one_hot_vectors)
Output: 
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[[1. 0. 0.]
 [0. 0. 1.]
 [0. 1. 0.]
 [1. 0. 0.]]
Explanation: 1. The `one_hot` function is decorated with `@jit` to enable JIT compilation by Jax. 2. Inside the function, we first create a 2D array `one_hot_vectors` of shape `(len(indexes), num_labels)` filled with zeros. 3. We then use Jax's advanced indexing (`one_hot_vectors.at[...].set(...)`) to set the corresponding elements in `one_hot_vectors` to 1 based on the indexes provided. 4. Finally, we return the `one_hot_vectors` array, which now contains the one-hot vector representation of the input indexes. Note that this implementation assumes that the input `indexes` are valid (i.e., within the range `[0, num_labels)`). If you need to handle out-of-range indexes, you may need to add additional checks or error handling.

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):
    """
    Convert a list of indexes to a one-hot vector representation.
    
    Args:
        indexes (jnp.ndarray): A 1D array of integers representing the indexes.
        num_labels (int): The number of possible labels.
        
    Returns:
        jnp.ndarray: A 2D array of shape (len(indexes), num_labels) with one-hot vector representation.
    """
    one_hot_vectors = jnp.zeros((len(indexes), num_labels), dtype=jnp.float32)
    one_hot_vectors = one_hot_vectors.at[jnp.arange(len(indexes)), indexes].set(1)
    return one_hot_vectors

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):
    """
    Convert a list of indexes to a one-hot vector representation.
    
    Args:
        indexes (jnp.ndarray): A 1D array of integers representing the indexes.
        num_labels (int): The number of possible labels.
        
    Returns:
        jnp.ndarray: A 2D array of shape (len(indexes), num_labels) with one-hot vector representation.
    """
    one_hot_vectors = jnp.zeros((len(indexes), num_labels), dtype=jnp.float32)
    one_hot_vectors = one_hot_vectors.at[jnp.arange(len(indexes)), indexes].set(1)
    return 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: `` 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_vectors = jnp.zeros((len(indexes), num_labels), dtype=jnp.float32) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 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

Here's the code for the `one_hot` function in JAX that takes in a list of indexes and the number of possible labels, and returns a one-hot vector representation: 

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import jax.numpy as jnp
from jax import jit

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

    Args:
        indexes (jnp.ndarray): A 1D array of integers representing the indexes.
        num_labels (int): The number of possible labels.

    Returns:
        jnp.ndarray: A 2D array of shape (len(indexes), num_labels) with one-hot encoding.
    """
    one_hot_arr = jnp.zeros((len(indexes), num_labels), dtype=jnp.float32)
    one_hot_arr = one_hot_arr.at[jnp.arange(len(indexes)), indexes].set(1)
    return one_hot_arr
Here's how the function works: 1. The `@jit` decorator is used to ensure that the function is JIT-compiled when called, which can significantly improve performance for certain workloads. 2. The function takes two arguments: `indexes` (a 1D NumPy array of integers representing the indexes) and `num_labels` (an integer representing the number of possible labels). 3. Inside the function, a 2D NumPy array `one_hot_arr` is initialized with zeros, with a shape of `(len(indexes), num_labels)`. This array will store the one-hot encoding. 4. The `one_hot_arr` is updated using the `at` and `set` methods of JAX. The `jnp.arange(len(indexes))` creates a range of indices corresponding to each row in `one_hot_arr`. The `indexes` array provides the column indices where the value should be set to 1 for each row. 5. The updated `one_hot_arr` is returned, which now contains the one-hot encoding of the input `indexes`. You can use this function like this: 
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import jax.numpy as jnp

indexes = jnp.array([0, 2, 1, 0])
num_labels = 3

one_hot_vec = one_hot(indexes, num_labels)
print(one_hot_vec)
This will output: 
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[[1. 0. 0.]
 [0. 0. 1.]
 [0. 1. 0.]
 [1. 0. 0.]]
Note that the `one_hot` function is JIT-compiled when called, so the first invocation may take longer due to the JIT compilation overhead, but subsequent calls will be significantly faster.

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

    Args:
        indexes (jnp.ndarray): A 1D array of integers representing the indexes.
        num_labels (int): The number of possible labels.

    Returns:
        jnp.ndarray: A 2D array of shape (len(indexes), num_labels) with one-hot encoding.
    """
    one_hot_arr = jnp.zeros((len(indexes), num_labels), dtype=jnp.float32)
    one_hot_arr = one_hot_arr.at[jnp.arange(len(indexes)), indexes].set(1)
    return one_hot_arr

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

    Args:
        indexes (jnp.ndarray): A 1D array of integers representing the indexes.
        num_labels (int): The number of possible labels.

    Returns:
        jnp.ndarray: A 2D array of shape (len(indexes), num_labels) with one-hot encoding.
    """
    one_hot_arr = jnp.zeros((len(indexes), num_labels), dtype=jnp.float32)
    one_hot_arr = one_hot_arr.at[jnp.arange(len(indexes)), indexes].set(1)
    return one_hot_arr



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_arr = jnp.zeros((len(indexes), num_labels), dtype=jnp.float32) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 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

Sure, here's the code for the `one_hot` function in JAX that takes a list of indexes and the number of possible labels as arguments, and returns a one-hot vector representation: 

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import jax.numpy as jnp
from jax import jit

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

    Args:
        indexes (jnp.ndarray): A 1D array of integers representing the indexes.
        num_labels (int): The number of possible labels.

    Returns:
        jnp.ndarray: A 2D array of shape (len(indexes), num_labels) with one-hot vectors.
    """
    one_hot_vectors = jnp.zeros((len(indexes), num_labels), dtype=jnp.float32)
    one_hot_vectors = one_hot_vectors.at[jnp.arange(len(indexes)), indexes].set(1)
    return one_hot_vectors
Here's how the `one_hot` function works: 1. The function is decorated with `@jit` to enable just-in-time (JIT) compilation for better performance. 2. The `one_hot_vectors` array is initialized with zeros of shape `(len(indexes), num_labels)` and data type `float32`. 3. The `one_hot_vectors` array is updated using the `at` method of JAX, which sets the values at the specified indices to 1. The indices are determined by `jnp.arange(len(indexes))` (row indices) and `indexes` (column indices). 4. The updated `one_hot_vectors` array is returned, which now contains the one-hot vector representation of the input indexes. You can use the `one_hot` function like this: 
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import jax.numpy as jnp

indexes = jnp.array([0, 2, 1, 0])
num_labels = 3

one_hot_vectors = one_hot(indexes, num_labels)
print(one_hot_vectors)
This will output: 
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[[1. 0. 0.]
 [0. 0. 1.]
 [0. 1. 0.]
 [1. 0. 0.]]
Note that the `one_hot` function expects the input `indexes` to be a JAX NumPy array (`jnp.ndarray`). If you have a regular Python list, you can convert it to a JAX NumPy array using `jnp.array(your_list)`.

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

    Args:
        indexes (jnp.ndarray): A 1D array of integers representing the indexes.
        num_labels (int): The number of possible labels.

    Returns:
        jnp.ndarray: A 2D array of shape (len(indexes), num_labels) with one-hot vectors.
    """
    one_hot_vectors = jnp.zeros((len(indexes), num_labels), dtype=jnp.float32)
    one_hot_vectors = one_hot_vectors.at[jnp.arange(len(indexes)), indexes].set(1)
    return one_hot_vectors

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

    Args:
        indexes (jnp.ndarray): A 1D array of integers representing the indexes.
        num_labels (int): The number of possible labels.

    Returns:
        jnp.ndarray: A 2D array of shape (len(indexes), num_labels) with one-hot vectors.
    """
    one_hot_vectors = jnp.zeros((len(indexes), num_labels), dtype=jnp.float32)
    one_hot_vectors = one_hot_vectors.at[jnp.arange(len(indexes)), indexes].set(1)
    return 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: `` 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_vectors = jnp.zeros((len(indexes), num_labels), dtype=jnp.float32) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 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

Here's the code for the `one_hot` function in JAX that takes a list of indexes and the number of possible labels as input, and returns a one-hot encoded vector. The function is designed to be jittable, meaning it can be compiled by JAX's just-in-time (JIT) compiler for improved performance. 

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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 encoded vector.
    
    Args:
        indexes (jnp.ndarray): Array of indices to be one-hot encoded.
        num_labels (int): Number of possible labels/classes.
        
    Returns:
        jnp.ndarray: One-hot encoded vector of shape (len(indexes), num_labels).
    """
    eye = jnp.eye(num_labels)
    return eye[indexes]
Here's how you can use the `one_hot` function: 
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import jax.numpy as jnp

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

one_hot_vector = one_hot(indexes, num_labels)
print(one_hot_vector)
Output: 
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[[1. 0. 0.]
 [0. 0. 1.]
 [0. 1. 0.]
 [1. 0. 0.]]
Explanation: 1. The `one_hot` function is decorated with `@jit` to enable JIT compilation by JAX. 2. Inside the function, we create an identity matrix `eye` of shape `(num_labels, num_labels)` using `jnp.eye(num_labels)`. This matrix will be used to perform the one-hot encoding. 3. We then use the `indexes` array to index into the `eye` matrix and select the corresponding rows, which represent the one-hot encoded vectors for each index. 4. The resulting one-hot encoded vector has shape `(len(indexes), num_labels)`, where each row corresponds to the one-hot encoding of the corresponding index in the `indexes` array. Note that the `one_hot` function assumes that the input `indexes` array contains valid indices within the range `[0, num_labels)`. If this assumption is violated, the function will raise an error.

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 encoded vector.
    
    Args:
        indexes (jnp.ndarray): Array of indices to be one-hot encoded.
        num_labels (int): Number of possible labels/classes.
        
    Returns:
        jnp.ndarray: One-hot encoded vector of shape (len(indexes), num_labels).
    """
    eye = jnp.eye(num_labels)
    return eye[indexes]

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 encoded vector.
    
    Args:
        indexes (jnp.ndarray): Array of indices to be one-hot encoded.
        num_labels (int): Number of possible labels/classes.
        
    Returns:
        jnp.ndarray: One-hot encoded vector of shape (len(indexes), num_labels).
    """
    eye = jnp.eye(num_labels)
    return eye[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 24, in <module> answer = str(one_hot(jnp.array([1,2,0]),5)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 17, in one_hot eye = jnp.eye(num_labels) ^^^^^^^^^^^^^^^^^^^ 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>

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 the `one_hot` function in JAX that takes a list of indexes and the number of possible labels, and returns the corresponding one-hot vector representation. This function will be JIT-compiled: 

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import jax.numpy as jnp
from jax import jit

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

    Args:
        indexes (jnp.ndarray): A 1D array of integers representing the indexes.
        num_labels (int): The number of possible labels or categories.

    Returns:
        jnp.ndarray: A 2D array representing the one-hot vector encoding.
    """
    one_hot_vec = jnp.zeros((len(indexes), num_labels), dtype=jnp.float32)
    one_hot_vec = one_hot_vec.at[jnp.arange(len(indexes)), indexes].set(1)
    return one_hot_vec
Here's how the function works: 1. The function is decorated with `@jit` to enable JIT compilation. 2. It takes two arguments: `indexes` (a JAX NumPy array of integers representing the indexes) and `num_labels` (an integer representing the number of possible labels or categories). 3. Inside the function, a 2D JAX NumPy array `one_hot_vec` is created with shape `(len(indexes), num_labels)` and filled with zeros. 4. The `at` method is used to update the values in `one_hot_vec` at the specified indices. `jnp.arange(len(indexes))` creates a range of indices corresponding to the length of `indexes`, and `indexes` provides the column indices where the value should be set to 1. 5. The updated `one_hot_vec` is returned, representing the one-hot vector encoding of the input indexes. You can use this function like this: 
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import jax.numpy as jnp

indexes = jnp.array([0, 2, 1, 0])
num_labels = 3

one_hot_vec = one_hot(indexes, num_labels)
print(one_hot_vec)
This will output: 
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[[1. 0. 0.]
 [0. 0. 1.]
 [0. 1. 0.]
 [1. 0. 0.]]
Note that since the function is JIT-compiled, it will be optimized for performance, especially when dealing with large arrays or repeated calls.

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

    Args:
        indexes (jnp.ndarray): A 1D array of integers representing the indexes.
        num_labels (int): The number of possible labels or categories.

    Returns:
        jnp.ndarray: A 2D array representing the one-hot vector encoding.
    """
    one_hot_vec = jnp.zeros((len(indexes), num_labels), dtype=jnp.float32)
    one_hot_vec = one_hot_vec.at[jnp.arange(len(indexes)), indexes].set(1)
    return one_hot_vec

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

    Args:
        indexes (jnp.ndarray): A 1D array of integers representing the indexes.
        num_labels (int): The number of possible labels or categories.

    Returns:
        jnp.ndarray: A 2D array representing the one-hot vector encoding.
    """
    one_hot_vec = jnp.zeros((len(indexes), num_labels), dtype=jnp.float32)
    one_hot_vec = one_hot_vec.at[jnp.arange(len(indexes)), indexes].set(1)
    return one_hot_vec


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 23, in <module> answer = str(one_hot(jnp.array([1,2,0]),5)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 16, in one_hot one_hot_vec = jnp.zeros((len(indexes), num_labels), dtype=jnp.float32) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 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:4 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>