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! Here's a simple implementation of a `one_hot` function using JAX, which converts a list of indices into one-hot vectors. The function is jitted using `jax.jit` for performance improvements. This function takes two arguments: the list of indices and the number of possible labels (depth of the one-hot encoding). 

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import jax
import jax.numpy as jnp

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

# Example usage
indices = jnp.array([0, 2, 1, 3])
num_classes = 4
one_hot_encoded = one_hot(indices, num_classes)
print(one_hot_encoded)
This script defines a jitted `one_hot` function using JAX. It utilizes the `jnp.eye` function to create an identity matrix of size `[num_classes, num_classes]` and then indexes this matrix with the provided indices to create the one-hot vectors. The resulting array will have a shape of `[len(indices), num_classes]`, where each row corresponds to a one-hot vector of the index at that position.

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(indices, num_classes):
    return jnp.eye(num_classes)[indices]

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

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp

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

# Example usage
indices = jnp.array([0, 2, 1, 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: `` 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 jnp.eye(num_classes)[indices] ^^^^^^^^^^^^^^^^^^^^ 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_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

Certainly! Below is a JAX code snippet that defines a `one_hot` function, which takes two arguments: `indexes` and `num_classes`. This function converts the given indexes into a one-hot encoded format and is JIT-compiled for efficiency using JAX's `jit` decorator. 

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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
if __name__ == "__main__":
    indexes = jnp.array([0, 1, 2, 3])
    num_classes = 4
    print(one_hot(indexes, num_classes))
This function utilizes JAX's built-in `one_hot` function from `jax.nn` module for the one-hot encoding and leverages JAX's `jit` decorator to ensure that the function is Just-In-Time compiled. When you use this function, make sure that both the `indexes` and `num_classes` are appropriate for your use case and that `indexes` contains valid indices for the specified number of classes (`num_classes`). The example usage provided demonstrates how to call this function with a simple list of indexes and a specified number of classes. The output will be a one-hot encoded representation of the input indexes.

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):
    return jax.nn.one_hot(indexes, num_classes)

# Example usage
if __name__ == "__main__":
    indexes = jnp.array([0, 1, 2, 3])
    num_classes = 4
    print(one_hot(indexes, num_classes))

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):
    return jax.nn.one_hot(indexes, num_classes)

# Example usage
if __name__ == "__main__":
    indexes = jnp.array([0, 1, 2, 3])
    num_classes = 4
    print(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: `` Traceback (most recent call last): File "/usr/src/app/main.py", line 13, in <module> print(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/python3.11/dist-packages/jax/_src/nn/functions.py", line 589, 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 argumentnum_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

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

Certainly! Below is a code snippet that defines a `one_hot` function using JAX. This function takes two arguments: `indexes`, which is an array of indices you want to convert to one-hot encoding, and `num_classes`, which represents the number of possible labels or the length of the one-hot encoded vectors. The function is compiled with JAX's JIT (Just-In-Time compilation) for performance optimization. 

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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:
if __name__ == "__main__":
    indexes = jnp.array([0, 2, 1, 3])
    num_classes = 4
    print(one_hot(indexes, num_classes))
This code first imports the necessary JAX modules. It then defines the `one_hot` function, which internally uses `jax.nn.one_hot` for converting the indices to one-hot encoded vectors. Notice that the function is decorated with `@jax.jit`, which instructs JAX to compile the function using JIT compilation, improving performance, especially for large inputs or repeated calls. The example usage at the bottom demonstrates how to call this function with a sample set of indices and a specified number of classes.

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):
    return jax.nn.one_hot(indexes, num_classes)

# Example usage:
if __name__ == "__main__":
    indexes = jnp.array([0, 2, 1, 3])
    num_classes = 4
    print(one_hot(indexes, num_classes))

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):
    return jax.nn.one_hot(indexes, num_classes)

# Example usage:
if __name__ == "__main__":
    indexes = jnp.array([0, 2, 1, 3])
    num_classes = 4
    print(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: `` Traceback (most recent call last): File "/usr/src/app/main.py", line 13, in <module> print(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/python3.11/dist-packages/jax/_src/nn/functions.py", line 589, 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 argumentnum_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

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

Certainly! Here is a concise implementation of the `one_hot` function using JAX that converts a list of indices to one-hot encoded vectors. This function is designed to be JIT-compiled using JAX's `jit` decorator for efficiency. It takes two arguments: the first is the array of indices, and the second is the number of possible labels (i.e., the length of the one-hot encoded vectors). 

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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:
if __name__ == "__main__":
    indices = jnp.array([0, 1, 2, 3])
    num_classes = 4
    one_hot_vectors = one_hot(indices, num_classes)
    print(one_hot_vectors)
In this example, `one_hot` function leverages JAX's built-in `one_hot` function for creating one-hot encoded vectors from the given indices. The `@jax.jit` decorator is used to JIT-compile the function, improving performance, especially for large arrays or when this function is called repeatedly in a computation-heavy context. Make sure to replace `indices` and `num_classes` in the example usage with your actual data and the number of labels you have, respectively.

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(indices, num_classes):
    return jax.nn.one_hot(indices, num_classes)

# Example Usage:
if __name__ == "__main__":
    indices = jnp.array([0, 1, 2, 3])
    num_classes = 4
    one_hot_vectors = one_hot(indices, num_classes)
    print(one_hot_vectors)

Run Code Interpreter

Running the following program: 

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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:
if __name__ == "__main__":
    indices = jnp.array([0, 1, 2, 3])
    num_classes = 4
    one_hot_vectors = one_hot(indices, num_classes)
    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: `` Traceback (most recent call last): File "/usr/src/app/main.py", line 13, in <module> one_hot_vectors = 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/python3.11/dist-packages/jax/_src/nn/functions.py", line 589, 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 argumentnum_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

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

You can achieve this by using the `jax` library in Python. First, ensure you have `jax` installed in your environment. If not, you can install it using pip: 

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pip install jax jaxlib
Here's a simple implementation of a `one_hot` function that takes two arguments: `indexes`, an array of indices, and `num_classes`, the number of possible labels. This function is jitted using `jax.jit` for performance improvements: 
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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
if __name__ == "__main__":
    indexes = jnp.array([0, 1, 2, 3])
    num_classes = 4
    print(one_hot(indexes, num_classes))
This code first imports the necessary modules from `jax`. It then defines the `one_hot` function, which internally uses `jax.nn.one_hot` for creating one-hot vectors from the provided indexes. The `@jax.jit` decorator is used to jit-compile the function, optimizing its execution. The example usage demonstrates how to call this function with a sample array of indexes and a specified number of classes. When you run this script, it should output the one-hot encoded matrix corresponding to the input indexes.

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):
    return jax.nn.one_hot(indexes, num_classes)

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):
    return jax.nn.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: `` 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(indexes, num_classes) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/jax/_src/nn/functions.py", line 589, 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 argumentnum_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

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>