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 simple function that should do what you're asking for using JAX: 

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import jax.numpy as jnp
from jax import jit, vmap

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

# If you want to apply the function to a batch of indexes, you can use vmap
one_hot_batched = vmap(one_hot, in_axes=(0, None))
In this code, `jnp.eye(num_labels)` creates an identity matrix of size `num_labels x num_labels`. The `[indexes]` part then selects the rows corresponding to the given indexes, effectively creating one-hot vectors. The `@jit` decorator jit-compiles the function for faster execution. The `vmap` function is used to vectorize the `one_hot` function, so it can be applied to a batch of indexes at once. The `in_axes=(0, None)` argument means that the function should be applied to each element in the first dimension of the first argument (the batch of indexes), and that the second argument (the number of labels) is a scalar.

Extract Code

I extracted the following code from that output: 

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import jax.numpy as jnp
from jax import jit, vmap

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

# If you want to apply the function to a batch of indexes, you can use vmap
one_hot_batched = vmap(one_hot, in_axes=(0, None))

Run Code Interpreter

Running the following program: 

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import jax.numpy as jnp
from jax import jit, vmap

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

# If you want to apply the function to a batch of indexes, you can use vmap
one_hot_batched = vmap(one_hot, in_axes=(0, None))



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 16, 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_labels)[indexes] ^^^^^^^^^^^^^^^^^^^ 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

Sure, here is a simple function that should do what you're asking for. This function uses the `jax.nn.one_hot` function to convert the indices to a one-hot vector. 

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import jax.numpy as jnp
from jax import jit, vmap
from jax.nn import one_hot

@jit
def one_hot(indices, num_classes):
    return vmap(one_hot)(indices, num_classes)
This function takes a list of indices and the number of possible labels (num_classes) as input. It uses `vmap` to vectorize the `one_hot` function, allowing it to operate on the entire list of indices at once. The `jit` decorator is used to compile the function with XLA for faster execution.

Extract Code

I extracted the following code from that output: 

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import jax.numpy as jnp
from jax import jit, vmap
from jax.nn import one_hot

@jit
def one_hot(indices, num_classes):
    return vmap(one_hot)(indices, num_classes)

Run Code Interpreter

Running the following program: 

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import jax.numpy as jnp
from jax import jit, vmap
from jax.nn import one_hot

@jit
def one_hot(indices, num_classes):
    return vmap(one_hot)(indices, 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: 
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Traceback (most recent call last):
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/api.py", line 1282, in _get_axis_size
    return shape[axis]
           ~~~~~^^^^^^
IndexError: tuple index out of range
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
  File "/usr/src/app/main.py", line 14, in <module>
    answer = str(one_hot(jnp.array([1,2,0]),5))
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/src/app/main.py", line 8, in one_hot
    return vmap(one_hot)(indices, num_classes)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: vmap was requested to map its argument along axis 0, which implies that its rank should be at least 1, but is only 0 (its shape is ())</p>
<hr />
<p>For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.

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 a simple function that should do what you asked for. This function uses the `jax.nn.one_hot` function to convert the indices to a one-hot vector. 

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import jax.numpy as jnp
from jax import jit, vmap
from jax.nn import one_hot

@jit
def one_hot(indices, num_classes):
    return vmap(one_hot)(indices, num_classes)
However, please note that `jax.nn.one_hot` is already jittable and it broadcasts, so you might not need to use `vmap` or `jit` here. You can simply use it like this: 
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def one_hot(indices, num_classes):
    return one_hot(indices, num_classes)
In both cases, `indices` should be an array of integers and `num_classes` should be an integer. The function will return a one-hot encoded array where the `i`th dimension is set to 1 for each `i` in `indices`.

Extract Code

I extracted the following code from that output: 

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import jax.numpy as jnp
from jax import jit, vmap
from jax.nn import one_hot

@jit
def one_hot(indices, num_classes):
    return vmap(one_hot)(indices, num_classes)

Run Code Interpreter

Running the following program: 

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import jax.numpy as jnp
from jax import jit, vmap
from jax.nn import one_hot

@jit
def one_hot(indices, num_classes):
    return vmap(one_hot)(indices, 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: 
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Traceback (most recent call last):
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/api.py", line 1282, in _get_axis_size
    return shape[axis]
           ~~~~~^^^^^^
IndexError: tuple index out of range
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
  File "/usr/src/app/main.py", line 14, in <module>
    answer = str(one_hot(jnp.array([1,2,0]),5))
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/src/app/main.py", line 8, in one_hot
    return vmap(one_hot)(indices, num_classes)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: vmap was requested to map its argument along axis 0, which implies that its rank should be at least 1, but is only 0 (its shape is ())</p>
<hr />
<p>For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.

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 a simple function that should do what you're asking for. This function uses the `jax.nn.one_hot` function to convert the indices to a one-hot vector. 

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import jax.numpy as jnp
from jax import jit, vmap
from jax.nn import one_hot

@jit
def one_hot(indices, num_classes):
    return one_hot(indices, num_classes)

# If you want to apply the function to a list of indices, you can use vmap
@jit
def one_hot_list(indices, num_classes):
    return vmap(one_hot)(indices, num_classes)
Please note that `jax.nn.one_hot` function is already jittable, so you don't need to jit it again. The `vmap` function is used to vectorize the `one_hot` function, so it can be applied to a list of indices. Also, please make sure that the `indices` are of integer type and `num_classes` is a positive integer. The `indices` should be in the range `[0, num_classes)`. If not, the function may not work as expected.

Extract Code

I extracted the following code from that output: 

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import jax.numpy as jnp
from jax import jit, vmap
from jax.nn import one_hot

@jit
def one_hot(indices, num_classes):
    return one_hot(indices, num_classes)

# If you want to apply the function to a list of indices, you can use vmap
@jit
def one_hot_list(indices, num_classes):
    return vmap(one_hot)(indices, num_classes)

Run Code Interpreter

Running the following program: 

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import jax.numpy as jnp
from jax import jit, vmap
from jax.nn import one_hot

@jit
def one_hot(indices, num_classes):
    return one_hot(indices, num_classes)

# If you want to apply the function to a list of indices, you can use vmap
@jit
def one_hot_list(indices, num_classes):
    return vmap(one_hot)(indices, 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: 
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Traceback (most recent call last):
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/traceback_util.py", line 179, in reraise_with_filtered_traceback
    return fun(<em>args, </em><em>kwargs)
           ^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/pjit.py", line 245, in cache_miss
    outs, out_flat, out_tree, args_flat, jaxpr, attrs_tracked = _python_pjit_helper(
                                                                ^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/pjit.py", line 133, in _python_pjit_helper
    infer_params_fn(</em>args, <strong>kwargs)
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/api.py", line 319, in infer_params
    return pjit.common_infer_params(pjit_info_args, *args, </strong>kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/pjit.py", line 426, in common_infer_params
    dbg = debug_info(jit_name, fun, args, kwargs, static_argnums, static_argnames)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/api_util.py", line 620, in debug_info
    arg_names = _arg_names(fun, args, kwargs, static_argnums, static_argnames)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/api_util.py", line 645, in _arg_names
    ba = inspect.signature(fn).bind(<em>args_, </em>*kwargs)
         ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/inspect.py", line 3280, in signature
    return Signature.from_callable(obj, follow_wrapped=follow_wrapped,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/inspect.py", line 3028, in from_callable
    return _signature_from_callable(obj, sigcls=cls,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/inspect.py", line 2465, in _signature_from_callable
    obj = unwrap(obj, stop=(lambda f: hasattr(f, "<strong>signature</strong>")
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/inspect.py", line 760, in unwrap
    memo = {id(f): f}
            ^^^^^
RecursionError: maximum recursion depth exceeded while calling a Python object
During handling of the above exception, another exception occurred:
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 8, in one_hot
    return one_hot(indices, num_classes)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/src/app/main.py", line 8, in one_hot
    return one_hot(indices, num_classes)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/src/app/main.py", line 8, in one_hot
    return one_hot(indices, num_classes)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  [Previous line repeated 73 more times]
  File "/usr/lib/python3.11/traceback.py", line 228, in extract_stack
    stack = StackSummary.extract(walk_stack(f), limit=limit)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/traceback.py", line 390, in extract
    return klass._extract_from_extended_frame_gen(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/traceback.py", line 433, in _extract_from_extended_frame_gen
    f.line
  File "/usr/lib/python3.11/traceback.py", line 318, in line
    self._line = linecache.getline(self.filename, self.lineno)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/linecache.py", line 30, in getline
    lines = getlines(filename, module_globals)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/linecache.py", line 46, in getlines
    return updatecache(filename, module_globals)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/linecache.py", line 136, in updatecache
    with tokenize.open(fullname) as fp:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/tokenize.py", line 396, in open
    buffer = _builtin_open(filename, 'rb')
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RecursionError: maximum recursion depth exceeded while calling a Python object</p>
<hr />
<p>For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.

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 a simple function that should do what you asked for. This function uses the `jax.nn.one_hot` function to convert the indices to a one-hot vector. 

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import jax.numpy as jnp
from jax import jit, vmap
from jax.nn import one_hot

@jit
def one_hot(indices, num_classes):
    return vmap(one_hot)(indices, num_classes)
Please note that `jnp.eye` or `jax.nn.one_hot` alone will not work for a list of indices. We need to use `vmap` to vectorize the function over the list of indices. Also, the `jit` decorator is used to compile the function using XLA for faster execution. Remember to install the `jax` library if you haven't done so already. You can install it using pip: 
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pip install --upgrade jax jaxlib

Extract Code

I extracted the following code from that output: 

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import jax.numpy as jnp
from jax import jit, vmap
from jax.nn import one_hot

@jit
def one_hot(indices, num_classes):
    return vmap(one_hot)(indices, num_classes)

Run Code Interpreter

Running the following program: 

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import jax.numpy as jnp
from jax import jit, vmap
from jax.nn import one_hot

@jit
def one_hot(indices, num_classes):
    return vmap(one_hot)(indices, 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: 
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Traceback (most recent call last):
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/api.py", line 1282, in _get_axis_size
    return shape[axis]
           ~~~~~^^^^^^
IndexError: tuple index out of range
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
  File "/usr/src/app/main.py", line 14, in <module>
    answer = str(one_hot(jnp.array([1,2,0]),5))
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/src/app/main.py", line 8, in one_hot
    return vmap(one_hot)(indices, num_classes)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: vmap was requested to map its argument along axis 0, which implies that its rank should be at least 1, but is only 0 (its shape is ())</p>
<hr />
<p>For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.

Substring Evaluation

Testing if the previous output contains the string All tests passed: False