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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
import jax
import jax.numpy as jnp

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

  Args:
    indexes: A 1D array of indexes.
    num_classes: The number of possible labels.

  Returns:
    A 2D array of one-hot vectors, where each row corresponds to an index
    in `indexes`.
  """
  return jnp.eye(num_classes)[indexes]

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

print(one_hot_vectors)

Extract Code

I extracted the following code from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
import jax
import jax.numpy as jnp

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

  Args:
    indexes: A 1D array of indexes.
    num_classes: The number of possible labels.

  Returns:
    A 2D array of one-hot vectors, where each row corresponds to an index
    in `indexes`.
  """
  return jnp.eye(num_classes)[indexes]

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

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
22
23
24
25
26
27
28
29
30
31
32
import jax
import jax.numpy as jnp

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

  Args:
    indexes: A 1D array of indexes.
    num_classes: The number of possible labels.

  Returns:
    A 2D array of one-hot vectors, where each row corresponds to an index
    in `indexes`.
  """
  return jnp.eye(num_classes)[indexes]

# Example usage:
indexes = jnp.array([1, 0, 2, 1])
num_classes = 4
one_hot_vectors = one_hot(indexes, 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: 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 22, in <module> one_hot_vectors = one_hot(indexes, num_classes) File "/usr/src/app/main.py", line 17, in one_hot return jnp.eye(num_classes)[indexes] File "/usr/local/lib/python3.10/dist-packages/jax/_src/numpy/lax_numpy.py", line 2910, 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 using `jit`, try using `static_argnums` or applying `jit` 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.

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
import jax
import jax.numpy as jnp

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

  Args:
    indexes: A 1D array of indexes.
    num_classes: The number of possible labels.

  Returns:
    A 2D array of one-hot vectors.
  """
  return jnp.eye(num_classes)[indexes]

Extract Code

I extracted the following code from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
import jax
import jax.numpy as jnp

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

  Args:
    indexes: A 1D array of indexes.
    num_classes: The number of possible labels.

  Returns:
    A 2D array of one-hot vectors.
  """
  return jnp.eye(num_classes)[indexes]

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
22
23
24
import jax
import jax.numpy as jnp

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

  Args:
    indexes: A 1D array of indexes.
    num_classes: The number of possible labels.

  Returns:
    A 2D array of one-hot vectors.
  """
  return jnp.eye(num_classes)[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: 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 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.eye(num_classes)[indexes] File "/usr/local/lib/python3.10/dist-packages/jax/_src/numpy/lax_numpy.py", line 2910, 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 using `jit`, try using `static_argnums` or applying `jit` 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.

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
import jax
import jax.numpy as jnp

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

  Args:
    indexes: A 1D array of indexes.
    num_classes: The number of possible labels.

  Returns:
    A 2D array of one-hot vectors.
  """
  return jnp.eye(num_classes)[indexes]

# Example usage
indexes = jnp.array([1, 0, 2, 1])
num_classes = 4

one_hot_vectors = one_hot(indexes, num_classes)
print(one_hot_vectors)

Extract Code

I extracted the following code from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
import jax
import jax.numpy as jnp

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

  Args:
    indexes: A 1D array of indexes.
    num_classes: The number of possible labels.

  Returns:
    A 2D array of one-hot vectors.
  """
  return jnp.eye(num_classes)[indexes]

# Example usage
indexes = jnp.array([1, 0, 2, 1])
num_classes = 4

one_hot_vectors = one_hot(indexes, num_classes)
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
22
23
24
25
26
27
28
29
30
31
import jax
import jax.numpy as jnp

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

  Args:
    indexes: A 1D array of indexes.
    num_classes: The number of possible labels.

  Returns:
    A 2D array of one-hot vectors.
  """
  return jnp.eye(num_classes)[indexes]

# Example usage
indexes = jnp.array([1, 0, 2, 1])
num_classes = 4

one_hot_vectors = one_hot(indexes, 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: 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 22, in <module> one_hot_vectors = one_hot(indexes, num_classes) File "/usr/src/app/main.py", line 16, in one_hot return jnp.eye(num_classes)[indexes] File "/usr/local/lib/python3.10/dist-packages/jax/_src/numpy/lax_numpy.py", line 2910, 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 using `jit`, try using `static_argnums` or applying `jit` 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.

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
import jax
import jax.numpy as jnp

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

  Args:
    indexes: A 1D array of indexes.
    num_classes: The number of possible labels.

  Returns:
    A 2D array of one-hot vectors.
  """
  return jnp.eye(num_classes)[indexes]

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

print(one_hot_vectors)

Extract Code

I extracted the following code from that output: 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
import jax
import jax.numpy as jnp

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

  Args:
    indexes: A 1D array of indexes.
    num_classes: The number of possible labels.

  Returns:
    A 2D array of one-hot vectors.
  """
  return jnp.eye(num_classes)[indexes]

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

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
22
23
24
25
26
27
28
29
30
31
import jax
import jax.numpy as jnp

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

  Args:
    indexes: A 1D array of indexes.
    num_classes: The number of possible labels.

  Returns:
    A 2D array of one-hot vectors.
  """
  return jnp.eye(num_classes)[indexes]

# Example usage
indexes = jnp.array([1, 0, 2, 0])
num_classes = 3
one_hot_vectors = one_hot(indexes, 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: 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 21, in <module> one_hot_vectors = one_hot(indexes, num_classes) File "/usr/src/app/main.py", line 16, in one_hot return jnp.eye(num_classes)[indexes] File "/usr/local/lib/python3.10/dist-packages/jax/_src/numpy/lax_numpy.py", line 2910, 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 using `jit`, try using `static_argnums` or applying `jit` 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.

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

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

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

  Args:
    indexes: A 1D array of indexes.
    num_classes: The number of possible labels.

  Returns:
    A 2D array of one-hot vectors, where each row corresponds to an index
    in `indexes`.
  """
  return jnp.eye(num_classes)[indexes]

Extract Code

I extracted the following code from that output: 

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

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

  Args:
    indexes: A 1D array of indexes.
    num_classes: The number of possible labels.

  Returns:
    A 2D array of one-hot vectors, where each row corresponds to an index
    in `indexes`.
  """
  return jnp.eye(num_classes)[indexes]

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
22
23
24
25
26
import jax
import jax.numpy as jnp

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

  Args:
    indexes: A 1D array of indexes.
    num_classes: The number of possible labels.

  Returns:
    A 2D array of one-hot vectors, where each row corresponds to an index
    in `indexes`.
  """
  return jnp.eye(num_classes)[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: 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 24, in <module> answer = str(one_hot(jnp.array([1,2,0]),5)) File "/usr/src/app/main.py", line 17, in one_hot return jnp.eye(num_classes)[indexes] File "/usr/local/lib/python3.10/dist-packages/jax/_src/numpy/lax_numpy.py", line 2910, 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 using `jit`, try using `static_argnums` or applying `jit` 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.

Substring Evaluation

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