:sparkles: only numpy and vanilla

1 files changed, 0 insertions, 132 deletions

diff --git a/challenge-307/conor-hoekstra/python/ch-1.py b/challenge-307/conor-hoekstra/python/ch-1.py
index de99d0885e..86d10de038 100644
--- a/challenge-307/conor-hoekstra/python/ch-1.py
+++ b/challenge-307/conor-hoekstra/python/ch-1.py
@@ -1,8 +1,4 @@
 import numpy as np
-import jax
-import jax.numpy as jnp
-import tensorflow as tf
-import torch
 
 
 def check_order(lst):
@@ -13,131 +9,3 @@ def check_order(lst):
 def check_order_np(lst):
     l = np.array(lst)
     return np.where(l != sorted(l))[0]
-
-
-def check_order_jax(lst):
-    l = jnp.array(lst)
-    return jnp.where(l != jnp.sort(l))[0]
-
-
-# @jax.jit
-# def check_order_jax_jit(lst):
-#     l = jnp.array(lst)
-#     return jnp.where(l != jnp.sort(l))[0]
-
-# jax.errors.ConcretizationTypeError: Abstract tracer value encountered
-#  where concrete value is expected: traced array with shape int32[]
-# The size argument of jnp.nonzero must be statically specified to use
-# jnp.nonzero within JAX transformations.
-
-
-# TensorFlow Solution (CPU)
-def check_order_tf(lst):
-    l = tf.constant(lst)
-    return tf.where(l != tf.sort(l))[:, 0].numpy()
-
-
-# TensorFlow Solution (CUDA/GPU)
-def check_order_tf_cuda(lst):
-    with tf.device("/GPU:0"):
-        l = tf.constant(lst)
-        return tf.where(l != tf.sort(l))[:, 0].numpy()
-
-
-# TensorFlow Function Solution (CPU)
-@tf.function
-def check_order_tf_fn(l):
-    return tf.where(l != tf.sort(l))[:, 0]
-
-
-# TensorFlow Function Solution (CUDA/GPU)
-@tf.function
-def check_order_tf_fn_cuda_impl(l):
-    return tf.where(l != tf.sort(l))[:, 0]
-
-
-def check_order_tf_fn_cuda(lst):
-    with tf.device("/GPU:0"):
-        l = tf.constant(lst)
-        return check_order_tf_fn_cuda_impl(l).numpy()
-
-
-# PyTorch Solution (CPU)
-def check_order_torch(lst):
-    l = torch.tensor(lst)
-    return torch.where(l != torch.sort(l)[0])[0].numpy()
-
-
-# PyTorch Solution (CUDA/GPU)
-def check_order_torch_cuda(lst):
-    l = torch.tensor(lst).cuda()
-    return torch.where(l != torch.sort(l)[0])[0].cpu().numpy()
-
-
-# PyTorch JIT Compile Solution (CPU)
-@torch.jit.script
-def check_order_torch_jit_impl(l):
-    return torch.where(l != torch.sort(l)[0])[0]
-
-
-def check_order_torch_jit(lst):
-    l = torch.tensor(lst)
-    return check_order_torch_jit_impl(l).numpy()
-
-
-# PyTorch JIT Compile Solution (CUDA/GPU)
-@torch.jit.script
-def check_order_torch_jit_cuda_impl(l):
-    return torch.where(l != torch.sort(l)[0])[0]
-
-
-def check_order_torch_jit_cuda(lst):
-    l = torch.tensor(lst).cuda()
-    return check_order_torch_jit_cuda_impl(l).cpu().numpy()
-
-
-# Tests
-print(check_order([5, 2, 4, 3, 1]))  # [0, 2, 3, 4]
-print(check_order([1, 2, 1, 1, 3]))  # [1, 3]
-print(check_order([3, 1, 3, 2, 3]))  # [0, 1, 3]
-print(check_order_np([5, 2, 4, 3, 1]))  # [0, 2, 3, 4]
-print(check_order_np([1, 2, 1, 1, 3]))  # [1, 3]
-print(check_order_np([3, 1, 3, 2, 3]))  # [0, 1, 3]
-print(check_order_jax([5, 2, 4, 3, 1]))  # [0, 2, 3, 4]
-print(check_order_jax([1, 2, 1, 1, 3]))  # [1, 3]
-print(check_order_jax([3, 1, 3, 2, 3]))  # [0, 1, 3]
-print(check_order_tf([5, 2, 4, 3, 1]))  # [0, 2, 3, 4]
-print(check_order_tf([1, 2, 1, 1, 3]))  # [1, 3]
-print(check_order_tf([3, 1, 3, 2, 3]))  # [0, 1, 3]
-print(check_order_tf_fn(tf.constant([5, 2, 4, 3, 1])).numpy())  # [0, 2, 3, 4]
-print(check_order_tf_fn(tf.constant([1, 2, 1, 1, 3])).numpy())  # [1, 3]
-print(check_order_tf_fn(tf.constant([3, 1, 3, 2, 3])).numpy())  # [0, 1, 3]
-print(check_order_torch([5, 2, 4, 3, 1]))  # [0, 2, 3, 4]
-print(check_order_torch([1, 2, 1, 1, 3]))  # [1, 3]
-print(check_order_torch([3, 1, 3, 2, 3]))  # [0, 1, 3]
-print(check_order_torch_jit([5, 2, 4, 3, 1]))  # [0, 2, 3, 4]
-print(check_order_torch_jit([1, 2, 1, 1, 3]))  # [1, 3]
-print(check_order_torch_jit([3, 1, 3, 2, 3]))  # [0, 1, 3]
-
-# CUDA/GPU Tests (requires CUDA-capable GPU)
-if tf.config.list_physical_devices("GPU"):
-    print("TensorFlow GPU available")
-    print(check_order_tf_cuda([5, 2, 4, 3, 1]))  # [0, 2, 3, 4]
-    print(check_order_tf_cuda([1, 2, 1, 1, 3]))  # [1, 3]
-    print(check_order_tf_cuda([3, 1, 3, 2, 3]))  # [0, 1, 3]
-    print(check_order_tf_fn_cuda([5, 2, 4, 3, 1]))  # [0, 2, 3, 4]
-    print(check_order_tf_fn_cuda([1, 2, 1, 1, 3]))  # [1, 3]
-    print(check_order_tf_fn_cuda([3, 1, 3, 2, 3]))  # [0, 1, 3]
-else:
-    print("TensorFlow GPU not available")
-
-if torch.cuda.is_available():
-    print("PyTorch CUDA available")
-    print(check_order_torch_cuda([5, 2, 4, 3, 1]))  # [0, 2, 3, 4]
-    print(check_order_torch_cuda([1, 2, 1, 1, 3]))  # [1, 3]
-    print(check_order_torch_cuda([3, 1, 3, 2, 3]))  # [0, 1, 3]
-    print(check_order_torch_jit_cuda([5, 2, 4, 3, 1]))  # [0, 2, 3, 4]
-    print(check_order_torch_jit_cuda([1, 2, 1, 1, 3]))  # [1, 3]
-    print(check_order_torch_jit_cuda([3, 1, 3, 2, 3]))  # [0, 1, 3]
-else:
-    print("PyTorch CUDA not available")