From 25794a477f4d309bb911a8838dcc1be55865cb29 Mon Sep 17 00:00:00 2001
From: Anshul Singhal <anshul3.mitmpl2023@learner.manipal.edu>
Date: Thu, 5 Jun 2025 04:00:48 +0530
Subject: [PATCH] tsai_core.py edit for correct conversion by importing torch

---
 tsai/data/core.py | 40 ++++++++++++++++++++++++++++++++++------
 1 file changed, 34 insertions(+), 6 deletions(-)

diff --git a/tsai/data/core.py b/tsai/data/core.py
index 69d554c7..10001374 100644
--- a/tsai/data/core.py
+++ b/tsai/data/core.py
@@ -335,8 +335,26 @@ def __init__(self, items, tfms=None, splits=None, split_idx=None, types=None, do
     def subset(self, i, **kwargs): return type(self)(self.items, splits=self.splits[i], split_idx=i, do_setup=False, types=self.types,
                                                      **kwargs)
     def __getitem__(self, it):
-        if hasattr(self.items, 'oindex'): return self.items.oindex[self._splits[it]]
-        else: return self.items[self._splits[it]]
+        # if hasattr(self.items, 'oindex'): return self.items.oindex[self._splits[it]]
+        # else: return self.items[self._splits[it]]
+        # changed the old code for our issue.
+        # 1) compute the raw index or slice from self._splits
+        idx = self._splits[it]
+
+        # 2) if it's a NumPy scalar (e.g. dtype=np.int8), turn it into a plain Python int
+        if isinstance(idx, np.generic):
+            idx = idx.item()
+        # 3) if it's a NumPy array (e.g. dtype=int8 or int32), turn it into a Python list
+        elif isinstance(idx, np.ndarray):
+            idx = idx.tolist()
+
+        # 4) finally, index self.items (or self.items.oindex) with that pure‐Python index
+        if hasattr(self.items, 'oindex'):
+            return self.items.oindex[idx]
+        else:
+            return self.items[idx]
+
+
     def __len__(self): return len(self._splits)
     def __repr__(self):
         if hasattr(self.items, "shape"):
@@ -486,12 +504,20 @@ def __init__(self, X=None, y=None, items=None, sel_vars=None, sel_steps=None, tf
             self.tls = L(lt(item, t, **kwargs) for lt,item,t in zip(lts, items, self.tfms))
             # if len(self.tls) > 0 and len(self.tls[0]) > 0:
             #     self.typs = [type(tl.items[0]) if isinstance(tl.items[0], torch.Tensor) else self.typs[i] for i,tl in enumerate(self.tls)]
+            # if self.inplace and (tfms is None or tfms == [None] * len(self.tls)):
+            #     for tl,typ in zip(self.tls, self.typs):
+            #         tl.items = typ(tl.items)
+            #replacing the above with the following safer version because torch.as_tensor can't automatically infer the dtype from a numpy.int64 array without a valid PyTorch tensor type
+            import torch
+
             if self.inplace and (tfms is None or tfms == [None] * len(self.tls)):
-                for tl,typ in zip(self.tls, self.typs):
-                    tl.items = typ(tl.items)
+                for tl in self.tls:
+                    if isinstance(tl.items, np.ndarray):
+                        tl.items = torch.as_tensor(tl.items, dtype=torch.float32)
+
                 self.ptls = self.tls
                 self.no_tfm = True
-            else:
+            else:   
                 self.ptls = L([typ(stack(tl[:]))[...,self.sel_vars, self.sel_steps] if (i==0 and self.multi_index) else typ(stack(tl[:])) \
                     for i,(tl,typ) in enumerate(zip(self.tls,self.typs))]) if inplace else self.tls
                 self.no_tfm = False
@@ -653,7 +679,9 @@ def create_batch(self, b):
         if hasattr(self, "split_idxs"):
             self.input_idxs = self.split_idxs[b]
         else: self.input_idxs = self.idxs
-        return self.dataset[b]
+        # return self.dataset[b]
+        return self.dataset[[int(i) for i in b]]
+
 
     def create_item(self, s):
         if self.indexed: return self.dataset[s or 0]