Bump dependency version ranges for numpy & lightning (#3226)

*Issue #, if available:*

*Description of changes:*


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Author: shchur

requirements/requirements-pytorch.txt

@@ -1,6 +1,6 @@
 torch>=1.9,<3
-lightning>=2.2.2,<2.4
+lightning>=2.2.2,<2.5
 # Capping `lightning` does not cap `pytorch_lightning`, so we cap manually
-pytorch_lightning>=2.2.2,<2.4
+pytorch_lightning>=2.2.2,<2.5
 scipy~=1.10; python_version > "3.7.0"
 scipy~=1.7.3; python_version <= "3.7.0"

requirements/requirements.txt

@@ -1,4 +1,4 @@
-numpy~=1.16
+numpy>=1.16,<2.2
 pandas>=1.0,<3
 pydantic>=1.7,<3
 tqdm~=4.23

src/gluonts/dataset/pandas.py

@@ -15,7 +15,7 @@
 
 import logging
 from dataclasses import dataclass, field, InitVar
-from typing import Any, Iterable, Optional, Type, Union, cast
+from typing import Any, Iterable, Optional, Type, Union
 
 import numpy as np
 import pandas as pd
@@ -351,4 +351,4 @@ def is_uniform(index: pd.PeriodIndex) -> bool:
         False
     """
 
-    return cast(bool, np.all(np.diff(index.asi8) == index.freq.n))
+    return bool(np.all(np.diff(index.asi8) == index.freq.n))

src/gluonts/itertools.py

@@ -240,7 +240,7 @@ def _location_for(self, idx, side="right") -> _SubIndex:
         else:
             local_idx = idx - self._offsets[part_no - 1]
 
-        return _SubIndex(part_no, local_idx)
+        return _SubIndex(int(part_no), int(local_idx))
 
     def __getitem__(self, idx):
         if isinstance(idx, slice):

src/gluonts/model/forecast_generator.py

@@ -59,7 +59,7 @@ def _unpack(batched) -> Iterator:
     This assumes that arrays are wrapped in a  nested structure of lists and
     tuples, and each array has the same shape::
 
-        >>> a = np.arange(5)
+        >>> a = np.arange(5, dtype="O")
         >>> batched = [a, (a, [a, a, a])]
         >>> list(_unpack(batched))
         [[0, (0, [0, 0, 0])],

src/gluonts/mx/trainer/_base.py

@@ -227,7 +227,7 @@ def base_path() -> str:
             best_epoch_info = {
                 "params_path": "{}-{}.params".format(base_path(), "init"),
                 "epoch_no": -1,
-                "score": np.Inf,
+                "score": float("inf"),
             }
 
             optimizer = mx.optimizer.Adam(

src/gluonts/mx/trainer/learning_rate_scheduler.py

@@ -50,15 +50,15 @@ def should_update(self, metric: float) -> bool:
 
 @dataclass
 class Min(Objective):
-    best: float = np.Inf
+    best: float = float("inf")
 
     def should_update(self, metric: float) -> bool:
         return metric < self.best
 
 
 @dataclass
 class Max(Objective):
-    best: float = -np.Inf
+    best: float = -float("inf")
 
     def should_update(self, metric: float) -> bool:
         return metric > self.best