chore: Clean up original code to get typehints passing

src/pyhf/cli/infer.py

@@ -9,7 +9,8 @@
 from pyhf.infer import hypotest
 from pyhf.infer import mle
 from pyhf.workspace import Workspace
-from pyhf import get_backend, set_backend, optimize
+from pyhf.tensor.manager import get_backend, set_backend
+from pyhf import optimize
 
 log = logging.getLogger(__name__)
 

src/pyhf/compat.py

@@ -19,7 +19,7 @@ def paramset_to_rootnames(paramset):
         paramset (:obj:`pyhf.paramsets.paramset`): The parameter set.
 
     Returns:
-        :obj:`List[str]` or :obj:`str`: The generated parameter names
+        :obj:`list[str]` or :obj:`str`: The generated parameter names
         (for the non-scalar/scalar case) respectively.
 
     Example:

src/pyhf/infer/__init__.py

@@ -1,7 +1,7 @@
 """Inference for Statistical Models."""
 
 from pyhf.infer import utils
-from pyhf import get_backend
+from pyhf.tensor.manager import get_backend
 from pyhf import exceptions
 
 

src/pyhf/infer/calculators.py

@@ -9,7 +9,7 @@
 """
 
 from pyhf.infer.mle import fixed_poi_fit
-from pyhf import get_backend
+from pyhf.tensor.manager import get_backend
 from pyhf.infer import utils
 import tqdm
 

src/pyhf/infer/intervals/upper_limits.py

@@ -3,7 +3,7 @@
 import numpy as np
 from scipy.optimize import toms748
 
-from pyhf import get_backend
+from pyhf.tensor.manager import get_backend
 from pyhf.infer import hypotest
 
 __all__ = ["linear_grid_scan", "toms748_scan", "upper_limit"]

src/pyhf/infer/mle.py

@@ -1,6 +1,6 @@
 """Module for Maximum Likelihood Estimation."""
 
-from pyhf import get_backend
+from pyhf.tensor.manager import get_backend
 from pyhf.exceptions import UnspecifiedPOI
 
 __all__ = ["fit", "fixed_poi_fit", "twice_nll"]

src/pyhf/infer/test_statistics.py

@@ -1,4 +1,4 @@
-from pyhf import get_backend
+from pyhf.tensor.manager import get_backend
 from pyhf.infer.mle import fixed_poi_fit, fit
 from pyhf.exceptions import UnspecifiedPOI
 

src/pyhf/modifiers/lumi.py

@@ -1,6 +1,7 @@
 import logging
 
-from pyhf import get_backend, events
+from pyhf.tensor.manager import get_backend
+from pyhf import events
 from pyhf.parameters import ParamViewer
 
 log = logging.getLogger(__name__)

src/pyhf/modifiers/normfactor.py

@@ -1,6 +1,7 @@
 import logging
 
-from pyhf import get_backend, events
+from pyhf.tensor.manager import get_backend
+from pyhf import events
 from pyhf.parameters import ParamViewer
 
 log = logging.getLogger(__name__)

src/pyhf/modifiers/normsys.py

@@ -1,6 +1,7 @@
 import logging
 
-from pyhf import get_backend, events
+from pyhf.tensor.manager import get_backend
+from pyhf import events
 from pyhf import interpolators
 from pyhf.parameters import ParamViewer
 

src/pyhf/modifiers/staterror.py

@@ -1,5 +1,6 @@
+from __future__ import annotations
+
 import logging
-from typing import List
 
 import pyhf
 from pyhf import events
@@ -10,7 +11,7 @@
 log = logging.getLogger(__name__)
 
 
-def required_parset(sigmas, fixed: List[bool]):
+def required_parset(sigmas, fixed: list[bool]):
     n_parameters = len(sigmas)
     return {
         'paramset_type': 'constrained_by_normal',

src/pyhf/optimize/opt_jax.py

@@ -1,6 +1,6 @@
 """JAX Backend Function Shim."""
 
-from pyhf import get_backend
+from pyhf.tensor.manager import get_backend
 from pyhf.tensor.common import _TensorViewer
 import jax
 import logging

src/pyhf/optimize/opt_numpy.py

@@ -1,6 +1,6 @@
 """Numpy Backend Function Shim."""
 
-from pyhf import get_backend
+from pyhf.tensor.manager import get_backend
 from pyhf import exceptions
 
 

src/pyhf/parameters/paramsets.py

@@ -1,5 +1,3 @@
-from typing import List
-
 import pyhf
 
 __all__ = [
@@ -29,7 +27,7 @@ def __init__(self, **kwargs):
             )
 
     @property
-    def suggested_fixed(self) -> List[bool]:
+    def suggested_fixed(self) -> list[bool]:
         if isinstance(self._suggested_fixed, bool):
             return [self._suggested_fixed] * self.n_parameters
         return self._suggested_fixed

src/pyhf/pdf.py

@@ -2,7 +2,7 @@
 
 import copy
 import logging
-from typing import List, Union
+from typing import Union
 
 import pyhf.parameters
 import pyhf
@@ -406,7 +406,7 @@ def param_set(self, name):
         """
         return self.par_map[name]['paramset']
 
-    def suggested_fixed(self) -> List[bool]:
+    def suggested_fixed(self) -> list[bool]:
         """
         Identify the fixed parameters in the model.
 

src/pyhf/probability.py

@@ -1,6 +1,6 @@
 """The probability density function module."""
 
-from pyhf import get_backend
+from pyhf.tensor.manager import get_backend
 
 __all__ = ["Independent", "Normal", "Poisson", "Simultaneous"]
 

src/pyhf/readxml.py

@@ -5,7 +5,6 @@
     IO,
     Callable,
     Iterable,
-    List,
     MutableMapping,
     MutableSequence,
     Sequence,
@@ -99,7 +98,7 @@ def extract_error(hist: uproot.behaviors.TH1.TH1) -> list[float]:
     """
 
     variance = hist.variances() if hist.weighted else hist.to_numpy()[0]
-    return cast(List[float], np.sqrt(variance).tolist())
+    return cast(list[float], np.sqrt(variance).tolist())
 
 
 def import_root_histogram(
@@ -222,7 +221,7 @@ def process_sample(
                     modtag.attrib.get('HistoPath', ''),
                     modtag.attrib['HistoName'],
                 )
-                staterr = np.multiply(extstat, data).tolist()
+                staterr = cast(list[float], np.multiply(extstat, data).tolist())
             if not staterr:
                 raise RuntimeError('cannot determine stat error.')
             modifier_staterror: StatError = {

src/pyhf/tensor/numpy_backend.py

@@ -3,7 +3,16 @@
 from __future__ import annotations
 
 import logging
-from typing import TYPE_CHECKING, Callable, Generic, Mapping, Sequence, TypeVar, Union
+from typing import (
+    Any,
+    TYPE_CHECKING,
+    Callable,
+    Generic,
+    Mapping,
+    Sequence,
+    TypeVar,
+    Union,
+)
 
 import numpy as np
 
@@ -205,9 +214,12 @@ def conditional(
         """
         return true_callable() if predicate else false_callable()
 
-    def tolist(self, tensor_in: Tensor[T] | list[T]) -> list[T]:
+    def tolist(
+        self, tensor_in: Tensor[T] | list[T]
+    ) -> int | float | complex | list[T] | list[Any]:
         try:
-            return tensor_in.tolist()  # type: ignore[union-attr,no-any-return]
+            result = tensor_in.tolist()  # type: ignore[union-attr]
+            return cast(Union[int, float, complex, list[T], list[Any]], result)
         except AttributeError:
             if isinstance(tensor_in, list):
                 return tensor_in
@@ -654,4 +666,5 @@ def transpose(self, tensor_in: Tensor[T]) -> ArrayLike:
 
         .. versionadded:: 0.7.0
         """
-        return tensor_in.transpose()
+        result = tensor_in.transpose()
+        return cast(ArrayLike, result)