Mod: Rename paths.py to globals.py, update internal globals behaviour…

… to handle non-path values and add temporary _get_device func.

src/aac_metrics/__init__.py

@@ -28,7 +28,7 @@
 from .classes.spider_max import SPIDErMax
 from .classes.vocab import Vocab
 from .functional.evaluate import evaluate, dcase2023_evaluate
-from .utils.paths import (
+from .utils.globals import (
     get_default_cache_path,
     get_default_java_path,
     get_default_tmp_path,

src/aac_metrics/classes/bert_score_mrefs.py

@@ -47,7 +47,11 @@ def __init__(
         verbose: int = 0,
     ) -> None:
         model, tokenizer = _load_model_and_tokenizer(
-            model, None, device, reset_state, verbose
+            model=model,
+            tokenizer=None,
+            device=device,
+            reset_state=reset_state,
+            verbose=verbose,
         )
 
         super().__init__()

src/aac_metrics/download.py

@@ -25,7 +25,7 @@
     check_spice_install,
 )
 from aac_metrics.utils.cmdline import _str_to_bool, _setup_logging
-from aac_metrics.utils.paths import (
+from aac_metrics.utils.globals import (
     _get_cache_path,
     _get_tmp_path,
     get_default_cache_path,

src/aac_metrics/eval.py

@@ -19,7 +19,7 @@
 )
 from aac_metrics.utils.checks import check_metric_inputs, check_java_path
 from aac_metrics.utils.cmdline import _str_to_bool, _str_to_opt_str, _setup_logging
-from aac_metrics.utils.paths import (
+from aac_metrics.utils.globals import (
     get_default_cache_path,
     get_default_java_path,
     get_default_tmp_path,

src/aac_metrics/functional/bert_score_mrefs.py

@@ -12,6 +12,7 @@
 from transformers import logging as tfmers_logging
 
 from aac_metrics.utils.collections import flat_list, unflat_list, duplicate_list
+from aac_metrics.utils.globals import _get_device
 
 
 def bert_score_mrefs(
@@ -61,8 +62,13 @@ def bert_score_mrefs(
             raise ValueError(
                 f"Invalid argument combinaison {model=} with {tokenizer=}."
             )
+
         model, tokenizer = _load_model_and_tokenizer(
-            model, tokenizer, device, reset_state, verbose
+            model=model,
+            tokenizer=tokenizer,
+            device=device,
+            reset_state=reset_state,
+            verbose=verbose,
         )
 
     elif isinstance(model, nn.Module):
@@ -76,11 +82,7 @@ def bert_score_mrefs(
             f"Invalid argument type {type(model)=}. (expected str or nn.Module)"
         )
 
-    if device == "auto":
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-    if isinstance(device, str):
-        device = torch.device(device)
-
+    device = _get_device(device)
     flat_mrefs, sizes = flat_list(mult_references)
     duplicated_cands = duplicate_list(candidates, sizes)
 
@@ -116,9 +118,9 @@ def bert_score_mrefs(
     if reduction == "mean":
         reduction_fn = torch.mean
     elif reduction == "max":
-        reduction_fn = max_reduce
+        reduction_fn = _max_reduce
     elif reduction == "min":
-        reduction_fn = min_reduce
+        reduction_fn = _min_reduce
     else:
         REDUCTIONS = ("mean", "max", "min")
         raise ValueError(
@@ -161,11 +163,7 @@ def _load_model_and_tokenizer(
 ) -> tuple[nn.Module, Optional[Callable]]:
     state = torch.random.get_rng_state()
 
-    if device == "auto":
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-    if isinstance(device, str):
-        device = torch.device(device)
-
+    device = _get_device(device)
     if isinstance(model, str):
         tfmers_verbosity = tfmers_logging.get_verbosity()
         if verbose <= 1:
@@ -188,14 +186,14 @@ def _load_model_and_tokenizer(
     return model, tokenizer  # type: ignore
 
 
-def max_reduce(x: Tensor, dim: Optional[int] = None) -> Tensor:
+def _max_reduce(x: Tensor, dim: Optional[int] = None) -> Tensor:
     if dim is None:
         return x.max()
     else:
         return x.max(dim=dim).values
 
 
-def min_reduce(x: Tensor, dim: Optional[int] = None) -> Tensor:
+def _min_reduce(x: Tensor, dim: Optional[int] = None) -> Tensor:
     if dim is None:
         return x.min()
     else:

src/aac_metrics/functional/fer.py

@@ -26,6 +26,8 @@
 from transformers.models.auto.tokenization_auto import AutoTokenizer
 from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
 
+from aac_metrics.utils.globals import _get_device
+
 
 # config according to the settings on your computer, this should be default setting of shadowsocks
 DEFAULT_PROXIES = {
@@ -182,13 +184,11 @@ def _load_echecker_and_tokenizer(
 ) -> tuple[BERTFlatClassifier, AutoTokenizer]:
     state = torch.random.get_rng_state()
 
-    if device == "auto":
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-    if isinstance(device, str):
-        device = torch.device(device)
-
+    device = _get_device(device)
     if isinstance(echecker, str):
-        echecker = __load_pretrain_echecker(echecker, device, verbose=verbose)
+        echecker = __load_pretrain_echecker(
+            echecker_model=echecker, device=device, verbose=verbose
+        )
 
     if echecker_tokenizer is None:
         echecker_tokenizer = AutoTokenizer.from_pretrained(echecker.model_type)  # type: ignore
@@ -211,10 +211,7 @@ def __detect_error_sents(
     device: Union[str, torch.device, None],
     max_len: int = 64,
 ) -> dict[str, np.ndarray]:
-    if device == "auto":
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-    if isinstance(device, str):
-        device = torch.device(device)
+    device = _get_device(device)
 
     if len(sents) <= batch_size:
         batch = __infer_preprocess(
@@ -280,11 +277,7 @@ def __infer_preprocess(
     device: Union[str, torch.device, None],
     dtype: torch.dtype,
 ) -> Mapping[str, Tensor]:
-    if device == "auto":
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-    if isinstance(device, str):
-        device = torch.device(device)
-
+    device = _get_device(device)
     texts = __text_preprocess(texts)  # type: ignore
     batch = tokenizer(texts, truncation=True, padding="max_length", max_length=max_len)
     for k in ("input_ids", "attention_mask", "token_type_ids"):
@@ -396,11 +389,7 @@ def __load_pretrain_echecker(
             f"Invalid argument {echecker_model=}. (expected one of {tuple(PRETRAIN_ECHECKERS_DICT.keys())})"
         )
 
-    if device == "auto":
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-    if isinstance(device, str):
-        device = torch.device(device)
-
+    device = _get_device(device)
     tfmers_logging.set_verbosity_error()  # suppress loading warnings
     url, checksum = PRETRAIN_ECHECKERS_DICT[echecker_model]
     remote = RemoteFileMetadata(

src/aac_metrics/functional/meteor.py

@@ -17,7 +17,7 @@
 from torch import Tensor
 
 from aac_metrics.utils.checks import check_java_path
-from aac_metrics.utils.paths import _get_cache_path, _get_java_path
+from aac_metrics.utils.globals import _get_cache_path, _get_java_path
 
 
 pylog = logging.getLogger(__name__)

src/aac_metrics/functional/sbert_sim.py

@@ -15,6 +15,8 @@
 from sentence_transformers import SentenceTransformer
 from torch import Tensor
 
+from aac_metrics.utils.globals import _get_device
+
 
 pylog = logging.getLogger(__name__)
 
@@ -91,11 +93,7 @@ def _load_sbert(
 ) -> SentenceTransformer:
     state = torch.random.get_rng_state()
 
-    if device == "auto":
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-    if isinstance(device, str):
-        device = torch.device(device)
-
+    device = _get_device(device)
     if isinstance(sbert_model, str):
         sbert_model = SentenceTransformer(sbert_model, device=device)  # type: ignore
     sbert_model.to(device=device)

src/aac_metrics/functional/spice.py

@@ -24,7 +24,7 @@
 from torch import Tensor
 
 from aac_metrics.utils.checks import check_java_path
-from aac_metrics.utils.paths import (
+from aac_metrics.utils.globals import (
     _get_cache_path,
     _get_java_path,
     _get_tmp_path,

src/aac_metrics/info.py

@@ -13,7 +13,7 @@
 import aac_metrics
 
 from aac_metrics.utils.checks import _get_java_version
-from aac_metrics.utils.paths import (
+from aac_metrics.utils.globals import (
     get_default_cache_path,
     get_default_java_path,
     get_default_tmp_path,