Merge pull request #3289 from flairNLP/3284-bug-support-transformers-…

…4310

3284 bug support transformers 4310

flair/data.py

@@ -143,7 +143,7 @@ def save(self, savefile):
     def __setstate__(self, d):
         self.__dict__ = d
         # set 'add_unk' if the dictionary was created with a version of Flair older than 0.9
-        if "add_unk" not in self.__dict__.keys():
+        if "add_unk" not in self.__dict__:
             self.__dict__["add_unk"] = b"<unk>" in self.__dict__["idx2item"]
 
     @classmethod

flair/embeddings/base.py

@@ -194,7 +194,7 @@ def embedding_type(self) -> str:
     def _everything_embedded(self, data_points: Sequence[Sentence]) -> bool:
         for sentence in data_points:
             for token in sentence.tokens:
-                if self.name not in token._embeddings.keys():
+                if self.name not in token._embeddings:
                     return False
         return True
 

flair/embeddings/image.py

@@ -73,7 +73,7 @@ class PrecomputedImageEmbeddings(ImageEmbeddings):
     def __init__(self, url2tensor_dict, name) -> None:
         self.url2tensor_dict = url2tensor_dict
         self.name = name
-        self.__embedding_length = len(list(self.url2tensor_dict.values())[0])
+        self.__embedding_length = len(next(iter(self.url2tensor_dict.values())))
         self.static_embeddings = True
         super().__init__()
 

flair/embeddings/legacy.py

@@ -1330,7 +1330,7 @@ def embed(self, sentences: Union[List[Sentence], Sentence]):
             sentences = [sentences]
 
         for sentence in sentences:
-            if self.name not in sentence._embeddings.keys():
+            if self.name not in sentence._embeddings:
                 everything_embedded = False
 
         if not everything_embedded:

flair/embeddings/transformer.py

@@ -11,6 +11,8 @@
 from typing import Any, Dict, List, Optional, Tuple, Type, Union, cast
 
 import torch
+import transformers
+from semver import Version
 from torch.jit import ScriptModule
 from transformers import (
     CONFIG_MAPPING,
@@ -1105,6 +1107,16 @@ def embedding_length(self) -> int:
 
         return self.embedding_length_internal
 
+    def _load_from_state_dict(
+        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+    ):
+        if transformers.__version__ >= Version(4, 31, 0):
+            assert isinstance(state_dict, dict)
+            state_dict.pop(f"{prefix}model.embeddings.position_ids", None)
+        super()._load_from_state_dict(
+            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+        )
+
     def _has_initial_cls_token(self) -> bool:
         # most models have CLS token as last token (GPT-1, GPT-2, TransfoXL, XLNet, XLM), but BERT is initial
         if self.tokenizer_needs_ocr_boxes:
@@ -1191,6 +1203,8 @@ def __setstate__(self, state):
             self.__dict__[key] = embedding.__dict__[key]
 
         if model_state_dict:
+            if transformers.__version__ >= Version(4, 31, 0):
+                model_state_dict.pop("embeddings.position_ids", None)
             self.model.load_state_dict(model_state_dict)
 
     @classmethod

flair/training_utils.py

@@ -66,12 +66,7 @@ def micro_avg_f_score(self):
         return self.mean_squared_error()
 
     def to_tsv(self):
-        return "{}\t{}\t{}\t{}".format(
-            self.mean_squared_error(),
-            self.mean_absolute_error(),
-            self.pearsonr(),
-            self.spearmanr(),
-        )
+        return f"{self.mean_squared_error()}\t{self.mean_absolute_error()}\t{self.pearsonr()}\t{self.spearmanr()}"
 
     @staticmethod
     def tsv_header(prefix=None):

pyproject.toml

@@ -85,6 +85,7 @@ ignore = [
   "D105",
   "D107",
   "E501",   # Ignore line too long
+  "RUF012",
 ]
 
 unfixable = [
@@ -96,6 +97,8 @@ unfixable = [
 
 [tool.ruff.per-file-ignores]
 "flair/embeddings/legacy.py" = ["D205"]
+"scripts/*" = ["INP001"]  #  no need for __ini__ for scripts
+"flair/datasets/*" = ["D417"]  # need to fix datasets in a unified way later.
 
 [tool.ruff.pydocstyle]
 convention = "google"

requirements.txt

@@ -25,4 +25,5 @@ tqdm>=4.63.0
 transformer-smaller-training-vocab>=0.2.3
 transformers[sentencepiece]>=4.18.0,<5.0.0
 urllib3<2.0.0,>=1.0.0  # pin below 2 to make dependency resolution faster.
-wikipedia-api>=0.5.7
+wikipedia-api>=0.5.7
+semver<4.0.0,>=3.0.0

tests/test_datasets.py

@@ -748,7 +748,7 @@ def test_masakhane_corpus(tasks_base_path):
             "bam": {"train": 4462, "dev": 638, "test": 1274},
             "bbj": {"train": 3384, "dev": 483, "test": 966},
             "ewe": {"train": 3505, "dev": 501, "test": 1001},
-            "fon": {"train": 4343, "dev": 621, "test": 1240},
+            "fon": {"train": 4343, "dev": 623, "test": 1228},
             "hau": {"train": 5716, "dev": 816, "test": 1633},
             "ibo": {"train": 7634, "dev": 1090, "test": 2181},
             "kin": {"train": 7825, "dev": 1118, "test": 2235},