From 9d5578631eeb294b4af0f2357c60e5934d31861f Mon Sep 17 00:00:00 2001
From: Benedikt Fuchs <benedikt.fuchs@rise-world.com>
Date: Mon, 31 Jul 2023 12:37:56 +0200
Subject: [PATCH 1/6] fix ruff coding

---
 flair/embeddings/image.py | 2 +-
 flair/training_utils.py   | 7 +------
 pyproject.toml            | 3 +++
 3 files changed, 5 insertions(+), 7 deletions(-)

diff --git a/flair/embeddings/image.py b/flair/embeddings/image.py
index 6a14a0e00..faf3a78b1 100644
--- a/flair/embeddings/image.py
+++ b/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__()
 
diff --git a/flair/training_utils.py b/flair/training_utils.py
index 51894386d..f36f95a91 100644
--- a/flair/training_utils.py
+++ b/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):
diff --git a/pyproject.toml b/pyproject.toml
index 635eee77f..12e62fdd8 100644
--- a/pyproject.toml
+++ b/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"

From 4478cf18e2ac0d8c035f96f2edbba5d21420a476 Mon Sep 17 00:00:00 2001
From: Benedikt Fuchs <benedikt.fuchs@rise-world.com>
Date: Mon, 31 Jul 2023 14:07:43 +0200
Subject: [PATCH 2/6] fix transformer embeddings created with <4.31.0 cannot be
 loaded with transformer version >=4.31.0

---
 flair/embeddings/transformer.py | 229 +++++++++++++++++---------------
 requirements.txt                |   3 +-
 2 files changed, 123 insertions(+), 109 deletions(-)

diff --git a/flair/embeddings/transformer.py b/flair/embeddings/transformer.py
index 3a92dab66..fb82c0bf7 100644
--- a/flair/embeddings/transformer.py
+++ b/flair/embeddings/transformer.py
@@ -8,9 +8,11 @@
 from abc import abstractmethod
 from io import BytesIO
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple, Type, Union, cast
+from semver import Version
+from typing import Any, Dict, List, Optional, Tuple, Type, Union, cast, Mapping
 
 import torch
+import transformers
 from torch.jit import ScriptModule
 from transformers import (
     CONFIG_MAPPING,
@@ -68,11 +70,11 @@ def truncate_hidden_states(hidden_states: torch.Tensor, input_ids: torch.Tensor)
 
 @torch.jit.script_if_tracing
 def combine_strided_tensors(
-    hidden_states: torch.Tensor,
-    overflow_to_sample_mapping: torch.Tensor,
-    half_stride: int,
-    max_length: int,
-    default_value: int,
+        hidden_states: torch.Tensor,
+        overflow_to_sample_mapping: torch.Tensor,
+        half_stride: int,
+        max_length: int,
+        default_value: int,
 ) -> torch.Tensor:
     _, counts = torch.unique(overflow_to_sample_mapping, sorted=True, return_counts=True)
     sentence_count = int(overflow_to_sample_mapping.max().item() + 1)
@@ -92,9 +94,9 @@ def combine_strided_tensors(
         selected_sentences = hidden_states[overflow_to_sample_mapping == sentence_id]
         if selected_sentences.size(0) > 1:
             start_part = selected_sentences[0, : half_stride + 1]
-            mid_part = selected_sentences[:, half_stride + 1 : max_length - 1 - half_stride]
+            mid_part = selected_sentences[:, half_stride + 1: max_length - 1 - half_stride]
             mid_part = torch.reshape(mid_part, (mid_part.shape[0] * mid_part.shape[1],) + mid_part.shape[2:])
-            end_part = selected_sentences[selected_sentences.shape[0] - 1, max_length - half_stride - 1 :]
+            end_part = selected_sentences[selected_sentences.shape[0] - 1, max_length - half_stride - 1:]
             sentence_hidden_state = torch.cat((start_part, mid_part, end_part), dim=0)
             sentence_hidden_states[sentence_id, : sentence_hidden_state.shape[0]] = torch.cat(
                 (start_part, mid_part, end_part), dim=0
@@ -107,11 +109,11 @@ def combine_strided_tensors(
 
 @torch.jit.script_if_tracing
 def fill_masked_elements(
-    all_token_embeddings: torch.Tensor,
-    sentence_hidden_states: torch.Tensor,
-    mask: torch.Tensor,
-    word_ids: torch.Tensor,
-    lengths: torch.LongTensor,
+        all_token_embeddings: torch.Tensor,
+        sentence_hidden_states: torch.Tensor,
+        mask: torch.Tensor,
+        word_ids: torch.Tensor,
+        lengths: torch.LongTensor,
 ):
     for i in torch.arange(int(all_token_embeddings.shape[0])):
         r = insert_missing_embeddings(sentence_hidden_states[i][mask[i] & (word_ids[i] >= 0)], word_ids[i], lengths[i])
@@ -121,7 +123,7 @@ def fill_masked_elements(
 
 @torch.jit.script_if_tracing
 def insert_missing_embeddings(
-    token_embeddings: torch.Tensor, word_id: torch.Tensor, length: torch.LongTensor
+        token_embeddings: torch.Tensor, word_id: torch.Tensor, length: torch.LongTensor
 ) -> torch.Tensor:
     # in some cases we need to insert zero vectors for tokens without embedding.
     if token_embeddings.shape[0] == 0:
@@ -164,10 +166,10 @@ def insert_missing_embeddings(
 
 @torch.jit.script_if_tracing
 def fill_mean_token_embeddings(
-    all_token_embeddings: torch.Tensor,
-    sentence_hidden_states: torch.Tensor,
-    word_ids: torch.Tensor,
-    token_lengths: torch.Tensor,
+        all_token_embeddings: torch.Tensor,
+        sentence_hidden_states: torch.Tensor,
+        word_ids: torch.Tensor,
+        token_lengths: torch.Tensor,
 ):
     for i in torch.arange(all_token_embeddings.shape[0]):
         for _id in torch.arange(token_lengths[i]):  # type: ignore[call-overload]
@@ -194,7 +196,7 @@ def document_max_pooling(sentence_hidden_states: torch.Tensor, sentence_lengths:
 
 
 def _legacy_reconstruct_word_ids(
-    embedding: "TransformerBaseEmbeddings", flair_tokens: List[List[str]]
+        embedding: "TransformerBaseEmbeddings", flair_tokens: List[List[str]]
 ) -> List[List[Optional[int]]]:
     word_ids_list = []
     max_len = 0
@@ -305,25 +307,25 @@ class TransformerBaseEmbeddings(Embeddings[Sentence]):
     """
 
     def __init__(
-        self,
-        name: str,
-        tokenizer: PreTrainedTokenizer,
-        embedding_length: int,
-        context_length: int,
-        context_dropout: float,
-        respect_document_boundaries: bool,
-        stride: int,
-        allow_long_sentences: bool,
-        fine_tune: bool,
-        truncate: bool,
-        use_lang_emb: bool,
-        is_document_embedding: bool = False,
-        is_token_embedding: bool = False,
-        force_device: Optional[torch.device] = None,
-        force_max_length: bool = False,
-        feature_extractor: Optional[FeatureExtractionMixin] = None,
-        needs_manual_ocr: Optional[bool] = None,
-        use_context_separator: bool = True,
+            self,
+            name: str,
+            tokenizer: PreTrainedTokenizer,
+            embedding_length: int,
+            context_length: int,
+            context_dropout: float,
+            respect_document_boundaries: bool,
+            stride: int,
+            allow_long_sentences: bool,
+            fine_tune: bool,
+            truncate: bool,
+            use_lang_emb: bool,
+            is_document_embedding: bool = False,
+            is_token_embedding: bool = False,
+            force_device: Optional[torch.device] = None,
+            force_max_length: bool = False,
+            feature_extractor: Optional[FeatureExtractionMixin] = None,
+            needs_manual_ocr: Optional[bool] = None,
+            use_context_separator: bool = True,
     ) -> None:
         self.name = name
         super().__init__()
@@ -471,32 +473,32 @@ def prepare_tensors(self, sentences: List[Sentence], device: Optional[torch.devi
 
         # random check some tokens to save performance.
         if (self.needs_manual_ocr or self.tokenizer_needs_ocr_boxes) and not all(
-            [
-                flair_tokens[0][0].has_metadata("bbox"),
-                flair_tokens[0][-1].has_metadata("bbox"),
-                flair_tokens[-1][0].has_metadata("bbox"),
-                flair_tokens[-1][-1].has_metadata("bbox"),
-            ]
+                [
+                    flair_tokens[0][0].has_metadata("bbox"),
+                    flair_tokens[0][-1].has_metadata("bbox"),
+                    flair_tokens[-1][0].has_metadata("bbox"),
+                    flair_tokens[-1][-1].has_metadata("bbox"),
+                ]
         ):
             raise ValueError(f"The embedding '{self.name}' requires the ocr 'bbox' set as metadata on all tokens.")
 
         if self.feature_extractor is not None and not all(
-            [
-                sentences[0].has_metadata("image"),
-                sentences[-1].has_metadata("image"),
-            ]
+                [
+                    sentences[0].has_metadata("image"),
+                    sentences[-1].has_metadata("image"),
+                ]
         ):
             raise ValueError(f"The embedding '{self.name}' requires the 'image' set as metadata for all sentences.")
 
         return self.__build_transformer_model_inputs(sentences, offsets, lengths, flair_tokens, device)
 
     def __build_transformer_model_inputs(
-        self,
-        sentences: List[Sentence],
-        offsets: List[int],
-        sentence_lengths: List[int],
-        flair_tokens: List[List[Token]],
-        device: torch.device,
+            self,
+            sentences: List[Sentence],
+            offsets: List[int],
+            sentence_lengths: List[int],
+            flair_tokens: List[List[Token]],
+            device: torch.device,
     ):
         tokenizer_kwargs: Dict[str, Any] = {}
         if self.tokenizer_needs_ocr_boxes:
@@ -557,7 +559,7 @@ def __build_transformer_model_inputs(
                 sentence_idx = 0
                 for sentence, part_length in zip(sentences, sentence_part_lengths):
                     lang_id = lang2id.get(sentence.get_language_code(), 0)
-                    model_kwargs["langs"][sentence_idx : sentence_idx + part_length] = lang_id
+                    model_kwargs["langs"][sentence_idx: sentence_idx + part_length] = lang_id
                     sentence_idx += part_length
 
         if "bbox" in batch_encoding:
@@ -799,12 +801,12 @@ def collect_dynamic_axes(cls, embedding: "TransformerEmbeddings", tensors):
 
     @classmethod
     def export_from_embedding(
-        cls,
-        path: Union[str, Path],
-        embedding: "TransformerEmbeddings",
-        example_sentences: List[Sentence],
-        opset_version: int = 14,
-        providers: Optional[List] = None,
+            cls,
+            path: Union[str, Path],
+            embedding: "TransformerEmbeddings",
+            example_sentences: List[Sentence],
+            opset_version: int = 14,
+            providers: Optional[List] = None,
     ):
         path = str(path)
         example_tensors = embedding.prepare_tensors(example_sentences)
@@ -897,7 +899,7 @@ def create_from_embedding(cls, module: ScriptModule, embedding: "TransformerEmbe
 
     @classmethod
     def parameter_to_list(
-        cls, embedding: "TransformerEmbeddings", wrapper: torch.nn.Module, sentences: List[Sentence]
+            cls, embedding: "TransformerEmbeddings", wrapper: torch.nn.Module, sentences: List[Sentence]
     ) -> Tuple[List[str], List[torch.Tensor]]:
         tensors = embedding.prepare_tensors(sentences)
         param_names = list(inspect.signature(wrapper.forward).parameters.keys())
@@ -910,8 +912,8 @@ def parameter_to_list(
 @register_embeddings
 class TransformerJitWordEmbeddings(TokenEmbeddings, TransformerJitEmbeddings):
     def __init__(
-        self,
-        **kwargs,
+            self,
+            **kwargs,
     ) -> None:
         TransformerJitEmbeddings.__init__(self, **kwargs)
 
@@ -919,8 +921,8 @@ def __init__(
 @register_embeddings
 class TransformerJitDocumentEmbeddings(DocumentEmbeddings, TransformerJitEmbeddings):
     def __init__(
-        self,
-        **kwargs,
+            self,
+            **kwargs,
     ) -> None:
         TransformerJitEmbeddings.__init__(self, **kwargs)
 
@@ -928,8 +930,8 @@ def __init__(
 @register_embeddings
 class TransformerOnnxWordEmbeddings(TokenEmbeddings, TransformerOnnxEmbeddings):
     def __init__(
-        self,
-        **kwargs,
+            self,
+            **kwargs,
     ) -> None:
         TransformerOnnxEmbeddings.__init__(self, **kwargs)
 
@@ -937,8 +939,8 @@ def __init__(
 @register_embeddings
 class TransformerOnnxDocumentEmbeddings(DocumentEmbeddings, TransformerOnnxEmbeddings):
     def __init__(
-        self,
-        **kwargs,
+            self,
+            **kwargs,
     ) -> None:
         TransformerOnnxEmbeddings.__init__(self, **kwargs)
 
@@ -948,27 +950,27 @@ class TransformerEmbeddings(TransformerBaseEmbeddings):
     onnx_cls: Type[TransformerOnnxEmbeddings] = TransformerOnnxEmbeddings
 
     def __init__(
-        self,
-        model: str = "bert-base-uncased",
-        fine_tune: bool = True,
-        layers: str = "-1",
-        layer_mean: bool = True,
-        subtoken_pooling: str = "first",
-        cls_pooling: str = "cls",
-        is_token_embedding: bool = True,
-        is_document_embedding: bool = True,
-        allow_long_sentences: bool = False,
-        use_context: Union[bool, int] = False,
-        respect_document_boundaries: bool = True,
-        context_dropout: float = 0.5,
-        saved_config: Optional[PretrainedConfig] = None,
-        tokenizer_data: Optional[BytesIO] = None,
-        feature_extractor_data: Optional[BytesIO] = None,
-        name: Optional[str] = None,
-        force_max_length: bool = False,
-        needs_manual_ocr: Optional[bool] = None,
-        use_context_separator: bool = True,
-        **kwargs,
+            self,
+            model: str = "bert-base-uncased",
+            fine_tune: bool = True,
+            layers: str = "-1",
+            layer_mean: bool = True,
+            subtoken_pooling: str = "first",
+            cls_pooling: str = "cls",
+            is_token_embedding: bool = True,
+            is_document_embedding: bool = True,
+            allow_long_sentences: bool = False,
+            use_context: Union[bool, int] = False,
+            respect_document_boundaries: bool = True,
+            context_dropout: float = 0.5,
+            saved_config: Optional[PretrainedConfig] = None,
+            tokenizer_data: Optional[BytesIO] = None,
+            feature_extractor_data: Optional[BytesIO] = None,
+            name: Optional[str] = None,
+            force_max_length: bool = False,
+            needs_manual_ocr: Optional[bool] = None,
+            use_context_separator: bool = True,
+            **kwargs,
     ) -> None:
         self.instance_parameters = self.get_instance_parameters(locals=locals())
         del self.instance_parameters["saved_config"]
@@ -1105,6 +1107,15 @@ 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(TransformerEmbeddings, self)._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 +1202,8 @@ 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
@@ -1235,23 +1248,23 @@ def to_params(self):
     def _can_document_embedding_shortcut(self):
         # cls first pooling can be done without recreating sentence hidden states
         return (
-            self.document_embedding
-            and not self.token_embedding
-            and self.cls_pooling == "cls"
-            and self.initial_cls_token
+                self.document_embedding
+                and not self.token_embedding
+                and self.cls_pooling == "cls"
+                and self.initial_cls_token
         )
 
     def forward(
-        self,
-        input_ids: torch.Tensor,
-        sub_token_lengths: Optional[torch.LongTensor] = None,
-        token_lengths: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        overflow_to_sample_mapping: Optional[torch.Tensor] = None,
-        word_ids: Optional[torch.Tensor] = None,
-        langs: Optional[torch.Tensor] = None,
-        bbox: Optional[torch.Tensor] = None,
-        pixel_values: Optional[torch.Tensor] = None,
+            self,
+            input_ids: torch.Tensor,
+            sub_token_lengths: Optional[torch.LongTensor] = None,
+            token_lengths: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            overflow_to_sample_mapping: Optional[torch.Tensor] = None,
+            word_ids: Optional[torch.Tensor] = None,
+            langs: Optional[torch.Tensor] = None,
+            bbox: Optional[torch.Tensor] = None,
+            pixel_values: Optional[torch.Tensor] = None,
     ):
         model_kwargs = {}
         if langs is not None:
@@ -1340,8 +1353,8 @@ def forward(
                     word_ids,
                     token_lengths,
                 )
-                all_token_embeddings[:, :, sentence_hidden_states.shape[2] :] = fill_masked_elements(
-                    all_token_embeddings[:, :, sentence_hidden_states.shape[2] :],
+                all_token_embeddings[:, :, sentence_hidden_states.shape[2]:] = fill_masked_elements(
+                    all_token_embeddings[:, :, sentence_hidden_states.shape[2]:],
                     sentence_hidden_states,
                     last_mask,
                     word_ids,
@@ -1361,7 +1374,7 @@ def _forward_tensors(self, tensors) -> Dict[str, torch.Tensor]:
         return self.forward(**tensors)
 
     def export_onnx(
-        self, path: Union[str, Path], example_sentences: List[Sentence], **kwargs
+            self, path: Union[str, Path], example_sentences: List[Sentence], **kwargs
     ) -> TransformerOnnxEmbeddings:
         """Export TransformerEmbeddings to OnnxFormat.
 
diff --git a/requirements.txt b/requirements.txt
index d3ca17e30..83f09e2ec 100644
--- a/requirements.txt
+++ b/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
\ No newline at end of file
+wikipedia-api>=0.5.7
+semver<4.0.0,>=3.0.0
\ No newline at end of file

From 270ef050625aa5f7dae29b2737cc2c3ddef18a16 Mon Sep 17 00:00:00 2001
From: Benedikt Fuchs <benedikt.fuchs@rise-world.com>
Date: Mon, 31 Jul 2023 15:47:24 +0200
Subject: [PATCH 3/6] fix ruff

---
 flair/embeddings/transformer.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/flair/embeddings/transformer.py b/flair/embeddings/transformer.py
index fb82c0bf7..4bf75c22d 100644
--- a/flair/embeddings/transformer.py
+++ b/flair/embeddings/transformer.py
@@ -8,11 +8,11 @@
 from abc import abstractmethod
 from io import BytesIO
 from pathlib import Path
-from semver import Version
-from typing import Any, Dict, List, Optional, Tuple, Type, Union, cast, Mapping
+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,
@@ -1113,7 +1113,7 @@ def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
         if transformers.__version__ >= Version(4, 31, 0):
             assert isinstance(state_dict, dict)
             state_dict.pop(f"{prefix}model.embeddings.position_ids", None)
-        super(TransformerEmbeddings, self)._load_from_state_dict(state_dict, prefix, local_metadata, strict,
+        super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,
                               missing_keys, unexpected_keys, error_msgs)
 
     def _has_initial_cls_token(self) -> bool:

From c492abff575af74681277f23d4eb447688e8444c Mon Sep 17 00:00:00 2001
From: Benedikt Fuchs <benedikt.fuchs@rise-world.com>
Date: Mon, 31 Jul 2023 19:04:31 +0200
Subject: [PATCH 4/6] fix extern dataset update

---
 flair/embeddings/transformer.py | 225 ++++++++++++++++----------------
 tests/test_datasets.py          |   2 +-
 2 files changed, 114 insertions(+), 113 deletions(-)

diff --git a/flair/embeddings/transformer.py b/flair/embeddings/transformer.py
index 4bf75c22d..c494c1cf8 100644
--- a/flair/embeddings/transformer.py
+++ b/flair/embeddings/transformer.py
@@ -70,11 +70,11 @@ def truncate_hidden_states(hidden_states: torch.Tensor, input_ids: torch.Tensor)
 
 @torch.jit.script_if_tracing
 def combine_strided_tensors(
-        hidden_states: torch.Tensor,
-        overflow_to_sample_mapping: torch.Tensor,
-        half_stride: int,
-        max_length: int,
-        default_value: int,
+    hidden_states: torch.Tensor,
+    overflow_to_sample_mapping: torch.Tensor,
+    half_stride: int,
+    max_length: int,
+    default_value: int,
 ) -> torch.Tensor:
     _, counts = torch.unique(overflow_to_sample_mapping, sorted=True, return_counts=True)
     sentence_count = int(overflow_to_sample_mapping.max().item() + 1)
@@ -94,9 +94,9 @@ def combine_strided_tensors(
         selected_sentences = hidden_states[overflow_to_sample_mapping == sentence_id]
         if selected_sentences.size(0) > 1:
             start_part = selected_sentences[0, : half_stride + 1]
-            mid_part = selected_sentences[:, half_stride + 1: max_length - 1 - half_stride]
+            mid_part = selected_sentences[:, half_stride + 1 : max_length - 1 - half_stride]
             mid_part = torch.reshape(mid_part, (mid_part.shape[0] * mid_part.shape[1],) + mid_part.shape[2:])
-            end_part = selected_sentences[selected_sentences.shape[0] - 1, max_length - half_stride - 1:]
+            end_part = selected_sentences[selected_sentences.shape[0] - 1, max_length - half_stride - 1 :]
             sentence_hidden_state = torch.cat((start_part, mid_part, end_part), dim=0)
             sentence_hidden_states[sentence_id, : sentence_hidden_state.shape[0]] = torch.cat(
                 (start_part, mid_part, end_part), dim=0
@@ -109,11 +109,11 @@ def combine_strided_tensors(
 
 @torch.jit.script_if_tracing
 def fill_masked_elements(
-        all_token_embeddings: torch.Tensor,
-        sentence_hidden_states: torch.Tensor,
-        mask: torch.Tensor,
-        word_ids: torch.Tensor,
-        lengths: torch.LongTensor,
+    all_token_embeddings: torch.Tensor,
+    sentence_hidden_states: torch.Tensor,
+    mask: torch.Tensor,
+    word_ids: torch.Tensor,
+    lengths: torch.LongTensor,
 ):
     for i in torch.arange(int(all_token_embeddings.shape[0])):
         r = insert_missing_embeddings(sentence_hidden_states[i][mask[i] & (word_ids[i] >= 0)], word_ids[i], lengths[i])
@@ -123,7 +123,7 @@ def fill_masked_elements(
 
 @torch.jit.script_if_tracing
 def insert_missing_embeddings(
-        token_embeddings: torch.Tensor, word_id: torch.Tensor, length: torch.LongTensor
+    token_embeddings: torch.Tensor, word_id: torch.Tensor, length: torch.LongTensor
 ) -> torch.Tensor:
     # in some cases we need to insert zero vectors for tokens without embedding.
     if token_embeddings.shape[0] == 0:
@@ -166,10 +166,10 @@ def insert_missing_embeddings(
 
 @torch.jit.script_if_tracing
 def fill_mean_token_embeddings(
-        all_token_embeddings: torch.Tensor,
-        sentence_hidden_states: torch.Tensor,
-        word_ids: torch.Tensor,
-        token_lengths: torch.Tensor,
+    all_token_embeddings: torch.Tensor,
+    sentence_hidden_states: torch.Tensor,
+    word_ids: torch.Tensor,
+    token_lengths: torch.Tensor,
 ):
     for i in torch.arange(all_token_embeddings.shape[0]):
         for _id in torch.arange(token_lengths[i]):  # type: ignore[call-overload]
@@ -196,7 +196,7 @@ def document_max_pooling(sentence_hidden_states: torch.Tensor, sentence_lengths:
 
 
 def _legacy_reconstruct_word_ids(
-        embedding: "TransformerBaseEmbeddings", flair_tokens: List[List[str]]
+    embedding: "TransformerBaseEmbeddings", flair_tokens: List[List[str]]
 ) -> List[List[Optional[int]]]:
     word_ids_list = []
     max_len = 0
@@ -307,25 +307,25 @@ class TransformerBaseEmbeddings(Embeddings[Sentence]):
     """
 
     def __init__(
-            self,
-            name: str,
-            tokenizer: PreTrainedTokenizer,
-            embedding_length: int,
-            context_length: int,
-            context_dropout: float,
-            respect_document_boundaries: bool,
-            stride: int,
-            allow_long_sentences: bool,
-            fine_tune: bool,
-            truncate: bool,
-            use_lang_emb: bool,
-            is_document_embedding: bool = False,
-            is_token_embedding: bool = False,
-            force_device: Optional[torch.device] = None,
-            force_max_length: bool = False,
-            feature_extractor: Optional[FeatureExtractionMixin] = None,
-            needs_manual_ocr: Optional[bool] = None,
-            use_context_separator: bool = True,
+        self,
+        name: str,
+        tokenizer: PreTrainedTokenizer,
+        embedding_length: int,
+        context_length: int,
+        context_dropout: float,
+        respect_document_boundaries: bool,
+        stride: int,
+        allow_long_sentences: bool,
+        fine_tune: bool,
+        truncate: bool,
+        use_lang_emb: bool,
+        is_document_embedding: bool = False,
+        is_token_embedding: bool = False,
+        force_device: Optional[torch.device] = None,
+        force_max_length: bool = False,
+        feature_extractor: Optional[FeatureExtractionMixin] = None,
+        needs_manual_ocr: Optional[bool] = None,
+        use_context_separator: bool = True,
     ) -> None:
         self.name = name
         super().__init__()
@@ -473,32 +473,32 @@ def prepare_tensors(self, sentences: List[Sentence], device: Optional[torch.devi
 
         # random check some tokens to save performance.
         if (self.needs_manual_ocr or self.tokenizer_needs_ocr_boxes) and not all(
-                [
-                    flair_tokens[0][0].has_metadata("bbox"),
-                    flair_tokens[0][-1].has_metadata("bbox"),
-                    flair_tokens[-1][0].has_metadata("bbox"),
-                    flair_tokens[-1][-1].has_metadata("bbox"),
-                ]
+            [
+                flair_tokens[0][0].has_metadata("bbox"),
+                flair_tokens[0][-1].has_metadata("bbox"),
+                flair_tokens[-1][0].has_metadata("bbox"),
+                flair_tokens[-1][-1].has_metadata("bbox"),
+            ]
         ):
             raise ValueError(f"The embedding '{self.name}' requires the ocr 'bbox' set as metadata on all tokens.")
 
         if self.feature_extractor is not None and not all(
-                [
-                    sentences[0].has_metadata("image"),
-                    sentences[-1].has_metadata("image"),
-                ]
+            [
+                sentences[0].has_metadata("image"),
+                sentences[-1].has_metadata("image"),
+            ]
         ):
             raise ValueError(f"The embedding '{self.name}' requires the 'image' set as metadata for all sentences.")
 
         return self.__build_transformer_model_inputs(sentences, offsets, lengths, flair_tokens, device)
 
     def __build_transformer_model_inputs(
-            self,
-            sentences: List[Sentence],
-            offsets: List[int],
-            sentence_lengths: List[int],
-            flair_tokens: List[List[Token]],
-            device: torch.device,
+        self,
+        sentences: List[Sentence],
+        offsets: List[int],
+        sentence_lengths: List[int],
+        flair_tokens: List[List[Token]],
+        device: torch.device,
     ):
         tokenizer_kwargs: Dict[str, Any] = {}
         if self.tokenizer_needs_ocr_boxes:
@@ -559,7 +559,7 @@ def __build_transformer_model_inputs(
                 sentence_idx = 0
                 for sentence, part_length in zip(sentences, sentence_part_lengths):
                     lang_id = lang2id.get(sentence.get_language_code(), 0)
-                    model_kwargs["langs"][sentence_idx: sentence_idx + part_length] = lang_id
+                    model_kwargs["langs"][sentence_idx : sentence_idx + part_length] = lang_id
                     sentence_idx += part_length
 
         if "bbox" in batch_encoding:
@@ -801,12 +801,12 @@ def collect_dynamic_axes(cls, embedding: "TransformerEmbeddings", tensors):
 
     @classmethod
     def export_from_embedding(
-            cls,
-            path: Union[str, Path],
-            embedding: "TransformerEmbeddings",
-            example_sentences: List[Sentence],
-            opset_version: int = 14,
-            providers: Optional[List] = None,
+        cls,
+        path: Union[str, Path],
+        embedding: "TransformerEmbeddings",
+        example_sentences: List[Sentence],
+        opset_version: int = 14,
+        providers: Optional[List] = None,
     ):
         path = str(path)
         example_tensors = embedding.prepare_tensors(example_sentences)
@@ -899,7 +899,7 @@ def create_from_embedding(cls, module: ScriptModule, embedding: "TransformerEmbe
 
     @classmethod
     def parameter_to_list(
-            cls, embedding: "TransformerEmbeddings", wrapper: torch.nn.Module, sentences: List[Sentence]
+        cls, embedding: "TransformerEmbeddings", wrapper: torch.nn.Module, sentences: List[Sentence]
     ) -> Tuple[List[str], List[torch.Tensor]]:
         tensors = embedding.prepare_tensors(sentences)
         param_names = list(inspect.signature(wrapper.forward).parameters.keys())
@@ -912,8 +912,8 @@ def parameter_to_list(
 @register_embeddings
 class TransformerJitWordEmbeddings(TokenEmbeddings, TransformerJitEmbeddings):
     def __init__(
-            self,
-            **kwargs,
+        self,
+        **kwargs,
     ) -> None:
         TransformerJitEmbeddings.__init__(self, **kwargs)
 
@@ -921,8 +921,8 @@ def __init__(
 @register_embeddings
 class TransformerJitDocumentEmbeddings(DocumentEmbeddings, TransformerJitEmbeddings):
     def __init__(
-            self,
-            **kwargs,
+        self,
+        **kwargs,
     ) -> None:
         TransformerJitEmbeddings.__init__(self, **kwargs)
 
@@ -930,8 +930,8 @@ def __init__(
 @register_embeddings
 class TransformerOnnxWordEmbeddings(TokenEmbeddings, TransformerOnnxEmbeddings):
     def __init__(
-            self,
-            **kwargs,
+        self,
+        **kwargs,
     ) -> None:
         TransformerOnnxEmbeddings.__init__(self, **kwargs)
 
@@ -939,8 +939,8 @@ def __init__(
 @register_embeddings
 class TransformerOnnxDocumentEmbeddings(DocumentEmbeddings, TransformerOnnxEmbeddings):
     def __init__(
-            self,
-            **kwargs,
+        self,
+        **kwargs,
     ) -> None:
         TransformerOnnxEmbeddings.__init__(self, **kwargs)
 
@@ -950,27 +950,27 @@ class TransformerEmbeddings(TransformerBaseEmbeddings):
     onnx_cls: Type[TransformerOnnxEmbeddings] = TransformerOnnxEmbeddings
 
     def __init__(
-            self,
-            model: str = "bert-base-uncased",
-            fine_tune: bool = True,
-            layers: str = "-1",
-            layer_mean: bool = True,
-            subtoken_pooling: str = "first",
-            cls_pooling: str = "cls",
-            is_token_embedding: bool = True,
-            is_document_embedding: bool = True,
-            allow_long_sentences: bool = False,
-            use_context: Union[bool, int] = False,
-            respect_document_boundaries: bool = True,
-            context_dropout: float = 0.5,
-            saved_config: Optional[PretrainedConfig] = None,
-            tokenizer_data: Optional[BytesIO] = None,
-            feature_extractor_data: Optional[BytesIO] = None,
-            name: Optional[str] = None,
-            force_max_length: bool = False,
-            needs_manual_ocr: Optional[bool] = None,
-            use_context_separator: bool = True,
-            **kwargs,
+        self,
+        model: str = "bert-base-uncased",
+        fine_tune: bool = True,
+        layers: str = "-1",
+        layer_mean: bool = True,
+        subtoken_pooling: str = "first",
+        cls_pooling: str = "cls",
+        is_token_embedding: bool = True,
+        is_document_embedding: bool = True,
+        allow_long_sentences: bool = False,
+        use_context: Union[bool, int] = False,
+        respect_document_boundaries: bool = True,
+        context_dropout: float = 0.5,
+        saved_config: Optional[PretrainedConfig] = None,
+        tokenizer_data: Optional[BytesIO] = None,
+        feature_extractor_data: Optional[BytesIO] = None,
+        name: Optional[str] = None,
+        force_max_length: bool = False,
+        needs_manual_ocr: Optional[bool] = None,
+        use_context_separator: bool = True,
+        **kwargs,
     ) -> None:
         self.instance_parameters = self.get_instance_parameters(locals=locals())
         del self.instance_parameters["saved_config"]
@@ -1107,14 +1107,15 @@ 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):
+    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)
+        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
@@ -1248,23 +1249,23 @@ def to_params(self):
     def _can_document_embedding_shortcut(self):
         # cls first pooling can be done without recreating sentence hidden states
         return (
-                self.document_embedding
-                and not self.token_embedding
-                and self.cls_pooling == "cls"
-                and self.initial_cls_token
+            self.document_embedding
+            and not self.token_embedding
+            and self.cls_pooling == "cls"
+            and self.initial_cls_token
         )
 
     def forward(
-            self,
-            input_ids: torch.Tensor,
-            sub_token_lengths: Optional[torch.LongTensor] = None,
-            token_lengths: Optional[torch.LongTensor] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            overflow_to_sample_mapping: Optional[torch.Tensor] = None,
-            word_ids: Optional[torch.Tensor] = None,
-            langs: Optional[torch.Tensor] = None,
-            bbox: Optional[torch.Tensor] = None,
-            pixel_values: Optional[torch.Tensor] = None,
+        self,
+        input_ids: torch.Tensor,
+        sub_token_lengths: Optional[torch.LongTensor] = None,
+        token_lengths: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        overflow_to_sample_mapping: Optional[torch.Tensor] = None,
+        word_ids: Optional[torch.Tensor] = None,
+        langs: Optional[torch.Tensor] = None,
+        bbox: Optional[torch.Tensor] = None,
+        pixel_values: Optional[torch.Tensor] = None,
     ):
         model_kwargs = {}
         if langs is not None:
@@ -1353,8 +1354,8 @@ def forward(
                     word_ids,
                     token_lengths,
                 )
-                all_token_embeddings[:, :, sentence_hidden_states.shape[2]:] = fill_masked_elements(
-                    all_token_embeddings[:, :, sentence_hidden_states.shape[2]:],
+                all_token_embeddings[:, :, sentence_hidden_states.shape[2] :] = fill_masked_elements(
+                    all_token_embeddings[:, :, sentence_hidden_states.shape[2] :],
                     sentence_hidden_states,
                     last_mask,
                     word_ids,
@@ -1374,7 +1375,7 @@ def _forward_tensors(self, tensors) -> Dict[str, torch.Tensor]:
         return self.forward(**tensors)
 
     def export_onnx(
-            self, path: Union[str, Path], example_sentences: List[Sentence], **kwargs
+        self, path: Union[str, Path], example_sentences: List[Sentence], **kwargs
     ) -> TransformerOnnxEmbeddings:
         """Export TransformerEmbeddings to OnnxFormat.
 
diff --git a/tests/test_datasets.py b/tests/test_datasets.py
index 91a1742f8..dc3f27ae0 100644
--- a/tests/test_datasets.py
+++ b/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},

From a2ab16b763a02932021f76f6239104c494d189d4 Mon Sep 17 00:00:00 2001
From: Benedikt Fuchs <benedikt.fuchs@rise-world.com>
Date: Mon, 31 Jul 2023 20:46:04 +0200
Subject: [PATCH 5/6] delete outdated poetry.lock file

---
 poetry.lock | 3374 ---------------------------------------------------
 1 file changed, 3374 deletions(-)
 delete mode 100644 poetry.lock

diff --git a/poetry.lock b/poetry.lock
deleted file mode 100644
index 3b965ccab..000000000
--- a/poetry.lock
+++ /dev/null
@@ -1,3374 +0,0 @@
-# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand.
-
-[[package]]
-name = "aiohttp"
-version = "3.8.4"
-description = "Async http client/server framework (asyncio)"
-category = "main"
-optional = false
-python-versions = ">=3.6"
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-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-typing-extensions = {version = ">=3.7.4", markers = "python_version < \"3.8\""}
-
-[[package]]
-name = "zipp"
-version = "3.15.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-category = "main"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "zipp-3.15.0-py3-none-any.whl", hash = "sha256:48904fc76a60e542af151aded95726c1a5c34ed43ab4134b597665c86d7ad556"},
-    {file = "zipp-3.15.0.tar.gz", hash = "sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b"},
-]
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
-
-[metadata]
-lock-version = "2.0"
-python-versions = ">=3.7,<4.0"
-content-hash = "6a0a8ac677b3c68630046f0885f6d97a2bd7c7617b5a261ba7680fa86e112150"

From 3695fe7242125c1bb5be1466294da82329b6dbc8 Mon Sep 17 00:00:00 2001
From: Benedikt Fuchs <benedikt.fuchs@rise-world.com>
Date: Mon, 31 Jul 2023 22:39:28 +0200
Subject: [PATCH 6/6] fix new ruff rules

---
 flair/data.py              | 2 +-
 flair/embeddings/base.py   | 2 +-
 flair/embeddings/legacy.py | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/flair/data.py b/flair/data.py
index 839f75006..ecbcb55ba 100644
--- a/flair/data.py
+++ b/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
diff --git a/flair/embeddings/base.py b/flair/embeddings/base.py
index a9521142b..bf3e7645a 100644
--- a/flair/embeddings/base.py
+++ b/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
 
diff --git a/flair/embeddings/legacy.py b/flair/embeddings/legacy.py
index 39275a16a..237bf6aff 100644
--- a/flair/embeddings/legacy.py
+++ b/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: