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Source code for linear_relational.CausalEditor

+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Callable, Literal, Optional, Sequence, TypeVar, Union, cast
+
+import torch
+from tokenizers import Tokenizer
+from torch import nn
+
+from linear_relational.Concept import Concept
+from linear_relational.lib.layer_matching import (
+    LayerMatcher,
+    collect_matching_layers,
+    get_layer_name,
+    guess_hidden_layer_matcher,
+)
+from linear_relational.lib.token_utils import (
+    ensure_tokenizer_has_pad_token,
+    find_final_word_token_index,
+    make_inputs,
+    predict_all_token_probs_from_input,
+    predict_next_tokens_greedy,
+)
+from linear_relational.lib.torch_utils import get_device, untuple_tensor
+from linear_relational.lib.TraceLayerDict import TraceLayerDict
+from linear_relational.lib.util import batchify
+
+EditorSubject = Union[str, int, Callable[[str, list[int]], int]]
+
+
+

+[docs]
+@dataclass
+class ConceptSwapRequest:
+    text: str
+    subject: EditorSubject
+    remove_concept: str
+    add_concept: str

+
+
+
+

+[docs]
+@dataclass
+class ConceptSwapAndPredictGreedyRequest(ConceptSwapRequest):
+    predict_num_tokens: int = 1

+
+
+
+T = TypeVar("T")
+
+
+

+[docs]
+class CausalEditor:
+    """Modify model activations during inference to swap concepts"""
+
+    concepts: list[Concept]
+    model: nn.Module
+    tokenizer: Tokenizer
+    layer_matcher: LayerMatcher
+    layer_name_to_num: dict[str, int]
+
+    def __init__(
+        self,
+        model: nn.Module,
+        tokenizer: Tokenizer,
+        concepts: list[Concept],
+        layer_matcher: Optional[LayerMatcher] = None,
+    ) -> None:
+        self.concepts = concepts
+        self.model = model
+        self.tokenizer = tokenizer
+        self.layer_matcher = layer_matcher or guess_hidden_layer_matcher(model)
+        ensure_tokenizer_has_pad_token(tokenizer)
+        num_layers = len(collect_matching_layers(self.model, self.layer_matcher))
+        self.layer_name_to_num = {}
+        for layer_num in range(num_layers):
+            self.layer_name_to_num[
+                get_layer_name(model, self.layer_matcher, layer_num)
+            ] = layer_num
+
+    @property
+    def device(self) -> torch.device:
+        return get_device(self.model)
+
+

+[docs]
+    def swap_subject_concepts_and_predict_greedy(
+        self,
+        text: str,
+        subject: EditorSubject,
+        remove_concept: str,
+        add_concept: str,
+        # if False, edit the subject token at every layer
+        edit_single_layer: int | Literal[False] = False,
+        predict_num_tokens: int = 1,
+        magnitude_multiplier: float = 1.0,
+        # if True, use the magnitude of the projection of the remove_concept against the subject's original activation
+        # if False, use the magnitude of the subject's original activation
+        use_remove_concept_projection_magnitude: bool = False,
+    ) -> str:
+        results = self.swap_subject_concepts_and_predict_greedy_bulk(
+            [
+                ConceptSwapAndPredictGreedyRequest(
+                    text, subject, remove_concept, add_concept, predict_num_tokens
+                )
+            ],
+            magnitude_multiplier=magnitude_multiplier,
+            edit_single_layer=edit_single_layer,
+            use_remove_concept_projection_magnitude=use_remove_concept_projection_magnitude,
+        )
+        return results[0]

+
+
+

+[docs]
+    def swap_subject_concepts_and_predict_greedy_bulk(
+        self,
+        requests: Sequence[ConceptSwapAndPredictGreedyRequest],
+        # if False, edit the subject token at every layer
+        edit_single_layer: int | Literal[False] = False,
+        magnitude_multiplier: float = 1.0,
+        # if True, use the magnitude of the projection of the remove_concept against the subject's original activation
+        # if False, use the magnitude of the subject's original activation
+        use_remove_concept_projection_magnitude: bool = False,
+    ) -> list[str]:
+        next_tokens = self.swap_subject_concepts_and_predict_tokens_greedy_bulk(
+            requests,
+            edit_single_layer=edit_single_layer,
+            magnitude_multiplier=magnitude_multiplier,
+            use_remove_concept_projection_magnitude=use_remove_concept_projection_magnitude,
+        )
+        return [self.tokenizer.decode(tokens) for tokens in next_tokens]

+
+
+

+[docs]
+    def swap_subject_concepts_and_predict_tokens_greedy_bulk(
+        self,
+        requests: Sequence[ConceptSwapAndPredictGreedyRequest],
+        # if False, edit the subject token at every layer
+        edit_single_layer: int | Literal[False],
+        magnitude_multiplier: float = 1.0,
+        # if True, use the magnitude of the projection of the remove_concept against the subject's original activation
+        # if False, use the magnitude of the subject's original activation
+        use_remove_concept_projection_magnitude: bool = False,
+        batch_size: int = 12,
+        show_progress: bool = False,
+    ) -> list[list[int]]:
+        results: list[list[int]] = []
+        for batch in batchify(requests, batch_size, show_progress=show_progress):
+
+            def run_batch_fn() -> list[list[int]]:
+                max_num_tokens = max(req.predict_num_tokens for req in batch)
+                next_tokens = predict_next_tokens_greedy(
+                    self.model,
+                    self.tokenizer,
+                    [req.text for req in batch],
+                    num_tokens=max_num_tokens,
+                    device=self.device,
+                )
+                return [
+                    tokens[: req.predict_num_tokens]
+                    for tokens, req in zip(next_tokens, batch)
+                ]
+
+            results.extend(
+                self._swap_subject_concepts_and_run_batch(
+                    batch,
+                    run_fn=run_batch_fn,
+                    edit_single_layer=edit_single_layer,
+                    magnitude_multiplier=magnitude_multiplier,
+                    use_remove_concept_projection_magnitude=use_remove_concept_projection_magnitude,
+                )
+            )
+        return results

+
+
+

+[docs]
+    def swap_subject_concepts_and_predict_all_token_probs_bulk(
+        self,
+        requests: Sequence[ConceptSwapRequest],
+        magnitude_multiplier: float = 1.0,
+        # if False, edit the subject token at every layer
+        edit_single_layer: int | Literal[False] = False,
+        # if True, use the magnitude of the projection of the remove_concept against the subject's original activation
+        # if False, use the magnitude of the subject's original activation
+        use_remove_concept_projection_magnitude: bool = False,
+        batch_size: int = 12,
+        show_progress: bool = False,
+    ) -> list[torch.Tensor]:
+        results: list[torch.Tensor] = []
+        for batch in batchify(requests, batch_size, show_progress=show_progress):
+
+            def run_batch_fn() -> list[torch.Tensor]:
+                inputs = make_inputs(
+                    self.tokenizer, [req.text for req in batch], device=self.device
+                )
+                return predict_all_token_probs_from_input(self.model, inputs)
+
+            results.extend(
+                self._swap_subject_concepts_and_run_batch(
+                    batch,
+                    run_fn=run_batch_fn,
+                    magnitude_multiplier=magnitude_multiplier,
+                    edit_single_layer=edit_single_layer,
+                    use_remove_concept_projection_magnitude=use_remove_concept_projection_magnitude,
+                )
+            )
+        return results

+
+
+    def _swap_subject_concepts_and_run_batch(
+        self,
+        requests: Sequence[ConceptSwapRequest],
+        run_fn: Callable[[], T],
+        edit_single_layer: int | Literal[False],
+        magnitude_multiplier: float = 1.0,
+        # if True, use the magnitude of the projection of the remove_concept against the subject's original activation
+        # if False, use the magnitude of the subject's original activation
+        use_remove_concept_projection_magnitude: bool = False,
+    ) -> T:
+        """
+        Helper to run the given run_fn while swapping the subject concept for each request.
+        The run_fn should run the model with the same batch of inputs as specified in the requests
+        """
+        subj_tokens = [self._find_subject_token(req) for req in requests]
+        with torch.no_grad():
+            remove_concept_vectors = [
+                (
+                    self._find_concept(req.remove_concept)
+                    .vector.detach()
+                    .clone()
+                    .type(cast(torch.dtype, self.model.dtype))
+                    .to(self.device)
+                )
+                for req in requests
+            ]
+            add_concept_vectors = [
+                (
+                    self._find_concept(req.add_concept)
+                    .vector.detach()
+                    .clone()
+                    .type(cast(torch.dtype, self.model.dtype))
+                    .to(self.device)
+                )
+                for req in requests
+            ]
+
+        def edit_model_output(output: torch.Tensor, layer_name: str) -> torch.Tensor:
+            if (
+                edit_single_layer is not False
+                and self.layer_name_to_num[layer_name] != edit_single_layer
+            ):
+                return output
+            fixed_output = untuple_tensor(output)
+            for i, subj_token in enumerate(subj_tokens):
+                remove_concept_vector = remove_concept_vectors[i]
+                add_concept_vector = add_concept_vectors[i]
+                original_subj_act = fixed_output[i][subj_token]
+                if use_remove_concept_projection_magnitude:
+                    base_magnitude = original_subj_act.dot(remove_concept_vector)
+                else:
+                    base_magnitude = original_subj_act.norm()
+                magnitude = base_magnitude * magnitude_multiplier
+                fixed_output[i][subj_token] = original_subj_act + magnitude * (
+                    add_concept_vector - remove_concept_vector
+                )
+            return output
+
+        with torch.no_grad(), TraceLayerDict(
+            self.model,
+            layers=self.layer_name_to_num.keys(),
+            edit_output=edit_model_output,
+        ):
+            return run_fn()
+
+    def _find_subject_token(self, query: ConceptSwapRequest) -> int:
+        text = query.text
+        subject = query.subject
+        if isinstance(subject, int):
+            return subject
+        if isinstance(subject, str):
+            return find_final_word_token_index(self.tokenizer, text, subject)
+        if callable(subject):
+            return subject(text, self.tokenizer.encode(text))
+        raise ValueError(f"Unknown subject type: {type(subject)}")
+
+    def _find_concept(self, concept_name: str) -> Concept:
+        for concept in self.concepts:
+            if concept.name == concept_name:
+                return concept
+        raise ValueError(f"Unknown concept: {concept_name}")

+
+
+
+
+
+ +
+ + +
+
+
+
+ Copyright © 2024, David Chanin +
+ Made with Sphinx and @pradyunsg's + + Furo + +
+
+ +
+
+ +
+
+ +
+
+ + + + + \ No newline at end of file diff --git a/_modules/linear_relational/Concept.html b/_modules/linear_relational/Concept.html new file mode 100644 index 0000000..7aca3d2 --- /dev/null +++ b/_modules/linear_relational/Concept.html @@ -0,0 +1,291 @@ + + + + + + + + linear_relational.Concept - linear-relational 0.4.0 documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
+
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+
+
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+
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+
+

Source code for linear_relational.Concept

+from __future__ import annotations
+
+from typing import Any, Optional
+
+import torch
+from torch import nn
+
+
+

+[docs]
+class Concept(nn.Module):
+    """Linear Relation Concept (LRC)"""
+
+    layer: int
+    vector: torch.Tensor
+    object: str
+    relation: str
+    name: str
+    metadata: dict[str, Any]
+
+    def __init__(
+        self,
+        layer: int,
+        vector: torch.Tensor,
+        object: str,
+        relation: str,
+        metadata: Optional[dict[str, Any]] = None,
+        name: Optional[str] = None,
+    ) -> None:
+        super().__init__()
+        self.layer = layer
+        self.vector = vector
+        self.object = object
+        self.relation = relation
+        self.metadata = metadata or {}
+        self.name = name or f"{self.relation}: {self.object}"
+
+

+[docs]
+    def forward(self, activations: torch.Tensor) -> torch.Tensor:
+        vector = self.vector.to(activations.device, dtype=activations.dtype)
+        if len(activations.shape) == 1:
+            return vector @ activations
+        return vector @ activations.T

+

+
+
+
+
+
+ +
+ + +
+
+
+
+ Copyright © 2024, David Chanin +
+ Made with Sphinx and @pradyunsg's + + Furo + +
+
+ +
+
+ +
+
+ +
+
+ + + + + \ No newline at end of file diff --git a/_modules/linear_relational/ConceptMatcher.html b/_modules/linear_relational/ConceptMatcher.html new file mode 100644 index 0000000..a6eb820 --- /dev/null +++ b/_modules/linear_relational/ConceptMatcher.html @@ -0,0 +1,416 @@ + + + + + + + + linear_relational.ConceptMatcher - linear-relational 0.4.0 documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
+
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+
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+
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+
+

Source code for linear_relational.ConceptMatcher

+from dataclasses import dataclass
+from typing import Callable, Optional, Sequence, Union
+
+import torch
+from tokenizers import Tokenizer
+from torch import nn
+
+from linear_relational.Concept import Concept
+from linear_relational.lib.extract_token_activations import (
+    TokenLayerActivationsList,
+    extract_token_activations,
+)
+from linear_relational.lib.layer_matching import (
+    LayerMatcher,
+    collect_matching_layers,
+    get_layer_name,
+    guess_hidden_layer_matcher,
+)
+from linear_relational.lib.token_utils import (
+    ensure_tokenizer_has_pad_token,
+    find_final_word_token_index,
+)
+from linear_relational.lib.torch_utils import get_device
+from linear_relational.lib.util import batchify
+
+QuerySubject = Union[str, int, Callable[[str, list[int]], int]]
+
+
+

+[docs]
+@dataclass
+class ConceptMatchQuery:
+    text: str
+    subject: QuerySubject

+
+
+
+

+[docs]
+@dataclass
+class ConceptMatchResult:
+    concept: str
+    score: float

+
+
+
+

+[docs]
+@dataclass
+class QueryResult:
+    concept_results: dict[str, ConceptMatchResult]
+
+    @property
+    def best_match(self) -> ConceptMatchResult:
+        return max(self.concept_results.values(), key=lambda x: x.score)

+
+
+
+

+[docs]
+class ConceptMatcher:
+    """Match concepts against subject activations in a model"""
+
+    concepts: list[Concept]
+    model: nn.Module
+    tokenizer: Tokenizer
+    layer_matcher: LayerMatcher
+    layer_name_to_num: dict[str, int]
+    map_activations_fn: (
+        Callable[[TokenLayerActivationsList], TokenLayerActivationsList] | None
+    )
+
+    def __init__(
+        self,
+        model: nn.Module,
+        tokenizer: Tokenizer,
+        concepts: list[Concept],
+        layer_matcher: Optional[LayerMatcher] = None,
+        map_activations_fn: (
+            Callable[[TokenLayerActivationsList], TokenLayerActivationsList] | None
+        ) = None,
+    ) -> None:
+        self.concepts = concepts
+        self.model = model
+        self.tokenizer = tokenizer
+        self.layer_matcher = layer_matcher or guess_hidden_layer_matcher(model)
+        self.map_activations_fn = map_activations_fn
+        ensure_tokenizer_has_pad_token(tokenizer)
+        num_layers = len(collect_matching_layers(self.model, self.layer_matcher))
+        self.layer_name_to_num = {}
+        for layer_num in range(num_layers):
+            self.layer_name_to_num[
+                get_layer_name(model, self.layer_matcher, layer_num)
+            ] = layer_num
+
+

+[docs]
+    def query(self, query: str, subject: QuerySubject) -> QueryResult:
+        return self.query_bulk([ConceptMatchQuery(query, subject)])[0]

+
+
+

+[docs]
+    def query_bulk(
+        self,
+        queries: Sequence[ConceptMatchQuery],
+        batch_size: int = 4,
+        verbose: bool = False,
+    ) -> list[QueryResult]:
+        results: list[QueryResult] = []
+        for batch in batchify(queries, batch_size, show_progress=verbose):
+            results.extend(self._query_batch(batch))
+        return results

+
+
+    def _query_batch(self, queries: Sequence[ConceptMatchQuery]) -> list[QueryResult]:
+        subj_tokens = [self._find_subject_token(query) for query in queries]
+        with torch.no_grad():
+            batch_subj_token_activations = extract_token_activations(
+                self.model,
+                self.tokenizer,
+                layers=self.layer_name_to_num.keys(),
+                texts=[q.text for q in queries],
+                token_indices=subj_tokens,
+                device=get_device(self.model),
+                # batching is handled already, so no need to batch here too
+                batch_size=len(queries),
+                show_progress=False,
+            )
+            if self.map_activations_fn is not None:
+                batch_subj_token_activations = self.map_activations_fn(
+                    batch_subj_token_activations
+                )
+
+        results: list[QueryResult] = []
+        for raw_subj_token_activations in batch_subj_token_activations:
+            concept_results: dict[str, ConceptMatchResult] = {}
+            # need to replace the layer name with the layer number
+            subj_token_activations = {
+                self.layer_name_to_num[layer_name]: layer_activations[0]
+                for layer_name, layer_activations in raw_subj_token_activations.items()
+            }
+            for concept in self.concepts:
+                concept_results[concept.name] = _apply_concept_to_activations(
+                    concept, subj_token_activations
+                )
+            results.append(QueryResult(concept_results))
+        return results
+
+    def _find_subject_token(self, query: ConceptMatchQuery) -> int:
+        text = query.text
+        subject = query.subject
+        if isinstance(subject, int):
+            return subject
+        if isinstance(subject, str):
+            return find_final_word_token_index(self.tokenizer, text, subject)
+        if callable(subject):
+            return subject(text, self.tokenizer.encode(text))
+        raise ValueError(f"Unknown subject type: {type(subject)}")

+
+
+
+@torch.no_grad()
+def _apply_concept_to_activations(
+    concept: Concept, activations: dict[int, torch.Tensor]
+) -> ConceptMatchResult:
+    score = concept.forward(activations[concept.layer]).item()
+    return ConceptMatchResult(
+        concept=concept.name,
+        score=score,
+    )
+
+
+
+
+ +
+ + +
+
+
+
+ Copyright © 2024, David Chanin +
+ Made with Sphinx and @pradyunsg's + + Furo + +
+
+ +
+
+ +
+
+ +
+
+ + + + + \ No newline at end of file diff --git a/_modules/linear_relational/Lre.html b/_modules/linear_relational/Lre.html new file mode 100644 index 0000000..81c79e4 --- /dev/null +++ b/_modules/linear_relational/Lre.html @@ -0,0 +1,491 @@ + + + + + + + + linear_relational.Lre - linear-relational 0.4.0 documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
+
+
+ +
+
+
linear-relational 0.4.0 documentation
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+
+
+ +
+ +
+
+ +
+
+
+ + + + + Back to top + +
+
+ +
+ +
+
+

Source code for linear_relational.Lre

+from typing import Any, Literal
+
+import torch
+from torch import nn
+
+
+

+[docs]
+class InvertedLre(nn.Module):
+    """Low-rank inverted LRE, used for calculating subject activations from object activations"""
+
+    relation: str
+    subject_layer: int
+    object_layer: int
+    # store u, v, s, and bias separately to avoid storing the full weight matrix
+    u: nn.Parameter
+    s: nn.Parameter
+    v: nn.Parameter
+    bias: nn.Parameter
+    object_aggregation: Literal["mean", "first_token"]
+    metadata: dict[str, Any] | None = None
+
+    def __init__(
+        self,
+        relation: str,
+        subject_layer: int,
+        object_layer: int,
+        object_aggregation: Literal["mean", "first_token"],
+        u: torch.Tensor,
+        s: torch.Tensor,
+        v: torch.Tensor,
+        bias: torch.Tensor,
+        metadata: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__()
+        self.relation = relation
+        self.subject_layer = subject_layer
+        self.object_layer = object_layer
+        self.object_aggregation = object_aggregation
+        self.u = nn.Parameter(u, requires_grad=False)
+        self.s = nn.Parameter(s, requires_grad=False)
+        self.v = nn.Parameter(v, requires_grad=False)
+        self.bias = nn.Parameter(bias, requires_grad=False)
+        self.metadata = metadata
+
+    @property
+    def rank(self) -> int:
+        return self.s.shape[0]
+
+

+[docs]
+    def w_inv_times_vec(self, vec: torch.Tensor) -> torch.Tensor:
+        # group u.T @ vec to avoid calculating larger matrices than needed
+        return self.v @ torch.diag(1 / self.s) @ (self.u.T @ vec)

+
+
+

+[docs]
+    def forward(
+        self,
+        object_activations: torch.Tensor,  # a tensor of shape (num_activations, hidden_activation_size)
+        normalize: bool = False,
+    ) -> torch.Tensor:
+        return self.calculate_subject_activation(
+            object_activations=object_activations,
+            normalize=normalize,
+        )

+
+
+

+[docs]
+    def calculate_subject_activation(
+        self,
+        object_activations: torch.Tensor,  # a tensor of shape (num_activations, hidden_activation_size)
+        normalize: bool = False,
+    ) -> torch.Tensor:
+        # match precision of weight_inverse and bias
+        unbiased_acts = object_activations - self.bias.unsqueeze(0)
+        vec = self.w_inv_times_vec(unbiased_acts.T).mean(dim=1)
+
+        if normalize:
+            vec = vec / vec.norm()
+        return vec

+

+
+
+
+

+[docs]
+class LowRankLre(nn.Module):
+    """Low-rank approximation of a LRE"""
+
+    relation: str
+    subject_layer: int
+    object_layer: int
+    # store u, v, s, and bias separately to avoid storing the full weight matrix
+    u: nn.Parameter
+    s: nn.Parameter
+    v: nn.Parameter
+    bias: nn.Parameter
+    object_aggregation: Literal["mean", "first_token"]
+    metadata: dict[str, Any] | None = None
+
+    def __init__(
+        self,
+        relation: str,
+        subject_layer: int,
+        object_layer: int,
+        object_aggregation: Literal["mean", "first_token"],
+        u: torch.Tensor,
+        s: torch.Tensor,
+        v: torch.Tensor,
+        bias: torch.Tensor,
+        metadata: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__()
+        self.relation = relation
+        self.subject_layer = subject_layer
+        self.object_layer = object_layer
+        self.object_aggregation = object_aggregation
+        self.u = nn.Parameter(u, requires_grad=False)
+        self.s = nn.Parameter(s, requires_grad=False)
+        self.v = nn.Parameter(v, requires_grad=False)
+        self.bias = nn.Parameter(bias, requires_grad=False)
+        self.metadata = metadata
+
+    @property
+    def rank(self) -> int:
+        return self.s.shape[0]
+
+

+[docs]
+    def w_times_vec(self, vec: torch.Tensor) -> torch.Tensor:
+        # group v.T @ vec to avoid calculating larger matrices than needed
+        return self.u @ torch.diag(self.s) @ (self.v.T @ vec)

+
+
+

+[docs]
+    def forward(
+        self,
+        subject_activations: torch.Tensor,  # a tensor of shape (num_activations, hidden_activation_size)
+        normalize: bool = False,
+    ) -> torch.Tensor:
+        return self.calculate_object_activation(
+            subject_activations=subject_activations,
+            normalize=normalize,
+        )

+
+
+

+[docs]
+    def calculate_object_activation(
+        self,
+        subject_activations: torch.Tensor,  # a tensor of shape (num_activations, hidden_activation_size)
+        normalize: bool = False,
+    ) -> torch.Tensor:
+        # match precision of weight_inverse and bias
+        ws = self.w_times_vec(subject_activations.T)
+        vec = (ws + self.bias.unsqueeze(-1)).mean(dim=1)
+        if normalize:
+            vec = vec / vec.norm()
+        return vec

+

+
+
+
+

+[docs]
+class Lre(nn.Module):
+    """Linear Relational Embedding"""
+
+    relation: str
+    subject_layer: int
+    object_layer: int
+    weight: nn.Parameter
+    bias: nn.Parameter
+    object_aggregation: Literal["mean", "first_token"]
+    metadata: dict[str, Any] | None = None
+
+    def __init__(
+        self,
+        relation: str,
+        subject_layer: int,
+        object_layer: int,
+        object_aggregation: Literal["mean", "first_token"],
+        weight: torch.Tensor,
+        bias: torch.Tensor,
+        metadata: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__()
+        self.relation = relation
+        self.subject_layer = subject_layer
+        self.object_layer = object_layer
+        self.object_aggregation = object_aggregation
+        self.weight = nn.Parameter(weight, requires_grad=False)
+        self.bias = nn.Parameter(bias, requires_grad=False)
+        self.metadata = metadata
+
+

+[docs]
+    def invert(self, rank: int) -> InvertedLre:
+        """Invert this LRE using a low-rank approximation"""
+        u, s, v = self._low_rank_svd(rank)
+        return InvertedLre(
+            relation=self.relation,
+            subject_layer=self.subject_layer,
+            object_layer=self.object_layer,
+            object_aggregation=self.object_aggregation,
+            u=u.detach().clone(),
+            s=s.detach().clone(),
+            v=v.detach().clone(),
+            bias=self.bias.detach().clone(),
+            metadata=self.metadata,
+        )

+
+
+

+[docs]
+    def to_low_rank(self, rank: int) -> LowRankLre:
+        """Create a low-rank approximation of this LRE"""
+        u, s, v = self._low_rank_svd(rank)
+        return LowRankLre(
+            relation=self.relation,
+            subject_layer=self.subject_layer,
+            object_layer=self.object_layer,
+            object_aggregation=self.object_aggregation,
+            u=u.detach().clone(),
+            s=s.detach().clone(),
+            v=v.detach().clone(),
+            bias=self.bias.detach().clone(),
+            metadata=self.metadata,
+        )

+
+
+    @torch.no_grad()
+    def _low_rank_svd(
+        self, rank: int
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        # use a float for the svd, then convert back to the original dtype
+        u, s, v = torch.svd(self.weight.float())
+        low_rank_u: torch.Tensor = u[:, :rank].to(self.weight.dtype)
+        low_rank_v: torch.Tensor = v[:, :rank].to(self.weight.dtype)
+        low_rank_s: torch.Tensor = s[:rank].to(self.weight.dtype)
+        return low_rank_u, low_rank_s, low_rank_v

+
+
+
+
+
+ +
+ + +
+
+
+
+ Copyright © 2024, David Chanin +
+ Made with Sphinx and @pradyunsg's + + Furo + +
+
+ +
+
+ +
+
+ +
+
+ + + + + \ No newline at end of file diff --git a/_modules/linear_relational/Prompt.html b/_modules/linear_relational/Prompt.html new file mode 100644 index 0000000..d0e9282 --- /dev/null +++ b/_modules/linear_relational/Prompt.html @@ -0,0 +1,266 @@ + + + + + + + + linear_relational.Prompt - linear-relational 0.4.0 documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
+
+
+ +
+
+
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+
+ +
+ +
+
+ +
+
+
+ + + + + Back to top + +
+
+ +
+ +
+
+

Source code for linear_relational.Prompt

+from dataclasses import dataclass
+
+
+

+[docs]
+@dataclass(frozen=True, slots=True)
+class Prompt:
+    """A prompt for training LREs and LRCs"""
+
+    text: str
+    answer: str
+    subject: str
+    subject_name: str = ""  # If not provided, will be set to subject
+    object_name: str = ""  # If not provided, will be set to answer
+
+    def __post_init__(self) -> None:
+        if self.subject_name == "":
+            object.__setattr__(self, "subject_name", self.subject)
+        if self.object_name == "":
+            object.__setattr__(self, "object_name", self.answer)

+
+
+
+
+
+ +
+ + +
+
+
+
+ Copyright © 2024, David Chanin +
+ Made with Sphinx and @pradyunsg's + + Furo + +
+
+ +
+
+ +
+
+ +
+
+ + + + + \ No newline at end of file diff --git a/_modules/linear_relational/training/Trainer.html b/_modules/linear_relational/training/Trainer.html new file mode 100644 index 0000000..0d9dc30 --- /dev/null +++ b/_modules/linear_relational/training/Trainer.html @@ -0,0 +1,568 @@ + + + + + + + + linear_relational.training.Trainer - linear-relational 0.4.0 documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
+
+
+ +
+
+
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+ +
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+ + + + + Back to top + +
+
+ +
+ +
+
+

Source code for linear_relational.training.Trainer

+from collections import defaultdict
+from time import time
+from typing import Callable, Literal, Optional
+
+import torch
+from tokenizers import Tokenizer
+from torch import nn
+
+from linear_relational.Concept import Concept
+from linear_relational.lib.balance_grouped_items import balance_grouped_items
+from linear_relational.lib.extract_token_activations import extract_token_activations
+from linear_relational.lib.layer_matching import (
+    LayerMatcher,
+    get_layer_name,
+    guess_hidden_layer_matcher,
+)
+from linear_relational.lib.logger import log_or_print, logger
+from linear_relational.lib.token_utils import PromptAnswerData, find_prompt_answer_data
+from linear_relational.lib.torch_utils import get_device
+from linear_relational.lib.util import group_items
+from linear_relational.Lre import InvertedLre, Lre
+from linear_relational.Prompt import Prompt
+from linear_relational.PromptValidator import PromptValidator
+from linear_relational.training.train_lre import ObjectAggregation, train_lre
+
+VectorAggregation = Literal["pre_mean", "post_mean"]
+
+
+

+[docs]
+class Trainer:
+    """Train LREs and concepts from prompts"""
+
+    model: nn.Module
+    tokenizer: Tokenizer
+    layer_matcher: LayerMatcher
+    prompt_validator: PromptValidator
+
+    def __init__(
+        self,
+        model: nn.Module,
+        tokenizer: Tokenizer,
+        layer_matcher: Optional[LayerMatcher] = None,
+        prompt_validator: Optional[PromptValidator] = None,
+    ) -> None:
+        self.model = model
+        self.tokenizer = tokenizer
+        self.layer_matcher = layer_matcher or guess_hidden_layer_matcher(model)
+        self.prompt_validator = prompt_validator or PromptValidator(model, tokenizer)
+
+

+[docs]
+    def train_lre(
+        self,
+        relation: str,
+        subject_layer: int,
+        object_layer: int,
+        prompts: list[Prompt],
+        max_lre_training_samples: int | None = None,
+        object_aggregation: ObjectAggregation = "mean",
+        validate_prompts: bool = True,
+        validate_prompts_batch_size: int = 4,
+        move_to_cpu: bool = False,
+        verbose: bool = True,
+        seed: int | str | float = 42,
+    ) -> Lre:
+        processed_prompts = self._process_relation_prompts(
+            relation=relation,
+            prompts=prompts,
+            validate_prompts=validate_prompts,
+            validate_prompts_batch_size=validate_prompts_batch_size,
+            verbose=verbose,
+        )
+        prompts_by_object = group_items(processed_prompts, lambda p: p.object_name)
+        lre_train_prompts = balance_grouped_items(
+            items_by_group=prompts_by_object,
+            max_total=max_lre_training_samples,
+            seed=seed,
+        )
+        return train_lre(
+            model=self.model,
+            tokenizer=self.tokenizer,
+            layer_matcher=self.layer_matcher,
+            relation=relation,
+            subject_layer=subject_layer,
+            object_layer=object_layer,
+            prompts=lre_train_prompts,
+            object_aggregation=object_aggregation,
+            move_to_cpu=move_to_cpu,
+        )

+
+
+

+[docs]
+    def train_relation_concepts(
+        self,
+        relation: str,
+        subject_layer: int,
+        object_layer: int,
+        prompts: list[Prompt],
+        max_lre_training_samples: int | None = 20,
+        object_aggregation: ObjectAggregation = "mean",
+        vector_aggregation: VectorAggregation = "post_mean",
+        inv_lre_rank: int = 200,
+        validate_prompts_batch_size: int = 4,
+        validate_prompts: bool = True,
+        verbose: bool = True,
+        name_concept_fn: Optional[Callable[[str, str], str]] = None,
+        seed: int | str | float = 42,
+    ) -> list[Concept]:
+        processed_prompts = self._process_relation_prompts(
+            relation=relation,
+            prompts=prompts,
+            validate_prompts=validate_prompts,
+            validate_prompts_batch_size=validate_prompts_batch_size,
+            verbose=verbose,
+        )
+        prompts_by_object = group_items(processed_prompts, lambda p: p.object_name)
+        if len(prompts_by_object) == 1:
+            logger.warning(
+                f"Only one valid object found for {relation}. Results may be poor."
+            )
+        lre_train_prompts = balance_grouped_items(
+            items_by_group=prompts_by_object,
+            max_total=max_lre_training_samples,
+            seed=seed,
+        )
+        inv_lre = train_lre(
+            model=self.model,
+            tokenizer=self.tokenizer,
+            layer_matcher=self.layer_matcher,
+            relation=relation,
+            subject_layer=subject_layer,
+            object_layer=object_layer,
+            prompts=lre_train_prompts,
+            object_aggregation=object_aggregation,
+        ).invert(inv_lre_rank)
+
+        return self.train_relation_concepts_from_inv_lre(
+            relation=relation,
+            inv_lre=inv_lre,
+            prompts=processed_prompts,
+            vector_aggregation=vector_aggregation,
+            object_aggregation=object_aggregation,
+            object_layer=object_layer,
+            validate_prompts_batch_size=validate_prompts_batch_size,
+            validate_prompts=False,  # we already validated the prompts above
+            name_concept_fn=name_concept_fn,
+            verbose=verbose,
+        )

+
+
+

+[docs]
+    def train_relation_concepts_from_inv_lre(
+        self,
+        inv_lre: InvertedLre | Callable[[str], InvertedLre],
+        prompts: list[Prompt],
+        vector_aggregation: VectorAggregation = "post_mean",
+        object_aggregation: ObjectAggregation | None = None,
+        relation: str | None = None,
+        object_layer: int | None = None,
+        validate_prompts_batch_size: int = 4,
+        extract_objects_batch_size: int = 4,
+        validate_prompts: bool = True,
+        name_concept_fn: Optional[Callable[[str, str], str]] = None,
+        verbose: bool = True,
+    ) -> list[Concept]:
+        if isinstance(inv_lre, InvertedLre):
+            if object_aggregation is None:
+                object_aggregation = inv_lre.object_aggregation
+            if object_layer is None:
+                object_layer = inv_lre.object_layer
+            if relation is None:
+                relation = inv_lre.relation
+        if object_aggregation is None:
+            raise ValueError(
+                "object_aggregation must be specified if inv_lre is a function"
+            )
+        if object_layer is None:
+            raise ValueError("object_layer must be specified if inv_lre is a function")
+        if relation is None:
+            raise ValueError("relation must be specified if inv_lre is a function")
+        processed_prompts = self._process_relation_prompts(
+            relation=relation,
+            prompts=prompts,
+            validate_prompts=validate_prompts,
+            validate_prompts_batch_size=validate_prompts_batch_size,
+            verbose=verbose,
+        )
+        start_time = time()
+        object_activations = self._extract_target_object_activations_for_inv_lre(
+            prompts=processed_prompts,
+            batch_size=extract_objects_batch_size,
+            object_aggregation=object_aggregation,
+            object_layer=object_layer,
+            show_progress=verbose,
+            move_to_cpu=True,
+        )
+        logger.info(
+            f"Extracted {len(object_activations)} object activations in {time() - start_time:.2f}s"
+        )
+        concepts: list[Concept] = []
+
+        with torch.no_grad():
+            for (
+                object_name,
+                activations,
+            ) in object_activations.items():
+                resolved_inv_lre = (
+                    inv_lre
+                    if isinstance(inv_lre, InvertedLre)
+                    else inv_lre(object_name)
+                )
+                name = None
+                if name_concept_fn is not None:
+                    name = name_concept_fn(relation, object_name)
+                concept = self._build_concept(
+                    relation_name=relation,
+                    layer=resolved_inv_lre.subject_layer,
+                    inv_lre=resolved_inv_lre,
+                    object_name=object_name,
+                    activations=activations,
+                    vector_aggregation=vector_aggregation,
+                    name=name,
+                )
+                concepts.append(concept)
+        return concepts

+
+
+    def _process_relation_prompts(
+        self,
+        relation: str,
+        prompts: list[Prompt],
+        validate_prompts: bool,
+        validate_prompts_batch_size: int,
+        verbose: bool,
+    ) -> list[Prompt]:
+        valid_prompts = prompts
+        if validate_prompts:
+            log_or_print(f"validating {len(prompts)} prompts", verbose=verbose)
+            valid_prompts = self.prompt_validator.filter_prompts(
+                prompts, validate_prompts_batch_size, verbose
+            )
+        if len(valid_prompts) == 0:
+            raise ValueError(f"No valid prompts found for {relation}.")
+        return valid_prompts
+
+    def _build_concept(
+        self,
+        layer: int,
+        relation_name: str,
+        object_name: str,
+        activations: list[torch.Tensor],
+        inv_lre: InvertedLre,
+        vector_aggregation: VectorAggregation,
+        name: str | None,
+    ) -> Concept:
+        device = inv_lre.bias.device
+        dtype = inv_lre.bias.dtype
+        if vector_aggregation == "pre_mean":
+            acts = [torch.stack(activations).to(device=device, dtype=dtype).mean(dim=0)]
+        elif vector_aggregation == "post_mean":
+            acts = [act.to(device=device, dtype=dtype) for act in activations]
+        else:
+            raise ValueError(f"Unknown vector aggregation method {vector_aggregation}")
+        vecs = [
+            inv_lre.calculate_subject_activation(act, normalize=False) for act in acts
+        ]
+        vec = torch.stack(vecs).mean(dim=0)
+        vec = vec / vec.norm()
+        return Concept(
+            name=name,
+            object=object_name,
+            relation=relation_name,
+            layer=layer,
+            vector=vec.detach().clone().cpu(),
+        )
+
+    @torch.no_grad()
+    def _extract_target_object_activations_for_inv_lre(
+        self,
+        object_layer: int,
+        object_aggregation: Literal["mean", "first_token"],
+        prompts: list[Prompt],
+        batch_size: int,
+        show_progress: bool = False,
+        move_to_cpu: bool = True,
+    ) -> dict[str, list[torch.Tensor]]:
+        activations_by_object: dict[str, list[torch.Tensor]] = defaultdict(list)
+        prompt_answer_data: list[PromptAnswerData] = []
+        for prompt in prompts:
+            prompt_answer_data.append(
+                find_prompt_answer_data(self.tokenizer, prompt.text, prompt.answer)
+            )
+
+        layer_name = get_layer_name(self.model, self.layer_matcher, object_layer)
+        raw_activations = extract_token_activations(
+            self.model,
+            self.tokenizer,
+            layers=[layer_name],
+            texts=[prompt_answer.full_prompt for prompt_answer in prompt_answer_data],
+            token_indices=[
+                prompt_answer.output_answer_token_indices
+                for prompt_answer in prompt_answer_data
+            ],
+            device=get_device(self.model),
+            batch_size=batch_size,
+            show_progress=show_progress,
+            move_results_to_cpu=move_to_cpu,
+        )
+        for prompt, raw_activation in zip(prompts, raw_activations):
+            if object_aggregation == "mean":
+                activation = torch.stack(raw_activation[layer_name]).mean(dim=0)
+            elif object_aggregation == "first_token":
+                activation = raw_activation[layer_name][0]
+            else:
+                raise ValueError(
+                    f"Unknown inv_lre.object_aggregation: {object_aggregation}"
+                )
+            activations_by_object[prompt.object_name].append(activation)
+        return activations_by_object

+
+
+
+
+
+ +
+ + +
+
+
+
+ Copyright © 2024, David Chanin +
+ Made with Sphinx and @pradyunsg's + + Furo + +
+
+ +
+
+ +
+
+ +
+
+ + + + + \ No newline at end of file diff --git a/_sources/about.rst.txt b/_sources/about.rst.txt new file mode 100644 index 0000000..e12c881 --- /dev/null +++ b/_sources/about.rst.txt @@ -0,0 +1,34 @@ +About +===== + +Acknowledgements +---------------- + +This library is inspired by and uses modified code from the following excellent projects: + +* `Locating and Editing Factual Associations in GPT `_ +* `Linearity of Relation Decoding in Transformer LMs `_ + +Contributing +------------ + +Any contributions to improve this project are welcome! Please open an issue or pull request in the `Github repo `_ with bugfixes, changes, and improvements. + +License +------- + +Linear-Relational is released under a MIT license. + +Citation +-------- + +If you use this library in your work, please cite the following: + +.. code-block:: bibtex + + @article{chanin2023identifying, + title={Identifying Linear Relational Concepts in Large Language Models}, + author={David Chanin and Anthony Hunter and Oana-Maria Camburu}, + journal={arXiv preprint arXiv:2311.08968}, + year={2023} + } \ No newline at end of file diff --git a/_sources/advanced_usage.rst.txt b/_sources/advanced_usage.rst.txt new file mode 100644 index 0000000..d85d36f --- /dev/null +++ b/_sources/advanced_usage.rst.txt @@ -0,0 +1,193 @@ +Advanced usage +============== + +Bulk editing +'''''''''''' + +Edits can be performed in batches to make better use of GPU resources using ``editor.swap_subject_concepts_and_predict_greedy_bulk()`` as below: + +.. code:: python + + from linear_relational import CausalEditor, ConceptSwapAndPredictGreedyRequest + + concepts = trainer.train_relation_concepts(...) + + editor = CausalEditor(model, tokenizer, concepts=concepts) + + swap_requests = [ + ConceptSwapAndPredictGreedyRequest( + text="Shanghai is located in the country of", + subject="Shanghai", + remove_concept="located in country: China", + add_concept="located in country: France", + predict_num_tokens=1, + ), + ConceptSwapAndPredictGreedyRequest( + text="Berlin is located in the country of", + subject="Berlin", + remove_concept="located in country: Germany", + add_concept="located in country: Japan", + predict_num_tokens=1, + ), + ] + edited_answers = editor.swap_subject_concepts_and_predict_greedy_bulk( + requests=swap_requests, + edit_single_layer=False, + magnitude_multiplier=0.1, + batch_size=4, + ) + print(edited_answers) # [" France", " Japan"] + + +Bulk concept matching +''''''''''''''''''''' + +We can perform concept matches in batches to better utilize GPU resources using ``matcher.query_bulk()`` as below: + +.. code:: python + + from linear_relational import ConceptMatcher, ConceptMatchQuery + + concepts = trainer.train_relation_concepts(...) + + matcher = ConceptMatcher(model, tokenizer, concepts=concepts) + + match_queries = [ + ConceptMatchQuery("Beijng is a northern city", subject="Beijing"), + ConceptMatchQuery("I sawi him in Marseille", subject="Marseille"), + ] + matches = matcher.query_bulk(match_queries, batch_size=4) + + print(matches[0].best_match.concept) # located in country: China + print(matches[1].best_match.concept) # located in country: France + + +Customizing LRC training +'''''''''''''''''''''''' + +The base ``trainer.train_relation_concepts()`` function is a convenience wrapper which trains a LRE, +performs a low-rank inverse of the LRE, and uses the inverted LRE to generate concepts. If you want to customize +this process, you can generate a LRE using ``trainer.train_lre()``, followed by inverting the LRE with ``lre.invert()``, +and finally training concepts from the inverted LRE with ``trainer.train_relation_concepts_from_inv_lre()``. This process +is shown below: + +.. code:: python + + from linear_relational import Trainer + + trainer = Trainer(model, tokenizer) + prompts = [...] + + lre = trainer.train_lre(...) + inv_lre = lre.invert(rank=200) + + concepts = trainer.train_relation_concepts_from_inv_lre( + inv_lre=inv_lre, + prompts=prompts, + ) + + +It's also possible to pass a lambda function as the ``inv_lre`` param to allow using a different inverted LRE +for each object. This lambda takes the object as a string and returns the inverted LRE for that object. However, +if you use this approach, you must also pass in ``relation``, ``object_aggregation`` and ``object_layer``, as these +cannot be inferred from the inverted LRE when passed as a function. + +This is shown below: + +.. code:: python + + from linear_relational import Trainer + + trainer = Trainer(model, tokenizer) + prompts = [...] + + lre1 = trainer.train_lre(...) + inv_lre1 = lre.invert(rank=200) + + lre2 = trainer.train_lre(...) + inv_lre2 = lre.invert(rank=200) + + def inv_lre_fn(object_name): + return inv_lre1 if object_name == "Paris" else inv_lre2 + + concepts = trainer.train_relation_concepts_from_inv_lre( + inv_lre=inv_lre_fn, + prompts=prompts, + relation="located_in_country", + object_aggregation="mean", + object_layer=20, + ) + + +Custom objects in prompts +''''''''''''''''''''''''' + +By default, when you create a ``Prompt``, the answer to the prompt is assumed to be the object +corresponding to a LRC. For instance, in the prompt ``Prompt("Paris is located in", "France", subject="Paris")``, +the answer, "France", is assumed to be the object. However, if this is not the case, you can specify the object +explicitly using the ``object_name`` parameter as below: + +.. code:: python + + from linear_relational import Prompt + + prompt1 = Prompt( + text="PARIS IS LOCATED IN", + answer="FRANCE", + subject="PARIS", + object_name="france", + ) + prompt2 = Prompt( + text="Paris is located in", + answer="France", + subject="Paris", + object_name="france", + ) + + +Skipping prompt validation +'''''''''''''''''''''''''' + +By default, the ``Trainer`` will validate that for every prompt passed in, that the model answers the prompt correctly, +and will filter out any prompts where this is not the case. +If you want to skip this validation, you can pass ``validate_prompts=False`` to all methods on the trainer +like ``Trainer.train_relation_concepts(prompts, validate_prompts=False)``. + + +Multi-token object aggregation +'''''''''''''''''''''''''''''' + +If a prompt has an answer which is multiple tokens, by default the ``Trainer`` will use the mean activation of +the tokens in the answer when training a LRE. An example of a prompt with a multi-token answer is "The CEO of Microsoft is Bill Gates", +where the object, "Bill Gates", has two tokens. Alternatively, you can use just the first token of the object by +passing ``object_aggregation="first_token"`` when training a LRE. For instance, you can run the following: + +.. code:: python + + lre = trainer.train_lre( + prompts=prompts, + object_aggregation="first_token", + ) + +If the answer is a single token, "mean" and "first_token" are equivalent. + + +Custom layer selection +'''''''''''''''''''''' + +By default, the library will try to guess which layers corresponding to hidden activations in the model, +and will use these layers for reading activations and training LREs. If the layers the library guesses are not +correct, or if you want to use different layers to extract activations and train LREs, you can pass in a +custom ``layer_matcher`` to the ``Trainer``, ``CausalEditor``, and ``ConceptMatcher`` when creating these +objects. + +A ``layer_matcher`` is typically A string, and must include the substring ``"{num}"`` which will be replaced +with the layer number to select a layer in the model. For instance, for GPT models, the matcher for +hidden layers is ``"transformer.h.{num}"``. You can find a list of all layers in a model by calling +``model.named_modules()``. + +For most cases, using a string is sufficient, but if you want to customize the layer matcher further +you can pass in a function to ``layer_matcher`` which takes in the layer number as an int and +returns the layer in the model as a string. For instance, for GPT models, this could be provided as +``lambda num: f"transformer.h.{num}"``. + diff --git a/_sources/api/causal_editor.rst.txt b/_sources/api/causal_editor.rst.txt new file mode 100644 index 0000000..29b099d --- /dev/null +++ b/_sources/api/causal_editor.rst.txt @@ -0,0 +1,14 @@ +CausalEditor +============ + +.. autoclass:: linear_relational.CausalEditor + :members: + :undoc-members: + +.. autoclass:: linear_relational.ConceptSwapRequest + :members: + :undoc-members: + +.. autoclass:: linear_relational.ConceptSwapAndPredictGreedyRequest + :members: + :undoc-members: \ No newline at end of file diff --git a/_sources/api/concept.rst.txt b/_sources/api/concept.rst.txt new file mode 100644 index 0000000..b8b767e --- /dev/null +++ b/_sources/api/concept.rst.txt @@ -0,0 +1,6 @@ +Concept +======= + +.. autoclass:: linear_relational.Concept + :members: + :undoc-members: diff --git a/_sources/api/concept_matcher.rst.txt b/_sources/api/concept_matcher.rst.txt new file mode 100644 index 0000000..086f74e --- /dev/null +++ b/_sources/api/concept_matcher.rst.txt @@ -0,0 +1,19 @@ +ConceptMatcher +============== + +.. autoclass:: linear_relational.ConceptMatcher + :members: + :undoc-members: + + +.. autoclass:: linear_relational.ConceptMatchQuery + :members: + :undoc-members: + +.. autoclass:: linear_relational.QueryResult + :members: + :undoc-members: + +.. autoclass:: linear_relational.ConceptMatchResult + :members: + :undoc-members: diff --git a/_sources/api/lre.rst.txt b/_sources/api/lre.rst.txt new file mode 100644 index 0000000..68681b4 --- /dev/null +++ b/_sources/api/lre.rst.txt @@ -0,0 +1,14 @@ +Lre +=== + +.. autoclass:: linear_relational.Lre + :members: + :undoc-members: + +.. autoclass:: linear_relational.LowRankLre + :members: + :undoc-members: + +.. autoclass:: linear_relational.InvertedLre + :members: + :undoc-members: \ No newline at end of file diff --git a/_sources/api/trainer.rst.txt b/_sources/api/trainer.rst.txt new file mode 100644 index 0000000..4638b76 --- /dev/null +++ b/_sources/api/trainer.rst.txt @@ -0,0 +1,12 @@ +Trainer +======= + + +.. autoclass:: linear_relational.Trainer + :members: + :undoc-members: + + +.. autoclass:: linear_relational.Prompt + :members: + :undoc-members: diff --git a/_sources/basic_usage.rst.txt b/_sources/basic_usage.rst.txt new file mode 100644 index 0000000..35472f1 --- /dev/null +++ b/_sources/basic_usage.rst.txt @@ -0,0 +1,176 @@ +Basic usage +=========== +This library assumes you're using PyTorch with a decoder-only generative language +model (e.g., GPT, LLaMa, etc...), and a tokenizer from Huggingface. + +Training a LRE +'''''''''''''' + +To train a LRE for a relation, first collect prompts which elicit the relation. +We provide a ``Prompt`` class to represent this data, and a ``Trainer`` class to make training +a LRE easy. Below, we train a LRE to represent the "located in country" relation. + +.. code-block:: python + + from transformers import GPT2LMHeadModel, GPT2TokenizerFast + from linear_relational import Prompt, Trainer + + # We load a generative LM from huggingface. The LMHead must be included. + model = GPT2LMHeadModel.from_pretrained("gpt2") + tokenizer = GPT2TokenizerFast.from_pretrained("gpt2") + + # Prompts consist of text, an answer, and subject. + # The subject must appear in the text. The answer + # is what the model should respond with, and corresponds to the "object" + prompts = [ + Prompt("Paris is located in the country of", "France", subject="Paris"), + Prompt("Shanghai is located in the country of", "China", subject="Shanghai"), + Prompt("Kyoto is located in the country of", "Japan", subject="Kyoto"), + Prompt("San Jose is located in the country of", "Costa Rica", subject="San Jose"), + ] + + trainer = Trainer(model, tokenizer) + + lre = trainer.train_lre( + relation="located in country", + subject_layer=8, # subject layer must be before the object layer + object_layer=10, + prompts=prompts, + ) + +Working with a LRE +'''''''''''''''''' + +A LRE is a PyTorch module, so once a LRE is trained, we can use it to predict object activations from subject activations: + +.. code-block:: python + + object_acts_estimate = lre(subject_acts) + +We can also create a low-rank estimate of the LRE: + +.. code-block:: python + + low_rank_lre = lre.to_low_rank(50) + low_rank_obj_acts_estimate = low_rank_lre(subject_acts) + +Finally we can invert the LRE: + +.. code-block:: python + + inv_lre = lre.invert(rank=50) + subject_acts_estimate = inv_lre(object_acts) + +Training LRCs for a relation +'''''''''''''''''''''''''''' + +The ``Trainer`` can also create LRCs for a relation. Internally, this first create a LRE, inverts it, +then generates LRCs from each object in the relation. Objects refer to the answers in the prompts, +e.g. in the example above, "France" is an object, "Japan" is an object, etc... + +.. code-block:: python + + from transformers import GPT2LMHeadModel, GPT2TokenizerFast + from linear_relational import Prompt, Trainer + + # We load a generative LM from huggingface. The LMHead must be included. + model = GPT2LMHeadModel.from_pretrained("gpt2") + tokenizer = GPT2TokenizerFast.from_pretrained("gpt2") + + # Prompts consist of text, an answer, and subject. + # The subject must appear in the text. The answer + # is what the model should respond with, and corresponds to the "object" + prompts = [ + Prompt("Paris is located in the country of", "France", subject="Paris"), + Prompt("Shanghai is located in the country of", "China", subject="Shanghai"), + Prompt("Kyoto is located in the country of", "Japan", subject="Kyoto"), + Prompt("San Jose is located in the country of", "Costa Rica", subject="San Jose"), + ] + + trainer = Trainer(model, tokenizer) + + concepts = trainer.train_relation_concepts( + relation="located in country", + subject_layer=8, + object_layer=10, + prompts=prompts, + max_lre_training_samples=10, + inv_lre_rank=50, + ) + +Causal editing +'''''''''''''' + +Once we have LRCs trained, we can use them to perform causal edits while the model is running. +For instance, we can perform a causal edit to make the model output that +"Shanghai is located in the country of France" by subtracting the +"located in country: China" concept from "Shanghai" and adding the +"located in country: France" concept. We can use the ``CausalEditor`` class to perform these edits. + +.. code-block:: python + + from linear_relational import CausalEditor + + concepts = trainer.train_relation_concepts(...) + + editor = CausalEditor(model, tokenizer, concepts=concepts) + + edited_answer = editor.swap_subject_concepts_and_predict_greedy( + text="Shanghai is located in the country of", + subject="Shanghai", + remove_concept="located in country: China", + add_concept="located in country: France", + edit_single_layer=8, + magnitude_multiplier=1.0, + predict_num_tokens=1, + ) + print(edited_answer) # " France" + + +Single-layer vs multi-layer edits +''''''''''''''''''''''''''''''''' + +Above we performed a single-layer edit, only modifying subject activations at layer 8. +However, we may want to perform an edit at all subject layers at the same time instead. +To do this, we can pass ``edit_single_layer=False`` to ``editor.swap_subject_concepts_and_predict_greedy()``. +We should also reduce the ``magnitude_multiplier`` since now we're going to make the edit at every layer, if we use +too large of a multiplier we'll drown out the rest of the activations in the model. The ``magnitude_multiplier`` is a +hyperparameter that requires tuning depending on the model being edited. + +.. code:: python + + from linear_relational import CausalEditor + + concepts = trainer.train_relation_concepts(...) + + editor = CausalEditor(model, tokenizer, concepts=concepts) + + edited_answer = editor.swap_subject_concepts_and_predict_greedy( + text="Shanghai is located in the country of", + subject="Shanghai", + remove_concept="located in country: China", + add_concept="located in country: France", + edit_single_layer=False, + magnitude_multiplier=0.1, + predict_num_tokens=1, + ) + print(edited_answer) # " France" + +Concept matching +'''''''''''''''' + +We can use learned concepts (LRCs) to act like classifiers and match them against subject activations in sentences. +We can use the ``ConceptMatcher`` class to do this matching. + +.. code:: python + + from linear_relational import ConceptMatcher + + concepts = trainer.train_relation_concepts(...) + + matcher = ConceptMatcher(model, tokenizer, concepts=concepts) + + match_info = matcher.query("Beijing is a northern city", subject="Beijing") + + print(match_info.best_match.concept) # located in country: China + print(match_info.best_match.score) # 0.832 diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 0000000..996939f --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,76 @@ +Linear-Relational +================= + +A library for working with Linear Relational Embeddings (LREs) and Linear Relational Concepts (LRCs) for LLMs in PyTorch + +.. image:: https://img.shields.io/pypi/v/linear-relational.svg?color=blue + :target: https://pypi.org/project/linear-relational + :alt: PyPI + +.. image:: https://img.shields.io/github/actions/workflow/status/chanind/linear-relational/ci.yaml?branch=main + :target: https://github.com/chanind/linear-relational + :alt: Build Status + + +Installation +------------ +Linear-Relational releases are hosted on `PyPI`_, and can be installed using `pip` as below: + +.. code-block:: bash + + pip install linear-relational + +This library assumes you're working with PyTorch and Huggingface Transformers. + +LREs and LRCs +------------- + +This library provides utilities and PyTorch modules for working with LREs and LRCs. LREs estimate the relation between a subject and object in a transformer language model (LM) as a linear map. + +This library assumes you're working with sentences with a subject, relation, and object. For instance, in the sentence: "Lyon is located in the country of France" would have the subject "Lyon", relation "located in country", and object "France". A LRE models a relation like "located in country" as a linear map consisting of a weight matrix :math:`W` and a bias term :math:`b`, so a LRE would map from the activations of the subject (Lyon) at layer :math:`l_s` to the activations of the object (France) at layer :math:`l_o`. So: + +.. math:: + LRE(s) = W s + b + +LREs can be inverted using a low-rank inverse, shown as :math:`LRE^{\dagger}`, to estimate :math:`s` from :math:`o`: + +.. math:: + LRE^{\dagger}(o) = W^{\dagger}(o - b) + +Linear Relational Concepts (LRCs) represent a concept :math:`(r, o)` as a direction vector $v$ on subject tokens, and can act like a simple linear classifier. For instance, while a LRE can represent the relation "located in country", we could learn a LRC for "located in the country: France", "located in country: Germany", "located in country: China", etc... This is just the result from passing in an object activation into the inverse LRE equation above. + +.. math:: + LRC(o) = W^{\dagger}(o - b) + +For more information on LREs and LRCs, check out `these `_ `papers `_. + +------------ + +Contents +-------- + +.. toctree:: + :maxdepth: 2 + + Home + basic_usage + advanced_usage + about + +.. toctree:: + :maxdepth: 2 + :caption: API Reference: + + api/trainer + api/lre + api/concept + api/causal_editor + api/concept_matcher + +.. toctree:: + :caption: Project Links + + GitHub + PyPI + +.. _PyPI: https://pypi.org/project/linear-relational/ diff --git a/_static/basic.css b/_static/basic.css new file mode 100644 index 0000000..f316efc --- /dev/null +++ b/_static/basic.css @@ -0,0 +1,925 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; 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+} +.sb-footer-content { + background: plum; +} +.sb-footer-content__inner { + background: palevioletred; +} +.sb-footer { + background: pink; +} +.sb-footer__inner { + background: salmon; +} +.sb-article { + background: white; +} diff --git a/_static/doctools.js b/_static/doctools.js new file mode 100644 index 0000000..4d67807 --- /dev/null +++ b/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/_static/documentation_options.js b/_static/documentation_options.js new file mode 100644 index 0000000..025330c --- /dev/null +++ b/_static/documentation_options.js @@ -0,0 +1,13 @@ +const DOCUMENTATION_OPTIONS = { + VERSION: '0.4.0', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/_static/file.png b/_static/file.png new file mode 100644 index 0000000000000000000000000000000000000000..a858a410e4faa62ce324d814e4b816fff83a6fb3 GIT binary patch literal 286 zcmV+(0pb3MP)s`hMrGg#P~ix$^RISR_I47Y|r1 z_CyJOe}D1){SET-^Amu_i71Lt6eYfZjRyw@I6OQAIXXHDfiX^GbOlHe=Ae4>0m)d(f|Me07*qoM6N<$f}vM^LjV8( literal 0 HcmV?d00001 diff --git a/_static/language_data.js b/_static/language_data.js new file mode 100644 index 0000000..367b8ed --- /dev/null +++ b/_static/language_data.js @@ -0,0 +1,199 @@ +/* + * language_data.js + * ~~~~~~~~~~~~~~~~ + * + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer and splitter. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, if available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); 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false,\n nestedClass: \"active\",\n\n // Offset & reflow\n offset: 0,\n reflow: false,\n\n // Event support\n events: true,\n };\n\n //\n // Methods\n //\n\n /**\n * Merge two or more objects together.\n * @param {Object} objects The objects to merge together\n * @returns {Object} Merged values of defaults and options\n */\n var extend = function () {\n var merged = {};\n Array.prototype.forEach.call(arguments, function (obj) {\n for (var key in obj) {\n if (!obj.hasOwnProperty(key)) return;\n merged[key] = obj[key];\n }\n });\n return merged;\n };\n\n /**\n * Emit a custom event\n * @param {String} type The event type\n * @param {Node} elem The element to attach the event to\n * @param {Object} detail Any details to pass along with the event\n */\n var emitEvent = function (type, elem, detail) {\n // Make sure events are enabled\n if (!detail.settings.events) return;\n\n // Create a new event\n var event = new CustomEvent(type, {\n bubbles: true,\n cancelable: true,\n detail: detail,\n });\n\n // Dispatch the event\n elem.dispatchEvent(event);\n };\n\n /**\n * Get an element's distance from the top of the Document.\n * @param {Node} elem The element\n * @return {Number} Distance from the top in pixels\n */\n var getOffsetTop = function (elem) {\n var location = 0;\n if (elem.offsetParent) {\n while (elem) {\n location += elem.offsetTop;\n elem = elem.offsetParent;\n }\n }\n return location >= 0 ? location : 0;\n };\n\n /**\n * Sort content from first to last in the DOM\n * @param {Array} contents The content areas\n */\n var sortContents = function (contents) {\n if (contents) {\n contents.sort(function (item1, item2) {\n var offset1 = getOffsetTop(item1.content);\n var offset2 = getOffsetTop(item2.content);\n if (offset1 < offset2) return -1;\n return 1;\n });\n }\n };\n\n /**\n * Get the offset to use for calculating position\n * @param {Object} settings The settings for this instantiation\n * @return {Float} The number of pixels to offset the calculations\n */\n var getOffset = function (settings) {\n // if the offset is a function run it\n if (typeof settings.offset === \"function\") {\n return parseFloat(settings.offset());\n }\n\n // Otherwise, return it as-is\n return parseFloat(settings.offset);\n };\n\n /**\n * Get the document element's height\n * @private\n * @returns {Number}\n */\n var getDocumentHeight = function () {\n return Math.max(\n document.body.scrollHeight,\n document.documentElement.scrollHeight,\n document.body.offsetHeight,\n document.documentElement.offsetHeight,\n document.body.clientHeight,\n document.documentElement.clientHeight,\n );\n };\n\n /**\n * Determine if an element is in view\n * @param {Node} elem The element\n * @param {Object} settings The settings for this instantiation\n * @param {Boolean} bottom If true, check if element is above bottom of viewport instead\n * @return {Boolean} Returns true if element is in the viewport\n */\n var isInView = function (elem, settings, bottom) {\n 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content areas\n * @param {Object} settings The settings for this instantiation\n * @return {Object} The content area and matching navigation link\n */\n var getActive = function (contents, settings) {\n var last = contents[contents.length - 1];\n if (useLastItem(last, settings)) return last;\n for (var i = contents.length - 1; i >= 0; i--) {\n if (isInView(contents[i].content, settings)) return contents[i];\n }\n };\n\n /**\n * Deactivate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var deactivateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested || !nav.parentNode) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Remove the active class\n li.classList.remove(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n deactivateNested(li, settings);\n };\n\n /**\n * Deactivate a nav and content area\n * @param {Object} items The nav item and content to deactivate\n * @param {Object} settings The settings for this instantiation\n */\n var deactivate = function (items, settings) {\n // Make sure there are items to deactivate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Remove the active class from the nav and content\n li.classList.remove(settings.navClass);\n items.content.classList.remove(settings.contentClass);\n\n // Deactivate any parent navs in a nested navigation\n deactivateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeDeactivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Activate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var activateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Add the active class\n li.classList.add(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n activateNested(li, settings);\n };\n\n /**\n * Activate a nav and content area\n * @param {Object} items The nav item and content to activate\n * @param {Object} settings The settings for this instantiation\n */\n var activate = function (items, settings) {\n // Make sure there are items to activate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Add the active class to the nav and content\n li.classList.add(settings.navClass);\n items.content.classList.add(settings.contentClass);\n\n // Activate any parent navs in a nested navigation\n activateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeActivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Create the Constructor object\n * @param {String} selector The selector to use for navigation items\n * @param {Object} options User options and settings\n */\n var Constructor = function (selector, options) {\n //\n // Variables\n //\n\n var publicAPIs = {};\n var navItems, contents, current, timeout, settings;\n\n //\n // Methods\n //\n\n /**\n * Set variables from DOM elements\n */\n publicAPIs.setup = function () {\n // Get all nav items\n navItems = document.querySelectorAll(selector);\n\n // Create contents array\n contents = [];\n\n // Loop through each item, get it's matching content, and push to the array\n Array.prototype.forEach.call(navItems, function (item) {\n // Get the content for the nav item\n var content = document.getElementById(\n decodeURIComponent(item.hash.substr(1)),\n );\n if (!content) return;\n\n // Push to the contents array\n 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\ No newline at end of file diff --git a/_static/searchtools.js b/_static/searchtools.js new file mode 100644 index 0000000..92da3f8 --- /dev/null +++ b/_static/searchtools.js @@ -0,0 +1,619 @@ +/* + * searchtools.js + * ~~~~~~~~~~~~~~~~ + * + * Sphinx JavaScript utilities for the full-text search. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + "Search finished, found ${resultCount} page(s) matching the search query." + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlinks", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/skeleton.css b/_static/skeleton.css new file mode 100644 index 0000000..467c878 --- /dev/null +++ b/_static/skeleton.css @@ -0,0 +1,296 @@ +/* Some sane resets. */ +html { + height: 100%; +} + +body { + margin: 0; + min-height: 100%; +} + +/* All the flexbox magic! */ +body, +.sb-announcement, +.sb-content, +.sb-main, +.sb-container, +.sb-container__inner, +.sb-article-container, +.sb-footer-content, +.sb-header, +.sb-header-secondary, +.sb-footer { + display: flex; +} + +/* These order things vertically */ +body, +.sb-main, +.sb-article-container { + flex-direction: column; +} + +/* Put elements in the center */ +.sb-header, +.sb-header-secondary, +.sb-container, +.sb-content, +.sb-footer, +.sb-footer-content { + justify-content: center; +} +/* Put elements at the ends */ +.sb-article-container { + justify-content: space-between; +} + +/* These elements grow. */ +.sb-main, +.sb-content, +.sb-container, +article { + flex-grow: 1; +} + +/* Because padding making this wider is not fun */ +article { + box-sizing: border-box; +} + +/* The announcements element should never be wider than the page. */ +.sb-announcement { + max-width: 100%; +} + +.sb-sidebar-primary, +.sb-sidebar-secondary { + flex-shrink: 0; + width: 17rem; +} + +.sb-announcement__inner { + justify-content: center; + + box-sizing: border-box; + height: 3rem; + + overflow-x: auto; + white-space: nowrap; +} + +/* Sidebars, with checkbox-based toggle */ +.sb-sidebar-primary, +.sb-sidebar-secondary { + position: fixed; + height: 100%; + top: 0; +} + +.sb-sidebar-primary { + left: -17rem; + transition: left 250ms ease-in-out; +} +.sb-sidebar-secondary { + right: -17rem; + transition: right 250ms ease-in-out; +} + +.sb-sidebar-toggle { + display: none; +} +.sb-sidebar-overlay { + position: fixed; + top: 0; + width: 0; + height: 0; + + transition: width 0ms ease 250ms, height 0ms ease 250ms, opacity 250ms ease; + + opacity: 0; + background-color: rgba(0, 0, 0, 0.54); +} + +#sb-sidebar-toggle--primary:checked + ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--primary"], +#sb-sidebar-toggle--secondary:checked + ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--secondary"] { + width: 100%; + height: 100%; + opacity: 1; + transition: width 0ms ease, height 0ms ease, opacity 250ms ease; +} + +#sb-sidebar-toggle--primary:checked ~ .sb-container .sb-sidebar-primary { + left: 0; +} +#sb-sidebar-toggle--secondary:checked ~ .sb-container .sb-sidebar-secondary { + right: 0; +} + +/* Full-width mode */ +.drop-secondary-sidebar-for-full-width-content + .hide-when-secondary-sidebar-shown { + display: none !important; +} +.drop-secondary-sidebar-for-full-width-content .sb-sidebar-secondary { + display: none !important; +} + +/* Mobile views */ +.sb-page-width { + width: 100%; +} + +.sb-article-container, +.sb-footer-content__inner, +.drop-secondary-sidebar-for-full-width-content .sb-article, +.drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 100vw; +} + +.sb-article, +.match-content-width { + padding: 0 1rem; + box-sizing: border-box; +} + +@media (min-width: 32rem) { + .sb-article, + .match-content-width { + padding: 0 2rem; + } +} + +/* Tablet views */ +@media (min-width: 42rem) { + .sb-article-container { + width: auto; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 42rem; + } + .sb-article, + .match-content-width { + width: 42rem; + } +} +@media (min-width: 46rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 46rem; + } + .sb-article, + .match-content-width { + width: 46rem; + } +} +@media (min-width: 50rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 50rem; + } + .sb-article, + .match-content-width { + width: 50rem; + } +} + +/* Tablet views */ +@media (min-width: 59rem) { + .sb-sidebar-secondary { + position: static; + } + .hide-when-secondary-sidebar-shown { + display: none !important; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 59rem; + } + .sb-article, + .match-content-width { + width: 42rem; + } +} +@media (min-width: 63rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 63rem; + } + .sb-article, + .match-content-width { + width: 46rem; + } +} +@media (min-width: 67rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 67rem; + } + .sb-article, + .match-content-width { + width: 50rem; + } +} + +/* Desktop views */ +@media (min-width: 76rem) { + .sb-sidebar-primary { + position: static; + } + .hide-when-primary-sidebar-shown { + display: none !important; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 59rem; + } + .sb-article, + .match-content-width { + width: 42rem; + } +} + +/* Full desktop views */ +@media (min-width: 80rem) { + .sb-article, + .match-content-width { + width: 46rem; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 63rem; + } +} + +@media (min-width: 84rem) { + .sb-article, + .match-content-width { + width: 50rem; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 67rem; + } +} + +@media (min-width: 88rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 67rem; + } + .sb-page-width { + width: 88rem; + } +} diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 0000000..8a96c69 --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/_static/styles/furo-extensions.css b/_static/styles/furo-extensions.css new file mode 100644 index 0000000..bc447f2 --- /dev/null +++ b/_static/styles/furo-extensions.css @@ -0,0 +1,2 @@ +#furo-sidebar-ad-placement{padding:var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)}#furo-sidebar-ad-placement .ethical-sidebar{background:var(--color-background-secondary);border:none;box-shadow:none}#furo-sidebar-ad-placement .ethical-sidebar:hover{background:var(--color-background-hover)}#furo-sidebar-ad-placement .ethical-sidebar a{color:var(--color-foreground-primary)}#furo-sidebar-ad-placement .ethical-callout 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Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Hide icon container.\n .content-icon-container\n display: none !important\n\n // Hide showing header links if hovering over when printing.\n .headerlink\n display: none !important\n\n // Hide mobile header.\n .mobile-header\n display: none !important\n\n // Hide navigation links.\n .related-pages\n display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Apply a border around code which no longer have a color background.\n .highlight\n border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n ul, ol, dl, a, table, pre, blockquote\n page-break-inside: avoid\n\n h1, h2, h3, h4, h5, h6, img, figure, caption\n page-break-inside: avoid\n page-break-after: avoid\n\n ul, ol, dl\n page-break-before: avoid\n",".visually-hidden\n position: absolute !important\n width: 1px !important\n height: 1px !important\n padding: 0 !important\n margin: -1px !important\n overflow: hidden !important\n clip: rect(0,0,0,0) !important\n white-space: nowrap !important\n border: 0 !important\n\n:-moz-focusring\n outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n @include fonts\n @include spacing\n @include icons\n @include admonitions\n @include default-admonition(#651fff, \"abstract\")\n @include default-topic(#14B8A6, \"pencil\")\n\n @include colors\n\n.only-light\n display: block !important\nhtml body .only-dark\n display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n // Enable dark-mode, if requested.\n body[data-theme=\"dark\"]\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n // Enable dark mode, unless explicitly told to avoid.\n @media (prefers-color-scheme: dark)\n body:not([data-theme=\"light\"])\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n .theme-toggle svg.theme-icon-when-auto\n display: block\n\nbody[data-theme=\"dark\"]\n .theme-toggle svg.theme-icon-when-dark\n display: block\n\nbody[data-theme=\"light\"]\n .theme-toggle svg.theme-icon-when-light\n display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n // These are adapted from https://systemfontstack.com/\n --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n sans-serif, Apple Color Emoji, Segoe UI Emoji;\n --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n Liberation Mono, Lucida Console, monospace;\n\n --font-size--normal: 100%;\n --font-size--small: 87.5%;\n --font-size--small--2: 81.25%;\n --font-size--small--3: 75%;\n --font-size--small--4: 62.5%;\n\n // Sidebar\n --sidebar-caption-font-size: var(--font-size--small--2);\n --sidebar-item-font-size: var(--font-size--small);\n --sidebar-search-input-font-size: var(--font-size--small);\n\n // Table of Contents\n --toc-font-size: var(--font-size--small--3);\n --toc-font-size--mobile: var(--font-size--normal);\n --toc-title-font-size: var(--font-size--small--4);\n\n // Admonitions\n //\n // These aren't defined in terms of %ages, since nesting these is permitted.\n --admonition-font-size: 0.8125rem;\n --admonition-title-font-size: 0.8125rem;\n\n // Code\n --code-font-size: var(--font-size--small--2);\n\n // API\n --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n // Header!\n --header-height: calc(\n var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n );\n --header-padding: 0.5rem;\n\n // Sidebar\n --sidebar-tree-space-above: 1.5rem;\n --sidebar-caption-space-above: 1rem;\n\n --sidebar-item-line-height: 1rem;\n --sidebar-item-spacing-vertical: 0.5rem;\n --sidebar-item-spacing-horizontal: 1rem;\n --sidebar-item-height: calc(\n var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n );\n\n --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n --sidebar-search-space-above: 0.5rem;\n --sidebar-search-input-spacing-vertical: 0.5rem;\n --sidebar-search-input-spacing-horizontal: 0.5rem;\n --sidebar-search-input-height: 1rem;\n --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n // Table of Contents\n --toc-title-padding: 0.25rem 0;\n --toc-spacing-vertical: 1.5rem;\n --toc-spacing-horizontal: 1.5rem;\n --toc-item-spacing-vertical: 0.4rem;\n --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n // Adapted from tabler-icons\n // url: https://tablericons.com/\n \"search\":\n url('data:image/svg+xml;charset=utf-8,'),\n // Factored out from mkdocs-material on 24-Aug-2020.\n // url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n \"pencil\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"abstract\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"info\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"flame\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"question\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"warning\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"failure\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"spark\":\n url('data:image/svg+xml;charset=utf-8,')\n);\n\n@mixin icons {\n @each $name, $glyph in $icons {\n --icon-#{$name}: #{$glyph};\n }\n}\n","// Admonitions\n\n// Structure of these is:\n// admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n // Each of these has an reST directives for it.\n \"caution\": #ff9100 \"spark\",\n \"warning\": #ff9100 \"warning\",\n \"danger\": #ff5252 \"spark\",\n \"attention\": #ff5252 \"warning\",\n \"error\": #ff5252 \"failure\",\n \"hint\": #00c852 \"question\",\n \"tip\": #00c852 \"info\",\n \"important\": #00bfa5 \"flame\",\n \"note\": #00b0ff \"pencil\",\n \"seealso\": #448aff \"info\",\n \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n --color-admonition-title: #{$color};\n --color-admonition-title-background: #{rgba($color, 0.2)};\n\n --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n --color-topic-title: #{$color};\n --color-topic-title-background: #{rgba($color, 0.2)};\n\n --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n @each $name, $values in $admonitions {\n --color-admonition-title--#{$name}: #{nth($values, 1)};\n --color-admonition-title-background--#{$name}: #{rgba(\n nth($values, 1),\n 0.2\n )};\n }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n --color-problematic: #b30000;\n\n // Base Colors\n --color-foreground-primary: black; // for main text and headings\n --color-foreground-secondary: #5a5c63; // for secondary text\n --color-foreground-muted: #646776; // for muted text\n --color-foreground-border: #878787; // for content borders\n\n --color-background-primary: white; // for content\n --color-background-secondary: #f8f9fb; // for navigation + ToC\n --color-background-hover: #efeff4ff; // for navigation-item hover\n --color-background-hover--transparent: #efeff400;\n --color-background-border: #eeebee; // for UI borders\n --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #2962ff;\n --color-brand-content: #2a5adf;\n\n // API documentation\n --color-api-background: var(--color-background-hover--transparent);\n --color-api-background-hover: var(--color-background-hover);\n --color-api-overall: var(--color-foreground-secondary);\n --color-api-name: var(--color-problematic);\n --color-api-pre-name: var(--color-problematic);\n --color-api-paren: var(--color-foreground-secondary);\n --color-api-keyword: var(--color-foreground-primary);\n --color-highlight-on-target: #ffffcc;\n\n // Inline code background\n --color-inline-code-background: var(--color-background-secondary);\n\n // Highlighted text (search)\n --color-highlighted-background: #ddeeff;\n --color-highlighted-text: var(--color-foreground-primary);\n\n // GUI Labels\n --color-guilabel-background: #ddeeff80;\n --color-guilabel-border: #bedaf580;\n --color-guilabel-text: var(--color-foreground-primary);\n\n // Admonitions!\n --color-admonition-background: transparent;\n\n //////////////////////////////////////////////////////////////////////////////\n // Everything below this should be one of:\n // - var(...)\n // - *-gradient(...)\n // - special literal values (eg: transparent, none)\n //////////////////////////////////////////////////////////////////////////////\n\n // Tables\n --color-table-header-background: var(--color-background-secondary);\n --color-table-border: var(--color-background-border);\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: transparent;\n --color-card-marginals-background: var(--color-background-secondary);\n\n // Header\n --color-header-background: var(--color-background-primary);\n --color-header-border: var(--color-background-border);\n --color-header-text: var(--color-foreground-primary);\n\n // Sidebar (left)\n --color-sidebar-background: var(--color-background-secondary);\n --color-sidebar-background-border: var(--color-background-border);\n\n --color-sidebar-brand-text: var(--color-foreground-primary);\n --color-sidebar-caption-text: var(--color-foreground-muted);\n --color-sidebar-link-text: var(--color-foreground-secondary);\n --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n --color-sidebar-item-background: var(--color-sidebar-background);\n --color-sidebar-item-background--current: var(\n --color-sidebar-item-background\n );\n --color-sidebar-item-background--hover: linear-gradient(\n 90deg,\n var(--color-background-hover--transparent) 0%,\n var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n var(--color-background-hover) 100%\n );\n\n --color-sidebar-item-expander-background: transparent;\n --color-sidebar-item-expander-background--hover: var(\n --color-background-hover\n );\n\n --color-sidebar-search-text: var(--color-foreground-primary);\n --color-sidebar-search-background: var(--color-background-secondary);\n --color-sidebar-search-background--focus: var(--color-background-primary);\n --color-sidebar-search-border: var(--color-background-border);\n --color-sidebar-search-icon: var(--color-foreground-muted);\n\n // Table of Contents (right)\n --color-toc-background: var(--color-background-primary);\n --color-toc-title-text: var(--color-foreground-muted);\n --color-toc-item-text: var(--color-foreground-secondary);\n --color-toc-item-text--hover: var(--color-foreground-primary);\n --color-toc-item-text--active: var(--color-brand-primary);\n\n // Actual page contents\n --color-content-foreground: var(--color-foreground-primary);\n --color-content-background: transparent;\n\n // Links\n --color-link: var(--color-brand-content);\n --color-link--hover: var(--color-brand-content);\n --color-link-underline: var(--color-background-border);\n --color-link-underline--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n --color-problematic: #ee5151;\n\n // Base Colors\n --color-foreground-primary: #ffffffcc; // for main text and headings\n --color-foreground-secondary: #9ca0a5; // for secondary text\n --color-foreground-muted: #81868d; // for muted text\n --color-foreground-border: #666666; // for content borders\n\n --color-background-primary: #131416; // for content\n --color-background-secondary: #1a1c1e; // for navigation + ToC\n --color-background-hover: #1e2124ff; // for navigation-item hover\n --color-background-hover--transparent: #1e212400;\n --color-background-border: #303335; // for UI borders\n --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #2b8cee;\n --color-brand-content: #368ce2;\n\n // Highlighted text (search)\n --color-highlighted-background: #083563;\n\n // GUI Labels\n --color-guilabel-background: #08356380;\n --color-guilabel-border: #13395f80;\n\n // API documentation\n --color-api-keyword: var(--color-foreground-secondary);\n --color-highlight-on-target: #333300;\n\n // Admonitions\n --color-admonition-background: #18181a;\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: #18181a;\n --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n -webkit-font-smoothing: antialiased\n -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n line-height: 1.25\n font-weight: bold\n\n border-radius: 0.5rem\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n margin-left: -0.5rem\n margin-right: -0.5rem\n padding-left: 0.5rem\n padding-right: 0.5rem\n\n + p\n margin-top: 0\n\nh1\n font-size: 2.5em\n margin-top: 1.75rem\n margin-bottom: 1rem\nh2\n font-size: 2em\n margin-top: 1.75rem\nh3\n font-size: 1.5em\nh4\n font-size: 1.25em\nh5\n font-size: 1.125em\nh6\n font-size: 1em\n\nsmall\n opacity: 75%\n font-size: 80%\n\n// Paragraph\np\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n height: 1px\n padding: 0\n margin: 2rem 0\n background-color: var(--color-background-border)\n border: 0\n\n.centered\n text-align: center\n\n// Links\na\n text-decoration: underline\n\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &.muted-link\n color: inherit\n &:hover\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\nhtml\n overflow-x: hidden\n overflow-y: scroll\n scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n // Override Firefox scrollbar style\n scrollbar-width: thin\n scrollbar-color: var(--color-foreground-border) transparent\n\n // Override Chrome scrollbar styles\n &::-webkit-scrollbar\n width: 0.25rem\n height: 0.25rem\n &::-webkit-scrollbar-thumb\n background-color: var(--color-foreground-border)\n border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n height: 100%\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\narticle\n color: var(--color-content-foreground)\n background: var(--color-content-background)\n overflow-wrap: break-word\n\n.page\n display: flex\n // fill the viewport for pages with little content.\n min-height: 100%\n\n.mobile-header\n width: 100%\n height: var(--header-height)\n background-color: var(--color-header-background)\n color: var(--color-header-text)\n border-bottom: 1px solid var(--color-header-border)\n\n // Looks like sub-script/super-script have this, and we need this to\n // be \"on top\" of those.\n z-index: 10\n\n // We don't show the header on large screens.\n display: none\n\n // Add shadow when scrolled\n &.scrolled\n border-bottom: none\n box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n .header-center\n a\n color: var(--color-header-text)\n text-decoration: none\n\n.main\n display: flex\n flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n box-sizing: border-box\n\n border-right: 1px solid var(--color-sidebar-background-border)\n background: var(--color-sidebar-background)\n\n display: flex\n justify-content: flex-end\n // These next two lines took me two days to figure out.\n width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n box-sizing: border-box\n width: $sidebar-width\n\n.toc-drawer\n background: var(--color-toc-background)\n // See HACK described on top of this document\n padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n position: sticky\n top: 0\n height: min(100%, 100vh)\n height: 100vh\n\n display: flex\n flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n flex-grow: 1\n flex-shrink: 1\n\n overflow: auto\n scroll-behavior: smooth\n\n// Central items.\n.content\n padding: 0 $content-padding\n width: $content-width\n\n display: flex\n flex-direction: column\n justify-content: space-between\n\n.icon\n display: inline-block\n height: 1rem\n width: 1rem\n svg\n width: 100%\n height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n background-color: var(--color-announcement-background)\n color: var(--color-announcement-text)\n\n height: var(--header-height)\n display: flex\n align-items: center\n overflow-x: auto\n & + .page\n min-height: calc(100% - var(--header-height))\n\n.announcement-content\n box-sizing: border-box\n padding: 0.5rem\n min-width: 100%\n white-space: nowrap\n text-align: center\n\n a\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-announcement-text)\n\n &:hover\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container // don't show theme toggle if there's no JS\n display: none\n\n.theme-toggle-container\n vertical-align: middle\n\n.theme-toggle\n cursor: pointer\n border: none\n padding: 0\n background: transparent\n\n.theme-toggle svg\n vertical-align: middle\n height: 1rem\n width: 1rem\n color: var(--color-foreground-primary)\n display: none\n\n.theme-toggle-header\n float: left\n padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n display: none\n cursor: pointer\n\n .icon\n color: var(--color-foreground-secondary)\n height: 1rem\n width: 1rem\n\n.toc-header-icon, .nav-overlay-icon\n // for when we set display: flex\n justify-content: center\n align-items: center\n\n.toc-content-icon\n height: 1.5rem\n width: 1.5rem\n\n.content-icon-container\n float: right\n display: flex\n margin-top: 1.5rem\n margin-left: 1rem\n margin-bottom: 1rem\n gap: 0.5rem\n\n .edit-this-page svg\n color: inherit\n height: 1rem\n width: 1rem\n\n.sidebar-toggle\n position: absolute\n display: none\n// \n.sidebar-toggle[name=\"__toc\"]\n left: 20px\n.sidebar-toggle:checked\n left: 40px\n// \n\n.overlay\n position: fixed\n top: 0\n width: 0\n height: 0\n\n transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n opacity: 0\n background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n z-index: 20\n.toc-overlay\n z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n z-index: 30\n transition: left 250ms ease-in-out\n.toc-drawer\n z-index: 50\n transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n & ~ .sidebar-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .sidebar-drawer\n top: 0\n left: 0\n // Show the toc sidebar\n#__toc:checked\n & ~ .toc-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .toc-drawer\n top: 0\n right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n text-decoration: none\n\n display: none\n position: fixed\n left: 0\n top: 1rem\n padding: 0.5rem\n padding-right: 0.75rem\n border-radius: 1rem\n font-size: 0.8125rem\n\n background: var(--color-background-primary)\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n z-index: 10\n\n margin-left: 50%\n transform: translateX(-50%)\n svg\n height: 1rem\n width: 1rem\n fill: currentColor\n display: inline-block\n\n span\n margin-left: 0.25rem\n\n .show-back-to-top &\n display: flex\n align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n html\n font-size: 110%\n\n@media (max-width: $full-width)\n // Collapse \"toc\" into the icon.\n .toc-content-icon\n display: flex\n .toc-drawer\n position: fixed\n height: 100vh\n top: 0\n right: -$sidebar-width\n border-left: 1px solid var(--color-background-muted)\n .toc-tree\n border-left: none\n font-size: var(--toc-font-size--mobile)\n\n // Accomodate for a changed content width.\n .sidebar-drawer\n width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $full-width - $sidebar-width)\n // Collapse \"navigation\".\n .nav-overlay-icon\n display: flex\n .sidebar-drawer\n position: fixed\n height: 100vh\n width: $sidebar-width\n\n top: 0\n left: -$sidebar-width\n\n // Swap which icon is visible.\n .toc-header-icon\n display: flex\n .toc-content-icon, .theme-toggle-content\n display: none\n .theme-toggle-header\n display: block\n\n // Show the header.\n .mobile-header\n position: sticky\n top: 0\n display: flex\n justify-content: space-between\n align-items: center\n\n .header-left,\n .header-right\n display: flex\n height: var(--header-height)\n padding: 0 var(--header-padding)\n label\n height: 100%\n width: 100%\n user-select: none\n\n .nav-overlay-icon .icon,\n .theme-toggle svg\n height: 1.25rem\n width: 1.25rem\n\n // Add a scroll margin for the content\n :target\n scroll-margin-top: var(--header-height)\n\n // Show back-to-top below the header\n .back-to-top\n top: calc(var(--header-height) + 0.5rem)\n\n // Center the page, and accommodate for the header.\n .page\n flex-direction: column\n justify-content: center\n .content\n margin-left: auto\n margin-right: auto\n\n@media (max-width: $content-width + 2* $content-padding)\n // Content should respect window limits.\n .content\n width: 100%\n overflow-x: auto\n\n@media (max-width: $content-width)\n .content\n padding: 0 $content-padding--small\n // Don't float sidebars to the right.\n article aside.sidebar\n float: none\n width: 100%\n margin: 1rem 0\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n margin: 1rem auto\n padding: 0 0.5rem 0.5rem 0.5rem\n\n background: var(--color-admonition-background)\n\n border-radius: 0.2rem\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n font-size: var(--admonition-font-size)\n\n overflow: hidden\n page-break-inside: avoid\n\n // First element should have no margin, since the title has it.\n > :nth-child(2)\n margin-top: 0\n\n // Last item should have no margin, since we'll control that w/ padding\n > :last-child\n margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n position: relative\n margin: 0 -0.5rem 0.5rem\n padding-left: 2rem\n padding-right: .5rem\n padding-top: .4rem\n padding-bottom: .4rem\n\n font-weight: 500\n font-size: var(--admonition-title-font-size)\n line-height: 1.3\n\n // Our fancy icon\n &::before\n content: \"\"\n position: absolute\n left: 0.5rem\n width: 1rem\n height: 1rem\n\n// Default styles\np.admonition-title\n background-color: var(--color-admonition-title-background)\n &::before\n background-color: var(--color-admonition-title)\n mask-image: var(--icon-admonition-default)\n mask-repeat: no-repeat\n\np.topic-title\n background-color: var(--color-topic-title-background)\n &::before\n background-color: var(--color-topic-title)\n mask-image: var(--icon-topic-default)\n mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n border-left: 0.2rem solid var(--color-admonition-title)\n\n @each $type, $value in $admonitions\n &.#{$type}\n border-left-color: var(--color-admonition-title--#{$type})\n > .admonition-title\n background-color: var(--color-admonition-title-background--#{$type})\n &::before\n background-color: var(--color-admonition-title--#{$type})\n mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n // Tweak the spacing of all the things!\n dd\n margin-left: 2rem\n > :first-child\n margin-top: 0.125rem\n > :last-child\n margin-bottom: 0.75rem\n\n // This is used for the arguments\n .field-list\n margin-bottom: 0.75rem\n\n // \"Headings\" (like \"Parameters\" and \"Return\")\n > dt\n text-transform: uppercase\n font-size: var(--font-size--small)\n\n dd:empty\n margin-bottom: 0.5rem\n dd > ul\n margin-left: -1.2rem\n > li\n > p:nth-child(2)\n margin-top: 0\n // When the last-empty-paragraph follows a paragraph, it doesn't need\n // to augument the existing spacing.\n > p + p:last-child:empty\n margin-top: 0\n margin-bottom: 0\n\n // Colorize the elements\n > dt\n color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n font-weight: bold\n\n font-size: var(--api-font-size)\n font-family: var(--font-stack--monospace)\n\n margin-left: -0.25rem\n margin-right: -0.25rem\n padding-top: 0.25rem\n padding-bottom: 0.25rem\n padding-right: 0.5rem\n\n // These are intentionally em, to properly match the font size.\n padding-left: 3em\n text-indent: -2.5em\n\n border-radius: 0.25rem\n\n background: var(--color-api-background)\n transition: background 100ms ease-out\n\n &:hover\n background: var(--color-api-background-hover)\n\n // adjust the size of the [source] link on the right.\n a.reference\n .viewcode-link\n font-weight: normal\n width: 3.5rem\n\nem.property\n font-style: normal\n &:first-child\n color: var(--color-api-keyword)\n.sig-name\n color: var(--color-api-name)\n.sig-prename\n font-weight: normal\n color: var(--color-api-pre-name)\n.sig-paren\n color: var(--color-api-paren)\n.sig-param\n font-style: normal\n\n.versionmodified\n font-style: italic\ndiv.versionadded, div.versionchanged, div.deprecated\n p\n margin-top: 0.125rem\n margin-bottom: 0.125rem\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n float: right\n text-align: right\n",".line-block\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n .line-block\n margin-top: 0rem\n margin-bottom: 0rem\n padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n font-size: var(--font-size--small)\n text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n .caption, :not(.caption) > .caption-text\n font-size: var(--font-size--small)\n text-transform: uppercase\n\n text-align: initial\n margin-bottom: 0\n\n > ul\n margin-top: 0\n margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n background: var(--color-inline-code-background)\n border-radius: 0.2em\n // Make the font smaller, and use padding to recover.\n font-size: var(--font-size--small--2)\n padding: 0.1em 0.2em\n\n pre.literal-block &\n font-size: inherit\n padding: 0\n\n p &\n border: 1px solid var(--color-background-border)\n\n.sig-inline\n font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n margin: 1em 0\n display: flex\n\n .table-wrapper\n margin: 0\n padding: 0\n\npre\n margin: 0\n padding: 0\n overflow: auto\n\n // Needed to have more specificity than pygments' \"pre\" selector. :(\n article[role=\"main\"] .highlight &\n line-height: 1.5\n\n &.literal-block,\n .highlight &\n font-size: var(--code-font-size)\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n // Make it look like all the other blocks.\n &.literal-block\n margin-top: 1rem\n margin-bottom: 1rem\n\n border-radius: 0.2rem\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n width: 100%\n border-radius: 0.2rem\n\n // Make line numbers and prompts un-selectable.\n .gp, span.linenos\n user-select: none\n pointer-events: none\n\n // Expand the line-highlighting.\n .hll\n display: block\n margin-left: -$code-spacing-horizontal\n margin-right: -$code-spacing-horizontal\n padding-left: $code-spacing-horizontal\n padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n display: flex\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n border-radius: 0.25rem\n border-bottom-left-radius: 0\n border-bottom-right-radius: 0\n font-weight: 300\n border-bottom: 1px solid\n\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n border-color: var(--color-background-border)\n\n + div[class]\n margin-top: 0\n pre\n border-top-left-radius: 0\n border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n width: 100%\n display: block\n tbody\n display: block\n\n tr\n display: flex\n\n // Line numbers\n td.linenos\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n padding: $code-spacing-vertical $code-spacing-horizontal\n padding-right: 0\n border-top-left-radius: 0.2rem\n border-bottom-left-radius: 0.2rem\n\n .linenodiv\n padding-right: $code-spacing-horizontal\n font-size: var(--code-font-size)\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n // Actual code\n td.code\n padding: 0\n display: block\n flex: 1\n overflow: hidden\n\n .highlight\n border-top-left-radius: 0\n border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n span.linenos\n display: inline-block\n padding-left: 0\n padding-right: $code-spacing-horizontal\n margin-right: $code-spacing-horizontal\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n font-size: var(--font-size--small--4)\n vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\n display: grid\n grid-template-columns: max-content auto\n dt\n margin: 0\n > .fn-backref\n margin-left: 0.25rem\n\n &:after\n content: \":\"\n\n .brackets\n &:before\n content: \"[\"\n &:after\n content: \"]\"\n\n dd\n margin: 0\n padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n float: left\n font-weight: 500\n padding-right: 0.25rem\n\naside.footnote > p,\ndiv.citation > p\n margin-left: 2rem\n","//\n// Figures\n//\nimg\n box-sizing: border-box\n max-width: 100%\n height: auto\n\narticle\n figure, .figure\n border-radius: 0.2rem\n\n margin: 0\n :last-child\n margin-bottom: 0\n\n .align-left\n float: left\n clear: left\n margin: 0 1rem 1rem\n\n .align-right\n float: right\n clear: right\n margin: 0 1rem 1rem\n\n .align-default,\n .align-center\n display: block\n text-align: center\n margin-left: auto\n margin-right: auto\n\n // WELL, table needs to be stylised like a table.\n table.align-default\n display: table\n text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n border-top: 1px solid var(--color-background-border)\n border-bottom: 1px solid var(--color-background-border)\n padding: 0.25rem\n\n.genindex-section, .domainindex-section\n h2\n margin-top: 0.75rem\n margin-bottom: 0.5rem\n ul\n margin-top: 0\n margin-bottom: 0\n","ul,\nol\n padding-left: 1.2rem\n\n // Space lists out like paragraphs\n margin-top: 1rem\n margin-bottom: 1rem\n // reduce margins within li.\n li\n > p:first-child\n margin-top: 0.25rem\n margin-bottom: 0.25rem\n\n > p:last-child\n margin-top: 0.25rem\n\n > ul,\n > ol\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n\nol\n &.arabic\n list-style: decimal\n &.loweralpha\n list-style: lower-alpha\n &.upperalpha\n list-style: upper-alpha\n &.lowerroman\n list-style: lower-roman\n &.upperroman\n list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n li\n > ul,\n > ol\n margin-top: 0\n margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n dt\n font-weight: 500\n margin-top: 0.25rem\n + dt\n margin-top: 0\n\n .classifier::before\n content: \":\"\n margin-left: 0.2rem\n margin-right: 0.2rem\n\n dd\n > p:first-child,\n ul\n margin-top: 0.125rem\n\n ul\n margin-bottom: 0.125rem\n",".math-wrapper\n width: 100%\n overflow-x: auto\n\ndiv.math\n position: relative\n text-align: center\n\n .headerlink,\n &:focus .headerlink\n display: none\n\n &:hover .headerlink\n display: inline-block\n\n span.eqno\n position: absolute\n right: 0.5rem\n top: 50%\n transform: translate(0, -50%)\n z-index: 1\n","// Abbreviations\nabbr[title]\n cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n margin: 0 0.2rem\n padding: 0 0.2rem\n border-radius: 0.2rem\n border: 1px solid var(--color-foreground-border)\n color: var(--color-foreground-primary)\n vertical-align: text-bottom\n\n font-size: var(--font-size--small--3)\n display: inline-block\n\n box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n border-left: 4px solid var(--color-background-border)\n background: var(--color-background-secondary)\n\n margin-left: 0\n margin-right: 0\n padding: 0.5rem 1rem\n\n .attribution\n font-weight: 600\n text-align: right\n\n &.pull-quote,\n &.highlights\n font-size: 1.25em\n\n &.epigraph,\n &.pull-quote\n border-left-width: 0\n border-radius: 0.5rem\n\n &.highlights\n border-left-width: 0\n background: transparent\n\n// Center align embedded-in-text images\np .reference img\n vertical-align: middle\n","p.rubric\n line-height: 1.25\n font-weight: bold\n font-size: 1.125em\n\n // For Numpy-style documentation that's got rubrics within it.\n // https://github.com/pradyunsg/furo/discussions/505\n dd &\n line-height: inherit\n font-weight: inherit\n\n font-size: var(--font-size--small)\n text-transform: uppercase\n","article .sidebar\n float: right\n clear: right\n width: 30%\n\n margin-left: 1rem\n margin-right: 0\n\n border-radius: 0.2rem\n background-color: var(--color-background-secondary)\n border: var(--color-background-border) 1px solid\n\n > *\n padding-left: 1rem\n padding-right: 1rem\n\n > ul, > ol // lists need additional padding, because bullets.\n padding-left: 2.2rem\n\n .sidebar-title\n margin: 0\n padding: 0.5rem 1rem\n border-bottom: var(--color-background-border) 1px solid\n\n font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n",".table-wrapper\n width: 100%\n overflow-x: auto\n margin-top: 1rem\n margin-bottom: 0.5rem\n padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n border-radius: 0.2rem\n border-spacing: 0\n border-collapse: collapse\n\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n th\n background: var(--color-table-header-background)\n\n td,\n th\n // Space things out properly\n padding: 0 0.25rem\n\n // Get the borders looking just-right.\n border-left: 1px solid var(--color-table-border)\n border-right: 1px solid var(--color-table-border)\n border-bottom: 1px solid var(--color-table-border)\n\n p\n margin: 0.25rem\n\n &:first-child\n border-left: none\n &:last-child\n border-right: none\n\n // MyST-parser tables set these classes for control of column alignment\n &.text-left\n text-align: left\n &.text-right\n text-align: right\n &.text-center\n text-align: center\n",":target\n scroll-margin-top: 0.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n :target\n scroll-margin-top: calc(0.5rem + var(--header-height))\n\n // When a heading is selected\n section > span:target\n scroll-margin-top: calc(0.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n font-weight: 100\n user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n > .headerlink\n margin-left: 0.5rem\n visibility: hidden\n &:hover > .headerlink\n visibility: visible\n\n // Don't change to link-like, if someone adds the contents directive.\n > .toc-backref\n color: inherit\n text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n visibility: visible\n\n:target >, // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n h1,\n h2,\n h3,\n h4,\n h5,\n h6\n &:nth-of-type(1)\n background-color: var(--color-highlight-on-target)\n // .headerlink\n // visibility: visible\n code.literal\n background-color: transparent\n\ntable:target > caption,\nfigure:target\n background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n// There isn't really an alternative to !important here, due to the\n// high-specificity of API documentation's selector.\ndt:target\n background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n background-color: var(--color-highlight-on-target)\n",".guilabel\n background-color: var(--color-guilabel-background)\n border: 1px solid var(--color-guilabel-border)\n color: var(--color-guilabel-text)\n\n padding: 0 0.3em\n border-radius: 0.5em\n font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n font-size: var(--font-size--small)\n display: flex\n flex-direction: column\n\n margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n display: flex\n align-items: center\n justify-content: space-between\n\n margin-top: 1rem\n padding-top: 1rem\n padding-bottom: 1rem\n\n color: var(--color-foreground-secondary)\n border-top: 1px solid var(--color-background-border)\n\n line-height: 1.5\n\n @media (max-width: $content-width)\n text-align: center\n flex-direction: column-reverse\n gap: 0.25rem\n\n .left-details\n font-size: var(--font-size--small)\n\n .right-details\n display: flex\n flex-direction: column\n gap: 0.25rem\n text-align: right\n\n .icons\n display: flex\n justify-content: flex-end\n gap: 0.25rem\n font-size: 1rem\n\n a\n text-decoration: none\n\n svg,\n img\n font-size: 1.125rem\n height: 1em\n width: 1em\n\n// Next/Prev page information\n.related-pages\n a\n display: flex\n align-items: center\n\n text-decoration: none\n &:hover .page-info .title\n text-decoration: underline\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n svg.furo-related-icon,\n svg.furo-related-icon > use\n flex-shrink: 0\n\n color: var(--color-foreground-border)\n\n width: 0.75rem\n height: 0.75rem\n margin: 0 0.5rem\n\n &.next-page\n max-width: 50%\n\n float: right\n clear: right\n text-align: right\n\n &.prev-page\n max-width: 50%\n\n float: left\n clear: left\n\n svg\n transform: rotate(180deg)\n\n.page-info\n display: flex\n flex-direction: column\n overflow-wrap: anywhere\n\n .next-page &\n align-items: flex-end\n\n .context\n display: flex\n align-items: center\n\n padding-bottom: 0.1rem\n\n color: var(--color-foreground-muted)\n font-size: var(--font-size--small)\n text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n display: flex\n flex-direction: column\n flex-shrink: 0\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n text-decoration: none\n\n.sidebar-brand-text\n color: var(--color-sidebar-brand-text)\n overflow-wrap: break-word\n margin: var(--sidebar-item-spacing-vertical) 0\n font-size: 1.5rem\n\n.sidebar-logo-container\n margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n margin: 0 auto\n display: block\n max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n display: flex\n align-items: center\n margin-top: var(--sidebar-search-space-above)\n\n position: relative\n\n background: var(--color-sidebar-search-background)\n &:hover,\n &:focus-within\n background: var(--color-sidebar-search-background--focus)\n\n &::before\n content: \"\"\n position: absolute\n left: var(--sidebar-item-spacing-horizontal)\n width: var(--sidebar-search-icon-size)\n height: var(--sidebar-search-icon-size)\n\n background-color: var(--color-sidebar-search-icon)\n mask-image: var(--icon-search)\n\n.sidebar-search\n box-sizing: border-box\n\n border: none\n border-top: 1px solid var(--color-sidebar-search-border)\n border-bottom: 1px solid var(--color-sidebar-search-border)\n\n padding-top: var(--sidebar-search-input-spacing-vertical)\n padding-bottom: var(--sidebar-search-input-spacing-vertical)\n padding-right: var(--sidebar-search-input-spacing-horizontal)\n padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n width: 100%\n\n color: var(--color-sidebar-search-foreground)\n background: transparent\n z-index: 10\n\n &:focus\n outline: none\n\n &::placeholder\n font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n margin: 0\n text-align: center\n\n a\n color: var(--color-sidebar-search-icon)\n font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n font-size: var(--sidebar-item-font-size)\n margin-top: var(--sidebar-tree-space-above)\n margin-bottom: var(--sidebar-item-spacing-vertical)\n\n ul\n padding: 0\n margin-top: 0\n margin-bottom: 0\n\n display: flex\n flex-direction: column\n\n list-style: none\n\n li\n position: relative\n margin: 0\n\n > ul\n margin-left: var(--sidebar-item-spacing-horizontal)\n\n .icon\n color: var(--color-sidebar-link-text)\n\n .reference\n box-sizing: border-box\n color: var(--color-sidebar-link-text)\n\n // Fill the parent.\n display: inline-block\n line-height: var(--sidebar-item-line-height)\n text-decoration: none\n\n // Don't allow long words to cause wrapping.\n overflow-wrap: anywhere\n\n height: 100%\n width: 100%\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n &:hover\n background: var(--color-sidebar-item-background--hover)\n\n // Add a nice little \"external-link\" arrow here.\n &.external::after\n content: url('data:image/svg+xml,')\n margin: 0 0.25rem\n vertical-align: middle\n color: var(--color-sidebar-link-text)\n\n // Make the current page reference bold.\n .current-page > .reference\n font-weight: bold\n\n label\n position: absolute\n top: 0\n right: 0\n height: var(--sidebar-item-height)\n width: var(--sidebar-expander-width)\n\n cursor: pointer\n user-select: none\n\n display: flex\n justify-content: center\n align-items: center\n\n .caption, :not(.caption) > .caption-text\n font-size: var(--sidebar-caption-font-size)\n color: var(--color-sidebar-caption-text)\n\n font-weight: bold\n text-transform: uppercase\n\n margin: var(--sidebar-caption-space-above) 0 0 0\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n // If it has children, add a bit more padding to wrap the content to avoid\n // overlapping with the