apply lint

Author: Mec-iS

.githooks/commit-msg

@@ -0,0 +1,4 @@
+#!/bin/sh
+# Entire CLI hooks
+# Commit-msg hook: strip trailer if no user content (allows aborting empty commits)
+if command -v entire >/dev/null 2>&1; then entire hooks git commit-msg "$1" || true; else printf '%s\n' '[entire] Entire CLI is enabled but not installed or not on PATH. Skipping Entire Git hook; continuing. Installation guide: https://docs.entire.io/cli/installation#installation-methods' >&2 || :; fi

.githooks/post-commit

@@ -0,0 +1,4 @@
+#!/bin/sh
+# Entire CLI hooks
+# Post-commit hook: condense session data if commit has Entire-Checkpoint trailer
+if command -v entire >/dev/null 2>&1; then entire hooks git post-commit 2>/dev/null || true; else :; fi

.githooks/post-rewrite

@@ -0,0 +1,4 @@
+#!/bin/sh
+# Entire CLI hooks
+# Post-rewrite hook: remap session linkage after amend/rebase rewrites
+if command -v entire >/dev/null 2>&1; then entire hooks git post-rewrite "$1" 2>/dev/null || true; else :; fi

.githooks/pre-push

@@ -0,0 +1,5 @@
+#!/bin/sh
+# Entire CLI hooks
+# Pre-push hook: push session logs alongside user's push
+# $1 is the remote name (e.g., "origin")
+if command -v entire >/dev/null 2>&1; then entire hooks git pre-push "$1"; else :; fi

.githooks/prepare-commit-msg

@@ -0,0 +1,3 @@
+#!/bin/sh
+# Entire CLI hooks
+if command -v entire >/dev/null 2>&1; then entire hooks git prepare-commit-msg "$1" "$2" 2>/dev/null || true; else :; fi

src/ald_sc/audio_codec.py

@@ -144,7 +144,9 @@ def _load_model(self) -> None:
             logger.warning("Failed to load EnCodec model: %s", e)
             raise
 
-    def encode(self, x: Tensor, prior: ArrowSpacePrior) -> tuple[Tensor, Tensor, Tensor]:
+    def encode(
+        self, x: Tensor, prior: ArrowSpacePrior
+    ) -> tuple[Tensor, Tensor, Tensor]:
         """Encode audio waveform to (z, A, c_spec).
 
         Parameters
@@ -271,7 +273,9 @@ def __init__(
                 )
             )
 
-        self.dec_out = nn.Conv1d(channel_steps[len(upsample_strides)], out_channels, 3, padding=1)
+        self.dec_out = nn.Conv1d(
+            channel_steps[len(upsample_strides)], out_channels, 3, padding=1
+        )
 
     def forward(self, z: Tensor) -> Tensor:
         """Decode 1-D latent to waveform (no spectral conditioning).

src/ald_sc/graph_decoder.py

@@ -190,7 +190,9 @@ def __init__(
                 )
             )
 
-        self.dec_out = nn.Conv1d(channel_steps[len(upsample_strides)], out_channels, 3, padding=1)
+        self.dec_out = nn.Conv1d(
+            channel_steps[len(upsample_strides)], out_channels, 3, padding=1
+        )
 
     def forward(self, z: Tensor, c_spec: Tensor) -> Tensor:
         """Decode 1-D latent under graph-structured reconstruction.
@@ -291,7 +293,9 @@ def __init__(
                 )
             )
 
-        self.dec_out = nn.Conv1d(channel_steps[len(upsample_strides)], out_channels, 3, padding=1)
+        self.dec_out = nn.Conv1d(
+            channel_steps[len(upsample_strides)], out_channels, 3, padding=1
+        )
 
     def forward(
         self,

src/ald_sc/losses.py

@@ -95,11 +95,17 @@ def stft_loss(self, x: Tensor, x_hat: Tensor) -> Tensor:
             hop = n_fft // 4
             window = self._windows[n_fft].to(device=x.device, dtype=x.dtype)
             spec_x = torch.stft(
-                x, n_fft, hop_length=hop, return_complex=True,
+                x,
+                n_fft,
+                hop_length=hop,
+                return_complex=True,
                 window=window,
             )
             spec_xhat = torch.stft(
-                x_hat, n_fft, hop_length=hop, return_complex=True,
+                x_hat,
+                n_fft,
+                hop_length=hop,
+                return_complex=True,
                 window=window,
             )
 

src/ald_sc/sampling.py

@@ -51,8 +51,12 @@ def _init_noise(
         )
     latent_size = getattr(model, "latent_size", 32)
     return torch.randn(
-        batch_size, latent_channels, latent_size, latent_size,
-        device=device, generator=gen,
+        batch_size,
+        latent_channels,
+        latent_size,
+        latent_size,
+        device=device,
+        generator=gen,
     )
 
 

tests/test_audio_codec.py

@@ -38,7 +38,9 @@ def __init__(self, latent_dim: int = 128) -> None:
         self.latent_dim = latent_dim
         self.proj = nn.Conv1d(1, latent_dim, 320, stride=320)
 
-    def encode(self, x: Tensor, prior: ArrowSpacePrior) -> tuple[Tensor, Tensor, Tensor]:
+    def encode(
+        self, x: Tensor, prior: ArrowSpacePrior
+    ) -> tuple[Tensor, Tensor, Tensor]:
         z = self.proj(x).float()
         a = z.mean(dim=2)
         c_spec = prior.chart_energy_descriptor(a)
@@ -133,9 +135,7 @@ class TestExtractFeatures:
     def test_extract_features_shape(self) -> None:
         """Extract features from a stub encoder."""
         encoder = StubEncoder(latent_dim=128)
-        loader = torch.utils.data.DataLoader(
-            torch.randn(8, 1, 320 * 4), batch_size=4
-        )
+        loader = torch.utils.data.DataLoader(torch.randn(8, 1, 320 * 4), batch_size=4)
         features = extract_encodec_features(loader, encoder)  # type: ignore
         assert features.shape == (8, 128)