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              "source": "        repository,"
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              "source": "    )"
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            },
            {
              "line": 498,
              "source": "        )"
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              "source": "    return LoadedBackbone("
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              "source": "        root=root,"
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              "source": "    _ = loaded.root.eval()"
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              "line": 525,
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              "line": 526,
              "source": "    for parameter in loaded.root.parameters():"
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              "source": "        parameter.requires_grad = False"
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              "source": "    context: AudioContext,"
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              "line": 420,
              "source": "    *,"
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              "source": "    window_start: float,"
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              "line": 422,
              "source": "    expected_width: int = EXPECTED_HIDDEN_WIDTH,"
            },
            {
              "line": 423,
              "source": ") -> LayerFrames:"
            },
            {
              "line": 424,
              "source": "    \"\"\"Run one context once and retain every hidden state with its alignment.\"\"\""
            },
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              "line": 425,
              "source": "    with torch.autocast(device_type=context.waveform.device.type, enabled=False):"
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              "source": "        output = model("
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            {
              "line": 428,
              "source": "            output_hidden_states=True,"
            },
            {
              "line": 429,
              "source": "        )"
            },
            {
              "line": 430,
              "source": "    layers = _all_hidden_states(output.hidden_states, expected_width)"
            },
            {
              "line": 431,
              "source": "    frame_timestamps, valid_frame_mask = _frame_alignment("
            },
            {
              "line": 432,
              "source": "        layers[0].shape[1], layers[0].device, context, window_start"
            },
            {
              "line": 433,
              "source": "    )"
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              "line": 434,
              "source": "    return LayerFrames("
            },
            {
              "line": 435,
              "source": "        layers=layers,"
            },
            {
              "line": 436,
              "source": "        frame_timestamps=frame_timestamps,"
            },
            {
              "line": 437,
              "source": "        valid_frame_mask=valid_frame_mask,"
            }
          ]
        }
      ]
    },
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      "path": "/home/work/Projects/Raina-laya/src/raina_laya/features/audio_features/infrastructure/features.py",
      "sha256": "75f22e9164b4a10fec27fa2f3cb118656ea103c641fbf20f59370e8815f8318e",
      "excerpts": [
        {
          "start_line": 243,
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              "line": 243,
              "source": "    pooled: list[torch.Tensor] = []"
            },
            {
              "line": 244,
              "source": "    for interval in bins:"
            },
            {
              "line": 245,
              "source": "        selected = ("
            },
            {
              "line": 246,
              "source": "            valid_frame_mask"
            },
            {
              "line": 247,
              "source": "            & (frame_timestamps >= interval.start)"
            },
            {
              "line": 248,
              "source": "            & (frame_timestamps < interval.end)"
            },
            {
              "line": 249,
              "source": "        )"
            },
            {
              "line": 250,
              "source": "        if not torch.any(selected):"
            },
            {
              "line": 251,
              "source": "            raise EmptyPoolingBinError(start=interval.start, end=interval.end)"
            },
            {
              "line": 252,
              "source": "        pooled.append("
            },
            {
              "line": 253,
              "source": "            torch.cat("
            },
            {
              "line": 254,
              "source": "                ("
            },
            {
              "line": 255,
              "source": "                    layer_10[0, selected].mean(dim=0),"
            },
            {
              "line": 256,
              "source": "                    layer_9[0, selected].mean(dim=0),"
            },
            {
              "line": 257,
              "source": "                ),"
            },
            {
              "line": 258,
              "source": "            ),"
            },
            {
              "line": 259,
              "source": "        )"
            },
            {
              "line": 260,
              "source": "    return torch.stack(pooled).to(dtype=torch.float32)"
            }
          ]
        },
        {
          "start_line": 464,
          "end_line": 479,
          "lines": [
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              "line": 464,
              "source": "    pooled: list[torch.Tensor] = []"
            },
            {
              "line": 465,
              "source": "    for interval in bins:"
            },
            {
              "line": 466,
              "source": "        selected = ("
            },
            {
              "line": 467,
              "source": "            frames.valid_frame_mask"
            },
            {
              "line": 468,
              "source": "            & (frames.frame_timestamps >= interval.start)"
            },
            {
              "line": 469,
              "source": "            & (frames.frame_timestamps < interval.end)"
            },
            {
              "line": 470,
              "source": "        )"
            },
            {
              "line": 471,
              "source": "        rows = selected.nonzero().squeeze(1)"
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            {
              "line": 472,
              "source": "        if rows.numel() == 0:"
            },
            {
              "line": 473,
              "source": "            raise EmptyPoolingBinError(start=interval.start, end=interval.end)"
            },
            {
              "line": 474,
              "source": "        pooled.append("
            },
            {
              "line": 475,
              "source": "            torch.stack("
            },
            {
              "line": 476,
              "source": "                [layer[0].index_select(0, rows).mean(dim=0) for layer in frames.layers],"
            },
            {
              "line": 477,
              "source": "            ),"
            },
            {
              "line": 478,
              "source": "        )"
            },
            {
              "line": 479,
              "source": "    return torch.stack(pooled).to(dtype=torch.float32)"
            }
          ]
        }
      ]
    },
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      "id": "feature_width_contract",
      "path": "/home/work/Projects/Raina-laya/src/raina_laya/features/grade_model/domain/model_inputs.py",
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            },
            {
              "line": 9,
              "source": "LAYER_WIDTH: Final = 1024"
            },
            {
              "line": 10,
              "source": "LAYER_COUNT: Final = 13"
            }
          ]
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    {
      "id": "config_feature_width",
      "path": "/home/work/Projects/Raina-laya/src/raina_laya/features/grade_model/application/v4_training_types.py",
      "sha256": "27e050e94120b536e277e2d658f278985a42f0cffc2de0592ca57fb8420c1055",
      "excerpts": [
        {
          "start_line": 97,
          "end_line": 99,
          "lines": [
            {
              "line": 97,
              "source": "    embedding_layers: tuple[int, ...] | None = None"
            },
            {
              "line": 98,
              "source": "    gate_soft_target_mix: float | None = None"
            },
            {
              "line": 99,
              "source": "    gate_soft_targets_sha256: str | None = None"
            }
          ]
        },
        {
          "start_line": 105,
          "end_line": 110,
          "lines": [
            {
              "line": 105,
              "source": "    @property"
            },
            {
              "line": 106,
              "source": "    def feature_width(self) -> int:"
            },
            {
              "line": 107,
              "source": "        \"\"\"Return the head input width: the legacy L10+L9 cache or chosen layers.\"\"\""
            },
            {
              "line": 108,
              "source": "        if self.embedding_layers is None:"
            },
            {
              "line": 109,
              "source": "            return FEATURE_WIDTH"
            },
            {
              "line": 110,
              "source": "        return LAYER_WIDTH * len(self.embedding_layers)"
            }
          ]
        }
      ]
    },
    {
      "id": "judgment_tokens",
      "path": "/home/work/Projects/Raina-laya/src/raina_laya/features/grade_model/domain/model.py",
      "sha256": "ca8996df129dcffe6ebd3cf1a69d3bd20b18e69c31b633b1127a1ad5c7a777a1",
      "excerpts": [
        {
          "start_line": 115,
          "end_line": 119,
          "lines": [
            {
              "line": 115,
              "source": "        self.feature_projection: nn.Linear = nn.Linear(feature_width, model_width)"
            },
            {
              "line": 116,
              "source": "        self.projection_norm: nn.LayerNorm = nn.LayerNorm(model_width)"
            },
            {
              "line": 117,
              "source": "        self.ratio_embedding: nn.Linear = nn.Linear(1, model_width)"
            },
            {
              "line": 118,
              "source": "        self.grade_tokens = nn.Parameter(torch.empty(GRADE_COUNT, model_width))"
            },
            {
              "line": 119,
              "source": "        self.type_embedding: nn.Embedding = nn.Embedding(2, model_width)"
            }
          ]
        },
        {
          "start_line": 229,
          "end_line": 243,
          "lines": [
            {
              "line": 229,
              "source": "        grade_tokens = self.grade_tokens.unsqueeze(0).expand(batch_size, -1, -1)"
            },
            {
              "line": 230,
              "source": "        grade_tokens = grade_tokens + self.type_embedding.weight[GRADE_TYPE_INDEX]"
            },
            {
              "line": 231,
              "source": "        sequence: torch.Tensor = torch.cat((grade_tokens, music_tokens), dim=1)"
            },
            {
              "line": 232,
              "source": "        grade_mask = torch.zeros("
            },
            {
              "line": 233,
              "source": "            (batch_size, GRADE_COUNT),"
            },
            {
              "line": 234,
              "source": "            dtype=torch.bool,"
            },
            {
              "line": 235,
              "source": "            device=padding_mask.device,"
            },
            {
              "line": 236,
              "source": "        )"
            },
            {
              "line": 237,
              "source": "        key_padding_mask = torch.cat((grade_mask, padding_mask), dim=1)"
            },
            {
              "line": 238,
              "source": "        for layer in self.transformer_layers:"
            },
            {
              "line": 239,
              "source": "            sequence = layer("
            },
            {
              "line": 240,
              "source": "                sequence,"
            },
            {
              "line": 241,
              "source": "                src_key_padding_mask=key_padding_mask,"
            },
            {
              "line": 242,
              "source": "            )"
            },
            {
              "line": 243,
              "source": "        logits: torch.Tensor = self.scorer(sequence[:, :GRADE_COUNT])"
            }
          ]
        }
      ]
    },
    {
      "id": "cache_route",
      "path": "/home/work/Projects/Raina-laya/src/raina_laya/workflows/training_inputs.py",
      "sha256": "4ddbbf907907572d6fbe3360dcec78b9ad44e20761c8d0d8c07f14baee89cb14",
      "excerpts": [
        {
          "start_line": 214,
          "end_line": 228,
          "lines": [
            {
              "line": 214,
              "source": "        if self.layer_dir is not None:"
            },
            {
              "line": 215,
              "source": "            source_hashes, inventory_digest = _source_inventory("
            },
            {
              "line": 216,
              "source": "                self.cache_inventory_path,"
            },
            {
              "line": 217,
              "source": "                expected_sources,"
            },
            {
              "line": 218,
              "source": "            )"
            },
            {
              "line": 219,
              "source": "            try:"
            },
            {
              "line": 220,
              "source": "                store = self.audio_features.open_layer_stack("
            },
            {
              "line": 221,
              "source": "                    self.layer_dir,"
            },
            {
              "line": 222,
              "source": "                    self.layers,"
            },
            {
              "line": 223,
              "source": "                )"
            },
            {
              "line": 224,
              "source": "            except CacheIncompleteError as error:"
            },
            {
              "line": 225,
              "source": "                raise TrainingRunInputError("
            },
            {
              "line": 226,
              "source": "                    detail=f\"layer store is missing: {error.path}\","
            },
            {
              "line": 227,
              "source": "                ) from error"
            },
            {
              "line": 228,
              "source": "            inventory_digest = store.lineage_digest(inventory_digest)"
            }
          ]
        },
        {
          "start_line": 240,
          "end_line": 247,
          "lines": [
            {
              "line": 240,
              "source": "        else:"
            },
            {
              "line": 241,
              "source": "            source_hashes, inventory_digest = _source_inventory("
            },
            {
              "line": 242,
              "source": "                self.cache_inventory_path,"
            },
            {
              "line": 243,
              "source": "                expected_sources,"
            },
            {
              "line": 244,
              "source": "            )"
            },
            {
              "line": 245,
              "source": "            if not self.embedding_database.is_file():"
            },
            {
              "line": 246,
              "source": "                raise TrainingRunInputError(detail=\"embedding database is missing\")"
            },
            {
              "line": 247,
              "source": "            store = self.audio_features.open_sqlite_store(self.embedding_database)"
            }
          ]
        }
      ]
    },
    {
      "id": "installed_wrapper_loader",
      "path": "/home/work/Projects/Raina-laya/.venv/lib/python3.13/site-packages/muq/muq_mulan/models/audio.py",
      "sha256": "05231129cffeb5132aded81b9aabc79ee29fc01513f26879ef98b1b0fbb14b9c",
      "excerpts": [
        {
          "start_line": 240,
          "end_line": 243,
          "lines": [
            {
              "line": 240,
              "source": "    def _init_pretrained_model(self, model_name):"
            },
            {
              "line": 241,
              "source": "        if 'muq' in model_name.lower():"
            },
            {
              "line": 242,
              "source": "            from muq import MuQ"
            },
            {
              "line": 243,
              "source": "            self.model = MuQ.from_pretrained(model_name, cache_dir=self.hf_hub_cache_dir)"
            }
          ]
        },
        {
          "start_line": 258,
          "end_line": 261,
          "lines": [
            {
              "line": 258,
              "source": "    def _forward_pretrained_model(self, x):"
            },
            {
              "line": 259,
              "source": "        if 'muq' in self.model_name.lower():"
            },
            {
              "line": 260,
              "source": "            outputs = self.model(x, output_hidden_states=True)"
            },
            {
              "line": 261,
              "source": "            return outputs.hidden_states # 13 layer x [batch_size, Time steps, 1024 feature_dim]"
            }
          ]
        },
        {
          "start_line": 279,
          "end_line": 288,
          "lines": [
            {
              "line": 279,
              "source": "        layer_hidden_states = outputs[self.use_layer_idx]"
            },
            {
              "line": 280,
              "source": ""
            },
            {
              "line": 281,
              "source": "        if no_proj:"
            },
            {
              "line": 282,
              "source": "            outputs = layer_hidden_states"
            },
            {
              "line": 283,
              "source": "        else:"
            },
            {
              "line": 284,
              "source": "            outputs = self.proj(layer_hidden_states)"
            },
            {
              "line": 285,
              "source": "        outputs, layer_results = self.transformer(outputs, return_all_layers=True)"
            },
            {
              "line": 286,
              "source": ""
            },
            {
              "line": 287,
              "source": "        if return_mean:"
            },
            {
              "line": 288,
              "source": "            outputs = outputs.mean(dim = -2)"
            }
          ]
        }
      ]
    },
    {
      "id": "installed_wrapper_construction",
      "path": "/home/work/Projects/Raina-laya/.venv/lib/python3.13/site-packages/muq/muq_mulan/muq_mulan.py",
      "sha256": "594843bf29efed147f97768c60946d8e247dce183af085fc5dffcd3d03a7164e",
      "excerpts": [
        {
          "start_line": 95,
          "end_line": 104,
          "lines": [
            {
              "line": 95,
              "source": "    def create_MuLan_from_config(cls, config:MuQMuLanConfig, hf_hub_cache_dir=None) -> MuLanModel:"
            },
            {
              "line": 96,
              "source": ""
            },
            {
              "line": 97,
              "source": "        audio_transformer = AudioSpectrogramTransformerPretrained("
            },
            {
              "line": 98,
              "source": "            model_name = config.audio_model.name, "
            },
            {
              "line": 99,
              "source": "            model_dim = config.audio_model.model_dim,"
            },
            {
              "line": 100,
              "source": "            use_layer_idx = config.audio_model.use_layer_idx,"
            },
            {
              "line": 101,
              "source": "            **config.audio_transformer,"
            },
            {
              "line": 102,
              "source": "            frozen_pretrained = False,"
            },
            {
              "line": 103,
              "source": "            hf_hub_cache_dir = hf_hub_cache_dir,"
            },
            {
              "line": 104,
              "source": "        )"
            }
          ]
        }
      ]
    },
    {
      "id": "installed_original_muq",
      "path": "/home/work/Projects/Raina-laya/.venv/lib/python3.13/site-packages/muq/muq/muq.py",
      "sha256": "dbec360f5e3ef622d83f1a73085e67ddde946eb626acad768cc01c1f81374211",
      "excerpts": [
        {
          "start_line": 19,
          "end_line": 20,
          "lines": [
            {
              "line": 19,
              "source": "    encoder_dim:int = field(default=1024)"
            },
            {
              "line": 20,
              "source": "    encoder_depth:int = field(default=12)"
            }
          ]
        },
        {
          "start_line": 35,
          "end_line": 41,
          "lines": [
            {
              "line": 35,
              "source": "class MuQ(nn.Module, PyTorchModelHubMixin):"
            },
            {
              "line": 36,
              "source": "    def __init__(self, config: MuQConfig):"
            },
            {
              "line": 37,
              "source": "        super().__init__()"
            },
            {
              "line": 38,
              "source": "        if isinstance(config, dict):"
            },
            {
              "line": 39,
              "source": "            config = MuQConfig(**config)"
            },
            {
              "line": 40,
              "source": "        self.config = config"
            },
            {
              "line": 41,
              "source": "        self.model = MuQModel("
            }
          ]
        },
        {
          "start_line": 83,
          "end_line": 90,
          "lines": [
            {
              "line": 83,
              "source": "        _, hidden_states = self.model.get_predictions(x, attention_mask=attention_mask, is_features_only=True)"
            },
            {
              "line": 84,
              "source": "        last_hidden_state = hidden_states[-1]"
            },
            {
              "line": 85,
              "source": "        if not output_hidden_states:"
            },
            {
              "line": 86,
              "source": "            return BaseModelOutput(last_hidden_state=last_hidden_state)"
            },
            {
              "line": 87,
              "source": "        return BaseModelOutput("
            },
            {
              "line": 88,
              "source": "            last_hidden_state=last_hidden_state,"
            },
            {
              "line": 89,
              "source": "            hidden_states=hidden_states"
            },
            {
              "line": 90,
              "source": "        )"
            }
          ]
        }
      ]
    },
    {
      "id": "wrapper_cached_config",
      "path": "/home/work/Projects/Raina-laya/model/raina-laya-best-20261001/huggingface/hub/models--OpenMuQ--MuQ-MuLan-large/snapshots/2e01c796b71dca71b45251384c04cd7b237c9020/config.json",
      "sha256": "8fefc545ef87ecd9bcde7417dd03464370c48c321f36dcff20266a752079e468",
      "excerpts": [
        {
          "start_line": 12,
          "end_line": 16,
          "lines": [
            {
              "line": 12,
              "source": "  \"audio_model\": {"
            },
            {
              "line": 13,
              "source": "    \"name\": \"OpenMuQ/MuQ-large-msd-iter\","
            },
            {
              "line": 14,
              "source": "    \"model_dim\": 1024,"
            },
            {
              "line": 15,
              "source": "    \"use_layer_idx\": -1"
            },
            {
              "line": 16,
              "source": "  },"
            }
          ]
        }
      ]
    },
    {
      "id": "original_cached_config",
      "path": "/home/work/Projects/Raina-laya/model/raina-laya-best-20261001/huggingface/hub/models--OpenMuQ--MuQ-large-msd-iter/snapshots/0562a57814f6f8bbd9fdea0a25921a2fce1a841a/config.json",
      "sha256": "237335ee27d8fb951ce778701a12a79e06c51ae636dd786f97e45f51ce532543",
      "excerpts": [
        {
          "start_line": 5,
          "end_line": 7,
          "lines": [
            {
              "line": 5,
              "source": "  \"encoder_depth\": 12,"
            },
            {
              "line": 6,
              "source": "  \"encoder_dim\": 1024,"
            },
            {
              "line": 7,
              "source": "  \"features\": ["
            }
          ]
        }
      ]
    }
  ]
}