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-  <rect x="614.0" y="512" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="120.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="146.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="172.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="198.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="224.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="250.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="276.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="302.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="328.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="354.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="380.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="406.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="432.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="458.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="484.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="510.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="588.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="614.0" y="538" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="120.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="146.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="172.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="198.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="224.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="250.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="276.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="302.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="328.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="354.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="380.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="406.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="432.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="458.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="484.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="510.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="588.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="614.0" y="564" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="120.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="146.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="172.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="198.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="224.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="250.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="276.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="302.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#1e293b" />
-  <rect x="328.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="354.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="380.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="406.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="432.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="458.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="484.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="510.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="588.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="614.0" y="590" width="24.0" height="24" rx="3" ry="3" fill="#22c55e" />
-  <rect x="120" y="632" width="14" height="14" rx="3" ry="3" fill="#22c55e" />
-  <text x="140" y="644" fill="#f8fafc">attended-to</text>
-  <rect x="235.5" y="632" width="14" height="14" rx="3" ry="3" fill="#1e293b" />
-  <text x="255.5" y="644" fill="#f8fafc">masked (not causally ready)</text>
-  <text x="120" y="668" fill="#94a3b8" font-size="11" font-weight="600">Compressed cache contents</text>
-  <text x="120" y="686" fill="#f8fafc" font-size="11">C0  =  compress( The,  quick,  brown,  fox,  jumps,  over,  the,  lazy )</text>
-  <text x="120" y="704" fill="#f8fafc" font-size="11">C1  =  compress( dog,  ate,  a,  small,  red,  fish,  this,  morning )</text>
-</svg>
\ No newline at end of file
diff --git a/deepseek_v4/visualize_attention_masks.py b/deepseek_v4/visualize_attention_masks.py
deleted file mode 100644
index acac4f87c7e2eced8f37f60c5407b0cc68bb2926..0000000000000000000000000000000000000000
--- a/deepseek_v4/visualize_attention_masks.py
+++ /dev/null
@@ -1,591 +0,0 @@
-"""Visualise the per-layer-type attention mask DeepSeek-V4 actually feeds to
-`eager_attention_forward`, for the three attention layer types (sliding, CSA, HCA).
-Generates the SVGs embedded in `docs/source/en/model_doc/deepseek_v4.md`.
-
-We build a tiny V4 model (small enough that 16 source tokens × all compressed
-slots fits on screen), forward a dummy batch through it, wrap each attention
-layer's forward with a thin shim that replays the mask-extension logic
-(`cat([sliding_mask, block_bias])`) and captures the exact post-cat mask, then
-render each layer's mask in either:
-
-  * the `■` / `⬚` ANSI-coloured style of `transformers.utils.attention_visualizer`
-    (default; suppressed when stdout isn't a TTY or `NO_COLOR=1` is set), or
-  * an SVG grid suitable for embedding in markdown (with `--svg <DIR>`).
-
-For CSA layers the visualizer remaps the literal `[S, S·k]` flat-slot `block_bias`
-back to the more readable `[S, T_entries]` entry-visibility view (each compressor
-column is then a compressed *entry* rather than a gather slot), and shades cells
-red when an entry is causally available but the indexer's top-k did not pick it.
-
-Run from the repository root::
-
-    python docs/source/en/imgs/deepseek_v4/visualize_attention_masks.py
-    python docs/source/en/imgs/deepseek_v4/visualize_attention_masks.py \\
-        --svg docs/source/en/imgs/deepseek_v4
-"""
-
-from __future__ import annotations
-
-import argparse
-import os
-from pathlib import Path
-
-import torch
-import torch.nn.functional as F
-
-from transformers import DeepseekV4Config, DeepseekV4ForCausalLM
-from transformers.models.deepseek_v4.modeling_deepseek_v4 import (
-    DeepseekV4CSACompressor,
-    DeepseekV4HCACompressor,
-    DeepseekV4Indexer,
-    DeepseekV4PreTrainedModel,
-)
-from transformers.utils.output_capturing import OutputRecorder
-
-
-# Tiny config so the visualised matrices fit in a terminal. Compress rates are dialled
-# down (m_csa=4, m_hca=8 — vs the real 4/128) so HCA actually emits an entry at S=16.
-CFG = DeepseekV4Config(
-    vocab_size=64,
-    hidden_size=64,
-    moe_intermediate_size=64,
-    num_hidden_layers=3,
-    num_attention_heads=4,
-    num_key_value_heads=1,
-    head_dim=32,
-    partial_rotary_factor=8 / 32,
-    q_lora_rank=32,
-    o_groups=2,
-    o_lora_rank=16,
-    num_experts_per_tok=2,
-    n_routed_experts=4,
-    n_shared_experts=1,
-    mlp_layer_types=["moe", "moe", "moe"],
-    layer_types=["sliding_attention", "compressed_sparse_attention", "heavily_compressed_attention"],
-    compress_rates={"compressed_sparse_attention": 4, "heavily_compressed_attention": 8},
-    sliding_window=8,
-    hc_mult=2,
-    hc_sinkhorn_iters=3,
-    hc_eps=1.0e-6,
-    index_n_heads=2,
-    index_head_dim=16,
-    index_topk=2,
-    num_nextn_predict_layers=0,
-    swiglu_limit=10.0,
-    rope_theta=10000.0,
-    compress_rope_theta=160000.0,
-    max_position_embeddings=64,
-)
-
-
-# ANSI colours, matching `transformers.utils.attention_visualizer`.
-GREEN = "\033[92m"
-YELLOW = "\033[93m"
-DIM = "\033[90m"
-RESET = "\033[0m"
-BLACK_SQUARE = "■"
-WHITE_SQUARE = "⬚"
-
-
-def _meta_subtitle(meta: dict | None) -> str:
-    """One-liner subtitle describing the layer's compressor config."""
-    if meta is None:
-        return ""
-    parts: list[str] = []
-    if meta.get("compress_rate") is not None:
-        parts.append(f"m={meta['compress_rate']}")
-    if meta.get("index_topk") is not None:
-        parts.append(f"k={meta['index_topk']}")
-    if meta.get("T_entries"):
-        parts.append(f"{meta['T_entries']} entries")
-    return " · ".join(parts)
-
-
-def _render(
-    mask_2d: torch.Tensor,
-    query_labels: list[str],
-    kv_labels: list[str],
-    title: str,
-    *,
-    color: bool = True,
-    meta: dict | None = None,
-) -> str:
-    """Render a 2D additive mask (0 = visible, -inf = masked) as a ■ / ⬚ grid.
-
-    `mask_2d` is `[S_q, S_kv]`. Columns past `len(query_labels)` are the
-    compressor / indexer entries the caller cat'd into the mask via
-    `cat([sliding_causal_mask, block_bias], dim=-1)`. When `color=False`,
-    diagonal / compressor slots are marked with the distinguishing glyphs
-    `◆` and `▲` instead of ANSI colours so the output renders in markdown.
-    """
-    visible = (mask_2d > -1e30).bool()
-    n_q, n_kv = visible.shape
-    sliding_kv = len(query_labels)  # by construction kv labels begin with the sliding tokens
-
-    if color:
-        g, y, d, r = GREEN, YELLOW, DIM, RESET
-        visible_glyph = f"{g}{BLACK_SQUARE}{r}"
-        compr_glyph = f"{y}{BLACK_SQUARE}{r}"
-    else:
-        g = y = d = r = ""
-        visible_glyph = "▣"
-        compr_glyph = "▲"
-
-    max_q_label = max(len(q) for q in query_labels)
-    out: list[str] = []
-    out.append(title)
-    out.append("=" * len(title))
-    out.append(
-        f"  shape=[{n_q}, {n_kv}]   "
-        f"{visible_glyph} visible (sliding KV)   {compr_glyph} visible (compressor entry)   {WHITE_SQUARE} masked"
-    )
-    subtitle = _meta_subtitle(meta)
-    if subtitle:
-        out.append(f"  {subtitle}")
-    if meta and meta.get("topk_picks") is not None:
-        # Append per-query indexer picks so warm-up sentinels and entry choices are auditable.
-        picks_lines = [f"  indexer topk picks (entry id, -1 = warm-up sentinel):"]
-        for i, picks in enumerate(meta["topk_picks"][0]):  # B=1
-            picks_lines.append(f"    q{i:>2}: {picks}")
-        out.append("\n".join(picks_lines))
-
-    # Column headers (kv indices, stacked vertically across `width` lines).
-    width = max(2, len(str(n_kv - 1)))
-    for line in range(width):
-        header_chars = []
-        for j in range(n_kv):
-            label = str(j).rjust(width)
-            ch = label[line] if line < len(label) else " "
-            # Tint compressor-section header so it visually separates from the sliding section.
-            header_chars.append(f"{d}{ch}{r}" if j >= sliding_kv else ch)
-        prefix = " " * (max_q_label + 6)
-        out.append(prefix + " ".join(header_chars))
-
-    # Body rows.
-    for i in range(n_q):
-        row_cells = []
-        for j in range(n_kv):
-            if not visible[i, j]:
-                row_cells.append(WHITE_SQUARE)
-            elif j >= sliding_kv:
-                row_cells.append(compr_glyph)
-            else:
-                row_cells.append(visible_glyph)
-        out.append(f"{query_labels[i].rjust(max_q_label)} : {str(i).rjust(2)}  " + " ".join(row_cells))
-
-    out.append(f"  {compr_glyph} columns past index {sliding_kv - 1} are compressor / indexer slots (block_bias).")
-    return "\n".join(out)
-
-
-# SVG palette + geometry. Bigger cells so we can fit token-string labels comfortably,
-# and compressor columns are drawn `m` cells wide so the visual encodes the fact that
-# one compressed entry summarizes m source tokens (rather than a 1:1 KV slot like the
-# sliding section).
-SVG_CELL = 24
-SVG_GAP = 2
-SVG_PAD_LEFT = 120     # roomy enough for the longest row-label token ("morning") at 11pt
-SVG_PAD_TOP = 200      # title (20) + shape (40) + meta (60) + section header (~80) + col idx + rotated tokens
-SVG_PAD_RIGHT = 48
-SVG_PAD_BOTTOM = 56
-SVG_COLORS = {
-    # Dark-mode palette: black background, light text.
-    "background": "#0b1220",    # near-black with a hint of blue
-    "visible": "#22c55e",       # green-500, pops against black
-    "masked": "#1e293b",        # slate-800 — dim cells that are visible against bg but recede
-    "indexer_masked": "#ef4444",# red-500, also pops
-    "compressor": "#22c55e",    # same green as visible (single-color attended-to)
-    "separator": "#475569",     # slate-600
-    "text": "#f8fafc",          # slate-50 — main text on black
-    "muted": "#94a3b8",         # slate-400 — secondary text
-}
-
-# Static word list used as token labels on the SVG axes. Real token semantics don't
-# affect the mask (which depends only on positions), so these labels are decorative.
-SVG_TOKENS = [
-    "The", "quick", "brown", "fox", "jumps", "over", "the", "lazy",
-    "dog", "ate", "a", "small", "red", "fish", "this", "morning",
-]
-
-
-def _render_svg(mask_2d: torch.Tensor, title: str, sliding_kv: int, meta: dict | None = None) -> str:
-    """Render the mask as a standalone SVG document.
-
-    Rows = queries (top → bottom). The sliding K=V section is one cell per
-    source position (key index). The compressor section is `T_entries`
-    blocks each `m·cell` wide — the wide rectangle is the visual hint that
-    one compressed entry summarizes `m` source tokens (rather than 1:1).
-
-    Cells are coloured: green = visible (the query attends to this slot),
-    light gray = masked. Compressor entries are tinted amber when visible
-    to a given query and pale amber when masked, so the compressor section
-    reads as a distinct "compression band" even at a glance.
-    """
-    visible = (mask_2d > -1e30).bool()
-    n_q, n_kv = visible.shape
-    n_compr = n_kv - sliding_kv
-    m = (meta or {}).get("compress_rate") or 1
-    stride = SVG_CELL + SVG_GAP
-
-    # All cells (sliding + compressor) are the same `SVG_CELL` width — a single KV slot
-    # is a single KV slot regardless of which cache produced it. The compressor section
-    # is shifted right by `section_gap` so the `cat([sliding_mask, block_bias])` boundary
-    # reads clearly.
-    section_gap = stride * 2
-    col_x: list[float] = []
-    col_w: list[float] = []
-    cursor = float(SVG_PAD_LEFT)
-    for j in range(sliding_kv):
-        col_x.append(cursor)
-        col_w.append(SVG_CELL)
-        cursor += stride
-    if n_compr > 0:
-        cursor += section_gap
-    for _w in range(n_compr):
-        col_x.append(cursor)
-        col_w.append(SVG_CELL)
-        cursor += stride
-    grid_w = cursor - SVG_PAD_LEFT - stride
-    grid_h = n_q * stride - SVG_GAP
-
-    # Footer legend: one line per compressed entry mapping it to its source tokens.
-    legend_lines = []
-    for w_idx in range(n_compr):
-        a, b = w_idx * m, (w_idx + 1) * m - 1
-        toks = [SVG_TOKENS[p] if p < len(SVG_TOKENS) else f"t{p}" for p in range(a, b + 1)]
-        legend_lines.append(f"C{w_idx}  =  compress( {',  '.join(toks)} )")
-    footer_h = (len(legend_lines) * 18 + 32) if legend_lines else 0
-
-    w = SVG_PAD_LEFT + grid_w + SVG_PAD_RIGHT
-    h = SVG_PAD_TOP + grid_h + SVG_PAD_BOTTOM + footer_h
-
-    subtitle = _meta_subtitle(meta)
-    # Explicit `width` / `height` (matching viewBox) so renderers don't fall back to the
-    # 150×150 SVG default when the file is embedded without sizing CSS. `preserveAspectRatio`
-    # keeps it sharp when scaled.
-    elems: list[str] = [
-        f'<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 {w} {h}" '
-        f'width="{w}" height="{h}" preserveAspectRatio="xMinYMin meet" '
-        f'font-family="ui-monospace, SFMono-Regular, Menlo, monospace" font-size="11">',
-        f'  <rect x="0" y="0" width="{w}" height="{h}" fill="{SVG_COLORS["background"]}" />',
-        f'  <text x="{SVG_PAD_LEFT}" y="20" font-size="15" font-weight="600" fill="{SVG_COLORS["text"]}">{title}</text>',
-        f'  <text x="{SVG_PAD_LEFT}" y="40" fill="{SVG_COLORS["muted"]}">'
-        f'shape=[{n_q}, {n_kv}]   keys →   queries ↓</text>',
-    ]
-    if subtitle:
-        elems.append(
-            f'  <text x="{SVG_PAD_LEFT}" y="60" fill="{SVG_COLORS["compressor"]}">{subtitle}</text>'
-        )
-
-    # Column headers, top-down within the `SVG_PAD_TOP` gutter (above the grid line):
-    #   y =  78  : `C_w` (centered over the compressor block, bold green)
-    #   y =  92  : `pos a–b` source-position range for the compressor block
-    #   y = SVG_PAD_TOP - 70 = 270 : numeric column index (sliding) — just above the
-    #                                tallest rotated token so it reads as a tick label.
-    #   y = SVG_PAD_TOP - 60 .. SVG_PAD_TOP : rotated (-90°) per-position token labels
-    #                                          (sliding + per-source-position inside blocks)
-    #   y = SVG_PAD_TOP                       : grid begins.
-    idx_y = SVG_PAD_TOP - 70
-    token_baseline_y = SVG_PAD_TOP - 8
-    # Vertical extent of the rotated-token area we want the dashed separators to span —
-    # just the token region, not the full title/subtitle gutter.
-    token_top_y = SVG_PAD_TOP - 60
-    for j in range(sliding_kv):
-        cx = col_x[j] + col_w[j] / 2
-        elems.append(
-            f'  <text x="{cx:.1f}" y="{idx_y}" text-anchor="middle" '
-            f'fill="{SVG_COLORS["muted"]}" font-size="10">{j}</text>'
-        )
-        tok = SVG_TOKENS[j] if j < len(SVG_TOKENS) else f"t{j}"
-        elems.append(
-            f'  <text x="{cx:.1f}" y="{token_baseline_y}" text-anchor="start" '
-            f'transform="rotate(-90 {cx:.1f} {token_baseline_y})" '
-            f'fill="{SVG_COLORS["text"]}" font-size="12" font-style="italic">{tok}</text>'
-        )
-    # `C_w` label sits on the same row as the sliding-section indices — the compressor
-    # cell looks like any other KV slot, with its `C_w` identifier centered above it
-    # in the column-index band. The actual source-tokens-that-got-compressed mapping
-    # lives in the footer legend so the body of the diagram stays clean.
-    for w_idx in range(n_compr):
-        j = sliding_kv + w_idx
-        cx = col_x[j] + col_w[j] / 2
-        elems.append(
-            f'  <text x="{cx:.1f}" y="{idx_y}" text-anchor="middle" '
-            f'fill="{SVG_COLORS["visible"]}" font-size="11" font-weight="700">C{w_idx}</text>'
-        )
-
-    # Section headers + `cat` indicator above the column-index band. The dashed
-    # separator line marks the `cat([sliding_mask, block_bias], dim=-1)` join.
-    if sliding_kv > 0:
-        sliding_mid_x = (col_x[0] + col_x[sliding_kv - 1] + col_w[sliding_kv - 1]) / 2
-        elems.append(
-            f'  <text x="{sliding_mid_x:.1f}" y="{idx_y - 32}" text-anchor="middle" '
-            f'fill="{SVG_COLORS["text"]}" font-size="12" font-weight="600">Sliding K=V cache</text>'
-        )
-    if n_compr > 0:
-        compr_mid_x = (col_x[sliding_kv] + col_x[-1] + col_w[-1]) / 2
-        elems.append(
-            f'  <text x="{compr_mid_x:.1f}" y="{idx_y - 32}" text-anchor="middle" '
-            f'fill="{SVG_COLORS["text"]}" font-size="12" font-weight="600">Compressed cache</text>'
-        )
-        # `cat` operator centered in the section gap between the two sections.
-        cat_x = (col_x[sliding_kv - 1] + col_w[sliding_kv - 1] + col_x[sliding_kv]) / 2
-        elems.append(
-            f'  <text x="{cat_x:.1f}" y="{idx_y - 32}" text-anchor="middle" '
-            f'fill="{SVG_COLORS["muted"]}" font-size="14" font-weight="700">⊕</text>'
-        )
-        elems.append(
-            f'  <text x="{cat_x:.1f}" y="{idx_y - 18}" text-anchor="middle" '
-            f'fill="{SVG_COLORS["muted"]}" font-size="9">cat dim=-1</text>'
-        )
-
-    # Row labels: query index (small, muted) + token (italic, dark) on the left.
-    # `SVG_PAD_LEFT` is sized to fit the longest token in the static word list.
-    for i in range(n_q):
-        cy = SVG_PAD_TOP + i * stride + SVG_CELL / 2 + 4
-        elems.append(
-            f'  <text x="6" y="{cy:.1f}" fill="{SVG_COLORS["muted"]}" font-size="10">q{i:>2}</text>'
-        )
-        tok = SVG_TOKENS[i] if i < len(SVG_TOKENS) else f"t{i}"
-        elems.append(
-            f'  <text x="{SVG_PAD_LEFT - 8}" y="{cy:.1f}" text-anchor="end" '
-            f'fill="{SVG_COLORS["text"]}" font-size="12" font-style="italic">{tok}</text>'
-        )
-
-    # Dashed vertical separator between sliding K=V and compressor sections.
-    if 0 < sliding_kv < n_kv:
-        sep_x = col_x[sliding_kv - 1] + col_w[sliding_kv - 1] + section_gap / 2
-        elems.append(
-            f'  <line x1="{sep_x:.1f}" y1="{SVG_PAD_TOP - 28}" x2="{sep_x:.1f}" '
-            f'y2="{SVG_PAD_TOP + grid_h:.1f}" stroke="{SVG_COLORS["separator"]}" '
-            f'stroke-width="1" stroke-dasharray="4 3" />'
-        )
-
-    # Indexer-rejected mask (CSA only): causally available compressor entry that the
-    # indexer's top-k chose not to pick. Rendered red to distinguish from the plain
-    # "not yet causally ready" masking (light gray).
-    rejected = (meta or {}).get("indexer_rejected")
-    if rejected is not None:
-        if rejected.ndim == 4:
-            rejected = rejected[0, 0]
-        elif rejected.ndim == 3:
-            rejected = rejected[0]
-
-    # Cells.
-    for i in range(n_q):
-        for j in range(n_kv):
-            x = col_x[j]
-            y = SVG_PAD_TOP + i * stride
-            in_compressor = j >= sliding_kv
-            is_rejected = rejected is not None and bool(rejected[i, j])
-            if visible[i, j]:
-                fill = SVG_COLORS["compressor"] if in_compressor else SVG_COLORS["visible"]
-            elif is_rejected:
-                fill = SVG_COLORS["indexer_masked"]
-            else:
-                fill = SVG_COLORS["masked"]
-            elems.append(
-                f'  <rect x="{x:.1f}" y="{y}" width="{col_w[j]:.1f}" height="{SVG_CELL}" '
-                f'rx="3" ry="3" fill="{fill}" />'
-            )
-
-    # Legend at the bottom. The red legend item is only meaningful for CSA (the only
-    # layer type that has an indexer + top-k pick step), so we include it only when
-    # the captured mask actually has rejected cells.
-    legend_y = SVG_PAD_TOP + grid_h + 30
-    legend_items = [
-        ("attended-to", SVG_COLORS["visible"]),
-        ("masked (not causally ready)", SVG_COLORS["masked"]),
-    ]
-    if rejected is not None and bool(rejected.any()):
-        legend_items.append(("indexer-masked (top-k did not pick)", SVG_COLORS["indexer_masked"]))
-    x_cursor = SVG_PAD_LEFT
-    for label, color in legend_items:
-        elems.append(
-            f'  <rect x="{x_cursor}" y="{legend_y - 12}" width="14" height="14" rx="3" ry="3" fill="{color}" />'
-        )
-        elems.append(
-            f'  <text x="{x_cursor + 20}" y="{legend_y}" fill="{SVG_COLORS["text"]}">{label}</text>'
-        )
-        x_cursor += 20 + len(label) * 6.5 + 24
-
-    # Footer: `C_w = compress(token, token, …)` lines, one per compressed entry. Says
-    # explicitly which source tokens each compressed-cache slot summarises — replaces
-    # the per-cell rotated-token annotations we used to render above each C_w block
-    # when the compressor section had wide cells.
-    if legend_lines:
-        footer_y = legend_y + 24
-        elems.append(
-            f'  <text x="{SVG_PAD_LEFT}" y="{footer_y}" '
-            f'fill="{SVG_COLORS["muted"]}" font-size="11" font-weight="600">Compressed cache contents</text>'
-        )
-        for line_idx, line in enumerate(legend_lines):
-            elems.append(
-                f'  <text x="{SVG_PAD_LEFT}" y="{footer_y + 18 + line_idx * 18}" '
-                f'fill="{SVG_COLORS["text"]}" font-size="11">{line}</text>'
-            )
-
-    elems.append("</svg>")
-    return "\n".join(elems)
-
-
-def _sliding_causal_mask(seq_len: int, sliding_window: int) -> torch.Tensor:
-    """Standard `[1, 1, S, S]` sliding-window-causal additive mask (0 visible, -inf masked).
-    Identical to what `create_sliding_window_causal_mask` builds for the sliding section
-    of every V4 attention layer — we synthesise it here so the visualiser stays decoupled
-    from the model's internal mask-building."""
-    q = torch.arange(seq_len)
-    k = torch.arange(seq_len)
-    diff = q.view(-1, 1) - k.view(1, -1)
-    visible = (diff >= 0) & (diff < sliding_window)
-    return torch.where(visible, torch.zeros((), dtype=torch.float32), torch.full((), float("-inf"))).view(1, 1, seq_len, seq_len)
-
-
-def main() -> None:
-    torch.manual_seed(0)
-
-    # ────────────────────────────────────────────────────────────────────────────────
-    # Extend `DeepseekV4PreTrainedModel._can_record_outputs` with the compressor /
-    # indexer outputs we need to reconstruct the visualised mask. The base class
-    # already registers `router_logits` / `hidden_states` / `attentions`; we add three
-    # more keys, each routed by `OutputRecorder(target_class=…, index=…)`:
-    #
-    #   * `csa_block_bias`  ← `DeepseekV4CSACompressor.forward()` returns `(gathered, block_bias)`,
-    #                          we grab index 1 (the per-query bias `[B, 1, S, S*k]`).
-    #   * `hca_block_bias`  ← `DeepseekV4HCACompressor.forward()` likewise (already entry-resolved).
-    #   * `indexer_topk`    ← `DeepseekV4Indexer.forward()` returns a single tensor `[B, S, k]`.
-    #
-    # Setting this *before* instantiating the model is what `PreTrainedModel.__init__`
-    # picks up to populate `_CAN_RECORD_REGISTRY`. The `@capture_outputs` decorator on
-    # `DeepseekV4Model.forward` then installs the corresponding `register_forward_hook`
-    # calls lazily on first request and stashes the captures on the returned
-    # `ModelOutput`. We just flip `config.output_<key>=True` to switch them on.
-    # ────────────────────────────────────────────────────────────────────────────────
-    DeepseekV4PreTrainedModel._can_record_outputs = {
-        **(DeepseekV4PreTrainedModel._can_record_outputs or {}),
-        "csa_block_bias": OutputRecorder(DeepseekV4CSACompressor, index=1),
-        "hca_block_bias": OutputRecorder(DeepseekV4HCACompressor, index=1),
-        "indexer_topk": OutputRecorder(DeepseekV4Indexer, index=0),
-    }
-
-    cfg = CFG
-    for key in ("csa_block_bias", "hca_block_bias", "indexer_topk"):
-        setattr(cfg, f"output_{key}", True)
-    full_model = DeepseekV4ForCausalLM(cfg).eval()
-    # `@capture_outputs` decorates `DeepseekV4Model.forward` (the inner model). The outer
-    # `DeepseekV4ForCausalLM.forward` builds a `CausalLMOutput` that only forwards logits
-    # and last_hidden_state, so our extra capture keys disappear if we go via the outer.
-    # Call the inner directly — we don't need logits to visualise masks.
-    inner_model = full_model.model
-
-    seq_len = 16
-    input_ids = torch.arange(seq_len).unsqueeze(0) % cfg.vocab_size  # [1, S]
-    with torch.no_grad():
-        out = inner_model(input_ids=input_ids)
-
-    # Tuples (one entry per layer of the matching module class). With our 3-layer
-    # config: 1 CSA layer + 1 HCA layer + 1 sliding-only layer.
-    csa_block_biases = getattr(out, "csa_block_bias", ()) or ()
-    hca_block_biases = getattr(out, "hca_block_bias", ()) or ()
-    indexer_topks = getattr(out, "indexer_topk", ()) or ()
-
-    # ────────────────────────────────────────────────────────────────────────────────
-    # Build the display mask + metadata per layer from the captured outputs.
-    # ────────────────────────────────────────────────────────────────────────────────
-    sliding_mask = _sliding_causal_mask(seq_len, cfg.sliding_window)  # [1, 1, S, S]
-    captured: dict[int, tuple[str, torch.Tensor, dict]] = {}
-    csa_iter = iter(csa_block_biases)
-    hca_iter = iter(hca_block_biases)
-    indexer_iter = iter(indexer_topks)
-    for layer_idx, layer in enumerate(inner_model.layers):
-        attn = layer.self_attn
-        layer_type = attn.layer_type
-        meta = {
-            "compress_rate": (attn.compressor.compress_rate if attn.compressor is not None else None),
-            "index_topk": (
-                attn.compressor.indexer.index_topk
-                if attn.compressor is not None and getattr(attn.compressor, "indexer", None) is not None
-                else None
-            ),
-            "T_entries": seq_len // attn.compressor.compress_rate if attn.compressor is not None else 0,
-            "topk_picks": None,
-            "indexer_rejected": None,
-        }
-        if attn.compressor is None:
-            captured[layer_idx] = (layer_type, sliding_mask, meta)
-            continue
-
-        if layer_type == "heavily_compressed_attention":
-            block_bias = next(hca_iter).detach().cpu()
-            display_mask = torch.cat([sliding_mask, block_bias.to(sliding_mask.dtype)], dim=-1)
-        elif layer_type == "compressed_sparse_attention":
-            _ = next(csa_iter)  # consume the (literal) `[B, 1, S, S*k]` flat-slot bias
-            topk = next(indexer_iter).detach().cpu()  # [B, S, k]
-            meta["topk_picks"] = topk.tolist()
-            # Remap the flat-slot view to the more readable entry-visibility view via
-            # the indexer's per-query top-k picks (one-hot + OR-reduce sidesteps the
-            # duplicate-index clobber that `scatter_` would hit on warm-up sentinels).
-            T_entries = meta["T_entries"]
-            valid = topk >= 0
-            safe = topk.clamp(min=0)
-            oh = F.one_hot(safe, num_classes=T_entries).bool() & valid.unsqueeze(-1)
-            em = oh.any(dim=-2)  # [B, S, T_entries]
-            entry_bias = torch.where(em, torch.zeros((), dtype=torch.float32), torch.full((), float("-inf")))
-            display_mask = torch.cat([sliding_mask, entry_bias.unsqueeze(1)], dim=-1)
-            # Red-highlight cells: causally available entry that the indexer rejected.
-            causal_threshold = torch.arange(seq_len).add(1).floor_divide(attn.compressor.compress_rate)
-            entry_idx = torch.arange(T_entries)
-            causally_available = entry_idx.view(1, -1) < causal_threshold.view(-1, 1)
-            rejected = causally_available & ~em[0]
-            meta["indexer_rejected"] = torch.cat(
-                [torch.zeros((seq_len, seq_len), dtype=torch.bool), rejected], dim=-1
-            )
-        else:
-            display_mask = sliding_mask
-
-        captured[layer_idx] = (layer_type, display_mask, meta)
-
-    color = os.environ.get("NO_COLOR") is None and os.isatty(1)
-
-    svg_dir = main.svg_dir if hasattr(main, "svg_dir") else None
-    if svg_dir is not None:
-        svg_dir.mkdir(parents=True, exist_ok=True)
-
-    print()
-    for layer_idx in sorted(captured):
-        layer_type, mask, meta = captured[layer_idx]
-        if mask is None:
-            continue
-        if mask.ndim == 4:
-            mask_2d = mask[0, 0]
-        elif mask.ndim == 3:
-            mask_2d = mask[0]
-        else:
-            mask_2d = mask
-
-        q_labels = [f"q{i}" for i in range(mask_2d.shape[0])]
-        kv_labels_sliding = [f"k{i}" for i in range(seq_len)]
-        n_compressor = mask_2d.shape[1] - seq_len
-        kv_labels_compressor = [f"c{i}" for i in range(n_compressor)]
-        kv_labels = kv_labels_sliding + kv_labels_compressor
-
-        title = f"Layer {layer_idx}: {layer_type}"
-        print(_render(mask_2d, q_labels, kv_labels, title, color=color, meta=meta))
-        print()
-        if svg_dir is not None:
-            svg_path = svg_dir / f"deepseek_v4_mask_layer{layer_idx}_{layer_type}.svg"
-            svg_path.write_text(_render_svg(mask_2d, title, sliding_kv=seq_len, meta=meta))
-            print(f"  wrote {svg_path}")
-            print()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument(
-        "--svg",
-        type=Path,
-        default=None,
-        help="Directory to write one SVG per layer (in addition to the ANSI / plain-text terminal output).",
-    )
-    args = parser.parse_args()
-    main.svg_dir = args.svg  # type: ignore[attr-defined]
-    main()
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diff --git a/diffusers/helios/interactive_showcases1.txt b/diffusers/helios/interactive_showcases1.txt
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-A stylish, young woman riding a sleek black motorcycle down a bustling city street. She wears a fitted leather jacket, dark sunglasses, and a helmet with a visor up, revealing her confident expression. Her posture is relaxed yet controlled as she grips the handlebars firmly. The motorcycle's engine roars as she navigates through traffic. The urban backdrop includes tall buildings, street signs, and passing cars, creating a vibrant, dynamic scene. Medium shot focusing on the girl and the motorcycle, capturing the energy of the city.
-The stylish young woman on the sleek black motorcycle leans slightly to the right as she smoothly changes lanes. Her fitted leather jacket gleams under the city lights, and her dark sunglasses reflect the passing scenery. With her helmet visor up, a faint smile plays on her lips, showing her enjoyment of the ride. Suddenly, she extends her left hand to adjust the side mirror, briefly checking the reflection of the vibrant urban backdrop behind her before returning her grip to the handlebars. Medium shot focusing on the girl and the motorcycle, capturing the energy of the city.
-The stylish young woman on the sleek black motorcycle accelerates gently, causing her hair to flutter beneath the edge of her helmet. Her fitted leather jacket remains snug against the wind, while her dark sunglasses shield her eyes from the glare. As she cruises past towering skyscrapers and vibrant street signs, she reaches down with her right hand to toggle a switch on the dashboard, activating the motorcycle's bright turn signal. The urban environment blurs slightly in the background, emphasizing her focused movement through the traffic. Medium shot focusing on the girl and the motorcycle, capturing the energy of the city.
-The stylish young woman on the sleek black motorcycle maintains her steady pace through the busy urban corridor. Her fitted leather jacket and dark sunglasses remain prominent against the backdrop of tall buildings and passing cars. With her helmet visor up, her confident expression is clear as she navigates the road. As she rides, she briefly lifts her left boot to shift gears, pressing down decisively on the pedal near the footrest. The engine note changes pitch slightly, blending with the city noise, while street signs flash by in the background. Medium shot focusing on the girl and the motorcycle, capturing the energy of the city.
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