code: def test_pattern_to_layout():
      BLOCK = 16
      SIZE = 128
      LAYOUT_SIZE = SIZE // BLOCK
  
      # All ones
      mask1 = torch.ones((SIZE, SIZE), dtype=torch.bool)
      layout1 = AP.pattern_to_layout(mask1, BLOCK)
      ref1 = torch.ones((LAYOUT_SIZE, LAYOUT_SIZE), dtype=torch.long)
      assert torch.allclose(layout1, ref1)
  
      # Diagonal -> expect block diagonal
      mask2 = torch.eye(SIZE, dtype=torch.bool)
      layout2 = AP.pattern_to_layout(mask2, BLOCK)
      ref2 = torch.eye(LAYOUT_SIZE, dtype=torch.long)
      assert torch.allclose(layout2, ref2)
  
      # Lower triangular, without the diagonal
      # note that the layout will need to have the diagonal, else the coefficients close enough would not be computed
      mask3 = torch.tril(torch.ones((SIZE, SIZE)), diagonal=-1).to(
          torch.bool
      )
      layout3 = AP.pattern_to_layout(mask3, BLOCK)
      ref3 = torch.tril(
          torch.ones((LAYOUT_SIZE, LAYOUT_SIZE)), diagonal=0
      ).to(torch.long)
      assert torch.allclose(layout3, ref3)
  
      # Handle heads properly
      mask = torch.cat((mask1, mask2, mask3))
      layout = AP.pattern_to_layout(mask, BLOCK)
      assert torch.allclose(layout, torch.cat((ref1, ref2, ref3)))
  
      # Catch problematic dimensions
      mask_off = torch.ones((SIZE + 3, SIZE), dtype=torch.bool)
      with pytest.raises(AssertionError):
          AP.pattern_to_layout(mask_off, BLOCK)
  
imports: import itertools
  import pytest
  import torch
  import xformers.components.attention.attention_patterns
  from xformers.components.attention.sparsity_config import BigBirdSparsityConfig, BSLongformerSparsityConfig, DenseSparsityConfig, FixedSparsityConfig, VariableSparsityConfig
label_desc: Use image node_id 14 for calling a global function with example usage: test_pattern_to_layout() without return types