+# This is one order of magnitude more complicated than I expected
+
+
+def flash_back_time_src(N, H, t0, t1, CL, CH, proba, device):
+ # starting flash backs
+ fb_start = (torch.rand(N, CH, t1 - t0, device=device) <= proba).long()
+ fb_start[:, :, -CL:] = 0
+ fb_start[:, :, :CL] = 0
+
+ # Remove series longer than CL
+ fb_body = fb_start.clone()
+ fb_body[:, :, CL + 1 :] -= fb_start[:, :, : -(CL + 1)]
+ fb_body = fb_body.cumsum(dim=2)
+ fb_start = fb_start * (fb_body == 1)
+
+ # pick past starting source times
+ src_time = (
+ fb_start
+ * (
+ torch.rand(fb_start.size(), device=fb_start.device)
+ * (torch.arange(fb_start.size(2), device=fb_start.device) - CL)[
+ None, None, :
+ ]
+ ).long()
+ )
+ src_time[:, :, CL:] -= src_time.clone()[:, :, :-CL]
+ src_time = src_time.cumsum(dim=2)
+
+ src_head = fb_start * torch.randint(H, fb_start.size(), device=fb_start.device)
+ src_head[:, :, CL:] -= src_head.clone()[:, :, :-CL]
+ src_head = src_head.cumsum(dim=2)
+
+ # combine
+ src_delta = fb_start.clone()
+ src_delta[:, :, CL:] -= fb_start[:, :, :-CL]
+ src_delta = src_delta.cumsum(dim=2)
+ src_delta[:, :, CL:] -= CL * fb_start[:, :, :-CL]
+ src_time += src_delta.cumsum(dim=2) - 1
+
+ return src_time, src_head
+
+
+def insert_flash_back(rec_V, V, rec_K, K, t0, t1, CL, proba):
+ N, H, CH = V.size(0), V.size(1), rec_V.size(1)
+
+ fbt, fbh = flash_back_time_src(N, H, t0, t1, CL, CH, proba, rec_V.device)
+
+ fbt_V = fbt[:, :, :, None].expand_as(rec_V[:, :, t0:t1])
+ fbh_V = fbh[:, :, :, None].expand_as(rec_V[:, :, t0:t1])
+ t = fbt_V.clamp(min=0)
+ n = torch.arange(V.size(0), device=V.device)[:, None, None, None].expand_as(
+ rec_V[:, :, t0:t1]
+ )
+ d = torch.arange(V.size(3), device=V.device)[None, None, None, :].expand_as(
+ rec_V[:, :, t0:t1]
+ )
+ q = V[:, :, t0:t1][n, fbh_V, t, d]
+ rec_V[:, :, t0:t1] = q * (fbt_V >= 0) + rec_V[:, :, t0:t1] * (fbt_V < 0)
+
+ fbt_K = fbt[:, :, :, None].expand_as(rec_K[:, :, t0:t1])
+ fbh_K = fbh[:, :, :, None].expand_as(rec_K[:, :, t0:t1])
+ t = fbt_K.clamp(min=0)
+ n = torch.arange(K.size(0), device=K.device)[:, None, None, None].expand_as(
+ rec_K[:, :, t0:t1]
+ )
+ d = torch.arange(K.size(3), device=K.device)[None, None, None, :].expand_as(
+ rec_K[:, :, t0:t1]
+ )
+ q = K[:, :, t0:t1][n, fbh_K, t, d]
+ rec_K[:, :, t0:t1] = q * (fbt_K >= 0) + rec_K[:, :, t0:t1] * (fbt_K < 0)
+
+ # print("SANITY", (fbt_K >=0).float().sum()/fbt_K.numel())
+
+
+######################################################################
+