From 20f8ec877a99ce2ebf193cb1e2e773cfc77b7c41 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 00:09:32 +0300
Subject: [PATCH] remove type annotations in new code because presumably they
 don't work in 3.7

---
 modules/prompt_parser.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 800b12c7..ee4c5d02 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -175,14 +175,14 @@ def get_multicond_prompt_list(prompts):
 
 class ComposableScheduledPromptConditioning:
     def __init__(self, schedules, weight=1.0):
-        self.schedules: list[ScheduledPromptConditioning] = schedules
+        self.schedules = schedules  # : list[ScheduledPromptConditioning]
         self.weight: float = weight
 
 
 class MulticondLearnedConditioning:
     def __init__(self, shape, batch):
         self.shape: tuple = shape  # the shape field is needed to send this object to DDIM/PLMS
-        self.batch: list[list[ComposableScheduledPromptConditioning]] = batch
+        self.batch = batch  # : list[list[ComposableScheduledPromptConditioning]]
 
 
 def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
@@ -203,7 +203,7 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne
     return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
 
 
-def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
+def reconstruct_cond_batch(c, current_step):  # c: list[list[ScheduledPromptConditioning]]
     param = c[0][0].cond
     res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
     for i, cond_schedule in enumerate(c):