From f79926b73d9a731208b5f67601e263aa848c41a9 Mon Sep 17 00:00:00 2001
From: vfbd <ve.forbryderne@gmail.com>
Date: Mon, 22 Aug 2022 16:51:09 -0400
Subject: [PATCH] Fix some more typos in prompt_tuner.py

---
 prompt_tuner.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/prompt_tuner.py b/prompt_tuner.py
index 2fbd6ee2..ea0efd3b 100644
--- a/prompt_tuner.py
+++ b/prompt_tuner.py
@@ -127,28 +127,28 @@ def get_tokenizer(model_id, revision=None) -> transformers.PreTrainedTokenizerBa
                 tokenizer = GPT2TokenizerFast.from_pretrained(model_id, revision=revision, cache_dir="cache")
             except Exception as e:
                 tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=revision, cache_dir="cache")
-    elif(os.path.isdir("models/{}".format(vars.model.replace('/', '_')))):
+    elif(os.path.isdir("models/{}".format(model_id.replace('/', '_')))):
         try:
-            tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=revision, cache_dir="cache")
+            tokenizer = AutoTokenizer.from_pretrained("models/{}".format(model_id.replace('/', '_')), revision=revision, cache_dir="cache")
         except Exception as e:
             pass
         try:
-            tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=revision, cache_dir="cache", use_fast=False)
+            tokenizer = AutoTokenizer.from_pretrained("models/{}".format(model_id.replace('/', '_')), revision=revision, cache_dir="cache", use_fast=False)
         except Exception as e:
             try:
-                tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=revision, cache_dir="cache")
+                tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(model_id.replace('/', '_')), revision=revision, cache_dir="cache")
             except Exception as e:
                 tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=revision, cache_dir="cache")
     else:
         try:
-            tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=revision, cache_dir="cache")
+            tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision, cache_dir="cache")
         except Exception as e:
             pass
         try:
-            tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=revision, cache_dir="cache", use_fast=False)
+            tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision, cache_dir="cache", use_fast=False)
         except Exception as e:
             try:
-                tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, revision=revision, cache_dir="cache")
+                tokenizer = GPT2TokenizerFast.from_pretrained(model_id, revision=revision, cache_dir="cache")
             except Exception as e:
                 tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=revision, cache_dir="cache")
 
@@ -474,20 +474,20 @@ class TrainerBase(abc.ABC):
                 if("out of memory" in traceback.format_exc().lower()):
                     raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
                 model     = GPTNeoPromptTuningLM.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache")
-        elif(os.path.isdir("models/{}".format(vars.model.replace('/', '_')))):
+        elif(os.path.isdir("models/{}".format(self.data.ckpt_path.replace('/', '_')))):
             try:
-                model     = AutoPromptTuningLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=REVISION, cache_dir="cache")
+                model     = AutoPromptTuningLM.from_pretrained("models/{}".format(self.data.ckpt_path.replace('/', '_')), revision=REVISION, cache_dir="cache")
             except Exception as e:
                 if("out of memory" in traceback.format_exc().lower()):
                     raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
-                model     = GPTNeoPromptTuningLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=REVISION, cache_dir="cache")
+                model     = GPTNeoPromptTuningLM.from_pretrained("models/{}".format(self.data.ckpt_path.replace('/', '_')), revision=REVISION, cache_dir="cache")
         else:
             try:
-                model     = AutoPromptTuningLM.from_pretrained(vars.model, revision=REVISION, cache_dir="cache")
+                model     = AutoPromptTuningLM.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache")
             except Exception as e:
                 if("out of memory" in traceback.format_exc().lower()):
                     raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
-                model     = GPTNeoPromptTuningLM.from_pretrained(vars.model, revision=REVISION, cache_dir="cache")
+                model     = GPTNeoPromptTuningLM.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache")
 
         if step == 0:
             soft_embeddings = self.get_initial_soft_embeddings(model)