From d820e1fecfa485712696d38533341784f7394be2 Mon Sep 17 00:00:00 2001
From: Hayk Martiros <hayk.mart@gmail.com>
Date: Mon, 26 Dec 2022 17:39:55 -0800
Subject: [PATCH] Fix minor errors

Topic: clean_rewrite
---
 riffusion/external/prompt_weighting.py | 26 +++++++++++++-------------
 test/print_exif_test.py                |  4 ++--
 2 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/riffusion/external/prompt_weighting.py b/riffusion/external/prompt_weighting.py
index 5c71486..160b7a9 100644
--- a/riffusion/external/prompt_weighting.py
+++ b/riffusion/external/prompt_weighting.py
@@ -8,10 +8,10 @@ License: Apache 2.0
 # ruff: noqa
 # mypy: ignore-errors
 
-import re
-from typing import List, Optional, Union
-
 import logging
+import re
+import typing as T
+
 import torch
 
 from diffusers import StableDiffusionPipeline
@@ -126,7 +126,7 @@ def parse_prompt_attention(text):
     return res
 
 
-def get_prompts_with_weights(pipe: StableDiffusionPipeline, prompt: List[str], max_length: int):
+def get_prompts_with_weights(pipe: StableDiffusionPipeline, prompt: T.List[str], max_length: int):
     r"""
     Tokenize a list of prompts and return its tokens with weights of each token.
     No padding, starting or ending token is included.
@@ -195,7 +195,7 @@ def get_unweighted_text_embeddings(
     pipe: StableDiffusionPipeline,
     text_input: torch.Tensor,
     chunk_length: int,
-    no_boseos_middle: Optional[bool] = True,
+    no_boseos_middle: T.Optional[bool] = True,
 ) -> torch.FloatTensor:
     """
     When the length of tokens is a multiple of the capacity of the text encoder,
@@ -235,12 +235,12 @@ def get_unweighted_text_embeddings(
 
 def get_weighted_text_embeddings(
     pipe: StableDiffusionPipeline,
-    prompt: Union[str, List[str]],
-    uncond_prompt: Optional[Union[str, List[str]]] = None,
-    max_embeddings_multiples: Optional[int] = 3,
-    no_boseos_middle: Optional[bool] = False,
-    skip_parsing: Optional[bool] = False,
-    skip_weighting: Optional[bool] = False,
+    prompt: T.Union[str, T.List[str]],
+    uncond_prompt: T.Optional[T.Union[str, T.List[str]]] = None,
+    max_embeddings_multiples: T.Optional[int] = 3,
+    no_boseos_middle: T.Optional[bool] = False,
+    skip_parsing: T.Optional[bool] = False,
+    skip_weighting: T.Optional[bool] = False,
     **kwargs,
 ) -> T.Tuple[torch.FloatTensor, T.Optional[torch.FloatTensor]]:
     r"""
@@ -251,9 +251,9 @@ def get_weighted_text_embeddings(
     Args:
         pipe (`StableDiffusionPipeline`):
             Pipe to provide access to the tokenizer and the text encoder.
-        prompt (`str` or `List[str]`):
+        prompt (`str` or `T.List[str]`):
             The prompt or prompts to guide the image generation.
-        uncond_prompt (`str` or `List[str]`):
+        uncond_prompt (`str` or `T.List[str]`):
             The unconditional prompt or prompts for guide the image generation. If unconditional prompt
             is provided, the embeddings of prompt and uncond_prompt are concatenated.
         max_embeddings_multiples (`int`, *optional*, defaults to `3`):
diff --git a/test/print_exif_test.py b/test/print_exif_test.py
index 82b6d94..ad32b00 100644
--- a/test/print_exif_test.py
+++ b/test/print_exif_test.py
@@ -28,5 +28,5 @@ class PrintExifTest(TestCase):
             print_exif(image=str(image_path))
 
         # Check that a couple of values are printed
-        self.assertTrue("NUM_FREQUENCIES: 512" in stdout.getvalue())
-        self.assertTrue("SAMPLE_RATE: 44100" in stdout.getvalue())
+        self.assertTrue("NUM_FREQUENCIES      =             512" in stdout.getvalue())
+        self.assertTrue("SAMPLE_RATE          =           44100" in stdout.getvalue())