Migrate from yapf to black

.pre-commit-config.yaml

@@ -1,7 +1,6 @@
-exclude: patch
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.2.0
+  rev: v4.4.0
   hooks:
   - id: check-executables-have-shebangs
   - id: check-json
@@ -9,29 +8,43 @@ repos:
   - id: check-shebang-scripts-are-executable
   - id: check-toml
   - id: check-yaml
-  - id: double-quote-string-fixer
   - id: end-of-file-fixer
   - id: mixed-line-ending
-    args: ['--fix=lf']
+    args: ["--fix=lf"]
   - id: requirements-txt-fixer
   - id: trailing-whitespace
 - repo: https://github.com/myint/docformatter
-  rev: v1.4
+  rev: v1.7.5
   hooks:
   - id: docformatter
-    args: ['--in-place']
+    args: ["--in-place"]
 - repo: https://github.com/pycqa/isort
   rev: 5.12.0
   hooks:
     - id: isort
+      args: ["--profile", "black"]
 - repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.991
+  rev: v1.5.1
   hooks:
     - id: mypy
-      args: ['--ignore-missing-imports']
-      additional_dependencies: ['types-python-slugify']
-- repo: https://github.com/google/yapf
-  rev: v0.32.0
+      args: ["--ignore-missing-imports"]
+      additional_dependencies: ["types-python-slugify", "types-requests", "types-PyYAML"]
+- repo: https://github.com/psf/black
+  rev: 23.9.1
   hooks:
-  - id: yapf
-    args: ['--parallel', '--in-place']
+    - id: black
+      language_version: python3.10
+      args: ["--line-length", "119"]
+- repo: https://github.com/kynan/nbstripout
+  rev: 0.6.1
+  hooks:
+    - id: nbstripout
+      args: ["--extra-keys", "metadata.interpreter metadata.kernelspec cell.metadata.pycharm"]
+- repo: https://github.com/nbQA-dev/nbQA
+  rev: 1.7.0
+  hooks:
+    - id: nbqa-black
+    - id: nbqa-pyupgrade
+      args: ["--py37-plus"]
+    - id: nbqa-isort
+      args: ["--float-to-top"]

.style.yapf

@@ -1,5 +0,0 @@
-[style]
-based_on_style = pep8
-blank_line_before_nested_class_or_def = false
-spaces_before_comment = 2
-split_before_logical_operator = true

.vscode/settings.json

@@ -0,0 +1,21 @@
+{
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter",
+        "editor.formatOnType": true,
+        "editor.codeActionsOnSave": {
+            "source.organizeImports": true
+        }
+    },
+    "black-formatter.args": [
+        "--line-length=119"
+    ],
+    "isort.args": ["--profile", "black"],
+    "flake8.args": [
+        "--max-line-length=119"
+    ],
+    "ruff.args": [
+        "--line-length=119"
+    ],
+    "editor.formatOnSave": true,
+    "files.insertFinalNewline": true
+}

app.py

@@ -10,92 +10,99 @@ import PIL.Image
 import torch
 from transformers import AutoProcessor, Blip2ForConditionalGeneration
 
-DESCRIPTION = '# [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2)'
+DESCRIPTION = "# [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2)"
 
-if (SPACE_ID := os.getenv('SPACE_ID')) is not None:
+if (SPACE_ID := os.getenv("SPACE_ID")) is not None:
     DESCRIPTION += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
 if not torch.cuda.is_available():
-    DESCRIPTION += '\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>'
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
 
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-MODEL_ID_OPT_6_7B = 'Salesforce/blip2-opt-6.7b'
-MODEL_ID_FLAN_T5_XXL = 'Salesforce/blip2-flan-t5-xxl'
+MODEL_ID_OPT_6_7B = "Salesforce/blip2-opt-6.7b"
+MODEL_ID_FLAN_T5_XXL = "Salesforce/blip2-flan-t5-xxl"
 
 if torch.cuda.is_available():
     model_dict = {
-        #MODEL_ID_OPT_6_7B: {
+        # MODEL_ID_OPT_6_7B: {
         #    'processor':
         #    AutoProcessor.from_pretrained(MODEL_ID_OPT_6_7B),
         #    'model':
         #    Blip2ForConditionalGeneration.from_pretrained(MODEL_ID_OPT_6_7B,
         #                                                  device_map='auto',
         #                                                  load_in_8bit=True),
-        #},
+        # },
         MODEL_ID_FLAN_T5_XXL: {
-            'processor':
-            AutoProcessor.from_pretrained(MODEL_ID_FLAN_T5_XXL),
-            'model':
-            Blip2ForConditionalGeneration.from_pretrained(MODEL_ID_FLAN_T5_XXL,
-                                                          device_map='auto',
-                                                          load_in_8bit=True),
+            "processor": AutoProcessor.from_pretrained(MODEL_ID_FLAN_T5_XXL),
+            "model": Blip2ForConditionalGeneration.from_pretrained(
+                MODEL_ID_FLAN_T5_XXL, device_map="auto", load_in_8bit=True
+            ),
         }
     }
 else:
     model_dict = {}
 
 
-def generate_caption(model_id: str, image: PIL.Image.Image,
-                     decoding_method: str, temperature: float,
-                     length_penalty: float, repetition_penalty: float) -> str:
+def generate_caption(
+    model_id: str,
+    image: PIL.Image.Image,
+    decoding_method: str,
+    temperature: float,
+    length_penalty: float,
+    repetition_penalty: float,
+) -> str:
     model_info = model_dict[model_id]
-    processor = model_info['processor']
-    model = model_info['model']
+    processor = model_info["processor"]
+    model = model_info["model"]
 
-    inputs = processor(images=image,
-                       return_tensors='pt').to(device, torch.float16)
+    inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
     generated_ids = model.generate(
         pixel_values=inputs.pixel_values,
-        do_sample=decoding_method == 'Nucleus sampling',
+        do_sample=decoding_method == "Nucleus sampling",
         temperature=temperature,
         length_penalty=length_penalty,
         repetition_penalty=repetition_penalty,
         max_length=50,
         min_length=1,
         num_beams=5,
-        top_p=0.9)
-    result = processor.batch_decode(generated_ids,
-                                    skip_special_tokens=True)[0].strip()
+        top_p=0.9,
+    )
+    result = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
     return result
 
 
-def answer_question(model_id: str, image: PIL.Image.Image, text: str,
-                    decoding_method: str, temperature: float,
-                    length_penalty: float, repetition_penalty: float) -> str:
+def answer_question(
+    model_id: str,
+    image: PIL.Image.Image,
+    text: str,
+    decoding_method: str,
+    temperature: float,
+    length_penalty: float,
+    repetition_penalty: float,
+) -> str:
     model_info = model_dict[model_id]
-    processor = model_info['processor']
-    model = model_info['model']
+    processor = model_info["processor"]
+    model = model_info["model"]
 
-    inputs = processor(images=image, text=text,
-                       return_tensors='pt').to(device, torch.float16)
-    generated_ids = model.generate(**inputs,
-                                   do_sample=decoding_method ==
-                                   'Nucleus sampling',
-                                   temperature=temperature,
-                                   length_penalty=length_penalty,
-                                   repetition_penalty=repetition_penalty,
-                                   max_length=30,
-                                   min_length=1,
-                                   num_beams=5,
-                                   top_p=0.9)
-    result = processor.batch_decode(generated_ids,
-                                    skip_special_tokens=True)[0].strip()
+    inputs = processor(images=image, text=text, return_tensors="pt").to(device, torch.float16)
+    generated_ids = model.generate(
+        **inputs,
+        do_sample=decoding_method == "Nucleus sampling",
+        temperature=temperature,
+        length_penalty=length_penalty,
+        repetition_penalty=repetition_penalty,
+        max_length=30,
+        min_length=1,
+        num_beams=5,
+        top_p=0.9,
+    )
+    result = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
     return result
 
 
 def postprocess_output(output: str) -> str:
-    if output and not output[-1] in string.punctuation:
-        output += '.'
+    if output and output[-1] not in string.punctuation:
+        output += "."
     return output
 
 
@@ -111,9 +118,9 @@ def chat(
     history_qa: list[str] = [],
 ) -> tuple[dict[str, list[str]], dict[str, list[str]], dict[str, list[str]]]:
     history_orig.append(text)
-    text_qa = f'Question: {text} Answer:'
+    text_qa = f"Question: {text} Answer:"
     history_qa.append(text_qa)
-    prompt = ' '.join(history_qa)
+    prompt = " ".join(history_qa)
 
     output = answer_question(
         model_id,
@@ -129,73 +136,73 @@ def chat(
     history_qa.append(output)
 
     chat_val = list(zip(history_orig[0::2], history_orig[1::2]))
-    return gr.update(value=chat_val), gr.update(value=history_orig), gr.update(
-        value=history_qa)
+    return gr.update(value=chat_val), gr.update(value=history_orig), gr.update(value=history_qa)
 
 
 examples = [
     [
-        'house.png',
-        'How could someone get out of the house?',
+        "house.png",
+        "How could someone get out of the house?",
     ],
     [
-        'flower.jpg',
-        'What is this flower and where is it\'s origin?',
+        "flower.jpg",
+        "What is this flower and where is it's origin?",
     ],
     [
-        'pizza.jpg',
-        'What are steps to cook it?',
+        "pizza.jpg",
+        "What are steps to cook it?",
     ],
     [
-        'sunset.jpg',
-        'Here is a romantic message going along the photo:',
+        "sunset.jpg",
+        "Here is a romantic message going along the photo:",
     ],
     [
-        'forbidden_city.webp',
-        'In what dynasties was this place built?',
+        "forbidden_city.webp",
+        "In what dynasties was this place built?",
     ],
 ]
 
-with gr.Blocks(css='style.css') as demo:
+with gr.Blocks(css="style.css") as demo:
     gr.Markdown(DESCRIPTION)
 
-    image = gr.Image(type='pil')
-    with gr.Accordion(label='Advanced settings', open=False):
+    image = gr.Image(type="pil")
+    with gr.Accordion(label="Advanced settings", open=False):
         with gr.Row():
             model_id_caption = gr.Dropdown(
-                label='Model ID for image captioning',
+                label="Model ID for image captioning",
                 choices=[MODEL_ID_OPT_6_7B, MODEL_ID_FLAN_T5_XXL],
                 value=MODEL_ID_FLAN_T5_XXL,
                 interactive=False,
-                visible=False)
+                visible=False,
+            )
             model_id_chat = gr.Dropdown(
-                label='Model ID for VQA',
+                label="Model ID for VQA",
                 choices=[MODEL_ID_OPT_6_7B, MODEL_ID_FLAN_T5_XXL],
                 value=MODEL_ID_FLAN_T5_XXL,
                 interactive=False,
-                visible=False)
+                visible=False,
+            )
         sampling_method = gr.Radio(
-            label='Text Decoding Method',
-            choices=['Beam search', 'Nucleus sampling'],
-            value='Beam search',
+            label="Text Decoding Method",
+            choices=["Beam search", "Nucleus sampling"],
+            value="Beam search",
         )
         temperature = gr.Slider(
-            label='Temperature (used with nucleus sampling)',
+            label="Temperature (used with nucleus sampling)",
             minimum=0.5,
             maximum=1.0,
             value=1.0,
             step=0.1,
         )
         length_penalty = gr.Slider(
-            label=
-            'Length Penalty (set to larger for longer sequence, used with beam search)',
+            label="Length Penalty (set to larger for longer sequence, used with beam search)",
             minimum=-1.0,
             maximum=2.0,
             value=1.0,
             step=0.2,
         )
         rep_penalty = gr.Slider(
-            label='Repeat Penalty (larger value prevents repetition)',
+            label="Repeat Penalty (larger value prevents repetition)",
             minimum=1.0,
             maximum=5.0,
             value=1.5,
@@ -204,21 +211,17 @@ with gr.Blocks(css='style.css') as demo:
     with gr.Row():
         with gr.Column():
             with gr.Box():
-                caption_button = gr.Button(value='Caption it!')
-                caption_output = gr.Textbox(
-                    label='Caption Output',
-                    show_label=False).style(container=False)
+                caption_button = gr.Button(value="Caption it!")
+                caption_output = gr.Textbox(label="Caption Output", show_label=False).style(container=False)
         with gr.Column():
             with gr.Box():
-                chatbot = gr.Chatbot(label='VQA Chat')
+                chatbot = gr.Chatbot(label="VQA Chat")
                 history_orig = gr.State(value=[])
                 history_qa = gr.State(value=[])
-                vqa_input = gr.Text(label='Chat Input',
-                                    show_label=False,
-                                    max_lines=1).style(container=False)
+                vqa_input = gr.Text(label="Chat Input", show_label=False, max_lines=1).style(container=False)
                 with gr.Row():
-                    clear_chat_button = gr.Button(value='Clear')
-                    chat_button = gr.Button(value='Submit')
+                    clear_chat_button = gr.Button(value="Clear")
+                    chat_button = gr.Button(value="Submit")
 
     gr.Examples(
         examples=examples,
@@ -239,7 +242,7 @@ with gr.Blocks(css='style.css') as demo:
             rep_penalty,
         ],
         outputs=caption_output,
-        api_name='caption',
+        api_name="caption",
     )
 
     chat_inputs = [
@@ -267,10 +270,10 @@ with gr.Blocks(css='style.css') as demo:
         fn=chat,
         inputs=chat_inputs,
         outputs=chat_outputs,
-        api_name='chat',
+        api_name="chat",
     )
     clear_chat_button.click(
-        fn=lambda: ('', [], [], []),
+        fn=lambda: ("", [], [], []),
         inputs=None,
         outputs=[
             vqa_input,
@@ -279,10 +282,10 @@ with gr.Blocks(css='style.css') as demo:
             history_qa,
         ],
         queue=False,
-        api_name='clear',
+        api_name="clear",
     )
     image.change(
-        fn=lambda: ('', [], [], []),
+        fn=lambda: ("", [], [], []),
         inputs=None,
         outputs=[
             caption_output,