style: use isort

.github/workflows/black.yml

@@ -1,14 +0,0 @@
-name: Lint
-
-on:
-  push:
-    branches: [main]
-  pull_request:
-    branches: [main]
-
-jobs:
-  lint:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v2
-      - uses: psf/black@stable

app/gradio/app_gradio.py

@@ -7,26 +7,18 @@
 
 import random
 
+import gradio as gr
 import jax
-import flax.linen as nn
-from flax.training.common_utils import shard
+import numpy as np
 from flax.jax_utils import replicate
-
-from transformers import BartTokenizer
-
+from flax.training.common_utils import shard
 from PIL import Image, ImageDraw, ImageFont
-import numpy as np
-
-from vqgan_jax.modeling_flax_vqgan import VQModel
-from dalle_mini.model import CustomFlaxBartForConditionalGeneration
 
 # ## CLIP Scoring
-from transformers import CLIPProcessor, FlaxCLIPModel
-
-import gradio as gr
-
-from PIL import Image, ImageDraw, ImageFont
+from transformers import BartTokenizer, CLIPProcessor, FlaxCLIPModel
+from vqgan_jax.modeling_flax_vqgan import VQModel
 
+from dalle_mini.model import CustomFlaxBartForConditionalGeneration
 
 DALLE_REPO = "flax-community/dalle-mini"
 DALLE_COMMIT_ID = "4d34126d0df8bc4a692ae933e3b902a1fa8b6114"

app/streamlit/app.py

@@ -1,9 +1,10 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-from .backend import ServiceError, get_images_from_backend
 import streamlit as st
 
+from .backend import ServiceError, get_images_from_backend
+
 st.sidebar.markdown(
     """
 <style>

app/streamlit/backend.py

@@ -1,6 +1,7 @@
-import requests
-from io import BytesIO
 import base64
+from io import BytesIO
+
+import requests
 from PIL import Image
 
 

dalle_mini/data.py

@@ -1,10 +1,12 @@
 from dataclasses import dataclass, field
-from datasets import load_dataset, Dataset
 from functools import partial
-import numpy as np
+
 import jax
 import jax.numpy as jnp
+import numpy as np
+from datasets import Dataset, load_dataset
 from flax.training.common_utils import shard
+
 from .text import TextNormalizer
 
 

dalle_mini/model.py

@@ -1,16 +1,14 @@
-import jax
 import flax.linen as nn
-
+import jax
+from transformers import BartConfig
 from transformers.models.bart.modeling_flax_bart import (
-    FlaxBartModule,
-    FlaxBartForConditionalGenerationModule,
-    FlaxBartForConditionalGeneration,
-    FlaxBartEncoder,
     FlaxBartDecoder,
+    FlaxBartEncoder,
+    FlaxBartForConditionalGeneration,
+    FlaxBartForConditionalGenerationModule,
+    FlaxBartModule,
 )
 
-from transformers import BartConfig
-
 
 class CustomFlaxBartModule(FlaxBartModule):
     def setup(self):

dalle_mini/text.py

@@ -2,13 +2,15 @@
 Utilities for processing text.
 """
 
+import html
+import math
+import random
+import re
 from pathlib import Path
-from unidecode import unidecode
 
-import re, math, random, html
 import ftfy
-
 from huggingface_hub import hf_hub_download
+from unidecode import unidecode
 
 # based on wiki word occurence
 person_token = [("a person", 282265), ("someone", 121194), ("somebody", 12219)]

tools/train/train.py

@@ -18,37 +18,31 @@ Fine-tuning the library models for seq2seq, text to image.
 Script adapted from run_summarization_flax.py
 """
 
-import os
+import json
 import logging
+import os
 import sys
 import time
-from dataclasses import dataclass, field
+from dataclasses import asdict, dataclass, field
 from pathlib import Path
 from typing import Callable, Optional
-import json
 
 import datasets
-from datasets import Dataset
-from tqdm import tqdm
-from dataclasses import asdict
-
 import jax
 import jax.numpy as jnp
 import optax
 import transformers
+import wandb
+from datasets import Dataset
 from flax import jax_utils, traverse_util
-from flax.serialization import from_bytes, to_bytes
 from flax.jax_utils import unreplicate
+from flax.serialization import from_bytes, to_bytes
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard_prng_key
-from transformers import (
-    AutoTokenizer,
-    HfArgumentParser,
-)
+from tqdm import tqdm
+from transformers import AutoTokenizer, HfArgumentParser
 from transformers.models.bart.modeling_flax_bart import BartConfig
 
-import wandb
-
 from dalle_mini.data import Dataset
 from dalle_mini.model import CustomFlaxBartForConditionalGeneration