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import glob
import torch
import scipy
import random
import matplotlib.pyplot as plt
from PIL import Image
from torchvision import transforms
from train import LitClassification, ClassificationData

# load test association to the numerical labels

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import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import models, datasets, ops
from torchvision.transforms import v2 as transforms
import pytorch_lightning as pl


# Step 1a: Define the transform
transform = transforms.Compose([

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name: Update git submodules

on:
  pull_request:
    branches: ["main"]
    paths:
      - ".github/workflows/ci-update-submodules.yml"
  schedule:
    # on Sundays
    - cron: "0 0 * * 0"

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name: Sample build
on:
  - push
  - pull_request
  - workflow_dispatch

jobs:
  build:
    runs-on: ubuntu-latest
    steps:

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preds = []
# move model to GPU for faster inference and set evaluation
model.cuda().eval()
for imgs, names in dm.test_dataloader():
    # for the prediction we do not need gradients
    with torch.no_grad():
        onehots = model(imgs.cuda()).cpu()
    # aggregate particular preditions
    for oh, name in zip(onehots, names):
        lbs = dm.onehot_to_labels(oh)

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from pytorch_lightning import Trainer
from kaggle_plantpatho.data import PlantPathologyDM
from kaggle_plantpatho.models import LitResnet, MultiPlantPathology

# create DataModule with training/validation split
dm = PlantPathologyDM(batch_size=98)
# initialize ResNet50 network
net = LitResnet(arch='resnet50', num_classes=dm.num_classes)
# initialize PL module with ResNet50
model = MultiPlantPathology(model=net, lr=6e-4)

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fig = plt.figure(figsize=(3, 7))
for imgs, lbs in dm.val_dataloader():
    # some stats about the batch - label distribution
    print(f'batch labels: {torch.sum(lbs, axis=0)}')
    print(f'image size: {imgs[0].shape}')
    # similar as above show just first images from the batch
    for i in range(3):
        ax = fig.add_subplot(3, 1, i + 1, xticks=[], yticks=[])
        ax.imshow(np.rollaxis(imgs[i].numpy(), 0, 3))
        ax.set_title(lbs[i])

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import os, tqdm, glob
import multiprocessing as mproc

ls_images = glob.glob("plant-pathology/train_images/*.jpg")
print(f'found images: {len(ls_images)}')

def _convert(pimg: str):
    # it can be replaced by opencv implementation...
    os.system(f'convert -resize 640 point {pimg} {pimg}')

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import pydicom
from pydicom.pixel_data_handlers import apply_voi_lut

# load the DICOM file
dicom = pydicom.dcmread(dicom_path)
# extract the image bitmap
img = apply_voi_lut(dicom.pixel_array, dicom)

import matplotlib.pyplot as plt

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"""
Make a simple repository statistic about user contributions.

## Resources
 - https://developer.github.com/v3/pulls/
 - http://zetcode.com/python/requests/
 - https://developer.github.com/v3/#authentication

**Requirements:**
```