# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.

# -------------------------------------------------------------------------------------
# CircleCI configuration file.
# Specifies automated environment setup and tests.
#
# See https://circleci.com/docs/2.0/language-python/ for more details
# Available Machine Images:
# https://circleci.com/docs/2.0/configuration-reference/#available-machine-images
# -------------------------------------------------------------------------------------

version: 2.1

# -------------------------------------------------------------------------------------
# Environments to run the jobs in
# -------------------------------------------------------------------------------------
cpu: &cpu
  machine:
      image: ubuntu-2004:202101-01

gpu: &gpu
  environment:
    CUDA_VERSION: "10.2"
  resource_class: gpu.nvidia.small.multi
  machine:
      image: ubuntu-2004:202101-01

setup_cuda: &setup_cuda
  run:
    name: Setup CUDA
    working_directory: ~/
    command: |
      # download and install nvidia drivers, cuda, etc
      wget --no-verbose --no-clobber -P ~/nvidia-downloads https://developer.download.nvidia.com/compute/cuda/11.2.2/local_installers/cuda_11.2.2_460.32.03_linux.run
      sudo sh ~/nvidia-downloads/cuda_11.2.2_460.32.03_linux.run --silent
      echo "Done installing CUDA."
      nvidia-smi

# -------------------------------------------------------------------------------------
# Re-usable commands
# -------------------------------------------------------------------------------------
install_conda: &install_conda
  run:
    name: Setup Conda
    working_directory: ~/
    command: |
      curl --retry 3 -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
      sh conda.sh -b -p $HOME/miniconda3

setup_ptv_conda: &setup_ptv_conda
  run:
    name: Setup Conda Environment
    command: |
      pyenv versions
      export PATH="$HOME/miniconda3/bin:$PATH"
      conda update -y conda
      conda init bash
      source ~/.bashrc
      conda create --name pytorchvideo python=3.7.9

install_pytorch: &install_pytorch
  - run:
      name: Install Pytorch
      command: |
        export PATH="$HOME/miniconda3/bin:$PATH"
        conda activate pytorchvideo
        conda install pytorch torchvision -c pytorch
        python -c 'import torch; print(torch.__version__)'
        python -c 'import torch; print("CUDA:", torch.cuda.is_available())'
        python -c 'import torchvision; print(torchvision.__version__)'

install_pytorchvideo: &install_pytorchvideo
  - run:
      name: Install PyTorchVideo
      command: |
        export PATH="$HOME/miniconda3/bin:$PATH"
        conda activate pytorchvideo
        pip install -U --progress-bar off -e .[test]
        python -c 'import pytorchvideo; print(pytorchvideo.__version__)'

build_wheels: &build_wheels
  - run:
      name: Install PyTorchVideo
      command: |
        export PATH="$HOME/miniconda3/bin:$PATH"
        conda activate pytorchvideo
        python setup.py sdist

        export BUILD_NIGHTLY="1"
        python setup.py sdist

run_unittests: &run_unittests
  - run:
      name: Run Unit Tests
      command: |
        export PATH="$HOME/miniconda3/bin:$PATH"
        conda activate pytorchvideo
        python -m unittest discover -v -s tests

run_unittests_with_coverage: &run_unittests_with_coverage
  - run:
      name: Run Unit Tests
      command: |
        export PATH="$HOME/miniconda3/bin:$PATH"
        conda activate pytorchvideo
        coverage run -m unittest discover -v -s tests
        bash <(curl -s https://codecov.io/bash)

# -------------------------------------------------------------------------------------
# Jobs to run
# -------------------------------------------------------------------------------------
jobs:
  cpu_tests:
    <<: *cpu
    working_directory: ~/pytorchvideo
    steps:
      - checkout
      - <<: *install_conda
      - <<: *setup_ptv_conda
      - <<: *install_pytorch
      - <<: *install_pytorchvideo
      - <<: *build_wheels
      - <<: *run_unittests_with_coverage
      - store_artifacts:
          path: ~/pytorchvideo/dist
      - persist_to_workspace:
          root: ~/pytorchvideo/dist
          paths:
            - "*"

  gpu_tests:
    working_directory: ~/pytorchvideo
    <<: *gpu
    steps:
      - checkout
      - <<: *setup_cuda
      - <<: *install_conda
      - <<: *setup_ptv_conda
      - <<: *install_pytorch
      - <<: *install_pytorchvideo
      - <<: *run_unittests

  upload_wheel:
    docker:
      - image: circleci/python:3.7
        auth:
          username: $DOCKERHUB_USERNAME
          password: $DOCKERHUB_TOKEN
    working_directory: ~/pytorchvideo
    steps:
      - checkout
      - attach_workspace:
          at: ~/workspace
      - run:
          command: |
            # no commits in the last 25 hours
            if [[ -z $(git log --since="25 hours ago") ]]; then
              echo "No commits in the last day."
              exit 0
            fi
            pip install --progress-bar off --user twine
            for pkg in ~/workspace/*.tar.gz; do
              if [[ "$pkg" == *"nightly"* ]];
              then
                twine upload --verbose --skip-existing --username __token__ --password $PTV_NIGHTLY_PYPI_TOKEN $pkg
              else
                twine upload --verbose --skip-existing --username __token__ --password $PTV_PYPI_TOKEN $pkg
              fi
            done
# -------------------------------------------------------------------------------------
# Workflows to launch
# -------------------------------------------------------------------------------------
workflows:
  version: 2
  regular_test:
    jobs:
      - cpu_tests:
          context:
            - DOCKERHUB_TOKEN
      - gpu_tests:
          context:
            - DOCKERHUB_TOKEN

  nightly:
    jobs:
      # https://circleci.com/docs/2.0/contexts/#creating-and-using-a-context
      - cpu_tests:
          context:
            - DOCKERHUB_TOKEN
      - gpu_tests:
          context:
            - DOCKERHUB_TOKEN
      - upload_wheel:
          requires:
            - cpu_tests
            - gpu_tests
          context:
            - DOCKERHUB_TOKEN
    triggers:
      - schedule:
          cron: "0 0 * * *"
          filters:
            branches:
              only:
                - main