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#!/bin/bash
# This script runs through the code in each of the python examples.
# The purpose is just as an integration test, not to actually train models in any meaningful way.
# For that reason, most of these set epochs = 1 and --dry-run.
# Optionally specify a comma separated list of examples to run. Can be run as:
# * To run all examples:
#   ./run_python_examples.sh
# * To run few specific examples:
#   ./run_python_examples.sh "dcgan,fast_neural_style"
# To test examples on CUDA accelerator, run as:
#   USE_CUDA=True ./run_python_examples.sh
# To test examples on hardware accelerator (CUDA, MPS, XPU, etc.), run as:
#   USE_ACCEL=True ./run_python_examples.sh
# NOTE: USE_ACCEL relies on torch.accelerator API and not all examples are converted
# to use it at the moment. Thus, expect failures using this flag on non-CUDA accelerators
# and consider to run examples one by one.
# Script requires uv to be installed. When executed, script will install prerequisites from
# `requirements.txt` for each example. If ran within activated virtual environment (uv venv,
# python -m venv, conda) this might reinstall some of the packages. To change pip installation
# index or to pass additional pip install options, run as:
#   PIP_INSTALL_ARGS="--pre -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html" \
#     ./run_python_examples.sh
# To force script to create virtual environment for each example, run as:
#   VIRTUAL_ENV=".venv" ./run_python_examples.sh
# Script will remove environments it creates in a teardown step after execution of each example.
BASE_DIR="$(pwd)/$(dirname $0)"
source $BASE_DIR/utils.sh
# TODO: Leave only USE_ACCEL and drop USE_CUDA once all examples will be converted
# to torch.accelerator API. For now, just add USE_ACCEL as an alias for USE_CUDA.
if [ -n "$USE_ACCEL" ]; then
  USE_CUDA=$USE_ACCEL
USE_CUDA=${USE_CUDA:-False}
case $USE_CUDA in
    echo "using cuda"
    CUDA_FLAG="--cuda"
    ACCEL_FLAG="--accel"
    echo "not using cuda"
    CUDA_FLAG=""
    ACCEL_FLAG=""
    exit 1;
function dcgan() {
  uv run main.py --dataset fake $ACCEL_FLAG --dry-run || error "dcgan failed"
function fast_neural_style() {
  if [ ! -d "saved_models" ]; then
    echo "downloading saved models for fast neural style"
    uv run download_saved_models.py
  test -d "saved_models" || { error "saved models not found"; return; }
  echo "running fast neural style model"
  uv run neural_style/neural_style.py eval --content-image images/content-images/amber.jpg --model saved_models/candy.pth --output-image images/output-images/amber-candy.jpg $ACCEL_FLAG || error "neural_style.py failed"
function imagenet() {
  if [[ ! -d "sample/val" || ! -d "sample/train" ]]; then
    mkdir -p sample/val/n
    mkdir -p sample/train/n
    curl -O "https://upload.wikimedia.org/wikipedia/commons/5/5a/Socks-clinton.jpg" || { error "couldn't download sample image for imagenet"; return; }
    mv Socks-clinton.jpg sample/train/n
    cp sample/train/n/* sample/val/n/
  uv run main.py --epochs 1 sample/ || error "imagenet example failed"
  uv run main.py --epochs 1 --gpu 0 sample/ || error "imagenet example failed"
function language_translation() {
  uv run -m spacy download en || error "couldn't download en package from spacy"
  uv run -m spacy download de || error "couldn't download de package from spacy"
  uv run main.py -e 1 --enc_layers 1 --dec_layers 1 --backend cpu --logging_dir output/ --dry_run || error "language translation example failed"
function mnist() {
  uv run main.py --epochs 1 --dry-run || error "mnist example failed"
function mnist_forward_forward() {
  uv run main.py --epochs 1 --no_accel || error "mnist forward forward failed"
function mnist_hogwild() {
  uv run main.py --epochs 1 --dry-run $CUDA_FLAG || error "mnist hogwild failed"
function mnist_rnn() {
  uv run main.py --epochs 1 --dry-run || error "mnist rnn example failed"
function regression() {
  uv run main.py --epochs 1 $CUDA_FLAG || error "regression failed"
function siamese_network() {
  uv run main.py --epochs 1 --dry-run || error "siamese network example failed"
function reinforcement_learning() {
  uv run reinforce.py || error "reinforcement learning reinforce failed"
  uv run actor_critic.py || error "reinforcement learning actor_critic failed"
function snli() {
  echo "installing 'en' model if not installed"
  uv run -m spacy download en || { error "couldn't download 'en' model needed for snli";  return; }
  echo "training..."
  uv run train.py --epochs 1 --dev_every 1 --no-bidirectional --dry-run || error "couldn't train snli"
function fx() {
  # uv run custom_tracer.py || error "fx custom tracer has failed" UnboundLocalError: local variable 'tabulate' referenced before assignment
  uv run invert.py || error "fx invert has failed"
  uv run module_tracer.py || error "fx module tracer has failed"
  uv run primitive_library.py || error "fx primitive library has failed"
  uv run profiling_tracer.py || error "fx profiling tracer has failed"
  uv run replace_op.py || error "fx replace op has failed"
  uv run subgraph_rewriter_basic_use.py || error "fx subgraph has failed"
  uv run wrap_output_dynamically.py || error "vmap output dynamically has failed"
function super_resolution() {
  uv run main.py --upscale_factor 3 --batchSize 4 --testBatchSize 100 --nEpochs 1 --lr 0.001 $ACCEL_FLAG || error "super resolution failed"
  uv run super_resolve.py --input_image dataset/BSDS300/images/test/16077.jpg --model model_epoch_1.pth --output_filename out.png $ACCEL_FLAG || error "super resolution upscaling failed"
function time_sequence_prediction() {
  uv run generate_sine_wave.py || { error "generate sine wave failed";  return; }
  uv run train.py --steps 2 || error "time sequence prediction training failed"
function vae() {
  uv run main.py --epochs 1 || error "vae failed"
function vision_transformer() {
  uv run main.py --epochs 1 --dry-run || error "vision transformer example failed"
function word_language_model() {
  uv run main.py --epochs 1 --dry-run $ACCEL_FLAG || error "word_language_model failed"
  uv run generate.py $ACCEL_FLAG || error "word_language_model generate failed"
  for model in "RNN_TANH" "RNN_RELU" "LSTM" "GRU" "Transformer"; do
    uv run main.py --model $model --epochs 1 --dry-run $ACCEL_FLAG || error "word_language_model failed"
    uv run generate.py $ACCEL_FLAG || error "word_language_model generate failed"
function gcn() {
  uv run main.py --epochs 1 --dry-run || error "graph convolutional network failed"
function gat() {
  uv run main.py --epochs 1 --dry-run || error "graph attention network failed"
eval "base_$(declare -f stop)"
function stop() {
  cd $BASE_DIR
  rm -rf dcgan/fake_samples_epoch_000.png \
    dcgan/netD_epoch_0.pth \
    dcgan/netG_epoch_0.pth \
    dcgan/real_samples.png \
    fast_neural_style/saved_models.zip \
    fast_neural_style/saved_models/ \
    imagenet/checkpoint.pth.tar \
    imagenet/lsun/ \
    imagenet/model_best.pth.tar \
    imagenet/sample/ \
    language_translation/output/ \
    snli/.data/ \
    snli/.vector_cache/ \
    snli/results/ \
    time_sequence_prediction/predict*.pdf \
    time_sequence_prediction/traindata.pt \
    word_language_model/model.pt \
    gcn/cora/ \
    gat/cora/ || error "couldn't clean up some files"
  git checkout fast_neural_style/images/output-images/amber-candy.jpg || error "couldn't clean up fast neural style image"
  base_stop "$1"
function run_all() {
  # cpp moved to `run_cpp_examples.sh```
  run dcgan
  # distributed moved to `run_distributed_examples.sh`
  run fast_neural_style
  run imagenet
  # language_translation
  run mnist
  run mnist_forward_forward
  run mnist_hogwild
  run mnist_rnn
  run regression
  run reinforcement_learning
  run siamese_network
  # run super_resolution - flaky
  run time_sequence_prediction
  # vision_transformer - example broken see https://github.com/pytorch/examples/issues/1184 and https://github.com/pytorch/examples/pull/1258 for more details
  run word_language_model
# by default, run all examples
if [ "" == "$EXAMPLES" ]; then
  for i in $(echo $EXAMPLES | sed "s/,/ /g")
    echo "Starting $i"
    echo "Finished $i, status $?"
if [ "" == "$ERRORS" ]; then
  echo "Completed successfully with status $?"
  echo "Some python examples failed:"
  printf "$ERRORS\n"
  #Exit with error (0-255) in case of failure in one of the tests.
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