code: import argparse
  import logging
  import os
  import sys
  import tempfile
  from pathlib import Path
  
  import pytest
  import pytorch_lightning as pl
  import torch
  
  import lightning_base
  from convert_pl_checkpoint_to_hf import convert_pl_to_hf
  from distillation import distill_main
  from finetune import SummarizationModule, main
  from parameterized import parameterized
  from run_eval import generate_summaries_or_translations
  from transformers import AutoConfig, AutoModelForSeq2SeqLM
  from transformers.hf_api import HfApi
  from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
  from utils import label_smoothed_nll_loss, lmap, load_json
  
  
  logging.basicConfig(level=logging.DEBUG)
  
  logger = logging.getLogger()
  CUDA_AVAILABLE = torch.cuda.is_available()
  CHEAP_ARGS = {
      "max_tokens_per_batch": None,
      "supervise_forward": True,
      "normalize_hidden": True,
      "label_smoothing": 0.2,
      "eval_max_gen_length": None,
      "eval_beams": 1,
      "val_metric": "loss",
      "save_top_k": 1,
      "adafactor": True,
      "early_stopping_patience": 2,
      "logger_name": "default",
      "length_penalty": 0.5,
      "cache_dir": "",
      "task": "summarization",
      "num_workers": 2,
      "alpha_hid": 0,
      "freeze_embeds": True,
      "enc_only": False,
      "tgt_suffix": "",
      "resume_from_checkpoint": None,
      "sortish_sampler": True,
      "student_decoder_layers": 1,
      "val_check_interval": 1.0,
      "output_dir": "",
      "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
      "no_teacher": False,
      "fp16_opt_level": "O1",
      "gpus": 1 if CUDA_AVAILABLE else 0,
      "n_tpu_cores": 0,
      "max_grad_norm": 1.0,
      "do_train": True,
      "do_predict": True,
      "accumulate_grad_batches": 1,
      "server_ip": "",
      "server_port": "",
      "seed": 42,
      "model_name_or_path": "sshleifer/bart-tiny-random",
      "config_name": "",
      "tokenizer_name": "facebook/bart-large",
      "do_lower_case": False,
      "learning_rate": 0.3,
      "lr_scheduler": "linear",
      "weight_decay": 0.0,
      "adam_epsilon": 1e-08,
      "warmup_steps": 0,
      "max_epochs": 1,
      "train_batch_size": 2,
      "eval_batch_size": 2,
      "max_source_length": 12,
      "max_target_length": 12,
      "val_max_target_length": 12,
      "test_max_target_length": 12,
      "fast_dev_run": False,
      "no_cache": False,
      "n_train": -1,
      "n_val": -1,
      "n_test": -1,
      "student_encoder_layers": 1,
      "freeze_encoder": False,
      "auto_scale_batch_size": False,
      "overwrite_output_dir": False,
      "student": None,
  }
  
  
  def _dump_articles(path: Path, articles: list):
      content = "\n".join(articles)
      Path(path).open("w").writelines(content)
  
  
  ARTICLES = [" Sam ate lunch today.", "Sams lunch ingredients."]
  SUMMARIES = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
  T5_TINY = "patrickvonplaten/t5-tiny-random"
  T5_TINIER = "sshleifer/t5-tinier-random"
  BART_TINY = "sshleifer/bart-tiny-random"
  MBART_TINY = "sshleifer/tiny-mbart"
  MARIAN_TINY = "sshleifer/tiny-marian-en-de"
  FSMT_TINY = "stas/tiny-wmt19-en-de"
  
  
  stream_handler = logging.StreamHandler(sys.stdout)
  logger.addHandler(stream_handler)
  logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
  
  
  def make_test_data_dir(tmp_dir):
      for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(tmp_dir, f"{split}.source"), ARTICLES)
          _dump_articles(os.path.join(tmp_dir, f"{split}.target"), SUMMARIES)
      return tmp_dir
  
  
  class TestSummarizationDistiller(TestCasePlus):
      @classmethod
      def setUpClass(cls):
          logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
          return cls
  
      @slow
      @require_torch_gpu
      def test_hub_configs(self):
          """I put require_torch_gpu cause I only want this to run with self-scheduled."""
  
          model_list = HfApi().model_list()
          org = "sshleifer"
          model_ids = [x.modelId for x in model_list if x.modelId.startswith(org)]
          allowed_to_be_broken = ["sshleifer/blenderbot-3B", "sshleifer/blenderbot-90M"]
          failures = []
          for m in model_ids:
              if m in allowed_to_be_broken:
                  continue
              try:
                  AutoConfig.from_pretrained(m)
              except Exception:
                  failures.append(m)
          assert not failures, f"The following models could not be loaded through AutoConfig: {failures}"
  
      def test_distill_no_teacher(self):
          updates = dict(student_encoder_layers=2, student_decoder_layers=1, no_teacher=True)
          self._test_distiller_cli(updates)
  
      def test_distill_checkpointing_with_teacher(self):
          updates = dict(
              student_encoder_layers=2,
              student_decoder_layers=1,
              max_epochs=4,
              val_check_interval=0.25,
              alpha_hid=2.0,
              model_name_or_path="IGNORE_THIS_IT_DOESNT_GET_USED",
          )
          model = self._test_distiller_cli(updates, check_contents=False)
  
          ckpts = list(Path(model.output_dir).glob("*.ckpt"))
          self.assertEqual(1, len(ckpts))
          transformer_ckpts = list(Path(model.output_dir).glob("**/*.bin"))
          self.assertEqual(len(transformer_ckpts), 2)
          examples = lmap(str.strip, Path(model.hparams.data_dir).joinpath("test.source").open().readlines())
          out_path = tempfile.mktemp()  # XXX: not being cleaned up
          generate_summaries_or_translations(examples, out_path, str(model.output_dir / "best_tfmr"))
          self.assertTrue(Path(out_path).exists())
  
          out_path_new = self.get_auto_remove_tmp_dir()
          convert_pl_to_hf(ckpts[0], transformer_ckpts[0].parent, out_path_new)
          assert os.path.exists(os.path.join(out_path_new, "pytorch_model.bin"))
  
      def test_loss_fn(self):
          model = AutoModelForSeq2SeqLM.from_pretrained(BART_TINY)
          input_ids, mask = model.dummy_inputs["input_ids"], model.dummy_inputs["attention_mask"]
          target_ids = torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]], dtype=torch.long, device=model.device)
          decoder_input_ids = target_ids[:, :-1].contiguous()  # Why this line?
          lm_labels = target_ids[:, 1:].clone()  # why clone?
          model_computed_loss = model(
              input_ids, attention_mask=mask, decoder_input_ids=decoder_input_ids, labels=lm_labels, use_cache=False
          ).loss
  
          logits = model(input_ids, attention_mask=mask, decoder_input_ids=decoder_input_ids, use_cache=False).logits
  
          lprobs = torch.nn.functional.log_softmax(logits, dim=-1)
          smoothed_loss, nll_loss = label_smoothed_nll_loss(
              lprobs, lm_labels, 0.1, ignore_index=model.config.pad_token_id
          )
          with self.assertRaises(AssertionError):
              # TODO: understand why this breaks
              self.assertEqual(nll_loss, model_computed_loss)
  
      def test_distill_mbart(self):
          updates = dict(
              student_encoder_layers=2,
              student_decoder_layers=1,
              num_train_epochs=4,
              val_check_interval=0.25,
              alpha_hid=2.0,
              task="translation",
              model_name_or_path="IGNORE_THIS_IT_DOESNT_GET_USED",
              tokenizer_name=MBART_TINY,
              teacher=MBART_TINY,
              src_lang="en_XX",
              tgt_lang="ro_RO",
          )
          model = self._test_distiller_cli(updates, check_contents=False)
          assert model.model.config.model_type == "mbart"
  
          ckpts = list(Path(model.output_dir).glob("*.ckpt"))
          self.assertEqual(1, len(ckpts))
          transformer_ckpts = list(Path(model.output_dir).glob("**/*.bin"))
          all_files = list(Path(model.output_dir).glob("best_tfmr/*"))
          assert len(all_files) > 2
          self.assertEqual(len(transformer_ckpts), 2)
  
      def test_distill_t5(self):
          updates = dict(
              student_encoder_layers=1,
              student_decoder_layers=1,
              alpha_hid=2.0,
              teacher=T5_TINY,
              model_name_or_path=T5_TINY,
              tokenizer_name=T5_TINY,
          )
          self._test_distiller_cli(updates)
  
      def test_distill_different_base_models(self):
          updates = dict(
              teacher=T5_TINY,
              student=T5_TINIER,
              model_name_or_path=T5_TINIER,
              tokenizer_name=T5_TINIER,
          )
          self._test_distiller_cli(updates)
  
      def _test_distiller_cli(self, updates, check_contents=True):
          default_updates = dict(
              label_smoothing=0.0,
              early_stopping_patience=-1,
              train_batch_size=1,
              eval_batch_size=2,
              max_epochs=2,
              alpha_mlm=0.2,
              alpha_ce=0.8,
              do_predict=True,
              model_name_or_path="sshleifer/tinier_bart",
              teacher=CHEAP_ARGS["model_name_or_path"],
              val_check_interval=0.5,
          )
          default_updates.update(updates)
          args_d: dict = CHEAP_ARGS.copy()
          tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
          output_dir = self.get_auto_remove_tmp_dir()
  
          args_d.update(data_dir=tmp_dir, output_dir=output_dir, **default_updates)
          model = distill_main(argparse.Namespace(**args_d))
          if not check_contents:
              return model
          contents = os.listdir(output_dir)
          contents = {os.path.basename(p) for p in contents}
          ckpt_files = [p for p in contents if p.endswith("ckpt")]
          assert len(ckpt_files) > 0
  
          self.assertIn("test_generations.txt", contents)
          self.assertIn("test_results.txt", contents)
  
          metrics = load_json(model.metrics_save_path)
          last_step_stats = metrics["val"][-1]
          self.assertGreaterEqual(last_step_stats["val_avg_gen_time"], 0.01)
          self.assertGreaterEqual(1.0, last_step_stats["val_avg_gen_time"])
          self.assertIsInstance(last_step_stats[f"val_avg_{model.val_metric}"], float)
          desired_n_evals = int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1)
          self.assertEqual(len(metrics["val"]), desired_n_evals)
          self.assertEqual(len(metrics["test"]), 1)
          return model
  
  
  class TestTheRest(TestCasePlus):
      @parameterized.expand(
          [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY],
      )
      def test_finetune(self, model):
          args_d: dict = CHEAP_ARGS.copy()
          task = "translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
          args_d["label_smoothing"] = 0.1 if task == "translation" else 0
  
          tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
          output_dir = self.get_auto_remove_tmp_dir()
          args_d.update(
              data_dir=tmp_dir,
              model_name_or_path=model,
              tokenizer_name=None,
              train_batch_size=2,
              eval_batch_size=2,
              output_dir=output_dir,
              do_predict=True,
              task=task,
              src_lang="en_XX",
              tgt_lang="ro_RO",
              freeze_encoder=True,
              freeze_embeds=True,
          )
          assert "n_train" in args_d
          args = argparse.Namespace(**args_d)
          module = main(args)
  
          input_embeds = module.model.get_input_embeddings()
          assert not input_embeds.weight.requires_grad
          if model == T5_TINY:
              lm_head = module.model.lm_head
              assert not lm_head.weight.requires_grad
              assert (lm_head.weight == input_embeds.weight).all().item()
          elif model == FSMT_TINY:
              fsmt = module.model.model
              embed_pos = fsmt.decoder.embed_positions
              assert not embed_pos.weight.requires_grad
              assert not fsmt.decoder.embed_tokens.weight.requires_grad
              # check that embeds are not the same
              assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
          else:
              bart = module.model.model
              embed_pos = bart.decoder.embed_positions
              assert not embed_pos.weight.requires_grad
              assert not bart.shared.weight.requires_grad
              # check that embeds are the same
              assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
              assert bart.decoder.embed_tokens == bart.shared
  
          example_batch = load_json(module.output_dir / "text_batch.json")
          assert isinstance(example_batch, dict)
          assert len(example_batch) >= 4
  
      def test_finetune_extra_model_args(self):
          args_d: dict = CHEAP_ARGS.copy()
  
          task = "summarization"
          tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
  
          args_d.update(
              data_dir=tmp_dir,
              tokenizer_name=None,
              train_batch_size=2,
              eval_batch_size=2,
              do_predict=False,
              task=task,
              src_lang="en_XX",
              tgt_lang="ro_RO",
              freeze_encoder=True,
              freeze_embeds=True,
          )
  
          # test models whose config includes the extra_model_args
          model = BART_TINY
          output_dir = self.get_auto_remove_tmp_dir()
          args_d1 = args_d.copy()
          args_d1.update(
              model_name_or_path=model,
              output_dir=output_dir,
          )
          extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
          for p in extra_model_params:
              args_d1[p] = 0.5
          args = argparse.Namespace(**args_d1)
          model = main(args)
          for p in extra_model_params:
              assert getattr(model.config, p) == 0.5, f"failed to override the model config for param {p}"
  
          # test models whose config doesn't include the extra_model_args
          model = T5_TINY
          output_dir = self.get_auto_remove_tmp_dir()
          args_d2 = args_d.copy()
          args_d2.update(
              model_name_or_path=model,
              output_dir=output_dir,
          )
          unsupported_param = "encoder_layerdrop"
          args_d2[unsupported_param] = 0.5
          args = argparse.Namespace(**args_d2)
          with pytest.raises(Exception) as excinfo:
              model = main(args)
          assert str(excinfo.value) == f"model config doesn't have a `{unsupported_param}` attribute"
  
      def test_finetune_lr_schedulers(self):
          args_d: dict = CHEAP_ARGS.copy()
  
          task = "summarization"
          tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
  
          model = BART_TINY
          output_dir = self.get_auto_remove_tmp_dir()
  
          args_d.update(
              data_dir=tmp_dir,
              model_name_or_path=model,
              output_dir=output_dir,
              tokenizer_name=None,
              train_batch_size=2,
              eval_batch_size=2,
              do_predict=False,
              task=task,
              src_lang="en_XX",
              tgt_lang="ro_RO",
              freeze_encoder=True,
              freeze_embeds=True,
          )
  
          # emulate finetune.py
          parser = argparse.ArgumentParser()
          parser = pl.Trainer.add_argparse_args(parser)
          parser = SummarizationModule.add_model_specific_args(parser, os.getcwd())
          args = {"--help": True}
  
          # --help test
          with pytest.raises(SystemExit) as excinfo:
              with CaptureStdout() as cs:
                  args = parser.parse_args(args)
              assert False, "--help is expected to sys.exit"
          assert excinfo.type == SystemExit
          expected = lightning_base.arg_to_scheduler_metavar
          assert expected in cs.out, "--help is expected to list the supported schedulers"
  
          # --lr_scheduler=non_existing_scheduler test
          unsupported_param = "non_existing_scheduler"
          args = {f"--lr_scheduler={unsupported_param}"}
          with pytest.raises(SystemExit) as excinfo:
              with CaptureStderr() as cs:
                  args = parser.parse_args(args)
              assert False, "invalid argument is expected to sys.exit"
          assert excinfo.type == SystemExit
          expected = f"invalid choice: '{unsupported_param}'"
          assert expected in cs.err, f"should have bailed on invalid choice of scheduler {unsupported_param}"
  
          # --lr_scheduler=existing_scheduler test
          supported_param = "cosine"
          args_d1 = args_d.copy()
          args_d1["lr_scheduler"] = supported_param
          args = argparse.Namespace(**args_d1)
          model = main(args)
          assert (
              getattr(model.hparams, "lr_scheduler") == supported_param
          ), f"lr_scheduler={supported_param} shouldn't fail"
  