From d804861fb20d1a9cea4fb191f33cfb6a1349e5a0 Mon Sep 17 00:00:00 2001
From: Timo Moeller <timo.moeller@deepset.ai>
Date: Mon, 13 Sep 2021 20:00:22 +0200
Subject: [PATCH] Fix tests

---
 test/test_modeling_question_answering.py | 81 ------------------------
 1 file changed, 81 deletions(-)
 delete mode 100644 test/test_modeling_question_answering.py

diff --git a/test/test_modeling_question_answering.py b/test/test_modeling_question_answering.py
deleted file mode 100644
index c3eb9b011..000000000
--- a/test/test_modeling_question_answering.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import logging
-from pathlib import Path
-
-from haystack.modeling.data_handler.data_silo import DataSilo
-from haystack.modeling.data_handler.processor import SquadProcessor
-from haystack.modeling.model.adaptive_model import AdaptiveModel
-from haystack.modeling.model.language_model import LanguageModel
-from haystack.modeling.model.optimization import initialize_optimizer
-from haystack.modeling.model.prediction_head import QuestionAnsweringHead
-from haystack.modeling.model.tokenization import Tokenizer
-from haystack.modeling.training.base import Trainer
-from haystack.modeling.utils import set_all_seeds, initialize_device_settings
-
-
-def test_training(caplog=None):
-    if caplog:
-        caplog.set_level(logging.CRITICAL)
-
-    set_all_seeds(seed=42)
-    device, n_gpu = initialize_device_settings(use_cuda=False)
-    batch_size = 2
-    n_epochs = 1
-    evaluate_every = 4
-    base_LM_model = "distilbert-base-uncased"
-
-    tokenizer = Tokenizer.load(
-        pretrained_model_name_or_path=base_LM_model,
-        do_lower_case=True,
-        use_fast=True  # TODO parametrize this to test slow as well
-    )
-    label_list = ["start_token", "end_token"]
-    processor = SquadProcessor(
-        tokenizer=tokenizer,
-        max_seq_len=256,
-        doc_stride=10,
-        max_query_length=6,
-        train_filename="train-sample.json",
-        dev_filename="dev-sample.json",
-        test_filename=None,
-        data_dir=Path("samples/qa"),
-        label_list=label_list,
-        metric="squad"
-    )
-
-    data_silo = DataSilo(processor=processor, batch_size=batch_size, max_processes=1)
-    language_model = LanguageModel.load(base_LM_model)
-    prediction_head = QuestionAnsweringHead()
-    model = AdaptiveModel(
-        language_model=language_model,
-        prediction_heads=[prediction_head],
-        embeds_dropout_prob=0.1,
-        lm_output_types=["per_token"],
-        device=device,
-    )
-
-    model, optimizer, lr_schedule = initialize_optimizer(
-        model=model,
-        learning_rate=2e-5,
-        # optimizer_opts={'name': 'AdamW', 'lr': 2E-05},
-        n_batches=len(data_silo.loaders["train"]),
-        n_epochs=n_epochs,
-        device=device
-    )
-    trainer = Trainer(
-        model=model,
-        optimizer=optimizer,
-        data_silo=data_silo,
-        epochs=n_epochs,
-        n_gpu=n_gpu,
-        lr_schedule=lr_schedule,
-        evaluate_every=evaluate_every,
-        device=device
-    )
-    trainer.train()
-
-    assert type(model) == AdaptiveModel
-    assert type(processor) == SquadProcessor
-
-
-if __name__ == "__main__":
-    test_training()