From 72156d18864b43bc976ae675e581de7a06db9c28 Mon Sep 17 00:00:00 2001
From: Sebastian Husch Lee <sjrl423@gmail.com>
Date: Tue, 6 Jun 2023 16:01:35 +0200
Subject: [PATCH] Use mocking to skip the actual training part

---
 test/nodes/test_retriever.py | 87 ++++++++++++------------------------
 1 file changed, 28 insertions(+), 59 deletions(-)

diff --git a/test/nodes/test_retriever.py b/test/nodes/test_retriever.py
index b13852e9b8..dd62bbf6ec 100644
--- a/test/nodes/test_retriever.py
+++ b/test/nodes/test_retriever.py
@@ -650,66 +650,35 @@ def test_table_text_retriever_training(tmp_path, document_store, samples_path):
     )
 
 
-def test_sentence_transformers_retriever_training():
-    retriever = EmbeddingRetriever(
-        embedding_model="sentence-transformers/all-MiniLM-L6-v2", model_format="sentence_transformers", use_gpu=False
-    )
-    retriever.train(
-        training_data=[
-            {
-                "question": "What color is the sky?",
-                "pos_doc": "The color of the sky is blue.",
-                "neg_doc": "The color of the grass is brown.",
-            },
-            {
-                "question": "What is the capital of Germany?",
-                "pos_doc": "The capital of Germany is Berlin.",
-                "neg_doc": "The capital of France is Paris.",
-            },
-        ],
-        train_loss="mnrl",
-        n_epochs=1,
-        batch_size=1,
-        num_warmup_steps=0,
-    )
+def test_sentence_transformers_retriever_training_with_gradient_checkpointing():
+    def mock_fit(*args, **kwargs):
+        return None
 
-
-# # TODO Mock _SentenceTransformerEmbeddingEncoder, create a normal EmbeddingRetriever
-# #      When creating the mock _SentenceTransformerEmbeddingEncoder add a train method that checks the correct
-# #      parameters were passed?
-# def test_sentence_transformers_retriever_training_with_gradient_checkpointing():
-#     def mock_train(model_name_or_path, **kwargs):
-#         return model_name_or_path == model_name
-#
-#     class MockSentenceTransformers():
-#         def train(self):
-#
-#
-#     with patch("haystack.nodes._embedding_encoder._SentenceTransformersEmbeddingEncoder", mock_encoder):
-#         retriever = EmbeddingRetriever(
-#             embedding_model="sentence-transformers/all-MiniLM-L6-v2",
-#             model_format="sentence_transformers",
-#             use_gpu=False,
-#         )
-#         retriever.train(
-#             training_data=[
-#                 {
-#                     "question": "What color is the sky?",
-#                     "pos_doc": "The color of the sky is blue.",
-#                     "neg_doc": "The color of the grass is brown.",
-#                 },
-#                 {
-#                     "question": "What is the capital of Germany?",
-#                     "pos_doc": "The capital of Germany is Berlin.",
-#                     "neg_doc": "The capital of France is Paris.",
-#                 },
-#             ],
-#             train_loss="mnrl",
-#             n_epochs=1,
-#             batch_size=1,
-#             num_warmup_steps=0,
-#             gradient_checkpointing=True,
-#         )
+    with patch("sentence_transformers.SentenceTransformer.fit", mock_fit):
+        retriever = EmbeddingRetriever(
+            embedding_model="sentence-transformers/all-MiniLM-L6-v2",
+            model_format="sentence_transformers",
+            use_gpu=False,
+        )
+        retriever.train(
+            training_data=[
+                {
+                    "question": "What color is the sky?",
+                    "pos_doc": "The color of the sky is blue.",
+                    "neg_doc": "The color of the grass is brown.",
+                },
+                {
+                    "question": "What is the capital of Germany?",
+                    "pos_doc": "The capital of Germany is Berlin.",
+                    "neg_doc": "The capital of France is Paris.",
+                },
+            ],
+            train_loss="mnrl",
+            n_epochs=1,
+            batch_size=1,
+            num_warmup_steps=0,
+            gradient_checkpointing=True,
+        )
 
 
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