✨ added run_tokenizer in text_generation_local.py

- 🎨 tox formatting
- 🚧 added a test to assert the length of the run_tokenizer output
- 🚧 made a more comprehensive test for the run tokenizer method

caikit_nlp/modules/text_generation/text_generation_local.py

@@ -55,6 +55,7 @@
 
 TRAINING_LOSS_LOG_FILENAME = "training_logs.jsonl"
 
+
 # pylint: disable=too-many-lines,too-many-instance-attributes
 @module(
     id="f9181353-4ccf-4572-bd1e-f12bcda26792",
@@ -590,7 +591,11 @@ def run_tokenizer(
             TokenizationResults
                 The token count
         """
-        raise NotImplementedError("Tokenization not implemented for local")
+        error.type_check("<NLP48137045E>", str, text=text)
+        tokenized_output = self.model.tokenizer(text)
+        return TokenizationResults(
+            token_count=len(tokenized_output["input_ids"]),
+        )
 
     ################################## Private Functions ######################################
 

tests/modules/text_generation/test_text_generation_local.py

@@ -1,5 +1,6 @@
 """Tests for text-generation module
 """
+
 # Standard
 import os
 import platform
@@ -10,7 +11,7 @@
 import torch
 
 # First Party
-from caikit.interfaces.nlp.data_model import GeneratedTextResult
+from caikit.interfaces.nlp.data_model import GeneratedTextResult, TokenizationResults
 import caikit
 
 # Local
@@ -211,7 +212,26 @@ def test_zero_epoch_case(disable_wip):
     assert isinstance(model.model, HFAutoSeq2SeqLM)
 
 
-def test_run_tokenizer_not_implemented():
-    with pytest.raises(NotImplementedError):
-        model = TextGeneration.bootstrap(SEQ2SEQ_LM_MODEL)
-        model.run_tokenizer("This text doesn't matter")
+# ############################## Run Tokenizer ################################
+
+
+def test_run_tokenizer_edge_cases(disable_wip, set_cpu_device):
+    """Test tokenizer on edge cases like empty strings and long input."""
+    model = TextGeneration.bootstrap(CAUSAL_LM_MODEL)
+
+    # Edge case: Empty string
+    empty_result = model.run_tokenizer("")
+    assert isinstance(empty_result, TokenizationResults)
+    assert empty_result.token_count == 0
+
+    # Normal case: short sentence
+    short_text = "This is a test sentence."
+    short_result = model.run_tokenizer(short_text)
+    assert isinstance(short_result, TokenizationResults)
+    assert short_result.token_count > 0
+
+    # Edge case: Long input
+    long_text = "This is a test sentence. " * 1000
+    long_result = model.run_tokenizer(long_text)
+    assert isinstance(long_result, TokenizationResults)
+    assert long_result.token_count > 0