# Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import gc import shutil import tempfile import unittest from transformers import ClvpFeatureExtractor, ClvpProcessor, ClvpTokenizer from transformers.testing_utils import require_torch from .test_feature_extraction_clvp import floats_list @require_torch class ClvpProcessorTest(unittest.TestCase): def setUp(self): self.checkpoint = "susnato/clvp_dev" self.tmpdirname = tempfile.mkdtemp() def tearDown(self): super().tearDown() shutil.rmtree(self.tmpdirname) gc.collect() # Copied from transformers.tests.models.whisper.test_processing_whisper.WhisperProcessorTest.get_tokenizer with Whisper->Clvp def get_tokenizer(self, **kwargs): return ClvpTokenizer.from_pretrained(self.checkpoint, **kwargs) # Copied from transformers.tests.models.whisper.test_processing_whisper.WhisperProcessorTest.get_feature_extractor with Whisper->Clvp def get_feature_extractor(self, **kwargs): return ClvpFeatureExtractor.from_pretrained(self.checkpoint, **kwargs) # Copied from transformers.tests.models.whisper.test_processing_whisper.WhisperProcessorTest.test_save_load_pretrained_default with Whisper->Clvp def test_save_load_pretrained_default(self): tokenizer = self.get_tokenizer() feature_extractor = self.get_feature_extractor() processor = ClvpProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) processor.save_pretrained(self.tmpdirname) processor = ClvpProcessor.from_pretrained(self.tmpdirname) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab()) self.assertIsInstance(processor.tokenizer, ClvpTokenizer) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string()) self.assertIsInstance(processor.feature_extractor, ClvpFeatureExtractor) # Copied from transformers.tests.models.whisper.test_processing_whisper.WhisperProcessorTest.test_feature_extractor with Whisper->Clvp,processor(raw_speech->processor(raw_speech=raw_speech def test_feature_extractor(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = ClvpProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) raw_speech = floats_list((3, 1000)) input_feat_extract = feature_extractor(raw_speech, return_tensors="np") input_processor = processor(raw_speech=raw_speech, return_tensors="np") for key in input_feat_extract: self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2) # Copied from transformers.tests.models.whisper.test_processing_whisper.WhisperProcessorTest.test_tokenizer with Whisper->Clvp def test_tokenizer(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = ClvpProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) input_str = "This is a test string" encoded_processor = processor(text=input_str) encoded_tok = tokenizer(input_str) for key in encoded_tok: self.assertListEqual(encoded_tok[key], encoded_processor[key]) # Copied from transformers.tests.models.whisper.test_processing_whisper.WhisperProcessorTest.test_tokenizer_decode with Whisper->Clvp def test_tokenizer_decode(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = ClvpProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]] decoded_processor = processor.batch_decode(predicted_ids) decoded_tok = tokenizer.batch_decode(predicted_ids) self.assertListEqual(decoded_tok, decoded_processor) def test_save_load_pretrained_additional_features(self): processor = ClvpProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()) processor.save_pretrained(self.tmpdirname) tokenizer_add_kwargs = self.get_tokenizer(pad_token="(PAD)") feature_extractor_add_kwargs = self.get_feature_extractor(sampling_rate=16000) processor = ClvpProcessor.from_pretrained( self.tmpdirname, pad_token="(PAD)", sampling_rate=16000, ) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) self.assertIsInstance(processor.tokenizer, ClvpTokenizer) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string()) self.assertIsInstance(processor.feature_extractor, ClvpFeatureExtractor) def test_text_and_audio_attention_mask(self): # When both `text` and `audio` are passed, the CLVP model consumes the *text* attention mask. # Ensure the audio feature extractor's (much longer) attention mask does not override the text one # in the merged output. Regression test for the merged-output attention mask collision. feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = ClvpProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) raw_speech = floats_list((3, 1000)) input_str = "This is a test string" inputs = processor(text=input_str, raw_speech=raw_speech, return_tensors="pt") self.assertIn("input_ids", inputs) self.assertIn("input_features", inputs) self.assertIn("attention_mask", inputs) # The attention mask must match the text `input_ids`, not the audio features. self.assertEqual(inputs["attention_mask"].shape, inputs["input_ids"].shape) def test_text_and_audio_flat_kwargs(self): # Flat (backward-compatible) kwargs must still be forwarded to the tokenizer when both `text` and # `audio` are passed. Regression test ensuring the audio-mask handling does not discard flat kwargs. feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = ClvpProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) raw_speech = floats_list((3, 1000)) input_str = "This is a test string" inputs = processor( text=input_str, raw_speech=raw_speech, return_tensors="pt", padding="max_length", max_length=20, ) self.assertEqual(inputs["input_ids"].shape[-1], 20) self.assertEqual(inputs["attention_mask"].shape[-1], 20)