mirror of
https://github.com/huggingface/transformers.git
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905e5773a3
* [processor] Add 'model input names' property * add test * no f string * add generic property method to mixin * copy to multimodal * copy to vision * tests for all audio * remove ad-hoc tests * style * fix flava test * fix test * fix processor code
247 lines
9.7 KiB
Python
247 lines
9.7 KiB
Python
# Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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import random
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import shutil
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import tempfile
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import unittest
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import numpy as np
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import pytest
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from transformers import BertTokenizer, BertTokenizerFast
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from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
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from transformers.testing_utils import require_vision
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from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
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if is_vision_available():
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from PIL import Image
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from transformers import FlavaFeatureExtractor, FlavaProcessor
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from transformers.models.flava.image_processing_flava import (
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FLAVA_CODEBOOK_MEAN,
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FLAVA_CODEBOOK_STD,
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FLAVA_IMAGE_MEAN,
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FLAVA_IMAGE_STD,
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)
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@require_vision
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class FlavaProcessorTest(unittest.TestCase):
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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# fmt: off
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vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
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# fmt: on
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as fp:
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fp.write("".join([x + "\n" for x in vocab_tokens]))
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feature_extractor_map = {
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"image_mean": FLAVA_IMAGE_MEAN,
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"image_std": FLAVA_IMAGE_STD,
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"do_normalize": True,
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"do_resize": True,
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"size": 224,
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"do_center_crop": True,
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"crop_size": 224,
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"input_size_patches": 14,
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"total_mask_patches": 75,
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"mask_group_max_patches": None,
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"mask_group_min_patches": 16,
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"mask_group_min_aspect_ratio": 0.3,
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"mask_group_max_aspect_ratio": None,
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"codebook_do_resize": True,
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"codebook_size": 112,
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"codebook_do_center_crop": True,
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"codebook_crop_size": 112,
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"codebook_do_map_pixels": True,
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"codebook_do_normalize": True,
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"codebook_image_mean": FLAVA_CODEBOOK_MEAN,
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"codebook_image_std": FLAVA_CODEBOOK_STD,
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}
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self.feature_extractor_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME)
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with open(self.feature_extractor_file, "w", encoding="utf-8") as fp:
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json.dump(feature_extractor_map, fp)
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def get_tokenizer(self, **kwargs):
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return BertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def get_rust_tokenizer(self, **kwargs):
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return BertTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
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def get_feature_extractor(self, **kwargs):
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return FlavaFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def prepare_image_inputs(self):
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"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
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or a list of PyTorch tensors if one specifies torchify=True.
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"""
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image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
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image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
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return image_inputs
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def test_save_load_pretrained_default(self):
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tokenizer_slow = self.get_tokenizer()
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tokenizer_fast = self.get_rust_tokenizer()
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feature_extractor = self.get_feature_extractor()
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processor_slow = FlavaProcessor(tokenizer=tokenizer_slow, feature_extractor=feature_extractor)
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processor_slow.save_pretrained(self.tmpdirname)
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processor_slow = FlavaProcessor.from_pretrained(self.tmpdirname, use_fast=False)
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processor_fast = FlavaProcessor(tokenizer=tokenizer_fast, feature_extractor=feature_extractor)
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processor_fast.save_pretrained(self.tmpdirname)
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processor_fast = FlavaProcessor.from_pretrained(self.tmpdirname)
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self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab())
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self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab())
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self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab())
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self.assertIsInstance(processor_slow.tokenizer, BertTokenizer)
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self.assertIsInstance(processor_fast.tokenizer, BertTokenizerFast)
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self.assertEqual(processor_slow.feature_extractor.to_json_string(), feature_extractor.to_json_string())
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self.assertEqual(processor_fast.feature_extractor.to_json_string(), feature_extractor.to_json_string())
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self.assertIsInstance(processor_slow.feature_extractor, FlavaFeatureExtractor)
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self.assertIsInstance(processor_fast.feature_extractor, FlavaFeatureExtractor)
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def test_save_load_pretrained_additional_features(self):
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processor = FlavaProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
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processor.save_pretrained(self.tmpdirname)
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tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
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feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
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processor = FlavaProcessor.from_pretrained(
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self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
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)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
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self.assertIsInstance(processor.tokenizer, BertTokenizerFast)
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self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
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self.assertIsInstance(processor.feature_extractor, FlavaFeatureExtractor)
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def test_feature_extractor(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = FlavaProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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image_input = self.prepare_image_inputs()
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input_feat_extract = feature_extractor(image_input, return_tensors="np")
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input_processor = processor(images=image_input, return_tensors="np")
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for key in input_feat_extract.keys():
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self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
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# With rest of the args
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random.seed(1234)
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input_feat_extract = feature_extractor(
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image_input, return_image_mask=True, return_codebook_pixels=True, return_tensors="np"
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)
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random.seed(1234)
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input_processor = processor(
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images=image_input, return_image_mask=True, return_codebook_pixels=True, return_tensors="np"
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)
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for key in input_feat_extract.keys():
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self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
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def test_tokenizer(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = FlavaProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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input_str = "lower newer"
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encoded_processor = processor(text=input_str)
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encoded_tok = tokenizer(input_str)
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for key in encoded_tok.keys():
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self.assertListEqual(encoded_tok[key], encoded_processor[key])
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def test_processor(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = FlavaProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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inputs = processor(text=input_str, images=image_input)
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self.assertListEqual(list(inputs.keys()), ["input_ids", "token_type_ids", "attention_mask", "pixel_values"])
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# add extra args
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inputs = processor(text=input_str, images=image_input, return_codebook_pixels=True, return_image_mask=True)
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self.assertListEqual(
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list(inputs.keys()),
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[
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"input_ids",
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"token_type_ids",
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"attention_mask",
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"pixel_values",
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"codebook_pixel_values",
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"bool_masked_pos",
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],
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)
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# test if it raises when no input is passed
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with pytest.raises(ValueError):
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processor()
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def test_tokenizer_decode(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = FlavaProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
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decoded_processor = processor.batch_decode(predicted_ids)
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decoded_tok = tokenizer.batch_decode(predicted_ids)
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self.assertListEqual(decoded_tok, decoded_processor)
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def test_model_input_names(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = FlavaProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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inputs = processor(text=input_str, images=image_input)
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self.assertListEqual(list(inputs.keys()), processor.model_input_names)
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