File size: 53,734 Bytes
e0be88b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 |
# Copyright 2024 The HuggingFace Inc. 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 inspect
import json
import random
import tempfile
from pathlib import Path
from typing import Optional
import numpy as np
from huggingface_hub import hf_hub_download
from parameterized import parameterized
from transformers.models.auto.processing_auto import processor_class_from_name
from transformers.processing_utils import Unpack
from transformers.testing_utils import (
check_json_file_has_correct_format,
require_av,
require_librosa,
require_torch,
require_vision,
)
from transformers.utils import is_torch_available, is_vision_available
global_rng = random.Random()
if is_vision_available():
from PIL import Image
if is_torch_available():
import torch
MODALITY_INPUT_DATA = {
"images": [
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
],
"videos": [
"https://test-videos.co.uk/vids/bigbuckbunny/mp4/h264/720/Big_Buck_Bunny_720_10s_10MB.mp4",
["https://www.ilankelman.org/stopsigns/australia.jpg", "https://www.ilankelman.org/stopsigns/australia.jpg"],
],
"audio": [
"https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3",
"https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/f2641_0_throatclearing.wav",
],
}
def prepare_image_inputs():
"""This function prepares a list of PIL images"""
image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
return image_inputs
# Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list
def floats_list(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
values = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
@require_torch
@require_vision
class ProcessorTesterMixin:
processor_class = None
text_input_name = "input_ids"
images_input_name = "pixel_values"
videos_input_name = "pixel_values_videos"
audio_input_name = "input_features"
@staticmethod
def prepare_processor_dict():
return {}
def get_component(self, attribute, **kwargs):
assert attribute in self.processor_class.attributes
component_class_name = getattr(self.processor_class, f"{attribute}_class")
if isinstance(component_class_name, tuple):
component_class_name = component_class_name[0]
component_class = processor_class_from_name(component_class_name)
component = component_class.from_pretrained(self.tmpdirname, **kwargs) # noqa
if "tokenizer" in attribute and not component.pad_token:
component.pad_token = "[TEST_PAD]"
if component.pad_token_id is None:
component.pad_token_id = 0
return component
def prepare_components(self):
components = {}
for attribute in self.processor_class.attributes:
component = self.get_component(attribute)
components[attribute] = component
return components
def get_processor(self):
components = self.prepare_components()
processor = self.processor_class(**components, **self.prepare_processor_dict())
return processor
def prepare_text_inputs(self, batch_size: Optional[int] = None, modality: Optional[str] = None):
if modality is not None:
special_token_to_add = getattr(self, f"{modality}_token", "")
else:
special_token_to_add = ""
if batch_size is None:
return f"lower newer {special_token_to_add}"
if batch_size < 1:
raise ValueError("batch_size must be greater than 0")
if batch_size == 1:
return [f"lower newer {special_token_to_add}"]
return [f"lower newer {special_token_to_add}", f" {special_token_to_add} upper older longer string"] + [
f"lower newer {special_token_to_add}"
] * (batch_size - 2)
@require_vision
def prepare_image_inputs(self, batch_size: Optional[int] = None):
"""This function prepares a list of PIL images for testing"""
if batch_size is None:
return prepare_image_inputs()[0]
if batch_size < 1:
raise ValueError("batch_size must be greater than 0")
return prepare_image_inputs() * batch_size
@require_vision
def prepare_video_inputs(self, batch_size: Optional[int] = None):
"""This function prepares a list of numpy videos."""
video_input = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)] * 8
if batch_size is None:
return video_input
return [video_input] * batch_size
def test_processor_to_json_string(self):
processor = self.get_processor()
obj = json.loads(processor.to_json_string())
for key, value in self.prepare_processor_dict().items():
# Chat template is saved as a separate file
if key not in "chat_template":
# json converts dict keys to str, but some processors force convert back to int when init
if (
isinstance(obj[key], dict)
and isinstance(list(obj[key].keys())[0], str)
and isinstance(list(value.keys())[0], int)
):
obj[key] = {int(k): v for k, v in obj[key].items()}
self.assertEqual(obj[key], value)
self.assertEqual(getattr(processor, key, None), value)
def test_processor_from_and_save_pretrained(self):
processor_first = self.get_processor()
with tempfile.TemporaryDirectory() as tmpdirname:
saved_files = processor_first.save_pretrained(tmpdirname)
if len(saved_files) > 0:
check_json_file_has_correct_format(saved_files[0])
processor_second = self.processor_class.from_pretrained(tmpdirname)
self.assertEqual(processor_second.to_dict(), processor_first.to_dict())
for attribute in processor_first.attributes:
attribute_first = getattr(processor_first, attribute)
attribute_second = getattr(processor_second, attribute)
# tokenizer repr contains model-path from where we loaded
if "tokenizer" not in attribute:
self.assertEqual(repr(attribute_first), repr(attribute_second))
# These kwargs-related tests ensure that processors are correctly instantiated.
# they need to be applied only if an image_processor exists.
def skip_processor_without_typed_kwargs(self, processor):
# TODO this signature check is to test only uniformized processors.
# Once all are updated, remove it.
is_kwargs_typed_dict = False
call_signature = inspect.signature(processor.__call__)
for param in call_signature.parameters.values():
if param.kind == param.VAR_KEYWORD and param.annotation != param.empty:
is_kwargs_typed_dict = (
hasattr(param.annotation, "__origin__") and param.annotation.__origin__ == Unpack
)
if not is_kwargs_typed_dict:
self.skipTest(f"{self.processor_class} doesn't have typed kwargs.")
def test_tokenizer_defaults_preserved_by_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length")
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(modality="image")
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input, return_tensors="pt")
self.assertEqual(inputs[self.text_input_name].shape[-1], 117)
def test_image_processor_defaults_preserved_by_image_kwargs(self):
"""
We use do_rescale=True, rescale_factor=-1 to ensure that image_processor kwargs are preserved in the processor.
We then check that the mean of the pixel_values is less than or equal to 0 after processing.
Since the original pixel_values are in [0, 255], this is a good indicator that the rescale_factor is indeed applied.
"""
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_components["image_processor"] = self.get_component(
"image_processor", do_rescale=True, rescale_factor=-1
)
processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length")
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(modality="image")
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input, return_tensors="pt")
self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0)
def test_kwargs_overrides_default_tokenizer_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_components["tokenizer"] = self.get_component("tokenizer", padding="longest")
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(modality="image")
image_input = self.prepare_image_inputs()
inputs = processor(
text=input_str, images=image_input, return_tensors="pt", max_length=112, padding="max_length"
)
self.assertEqual(inputs[self.text_input_name].shape[-1], 112)
def test_kwargs_overrides_default_image_processor_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_components["image_processor"] = self.get_component(
"image_processor", do_rescale=True, rescale_factor=1
)
processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length")
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(modality="image")
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input, do_rescale=True, rescale_factor=-1, return_tensors="pt")
self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0)
def test_unstructured_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(modality="image")
image_input = self.prepare_image_inputs()
inputs = processor(
text=input_str,
images=image_input,
return_tensors="pt",
do_rescale=True,
rescale_factor=-1,
padding="max_length",
max_length=76,
)
self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0)
self.assertEqual(inputs[self.text_input_name].shape[-1], 76)
def test_unstructured_kwargs_batched(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(batch_size=2, modality="image")
image_input = self.prepare_image_inputs(batch_size=2)
inputs = processor(
text=input_str,
images=image_input,
return_tensors="pt",
do_rescale=True,
rescale_factor=-1,
padding="longest",
max_length=76,
)
self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0)
self.assertTrue(
len(inputs[self.text_input_name][0]) == len(inputs[self.text_input_name][1])
and len(inputs[self.text_input_name][1]) < 76
)
def test_doubly_passed_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = [self.prepare_text_inputs(modality="image")]
image_input = self.prepare_image_inputs()
with self.assertRaises(ValueError):
_ = processor(
text=input_str,
images=image_input,
images_kwargs={"do_rescale": True, "rescale_factor": -1},
do_rescale=True,
return_tensors="pt",
)
def test_structured_kwargs_nested(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(modality="image")
image_input = self.prepare_image_inputs()
# Define the kwargs for each modality
all_kwargs = {
"common_kwargs": {"return_tensors": "pt"},
"images_kwargs": {"do_rescale": True, "rescale_factor": -1},
"text_kwargs": {"padding": "max_length", "max_length": 76},
}
inputs = processor(text=input_str, images=image_input, **all_kwargs)
self.skip_processor_without_typed_kwargs(processor)
self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0)
self.assertEqual(inputs[self.text_input_name].shape[-1], 76)
def test_structured_kwargs_nested_from_dict(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(modality="image")
image_input = self.prepare_image_inputs()
# Define the kwargs for each modality
all_kwargs = {
"common_kwargs": {"return_tensors": "pt"},
"images_kwargs": {"do_rescale": True, "rescale_factor": -1},
"text_kwargs": {"padding": "max_length", "max_length": 76},
}
inputs = processor(text=input_str, images=image_input, **all_kwargs)
self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0)
self.assertEqual(inputs[self.text_input_name].shape[-1], 76)
# text + audio kwargs testing
@require_torch
def test_tokenizer_defaults_preserved_by_kwargs_audio(self):
if "feature_extractor" not in self.processor_class.attributes:
self.skipTest(f"feature_extractor attribute not present in {self.processor_class}")
feature_extractor = self.get_component("feature_extractor")
tokenizer = self.get_component("tokenizer", max_length=300, padding="max_length")
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(tokenizer=tokenizer, feature_extractor=feature_extractor, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(batch_size=3, modality="audio")
raw_speech = floats_list((3, 1000))
raw_speech = [np.asarray(audio) for audio in raw_speech]
inputs = processor(text=input_str, audio=raw_speech, return_tensors="pt")
self.assertEqual(len(inputs[self.text_input_name][0]), 300)
@require_torch
def test_kwargs_overrides_default_tokenizer_kwargs_audio(self):
if "feature_extractor" not in self.processor_class.attributes:
self.skipTest(f"feature_extractor attribute not present in {self.processor_class}")
feature_extractor = self.get_component("feature_extractor")
tokenizer = self.get_component("tokenizer", max_length=117)
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(tokenizer=tokenizer, feature_extractor=feature_extractor, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(batch_size=3, modality="audio")
raw_speech = floats_list((3, 1000))
raw_speech = [np.asarray(audio) for audio in raw_speech]
inputs = processor(text=input_str, audio=raw_speech, return_tensors="pt", max_length=300, padding="max_length")
self.assertEqual(len(inputs[self.text_input_name][0]), 300)
@require_torch
def test_unstructured_kwargs_audio(self):
if "feature_extractor" not in self.processor_class.attributes:
self.skipTest(f"feature_extractor attribute not present in {self.processor_class}")
feature_extractor = self.get_component("feature_extractor")
tokenizer = self.get_component("tokenizer")
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(tokenizer=tokenizer, feature_extractor=feature_extractor, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(batch_size=3, modality="audio")
raw_speech = floats_list((3, 1000))
raw_speech = [np.asarray(audio) for audio in raw_speech]
inputs = processor(text=input_str, audio=raw_speech, return_tensors="pt", max_length=300, padding="max_length")
self.assertEqual(len(inputs[self.text_input_name][0]), 300)
@require_torch
def test_doubly_passed_kwargs_audio(self):
if "feature_extractor" not in self.processor_class.attributes:
self.skipTest(f"feature_extractor attribute not present in {self.processor_class}")
feature_extractor = self.get_component("feature_extractor")
tokenizer = self.get_component("tokenizer")
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(tokenizer=tokenizer, feature_extractor=feature_extractor, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(batch_size=3, modality="audio")
raw_speech = floats_list((3, 1000))
raw_speech = [np.asarray(audio) for audio in raw_speech]
with self.assertRaises(ValueError):
_ = processor(
text=input_str,
audio=raw_speech,
text_kwargs={"padding": "max_length"},
padding="max_length",
)
@require_torch
@require_vision
def test_structured_kwargs_audio_nested(self):
if "feature_extractor" not in self.processor_class.attributes:
self.skipTest(f"feature_extractor attribute not present in {self.processor_class}")
feature_extractor = self.get_component("feature_extractor")
tokenizer = self.get_component("tokenizer", max_length=117)
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(tokenizer=tokenizer, feature_extractor=feature_extractor, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(batch_size=3, modality="audio")
raw_speech = floats_list((3, 1000))
raw_speech = [np.asarray(audio) for audio in raw_speech]
# Define the kwargs for each modality
all_kwargs = {
"common_kwargs": {"return_tensors": "pt"},
"text_kwargs": {"padding": "max_length", "max_length": 76},
"audio_kwargs": {"padding": "max_length", "max_length": 300},
}
inputs = processor(text=input_str, audio=raw_speech, **all_kwargs)
self.assertEqual(len(inputs[self.text_input_name][0]), 76)
def test_tokenizer_defaults_preserved_by_kwargs_video(self):
if "video_processor" not in self.processor_class.attributes:
self.skipTest(f"video_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length")
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(modality="video")
video_input = self.prepare_video_inputs()
inputs = processor(text=input_str, videos=video_input, return_tensors="pt")
self.assertEqual(inputs[self.text_input_name].shape[-1], 117)
def test_video_processor_defaults_preserved_by_video_kwargs(self):
"""
We use do_rescale=True, rescale_factor=-1 to ensure that image_processor kwargs are preserved in the processor.
We then check that the mean of the pixel_values is less than or equal to 0 after processing.
Since the original pixel_values are in [0, 255], this is a good indicator that the rescale_factor is indeed applied.
"""
if "video_processor" not in self.processor_class.attributes:
self.skipTest(f"video_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_components["video_processor"] = self.get_component(
"video_processor", do_rescale=True, rescale_factor=-1
)
processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length")
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(modality="video")
video_input = self.prepare_video_inputs()
inputs = processor(text=input_str, videos=video_input, return_tensors="pt")
self.assertLessEqual(inputs[self.videos_input_name][0].mean(), 0)
def test_kwargs_overrides_default_tokenizer_kwargs_video(self):
if "video_processor" not in self.processor_class.attributes:
self.skipTest(f"video_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_components["tokenizer"] = self.get_component("tokenizer", padding="longest")
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(modality="video")
video_input = self.prepare_video_inputs()
inputs = processor(
text=input_str, videos=video_input, return_tensors="pt", max_length=112, padding="max_length"
)
self.assertEqual(inputs[self.text_input_name].shape[-1], 112)
def test_kwargs_overrides_default_video_processor_kwargs(self):
if "video_processor" not in self.processor_class.attributes:
self.skipTest(f"video_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_components["video_processor"] = self.get_component(
"video_processor", do_rescale=True, rescale_factor=1
)
processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length")
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(modality="video")
video_input = self.prepare_video_inputs()
inputs = processor(text=input_str, videos=video_input, do_rescale=True, rescale_factor=-1, return_tensors="pt")
self.assertLessEqual(inputs[self.videos_input_name][0].mean(), 0)
def test_unstructured_kwargs_video(self):
if "video_processor" not in self.processor_class.attributes:
self.skipTest(f"video_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(modality="video")
video_input = self.prepare_video_inputs()
inputs = processor(
text=input_str,
videos=video_input,
return_tensors="pt",
do_rescale=True,
rescale_factor=-1,
padding="max_length",
max_length=76,
)
self.assertLessEqual(inputs[self.videos_input_name][0].mean(), 0)
self.assertEqual(inputs[self.text_input_name].shape[-1], 76)
def test_unstructured_kwargs_batched_video(self):
if "video_processor" not in self.processor_class.attributes:
self.skipTest(f"video_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(batch_size=2, modality="video")
video_input = self.prepare_video_inputs(batch_size=2)
inputs = processor(
text=input_str,
videos=video_input,
return_tensors="pt",
do_rescale=True,
rescale_factor=-1,
padding="longest",
max_length=76,
)
self.assertLessEqual(inputs[self.videos_input_name][0].mean(), 0)
self.assertTrue(
len(inputs[self.text_input_name][0]) == len(inputs[self.text_input_name][1])
and len(inputs[self.text_input_name][1]) < 76
)
def test_doubly_passed_kwargs_video(self):
if "video_processor" not in self.processor_class.attributes:
self.skipTest(f"video_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = [self.prepare_text_inputs(modality="video")]
video_input = self.prepare_video_inputs()
with self.assertRaises(ValueError):
_ = processor(
text=input_str,
videos=video_input,
videos_kwargs={"do_rescale": True, "rescale_factor": -1},
do_rescale=True,
return_tensors="pt",
)
def test_structured_kwargs_nested_video(self):
if "video_processor" not in self.processor_class.attributes:
self.skipTest(f"video_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(modality="video")
video_input = self.prepare_video_inputs()
# Define the kwargs for each modality
all_kwargs = {
"common_kwargs": {"return_tensors": "pt"},
"videos_kwargs": {"do_rescale": True, "rescale_factor": -1},
"text_kwargs": {"padding": "max_length", "max_length": 76},
}
inputs = processor(text=input_str, videos=video_input, **all_kwargs)
self.skip_processor_without_typed_kwargs(processor)
self.assertLessEqual(inputs[self.videos_input_name][0].mean(), 0)
self.assertEqual(inputs[self.text_input_name].shape[-1], 76)
def test_structured_kwargs_nested_from_dict_video(self):
if "video_processor" not in self.processor_class.attributes:
self.skipTest(f"video_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(modality="video")
video_input = self.prepare_video_inputs()
# Define the kwargs for each modality
all_kwargs = {
"common_kwargs": {"return_tensors": "pt"},
"videos_kwargs": {"do_rescale": True, "rescale_factor": -1},
"text_kwargs": {"padding": "max_length", "max_length": 76},
}
inputs = processor(text=input_str, videos=video_input, **all_kwargs)
self.assertLessEqual(inputs[self.videos_input_name][0].mean(), 0)
self.assertEqual(inputs[self.text_input_name].shape[-1], 76)
# TODO: the same test, but for audio + text processors that have strong overlap in kwargs
# TODO (molbap) use the same structure of attribute kwargs for other tests to avoid duplication
def test_overlapping_text_image_kwargs_handling(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor = self.processor_class(**processor_components)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(modality="image")
image_input = self.prepare_image_inputs()
with self.assertRaises(ValueError):
_ = processor(
text=input_str,
images=image_input,
return_tensors="pt",
padding="max_length",
text_kwargs={"padding": "do_not_pad"},
)
def test_overlapping_text_audio_kwargs_handling(self):
"""
Checks that `padding`, or any other overlap arg between audio extractor and tokenizer
is be passed to only text and ignored for audio for BC purposes
"""
if "feature_extractor" not in self.processor_class.attributes:
self.skipTest(f"feature_extractor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(batch_size=3, modality="audio")
audio_lengths = [4000, 8000, 16000, 32000]
raw_speech = [np.asarray(audio)[:length] for audio, length in zip(floats_list((3, 32_000)), audio_lengths)]
# padding = True should not raise an error and will if the audio processor popped its value to None
_ = processor(text=input_str, audio=raw_speech, padding=True, return_tensors="pt")
def test_prepare_and_validate_optional_call_args(self):
processor = self.get_processor()
optional_call_args_name = getattr(processor, "optional_call_args", [])
num_optional_call_args = len(optional_call_args_name)
if num_optional_call_args == 0:
self.skipTest("No optional call args")
# test all optional call args are given
optional_call_args = processor.prepare_and_validate_optional_call_args(
*(f"optional_{i}" for i in range(num_optional_call_args))
)
self.assertEqual(
optional_call_args, {arg_name: f"optional_{i}" for i, arg_name in enumerate(optional_call_args_name)}
)
# test only one optional call arg is given
optional_call_args = processor.prepare_and_validate_optional_call_args("optional_1")
self.assertEqual(optional_call_args, {optional_call_args_name[0]: "optional_1"})
# test no optional call arg is given
optional_call_args = processor.prepare_and_validate_optional_call_args()
self.assertEqual(optional_call_args, {})
# test too many optional call args are given
with self.assertRaises(ValueError):
processor.prepare_and_validate_optional_call_args(
*(f"optional_{i}" for i in range(num_optional_call_args + 1))
)
def test_chat_template_save_loading(self):
processor = self.processor_class.from_pretrained(self.tmpdirname)
signature = inspect.signature(processor.__init__)
if "chat_template" not in {*signature.parameters.keys()}:
self.skipTest("Processor doesn't accept chat templates at input")
existing_tokenizer_template = getattr(processor.tokenizer, "chat_template", None)
processor.chat_template = "test template"
with tempfile.TemporaryDirectory() as tmpdirname:
processor.save_pretrained(tmpdirname, save_jinja_files=False)
self.assertTrue(Path(tmpdirname, "chat_template.json").is_file())
self.assertFalse(Path(tmpdirname, "chat_template.jinja").is_file())
reloaded_processor = self.processor_class.from_pretrained(tmpdirname)
self.assertEqual(processor.chat_template, reloaded_processor.chat_template)
# When we don't use single-file chat template saving, processor and tokenizer chat templates
# should remain separate
self.assertEqual(getattr(reloaded_processor.tokenizer, "chat_template", None), existing_tokenizer_template)
with tempfile.TemporaryDirectory() as tmpdirname:
processor.save_pretrained(tmpdirname)
self.assertTrue(Path(tmpdirname, "chat_template.jinja").is_file())
self.assertFalse(Path(tmpdirname, "chat_template.json").is_file())
self.assertFalse(Path(tmpdirname, "additional_chat_templates").is_dir())
reloaded_processor = self.processor_class.from_pretrained(tmpdirname)
self.assertEqual(processor.chat_template, reloaded_processor.chat_template)
# When we save as single files, tokenizers and processors share a chat template, which means
# the reloaded tokenizer should get the chat template as well
self.assertEqual(reloaded_processor.chat_template, reloaded_processor.tokenizer.chat_template)
with tempfile.TemporaryDirectory() as tmpdirname:
processor.chat_template = {"default": "a", "secondary": "b"}
processor.save_pretrained(tmpdirname)
self.assertTrue(Path(tmpdirname, "chat_template.jinja").is_file())
self.assertFalse(Path(tmpdirname, "chat_template.json").is_file())
self.assertTrue(Path(tmpdirname, "additional_chat_templates").is_dir())
reloaded_processor = self.processor_class.from_pretrained(tmpdirname)
self.assertEqual(processor.chat_template, reloaded_processor.chat_template)
# When we save as single files, tokenizers and processors share a chat template, which means
# the reloaded tokenizer should get the chat template as well
self.assertEqual(reloaded_processor.chat_template, reloaded_processor.tokenizer.chat_template)
with self.assertRaises(ValueError):
# Saving multiple templates in the legacy format is not permitted
with tempfile.TemporaryDirectory() as tmpdirname:
processor.chat_template = {"default": "a", "secondary": "b"}
processor.save_pretrained(tmpdirname, save_jinja_files=False)
@require_torch
def _test_apply_chat_template(
self,
modality: str,
batch_size: int,
return_tensors: str,
input_name: str,
processor_name: str,
input_data: list[str],
):
processor = self.get_processor()
if processor.chat_template is None:
self.skipTest("Processor has no chat template")
if processor_name not in self.processor_class.attributes:
self.skipTest(f"{processor_name} attribute not present in {self.processor_class}")
# some models have only Fast image processor
if getattr(processor, processor_name).__class__.__name__.endswith("Fast"):
return_tensors = "pt"
batch_messages = [
[
{
"role": "user",
"content": [{"type": "text", "text": "Describe this."}],
},
]
] * batch_size
# Test that jinja can be applied
formatted_prompt = processor.apply_chat_template(batch_messages, add_generation_prompt=True, tokenize=False)
self.assertEqual(len(formatted_prompt), batch_size)
# Test that tokenizing with template and directly with `self.tokenizer` gives same output
formatted_prompt_tokenized = processor.apply_chat_template(
batch_messages, add_generation_prompt=True, tokenize=True, return_tensors=return_tensors
)
add_special_tokens = True
if processor.tokenizer.bos_token is not None and formatted_prompt[0].startswith(processor.tokenizer.bos_token):
add_special_tokens = False
tok_output = processor.tokenizer(
formatted_prompt, return_tensors=return_tensors, add_special_tokens=add_special_tokens
)
expected_output = tok_output.input_ids
self.assertListEqual(expected_output.tolist(), formatted_prompt_tokenized.tolist())
# Test that kwargs passed to processor's `__call__` are actually used
tokenized_prompt_100 = processor.apply_chat_template(
batch_messages,
add_generation_prompt=True,
tokenize=True,
padding="max_length",
truncation=True,
return_tensors=return_tensors,
max_length=100,
)
self.assertEqual(len(tokenized_prompt_100[0]), 100)
# Test that `return_dict=True` returns text related inputs in the dict
out_dict_text = processor.apply_chat_template(
batch_messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors=return_tensors,
)
self.assertTrue(all(key in out_dict_text for key in ["input_ids", "attention_mask"]))
self.assertEqual(len(out_dict_text["input_ids"]), batch_size)
self.assertEqual(len(out_dict_text["attention_mask"]), batch_size)
# Test that with modality URLs and `return_dict=True`, we get modality inputs in the dict
for idx, url in enumerate(input_data[:batch_size]):
batch_messages[idx][0]["content"] = [batch_messages[idx][0]["content"][0], {"type": modality, "url": url}]
out_dict = processor.apply_chat_template(
batch_messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors=return_tensors,
num_frames=4, # by default no more than 4 frames, otherwise too slow
)
input_name = getattr(self, input_name)
self.assertTrue(input_name in out_dict)
self.assertEqual(len(out_dict["input_ids"]), batch_size)
self.assertEqual(len(out_dict["attention_mask"]), batch_size)
self.assertEqual(len(out_dict[input_name]), batch_size)
return_tensor_to_type = {"pt": torch.Tensor, "np": np.ndarray, None: list}
for k in out_dict:
self.assertIsInstance(out_dict[k], return_tensor_to_type[return_tensors])
# Test continue from final message
assistant_message = {
"role": "assistant",
"content": [{"type": "text", "text": "It is the sound of"}],
}
for idx, url in enumerate(input_data[:batch_size]):
batch_messages[idx] = batch_messages[idx] + [assistant_message]
continue_prompt = processor.apply_chat_template(batch_messages, continue_final_message=True, tokenize=False)
for prompt in continue_prompt:
self.assertTrue(prompt.endswith("It is the sound of")) # no `eos` token at the end
@require_librosa
@parameterized.expand([(1, "np"), (1, "pt"), (2, "np"), (2, "pt")])
def test_apply_chat_template_audio(self, batch_size: int, return_tensors: str):
self._test_apply_chat_template(
"audio", batch_size, return_tensors, "audio_input_name", "feature_extracttor", MODALITY_INPUT_DATA["audio"]
)
@require_av
@parameterized.expand([(1, "pt"), (2, "pt")]) # video processor supports only torchvision
def test_apply_chat_template_video(self, batch_size: int, return_tensors: str):
self._test_apply_chat_template(
"video", batch_size, return_tensors, "videos_input_name", "video_processor", MODALITY_INPUT_DATA["videos"]
)
@parameterized.expand([(1, "np"), (1, "pt"), (2, "np"), (2, "pt")])
def test_apply_chat_template_image(self, batch_size: int, return_tensors: str):
self._test_apply_chat_template(
"image", batch_size, return_tensors, "images_input_name", "image_processor", MODALITY_INPUT_DATA["images"]
)
@require_torch
def test_apply_chat_template_video_frame_sampling(self):
processor = self.get_processor()
if processor.chat_template is None:
self.skipTest("Processor has no chat template")
signature = inspect.signature(processor.__call__)
if "videos" not in {*signature.parameters.keys()} or (
signature.parameters.get("videos") is not None
and signature.parameters["videos"].annotation == inspect._empty
):
self.skipTest("Processor doesn't accept videos at input")
messages = [
[
{
"role": "user",
"content": [
{
"type": "video",
"url": "https://test-videos.co.uk/vids/bigbuckbunny/mp4/h264/720/Big_Buck_Bunny_720_10s_10MB.mp4",
},
{"type": "text", "text": "What is shown in this video?"},
],
},
]
]
num_frames = 3
out_dict_with_video = processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
num_frames=num_frames,
return_tensors="pt",
)
self.assertTrue(self.videos_input_name in out_dict_with_video)
self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 1)
self.assertEqual(len(out_dict_with_video[self.videos_input_name][0]), num_frames)
# Load with `video_fps` arg
video_fps = 1
out_dict_with_video = processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
video_fps=video_fps,
return_tensors="pt",
)
self.assertTrue(self.videos_input_name in out_dict_with_video)
self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 1)
self.assertEqual(len(out_dict_with_video[self.videos_input_name][0]), video_fps * 10)
# Load with `video_fps` and `num_frames` args, should raise an error
with self.assertRaises(ValueError):
out_dict_with_video = processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
video_fps=video_fps,
num_frames=num_frames,
)
# Load without any arg should load the whole video
out_dict_with_video = processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
)
self.assertTrue(self.videos_input_name in out_dict_with_video)
self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 1)
self.assertEqual(len(out_dict_with_video[self.videos_input_name][0]), 300)
# Load video as a list of frames (i.e. images). NOTE: each frame should have same size
# because we assume they come from one video
messages[0][0]["content"][0] = {
"type": "video",
"url": [
"https://www.ilankelman.org/stopsigns/australia.jpg",
"https://www.ilankelman.org/stopsigns/australia.jpg",
],
}
out_dict_with_video = processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
)
self.assertTrue(self.videos_input_name in out_dict_with_video)
self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 1)
self.assertEqual(len(out_dict_with_video[self.videos_input_name][0]), 2)
@require_av
@require_torch
def test_apply_chat_template_video_special_processing(self):
"""
Tests that models can use their own preprocessing to preprocess conversations.
"""
processor = self.get_processor()
if processor.chat_template is None:
self.skipTest("Processor has no chat template")
signature = inspect.signature(processor.__call__)
if "videos" not in {*signature.parameters.keys()} or (
signature.parameters.get("videos") is not None
and signature.parameters["videos"].annotation == inspect._empty
):
self.skipTest("Processor doesn't accept videos at input")
video_file_path = hf_hub_download(
repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset"
)
messages = [
[
{
"role": "user",
"content": [
{"type": "video", "path": video_file_path},
{"type": "text", "text": "What is shown in this video?"},
],
},
]
]
def _process_messages_for_chat_template(
conversation,
batch_images,
batch_videos,
batch_video_metadata,
**chat_template_kwargs,
):
# Let us just always return a dummy prompt
new_msg = [
[
{
"role": "user",
"content": [
{"type": "video"}, # no need to use path, video is loaded already by this moment
{"type": "text", "text": "Dummy prompt for preprocess testing"},
],
},
]
]
return new_msg
processor._process_messages_for_chat_template = _process_messages_for_chat_template
out_dict_with_video = processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
)
self.assertTrue(self.videos_input_name in out_dict_with_video)
# Check with `in` because we don't know how each template formats the prompt with BOS/EOS/etc
formatted_text = processor.batch_decode(out_dict_with_video["input_ids"], skip_special_tokens=True)[0]
self.assertTrue("Dummy prompt for preprocess testing" in formatted_text)
self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 1)
self.assertEqual(len(out_dict_with_video[self.videos_input_name][0]), 243)
@require_librosa
@require_av
def test_chat_template_audio_from_video(self):
processor = self.get_processor()
if processor.chat_template is None:
self.skipTest("Processor has no chat template")
signature = inspect.signature(processor.__call__)
if "videos" not in {*signature.parameters.keys()} or (
signature.parameters.get("videos") is not None
and signature.parameters["videos"].annotation == inspect._empty
):
self.skipTest(f"{self.processor_class} does not support video inputs")
if "feature_extractor" not in self.processor_class.attributes:
self.skipTest(f"feature_extractor attribute not present in {self.processor_class}")
video_file_path = hf_hub_download(
repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset"
)
messages = [
{
"role": "user",
"content": [
{"type": "video", "path": video_file_path},
{"type": "text", "text": "Which of these animals is making the sound?"},
],
},
{
"role": "assistant",
"content": [{"type": "text", "text": "It is a cow."}],
},
{
"role": "user",
"content": [
{"type": "text", "text": "Tell me all about this animal."},
],
},
]
formatted_prompt = processor.apply_chat_template([messages], add_generation_prompt=True, tokenize=False)
self.assertEqual(len(formatted_prompt), 1) # batch size=1
out_dict = processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="np",
load_audio_from_video=True,
)
self.assertTrue(self.audio_input_name in out_dict)
self.assertTrue(self.videos_input_name in out_dict)
# should always have input_ids and attention_mask
self.assertEqual(len(out_dict["input_ids"]), 1) # batch-size=1
self.assertEqual(len(out_dict["attention_mask"]), 1) # batch-size=1
self.assertEqual(len(out_dict[self.audio_input_name]), 1) # 1 audio in the conversation
self.assertEqual(len(out_dict[self.videos_input_name]), 1) # 1 video in the conversation
|