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Remove repeated prepare_images in processor tests (#33163)

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* Remove repeated prepare_images

* Address comments - update docstring; explanatory comment
This commit is contained in:
amyeroberts
2024-09-09 13:20:27 +01:00
committed by GitHub
parent 0574fa668b
commit f745e7d3f9
21 changed files with 140 additions and 304 deletions
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22
tests/models/layoutlmv3/test_processor_layoutlmv3.py
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@@ -19,26 +19,27 @@ import tempfile
import unittest
from typing import List
import numpy as np
from transformers import PreTrainedTokenizer, PreTrainedTokenizerBase, PreTrainedTokenizerFast
from transformers.models.layoutlmv3 import LayoutLMv3Tokenizer, LayoutLMv3TokenizerFast
from transformers.models.layoutlmv3 import LayoutLMv3Processor, LayoutLMv3Tokenizer, LayoutLMv3TokenizerFast
from transformers.models.layoutlmv3.tokenization_layoutlmv3 import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pytesseract, require_tokenizers, require_torch, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, cached_property, is_pytesseract_available
from ...test_processing_common import ProcessorTesterMixin
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMv3ImageProcessor, LayoutLMv3Processor
from transformers import LayoutLMv3ImageProcessor
@require_pytesseract
@require_tokenizers
class LayoutLMv3ProcessorTest(unittest.TestCase):
class LayoutLMv3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
tokenizer_class = LayoutLMv3Tokenizer
rust_tokenizer_class = LayoutLMv3TokenizerFast
processor_class = LayoutLMv3Processor
def setUp(self):
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
@@ -101,17 +102,6 @@ class LayoutLMv3ProcessorTest(unittest.TestCase):
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def prepare_image_inputs(self):
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
or a list of PyTorch tensors if one specifies torchify=True.
"""
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
def test_save_load_pretrained_default(self):
image_processor = self.get_image_processor()
tokenizers = self.get_tokenizers()