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# coding: utf-8
#! pip install pillow transformers optimum[onnxruntime]
from PIL import Image
from transformers import TrOCRProcessor
from optimum.onnxruntime import ORTModelForVision2Seq
from transformers import VisionEncoderDecoderModel
def test_tokenizer_consistency(processor, test_strings=None):
"""
测试Tokenizer的编码和解码是否一致
Args:
processor: TrOCRProcessor实例
test_strings (list): 要测试的字符串列表
"""
if test_strings is None:
test_strings = [
# "Hello, world!",
# "你好，世界！",
# "12345",
# "1 + 1 = 2",
# "The quick brown fox jumps over the lazy dog.",
# "测试一下中文和English混合的情况",
# "\mathcal{L}_{\mathrm{e y e l i d}} \,=\sum_{t=1}^{T} \sum_{v=1}^{V} \mathcal{M}_{v}^{\mathrm{( e y e l i d )}} \left( \left\| \hat{h}_{t, v}-x_{t, v} \right\|^{2} \right)",
"\\hat { N } _ { 3 } = \\sum \\sp f _ { j = 1 } a _ { j } \\sp { \\dagger } a _ { j } .",
]
print("\n" + "="*50)
print("Testing Tokenizer Consistency")
print("="*50)
all_passed = True
for text in test_strings:
# 编码
encoded = processor.tokenizer.encode_plus(text, return_tensors="pt")
outs = processor.tokenizer(
[text],
padding="max_length",
truncation=True,
max_length=512,
)["input_ids"]
input_ids = encoded["input_ids"][0]
breakpoint()
# 解码
decoded = processor.tokenizer.decode(input_ids, skip_special_tokens=True)
# 比较
is_match = (text == decoded)
if not is_match:
all_passed = False
print(f"\nOriginal: {repr(text)}")
print(f"Encoded: {input_ids.tolist()}")
print(f"Decoded: {repr(decoded)}")
print(f"Match: {is_match}")
print("\n" + "="*50)
if all_passed:
print("✅ All tests passed! Tokenizer encoding and decoding are consistent.")
else:
print("❌ Some tests failed. Tokenizer encoding and decoding are not consistent.")
print("="*50 + "\n")
model = 'breezedeus/pix2text-mfr'
processor = TrOCRProcessor.from_pretrained(model)
# 测试Tokenizer的编码和解码是否一致
# test_tokenizer_consistency(processor)
# model = ORTModelForVision2Seq.from_pretrained(model, use_cache=False)
model = 'models/checkpoint-683356'
model = VisionEncoderDecoderModel.from_pretrained(model)
image_fps = [
# 'https://github.com/breezedeus/Pix2Text/blob/main/docs/examples/formula.jpg?raw=true',
'docs/examples/formula.jpg',
# '/Users/king/Documents/WhatIHaveDone/Test/syndoc/output-latex/sqrt_tex/150-cmbright.jpg'
# 'examples/0000186.png',
]
images = [Image.open(fp).convert('RGB') for fp in image_fps]
pixel_values = processor(images=images, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(f'generated_ids: {generated_ids}, \ngenerated text: {generated_text}')
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