129 lines
4.6 KiB
Python
129 lines
4.6 KiB
Python
# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. 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 tempfile
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import unittest
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from transformers import CLIPTokenizerFast, ProcessorMixin
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from transformers.models.auto.processing_auto import processor_class_from_name
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from transformers.testing_utils import (
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check_json_file_has_correct_format,
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require_tokenizers,
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require_torch,
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require_vision,
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)
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from transformers.utils import is_vision_available
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if is_vision_available():
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from transformers import CLIPImageProcessor
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@require_torch
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class ProcessorTesterMixin:
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processor_class = None
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def prepare_processor_dict(self):
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return {}
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def get_component(self, attribute, **kwargs):
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assert attribute in self.processor_class.attributes
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component_class_name = getattr(self.processor_class, f"{attribute}_class")
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if isinstance(component_class_name, tuple):
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component_class_name = component_class_name[0]
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component_class = processor_class_from_name(component_class_name)
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component = component_class.from_pretrained(self.tmpdirname, **kwargs) # noqa
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return component
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def prepare_components(self):
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components = {}
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for attribute in self.processor_class.attributes:
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component = self.get_component(attribute)
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components[attribute] = component
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return components
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def get_processor(self):
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components = self.prepare_components()
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processor = self.processor_class(**components, **self.prepare_processor_dict())
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return processor
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def test_processor_to_json_string(self):
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processor = self.get_processor()
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obj = json.loads(processor.to_json_string())
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for key, value in self.prepare_processor_dict().items():
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self.assertEqual(obj[key], value)
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self.assertEqual(getattr(processor, key, None), value)
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def test_processor_from_and_save_pretrained(self):
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processor_first = self.get_processor()
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with tempfile.TemporaryDirectory() as tmpdirname:
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saved_files = processor_first.save_pretrained(tmpdirname)
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if len(saved_files) > 0:
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check_json_file_has_correct_format(saved_files[0])
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processor_second = self.processor_class.from_pretrained(tmpdirname)
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self.assertEqual(processor_second.to_dict(), processor_first.to_dict())
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class MyProcessor(ProcessorMixin):
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = "CLIPImageProcessor"
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tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast")
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def __init__(self, image_processor=None, tokenizer=None, processor_attr_1=1, processor_attr_2=True):
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super().__init__(image_processor, tokenizer)
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self.processor_attr_1 = processor_attr_1
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self.processor_attr_2 = processor_attr_2
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@require_tokenizers
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@require_vision
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class ProcessorTest(unittest.TestCase):
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processor_class = MyProcessor
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def prepare_processor_dict(self):
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return {"processor_attr_1": 1, "processor_attr_2": False}
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def get_processor(self):
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image_processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14")
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tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-large-patch14")
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processor = MyProcessor(image_processor, tokenizer, **self.prepare_processor_dict())
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return processor
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def test_processor_to_json_string(self):
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processor = self.get_processor()
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obj = json.loads(processor.to_json_string())
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for key, value in self.prepare_processor_dict().items():
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self.assertEqual(obj[key], value)
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self.assertEqual(getattr(processor, key, None), value)
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def test_processor_from_and_save_pretrained(self):
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processor_first = self.get_processor()
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with tempfile.TemporaryDirectory() as tmpdirname:
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saved_file = processor_first.save_pretrained(tmpdirname)[0]
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check_json_file_has_correct_format(saved_file)
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processor_second = self.processor_class.from_pretrained(tmpdirname)
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self.assertEqual(processor_second.to_dict(), processor_first.to_dict())
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