2023 the huggingface team 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 orglicenseslicense2 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 this function prepares a list of pil images or a list of numpy arrays if one specifies numpifytrue or a list of pytorch tensors if one specifies torchifytrue should also work with np this function prepares a list of pil images or a list of numpy arrays if one specifies numpifytrue or a list of pytorch tensors if one specifies torchifytrue should also work with np this function prepares a list of pil images or a list of numpy arrays if one specifies numpifytrue or a list of pytorch tensors if one specifies torchifytrue 2023 the huggingface team 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 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 pop original_sizes as it is popped in the processor pop original_sizes as it is popped in the processor should also work with np 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 pop original_sizes as it is popped in the processor pop reshaped_input_sizes as it is popped in the processor should also work with np 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
import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
    import torch

if is_tf_available():
    import tensorflow as tf


@require_vision
@require_torchvision
class SamProcessorTest(unittest.TestCase):
    def setUp(self):
        self.tmpdirname = tempfile.mkdtemp()
        image_processor = SamImageProcessor()
        processor = SamProcessor(image_processor)
        processor.save_pretrained(self.tmpdirname)

    def get_image_processor(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor

    def tearDown(self):
        shutil.rmtree(self.tmpdirname)

    def prepare_image_inputs(self):
        

        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_additional_features(self):
        processor = SamProcessor(image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)

        processor = SamProcessor.from_pretrained(self.tmpdirname, do_normalize=False, padding_value=1.0)

        self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor, SamImageProcessor)

    def test_image_processor(self):
        image_processor = self.get_image_processor()

        processor = SamProcessor(image_processor=image_processor)

        image_input = self.prepare_image_inputs()

        input_feat_extract = image_processor(image_input, return_tensors="np")
        input_processor = processor(images=image_input, return_tensors="np")

        input_feat_extract.pop("original_sizes")  
        input_feat_extract.pop("reshaped_input_sizes")  

        for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)

    @require_torch
    def test_post_process_masks(self):
        image_processor = self.get_image_processor()

        processor = SamProcessor(image_processor=image_processor)
        dummy_masks = [torch.ones((1, 3, 5, 5))]

        original_sizes = [[1764, 2646]]

        reshaped_input_size = [[683, 1024]]
        masks = processor.post_process_masks(dummy_masks, original_sizes, reshaped_input_size)
        self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))

        masks = processor.post_process_masks(
            dummy_masks, torch.tensor(original_sizes), torch.tensor(reshaped_input_size)
        )
        self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))

        
        dummy_masks = [np.ones((1, 3, 5, 5))]
        masks = processor.post_process_masks(dummy_masks, np.array(original_sizes), np.array(reshaped_input_size))

        self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))

        dummy_masks = [[1, 0], [0, 1]]
        with self.assertRaises(ValueError):
            masks = processor.post_process_masks(dummy_masks, np.array(original_sizes), np.array(reshaped_input_size))


@require_vision
@require_tf
class TFSamProcessorTest(unittest.TestCase):
    def setUp(self):
        self.tmpdirname = tempfile.mkdtemp()
        image_processor = SamImageProcessor()
        processor = SamProcessor(image_processor)
        processor.save_pretrained(self.tmpdirname)

    def get_image_processor(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor

    def tearDown(self):
        shutil.rmtree(self.tmpdirname)

    def prepare_image_inputs(self):
        

        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_additional_features(self):
        processor = SamProcessor(image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)

        processor = SamProcessor.from_pretrained(self.tmpdirname, do_normalize=False, padding_value=1.0)

        self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor, SamImageProcessor)

    def test_image_processor(self):
        image_processor = self.get_image_processor()

        processor = SamProcessor(image_processor=image_processor)

        image_input = self.prepare_image_inputs()

        input_feat_extract = image_processor(image_input, return_tensors="np")
        input_processor = processor(images=image_input, return_tensors="np")

        input_feat_extract.pop("original_sizes")  
        input_feat_extract.pop("reshaped_input_sizes")  

        for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)

    @require_tf
    def test_post_process_masks(self):
        image_processor = self.get_image_processor()

        processor = SamProcessor(image_processor=image_processor)
        dummy_masks = [tf.ones((1, 3, 5, 5))]

        original_sizes = [[1764, 2646]]

        reshaped_input_size = [[683, 1024]]
        masks = processor.post_process_masks(dummy_masks, original_sizes, reshaped_input_size, return_tensors="tf")
        self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))

        masks = processor.post_process_masks(
            dummy_masks,
            tf.convert_to_tensor(original_sizes),
            tf.convert_to_tensor(reshaped_input_size),
            return_tensors="tf",
        )
        self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))

        
        dummy_masks = [np.ones((1, 3, 5, 5))]
        masks = processor.post_process_masks(
            dummy_masks, np.array(original_sizes), np.array(reshaped_input_size), return_tensors="tf"
        )

        self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))

        dummy_masks = [[1, 0], [0, 1]]
        with self.assertRaises(tf.errors.InvalidArgumentError):
            masks = processor.post_process_masks(
                dummy_masks, np.array(original_sizes), np.array(reshaped_input_size), return_tensors="tf"
            )


@require_vision
@require_torchvision
class SamProcessorEquivalenceTest(unittest.TestCase):
    def setUp(self):
        self.tmpdirname = tempfile.mkdtemp()
        image_processor = SamImageProcessor()
        processor = SamProcessor(image_processor)
        processor.save_pretrained(self.tmpdirname)

    def get_image_processor(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor

    def tearDown(self):
        shutil.rmtree(self.tmpdirname)

    def prepare_image_inputs(self):
        

        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

    @is_pt_tf_cross_test
    def test_post_process_masks_equivalence(self):
        image_processor = self.get_image_processor()

        processor = SamProcessor(image_processor=image_processor)
        dummy_masks = np.random.randint(0, 2, size=(1, 3, 5, 5)).astype(np.float32)
        tf_dummy_masks = [tf.convert_to_tensor(dummy_masks)]
        pt_dummy_masks = [torch.tensor(dummy_masks)]

        original_sizes = [[1764, 2646]]

        reshaped_input_size = [[683, 1024]]
        tf_masks = processor.post_process_masks(
            tf_dummy_masks, original_sizes, reshaped_input_size, return_tensors="tf"
        )
        pt_masks = processor.post_process_masks(
            pt_dummy_masks, original_sizes, reshaped_input_size, return_tensors="pt"
        )

        self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()))

    @is_pt_tf_cross_test
    def test_image_processor_equivalence(self):
        image_processor = self.get_image_processor()

        processor = SamProcessor(image_processor=image_processor)

        image_input = self.prepare_image_inputs()

        pt_input_feat_extract = image_processor(image_input, return_tensors="pt")["pixel_values"].numpy()
        pt_input_processor = processor(images=image_input, return_tensors="pt")["pixel_values"].numpy()

        tf_input_feat_extract = image_processor(image_input, return_tensors="tf")["pixel_values"].numpy()
        tf_input_processor = processor(images=image_input, return_tensors="tf")["pixel_values"].numpy()

        self.assertTrue(np.allclose(pt_input_feat_extract, pt_input_processor))
        self.assertTrue(np.allclose(pt_input_feat_extract, tf_input_feat_extract))
        self.assertTrue(np.allclose(pt_input_feat_extract, tf_input_processor))