codingutf8 2022 huggingface inc 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 initialize imageprocessing create random pil videos test not batched input test batched initialize imageprocessing create random numpy tensors test not batched input test batched initialize imageprocessing create random numpy tensors test not batched input test batched initialize imageprocessing create random pytorch tensors test not batched input test batched coding utf 8 2022 huggingface inc 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 initialize image_processing create random pil videos test not batched input test batched initialize image_processing create random numpy tensors test not batched input test batched initialize image_processing create random numpy tensors test not batched input test batched initialize image_processing create random pytorch tensors test not batched input test batched
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingTestMixin, prepare_video_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import VideoMAEImageProcessor


class VideoMAEImageProcessingTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        num_frames=10,
        image_size=18,
        min_resolution=30,
        max_resolution=400,
        do_resize=True,
        size=None,
        do_normalize=True,
        image_mean=[0.5, 0.5, 0.5],
        image_std=[0.5, 0.5, 0.5],
        crop_size=None,
    ):
        size = size if size is not None else {"shortest_edge": 18}
        crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}

        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.num_frames = num_frames
        self.image_size = image_size
        self.min_resolution = min_resolution
        self.max_resolution = max_resolution
        self.do_resize = do_resize
        self.size = size
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std
        self.crop_size = crop_size

    def prepare_image_processor_dict(self):
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "crop_size": self.crop_size,
        }

    def expected_output_image_shape(self, images):
        return self.num_frames, self.num_channels, self.crop_size["height"], self.crop_size["width"]

    def prepare_video_inputs(self, equal_resolution=False, numpify=False, torchify=False):
        return prepare_video_inputs(
            batch_size=self.batch_size,
            num_frames=self.num_frames,
            num_channels=self.num_channels,
            min_resolution=self.min_resolution,
            max_resolution=self.max_resolution,
            equal_resolution=equal_resolution,
            numpify=numpify,
            torchify=torchify,
        )


@require_torch
@require_vision
class VideoMAEImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = VideoMAEImageProcessor if is_vision_available() else None

    def setUp(self):
        self.image_processor_tester = VideoMAEImageProcessingTester(self)

    @property
    def image_processor_dict(self):
        return self.image_processor_tester.prepare_image_processor_dict()

    def test_image_processor_properties(self):
        image_processing = self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(image_processing, "image_mean"))
        self.assertTrue(hasattr(image_processing, "image_std"))
        self.assertTrue(hasattr(image_processing, "do_normalize"))
        self.assertTrue(hasattr(image_processing, "do_resize"))
        self.assertTrue(hasattr(image_processing, "do_center_crop"))
        self.assertTrue(hasattr(image_processing, "size"))

    def test_image_processor_from_dict_with_kwargs(self):
        image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size, {"shortest_edge": 18})
        self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})

        image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
        self.assertEqual(image_processor.size, {"shortest_edge": 42})
        self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})

    def test_call_pil(self):
        
        image_processing = self.image_processing_class(**self.image_processor_dict)
        
        video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False)
        for video in video_inputs:
            self.assertIsInstance(video, list)
            self.assertIsInstance(video[0], Image.Image)

        
        encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
        expected_output_video_shape = self.image_processor_tester.expected_output_image_shape([encoded_videos[0]])
        self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))

        
        encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
        expected_output_video_shape = self.image_processor_tester.expected_output_image_shape(encoded_videos)
        self.assertEqual(
            tuple(encoded_videos.shape), (self.image_processor_tester.batch_size, *expected_output_video_shape)
        )

    def test_call_numpy(self):
        
        image_processing = self.image_processing_class(**self.image_processor_dict)
        
        video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, numpify=True)
        for video in video_inputs:
            self.assertIsInstance(video, list)
            self.assertIsInstance(video[0], np.ndarray)

        
        encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
        expected_output_video_shape = self.image_processor_tester.expected_output_image_shape([encoded_videos[0]])
        self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))

        
        encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
        expected_output_video_shape = self.image_processor_tester.expected_output_image_shape(encoded_videos)
        self.assertEqual(
            tuple(encoded_videos.shape), (self.image_processor_tester.batch_size, *expected_output_video_shape)
        )

    def test_call_numpy_4_channels(self):
        
        image_processing = self.image_processing_class(**self.image_processor_dict)
        
        self.image_processor_tester.num_channels = 4
        video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, numpify=True)
        for video in video_inputs:
            self.assertIsInstance(video, list)
            self.assertIsInstance(video[0], np.ndarray)

        
        encoded_videos = image_processing(
            video_inputs[0], return_tensors="pt", image_mean=0, image_std=1, input_data_format="channels_first"
        ).pixel_values
        expected_output_video_shape = self.image_processor_tester.expected_output_image_shape([encoded_videos[0]])
        self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))

        
        encoded_videos = image_processing(
            video_inputs, return_tensors="pt", image_mean=0, image_std=1, input_data_format="channels_first"
        ).pixel_values
        expected_output_video_shape = self.image_processor_tester.expected_output_image_shape(encoded_videos)
        self.assertEqual(
            tuple(encoded_videos.shape), (self.image_processor_tester.batch_size, *expected_output_video_shape)
        )
        self.image_processor_tester.num_channels = 3

    def test_call_pytorch(self):
        
        image_processing = self.image_processing_class(**self.image_processor_dict)
        
        video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, torchify=True)
        for video in video_inputs:
            self.assertIsInstance(video, list)
            self.assertIsInstance(video[0], torch.Tensor)

        
        encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
        expected_output_video_shape = self.image_processor_tester.expected_output_image_shape([encoded_videos[0]])
        self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))

        
        encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
        expected_output_video_shape = self.image_processor_tester.expected_output_image_shape(encoded_videos)
        self.assertEqual(
            tuple(encoded_videos.shape), (self.image_processor_tester.batch_size, *expected_output_video_shape)
        )