codingutf8 2023 the intel team s the huggingface inc 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 computes the expected height and width when providing images to tvpimageprocessor assuming doresize is set to true with a scalar size 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 2023 the intel team s the huggingface inc 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 computes the expected height and width when providing images to tvpimageprocessor assuming do_resize is set to true with a scalar size 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
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.image_transforms import PaddingMode
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 TvpImageProcessor


class TvpImageProcessingTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        do_resize: bool = True,
        size: Dict[str, int] = {"longest_edge": 40},
        do_center_crop: bool = False,
        crop_size: Dict[str, int] = None,
        do_rescale: bool = False,
        rescale_factor: Union[int, float] = 1 / 255,
        do_pad: bool = True,
        pad_size: Dict[str, int] = {"height": 80, "width": 80},
        fill: int = None,
        pad_mode: PaddingMode = None,
        do_normalize: bool = True,
        image_mean: Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073],
        image_std: Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711],
        batch_size=2,
        min_resolution=40,
        max_resolution=80,
        num_channels=3,
        num_frames=2,
    ):
        self.do_resize = do_resize
        self.size = size
        self.do_center_crop = do_center_crop
        self.crop_size = crop_size
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_pad = do_pad
        self.pad_size = pad_size
        self.fill = fill
        self.pad_mode = pad_mode
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.min_resolution = min_resolution
        self.max_resolution = max_resolution
        self.num_frames = num_frames

    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,
            "do_rescale": self.do_rescale,
            "do_center_crop": self.do_center_crop,
            "do_pad": self.do_pad,
            "pad_size": self.pad_size,
        }

    def get_expected_values(self, image_inputs, batched=False):
        
        if not batched:
            return (int(self.pad_size["height"]), int(self.pad_size["width"]))

        else:
            expected_values = []
            for image in image_inputs:
                expected_height, expected_width = self.get_expected_values([image])
                expected_values.append((expected_height, expected_width))
            expected_height = max(expected_values, key=lambda item: item[0])[0]
            expected_width = max(expected_values, key=lambda item: item[1])[1]

        return expected_height, expected_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 TvpImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = TvpImageProcessor if is_vision_available() else None

    def setUp(self):
        self.image_processor_tester = TvpImageProcessingTester(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"))
        self.assertTrue(hasattr(image_processing, "do_rescale"))
        self.assertTrue(hasattr(image_processing, "do_pad"))
        self.assertTrue(hasattr(image_processing, "pad_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, {"longest_edge": 40})

        image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size={"longest_edge": 12})
        self.assertEqual(image_processor.size, {"longest_edge": 12})

    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)

        
        expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs)
        encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_videos.shape,
            (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),
        )

        
        expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs, batched=True)
        encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_videos.shape,
            (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),
        )

    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)

        
        expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs)
        encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_videos.shape,
            (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),
        )

        
        expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs, batched=True)
        encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_videos.shape,
            (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),
        )

    def test_call_numpy_4_channels(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)

        
        expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs)
        encoded_videos = image_processing(
            video_inputs[0], return_tensors="pt", image_mean=0, image_std=1, input_data_format="channels_first"
        ).pixel_values
        self.assertEqual(
            encoded_videos.shape,
            (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),
        )

        
        expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs, batched=True)
        encoded_videos = image_processing(
            video_inputs, return_tensors="pt", image_mean=0, image_std=1, input_data_format="channels_first"
        ).pixel_values
        self.assertEqual(
            encoded_videos.shape,
            (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),
        )
        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)

        
        expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs)
        encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_videos.shape,
            (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),
        )

        
        expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs, batched=True)
        encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_videos.shape,
            (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),
        )