# What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
Fix last models for common tests that are too big.. # What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
# coding=utf-8
# Copyright 2021 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.


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 ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor


class ViTImageProcessingTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        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],
    ):
        size = size if size is not None else {"height": 18, "width": 18}
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        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

    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,
        }


@require_torch
@require_vision
class ViTImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
    image_processing_class = ViTImageProcessor if is_vision_available() else None

    def setUp(self):
        self.image_processor_tester = ViTImageProcessingTester(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, "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, {"height": 18, "width": 18})

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

    def test_batch_feature(self):
        pass

    def test_call_pil(self):
        # Initialize image_processing
        image_processing = self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
        for image in image_inputs:
            self.assertIsInstance(image, Image.Image)

        # Test not batched input
        encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            ),
        )

        # Test batched
        encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            ),
        )

    def test_call_numpy(self):
        # Initialize image_processing
        image_processing = self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, numpify=True)
        for image in image_inputs:
            self.assertIsInstance(image, np.ndarray)

        # Test not batched input
        encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            ),
        )

        # Test batched
        encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            ),
        )

    def test_call_pytorch(self):
        # Initialize image_processing
        image_processing = self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
        for image in image_inputs:
            self.assertIsInstance(image, torch.Tensor)

        # Test not batched input
        encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            ),
        )

        # Test batched
        encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            ),
        )

# coding=utf-8
# Copyright 2021 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.


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 ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor


class ViTImageProcessingTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        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],
    ):
        size = size if size is not None else {"height": 18, "width": 18}
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        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

    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,
        }


@require_torch
@require_vision
class ViTImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
    image_processing_class = ViTImageProcessor if is_vision_available() else None

    def setUp(self):
        self.image_processor_tester = ViTImageProcessingTester(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, "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, {"height": 18, "width": 18})

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

    def test_batch_feature(self):
        pass

    def test_call_pil(self):
        # Initialize image_processing
        image_processing = self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
        for image in image_inputs:
            self.assertIsInstance(image, Image.Image)

        # Test not batched input
        encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            ),
        )

        # Test batched
        encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            ),
        )

    def test_call_numpy(self):
        # Initialize image_processing
        image_processing = self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, numpify=True)
        for image in image_inputs:
            self.assertIsInstance(image, np.ndarray)

        # Test not batched input
        encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            ),
        )

        # Test batched
        encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            ),
        )

    def test_call_pytorch(self):
        # Initialize image_processing
        image_processing = self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
        for image in image_inputs:
            self.assertIsInstance(image, torch.Tensor)

        # Test not batched input
        encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            ),
        )

        # Test batched
        encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            ),
        )
