# Copyright 2023 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.image_utils import load_image
from transformers.testing_utils import require_torch, require_torch_accelerator, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
from ...test_processing_common import url_to_local_path


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image


class Pix2StructImageProcessingTester:
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        image_size=18,
        min_resolution=30,
        max_resolution=400,
        size=None,
        do_normalize=True,
        do_convert_rgb=True,
        patch_size=None,
    ):
        size = size if size is not None else {"height": 20, "width": 20}
        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.size = size
        self.do_normalize = do_normalize
        self.do_convert_rgb = do_convert_rgb
        self.max_patches = [512, 1024, 2048, 4096]
        self.patch_size = patch_size if patch_size is not None else {"height": 16, "width": 16}

    def prepare_image_processor_dict(self):
        return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}

    def prepare_dummy_image(self):
        img_url = url_to_local_path(
            "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
        )
        raw_image = load_image(img_url).convert("RGB")
        return raw_image

    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
        return prepare_image_inputs(
            batch_size=self.batch_size,
            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 Pix2StructImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    def setUp(self):
        super().setUp()
        self.image_processor_tester = Pix2StructImageProcessingTester(self)

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

    @require_vision
    @require_torch
    def test_backends_equivalence(self):
        """Override to use flattened_patches instead of pixel_values."""
        if len(self.image_processing_classes) < 2:
            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")

        import io

        import httpx
        from PIL import Image

        dummy_image = Image.open(
            io.BytesIO(
                httpx.get("http://images.cocodataset.org/val2017/000000039769.jpg", follow_redirects=True).content
            )
        )

        # Create processors for each backend
        encodings = {}
        for backend_name, image_processing_class in self.image_processing_classes.items():
            image_processor = image_processing_class(**self.image_processor_dict)
            encodings[backend_name] = image_processor(dummy_image, return_tensors="pt", max_patches=2048)

        # Compare all backends to the first one (reference backend)
        backend_names = list(encodings.keys())
        reference_backend = backend_names[0]
        reference_encoding = encodings[reference_backend].flattened_patches
        for backend_name in backend_names[1:]:
            current_encoding = encodings[backend_name].flattened_patches
            self._assert_tensors_equivalence(reference_encoding, current_encoding)

    @require_vision
    @require_torch
    def test_backends_equivalence_batched(self):
        """Override to use flattened_patches instead of pixel_values."""
        if len(self.image_processing_classes) < 2:
            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")

        dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)

        # Create processors for each backend
        encodings = {}
        for backend_name, image_processing_class in self.image_processing_classes.items():
            image_processor = image_processing_class(**self.image_processor_dict)
            encodings[backend_name] = image_processor(dummy_images, return_tensors="pt", max_patches=2048)

        # Compare all backends to the first one (reference backend)
        backend_names = list(encodings.keys())
        reference_backend = backend_names[0]
        reference_encoding = encodings[reference_backend].flattened_patches
        for backend_name in backend_names[1:]:
            self._assert_tensors_equivalence(reference_encoding, encodings[backend_name].flattened_patches)

    def test_image_processor_properties(self):
        for image_processing_class in self.image_processing_classes.values():
            image_processor = image_processing_class(**self.image_processor_dict)
            self.assertTrue(hasattr(image_processor, "do_normalize"))
            self.assertTrue(hasattr(image_processor, "do_convert_rgb"))

    def test_expected_patches(self):
        dummy_image = self.image_processor_tester.prepare_dummy_image()

        for image_processing_class in self.image_processing_classes.values():
            image_processor = image_processing_class(**self.image_processor_dict)
            max_patch = 2048

            inputs = image_processor(dummy_image, return_tensors="pt", max_patches=max_patch)
            torch.testing.assert_close(inputs.flattened_patches.mean(), torch.tensor(0.0606), rtol=1e-3, atol=1e-3)

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

            # Test not batched input
            expected_hidden_dim = (
                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                # Test not batched input
                encoded_images = image_processor(
                    image_inputs[0], return_tensors="pt", max_patches=max_patch
                ).flattened_patches
                self.assertEqual(
                    encoded_images.shape,
                    (1, max_patch, expected_hidden_dim),
                )

                # Test batched
                encoded_images = image_processor(
                    image_inputs, return_tensors="pt", max_patches=max_patch
                ).flattened_patches
                self.assertEqual(
                    encoded_images.shape,
                    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
                )

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

            # Test not batched input
            expected_hidden_dim = (
                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                * self.image_processor_tester.num_channels
            ) + 2

            image_processor.is_vqa = True

            for max_patch in self.image_processor_tester.max_patches:
                # Test not batched input
                with self.assertRaises(ValueError):
                    encoded_images = image_processor(
                        image_inputs[0], return_tensors="pt", max_patches=max_patch
                    ).flattened_patches

                dummy_text = "Hello"

                encoded_images = image_processor(
                    image_inputs[0], return_tensors="pt", max_patches=max_patch, header_text=dummy_text
                ).flattened_patches
                self.assertEqual(
                    encoded_images.shape,
                    (1, max_patch, expected_hidden_dim),
                )

                # Test batched
                encoded_images = image_processor(
                    image_inputs, return_tensors="pt", max_patches=max_patch, header_text=dummy_text
                ).flattened_patches
                self.assertEqual(
                    encoded_images.shape,
                    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
                )

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

            expected_hidden_dim = (
                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                # Test not batched input
                encoded_images = image_processor(
                    image_inputs[0], return_tensors="pt", max_patches=max_patch
                ).flattened_patches
                self.assertEqual(
                    encoded_images.shape,
                    (1, max_patch, expected_hidden_dim),
                )

                # Test batched
                encoded_images = image_processor(
                    image_inputs, return_tensors="pt", max_patches=max_patch
                ).flattened_patches
                self.assertEqual(
                    encoded_images.shape,
                    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
                )

    def test_call_numpy_4_channels(self):
        for image_processing_class in self.image_processing_classes.values():
            # Initialize image_processor
            image_processor = image_processing_class(**self.image_processor_dict)
            # create random numpy tensors
            self.image_processor_tester.num_channels = 4
            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
            for image in image_inputs:
                self.assertIsInstance(image, np.ndarray)

            expected_hidden_dim = (
                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                # Test not batched input
                encoded_images = image_processor(
                    image_inputs[0], return_tensors="pt", max_patches=max_patch, input_data_format="channels_last"
                ).flattened_patches
                self.assertEqual(
                    encoded_images.shape,
                    (1, max_patch, expected_hidden_dim),
                )

                # Test batched
                encoded_images = image_processor(
                    image_inputs, return_tensors="pt", max_patches=max_patch, input_data_format="channels_last"
                ).flattened_patches
                self.assertEqual(
                    encoded_images.shape,
                    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
                )
            self.image_processor_tester.num_channels = 3

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

            # Test not batched input
            expected_hidden_dim = (
                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                # Test not batched input
                encoded_images = image_processor(
                    image_inputs[0], return_tensors="pt", max_patches=max_patch
                ).flattened_patches
                self.assertEqual(
                    encoded_images.shape,
                    (1, max_patch, expected_hidden_dim),
                )

                # Test batched
                encoded_images = image_processor(
                    image_inputs, return_tensors="pt", max_patches=max_patch
                ).flattened_patches
                self.assertEqual(
                    encoded_images.shape,
                    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
                )

    @slow
    @require_torch_accelerator
    @require_vision
    def test_can_compile_torchvision_backend(self):
        if "torchvision" not in self.image_processing_classes:
            self.skipTest("Skipping compilation test as torchvision backend is not available")

        torch.compiler.reset()
        input_image = torch.randint(0, 255, (3, 224, 224), dtype=torch.uint8)
        image_processor = self.image_processing_classes["torchvision"](**self.image_processor_dict)
        output_eager = image_processor(input_image, device=torch_device, return_tensors="pt")

        image_processor = torch.compile(image_processor, mode="reduce-overhead")
        output_compiled = image_processor(input_image, device=torch_device, return_tensors="pt")
        # Pix2Struct uses flattened_patches instead of pixel_values
        self._assert_tensors_equivalence(
            output_eager.flattened_patches, output_compiled.flattened_patches, atol=1e-4, rtol=1e-4, mean_atol=1e-5
        )


@require_torch
@require_vision
class Pix2StructImageProcessingTestFourChannels(ImageProcessingTestMixin, unittest.TestCase):
    def setUp(self):
        super().setUp()
        self.image_processor_tester = Pix2StructImageProcessingTester(self, num_channels=4)
        self.expected_encoded_image_num_channels = 3

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

    def test_image_processor_properties(self):
        for image_processing_class in self.image_processing_classes.values():
            image_processor = image_processing_class(**self.image_processor_dict)
            self.assertTrue(hasattr(image_processor, "do_normalize"))
            self.assertTrue(hasattr(image_processor, "do_convert_rgb"))

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

            # Test not batched input
            expected_hidden_dim = (
                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                * (self.image_processor_tester.num_channels - 1)
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                # Test not batched input
                encoded_images = image_processor(
                    image_inputs[0], return_tensors="pt", max_patches=max_patch
                ).flattened_patches
                self.assertEqual(
                    encoded_images.shape,
                    (1, max_patch, expected_hidden_dim),
                )

                # Test batched
                encoded_images = image_processor(
                    image_inputs, return_tensors="pt", max_patches=max_patch
                ).flattened_patches
                self.assertEqual(
                    encoded_images.shape,
                    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
                )

    @unittest.skip(reason="Pix2StructImageProcessor does not support 4 channels yet")  # FIXME Amy
    def test_call_numpy(self):
        return super().test_call_numpy()

    @unittest.skip(reason="Pix2StructImageProcessor does not support 4 channels yet")  # FIXME Amy
    def test_call_pytorch(self):
        return super().test_call_torch()

    @unittest.skip(
        reason="Pix2StructImageProcessor does treat numpy and PIL 4 channel images consistently"
    )  # FIXME Amy
    def test_call_numpy_4_channels(self):
        return super().test_call_torch()

    @unittest.skip(reason="Pix2StructImageProcessor does not support 4 channels yet")
    def test_backends_equivalence(self):
        pass

    @unittest.skip(reason="Pix2StructImageProcessor does not support 4 channels yet")
    def test_backends_equivalence_batched(self):
        pass

    @unittest.skip(reason="Pix2StructImageProcessor does not support 4 channels yet")
    def test_can_compile_torchvision_backend(self):
        pass