# coding=utf-8
# Copyright 2024 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
import pytest
from packaging import version

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_torchvision_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

    from transformers import Kosmos2_5ImageProcessor

    if is_torchvision_available():
        from transformers import Kosmos2_5ImageProcessorFast


class Kosmos2_5ImageProcessingTester:
    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 Kosmos2_5ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = Kosmos2_5ImageProcessor if is_vision_available() else None
    fast_image_processing_class = Kosmos2_5ImageProcessorFast if is_torchvision_available() else None

    def setUp(self):
        super().setUp()
        self.image_processor_tester = Kosmos2_5ImageProcessingTester(self)

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

    # Overwrite from the common test to use `flattened_patches` instead of `pixel_values`.
    # TODO: enhance the common test to avoid overwriting
    @require_vision
    @require_torch
    def test_slow_fast_equivalence(self):
        if not self.test_slow_image_processor or not self.test_fast_image_processor:
            self.skipTest(reason="Skipping slow/fast equivalence test")

        if self.image_processing_class is None or self.fast_image_processing_class is None:
            self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")

        dummy_image = load_image(url_to_local_path("http://images.cocodataset.org/val2017/000000039769.jpg"))
        image_processor_slow = self.image_processing_class(**self.image_processor_dict)
        image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)

        encoding_slow = image_processor_slow(dummy_image, return_tensors="pt")
        encoding_fast = image_processor_fast(dummy_image, return_tensors="pt")
        self.assertTrue(torch.allclose(encoding_slow.flattened_patches, encoding_fast.flattened_patches, atol=1e-1))
        self.assertLessEqual(
            torch.mean(torch.abs(encoding_slow.flattened_patches - encoding_fast.flattened_patches)).item(), 1e-3
        )

    # Overwrite from the common test to use `flattened_patches` instead of `pixel_values`.
    # TODO: enhance the common test to avoid overwriting
    @require_vision
    @require_torch
    def test_slow_fast_equivalence_batched(self):
        if not self.test_slow_image_processor or not self.test_fast_image_processor:
            self.skipTest(reason="Skipping slow/fast equivalence test")

        if self.image_processing_class is None or self.fast_image_processing_class is None:
            self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")

        if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop:
            self.skipTest(
                reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors"
            )

        dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
        image_processor_slow = self.image_processing_class(**self.image_processor_dict)
        image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)

        encoding_slow = image_processor_slow(dummy_images, return_tensors="pt")
        encoding_fast = image_processor_fast(dummy_images, return_tensors="pt")

        self.assertTrue(torch.allclose(encoding_slow.flattened_patches, encoding_fast.flattened_patches, atol=1e-1))
        self.assertLessEqual(
            torch.mean(torch.abs(encoding_slow.flattened_patches - encoding_fast.flattened_patches)).item(), 1e-3
        )

    # Overwrite from the common test to use `flattened_patches` instead of `pixel_values`.
    # TODO: enhance the common test to avoid overwriting + fix this compile test.
    @unittest.skip("Failing with `AttributeError: 'StrictLessThan' object has no attribute 'diff'`.")
    @slow
    @require_torch_accelerator
    @require_vision
    @pytest.mark.torch_compile_test
    def test_can_compile_fast_image_processor(self):
        if self.fast_image_processing_class is None:
            self.skipTest("Skipping compilation test as fast image processor is not defined")
        if version.parse(torch.__version__) < version.parse("2.3"):
            self.skipTest(reason="This test requires torch >= 2.3 to run.")

        torch.compiler.reset()
        input_image = torch.randint(0, 255, (3, 224, 224), dtype=torch.uint8)
        image_processor = self.fast_image_processing_class(**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")
        self._assert_slow_fast_tensors_equivalence(
            output_eager.pixel_values, output_compiled.pixel_values, atol=1e-4, rtol=1e-4, mean_atol=1e-5
        )

    def test_image_processor_properties(self):
        image_processor = self.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()

        image_processor = self.image_processing_class(**self.image_processor_dict)
        max_patch = 2048

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

    def test_call_pil(self):
        # Initialize image_processor
        image_processor = self.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_numpy(self):
        # Initialize image_processor
        image_processor = self.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):
        # Initialize image_processor
        image_processor = self.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):
        # Initialize image_processor
        image_processor = self.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),
            )


@require_torch
@require_vision
class Kosmos2_5ImageProcessingTestFourChannels(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = Kosmos2_5ImageProcessor if is_vision_available() else None
    fast_image_processing_class = Kosmos2_5ImageProcessorFast if is_torchvision_available() else None

    def setUp(self):
        super().setUp()
        self.image_processor_tester = Kosmos2_5ImageProcessingTester(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()

    # Overwrite from the common test to use `flattened_patches` instead of `pixel_values`.
    # TODO: enhance the common test to avoid overwriting
    @unittest.skip(reason="Kosmos2_5ImageProcessor does not support 4 channels yet")  # FIXME Amy
    @require_vision
    @require_torch
    def test_slow_fast_equivalence(self):
        if not self.test_slow_image_processor or not self.test_fast_image_processor:
            self.skipTest(reason="Skipping slow/fast equivalence test")

        if self.image_processing_class is None or self.fast_image_processing_class is None:
            self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")

        dummy_image = load_image(url_to_local_path("http://images.cocodataset.org/val2017/000000039769.jpg"))
        image_processor_slow = self.image_processing_class(**self.image_processor_dict)
        image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)

        encoding_slow = image_processor_slow(dummy_image, return_tensors="pt")
        encoding_fast = image_processor_fast(dummy_image, return_tensors="pt")
        self.assertTrue(torch.allclose(encoding_slow.flattened_patches, encoding_fast.flattened_patches, atol=1e-1))
        self.assertLessEqual(
            torch.mean(torch.abs(encoding_slow.flattened_patches - encoding_fast.flattened_patches)).item(), 1e-3
        )

    @unittest.skip(reason="Kosmos2_5ImageProcessor does not support 4 channels yet")
    def test_slow_fast_equivalence_batched(self):
        return super().test_slow_fast_equivalence_batched()

    @unittest.skip(reason="Kosmos2_5ImageProcessor does not support 4 channels yet")
    def test_can_compile_fast_image_processor(self):
        return super().test_can_compile_fast_image_processor()

    def test_image_processor_properties(self):
        image_processor = self.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):
        # Initialize image_processor
        image_processor = self.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="Kosmos2_5ImageProcessor does not support 4 channels yet")  # FIXME Amy
    def test_call_numpy(self):
        return super().test_call_numpy()

    @unittest.skip(reason="Kosmos2_5ImageProcessor does not support 4 channels yet")  # FIXME Amy
    def test_call_pytorch(self):
        return super().test_call_pytorch()

    @unittest.skip(
        reason="Kosmos2_5ImageProcessor does treat numpy and PIL 4 channel images consistently"
    )  # FIXME Amy
    def test_call_numpy_4_channels(self):
        return super().test_call_pytorch()