# 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

from transformers.image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ChannelDimension
from transformers.models.llava_next.image_processing_llava_next import select_best_resolution
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available

from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import LlavaNextImageProcessor

    if is_torchvision_available():
        from transformers import LlavaNextImageProcessorFast


class LlavaNextImageProcessingTester:
    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_center_crop=True,
        crop_size=None,
        do_normalize=True,
        image_mean=OPENAI_CLIP_MEAN,
        image_std=OPENAI_CLIP_STD,
        do_convert_rgb=True,
    ):
        super().__init__()
        size = size if size is not None else {"shortest_edge": 20}
        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.image_size = image_size
        self.min_resolution = min_resolution
        self.max_resolution = max_resolution
        self.do_resize = do_resize
        self.size = size
        self.do_center_crop = do_center_crop
        self.crop_size = crop_size
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std
        self.do_convert_rgb = do_convert_rgb

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

    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTester.expected_output_image_shape
    def expected_output_image_shape(self, images):
        return self.num_channels, self.crop_size["height"], self.crop_size["width"]

    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTester.prepare_image_inputs
    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 LlavaNextImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = LlavaNextImageProcessor if is_vision_available() else None
    fast_image_processing_class = LlavaNextImageProcessorFast if is_torchvision_available() else None

    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.setUp with CLIP->LlavaNext
    def setUp(self):
        super().setUp()
        self.image_processor_tester = LlavaNextImageProcessingTester(self)

    @property
    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.image_processor_dict
    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_processor_list:
            image_processing = image_processing_class(**self.image_processor_dict)
            self.assertTrue(hasattr(image_processing, "do_resize"))
            self.assertTrue(hasattr(image_processing, "size"))
            self.assertTrue(hasattr(image_processing, "do_center_crop"))
            self.assertTrue(hasattr(image_processing, "center_crop"))
            self.assertTrue(hasattr(image_processing, "do_normalize"))
            self.assertTrue(hasattr(image_processing, "image_mean"))
            self.assertTrue(hasattr(image_processing, "image_std"))
            self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
            self.assertTrue(hasattr(image_processing, "image_grid_pinpoints"))

    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.test_image_processor_from_dict_with_kwargs
    def test_image_processor_from_dict_with_kwargs(self):
        for image_processing_class in self.image_processor_list:
            image_processor = image_processing_class.from_dict(self.image_processor_dict)
            self.assertEqual(image_processor.size, {"shortest_edge": 20})
            self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})

            image_processor = 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_select_best_resolution(self):
        possible_resolutions = [[672, 336], [336, 672], [672, 672], [336, 1008], [1008, 336]]

        # Test with a square aspect ratio
        best_resolution = select_best_resolution((336, 336), possible_resolutions)
        self.assertEqual(best_resolution, (672, 336))

    def test_call_pil(self):
        for image_processing_class in self.image_processor_list:
            # Initialize image_processing
            image_processing = image_processing_class(**self.image_processor_dict)
            # create random PIL images
            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
            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
            expected_output_image_shape = (1, 1445, 3, 18, 18)
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)

            # Test batched
            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
            expected_output_image_shape = (7, 1445, 3, 18, 18)
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)

    def test_call_numpy(self):
        for image_processing_class in self.image_processor_list:
            # Initialize image_processing
            image_processing = image_processing_class(**self.image_processor_dict)
            # create random numpy tensors
            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, 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
            expected_output_image_shape = (1, 1445, 3, 18, 18)
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)

            # Test batched
            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
            expected_output_image_shape = (7, 1445, 3, 18, 18)
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)

    def test_call_pytorch(self):
        for image_processing_class in self.image_processor_list:
            # Initialize image_processing
            image_processing = image_processing_class(**self.image_processor_dict)
            # create random PyTorch tensors
            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, 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
            expected_output_image_shape = (1, 1445, 3, 18, 18)
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)

            # Test batched
            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
            expected_output_image_shape = (7, 1445, 3, 18, 18)
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)

    @unittest.skip(
        reason="LlavaNextImageProcessor doesn't treat 4 channel PIL and numpy consistently yet"
    )  # FIXME Amy
    def test_call_numpy_4_channels(self):
        pass

    def test_nested_input(self):
        for image_processing_class in self.image_processor_list:
            image_processing = image_processing_class(**self.image_processor_dict)
            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)

            # Test batched as a list of images
            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
            expected_output_image_shape = (7, 1445, 3, 18, 18)
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)

            # Test batched as a nested list of images, where each sublist is one batch
            image_inputs_nested = [image_inputs[:3], image_inputs[3:]]
            encoded_images_nested = image_processing(image_inputs_nested, return_tensors="pt").pixel_values
            expected_output_image_shape = (7, 1445, 3, 18, 18)
            self.assertEqual(tuple(encoded_images_nested.shape), expected_output_image_shape)

            # Image processor should return same pixel values, independently of ipnut format
            self.assertTrue((encoded_images_nested == encoded_images).all())

    def test_pad_for_patching(self):
        for image_processing_class in self.image_processor_list:
            if image_processing_class == self.fast_image_processing_class:
                numpify = False
                torchify = True
                input_data_format = image_processing_class.data_format
            else:
                numpify = True
                torchify = False
                input_data_format = ChannelDimension.LAST
            image_processing = image_processing_class(**self.image_processor_dict)
            # Create odd-sized images
            image_input = self.image_processor_tester.prepare_image_inputs(
                equal_resolution=True,
                numpify=numpify,
                torchify=torchify,
            )[0]
            self.assertIn(image_input.shape, [(3, 400, 400), (400, 400, 3)])

            # Test odd-width
            image_shape = (400, 601)
            encoded_images = image_processing._pad_for_patching(image_input, image_shape, input_data_format)
            encoded_image_shape = (
                encoded_images.shape[:-1] if input_data_format == ChannelDimension.LAST else encoded_images.shape[1:]
            )
            self.assertEqual(encoded_image_shape, image_shape)

            # Test odd-height
            image_shape = (503, 400)
            encoded_images = image_processing._pad_for_patching(image_input, image_shape, input_data_format)
            encoded_image_shape = (
                encoded_images.shape[:-1] if input_data_format == ChannelDimension.LAST else encoded_images.shape[1:]
            )
            self.assertEqual(encoded_image_shape, image_shape)

    def test_call_without_padding(self):
        for image_processing_class in self.image_processor_list:
            # Initialize image_processing
            image_processing = image_processing_class(**self.image_processor_dict)
            # create random PyTorch tensors
            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)

            # Test not batched input
            encoded_images = image_processing(image_inputs[0], do_pad=False).pixel_values
            self.assertEqual(len(encoded_images), 1)

            # Test batched
            encoded_images = image_processing(image_inputs, do_pad=False).pixel_values
            self.assertEqual(len(encoded_images), len(image_inputs))