# Copyright 2025 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 itertools
import tempfile
import unittest

import numpy as np

from transformers.image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, load_image
from transformers.models.ernie4_5_vl_moe.image_processing_ernie4_5_vl_moe import smart_resize
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_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 Ernie4_5_VLMoeImageProcessorTester:
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        min_resolution=56,
        max_resolution=1024,
        size=None,
        do_resize=True,
        do_normalize=True,
        do_convert_rgb=True,
        image_mean=OPENAI_CLIP_MEAN,
        image_std=OPENAI_CLIP_STD,
        patch_size=14,
        merge_size=2,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.min_resolution = min_resolution
        self.max_resolution = max_resolution
        if size is None:
            size = {"shortest_edge": 56 * 56, "longest_edge": 6177 * 28 * 28}
        self.size = size
        self.do_resize = do_resize
        self.do_normalize = do_normalize
        self.do_convert_rgb = do_convert_rgb
        self.image_mean = image_mean
        self.image_std = image_std
        self.patch_size = patch_size
        self.merge_size = merge_size

    def prepare_image_processor_dict(self):
        return {
            "do_resize": self.do_resize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "size": self.size,
            "patch_size": self.patch_size,
            "merge_size": self.merge_size,
        }

    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
        images = 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,
        )
        return [[image] for image in images]


@require_torch
@require_vision
class Ernie4_5_VLMoeImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    def setUp(self):
        super().setUp()
        self.image_processor_tester = Ernie4_5_VLMoeImageProcessorTester(self)

    @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_processing = image_processing_class(**self.image_processor_dict)
            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_resize"))
            self.assertTrue(hasattr(image_processing, "size"))
            self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
            self.assertTrue(hasattr(image_processing, "patch_size"))
            self.assertTrue(hasattr(image_processing, "merge_size"))

    def test_image_processor_from_dict_with_kwargs(self):
        for image_processing_class in self.image_processing_classes.values():
            image_processor = image_processing_class.from_dict(self.image_processor_dict)
            self.assertEqual(image_processor.size["shortest_edge"], 56 * 56)
            self.assertEqual(image_processor.size["longest_edge"], 6177 * 28 * 28)

            image_processor = image_processing_class.from_dict(
                self.image_processor_dict,
                size={"shortest_edge": 256 * 256, "longest_edge": 640 * 640},
            )
            self.assertEqual(image_processor.size["shortest_edge"], 256 * 256)
            self.assertEqual(image_processor.size["longest_edge"], 640 * 640)

    def test_select_best_resolution(self):
        # Test with a final resize resolution
        best_resolution = smart_resize(561, 278, factor=28)
        self.assertEqual(best_resolution, (560, 280))

    def test_call_pil(self):
        for image_processing_class in self.image_processing_classes.values():
            # 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[0], Image.Image)

            # Test not batched input
            process_out = image_processing(image_inputs[0], return_tensors="pt")
            encoded_images = process_out.pixel_values
            image_grid_thws = process_out.image_grid_thw
            expected_output_image_shape = (5476, 588)
            expected_image_grid_thws = torch.Tensor([[1, 74, 74]])
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())

            # Test batched
            process_out = image_processing(image_inputs, return_tensors="pt")
            encoded_images = process_out.pixel_values
            image_grid_thws = process_out.image_grid_thw
            expected_output_image_shape = (38332, 588)
            expected_image_grid_thws = torch.Tensor([[1, 74, 74]] * 7)
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())

    def test_call_numpy(self):
        for image_processing_class in self.image_processing_classes.values():
            # 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[0], np.ndarray)

            # Test not batched input
            process_out = image_processing(image_inputs[0], return_tensors="pt")
            encoded_images = process_out.pixel_values
            image_grid_thws = process_out.image_grid_thw
            expected_output_image_shape = (5476, 588)
            expected_image_grid_thws = torch.Tensor([[1, 74, 74]])
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())

            # Test batched
            process_out = image_processing(image_inputs, return_tensors="pt")
            encoded_images = process_out.pixel_values
            image_grid_thws = process_out.image_grid_thw
            expected_output_image_shape = (38332, 588)
            expected_image_grid_thws = torch.Tensor([[1, 74, 74]] * 7)
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())

    def test_call_pytorch(self):
        for image_processing_class in self.image_processing_classes.values():
            # 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[0], torch.Tensor)

            # Test not batched input
            process_out = image_processing(image_inputs[0], return_tensors="pt")
            encoded_images = process_out.pixel_values
            image_grid_thws = process_out.image_grid_thw
            expected_output_image_shape = (5476, 588)
            expected_image_grid_thws = torch.Tensor([[1, 74, 74]])
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())

            # Test batched
            process_out = image_processing(image_inputs, return_tensors="pt")
            encoded_images = process_out.pixel_values
            image_grid_thws = process_out.image_grid_thw
            expected_output_image_shape = (38332, 588)
            expected_image_grid_thws = torch.Tensor([[1, 74, 74]] * 7)
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())

    @unittest.skip(reason="Erni4_5_VLImageProcessor doesn't treat 4 channel PIL and numpy consistently yet")
    def test_call_numpy_4_channels(self):
        pass

    def test_nested_input(self):
        for image_processing_class in self.image_processing_classes.values():
            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
            process_out = image_processing(image_inputs, return_tensors="pt")
            encoded_images = process_out.pixel_values
            image_grid_thws = process_out.image_grid_thw
            expected_output_image_shape = (38332, 588)
            expected_image_grid_thws = torch.Tensor([[1, 74, 74]] * 7)
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())

            # Test batched as a nested list of images, where each sublist is one batch
            image_inputs_nested = image_inputs[:3] + image_inputs[3:]
            process_out = image_processing(image_inputs_nested, return_tensors="pt")
            encoded_images_nested = process_out.pixel_values
            image_grid_thws_nested = process_out.image_grid_thw
            expected_output_image_shape = (38332, 588)
            expected_image_grid_thws = torch.Tensor([[1, 74, 74]] * 7)
            self.assertEqual(tuple(encoded_images_nested.shape), expected_output_image_shape)
            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())

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

    def test_custom_image_size(self):
        for image_processing_class in self.image_processing_classes.values():
            image_processing = image_processing_class(**self.image_processor_dict)
            with tempfile.TemporaryDirectory() as tmpdirname:
                image_processing.save_pretrained(tmpdirname)
                image_processor_loaded = image_processing_class.from_pretrained(
                    tmpdirname, size={"shortest_edge": 28 * 28, "longest_edge": 56 * 56}
                )

            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
            process_out = image_processor_loaded(image_inputs, return_tensors="pt")
            expected_output_image_shape = [112, 588]
            self.assertListEqual(list(process_out.pixel_values.shape), expected_output_image_shape)

    def test_custom_pixels(self):
        pixel_choices = frozenset(itertools.product((100, 150, 200, 20000), (100, 150, 200, 20000)))
        for image_processing_class in self.image_processing_classes.values():
            image_processor_dict = self.image_processor_dict.copy()
            for a_pixels, b_pixels in pixel_choices:
                image_processor_dict["size"] = {
                    "shortest_edge": min(a_pixels, b_pixels),
                    "longest_edge": max(a_pixels, b_pixels),
                }
                image_processor = image_processing_class(**image_processor_dict)
                image_inputs = self.image_processor_tester.prepare_image_inputs()
                # Just checking that it doesn't raise an error
                image_processor(image_inputs, return_tensors="pt")

    @require_vision
    @require_torch
    def test_backends_equivalence(self):
        """Override base test to also compare image_grid_thw."""
        if len(self.image_processing_classes) < 2:
            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")

        dummy_image = load_image(url_to_local_path("http://images.cocodataset.org/val2017/000000039769.jpg"))

        image_processor_pil = self.image_processing_classes["pil"](**self.image_processor_dict)
        image_processor_torchvision = self.image_processing_classes["torchvision"](**self.image_processor_dict)

        encoding_pil = image_processor_pil(dummy_image, return_tensors="pt")
        encoding_torchvision = image_processor_torchvision(dummy_image, return_tensors="pt")

        self._assert_tensors_equivalence(encoding_pil.pixel_values, encoding_torchvision.pixel_values)
        self.assertEqual(encoding_pil.image_grid_thw.dtype, encoding_torchvision.image_grid_thw.dtype)
        self._assert_tensors_equivalence(
            encoding_pil.image_grid_thw.float(), encoding_torchvision.image_grid_thw.float()
        )

    @require_vision
    @require_torch
    def test_backends_equivalence_batched(self):
        """Override base test to also compare image_grid_thw."""
        if len(self.image_processing_classes) < 2:
            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")

        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_pil = self.image_processing_classes["pil"](**self.image_processor_dict)
        image_processor_torchvision = self.image_processing_classes["torchvision"](**self.image_processor_dict)

        encoding_pil = image_processor_pil(dummy_images, return_tensors="pt")
        encoding_torchvision = image_processor_torchvision(dummy_images, return_tensors="pt")

        self._assert_tensors_equivalence(encoding_pil.pixel_values, encoding_torchvision.pixel_values)
        self.assertEqual(encoding_pil.image_grid_thw.dtype, encoding_torchvision.image_grid_thw.dtype)
        self._assert_tensors_equivalence(
            encoding_pil.image_grid_thw.float(), encoding_torchvision.image_grid_thw.float()
        )

    def test_get_num_patches_without_images(self):
        for image_processing_class in self.image_processing_classes.values():
            image_processing = image_processing_class(**self.image_processor_dict)
            num_patches = image_processing.get_number_of_image_patches(height=100, width=100, images_kwargs={})
            self.assertEqual(num_patches, 64)

            num_patches = image_processing.get_number_of_image_patches(height=200, width=50, images_kwargs={})
            self.assertEqual(num_patches, 56)

            num_patches = image_processing.get_number_of_image_patches(
                height=100, width=100, images_kwargs={"patch_size": 28}
            )
            self.assertEqual(num_patches, 16)
