# 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

from transformers.image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
from transformers.models.qwen2_vl.image_processing_qwen2_vl 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


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import Qwen2VLImageProcessor


class Qwen2VLImageProcessingTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        min_resolution=56,
        max_resolution=1024,
        min_pixels=56 * 56,
        max_pixels=28 * 28 * 1280,
        do_normalize=True,
        image_mean=OPENAI_CLIP_MEAN,
        image_std=OPENAI_CLIP_STD,
        do_resize=True,
        patch_size=14,
        temporal_patch_size=2,
        merge_size=2,
        do_convert_rgb=True,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.min_resolution = min_resolution
        self.max_resolution = max_resolution
        self.num_channels = num_channels
        self.image_mean = OPENAI_CLIP_MEAN
        self.image_std = OPENAI_CLIP_STD
        self.min_pixels = min_pixels
        self.max_pixels = max_pixels
        self.patch_size = patch_size
        self.temporal_patch_size = temporal_patch_size
        self.merge_size = merge_size
        self.do_resize = do_resize
        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,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "min_pixels": self.min_pixels,
            "max_pixels": self.max_pixels,
            "patch_size": self.patch_size,
            "temporal_patch_size": self.temporal_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 Qwen2VLImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = Qwen2VLImageProcessor if is_vision_available() else None

    def setUp(self):
        super().setUp()
        self.image_processor_tester = Qwen2VLImageProcessingTester(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, "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, "min_pixels"))
        self.assertTrue(hasattr(image_processing, "max_pixels"))
        self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
        self.assertTrue(hasattr(image_processing, "patch_size"))
        self.assertTrue(hasattr(image_processing, "temporal_patch_size"))
        self.assertTrue(hasattr(image_processing, "merge_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.min_pixels, 56 * 56)
        self.assertEqual(image_processor.max_pixels, 28 * 28 * 1280)

        image_processor = self.image_processing_class.from_dict(
            self.image_processor_dict, min_pixels=256 * 256, max_pixels=640 * 640
        )
        self.assertEqual(image_processor.min_pixels, 256 * 256)
        self.assertEqual(image_processor.max_pixels, 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):
        # Initialize image_processing
        image_processing = self.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
        prcocess_out = image_processing(image_inputs[0], return_tensors="pt")
        encoded_images = prcocess_out.pixel_values
        image_grid_thws = prcocess_out.image_grid_thw
        expected_output_image_shape = (4900, 1176)
        expected_image_grid_thws = torch.Tensor([[1, 70, 70]])
        self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
        self.assertTrue((image_grid_thws == expected_image_grid_thws).all())

        # Test batched
        prcocess_out = image_processing(image_inputs, return_tensors="pt")
        encoded_images = prcocess_out.pixel_values
        image_grid_thws = prcocess_out.image_grid_thw
        expected_output_image_shape = (34300, 1176)
        expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 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):
        # Initialize image_processing
        image_processing = self.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
        prcocess_out = image_processing(image_inputs[0], return_tensors="pt")
        encoded_images = prcocess_out.pixel_values
        image_grid_thws = prcocess_out.image_grid_thw
        expected_output_image_shape = (4900, 1176)
        expected_image_grid_thws = torch.Tensor([[1, 70, 70]])
        self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
        self.assertTrue((image_grid_thws == expected_image_grid_thws).all())

        # Test batched
        prcocess_out = image_processing(image_inputs, return_tensors="pt")
        encoded_images = prcocess_out.pixel_values
        image_grid_thws = prcocess_out.image_grid_thw
        expected_output_image_shape = (34300, 1176)
        expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 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):
        # Initialize image_processing
        image_processing = self.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
        prcocess_out = image_processing(image_inputs[0], return_tensors="pt")
        encoded_images = prcocess_out.pixel_values
        image_grid_thws = prcocess_out.image_grid_thw
        expected_output_image_shape = (4900, 1176)
        expected_image_grid_thws = torch.Tensor([[1, 70, 70]])
        self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
        self.assertTrue((image_grid_thws == expected_image_grid_thws).all())

        # Test batched
        prcocess_out = image_processing(image_inputs, return_tensors="pt")
        encoded_images = prcocess_out.pixel_values
        image_grid_thws = prcocess_out.image_grid_thw
        expected_output_image_shape = (34300, 1176)
        expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 7)
        self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
        self.assertTrue((image_grid_thws == expected_image_grid_thws).all())

    @unittest.skip(reason="Qwen2VLImageProcessor doesn't treat 4 channel PIL and numpy consistently yet")
    def test_call_numpy_4_channels(self):
        pass

    def test_nested_input(self):
        image_processing = self.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
        prcocess_out = image_processing(image_inputs, return_tensors="pt")
        encoded_images = prcocess_out.pixel_values
        image_grid_thws = prcocess_out.image_grid_thw
        expected_output_image_shape = (34300, 1176)
        expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 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:]
        prcocess_out = image_processing(image_inputs_nested, return_tensors="pt")
        encoded_images_nested = prcocess_out.pixel_values
        image_grid_thws_nested = prcocess_out.image_grid_thw
        expected_output_image_shape = (34300, 1176)
        expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 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())
