# 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.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 JanusImageProcessor

    if is_torchvision_available():
        from transformers import JanusImageProcessorFast


class JanusImageProcessingTester:
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        image_size=384,
        min_resolution=30,
        max_resolution=200,
        do_resize=True,
        size=None,
        do_normalize=True,
        image_mean=[0.48145466, 0.4578275, 0.40821073],
        image_std=[0.26862954, 0.26130258, 0.27577711],
        do_convert_rgb=True,
    ):
        size = size if size is not None else {"height": 384, "width": 384}
        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_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_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.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 JanusImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = JanusImageProcessor if is_vision_available() else None
    fast_image_processing_class = JanusImageProcessorFast if is_torchvision_available() else None

    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.setUp with CLIP->Janus
    def setUp(self):
        super().setUp()
        self.image_processor_tester = JanusImageProcessingTester(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_normalize"))
            self.assertTrue(hasattr(image_processing, "image_mean"))
            self.assertTrue(hasattr(image_processing, "image_std"))
            self.assertTrue(hasattr(image_processing, "do_convert_rgb"))

    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, {"height": 384, "width": 384})
            self.assertEqual(image_processor.image_mean, [0.48145466, 0.4578275, 0.40821073])

            image_processor = image_processing_class.from_dict(
                self.image_processor_dict, size=42, image_mean=[1.0, 2.0, 1.0]
            )
            self.assertEqual(image_processor.size, {"height": 42, "width": 42})
            self.assertEqual(image_processor.image_mean, [1.0, 2.0, 1.0])

    def test_call_pil(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)
            for image in image_inputs:
                self.assertIsInstance(image, Image.Image)

            # Test Non batched input
            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
            expected_output_image_shape = (1, 3, 384, 384)
            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, 3, 384, 384)
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)

    def test_call_numpy(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, numpify=True)
            for image in image_inputs:
                self.assertIsInstance(image, np.ndarray)

            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
            expected_output_image_shape = (1, 3, 384, 384)
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)

            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
            expected_output_image_shape = (7, 3, 384, 384)
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)

    def test_call_pytorch(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, torchify=True)

            for image in image_inputs:
                self.assertIsInstance(image, torch.Tensor)

            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
            expected_output_image_shape = (1, 3, 384, 384)
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)

            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
            expected_output_image_shape = (7, 3, 384, 384)
            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)

    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, 3, 384, 384)
            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, 3, 384, 384)
            self.assertEqual(tuple(encoded_images_nested.shape), expected_output_image_shape)

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

    @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=None)
        encoding_fast = image_processor_fast(dummy_images, return_tensors=None)

        # Overwrite as the outputs are not always all of the same shape (kept for BC)
        for i in range(len(encoding_slow.pixel_values)):
            self._assert_slow_fast_tensors_equivalence(
                torch.from_numpy(encoding_slow.pixel_values[i]), encoding_fast.pixel_values[i]
            )

    @require_vision
    @require_torch
    def test_slow_fast_equivalence_postprocess(self):
        dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
        dummy_images = [image / 255.0 for image in dummy_images]
        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.postprocess(dummy_images, return_tensors=None)
        encoding_fast = image_processor_fast.postprocess(dummy_images, return_tensors=None)

        # Overwrite as the outputs are not always all of the same shape (kept for BC)
        for i in range(len(encoding_slow.pixel_values)):
            self._assert_slow_fast_tensors_equivalence(
                torch.from_numpy(encoding_slow.pixel_values[i]).float(), encoding_fast.pixel_values[i].float()
            )

    @unittest.skip(reason="Not supported")
    def test_call_numpy_4_channels(self):
        pass