# 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 ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs


class PromptDepthAnythingImageProcessingTester(unittest.TestCase):
    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_normalize=True,
        image_mean=[0.5, 0.5, 0.5],
        image_std=[0.5, 0.5, 0.5],
    ):
        super().__init__()
        size = size if 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_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std

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

    def expected_output_image_shape(self, images):
        return self.num_channels, self.size["height"], self.size["width"]

    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 PromptDepthAnythingImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    def setUp(self):
        super().setUp()
        self.image_processor_tester = PromptDepthAnythingImageProcessingTester(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, "image_mean"))
            self.assertTrue(hasattr(image_processing, "image_std"))
            self.assertTrue(hasattr(image_processing, "do_normalize"))
            self.assertTrue(hasattr(image_processing, "do_resize"))
            self.assertTrue(hasattr(image_processing, "size"))
            self.assertTrue(hasattr(image_processing, "do_rescale"))
            self.assertTrue(hasattr(image_processing, "rescale_factor"))
            self.assertTrue(hasattr(image_processing, "do_pad"))
            self.assertTrue(hasattr(image_processing, "size_divisor"))
            self.assertTrue(hasattr(image_processing, "prompt_scale_to_meter"))

    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, {"height": 18, "width": 18})

            image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42)
            self.assertEqual(image_processor.size, {"height": 42, "width": 42})

    def test_keep_aspect_ratio(self):
        size = {"height": 512, "width": 512}
        for image_processing_class in self.image_processing_classes.values():
            image_processor = image_processing_class(size=size, keep_aspect_ratio=True, ensure_multiple_of=32)

            image = np.zeros((489, 640, 3))

            pixel_values = image_processor(image, return_tensors="pt").pixel_values

            self.assertEqual(list(pixel_values.shape), [1, 3, 512, 672])

    def test_prompt_depth_processing(self):
        size = {"height": 756, "width": 756}
        for image_processing_class in self.image_processing_classes.values():
            image_processor = image_processing_class(size=size, keep_aspect_ratio=True, ensure_multiple_of=32)

            image = np.zeros((756, 1008, 3))
            prompt_depth = np.random.random((192, 256))

            outputs = image_processor(image, prompt_depth=prompt_depth, return_tensors="pt")
            pixel_values = outputs.pixel_values
            prompt_depth_values = outputs.prompt_depth

            self.assertEqual(list(pixel_values.shape), [1, 3, 768, 1024])
            self.assertEqual(list(prompt_depth_values.shape), [1, 1, 192, 256])

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

        image = np.zeros((756, 1008, 3))
        prompt_depth = np.random.random((192, 256))

        size = {"height": 756, "width": 756}
        encodings = {}
        for backend_name, image_processing_class in self.image_processing_classes.items():
            image_processor = image_processing_class(
                size=size, keep_aspect_ratio=True, ensure_multiple_of=32, do_pad=True, size_divisor=51
            )
            encodings[backend_name] = image_processor(image, prompt_depth=prompt_depth, return_tensors="pt")

        backend_names = list(encodings.keys())
        reference_backend = backend_names[0]
        for backend_name in backend_names[1:]:
            self._assert_tensors_equivalence(
                encodings[reference_backend].pixel_values, encodings[backend_name].pixel_values
            )
            self.assertEqual(
                encodings[reference_backend].prompt_depth.dtype, encodings[backend_name].prompt_depth.dtype
            )
            self._assert_tensors_equivalence(
                encodings[reference_backend].prompt_depth, encodings[backend_name].prompt_depth
            )

    def test_slow_fast_equivalence_batched(self):
        """Override base class test to also compare prompt_depth."""
        if len(self.image_processing_classes) < 2:
            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")

        batch_size = self.image_processor_tester.batch_size
        images = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
        prompt_depths = [np.random.random((192, 256)) for _ in range(batch_size)]

        size = {"height": 756, "width": 756}
        encodings = {}
        for backend_name, image_processing_class in self.image_processing_classes.items():
            image_processor = image_processing_class(size=size, keep_aspect_ratio=False, ensure_multiple_of=32)
            encodings[backend_name] = image_processor(images, prompt_depth=prompt_depths, return_tensors="pt")

        backend_names = list(encodings.keys())
        reference_backend = backend_names[0]
        for backend_name in backend_names[1:]:
            self._assert_tensors_equivalence(
                encodings[reference_backend].pixel_values, encodings[backend_name].pixel_values
            )
            self.assertEqual(
                encodings[reference_backend].prompt_depth.dtype, encodings[backend_name].prompt_depth.dtype
            )
            self._assert_tensors_equivalence(
                encodings[reference_backend].prompt_depth, encodings[backend_name].prompt_depth
            )
