# Copyright 2021 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 inspect
import math
import unittest
import warnings

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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


class Phi4MultimodalImageProcessingTester:
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        image_size=100,
        min_resolution=30,
        max_resolution=400,
        dynamic_hd=36,
        do_resize=True,
        size=None,
        patch_size=14,
        do_normalize=True,
        image_mean=[0.5, 0.5, 0.5],
        image_std=[0.5, 0.5, 0.5],
        do_convert_rgb=True,
    ):
        super().__init__()
        size = size if size is not None else {"height": 100, "width": 100}
        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.dynamic_hd = dynamic_hd
        self.do_resize = do_resize
        self.size = size
        self.patch_size = patch_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,
            "patch_size": self.patch_size,
            "dynamic_hd": self.dynamic_hd,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
        }

    def expected_output_image_shape(self, images):
        max_num_patches = 0
        for image in images:
            if isinstance(image, Image.Image):
                width, height = image.size
            elif isinstance(image, np.ndarray):
                height, width = image.shape[:2]
            elif isinstance(image, torch.Tensor):
                height, width = image.shape[-2:]
            w_crop_num = math.ceil(width / float(self.size["width"]))
            h_crop_num = math.ceil(height / float(self.size["height"]))
            num_patches = min(w_crop_num * h_crop_num + 1, self.dynamic_hd)
            max_num_patches = max(max_num_patches, num_patches)
        num_patches = max_num_patches
        return num_patches, 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 Phi4MultimodalImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    def setUp(self):
        super().setUp()
        self.image_processor_tester = Phi4MultimodalImageProcessingTester(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_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"))

    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": 100, "width": 100})

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

    @unittest.skip(reason="Phi4MultimodalImageProcessor doesn't treat 4 channel PIL and numpy consistently yet")
    def test_call_numpy_4_channels(self):
        pass

    def test_cast_dtype_device(self):
        for image_processing_class in self.image_processing_classes.values():
            if self.test_cast_dtype is not None:
                # Initialize image_processor
                image_processor = image_processing_class(**self.image_processor_dict)

                # create random PyTorch tensors
                image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)

                encoding = image_processor(image_inputs, return_tensors="pt")
                # for layoutLM compatibility
                self.assertEqual(encoding.image_pixel_values.device, torch.device("cpu"))
                self.assertEqual(encoding.image_pixel_values.dtype, torch.float32)

                encoding = image_processor(image_inputs, return_tensors="pt").to(torch.float16)
                self.assertEqual(encoding.image_pixel_values.device, torch.device("cpu"))
                self.assertEqual(encoding.image_pixel_values.dtype, torch.float16)

                encoding = image_processor(image_inputs, return_tensors="pt").to("cpu", torch.bfloat16)
                self.assertEqual(encoding.image_pixel_values.device, torch.device("cpu"))
                self.assertEqual(encoding.image_pixel_values.dtype, torch.bfloat16)

                with self.assertRaises(TypeError):
                    _ = image_processor(image_inputs, return_tensors="pt").to(torch.bfloat16, "cpu")

                # Try with text + image feature
                encoding = image_processor(image_inputs, return_tensors="pt")
                encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]])})
                encoding = encoding.to(torch.float16)

                self.assertEqual(encoding.image_pixel_values.device, torch.device("cpu"))
                self.assertEqual(encoding.image_pixel_values.dtype, torch.float16)
                self.assertEqual(encoding.input_ids.dtype, torch.long)

    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=False)
            for image in image_inputs:
                self.assertIsInstance(image, Image.Image)

            # Test not batched input
            encoded_images = image_processing(image_inputs[0], return_tensors="pt").image_pixel_values
            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))

            # Test batched
            encoded_images = image_processing(image_inputs, return_tensors="pt").image_pixel_values
            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
            self.assertEqual(
                tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
            )

    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=False, 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").image_pixel_values
            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))

            # Test batched
            encoded_images = image_processing(image_inputs, return_tensors="pt").image_pixel_values
            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
            self.assertEqual(
                tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
            )

    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=False, 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").image_pixel_values
            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))

            # Test batched
            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
            encoded_images = image_processing(image_inputs, return_tensors="pt").image_pixel_values
            self.assertEqual(
                tuple(encoded_images.shape),
                (self.image_processor_tester.batch_size, *expected_output_image_shape),
            )

    def test_image_processor_preprocess_arguments(self):
        is_tested = False

        for image_processing_class in self.image_processing_classes.values():
            image_processor = image_processing_class(**self.image_processor_dict)

            # validation done by _valid_processor_keys attribute
            if hasattr(image_processor, "_valid_processor_keys") and hasattr(image_processor, "preprocess"):
                preprocess_parameter_names = inspect.getfullargspec(image_processor.preprocess).args
                preprocess_parameter_names.remove("self")
                preprocess_parameter_names.sort()
                valid_processor_keys = image_processor._valid_processor_keys
                valid_processor_keys.sort()
                self.assertEqual(preprocess_parameter_names, valid_processor_keys)
                is_tested = True

            # validation done by @filter_out_non_signature_kwargs decorator
            if hasattr(image_processor.preprocess, "_filter_out_non_signature_kwargs"):
                if hasattr(self.image_processor_tester, "prepare_image_inputs"):
                    inputs = self.image_processor_tester.prepare_image_inputs()
                elif hasattr(self.image_processor_tester, "prepare_video_inputs"):
                    inputs = self.image_processor_tester.prepare_video_inputs()
                else:
                    self.skipTest(reason="No valid input preparation method found")

                with warnings.catch_warnings(record=True) as raised_warnings:
                    warnings.simplefilter("always")
                    image_processor(inputs, extra_argument=True)

                messages = " ".join([str(w.message) for w in raised_warnings])
                self.assertGreaterEqual(len(raised_warnings), 1)
                self.assertIn("extra_argument", messages)
                is_tested = True

        if not is_tested:
            self.skipTest(reason="No validation found for `preprocess` method")

    @slow
    @pytest.mark.torch_compile_test
    def test_can_compile_torchvision_backend(self):
        if "torchvision" not in self.image_processing_classes:
            self.skipTest("Skipping compilation test as torchvision image processor is not defined")

        torch.compiler.reset()
        input_image = torch.randint(0, 255, (3, 224, 224), dtype=torch.uint8)
        image_processor = self.image_processing_classes["torchvision"](**self.image_processor_dict)
        output_eager = image_processor(input_image, device=torch_device, return_tensors="pt")

        image_processor = torch.compile(image_processor, mode="reduce-overhead")
        output_compiled = image_processor(input_image, device=torch_device, return_tensors="pt")

        torch.testing.assert_close(
            output_eager.image_pixel_values, output_compiled.image_pixel_values, rtol=1e-4, atol=1e-4
        )
