# coding=utf-8
# Copyright 2023 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
import requests

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

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    is_torch_greater_or_equal_than_1_11 = False

if is_vision_available():
    from PIL import Image

    from transformers import Pix2StructImageProcessor


class Pix2StructImageProcessingTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        image_size=18,
        min_resolution=30,
        max_resolution=400,
        size=None,
        do_normalize=True,
        do_convert_rgb=True,
        patch_size=None,
    ):
        size = size if size is not None else {"height": 20, "width": 20}
        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.size = size
        self.do_normalize = do_normalize
        self.do_convert_rgb = do_convert_rgb
        self.max_patches = [512, 1024, 2048, 4096]
        self.patch_size = patch_size if patch_size is not None else {"height": 16, "width": 16}

    def prepare_image_processor_dict(self):
        return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}

    def prepare_dummy_image(self):
        img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
        raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
        return raw_image

    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,
        )


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11,
    reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`.",
)
@require_torch
@require_vision
class Pix2StructImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = Pix2StructImageProcessor if is_vision_available() else None

    def setUp(self):
        self.image_processor_tester = Pix2StructImageProcessingTester(self)

    @property
    def image_processor_dict(self):
        return self.image_processor_tester.prepare_image_processor_dict()

    def test_image_processor_properties(self):
        image_processor = self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(image_processor, "do_normalize"))
        self.assertTrue(hasattr(image_processor, "do_convert_rgb"))

    def test_expected_patches(self):
        dummy_image = self.image_processor_tester.prepare_dummy_image()

        image_processor = self.image_processing_class(**self.image_processor_dict)
        max_patch = 2048

        inputs = image_processor(dummy_image, return_tensors="pt", max_patches=max_patch)
        self.assertTrue(torch.allclose(inputs.flattened_patches.mean(), torch.tensor(0.0606), atol=1e-3, rtol=1e-3))

    def test_call_pil(self):
        # Initialize image_processor
        image_processor = self.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
        expected_hidden_dim = (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            encoded_images = image_processor(
                image_inputs[0], return_tensors="pt", max_patches=max_patch
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (1, max_patch, expected_hidden_dim),
            )

            # Test batched
            encoded_images = image_processor(
                image_inputs, return_tensors="pt", max_patches=max_patch
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
            )

    def test_call_vqa(self):
        # Initialize image_processor
        image_processor = self.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
        expected_hidden_dim = (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        image_processor.is_vqa = True

        for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            with self.assertRaises(ValueError):
                encoded_images = image_processor(
                    image_inputs[0], return_tensors="pt", max_patches=max_patch
                ).flattened_patches

            dummy_text = "Hello"

            encoded_images = image_processor(
                image_inputs[0], return_tensors="pt", max_patches=max_patch, header_text=dummy_text
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (1, max_patch, expected_hidden_dim),
            )

            # Test batched
            encoded_images = image_processor(
                image_inputs, return_tensors="pt", max_patches=max_patch, header_text=dummy_text
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
            )

    def test_call_numpy(self):
        # Initialize image_processor
        image_processor = self.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)

        expected_hidden_dim = (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            encoded_images = image_processor(
                image_inputs[0], return_tensors="pt", max_patches=max_patch
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (1, max_patch, expected_hidden_dim),
            )

            # Test batched
            encoded_images = image_processor(
                image_inputs, return_tensors="pt", max_patches=max_patch
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
            )

    def test_call_numpy_4_channels(self):
        # Initialize image_processor
        image_processor = self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        self.image_processor_tester.num_channels = 4
        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
        for image in image_inputs:
            self.assertIsInstance(image, np.ndarray)

        expected_hidden_dim = (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            encoded_images = image_processor(
                image_inputs[0], return_tensors="pt", max_patches=max_patch, input_data_format="channels_first"
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (1, max_patch, expected_hidden_dim),
            )

            # Test batched
            encoded_images = image_processor(
                image_inputs, return_tensors="pt", max_patches=max_patch, input_data_format="channels_first"
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
            )
        self.image_processor_tester.num_channels = 3

    def test_call_pytorch(self):
        # Initialize image_processor
        image_processor = self.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
        expected_hidden_dim = (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            encoded_images = image_processor(
                image_inputs[0], return_tensors="pt", max_patches=max_patch
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (1, max_patch, expected_hidden_dim),
            )

            # Test batched
            encoded_images = image_processor(
                image_inputs, return_tensors="pt", max_patches=max_patch
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
            )


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11,
    reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`.",
)
@require_torch
@require_vision
class Pix2StructImageProcessingTestFourChannels(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = Pix2StructImageProcessor if is_vision_available() else None

    def setUp(self):
        self.image_processor_tester = Pix2StructImageProcessingTester(self, num_channels=4)
        self.expected_encoded_image_num_channels = 3

    @property
    def image_processor_dict(self):
        return self.image_processor_tester.prepare_image_processor_dict()

    def test_image_processor_properties(self):
        image_processor = self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(image_processor, "do_normalize"))
        self.assertTrue(hasattr(image_processor, "do_convert_rgb"))

    def test_call_pil(self):
        # Initialize image_processor
        image_processor = self.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
        expected_hidden_dim = (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * (self.image_processor_tester.num_channels - 1)
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            encoded_images = image_processor(
                image_inputs[0], return_tensors="pt", max_patches=max_patch
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (1, max_patch, expected_hidden_dim),
            )

            # Test batched
            encoded_images = image_processor(
                image_inputs, return_tensors="pt", max_patches=max_patch
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
            )

    @unittest.skip("Pix2StructImageProcessor does not support 4 channels yet")  # FIXME Amy
    def test_call_numpy(self):
        return super().test_call_numpy()

    @unittest.skip("Pix2StructImageProcessor does not support 4 channels yet")  # FIXME Amy
    def test_call_pytorch(self):
        return super().test_call_torch()

    @unittest.skip("Pix2StructImageProcessor does treat numpy and PIL 4 channel images consistently")  # FIXME Amy
    def test_call_numpy_4_channels(self):
        return super().test_call_torch()