# coding=utf-8
# Copyright 2025 the HuggingFace Team. All rights reserved.
#
# 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 shutil
import tempfile
import unittest

import numpy as np
import pytest
from PIL import Image

from transformers import AutoProcessor, Qwen2Tokenizer
from transformers.testing_utils import require_av, require_torch, require_torchvision, require_vision
from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available

from ...test_processing_common import ProcessorTesterMixin


if is_vision_available():
    from transformers import VideoLlama3Processor

    if is_torchvision_available():
        from transformers import VideoLlama3ImageProcessor, VideoLlama3VideoProcessor

if is_torch_available():
    import torch


def prepare_image_inputs():
    """This function prepares a list of PIL images"""
    image_inputs = [np.random.randint(255, size=(3, 15, 50), dtype=np.uint8)]
    image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
    return image_inputs


@require_vision
@require_torch
@require_torchvision
class VideoLlama3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
    processor_class = VideoLlama3Processor

    @classmethod
    def setUpClass(cls):
        cls.tmpdirname = tempfile.mkdtemp()
        processor = VideoLlama3Processor.from_pretrained(
            "lkhl/VideoLLaMA3-2B-Image-HF", patch_size=4, max_pixels=56 * 56, min_pixels=28 * 28
        )
        processor.save_pretrained(cls.tmpdirname)
        cls.image_token = processor.image_token

    def get_tokenizer(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer

    def get_image_processor(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor

    def get_video_processor(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).video_processor

    def get_processor(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs)

    @classmethod
    def tearDownClass(cls):
        shutil.rmtree(cls.tmpdirname, ignore_errors=True)

    @require_vision
    def prepare_image_inputs(self, batch_size: int | None = None):
        """This function prepares a list of PIL images for testing"""
        if batch_size is None:
            return prepare_image_inputs()[0]
        if batch_size < 1:
            raise ValueError("batch_size must be greater than 0")
        return prepare_image_inputs() * batch_size

    # Copied from tests.models.llava.test_processing_llava.LlavaProcessorTest.test_get_num_vision_tokens
    def test_get_num_vision_tokens(self):
        "Tests general functionality of the helper used internally in vLLM"

        processor = self.get_processor()

        output = processor._get_num_multimodal_tokens(image_sizes=[(100, 100), (300, 100), (500, 30)])
        self.assertTrue("num_image_tokens" in output)
        self.assertEqual(len(output["num_image_tokens"]), 3)

        self.assertTrue("num_image_patches" in output)
        self.assertEqual(len(output["num_image_patches"]), 3)

    def test_save_load_pretrained_default(self):
        tokenizer = self.get_tokenizer()
        image_processor = self.get_image_processor()
        video_processor = self.get_video_processor()

        processor = VideoLlama3Processor(
            tokenizer=tokenizer, image_processor=image_processor, video_processor=video_processor
        )
        processor.save_pretrained(self.tmpdirname)
        processor = VideoLlama3Processor.from_pretrained(self.tmpdirname, use_fast=False)

        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
        self.assertEqual(processor.image_processor.to_json_string(), image_processor.to_json_string())
        self.assertIsInstance(processor.tokenizer, Qwen2Tokenizer)
        self.assertIsInstance(processor.image_processor, VideoLlama3ImageProcessor)
        self.assertIsInstance(processor.video_processor, VideoLlama3VideoProcessor)

    def test_image_processor(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()
        video_processor = self.get_video_processor()

        processor = VideoLlama3Processor(
            tokenizer=tokenizer, image_processor=image_processor, video_processor=video_processor
        )

        image_input = self.prepare_image_inputs()

        input_image_proc = image_processor(image_input, return_tensors="pt")
        input_processor = processor(images=image_input, text="dummy", return_tensors="pt")

        for key in input_image_proc:
            self.assertAlmostEqual(input_image_proc[key].sum(), input_processor[key].sum(), delta=1e-2)

    def test_processor(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()
        video_processor = self.get_video_processor()

        processor = VideoLlama3Processor(
            tokenizer=tokenizer, image_processor=image_processor, video_processor=video_processor
        )

        input_str = "lower newer"
        image_input = self.prepare_image_inputs()
        inputs = processor(text=input_str, images=image_input)

        self.assertListEqual(
            list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values", "image_grid_thw", "image_merge_sizes"]
        )

        # test if it raises when no input is passed
        with pytest.raises(ValueError):
            processor()

        # test if it raises when no text is passed
        with pytest.raises(TypeError):
            processor(images=image_input)

    @require_torch
    @require_av
    def _test_apply_chat_template(
        self,
        modality: str,
        batch_size: int,
        return_tensors: str,
        input_name: str,
        processor_name: str,
        input_data: list[str],
    ):
        processor = self.get_processor()
        if processor.chat_template is None:
            self.skipTest("Processor has no chat template")

        if processor_name not in self.processor_class.attributes:
            self.skipTest(f"{processor_name} attribute not present in {self.processor_class}")

        batch_messages = [
            [
                {
                    "role": "user",
                    "content": [{"type": "text", "text": "Describe this."}],
                },
            ]
        ] * batch_size

        # Test that jinja can be applied
        formatted_prompt = processor.apply_chat_template(batch_messages, add_generation_prompt=True, tokenize=False)
        self.assertEqual(len(formatted_prompt), batch_size)

        # Test that tokenizing with template and directly with `self.tokenizer` gives same output
        formatted_prompt_tokenized = processor.apply_chat_template(
            batch_messages, add_generation_prompt=True, tokenize=True, return_tensors=return_tensors
        )
        add_special_tokens = True
        if processor.tokenizer.bos_token is not None and formatted_prompt[0].startswith(processor.tokenizer.bos_token):
            add_special_tokens = False
        tok_output = processor.tokenizer(
            formatted_prompt, return_tensors=return_tensors, add_special_tokens=add_special_tokens
        )
        expected_output = tok_output.input_ids
        self.assertListEqual(expected_output.tolist(), formatted_prompt_tokenized.tolist())

        # Test that kwargs passed to processor's `__call__` are actually used
        tokenized_prompt_100 = processor.apply_chat_template(
            batch_messages,
            add_generation_prompt=True,
            tokenize=True,
            padding="max_length",
            truncation=True,
            return_tensors=return_tensors,
            max_length=100,
        )
        self.assertEqual(len(tokenized_prompt_100[0]), 100)

        # Test that `return_dict=True` returns text related inputs in the dict
        out_dict_text = processor.apply_chat_template(
            batch_messages,
            add_generation_prompt=True,
            tokenize=True,
            return_dict=True,
            return_tensors=return_tensors,
        )
        self.assertTrue(all(key in out_dict_text for key in ["input_ids", "attention_mask"]))
        self.assertEqual(len(out_dict_text["input_ids"]), batch_size)
        self.assertEqual(len(out_dict_text["attention_mask"]), batch_size)

        # Test that with modality URLs and `return_dict=True`, we get modality inputs in the dict
        for idx, url in enumerate(input_data[:batch_size]):
            batch_messages[idx][0]["content"] = [batch_messages[idx][0]["content"][0], {"type": modality, "url": url}]

        out_dict = processor.apply_chat_template(
            batch_messages,
            add_generation_prompt=True,
            tokenize=True,
            return_dict=True,
            return_tensors=return_tensors,
            num_frames=2,  # by default no more than 2 frames, otherwise too slow
        )
        input_name = getattr(self, input_name)
        self.assertTrue(input_name in out_dict)
        self.assertEqual(len(out_dict["input_ids"]), batch_size)
        self.assertEqual(len(out_dict["attention_mask"]), batch_size)
        if modality == "video":
            # qwen pixels don't scale with bs same way as other models, calculate expected video token count based on video_grid_thw
            expected_video_token_count = 0
            for thw in out_dict["video_grid_thw"]:
                expected_video_token_count += thw[0] * thw[1] * thw[2]
            mm_len = expected_video_token_count
        else:
            mm_len = batch_size * 192
        self.assertEqual(len(out_dict[input_name]), mm_len)

        return_tensor_to_type = {"pt": torch.Tensor, "np": np.ndarray, None: list}
        for k in out_dict:
            self.assertIsInstance(out_dict[k], return_tensor_to_type[return_tensors])

    @require_av
    def test_apply_chat_template_video_frame_sampling(self):
        processor = self.get_processor()
        if processor.chat_template is None:
            self.skipTest("Processor has no chat template")

        signature = inspect.signature(processor.__call__)
        if "videos" not in {*signature.parameters.keys()} or (
            signature.parameters.get("videos") is not None
            and signature.parameters["videos"].annotation == inspect._empty
        ):
            self.skipTest("Processor doesn't accept videos at input")

        messages = [
            [
                {
                    "role": "user",
                    "content": [
                        {"type": "video"},
                        {"type": "text", "text": "What is shown in this video?"},
                    ],
                },
            ]
        ]

        formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
        self.assertEqual(len(formatted_prompt), 1)

        formatted_prompt_tokenized = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True)
        expected_output = processor.tokenizer(formatted_prompt, return_tensors=None).input_ids
        self.assertListEqual(expected_output, formatted_prompt_tokenized)

        out_dict = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True)
        self.assertListEqual(list(out_dict.keys()), ["input_ids", "attention_mask"])

        # Add video URL for return dict and load with `num_frames` arg
        messages[0][0]["content"][0] = {
            "type": "video",
            "url": "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/Big_Buck_Bunny_720_10s_10MB.mp4",
        }
        num_frames = 3
        out_dict_with_video = processor.apply_chat_template(
            messages,
            add_generation_prompt=True,
            tokenize=True,
            return_dict=True,
            num_frames=num_frames,
        )
        self.assertTrue(self.videos_input_name in out_dict_with_video)
        self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 180)

        # Load with `fps` arg
        fps = 1
        out_dict_with_video = processor.apply_chat_template(
            messages,
            add_generation_prompt=True,
            tokenize=True,
            return_dict=True,
            fps=fps,
        )
        self.assertTrue(self.videos_input_name in out_dict_with_video)
        self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 80)

        # Load with `fps` and `num_frames` args, should raise an error
        with self.assertRaises(ValueError):
            out_dict_with_video = processor.apply_chat_template(
                messages,
                add_generation_prompt=True,
                tokenize=True,
                return_dict=True,
                fps=fps,
                num_frames=num_frames,
            )

        # Load without any arg should load the whole video
        out_dict_with_video = processor.apply_chat_template(
            messages,
            add_generation_prompt=True,
            tokenize=True,
            return_dict=True,
        )
        self.assertTrue(self.videos_input_name in out_dict_with_video)
        self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 1200)

        # Load video as a list of frames (i.e. images). NOTE: each frame should have same size
        # because we assume they come from one video
        messages[0][0]["content"][0] = {
            "type": "video",
            "url": [
                "https://www.ilankelman.org/stopsigns/australia.jpg",
                "https://www.ilankelman.org/stopsigns/australia.jpg",
            ],
        }
        out_dict_with_video = processor.apply_chat_template(
            messages,
            add_generation_prompt=True,
            tokenize=True,
            return_dict=True,
        )
        self.assertTrue(self.videos_input_name in out_dict_with_video)
        self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 192)

        # When the inputs are frame URLs/paths we expect that those are already
        # sampled and will raise an error is asked to sample again.
        with self.assertRaisesRegex(
            ValueError, "Sampling frames from a list of images is not supported! Set `do_sample_frames=False`"
        ):
            out_dict_with_video = processor.apply_chat_template(
                messages,
                add_generation_prompt=True,
                tokenize=True,
                return_dict=True,
                do_sample_frames=True,
            )

    def test_kwargs_overrides_custom_image_processor_kwargs(self):
        processor = self.get_processor()
        self.skip_processor_without_typed_kwargs(processor)

        input_str = self.prepare_text_inputs()
        image_input = self.prepare_image_inputs()
        inputs = processor(text=input_str, images=image_input, return_tensors="pt")
        self.assertEqual(inputs[self.images_input_name].shape[0], 52)
        inputs = processor(text=input_str, images=image_input, max_pixels=56 * 56 * 4, return_tensors="pt")
        self.assertEqual(inputs[self.images_input_name].shape[0], 52)

    def test_special_mm_token_truncation(self):
        """Tests that special vision tokens do not get truncated when `truncation=True` is set."""

        processor = self.get_processor()

        input_str = self.prepare_text_inputs(batch_size=2, modalities="image")
        image_input = self.prepare_image_inputs(batch_size=2)

        _ = processor(
            text=input_str,
            images=image_input,
            return_tensors="pt",
            truncation=None,
            padding=True,
        )

        with self.assertRaises(ValueError):
            _ = processor(
                text=input_str,
                images=image_input,
                return_tensors="pt",
                truncation=True,
                padding=True,
                max_length=20,
            )
