# Copyright 2025 the HuggingFace Inc. team.
#
# 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 json
import sys
import tempfile
import unittest
from pathlib import Path
from unittest.mock import patch

import transformers
from transformers import (
    CONFIG_MAPPING,
    VIDEO_PROCESSOR_MAPPING,
    AutoConfig,
    AutoVideoProcessor,
    InternVLConfig,
    LlavaOnevisionConfig,
    LlavaOnevisionVideoProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_torch


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_video_processing import CustomVideoProcessor  # noqa E402


@require_torch
class AutoVideoProcessorTest(unittest.TestCase):
    def setUp(self):
        transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0

    def test_video_processor_from_model_shortcut(self):
        config = AutoVideoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-0.5b-ov-hf")
        self.assertIsInstance(config, LlavaOnevisionVideoProcessor)

    def test_video_processor_from_local_directory_from_key(self):
        with tempfile.TemporaryDirectory() as tmpdirname:
            processor_tmpfile = Path(tmpdirname) / "video_preprocessor_config.json"
            config_tmpfile = Path(tmpdirname) / "config.json"
            json.dump(
                {
                    "video_processor_type": "LlavaOnevisionVideoProcessor",
                    "processor_class": "LlavaOnevisionProcessor",
                },
                open(processor_tmpfile, "w", encoding="utf-8"),
            )
            json.dump({"model_type": "llava_onevision"}, open(config_tmpfile, "w", encoding="utf-8"))

            config = AutoVideoProcessor.from_pretrained(tmpdirname)
            self.assertIsInstance(config, LlavaOnevisionVideoProcessor)

    def test_video_processor_from_local_directory_from_preprocessor_key(self):
        # Ensure we can load the image processor from the feature extractor config
        with tempfile.TemporaryDirectory() as tmpdirname:
            processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json"
            config_tmpfile = Path(tmpdirname) / "config.json"
            json.dump(
                {
                    "video_processor_type": "LlavaOnevisionVideoProcessor",
                    "processor_class": "LlavaOnevisionProcessor",
                },
                open(processor_tmpfile, "w", encoding="utf-8"),
            )
            json.dump({"model_type": "llava_onevision"}, open(config_tmpfile, "w", encoding="utf-8"))

            config = AutoVideoProcessor.from_pretrained(tmpdirname)
            self.assertIsInstance(config, LlavaOnevisionVideoProcessor)

    def test_video_processor_from_local_directory_from_config(self):
        with tempfile.TemporaryDirectory() as tmpdirname:
            model_config = LlavaOnevisionConfig()

            # Create a dummy config file with image_processor_type
            processor_tmpfile = Path(tmpdirname) / "video_preprocessor_config.json"
            config_tmpfile = Path(tmpdirname) / "config.json"
            json.dump(
                {
                    "video_processor_type": "LlavaOnevisionVideoProcessor",
                    "processor_class": "LlavaOnevisionProcessor",
                },
                open(processor_tmpfile, "w", encoding="utf-8"),
            )
            json.dump({"model_type": "llava_onevision"}, open(config_tmpfile, "w", encoding="utf-8"))

            # remove video_processor_type to make sure config.json alone is enough to load image processor locally
            config_dict = AutoVideoProcessor.from_pretrained(tmpdirname).to_dict()

            config_dict.pop("video_processor_type")
            config = LlavaOnevisionVideoProcessor(**config_dict)

            # save in new folder
            model_config.save_pretrained(tmpdirname)
            config.save_pretrained(tmpdirname)

            config = AutoVideoProcessor.from_pretrained(tmpdirname)

            # make sure private variable is not incorrectly saved
            dict_as_saved = json.loads(config.to_json_string())
            self.assertTrue("_processor_class" not in dict_as_saved)

        self.assertIsInstance(config, LlavaOnevisionVideoProcessor)

    def test_video_processor_from_local_file(self):
        with tempfile.TemporaryDirectory() as tmpdirname:
            processor_tmpfile = Path(tmpdirname) / "video_preprocessor_config.json"
            json.dump(
                {
                    "video_processor_type": "LlavaOnevisionVideoProcessor",
                    "processor_class": "LlavaOnevisionProcessor",
                },
                open(processor_tmpfile, "w", encoding="utf-8"),
            )

            config = AutoVideoProcessor.from_pretrained(processor_tmpfile)
            self.assertIsInstance(config, LlavaOnevisionVideoProcessor)

    def test_video_processor_loading_does_not_resolve_other_models(self):
        """
        Image and video processors resolve class names for the requested backend without accessing the lazy mapping.
        `from_pretrained` imports the resolved class directly, so `_load_attr_from_module` should not be called
        for these named processor configurations.

        Regression test for https://github.com/huggingface/transformers/pull/48984.
        """
        for filename, processor_config in (
            ("video_preprocessor_config.json", {"video_processor_type": "LlavaOnevisionVideoProcessor"}),
            ("preprocessor_config.json", {"image_processor_type": "LlavaOnevisionImageProcessor"}),
        ):
            with self.subTest(filename=filename), tempfile.TemporaryDirectory() as tmpdirname:
                with open(Path(tmpdirname) / filename, "w", encoding="utf-8") as fp:
                    json.dump(processor_config, fp)

                with patch.object(
                    VIDEO_PROCESSOR_MAPPING,
                    "_load_attr_from_module",
                    wraps=VIDEO_PROCESSOR_MAPPING._load_attr_from_module,
                ) as load_attribute:
                    video_processor = AutoVideoProcessor.from_pretrained(tmpdirname)

                self.assertIsInstance(video_processor, LlavaOnevisionVideoProcessor)
                load_attribute.assert_not_called()

    def test_repo_not_found(self):
        with self.assertRaisesRegex(
            EnvironmentError,
            "llava-hf/llava-doesnt-exist is not a local folder and is not a valid model identifier",
        ):
            _ = AutoVideoProcessor.from_pretrained("llava-hf/llava-doesnt-exist")

    def test_revision_not_found(self):
        with self.assertRaisesRegex(
            EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"
        ):
            _ = AutoVideoProcessor.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa")

    def test_video_processor_not_found(self):
        with self.assertRaisesRegex(
            EnvironmentError,
            "Can't load video processor for 'hf-internal-testing/config-no-model'.",
        ):
            _ = AutoVideoProcessor.from_pretrained("hf-internal-testing/config-no-model")

    def test_unavailable_backend_error_mentions_missing_dependency(self):
        # Without torchvision, `VIDEO_PROCESSOR_MAPPING` resolves to `None` while
        # `type(config) in VIDEO_PROCESSOR_MAPPING` stays True, so the mapping value must not be
        # dereferenced unguarded. InternVL is such a case: its image processor is
        # `GotOCRImageProcessor`, no `GotOCRVideoProcessor` exists to infer from, and the class
        # stays `None`.
        with tempfile.TemporaryDirectory() as tmpdirname:
            with open(Path(tmpdirname) / "preprocessor_config.json", "w", encoding="utf-8") as fp:
                json.dump({"image_processor_type": "GotOCRImageProcessor"}, fp)
            with open(Path(tmpdirname) / "config.json", "w", encoding="utf-8") as fp:
                json.dump({"model_type": "internvl"}, fp)

            with (
                patch.dict(VIDEO_PROCESSOR_MAPPING._extra_content, {InternVLConfig: None}),
                patch(
                    "transformers.models.auto.video_processing_auto.is_torchvision_available",
                    return_value=False,
                ),
            ):
                with self.assertRaisesRegex(ValueError, "requires `torchvision` to be installed"):
                    AutoVideoProcessor.from_pretrained(tmpdirname)

    def test_from_pretrained_dynamic_video_processor(self):
        # If remote code is not set, we will time out when asking whether to load the model.
        with self.assertRaises(ValueError):
            video_processor = AutoVideoProcessor.from_pretrained("hf-internal-testing/test_dynamic_video_processor")
        # If remote code is disabled, we can't load this config.
        with self.assertRaises(ValueError):
            video_processor = AutoVideoProcessor.from_pretrained(
                "hf-internal-testing/test_dynamic_video_processor", trust_remote_code=False
            )

        video_processor = AutoVideoProcessor.from_pretrained(
            "hf-internal-testing/test_dynamic_video_processor", trust_remote_code=True
        )
        self.assertEqual(video_processor.__class__.__name__, "NewVideoProcessor")

        # Test the dynamic module is loaded only once.
        reloaded_video_processor = AutoVideoProcessor.from_pretrained(
            "hf-internal-testing/test_dynamic_video_processor", trust_remote_code=True
        )
        self.assertIs(video_processor.__class__, reloaded_video_processor.__class__)

        # Test image processor can be reloaded.
        with tempfile.TemporaryDirectory() as tmp_dir:
            video_processor.save_pretrained(tmp_dir)
            reloaded_video_processor = AutoVideoProcessor.from_pretrained(tmp_dir, trust_remote_code=True)
        self.assertEqual(reloaded_video_processor.__class__.__name__, "NewVideoProcessor")

    def test_from_pretrained_dynamic_video_processor_backend_dict(self):
        """Load and save a remote video processor selected from a backend dict."""
        original_auto_class = CustomVideoProcessor._auto_class
        try:
            CustomVideoProcessor.register_for_auto_class()
            with tempfile.TemporaryDirectory() as tmp_dir:
                CustomVideoProcessor().save_pretrained(tmp_dir)
                config_file = Path(tmp_dir) / "video_preprocessor_config.json"
                processor_config = json.loads(config_file.read_text(encoding="utf-8"))
                class_ref = processor_config["auto_map"]["AutoVideoProcessor"]
                processor_config["auto_map"]["AutoVideoProcessor"] = {"torchvision": class_ref}
                config_file.write_text(json.dumps(processor_config), encoding="utf-8")

                processor = AutoVideoProcessor.from_pretrained(tmp_dir, backend="torchvision", trust_remote_code=True)
                self.assertEqual(processor.__class__.__name__, "CustomVideoProcessor")
                with tempfile.TemporaryDirectory() as reload_dir:
                    processor.save_pretrained(reload_dir)
                    reloaded = AutoVideoProcessor.from_pretrained(
                        reload_dir, backend="torchvision", trust_remote_code=True
                    )
                    self.assertDictEqual(processor.to_dict(), reloaded.to_dict())
        finally:
            CustomVideoProcessor._auto_class = original_auto_class

    def test_from_pretrained_dynamic_video_processor_backend_list(self):
        """Load and save a remote video processor selected from a backend list."""
        original_auto_class = CustomVideoProcessor._auto_class
        try:
            CustomVideoProcessor.register_for_auto_class()
            with tempfile.TemporaryDirectory() as tmp_dir:
                CustomVideoProcessor().save_pretrained(tmp_dir)
                config_file = Path(tmp_dir) / "video_preprocessor_config.json"
                processor_config = json.loads(config_file.read_text(encoding="utf-8"))
                class_ref = processor_config["auto_map"]["AutoVideoProcessor"]
                processor_config["auto_map"]["AutoVideoProcessor"] = [None, class_ref]
                config_file.write_text(json.dumps(processor_config), encoding="utf-8")

                processor = AutoVideoProcessor.from_pretrained(tmp_dir, backend="torchvision", trust_remote_code=True)
                self.assertEqual(processor.__class__.__name__, "CustomVideoProcessor")
                with tempfile.TemporaryDirectory() as reload_dir:
                    processor.save_pretrained(reload_dir)
                    reloaded = AutoVideoProcessor.from_pretrained(
                        reload_dir, backend="torchvision", trust_remote_code=True
                    )
                    self.assertDictEqual(processor.to_dict(), reloaded.to_dict())
        finally:
            CustomVideoProcessor._auto_class = original_auto_class

    def test_new_video_processor_registration(self):
        try:
            AutoConfig.register("custom", CustomConfig)
            AutoVideoProcessor.register(CustomConfig, CustomVideoProcessor)
            # Trying to register something existing in the Transformers library will raise an error
            with self.assertRaises(ValueError):
                AutoVideoProcessor.register(LlavaOnevisionConfig, LlavaOnevisionVideoProcessor)

            with tempfile.TemporaryDirectory() as tmpdirname:
                processor_tmpfile = Path(tmpdirname) / "video_preprocessor_config.json"
                config_tmpfile = Path(tmpdirname) / "config.json"
                json.dump(
                    {
                        "video_processor_type": "LlavaOnevisionVideoProcessor",
                        "processor_class": "LlavaOnevisionProcessor",
                    },
                    open(processor_tmpfile, "w", encoding="utf-8"),
                )
                json.dump({"model_type": "llava_onevision"}, open(config_tmpfile, "w", encoding="utf-8"))

                video_processor = CustomVideoProcessor.from_pretrained(tmpdirname)

            # Now that the config is registered, it can be used as any other config with the auto-API
            with tempfile.TemporaryDirectory() as tmp_dir:
                video_processor.save_pretrained(tmp_dir)
                new_video_processor = AutoVideoProcessor.from_pretrained(tmp_dir)
                self.assertIsInstance(new_video_processor, CustomVideoProcessor)

        finally:
            if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in VIDEO_PROCESSOR_MAPPING._extra_content:
                del VIDEO_PROCESSOR_MAPPING._extra_content[CustomConfig]

    def test_video_processor_new_backend_registration(self):
        class Cv2VideoProcessor(LlavaOnevisionVideoProcessor):
            foo = True
            do_rescale = False

        try:
            AutoVideoProcessor.register(
                LlavaOnevisionConfig,
                video_processor_classes={"cv2": Cv2VideoProcessor},
                exist_ok=True,
            )

            with tempfile.TemporaryDirectory() as tmpdirname:
                processor_tmpfile = Path(tmpdirname) / "video_preprocessor_config.json"
                config_tmpfile = Path(tmpdirname) / "config.json"
                json.dump(
                    {
                        "video_processor_type": "LlavaOnevisionVideoProcessor",
                        "processor_class": "LlavaOnevisionProcessor",
                    },
                    open(processor_tmpfile, "w", encoding="utf-8"),
                )
                json.dump({"model_type": "llava_onevision"}, open(config_tmpfile, "w", encoding="utf-8"))

                cv2_processor = AutoVideoProcessor.from_pretrained(tmpdirname, backend="cv2")
                torch_processor = AutoVideoProcessor.from_pretrained(tmpdirname, backend="torchvision")
                default_processor = AutoVideoProcessor.from_pretrained(tmpdirname)

                self.assertEqual(cv2_processor.__class__.__name__, "Cv2VideoProcessor")
                self.assertEqual(cv2_processor.foo, True)

                self.assertEqual(torch_processor.__class__.__name__, "LlavaOnevisionVideoProcessor")
                self.assertFalse(hasattr(torch_processor, "foo"))

                self.assertEqual(default_processor.__class__.__name__, "LlavaOnevisionVideoProcessor")
                self.assertFalse(hasattr(default_processor, "foo"))

            # We can save any custom-backend processor and load it back with any backend
            # As long as we save the `config`, the backend just get chosen at load-time
            with tempfile.TemporaryDirectory() as tmp_dir:
                cv2_processor.save_pretrained(tmp_dir)
                new_video_processor = AutoVideoProcessor.from_pretrained(tmp_dir, backend="torchvision")
                self.assertEqual(new_video_processor.__class__.__name__, "LlavaOnevisionVideoProcessor")
                self.assertFalse(hasattr(new_video_processor, "foo"))
                self.assertFalse(new_video_processor.do_rescale)

                new_video_processor = AutoVideoProcessor.from_pretrained(tmp_dir, backend="cv2")
                self.assertEqual(new_video_processor.__class__.__name__, "Cv2VideoProcessor")
                self.assertTrue(hasattr(new_video_processor, "foo"))
                self.assertFalse(new_video_processor.do_rescale)

        finally:
            if LlavaOnevisionConfig in VIDEO_PROCESSOR_MAPPING._extra_content:
                del VIDEO_PROCESSOR_MAPPING._extra_content[LlavaOnevisionConfig]

    def test_from_pretrained_dynamic_video_processor_conflict(self):
        class NewVideoProcessor(LlavaOnevisionVideoProcessor):
            is_local = True

        try:
            AutoConfig.register("custom", CustomConfig)
            AutoVideoProcessor.register(CustomConfig, NewVideoProcessor)
            # If remote code is not set, the default is to use local
            video_processor = AutoVideoProcessor.from_pretrained("hf-internal-testing/test_dynamic_video_processor")
            self.assertEqual(video_processor.__class__.__name__, "NewVideoProcessor")
            self.assertTrue(video_processor.is_local)

            # If remote code is disabled, we load the local one.
            video_processor = AutoVideoProcessor.from_pretrained(
                "hf-internal-testing/test_dynamic_video_processor", trust_remote_code=False
            )
            self.assertEqual(video_processor.__class__.__name__, "NewVideoProcessor")
            self.assertTrue(video_processor.is_local)

            # If remote code is enabled but the user explicitly registered the local one, we load the local one.
            video_processor = AutoVideoProcessor.from_pretrained(
                "hf-internal-testing/test_dynamic_video_processor", trust_remote_code=True
            )
            self.assertEqual(video_processor.__class__.__name__, "NewVideoProcessor")
            self.assertTrue(video_processor.is_local)

            # If remote code is enabled but local code originated from transformers, we load the remote one.
            NewVideoProcessor.__module__ = "transformers.models.custom.configuration_custom"
            video_processor = AutoVideoProcessor.from_pretrained(
                "hf-internal-testing/test_dynamic_video_processor", trust_remote_code=True
            )
            self.assertEqual(video_processor.__class__.__name__, "NewVideoProcessor")
            self.assertTrue(not hasattr(video_processor, "is_local"))

        finally:
            if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in VIDEO_PROCESSOR_MAPPING._extra_content:
                del VIDEO_PROCESSOR_MAPPING._extra_content[CustomConfig]