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# reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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import shutil
import tempfile
import unittest

from parameterized import parameterized

from transformers import (
    AutoProcessor,
    AutoTokenizer,
    MusicFlamingoProcessor,
    WhisperFeatureExtractor,
)
from transformers.testing_utils import require_librosa, require_torch, require_torchaudio

from ...test_processing_common import MODALITY_INPUT_DATA, ProcessorTesterMixin


class MusicFlamingoProcessorTest(ProcessorTesterMixin, unittest.TestCase):
    processor_class = MusicFlamingoProcessor

    @classmethod
    @require_torch
    @require_torchaudio
    def setUpClass(cls):
        cls.checkpoint = "nvidia/music-flamingo-2601-hf"
        cls.tmpdirname = tempfile.mkdtemp()

        processor = MusicFlamingoProcessor.from_pretrained(cls.checkpoint)
        processor.save_pretrained(cls.tmpdirname)

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

    @require_torch
    @require_torchaudio
    def get_audio_processor(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).audio_processor

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

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

    @require_torch
    @require_torchaudio
    def test_can_load_various_tokenizers(self):
        processor = MusicFlamingoProcessor.from_pretrained(self.checkpoint)
        tokenizer = AutoTokenizer.from_pretrained(self.checkpoint)
        self.assertEqual(processor.tokenizer.__class__, tokenizer.__class__)

    @require_torch
    @require_torchaudio
    def test_save_load_pretrained_default(self):
        tokenizer = AutoTokenizer.from_pretrained(self.checkpoint)
        processor = MusicFlamingoProcessor.from_pretrained(self.checkpoint)
        feature_extractor = processor.feature_extractor

        processor = MusicFlamingoProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)

        with tempfile.TemporaryDirectory() as tmpdir:
            processor.save_pretrained(tmpdir)
            reloaded = MusicFlamingoProcessor.from_pretrained(tmpdir)

        self.assertEqual(reloaded.tokenizer.get_vocab(), tokenizer.get_vocab())
        self.assertEqual(reloaded.feature_extractor.to_json_string(), feature_extractor.to_json_string())
        self.assertIsInstance(reloaded.feature_extractor, WhisperFeatureExtractor)

    @require_torch
    @require_torchaudio
    def test_tokenizer_integration(self):
        slow_tokenizer = AutoTokenizer.from_pretrained(self.checkpoint, use_fast=False)
        fast_tokenizer = AutoTokenizer.from_pretrained(self.checkpoint, from_slow=True, legacy=False)

        prompt = (
            "<|im_start|>system\nAnswer the questions.<|im_end|>"
            "<|im_start|>user\n<sound>What is it?<|im_end|>"
            "<|im_start|>assistant\n"
        )
        EXPECTED_OUTPUT = [
            "<|im_start|>",
            "system",
            "Ċ",
            "Answer",
            "Ġthe",
            "Ġquestions",
            ".",
            "<|im_end|>",
            "<|im_start|>",
            "user",
            "Ċ",
            "<sound>",
            "What",
            "Ġis",
            "Ġit",
            "?",
            "<|im_end|>",
            "<|im_start|>",
            "assistant",
            "Ċ",
        ]

        self.assertEqual(slow_tokenizer.tokenize(prompt), EXPECTED_OUTPUT)
        self.assertEqual(fast_tokenizer.tokenize(prompt), EXPECTED_OUTPUT)

    @require_torch
    @require_torchaudio
    def test_chat_template(self):
        processor = AutoProcessor.from_pretrained(self.checkpoint)
        expected_prompt = (
            "<|im_start|>system\nYou are Music Flamingo, a multimodal assistant for language and music. "
            "On each turn you receive an audio clip which contains music and optional text, "
            "you will receive at least one or both; use your world knowledge and reasoning "
            "to help the user with any task. Interpret the entirety of the content any input music"
            "--regardlenss of whether the user calls it audio, music, or sound.<|im_end|>\n"
            "<|im_start|>user\n<sound>What is surprising about the relationship between the barking and the music?<|im_end|>\n"
            "<|im_start|>assistant\n"
        )

        conversations = [
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": "What is surprising about the relationship between the barking and the music?",
                    },
                    {
                        "type": "audio",
                        "path": "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/dogs_barking_in_sync_with_the_music.wav",
                    },
                ],
            }
        ]

        formatted = processor.tokenizer.apply_chat_template(conversations, tokenize=False, add_generation_prompt=True)
        self.assertEqual(expected_prompt, formatted)

    @require_torch
    @require_torchaudio
    def test_transcription_helpers_not_supported(self):
        processor = AutoProcessor.from_pretrained(self.checkpoint)
        self.assertFalse(hasattr(processor, "apply_transcription_request"))
        self.assertFalse(hasattr(processor, "_strip_assistant_prefix_and_quotes"))

    # Overwrite to remove skip numpy inputs (still need to keep as many cases as parent)
    @require_librosa
    @parameterized.expand([(1, "np"), (1, "pt"), (2, "np"), (2, "pt")])
    def test_apply_chat_template_audio(self, batch_size: int, return_tensors: str):
        if return_tensors == "np":
            self.skipTest("MusicFlamingo only supports PyTorch tensors")
        self._test_apply_chat_template(
            "audio", batch_size, return_tensors, "audio_input_name", "feature_extractor", MODALITY_INPUT_DATA["audio"]
        )
