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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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import shutil
import tempfile
import unittest

from transformers import AutoProcessor, AutoTokenizer, Qwen2AudioProcessor, WhisperFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_processing_common import ProcessorTesterMixin, url_to_local_path


@require_torch
@require_torchaudio
class Qwen2AudioProcessorTest(ProcessorTesterMixin, unittest.TestCase):
    processor_class = Qwen2AudioProcessor

    @classmethod
    def setUpClass(cls):
        cls.checkpoint = "Qwen/Qwen2-Audio-7B-Instruct"
        cls.tmpdirname = tempfile.mkdtemp()

        processor = Qwen2AudioProcessor.from_pretrained(cls.checkpoint)
        processor.save_pretrained(cls.tmpdirname)
        cls.audio_token = processor.audio_token

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

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

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

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

    def test_can_load_various_tokenizers(self):
        processor = Qwen2AudioProcessor.from_pretrained(self.checkpoint)
        tokenizer = AutoTokenizer.from_pretrained(self.checkpoint)
        self.assertEqual(processor.tokenizer.__class__, tokenizer.__class__)

    def test_save_load_pretrained_default(self):
        tokenizer = AutoTokenizer.from_pretrained(self.checkpoint)
        processor = Qwen2AudioProcessor.from_pretrained(self.checkpoint)
        feature_extractor = processor.feature_extractor

        processor = Qwen2AudioProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)

        with tempfile.TemporaryDirectory() as tmpdir:
            processor.save_pretrained(tmpdir)
            processor = Qwen2AudioProcessor.from_pretrained(tmpdir)

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

    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<|audio_bos|><|AUDIO|><|audio_eos|>\nWhat is it in this audio?<|im_end|><|im_start|>assistant\n"
        EXPECTED_OUTPUT = [
            "<|im_start|>",
            "system",
            "Ċ",
            "Answer",
            "Ġthe",
            "Ġquestions",
            ".",
            "<|im_end|>",
            "<|im_start|>",
            "user",
            "Ċ",
            "<|audio_bos|>",
            "<|AUDIO|>",
            "<|audio_eos|>",
            "Ċ",
            "What",
            "Ġis",
            "Ġit",
            "Ġin",
            "Ġthis",
            "Ġaudio",
            "?",
            "<|im_end|>",
            "<|im_start|>",
            "assistant",
            "Ċ",
        ]

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

    def test_chat_template(self):
        processor = AutoProcessor.from_pretrained(self.checkpoint)
        expected_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nAudio 1: <|audio_bos|><|AUDIO|><|audio_eos|>\nWhat's that sound?<|im_end|>\n<|im_start|>assistant\nIt is the sound of glass shattering.<|im_end|>\n<|im_start|>user\nAudio 2: <|audio_bos|><|AUDIO|><|audio_eos|>\nHow about this one?<|im_end|>\n<|im_start|>assistant\n"

        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {
                "role": "user",
                "content": [
                    {
                        "type": "audio",
                        "audio_url": url_to_local_path(
                            "https://huggingface.co/datasets/raushan-testing-hf/audio-test/resolve/main/glass-breaking-151256.mp3"
                        ),
                    },
                    {"type": "text", "text": "What's that sound?"},
                ],
            },
            {"role": "assistant", "content": "It is the sound of glass shattering."},
            {
                "role": "user",
                "content": [
                    {
                        "type": "audio",
                        "audio_url": url_to_local_path(
                            "https://huggingface.co/datasets/raushan-testing-hf/audio-test/resolve/main/f2641_0_throatclearing.wav"
                        ),
                    },
                    {"type": "text", "text": "How about this one?"},
                ],
            },
        ]

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