# Copyright (c) Alibaba, Inc. and its affiliates.

from collections import OrderedDict

from evalscope.api.benchmark import BenchmarkMeta, VisionLanguageAdapter
from evalscope.api.dataset import Sample
from evalscope.api.evaluator import TaskState
from evalscope.api.messages import ChatMessageUser, Content, ContentAudio, ContentText
from evalscope.api.metric.scorer import Score
from evalscope.api.registry import register_benchmark
from evalscope.constants import Tags
from evalscope.utils.io_utils import bytes_to_base64
from evalscope.utils.logger import get_logger

logger = get_logger()

FLEURS_LANG_TO_ID = OrderedDict([
    ('Afrikaans', 'af'),
    ('Amharic', 'am'),
    ('Arabic', 'ar'),
    ('Armenian', 'hy'),
    ('Assamese', 'as'),
    ('Asturian', 'ast'),
    ('Azerbaijani', 'az'),
    ('Belarusian', 'be'),
    ('Bengali', 'bn'),
    ('Bosnian', 'bs'),
    ('Bulgarian', 'bg'),
    ('Burmese', 'my'),
    ('Catalan', 'ca'),
    ('Cebuano', 'ceb'),
    ('Mandarin Chinese', 'cmn_hans'),
    ('Cantonese Chinese', 'yue_hant'),
    ('Croatian', 'hr'),
    ('Czech', 'cs'),
    ('Danish', 'da'),
    ('Dutch', 'nl'),
    ('English', 'en'),
    ('Estonian', 'et'),
    ('Filipino', 'fil'),
    ('Finnish', 'fi'),
    ('French', 'fr'),
    ('Fula', 'ff'),
    ('Galician', 'gl'),
    ('Ganda', 'lg'),
    ('Georgian', 'ka'),
    ('German', 'de'),
    ('Greek', 'el'),
    ('Gujarati', 'gu'),
    ('Hausa', 'ha'),
    ('Hebrew', 'he'),
    ('Hindi', 'hi'),
    ('Hungarian', 'hu'),
    ('Icelandic', 'is'),
    ('Igbo', 'ig'),
    ('Indonesian', 'id'),
    ('Irish', 'ga'),
    ('Italian', 'it'),
    ('Japanese', 'ja'),
    ('Javanese', 'jv'),
    ('Kabuverdianu', 'kea'),
    ('Kamba', 'kam'),
    ('Kannada', 'kn'),
    ('Kazakh', 'kk'),
    ('Khmer', 'km'),
    ('Korean', 'ko'),
    ('Kyrgyz', 'ky'),
    ('Lao', 'lo'),
    ('Latvian', 'lv'),
    ('Lingala', 'ln'),
    ('Lithuanian', 'lt'),
    ('Luo', 'luo'),
    ('Luxembourgish', 'lb'),
    ('Macedonian', 'mk'),
    ('Malay', 'ms'),
    ('Malayalam', 'ml'),
    ('Maltese', 'mt'),
    ('Maori', 'mi'),
    ('Marathi', 'mr'),
    ('Mongolian', 'mn'),
    ('Nepali', 'ne'),
    ('Northern-Sotho', 'nso'),
    ('Norwegian', 'nb'),
    ('Nyanja', 'ny'),
    ('Occitan', 'oc'),
    ('Oriya', 'or'),
    ('Oromo', 'om'),
    ('Pashto', 'ps'),
    ('Persian', 'fa'),
    ('Polish', 'pl'),
    ('Portuguese', 'pt'),
    ('Punjabi', 'pa'),
    ('Romanian', 'ro'),
    ('Russian', 'ru'),
    ('Serbian', 'sr'),
    ('Shona', 'sn'),
    ('Sindhi', 'sd'),
    ('Slovak', 'sk'),
    ('Slovenian', 'sl'),
    ('Somali', 'so'),
    ('Sorani-Kurdish', 'ckb'),
    ('Spanish', 'es'),
    ('Swahili', 'sw'),
    ('Swedish', 'sv'),
    ('Tajik', 'tg'),
    ('Tamil', 'ta'),
    ('Telugu', 'te'),
    ('Thai', 'th'),
    ('Turkish', 'tr'),
    ('Ukrainian', 'uk'),
    ('Umbundu', 'umb'),
    ('Urdu', 'ur'),
    ('Uzbek', 'uz'),
    ('Vietnamese', 'vi'),
    ('Welsh', 'cy'),
    ('Wolof', 'wo'),
    ('Xhosa', 'xh'),
    ('Yoruba', 'yo'),
    ('Zulu', 'zu'),
])


@register_benchmark(
    BenchmarkMeta(
        name='fleurs',
        pretty_name='FLEURS',
        dataset_id='lmms-lab/fleurs',
        tags=[Tags.AUDIO, Tags.MULTI_LINGUAL, Tags.SPEECH_RECOGNITION],
        description="""
## Overview

FLEURS (Few-shot Learning Evaluation of Universal Representations of Speech) is a massively multilingual benchmark covering 102 languages for evaluating automatic speech recognition (ASR), spoken language understanding, and speech translation.

## Task Description

- **Task Type**: Automatic Speech Recognition (ASR)
- **Input**: Audio recordings with speech in various languages
- **Output**: Transcribed text in the corresponding language
- **Languages**: 102 languages including Mandarin Chinese, Cantonese, English, and many more

## Key Features

- Massive multilingual coverage (102 languages)
- Derived from FLoRes-101 machine translation benchmark
- Includes diverse language families and scripts
- High-quality human recordings and transcriptions
- Metadata includes gender, language group, and speaker information

## Evaluation Notes

- Default configuration uses **test** split
- Primary metric: **Word Error Rate (WER)**
- Default subsets: `cmn_hans_cn` (Mandarin), `en_us` (English), `yue_hant_hk` (Cantonese)
- Language-specific text normalization applied during evaluation
- Prompt: "Please recognize the speech and only output the recognized content"
""",  # noqa: E501
        subset_list=['cmn_hans_cn', 'en_us', 'yue_hant_hk'],
        eval_split='test',
        metric_list=['wer'],
        prompt_template='Please recognize the speech and only output the recognized content:',
    )
)
class FLEURSAdapter(VisionLanguageAdapter):

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def record_to_sample(self, record) -> Sample:
        content_list = [ContentText(text=self.prompt_template)]
        audio_base64 = bytes_to_base64(record['audio']['bytes'], format='wav', add_header=True, content_type='audio')
        content_list.append(ContentAudio(audio=audio_base64, format='wav'))

        return Sample(
            input=[ChatMessageUser(content=content_list)],
            target=record['transcription'],
            metadata={
                'id': record['id'],
                'num_samples': record['num_samples'],
                'raw_transcription': record['raw_transcription'],
                'language': record['language'],
                'gender': record['gender'],
                'lang_id': FLEURS_LANG_TO_ID[record['language']],
                'lang_group_id': record['lang_group_id'],
            }
        )

    def match_score(self, original_prediction, filtered_prediction, reference, task_state):
        from evalscope.metrics.text_normalizer.wer import normalize_text, wer

        language = task_state.metadata['lang_id']

        normalized_prediction = normalize_text(original_prediction, language)
        normalized_reference = normalize_text(reference, language)
        score = Score(
            extracted_prediction=normalized_prediction,
            prediction=original_prediction,
        )

        wer_score = wer([normalized_reference], [normalized_prediction], language)
        score.value = {'wer': wer_score}
        return score
