import ast
import json
import numpy as np
from typing import Any, Dict, List

from evalscope.api.benchmark import BenchmarkMeta, VisionLanguageAdapter
from evalscope.api.dataset import Sample
from evalscope.api.messages import ChatMessageUser, Content, ContentImage, ContentText
from evalscope.api.metric.scorer import AggScore, SampleScore, Score
from evalscope.api.registry import register_benchmark
from evalscope.constants import Tags
from evalscope.utils.import_utils import check_import
from evalscope.utils.logger import get_logger

logger = get_logger()

PROMPT_TEMPLATE = r""" You are an AI assistant specialized in converting PDF images to Markdown format. Please follow these instructions for the conversion:

    1. Text Processing:
    - Accurately recognize all text content in the PDF image without guessing or inferring.
    - Convert the recognized text into Markdown format.
    - Maintain the original document structure, including headings, paragraphs, lists, etc.

    2. Mathematical Formula Processing:
    - Convert all mathematical formulas to LaTeX format.
    - Enclose inline formulas with \( \). For example: This is an inline formula \( E = mc^2 \)
    - Enclose block formulas with \\[ \\]. For example: \[ \frac{-b \pm \sqrt{b^2 - 4ac}}{2a} \]

    3. Table Processing:
    - Convert tables to HTML format.
    - Wrap the entire table with <table> and </table>.

    4. Figure Handling:
    - Ignore figures content in the PDF image. Do not attempt to describe or convert images.

    5. Output Format:
    - Ensure the output Markdown document has a clear structure with appropriate line breaks between elements.
    - For complex layouts, try to maintain the original document's structure and format as closely as possible.

    Please strictly follow these guidelines to ensure accuracy and consistency in the conversion. Your task is to accurately convert the content of the PDF image into Markdown format without adding any extra explanations or comments.
"""  # noqa: E501


@register_benchmark(
    BenchmarkMeta(
        name='omni_doc_bench',
        pretty_name='OmniDocBench',
        tags=[Tags.MULTI_MODAL, Tags.KNOWLEDGE, Tags.QA],
        description="""
## Overview

OmniDocBench is an evaluation dataset for diverse document parsing in real-world scenarios, covering 9 document types, 4 layout types, and 3 language types with 1,355 PDF pages.

## Task Description

- **Task Type**: Document Parsing and Understanding
- **Input**: PDF page image
- **Output**: Parsed document structure in Markdown format
- **Domain**: Document understanding, OCR, layout analysis

## Key Features

- 1,355 PDF pages across 9 document types
- Rich annotations: 15 block-level and 4 span-level element types
- Over 20k block-level and 80k span-level annotations
- Reading order annotations
- Coverage: academic papers, financial reports, newspapers, textbooks, handwritten notes

## Evaluation Notes

- Implements `end2end` and `quick_match` methods from official OmniDocBench-v1.5
- Metrics: Edit_dist, BLEU, METEOR (text), TEDS (tables)
- Requires: apted, distance, lxml, Polygon3, zss, rapidfuzz packages
- Output format: Markdown with LaTeX formulas and HTML tables
""",  # noqa: E501
        dataset_id='evalscope/OmniDocBench_tsv',
        metric_list=[
            {
                'text_block': {
                    'metric': ['Edit_dist', 'BLEU', 'METEOR']
                }
            },
            {
                'display_formula': {
                    'metric': ['Edit_dist']
                }
            },
            {
                'table': {
                    'metric': ['TEDS', 'Edit_dist']
                }
            },
            {
                'reading_order': {
                    'metric': ['Edit_dist']
                }
            },
        ],
        eval_split='train',
        prompt_template=PROMPT_TEMPLATE,
        extra_params={
            'match_method': {
                'type': 'str',
                'description': 'Scoring match method used for evaluation.',
                'value': 'quick_match',
                'choices': ['quick_match', 'simple_match', 'no_split']
            }
        }
    )
)
class OmniDocBenchAdapter(VisionLanguageAdapter):

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.add_aggregation_name = False
        self.match_method = self.extra_params.get('match_method', 'quick_match')

        check_import(
            module_name=['apted', 'distance', 'Levenshtein', 'lxml', 'bs4', 'jieba'],
            extra='omnidoc_bench',
            raise_error=True,
            feature_name='OmniDocBench'
        )

    def record_to_sample(self, record) -> Sample:
        content_list: List[Content] = [ContentText(text=self.prompt_template)]
        content_list.append(ContentImage(image=f'data:image/png;base64,{record["image"]}'))

        try:
            metadata = json.loads(record['answer'])
        except json.JSONDecodeError:
            metadata = ast.literal_eval(record['answer'])

        return Sample(input=[ChatMessageUser(content=content_list)], target='', metadata=metadata)

    def match_score(self, original_prediction, filtered_prediction, reference, task_state) -> Score:
        # Dummy implementation to comply with the interface

        score = Score(
            prediction=original_prediction,
            extracted_prediction=filtered_prediction,
        )

        return score

    def aggregate_scores(self, sample_scores: List[SampleScore]) -> List[AggScore]:
        from .end2end_eval import End2EndEvaluator

        if not sample_scores:
            return []

        predictions = [s.score.prediction for s in sample_scores]
        references = [s.sample_metadata for s in sample_scores]

        metric_dict = {
            metric_name: metric_params
            for metric in self.metric_list
            for metric_name, metric_params in metric.items()
        }
        evaluator = End2EndEvaluator(
            prediction=predictions,
            reference=references,
            metrics=metric_dict,
            match_method=self.match_method,
        )
        agg_results = evaluator.score()

        agg_scores = []
        for metric_name, agg_result in agg_results.items():
            if agg_result is not np.nan:
                agg_score = AggScore(
                    score=agg_result,
                    metric_name=metric_name,
                    num=len(sample_scores),
                )
                agg_scores.append(agg_score)

        return agg_scores
