"""Code generated by Speakeasy (https://speakeasy.com). DO NOT EDIT."""
# @generated-id: 7e4a07e4bdfd

from __future__ import annotations
from .ocrconfidencescore import OCRConfidenceScore, OCRConfidenceScoreTypedDict
from mistralai.client.types import BaseModel, UNSET_SENTINEL
from pydantic import model_serializer
from typing import List, Optional
from typing_extensions import NotRequired, TypedDict


class OCRPageConfidenceScoresTypedDict(TypedDict):
    r"""Confidence scores for an OCR page at various granularities.

    Note on page-level stats:
    - For 'page' granularity: average/minimum are computed from per-token exp(logprob).
    - For 'word' granularity: average/minimum are computed from per-word confidence,
    where each word's confidence is exp(mean(token_logprobs)) — a geometric mean
    over the word's subword tokens.
    """

    average_page_confidence_score: float
    r"""Average confidence score for the page"""
    minimum_page_confidence_score: float
    r"""Minimum confidence score for the page"""
    word_confidence_scores: NotRequired[List[OCRConfidenceScoreTypedDict]]
    r"""Word-level confidence scores (populated only for 'word' granularity)"""


class OCRPageConfidenceScores(BaseModel):
    r"""Confidence scores for an OCR page at various granularities.

    Note on page-level stats:
    - For 'page' granularity: average/minimum are computed from per-token exp(logprob).
    - For 'word' granularity: average/minimum are computed from per-word confidence,
    where each word's confidence is exp(mean(token_logprobs)) — a geometric mean
    over the word's subword tokens.
    """

    average_page_confidence_score: float
    r"""Average confidence score for the page"""

    minimum_page_confidence_score: float
    r"""Minimum confidence score for the page"""

    word_confidence_scores: Optional[List[OCRConfidenceScore]] = None
    r"""Word-level confidence scores (populated only for 'word' granularity)"""

    @model_serializer(mode="wrap")
    def serialize_model(self, handler):
        optional_fields = set(["word_confidence_scores"])
        serialized = handler(self)
        m = {}

        for n, f in type(self).model_fields.items():
            k = f.alias or n
            val = serialized.get(k, serialized.get(n))

            if val != UNSET_SENTINEL:
                if val is not None or k not in optional_fields:
                    m[k] = val

        return m