"""Code generated by Speakeasy (https://speakeasy.com). DO NOT EDIT."""

from __future__ import annotations
from openrouter.types import BaseModel, UNSET_SENTINEL
from pydantic import model_serializer
from typing import List, Optional
from typing_extensions import NotRequired, TypedDict


class STTSegmentTypedDict(TypedDict):
    r"""A timestamped transcript segment, returned when response_format is verbose_json"""

    end: float
    r"""Segment end time in seconds"""
    id: int
    r"""Segment index within the transcript"""
    start: float
    r"""Segment start time in seconds"""
    text: str
    r"""Transcribed text of the segment"""
    avg_logprob: NotRequired[float]
    r"""Average log probability of the segment"""
    compression_ratio: NotRequired[float]
    r"""Compression ratio of the segment"""
    no_speech_prob: NotRequired[float]
    r"""Probability the segment contains no speech"""
    seek: NotRequired[int]
    r"""Seek offset of the segment"""
    temperature: NotRequired[float]
    r"""Temperature used for the segment"""
    tokens: NotRequired[List[int]]
    r"""Token IDs of the segment"""


class STTSegment(BaseModel):
    r"""A timestamped transcript segment, returned when response_format is verbose_json"""

    end: float
    r"""Segment end time in seconds"""

    id: int
    r"""Segment index within the transcript"""

    start: float
    r"""Segment start time in seconds"""

    text: str
    r"""Transcribed text of the segment"""

    avg_logprob: Optional[float] = None
    r"""Average log probability of the segment"""

    compression_ratio: Optional[float] = None
    r"""Compression ratio of the segment"""

    no_speech_prob: Optional[float] = None
    r"""Probability the segment contains no speech"""

    seek: Optional[int] = None
    r"""Seek offset of the segment"""

    temperature: Optional[float] = None
    r"""Temperature used for the segment"""

    tokens: Optional[List[int]] = None
    r"""Token IDs of the segment"""

    @model_serializer(mode="wrap")
    def serialize_model(self, handler):
        optional_fields = set(
            [
                "avg_logprob",
                "compression_ratio",
                "no_speech_prob",
                "seek",
                "temperature",
                "tokens",
            ]
        )
        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