# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.

from typing import Optional

from .._models import BaseModel

__all__ = ["CompletionUsage", "CompletionTokensDetails", "PromptTokensDetails"]


class CompletionTokensDetails(BaseModel):
    """Breakdown of tokens used in a completion."""

    accepted_prediction_tokens: Optional[int] = None
    """
    When using Predicted Outputs, the number of tokens in the prediction that
    appeared in the completion.
    """

    audio_tokens: Optional[int] = None
    """Audio input tokens generated by the model."""

    reasoning_tokens: Optional[int] = None
    """Tokens generated by the model for reasoning."""

    rejected_prediction_tokens: Optional[int] = None
    """
    When using Predicted Outputs, the number of tokens in the prediction that did
    not appear in the completion. However, like reasoning tokens, these tokens are
    still counted in the total completion tokens for purposes of billing, output,
    and context window limits.
    """


class PromptTokensDetails(BaseModel):
    """Breakdown of tokens used in the prompt."""

    audio_tokens: Optional[int] = None
    """Audio input tokens present in the prompt."""

    cached_tokens: Optional[int] = None
    """Cached tokens present in the prompt."""


class CompletionUsage(BaseModel):
    """Usage statistics for the completion request."""

    completion_tokens: int
    """Number of tokens in the generated completion."""

    prompt_tokens: int
    """Number of tokens in the prompt."""

    total_tokens: int
    """Total number of tokens used in the request (prompt + completion)."""

    completion_tokens_details: Optional[CompletionTokensDetails] = None
    """Breakdown of tokens used in a completion."""

    prompt_tokens_details: Optional[PromptTokensDetails] = None
    """Breakdown of tokens used in the prompt."""
