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

from typing import Dict, List, Union, Optional
from typing_extensions import Literal, Annotated, TypeAlias

from pydantic import Field as FieldInfo

from ..._utils import PropertyInfo
from ..._models import BaseModel
from .eval_api_error import EvalAPIError
from ..responses.tool import Tool
from ..shared.metadata import Metadata
from ..shared.reasoning_effort import ReasoningEffort
from ..responses.response_input_text import ResponseInputText
from ..responses.response_input_audio import ResponseInputAudio
from .create_eval_jsonl_run_data_source import CreateEvalJSONLRunDataSource
from ..responses.response_format_text_config import ResponseFormatTextConfig
from .create_eval_completions_run_data_source import CreateEvalCompletionsRunDataSource

__all__ = [
    "RunListResponse",
    "DataSource",
    "DataSourceResponses",
    "DataSourceResponsesSource",
    "DataSourceResponsesSourceFileContent",
    "DataSourceResponsesSourceFileContentContent",
    "DataSourceResponsesSourceFileID",
    "DataSourceResponsesSourceResponses",
    "DataSourceResponsesInputMessages",
    "DataSourceResponsesInputMessagesTemplate",
    "DataSourceResponsesInputMessagesTemplateTemplate",
    "DataSourceResponsesInputMessagesTemplateTemplateChatMessage",
    "DataSourceResponsesInputMessagesTemplateTemplateEvalItem",
    "DataSourceResponsesInputMessagesTemplateTemplateEvalItemContent",
    "DataSourceResponsesInputMessagesTemplateTemplateEvalItemContentOutputText",
    "DataSourceResponsesInputMessagesTemplateTemplateEvalItemContentInputImage",
    "DataSourceResponsesInputMessagesItemReference",
    "DataSourceResponsesSamplingParams",
    "DataSourceResponsesSamplingParamsText",
    "PerModelUsage",
    "PerTestingCriteriaResult",
    "ResultCounts",
]


class DataSourceResponsesSourceFileContentContent(BaseModel):
    item: Dict[str, object]

    sample: Optional[Dict[str, object]] = None


class DataSourceResponsesSourceFileContent(BaseModel):
    content: List[DataSourceResponsesSourceFileContentContent]
    """The content of the jsonl file."""

    type: Literal["file_content"]
    """The type of jsonl source. Always `file_content`."""


class DataSourceResponsesSourceFileID(BaseModel):
    id: str
    """The identifier of the file."""

    type: Literal["file_id"]
    """The type of jsonl source. Always `file_id`."""


class DataSourceResponsesSourceResponses(BaseModel):
    type: Literal["responses"]
    """The type of run data source. Always `responses`."""

    created_after: Optional[int] = None
    """Only include items created after this timestamp (inclusive).

    This is a query parameter used to select responses.
    """

    created_before: Optional[int] = None
    """Only include items created before this timestamp (inclusive).

    This is a query parameter used to select responses.
    """

    instructions_search: Optional[str] = None
    """Optional string to search the 'instructions' field.

    This is a query parameter used to select responses.
    """

    metadata: Optional[object] = None
    """Metadata filter for the responses.

    This is a query parameter used to select responses.
    """

    model: Optional[str] = None
    """The name of the model to find responses for.

    This is a query parameter used to select responses.
    """

    reasoning_effort: Optional[ReasoningEffort] = None
    """
    Constrains effort on reasoning for
    [reasoning models](https://platform.openai.com/docs/guides/reasoning). Currently
    supported values are `minimal`, `low`, `medium`, and `high`. Reducing reasoning
    effort can result in faster responses and fewer tokens used on reasoning in a
    response.

    Note: The `gpt-5-pro` model defaults to (and only supports) `high` reasoning
    effort.
    """

    temperature: Optional[float] = None
    """Sampling temperature. This is a query parameter used to select responses."""

    tools: Optional[List[str]] = None
    """List of tool names. This is a query parameter used to select responses."""

    top_p: Optional[float] = None
    """Nucleus sampling parameter. This is a query parameter used to select responses."""

    users: Optional[List[str]] = None
    """List of user identifiers. This is a query parameter used to select responses."""


DataSourceResponsesSource: TypeAlias = Annotated[
    Union[DataSourceResponsesSourceFileContent, DataSourceResponsesSourceFileID, DataSourceResponsesSourceResponses],
    PropertyInfo(discriminator="type"),
]


class DataSourceResponsesInputMessagesTemplateTemplateChatMessage(BaseModel):
    content: str
    """The content of the message."""

    role: str
    """The role of the message (e.g. "system", "assistant", "user")."""


class DataSourceResponsesInputMessagesTemplateTemplateEvalItemContentOutputText(BaseModel):
    text: str
    """The text output from the model."""

    type: Literal["output_text"]
    """The type of the output text. Always `output_text`."""


class DataSourceResponsesInputMessagesTemplateTemplateEvalItemContentInputImage(BaseModel):
    image_url: str
    """The URL of the image input."""

    type: Literal["input_image"]
    """The type of the image input. Always `input_image`."""

    detail: Optional[str] = None
    """The detail level of the image to be sent to the model.

    One of `high`, `low`, or `auto`. Defaults to `auto`.
    """


DataSourceResponsesInputMessagesTemplateTemplateEvalItemContent: TypeAlias = Union[
    str,
    ResponseInputText,
    DataSourceResponsesInputMessagesTemplateTemplateEvalItemContentOutputText,
    DataSourceResponsesInputMessagesTemplateTemplateEvalItemContentInputImage,
    ResponseInputAudio,
    List[object],
]


class DataSourceResponsesInputMessagesTemplateTemplateEvalItem(BaseModel):
    content: DataSourceResponsesInputMessagesTemplateTemplateEvalItemContent
    """Inputs to the model - can contain template strings."""

    role: Literal["user", "assistant", "system", "developer"]
    """The role of the message input.

    One of `user`, `assistant`, `system`, or `developer`.
    """

    type: Optional[Literal["message"]] = None
    """The type of the message input. Always `message`."""


DataSourceResponsesInputMessagesTemplateTemplate: TypeAlias = Union[
    DataSourceResponsesInputMessagesTemplateTemplateChatMessage,
    DataSourceResponsesInputMessagesTemplateTemplateEvalItem,
]


class DataSourceResponsesInputMessagesTemplate(BaseModel):
    template: List[DataSourceResponsesInputMessagesTemplateTemplate]
    """A list of chat messages forming the prompt or context.

    May include variable references to the `item` namespace, ie {{item.name}}.
    """

    type: Literal["template"]
    """The type of input messages. Always `template`."""


class DataSourceResponsesInputMessagesItemReference(BaseModel):
    item_reference: str
    """A reference to a variable in the `item` namespace. Ie, "item.name" """

    type: Literal["item_reference"]
    """The type of input messages. Always `item_reference`."""


DataSourceResponsesInputMessages: TypeAlias = Annotated[
    Union[DataSourceResponsesInputMessagesTemplate, DataSourceResponsesInputMessagesItemReference],
    PropertyInfo(discriminator="type"),
]


class DataSourceResponsesSamplingParamsText(BaseModel):
    format: Optional[ResponseFormatTextConfig] = None
    """An object specifying the format that the model must output.

    Configuring `{ "type": "json_schema" }` enables Structured Outputs, which
    ensures the model will match your supplied JSON schema. Learn more in the
    [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).

    The default format is `{ "type": "text" }` with no additional options.

    **Not recommended for gpt-4o and newer models:**

    Setting to `{ "type": "json_object" }` enables the older JSON mode, which
    ensures the message the model generates is valid JSON. Using `json_schema` is
    preferred for models that support it.
    """


class DataSourceResponsesSamplingParams(BaseModel):
    max_completion_tokens: Optional[int] = None
    """The maximum number of tokens in the generated output."""

    reasoning_effort: Optional[ReasoningEffort] = None
    """
    Constrains effort on reasoning for
    [reasoning models](https://platform.openai.com/docs/guides/reasoning). Currently
    supported values are `minimal`, `low`, `medium`, and `high`. Reducing reasoning
    effort can result in faster responses and fewer tokens used on reasoning in a
    response.

    Note: The `gpt-5-pro` model defaults to (and only supports) `high` reasoning
    effort.
    """

    seed: Optional[int] = None
    """A seed value to initialize the randomness, during sampling."""

    temperature: Optional[float] = None
    """A higher temperature increases randomness in the outputs."""

    text: Optional[DataSourceResponsesSamplingParamsText] = None
    """Configuration options for a text response from the model.

    Can be plain text or structured JSON data. Learn more:

    - [Text inputs and outputs](https://platform.openai.com/docs/guides/text)
    - [Structured Outputs](https://platform.openai.com/docs/guides/structured-outputs)
    """

    tools: Optional[List[Tool]] = None
    """An array of tools the model may call while generating a response.

    You can specify which tool to use by setting the `tool_choice` parameter.

    The two categories of tools you can provide the model are:

    - **Built-in tools**: Tools that are provided by OpenAI that extend the model's
      capabilities, like
      [web search](https://platform.openai.com/docs/guides/tools-web-search) or
      [file search](https://platform.openai.com/docs/guides/tools-file-search).
      Learn more about
      [built-in tools](https://platform.openai.com/docs/guides/tools).
    - **Function calls (custom tools)**: Functions that are defined by you, enabling
      the model to call your own code. Learn more about
      [function calling](https://platform.openai.com/docs/guides/function-calling).
    """

    top_p: Optional[float] = None
    """An alternative to temperature for nucleus sampling; 1.0 includes all tokens."""


class DataSourceResponses(BaseModel):
    source: DataSourceResponsesSource
    """Determines what populates the `item` namespace in this run's data source."""

    type: Literal["responses"]
    """The type of run data source. Always `responses`."""

    input_messages: Optional[DataSourceResponsesInputMessages] = None
    """Used when sampling from a model.

    Dictates the structure of the messages passed into the model. Can either be a
    reference to a prebuilt trajectory (ie, `item.input_trajectory`), or a template
    with variable references to the `item` namespace.
    """

    model: Optional[str] = None
    """The name of the model to use for generating completions (e.g. "o3-mini")."""

    sampling_params: Optional[DataSourceResponsesSamplingParams] = None


DataSource: TypeAlias = Annotated[
    Union[CreateEvalJSONLRunDataSource, CreateEvalCompletionsRunDataSource, DataSourceResponses],
    PropertyInfo(discriminator="type"),
]


class PerModelUsage(BaseModel):
    cached_tokens: int
    """The number of tokens retrieved from cache."""

    completion_tokens: int
    """The number of completion tokens generated."""

    invocation_count: int
    """The number of invocations."""

    run_model_name: str = FieldInfo(alias="model_name")
    """The name of the model."""

    prompt_tokens: int
    """The number of prompt tokens used."""

    total_tokens: int
    """The total number of tokens used."""


class PerTestingCriteriaResult(BaseModel):
    failed: int
    """Number of tests failed for this criteria."""

    passed: int
    """Number of tests passed for this criteria."""

    testing_criteria: str
    """A description of the testing criteria."""


class ResultCounts(BaseModel):
    errored: int
    """Number of output items that resulted in an error."""

    failed: int
    """Number of output items that failed to pass the evaluation."""

    passed: int
    """Number of output items that passed the evaluation."""

    total: int
    """Total number of executed output items."""


class RunListResponse(BaseModel):
    id: str
    """Unique identifier for the evaluation run."""

    created_at: int
    """Unix timestamp (in seconds) when the evaluation run was created."""

    data_source: DataSource
    """Information about the run's data source."""

    error: EvalAPIError
    """An object representing an error response from the Eval API."""

    eval_id: str
    """The identifier of the associated evaluation."""

    metadata: Optional[Metadata] = None
    """Set of 16 key-value pairs that can be attached to an object.

    This can be useful for storing additional information about the object in a
    structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with
    a maximum length of 512 characters.
    """

    model: str
    """The model that is evaluated, if applicable."""

    name: str
    """The name of the evaluation run."""

    object: Literal["eval.run"]
    """The type of the object. Always "eval.run"."""

    per_model_usage: List[PerModelUsage]
    """Usage statistics for each model during the evaluation run."""

    per_testing_criteria_results: List[PerTestingCriteriaResult]
    """Results per testing criteria applied during the evaluation run."""

    report_url: str
    """The URL to the rendered evaluation run report on the UI dashboard."""

    result_counts: ResultCounts
    """Counters summarizing the outcomes of the evaluation run."""

    status: str
    """The status of the evaluation run."""
