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

from __future__ import annotations

from typing import List, Union, Iterable, Optional
from typing_extensions import Literal, Required, Annotated, TypeAlias, TypedDict

from ..._utils import PropertyInfo
from ..model_param import ModelParam
from .beta_tool_param import BetaToolParam
from .beta_message_param import BetaMessageParam
from ..anthropic_beta_param import AnthropicBetaParam
from .beta_text_block_param import BetaTextBlockParam
from .beta_mcp_toolset_param import BetaMCPToolsetParam
from .beta_tool_choice_param import BetaToolChoiceParam
from .beta_output_config_param import BetaOutputConfigParam
from .beta_thinking_config_param import BetaThinkingConfigParam
from .beta_json_output_format_param import BetaJSONOutputFormatParam
from .beta_tool_bash_20241022_param import BetaToolBash20241022Param
from .beta_tool_bash_20250124_param import BetaToolBash20250124Param
from .beta_memory_tool_20250818_param import BetaMemoryTool20250818Param
from .beta_advisor_tool_20260301_param import BetaAdvisorTool20260301Param
from .beta_cache_control_ephemeral_param import BetaCacheControlEphemeralParam
from .beta_web_fetch_tool_20250910_param import BetaWebFetchTool20250910Param
from .beta_web_fetch_tool_20260209_param import BetaWebFetchTool20260209Param
from .beta_web_fetch_tool_20260309_param import BetaWebFetchTool20260309Param
from .beta_web_fetch_tool_20260318_param import BetaWebFetchTool20260318Param
from .beta_web_search_tool_20250305_param import BetaWebSearchTool20250305Param
from .beta_web_search_tool_20260209_param import BetaWebSearchTool20260209Param
from .beta_web_search_tool_20260318_param import BetaWebSearchTool20260318Param
from .beta_context_management_config_param import BetaContextManagementConfigParam
from .beta_tool_text_editor_20241022_param import BetaToolTextEditor20241022Param
from .beta_tool_text_editor_20250124_param import BetaToolTextEditor20250124Param
from .beta_tool_text_editor_20250429_param import BetaToolTextEditor20250429Param
from .beta_tool_text_editor_20250728_param import BetaToolTextEditor20250728Param
from .beta_tool_computer_use_20241022_param import BetaToolComputerUse20241022Param
from .beta_tool_computer_use_20250124_param import BetaToolComputerUse20250124Param
from .beta_tool_computer_use_20251124_param import BetaToolComputerUse20251124Param
from .beta_code_execution_tool_20250522_param import BetaCodeExecutionTool20250522Param
from .beta_code_execution_tool_20250825_param import BetaCodeExecutionTool20250825Param
from .beta_code_execution_tool_20260120_param import BetaCodeExecutionTool20260120Param
from .beta_code_execution_tool_20260521_param import BetaCodeExecutionTool20260521Param
from .beta_tool_search_tool_bm25_20251119_param import BetaToolSearchToolBm25_20251119Param
from .beta_tool_search_tool_regex_20251119_param import BetaToolSearchToolRegex20251119Param
from .beta_request_mcp_server_url_definition_param import BetaRequestMCPServerURLDefinitionParam

__all__ = ["MessageCountTokensParams", "Tool"]


class MessageCountTokensParams(TypedDict, total=False):
    messages: Required[Iterable[BetaMessageParam]]
    """Input messages.

    Our models are trained to operate on alternating `user` and `assistant`
    conversational turns. When creating a new `Message`, you specify the prior
    conversational turns with the `messages` parameter, and the model then generates
    the next `Message` in the conversation. Consecutive `user` or `assistant` turns
    in your request will be combined into a single turn.

    Each input message must be an object with a `role` and `content`. You can
    specify a single `user`-role message, or you can include multiple `user` and
    `assistant` messages.

    If the final message uses the `assistant` role, the response content will
    continue immediately from the content in that message. This can be used to
    constrain part of the model's response.

    Example with a single `user` message:

    ```json
    [{ "role": "user", "content": "Hello, Claude" }]
    ```

    Example with multiple conversational turns:

    ```json
    [
      { "role": "user", "content": "Hello there." },
      { "role": "assistant", "content": "Hi, I'm Claude. How can I help you?" },
      { "role": "user", "content": "Can you explain LLMs in plain English?" }
    ]
    ```

    Example with a partially-filled response from Claude:

    ```json
    [
      {
        "role": "user",
        "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun"
      },
      { "role": "assistant", "content": "The best answer is (" }
    ]
    ```

    Each input message `content` may be either a single `string` or an array of
    content blocks, where each block has a specific `type`. Using a `string` for
    `content` is shorthand for an array of one content block of type `"text"`. The
    following input messages are equivalent:

    ```json
    { "role": "user", "content": "Hello, Claude" }
    ```

    ```json
    { "role": "user", "content": [{ "type": "text", "text": "Hello, Claude" }] }
    ```

    See
    [input examples](https://platform.claude.com/docs/en/build-with-claude/working-with-messages).

    Note that if you want to include a
    [system prompt](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices#give-claude-a-role),
    you can use the top-level `system` parameter — there is no `"system"` role for
    input messages in the Messages API.

    There is a limit of 100,000 messages in a single request.
    """

    model: Required[ModelParam]
    """The model that will complete your prompt.

    See [models](https://docs.anthropic.com/en/docs/models-overview) for additional
    details and options.
    """

    cache_control: Optional[BetaCacheControlEphemeralParam]
    """
    Top-level cache control automatically applies a cache_control marker to the last
    cacheable block in the request.
    """

    context_management: Optional[BetaContextManagementConfigParam]
    """Context management configuration.

    This allows you to control how Claude manages context across multiple requests,
    such as whether to clear function results or not.
    """

    mcp_servers: Iterable[BetaRequestMCPServerURLDefinitionParam]
    """MCP servers to be utilized in this request"""

    output_config: BetaOutputConfigParam
    """Configuration options for the model's output, such as the output format."""

    output_format: Optional[BetaJSONOutputFormatParam]
    """Deprecated: Use `output_config.format` instead.

    See
    [structured outputs](https://platform.claude.com/docs/en/build-with-claude/structured-outputs)

    A schema to specify Claude's output format in responses. This parameter will be
    removed in a future release.
    """

    speed: Optional[Literal["standard", "fast"]]
    """Inference speed mode.

    `fast` provides significantly faster output token generation at premium pricing.
    Not all models support `fast`; invalid combinations are rejected at create time.
    """

    system: Union[str, Iterable[BetaTextBlockParam]]
    """System prompt.

    A system prompt is a way of providing context and instructions to Claude, such
    as specifying a particular goal or role. See our
    [guide to system prompts](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices#give-claude-a-role).
    """

    thinking: BetaThinkingConfigParam
    """Configuration for enabling Claude's extended thinking.

    When enabled, responses include `thinking` content blocks showing Claude's
    thinking process before the final answer. Requires a minimum budget of 1,024
    tokens and counts towards your `max_tokens` limit.

    See
    [extended thinking](https://platform.claude.com/docs/en/build-with-claude/extended-thinking)
    for details.
    """

    tool_choice: BetaToolChoiceParam
    """How the model should use the provided tools.

    The model can use a specific tool, any available tool, decide by itself, or not
    use tools at all.
    """

    tools: Iterable[Tool]
    """Definitions of tools that the model may use.

    If you include `tools` in your API request, the model may return `tool_use`
    content blocks that represent the model's use of those tools. You can then run
    those tools using the tool input generated by the model and then optionally
    return results back to the model using `tool_result` content blocks.

    There are two types of tools: **client tools** and **server tools**. The
    behavior described below applies to client tools. For
    [server tools](https://platform.claude.com/docs/en/agents-and-tools/tool-use/server-tools),
    see their individual documentation as each has its own behavior (e.g., the
    [web search tool](https://platform.claude.com/docs/en/agents-and-tools/tool-use/web-search-tool)).

    Each tool definition includes:

    - `name`: Name of the tool.
    - `description`: Optional, but strongly-recommended description of the tool.
    - `input_schema`: [JSON schema](https://json-schema.org/draft/2020-12) for the
      tool `input` shape that the model will produce in `tool_use` output content
      blocks.

    For example, if you defined `tools` as:

    ```json
    [
      {
        "name": "get_stock_price",
        "description": "Get the current stock price for a given ticker symbol.",
        "input_schema": {
          "type": "object",
          "properties": {
            "ticker": {
              "type": "string",
              "description": "The stock ticker symbol, e.g. AAPL for Apple Inc."
            }
          },
          "required": ["ticker"]
        }
      }
    ]
    ```

    And then asked the model "What's the S&P 500 at today?", the model might produce
    `tool_use` content blocks in the response like this:

    ```json
    [
      {
        "type": "tool_use",
        "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV",
        "name": "get_stock_price",
        "input": { "ticker": "^GSPC" }
      }
    ]
    ```

    You might then run your `get_stock_price` tool with `{"ticker": "^GSPC"}` as an
    input, and return the following back to the model in a subsequent `user`
    message:

    ```json
    [
      {
        "type": "tool_result",
        "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV",
        "content": "259.75 USD"
      }
    ]
    ```

    Tools can be used for workflows that include running client-side tools and
    functions, or more generally whenever you want the model to produce a particular
    JSON structure of output.

    See our
    [guide](https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview)
    for more details.
    """

    betas: Annotated[List[AnthropicBetaParam], PropertyInfo(alias="anthropic-beta")]
    """Optional header to specify the beta version(s) you want to use."""

    user_profile_id: Annotated[str, PropertyInfo(alias="anthropic-user-profile-id")]
    """The user profile ID to attribute this request to.

    Use when acting on behalf of a party other than your organization. Requires the
    `user-profiles` beta header.
    """


Tool: TypeAlias = Union[
    BetaToolParam,
    BetaToolBash20241022Param,
    BetaToolBash20250124Param,
    BetaCodeExecutionTool20250522Param,
    BetaCodeExecutionTool20250825Param,
    BetaCodeExecutionTool20260120Param,
    BetaCodeExecutionTool20260521Param,
    BetaToolComputerUse20241022Param,
    BetaMemoryTool20250818Param,
    BetaToolComputerUse20250124Param,
    BetaToolTextEditor20241022Param,
    BetaToolComputerUse20251124Param,
    BetaToolTextEditor20250124Param,
    BetaToolTextEditor20250429Param,
    BetaToolTextEditor20250728Param,
    BetaWebSearchTool20250305Param,
    BetaWebFetchTool20250910Param,
    BetaWebSearchTool20260209Param,
    BetaWebFetchTool20260209Param,
    BetaWebFetchTool20260309Param,
    BetaWebSearchTool20260318Param,
    BetaWebFetchTool20260318Param,
    BetaAdvisorTool20260301Param,
    BetaToolSearchToolBm25_20251119Param,
    BetaToolSearchToolRegex20251119Param,
    BetaMCPToolsetParam,
]
