"""Public response/stream/iteration types for the LLM API.

These used to live in src/utils/clients.py and have been moved here as part
of the migration toward src/llm/ owning all non-embedding LLM orchestration.
"""

from __future__ import annotations

from collections.abc import AsyncIterator, Callable
from dataclasses import dataclass
from typing import Any, Generic, Literal, TypeVar

from anthropic import AsyncAnthropic
from google import genai
from openai import AsyncOpenAI
from pydantic import BaseModel, Field

T = TypeVar("T")

# OpenAI GPT-5 specific reasoning levels.
ReasoningEffortType = (
    Literal["none", "minimal", "low", "medium", "high", "xhigh", "max"] | None
)
VerbosityType = Literal["low", "medium", "high"] | None

# Raw SDK client union used by the provider-selection layer.
ProviderClient = AsyncAnthropic | AsyncOpenAI | genai.Client


@dataclass
class IterationData:
    """Data passed to iteration callbacks after each tool execution loop iteration."""

    iteration: int
    """1-indexed iteration number."""
    tool_calls: list[str]
    """List of tool names called in this iteration."""
    input_tokens: int
    """Input tokens used in this iteration's LLM call."""
    output_tokens: int
    """Output tokens generated in this iteration's LLM call."""
    cache_read_tokens: int = 0
    """Tokens read from cache in this iteration."""
    cache_creation_tokens: int = 0
    """Tokens written to cache in this iteration."""


IterationCallback = Callable[[IterationData], None]


class HonchoLLMCallResponse(BaseModel, Generic[T]):
    """Response object for LLM calls.

    Note:
        Uncached input tokens = input_tokens - cache_read_input_tokens
                              + cache_creation_input_tokens
        (cache_creation costs 25% more, cache_read costs 90% less)
    """

    content: T
    input_tokens: int = 0
    output_tokens: int
    cache_creation_input_tokens: int = 0
    cache_read_input_tokens: int = 0
    finish_reasons: list[str]
    tool_calls_made: list[dict[str, Any]] = Field(default_factory=list)
    iterations: int = 0
    """Number of LLM calls made in the tool execution loop."""
    thinking_content: str | None = None
    # Full thinking blocks with signatures for multi-turn replay (Anthropic only).
    thinking_blocks: list[dict[str, Any]] = Field(default_factory=list)
    # OpenRouter reasoning_details for Gemini models — must be preserved across turns.
    reasoning_details: list[dict[str, Any]] = Field(default_factory=list)


class HonchoLLMCallStreamChunk(BaseModel):
    """A single chunk in a streaming LLM response."""

    content: str
    is_done: bool = False
    finish_reasons: list[str] = Field(default_factory=list)
    output_tokens: int | None = None


class StreamingResponseWithMetadata:
    """Streaming response wrapper carrying metadata from a completed tool loop.

    Lets callers read tool_calls_made / token counts / thinking_content from
    the tool-execution phase while still iterating the final streamed answer.
    """

    _stream: AsyncIterator[HonchoLLMCallStreamChunk]
    tool_calls_made: list[dict[str, Any]]
    input_tokens: int
    output_tokens: int
    cache_creation_input_tokens: int
    cache_read_input_tokens: int
    thinking_content: str | None
    iterations: int

    def __init__(
        self,
        stream: AsyncIterator[HonchoLLMCallStreamChunk],
        tool_calls_made: list[dict[str, Any]],
        input_tokens: int,
        output_tokens: int,
        cache_creation_input_tokens: int,
        cache_read_input_tokens: int,
        thinking_content: str | None = None,
        iterations: int = 0,
    ):
        self._stream = stream
        self.tool_calls_made = tool_calls_made
        self.input_tokens = input_tokens
        self.output_tokens = output_tokens
        self.cache_creation_input_tokens = cache_creation_input_tokens
        self.cache_read_input_tokens = cache_read_input_tokens
        self.thinking_content = thinking_content
        self.iterations = iterations

    def __aiter__(self) -> AsyncIterator[HonchoLLMCallStreamChunk]:
        return self._stream.__aiter__()

    async def __anext__(self) -> HonchoLLMCallStreamChunk:
        return await self._stream.__anext__()


__all__ = [
    "HonchoLLMCallResponse",
    "HonchoLLMCallStreamChunk",
    "IterationCallback",
    "IterationData",
    "ProviderClient",
    "ReasoningEffortType",
    "StreamingResponseWithMetadata",
    "T",
    "VerbosityType",
]