from __future__ import annotations

from collections.abc import Mapping
from dataclasses import fields, replace
from typing import Annotated, Any, Literal, TypeAlias, cast

from openai import Omit as _Omit
from openai._types import Body, Query
from openai.types.responses import ResponseIncludable
from openai.types.responses.response_create_params import ContextManagement
from openai.types.shared import Reasoning
from pydantic import GetCoreSchemaHandler, TypeAdapter
from pydantic.dataclasses import dataclass
from pydantic_core import core_schema

from .retry import (
    ModelRetryBackoffInput,
    ModelRetryBackoffSettings,
    ModelRetrySettings,
    _coerce_backoff_settings,
)


class _OmitTypeAnnotation:
    @classmethod
    def __get_pydantic_core_schema__(
        cls,
        _source_type: Any,
        _handler: GetCoreSchemaHandler,
    ) -> core_schema.CoreSchema:
        def validate_from_none(value: None) -> _Omit:
            return _Omit()

        from_none_schema = core_schema.chain_schema(
            [
                core_schema.none_schema(),
                core_schema.no_info_plain_validator_function(validate_from_none),
            ]
        )
        return core_schema.json_or_python_schema(
            json_schema=from_none_schema,
            python_schema=core_schema.union_schema(
                [
                    # check if it's an instance first before doing any further work
                    core_schema.is_instance_schema(_Omit),
                    from_none_schema,
                ]
            ),
            serialization=core_schema.plain_serializer_function_ser_schema(lambda instance: None),
        )


@dataclass
class MCPToolChoice:
    server_label: str
    name: str


Omit = Annotated[_Omit, _OmitTypeAnnotation]
Headers: TypeAlias = Mapping[str, str | Omit]
ToolChoice: TypeAlias = Literal["auto", "required", "none"] | str | MCPToolChoice | None

_TRACEABLE_MODEL_SETTING_FIELDS = (
    "temperature",
    "top_p",
    "frequency_penalty",
    "presence_penalty",
    "tool_choice",
    "parallel_tool_calls",
    "truncation",
    "max_tokens",
    "reasoning",
    "verbosity",
    "metadata",
    "store",
    "prompt_cache_retention",
    "include_usage",
    "response_include",
    "top_logprobs",
    "retry",
    "context_management",
)


@dataclass
class ModelSettings:
    """Settings to use when calling an LLM.

    This class holds optional model configuration parameters (e.g. temperature,
    top_p, penalties, truncation, etc.).

    Not all models/providers support all of these parameters, so please check the API documentation
    for the specific model and provider you are using.
    """

    temperature: float | None = None
    """The temperature to use when calling the model."""

    top_p: float | None = None
    """The top_p to use when calling the model."""

    frequency_penalty: float | None = None
    """The frequency penalty to use when calling the model."""

    presence_penalty: float | None = None
    """The presence penalty to use when calling the model."""

    tool_choice: ToolChoice | None = None
    """The tool choice to use when calling the model."""

    parallel_tool_calls: bool | None = None
    """Controls whether the model can make multiple parallel tool calls in a single turn.
    If not provided (i.e., set to None), this behavior defers to the underlying
    model provider's default. For most current providers (e.g., OpenAI), this typically
    means parallel tool calls are enabled (True).
    Set to True to explicitly enable parallel tool calls, or False to restrict the
    model to at most one tool call per turn.
    """

    truncation: Literal["auto", "disabled"] | None = None
    """The truncation strategy to use when calling the model.
    See [Responses API documentation](https://platform.openai.com/docs/api-reference/responses/create#responses_create-truncation)
    for more details.
    """

    max_tokens: int | None = None
    """The maximum number of output tokens to generate."""

    reasoning: Reasoning | None = None
    """Configuration options for
    [reasoning models](https://platform.openai.com/docs/guides/reasoning).
    """

    verbosity: Literal["low", "medium", "high"] | None = None
    """Constrains the verbosity of the model's response.
    """

    metadata: dict[str, str] | None = None
    """Metadata to include with the model response call."""

    store: bool | None = None
    """Whether to store the generated model response for later retrieval.
    For Responses API: automatically enabled when not specified.
    For Chat Completions API: disabled when not specified."""

    prompt_cache_retention: Literal["in_memory", "24h"] | None = None
    """The retention policy for the prompt cache. Set to `24h` to enable extended
    prompt caching, which keeps cached prefixes active for longer, up to a maximum
    of 24 hours.
    [Learn more](https://platform.openai.com/docs/guides/prompt-caching#prompt-cache-retention)."""

    include_usage: bool | None = None
    """Whether to include usage chunk.
    Only available for Chat Completions API."""

    # TODO: revisit ResponseIncludable | str if ResponseIncludable covers more cases
    # We've added str to support missing ones like
    # "web_search_call.action.sources" etc.
    response_include: list[ResponseIncludable | str] | None = None
    """Additional output data to include in the model response.
    [include parameter](https://platform.openai.com/docs/api-reference/responses/create#responses-create-include)"""

    top_logprobs: int | None = None
    """Number of top tokens to return logprobs for. Setting this will
    automatically include ``"message.output_text.logprobs"`` in the response."""

    extra_query: Query | None = None
    """Additional query fields to provide with the request.
    Defaults to None if not provided."""

    extra_body: Body | None = None
    """Additional body fields to provide with the request.
    Defaults to None if not provided."""

    extra_headers: Headers | None = None
    """Additional headers to provide with the request.
    Defaults to None if not provided."""

    extra_args: dict[str, Any] | None = None
    """Arbitrary keyword arguments to pass to the model API call.
    These will be passed directly to the underlying model provider's API.
    Use with caution as not all models support all parameters."""

    retry: ModelRetrySettings | None = None
    """Opt-in runner-managed retry settings for model calls."""

    context_management: list[ContextManagement] | None = None
    """Context management entries for OpenAI Responses API requests.

    For example, use ``[{"type": "compaction", "compact_threshold": 200000}]``
    to enable server-side compaction when the rendered context crosses a token threshold.
    """

    def resolve(self, override: ModelSettings | None) -> ModelSettings:
        """Produce a new ModelSettings by overlaying any non-None values from the
        override on top of this instance."""
        if override is None:
            return self

        changes = {
            field.name: getattr(override, field.name)
            for field in fields(self)
            if getattr(override, field.name) is not None
        }

        # Handle extra_args merging specially - merge dictionaries instead of replacing.
        if self.extra_args is not None or override.extra_args is not None:
            merged_args = {}
            if self.extra_args:
                merged_args.update(self.extra_args)
            if override.extra_args:
                merged_args.update(override.extra_args)
            changes["extra_args"] = merged_args if merged_args else None

        if self.retry is not None or override.retry is not None:
            changes["retry"] = _merge_retry_settings(self.retry, override.retry)

        return replace(self, **changes)

    def to_json_dict(self) -> dict[str, Any]:
        return cast(dict[str, Any], TypeAdapter(ModelSettings).dump_python(self, mode="json"))

    def to_traceable_dict(self) -> dict[str, Any]:
        """Serialize settings for tracing without provider-specific request extras."""
        payload = self.to_json_dict()
        return {key: payload[key] for key in _TRACEABLE_MODEL_SETTING_FIELDS if key in payload}


def _merge_retry_settings(
    inherited: ModelRetrySettings | None,
    override: ModelRetrySettings | None,
) -> ModelRetrySettings | None:
    if inherited is None:
        return override
    if override is None:
        return inherited

    merged_backoff = _merge_backoff_settings(inherited.backoff, override.backoff)
    retry_changes = {
        field.name: getattr(override, field.name)
        for field in fields(inherited)
        if field.name != "backoff" and getattr(override, field.name) is not None
    }
    return replace(inherited, **retry_changes, backoff=merged_backoff)


def _merge_backoff_settings(
    inherited: ModelRetryBackoffInput | None,
    override: ModelRetryBackoffInput | None,
) -> ModelRetryBackoffSettings | None:
    inherited = _coerce_backoff_settings(inherited)
    override = _coerce_backoff_settings(override)
    if inherited is None:
        return override
    if override is None:
        return inherited

    changes = {
        field.name: getattr(override, field.name)
        for field in fields(inherited)
        if getattr(override, field.name) is not None
    }
    return replace(inherited, **changes)
