from __future__ import annotations

import warnings
from typing import Any, Literal, Optional, Union

from cognite.client import data_modeling as dm
from pydantic import Field
from pydantic import field_validator, model_validator

from ._core import (
    DEFAULT_INSTANCE_SPACE,
    DataRecord,
    DataRecordGraphQL,
    DataRecordWrite,
    DomainModel,
    DomainModelCore,
    DomainModelWrite,
    DomainModelWriteList,
    DomainModelList,
    DomainRelationWrite,
    GraphQLCore,
    ResourcesWrite,
)


__all__ = [
    "FunctionInput",
    "FunctionInputWrite",
    "FunctionInputApply",
    "FunctionInputList",
    "FunctionInputWriteList",
    "FunctionInputApplyList",
    "FunctionInputFields",
    "FunctionInputTextFields",
]


FunctionInputTextFields = Literal["workflow_execution_id", "function_name", "function_call_id"]
FunctionInputFields = Literal["workflow_execution_id", "workflow_step", "function_name", "function_call_id"]

_FUNCTIONINPUT_PROPERTIES_BY_FIELD = {
    "workflow_execution_id": "workflowExecutionId",
    "workflow_step": "workflowStep",
    "function_name": "functionName",
    "function_call_id": "functionCallId",
}


class FunctionInputGraphQL(GraphQLCore):
    """This represents the reading version of function input, used
    when data is retrieved from CDF using GraphQL.

    It is used when retrieving data from CDF using GraphQL.

    Args:
        space: The space where the node is located.
        external_id: The external id of the function input.
        data_record: The data record of the function input node.
        workflow_execution_id: The process associated with the function execution
        workflow_step: This is the step in the process.
        function_name: The name of the function
        function_call_id: The function call id
    """

    view_id = dm.ViewId("power_ops_core", "FunctionInput", "1")
    workflow_execution_id: Optional[str] = Field(None, alias="workflowExecutionId")
    workflow_step: Optional[int] = Field(None, alias="workflowStep")
    function_name: Optional[str] = Field(None, alias="functionName")
    function_call_id: Optional[str] = Field(None, alias="functionCallId")

    @model_validator(mode="before")
    def parse_data_record(cls, values: Any) -> Any:
        if not isinstance(values, dict):
            return values
        if "lastUpdatedTime" in values or "createdTime" in values:
            values["dataRecord"] = DataRecordGraphQL(
                created_time=values.pop("createdTime", None),
                last_updated_time=values.pop("lastUpdatedTime", None),
            )
        return values

    def as_read(self) -> FunctionInput:
        """Convert this GraphQL format of function input to the reading format."""
        if self.data_record is None:
            raise ValueError("This object cannot be converted to a read format because it lacks a data record.")
        return FunctionInput(
            space=self.space or DEFAULT_INSTANCE_SPACE,
            external_id=self.external_id,
            data_record=DataRecord(
                version=0,
                last_updated_time=self.data_record.last_updated_time,
                created_time=self.data_record.created_time,
            ),
            workflow_execution_id=self.workflow_execution_id,
            workflow_step=self.workflow_step,
            function_name=self.function_name,
            function_call_id=self.function_call_id,
        )

    def as_write(self) -> FunctionInputWrite:
        """Convert this GraphQL format of function input to the writing format."""
        return FunctionInputWrite(
            space=self.space or DEFAULT_INSTANCE_SPACE,
            external_id=self.external_id,
            data_record=DataRecordWrite(existing_version=0),
            workflow_execution_id=self.workflow_execution_id,
            workflow_step=self.workflow_step,
            function_name=self.function_name,
            function_call_id=self.function_call_id,
        )


class FunctionInput(DomainModel):
    """This represents the reading version of function input.

    It is used to when data is retrieved from CDF.

    Args:
        space: The space where the node is located.
        external_id: The external id of the function input.
        data_record: The data record of the function input node.
        workflow_execution_id: The process associated with the function execution
        workflow_step: This is the step in the process.
        function_name: The name of the function
        function_call_id: The function call id
    """

    space: str = DEFAULT_INSTANCE_SPACE
    node_type: Union[dm.DirectRelationReference, None] = None
    workflow_execution_id: str = Field(alias="workflowExecutionId")
    workflow_step: int = Field(alias="workflowStep")
    function_name: str = Field(alias="functionName")
    function_call_id: str = Field(alias="functionCallId")

    def as_write(self) -> FunctionInputWrite:
        """Convert this read version of function input to the writing version."""
        return FunctionInputWrite(
            space=self.space,
            external_id=self.external_id,
            data_record=DataRecordWrite(existing_version=self.data_record.version),
            workflow_execution_id=self.workflow_execution_id,
            workflow_step=self.workflow_step,
            function_name=self.function_name,
            function_call_id=self.function_call_id,
        )

    def as_apply(self) -> FunctionInputWrite:
        """Convert this read version of function input to the writing version."""
        warnings.warn(
            "as_apply is deprecated and will be removed in v1.0. Use as_write instead.",
            UserWarning,
            stacklevel=2,
        )
        return self.as_write()


class FunctionInputWrite(DomainModelWrite):
    """This represents the writing version of function input.

    It is used to when data is sent to CDF.

    Args:
        space: The space where the node is located.
        external_id: The external id of the function input.
        data_record: The data record of the function input node.
        workflow_execution_id: The process associated with the function execution
        workflow_step: This is the step in the process.
        function_name: The name of the function
        function_call_id: The function call id
    """

    space: str = DEFAULT_INSTANCE_SPACE
    node_type: Union[dm.DirectRelationReference, None] = None
    workflow_execution_id: str = Field(alias="workflowExecutionId")
    workflow_step: int = Field(alias="workflowStep")
    function_name: str = Field(alias="functionName")
    function_call_id: str = Field(alias="functionCallId")

    def _to_instances_write(
        self,
        cache: set[tuple[str, str]],
        view_by_read_class: dict[type[DomainModelCore], dm.ViewId] | None,
        write_none: bool = False,
        allow_version_increase: bool = False,
    ) -> ResourcesWrite:
        resources = ResourcesWrite()
        if self.as_tuple_id() in cache:
            return resources

        write_view = (view_by_read_class or {}).get(FunctionInput, dm.ViewId("power_ops_core", "FunctionInput", "1"))

        properties: dict[str, Any] = {}

        if self.workflow_execution_id is not None:
            properties["workflowExecutionId"] = self.workflow_execution_id

        if self.workflow_step is not None:
            properties["workflowStep"] = self.workflow_step

        if self.function_name is not None:
            properties["functionName"] = self.function_name

        if self.function_call_id is not None:
            properties["functionCallId"] = self.function_call_id

        if properties:
            this_node = dm.NodeApply(
                space=self.space,
                external_id=self.external_id,
                existing_version=None if allow_version_increase else self.data_record.existing_version,
                type=self.node_type,
                sources=[
                    dm.NodeOrEdgeData(
                        source=write_view,
                        properties=properties,
                    )
                ],
            )
            resources.nodes.append(this_node)
            cache.add(self.as_tuple_id())

        return resources


class FunctionInputApply(FunctionInputWrite):
    def __new__(cls, *args, **kwargs) -> FunctionInputApply:
        warnings.warn(
            "FunctionInputApply is deprecated and will be removed in v1.0. Use FunctionInputWrite instead."
            "The motivation for this change is that Write is a more descriptive name for the writing version of the"
            "FunctionInput.",
            UserWarning,
            stacklevel=2,
        )
        return super().__new__(cls)


class FunctionInputList(DomainModelList[FunctionInput]):
    """List of function inputs in the read version."""

    _INSTANCE = FunctionInput

    def as_write(self) -> FunctionInputWriteList:
        """Convert these read versions of function input to the writing versions."""
        return FunctionInputWriteList([node.as_write() for node in self.data])

    def as_apply(self) -> FunctionInputWriteList:
        """Convert these read versions of primitive nullable to the writing versions."""
        warnings.warn(
            "as_apply is deprecated and will be removed in v1.0. Use as_write instead.",
            UserWarning,
            stacklevel=2,
        )
        return self.as_write()


class FunctionInputWriteList(DomainModelWriteList[FunctionInputWrite]):
    """List of function inputs in the writing version."""

    _INSTANCE = FunctionInputWrite


class FunctionInputApplyList(FunctionInputWriteList): ...


def _create_function_input_filter(
    view_id: dm.ViewId,
    workflow_execution_id: str | list[str] | None = None,
    workflow_execution_id_prefix: str | None = None,
    min_workflow_step: int | None = None,
    max_workflow_step: int | None = None,
    function_name: str | list[str] | None = None,
    function_name_prefix: str | None = None,
    function_call_id: str | list[str] | None = None,
    function_call_id_prefix: str | None = None,
    external_id_prefix: str | None = None,
    space: str | list[str] | None = None,
    filter: dm.Filter | None = None,
) -> dm.Filter | None:
    filters = []
    if isinstance(workflow_execution_id, str):
        filters.append(dm.filters.Equals(view_id.as_property_ref("workflowExecutionId"), value=workflow_execution_id))
    if workflow_execution_id and isinstance(workflow_execution_id, list):
        filters.append(dm.filters.In(view_id.as_property_ref("workflowExecutionId"), values=workflow_execution_id))
    if workflow_execution_id_prefix is not None:
        filters.append(
            dm.filters.Prefix(view_id.as_property_ref("workflowExecutionId"), value=workflow_execution_id_prefix)
        )
    if min_workflow_step is not None or max_workflow_step is not None:
        filters.append(
            dm.filters.Range(view_id.as_property_ref("workflowStep"), gte=min_workflow_step, lte=max_workflow_step)
        )
    if isinstance(function_name, str):
        filters.append(dm.filters.Equals(view_id.as_property_ref("functionName"), value=function_name))
    if function_name and isinstance(function_name, list):
        filters.append(dm.filters.In(view_id.as_property_ref("functionName"), values=function_name))
    if function_name_prefix is not None:
        filters.append(dm.filters.Prefix(view_id.as_property_ref("functionName"), value=function_name_prefix))
    if isinstance(function_call_id, str):
        filters.append(dm.filters.Equals(view_id.as_property_ref("functionCallId"), value=function_call_id))
    if function_call_id and isinstance(function_call_id, list):
        filters.append(dm.filters.In(view_id.as_property_ref("functionCallId"), values=function_call_id))
    if function_call_id_prefix is not None:
        filters.append(dm.filters.Prefix(view_id.as_property_ref("functionCallId"), value=function_call_id_prefix))
    if external_id_prefix is not None:
        filters.append(dm.filters.Prefix(["node", "externalId"], value=external_id_prefix))
    if isinstance(space, str):
        filters.append(dm.filters.Equals(["node", "space"], value=space))
    if space and isinstance(space, list):
        filters.append(dm.filters.In(["node", "space"], values=space))
    if filter:
        filters.append(filter)
    return dm.filters.And(*filters) if filters else None
