# This file was auto-generated by Fern from our API Definition.

import typing
import urllib.parse
from json.decoder import JSONDecodeError

from ...core.api_error import ApiError
from ...core.client_wrapper import AsyncClientWrapper, SyncClientWrapper
from ...core.jsonable_encoder import jsonable_encoder
from ...errors.unprocessable_entity_error import UnprocessableEntityError
from ...types.data_source_sync_request import DataSourceSyncRequest
from ...types.http_validation_error import HttpValidationError
from ...types.managed_ingestion_status_response import ManagedIngestionStatusResponse
from ...types.pipeline import Pipeline
from ...types.pipeline_data_source import PipelineDataSource
from ...types.pipeline_data_source_create import PipelineDataSourceCreate

try:
    import pydantic
    if pydantic.__version__.startswith("1."):
        raise ImportError
    import pydantic.v1 as pydantic  # type: ignore
except ImportError:
    import pydantic  # type: ignore

# this is used as the default value for optional parameters
OMIT = typing.cast(typing.Any, ...)


class PipelineDataSourcesClient:
    def __init__(self, *, client_wrapper: SyncClientWrapper):
        self._client_wrapper = client_wrapper

    def list_pipeline_data_sources(self, pipeline_id: str) -> typing.List[PipelineDataSource]:
        """
        Get data sources for a pipeline.

        Parameters:
            - pipeline_id: str.
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.pipeline_data_sources.list_pipeline_data_sources(
            pipeline_id="string",
        )
        """
        _response = self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/", f"api/v1/pipelines/{pipeline_id}/data-sources"
            ),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.List[PipelineDataSource], _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _response_json = _response.json()
        except JSONDecodeError:
            raise ApiError(status_code=_response.status_code, body=_response.text)
        raise ApiError(status_code=_response.status_code, body=_response_json)

    def add_data_sources_to_pipeline(
        self, pipeline_id: str, *, request: typing.List[PipelineDataSourceCreate]
    ) -> typing.List[PipelineDataSource]:
        """
        Add data sources to a pipeline.

        Parameters:
            - pipeline_id: str.

            - request: typing.List[PipelineDataSourceCreate].
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.pipeline_data_sources.add_data_sources_to_pipeline(
            pipeline_id="string",
            request=[],
        )
        """
        _response = self._client_wrapper.httpx_client.request(
            "PUT",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/", f"api/v1/pipelines/{pipeline_id}/data-sources"
            ),
            json=jsonable_encoder(request),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.List[PipelineDataSource], _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _response_json = _response.json()
        except JSONDecodeError:
            raise ApiError(status_code=_response.status_code, body=_response.text)
        raise ApiError(status_code=_response.status_code, body=_response_json)

    def update_pipeline_data_source(
        self, data_source_id: str, pipeline_id: str, *, sync_interval: typing.Optional[float] = OMIT
    ) -> PipelineDataSource:
        """
        Update the configuration of a data source in a pipeline.

        Parameters:
            - data_source_id: str.

            - pipeline_id: str.

            - sync_interval: typing.Optional[float].
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.pipeline_data_sources.update_pipeline_data_source(
            data_source_id="string",
            pipeline_id="string",
        )
        """
        _request: typing.Dict[str, typing.Any] = {}
        if sync_interval is not OMIT:
            _request["sync_interval"] = sync_interval
        _response = self._client_wrapper.httpx_client.request(
            "PUT",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/",
                f"api/v1/pipelines/{pipeline_id}/data-sources/{data_source_id}",
            ),
            json=jsonable_encoder(_request),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(PipelineDataSource, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _response_json = _response.json()
        except JSONDecodeError:
            raise ApiError(status_code=_response.status_code, body=_response.text)
        raise ApiError(status_code=_response.status_code, body=_response_json)

    def get_pipeline_data_source_status(self, data_source_id: str, pipeline_id: str) -> ManagedIngestionStatusResponse:
        """
        Get the status of a data source for a pipeline.

        Parameters:
            - data_source_id: str.

            - pipeline_id: str.
        ---
        from llama_cloud.client import LlamaCloud

        client = LlamaCloud(
            token="YOUR_TOKEN",
        )
        client.pipeline_data_sources.get_pipeline_data_source_status(
            data_source_id="string",
            pipeline_id="string",
        )
        """
        _response = self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/",
                f"api/v1/pipelines/{pipeline_id}/data-sources/{data_source_id}/status",
            ),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(ManagedIngestionStatusResponse, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _response_json = _response.json()
        except JSONDecodeError:
            raise ApiError(status_code=_response.status_code, body=_response.text)
        raise ApiError(status_code=_response.status_code, body=_response_json)

    def sync_pipeline_data_source(
        self, data_source_id: str, pipeline_id: str, *, request: typing.Optional[DataSourceSyncRequest] = None
    ) -> Pipeline:
        """
        Run ingestion for the pipeline data source by incrementally updating the data-sink with upstream changes from data-source.

        Parameters:
            - data_source_id: str.

            - pipeline_id: str.

            - request: typing.Optional[DataSourceSyncRequest].
        """
        _response = self._client_wrapper.httpx_client.request(
            "POST",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/",
                f"api/v1/pipelines/{pipeline_id}/data-sources/{data_source_id}/sync",
            ),
            json=jsonable_encoder(request),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(Pipeline, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _response_json = _response.json()
        except JSONDecodeError:
            raise ApiError(status_code=_response.status_code, body=_response.text)
        raise ApiError(status_code=_response.status_code, body=_response_json)


class AsyncPipelineDataSourcesClient:
    def __init__(self, *, client_wrapper: AsyncClientWrapper):
        self._client_wrapper = client_wrapper

    async def list_pipeline_data_sources(self, pipeline_id: str) -> typing.List[PipelineDataSource]:
        """
        Get data sources for a pipeline.

        Parameters:
            - pipeline_id: str.
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.pipeline_data_sources.list_pipeline_data_sources(
            pipeline_id="string",
        )
        """
        _response = await self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/", f"api/v1/pipelines/{pipeline_id}/data-sources"
            ),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.List[PipelineDataSource], _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _response_json = _response.json()
        except JSONDecodeError:
            raise ApiError(status_code=_response.status_code, body=_response.text)
        raise ApiError(status_code=_response.status_code, body=_response_json)

    async def add_data_sources_to_pipeline(
        self, pipeline_id: str, *, request: typing.List[PipelineDataSourceCreate]
    ) -> typing.List[PipelineDataSource]:
        """
        Add data sources to a pipeline.

        Parameters:
            - pipeline_id: str.

            - request: typing.List[PipelineDataSourceCreate].
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.pipeline_data_sources.add_data_sources_to_pipeline(
            pipeline_id="string",
            request=[],
        )
        """
        _response = await self._client_wrapper.httpx_client.request(
            "PUT",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/", f"api/v1/pipelines/{pipeline_id}/data-sources"
            ),
            json=jsonable_encoder(request),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(typing.List[PipelineDataSource], _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _response_json = _response.json()
        except JSONDecodeError:
            raise ApiError(status_code=_response.status_code, body=_response.text)
        raise ApiError(status_code=_response.status_code, body=_response_json)

    async def update_pipeline_data_source(
        self, data_source_id: str, pipeline_id: str, *, sync_interval: typing.Optional[float] = OMIT
    ) -> PipelineDataSource:
        """
        Update the configuration of a data source in a pipeline.

        Parameters:
            - data_source_id: str.

            - pipeline_id: str.

            - sync_interval: typing.Optional[float].
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.pipeline_data_sources.update_pipeline_data_source(
            data_source_id="string",
            pipeline_id="string",
        )
        """
        _request: typing.Dict[str, typing.Any] = {}
        if sync_interval is not OMIT:
            _request["sync_interval"] = sync_interval
        _response = await self._client_wrapper.httpx_client.request(
            "PUT",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/",
                f"api/v1/pipelines/{pipeline_id}/data-sources/{data_source_id}",
            ),
            json=jsonable_encoder(_request),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(PipelineDataSource, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _response_json = _response.json()
        except JSONDecodeError:
            raise ApiError(status_code=_response.status_code, body=_response.text)
        raise ApiError(status_code=_response.status_code, body=_response_json)

    async def get_pipeline_data_source_status(
        self, data_source_id: str, pipeline_id: str
    ) -> ManagedIngestionStatusResponse:
        """
        Get the status of a data source for a pipeline.

        Parameters:
            - data_source_id: str.

            - pipeline_id: str.
        ---
        from llama_cloud.client import AsyncLlamaCloud

        client = AsyncLlamaCloud(
            token="YOUR_TOKEN",
        )
        await client.pipeline_data_sources.get_pipeline_data_source_status(
            data_source_id="string",
            pipeline_id="string",
        )
        """
        _response = await self._client_wrapper.httpx_client.request(
            "GET",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/",
                f"api/v1/pipelines/{pipeline_id}/data-sources/{data_source_id}/status",
            ),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(ManagedIngestionStatusResponse, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _response_json = _response.json()
        except JSONDecodeError:
            raise ApiError(status_code=_response.status_code, body=_response.text)
        raise ApiError(status_code=_response.status_code, body=_response_json)

    async def sync_pipeline_data_source(
        self, data_source_id: str, pipeline_id: str, *, request: typing.Optional[DataSourceSyncRequest] = None
    ) -> Pipeline:
        """
        Run ingestion for the pipeline data source by incrementally updating the data-sink with upstream changes from data-source.

        Parameters:
            - data_source_id: str.

            - pipeline_id: str.

            - request: typing.Optional[DataSourceSyncRequest].
        """
        _response = await self._client_wrapper.httpx_client.request(
            "POST",
            urllib.parse.urljoin(
                f"{self._client_wrapper.get_base_url()}/",
                f"api/v1/pipelines/{pipeline_id}/data-sources/{data_source_id}/sync",
            ),
            json=jsonable_encoder(request),
            headers=self._client_wrapper.get_headers(),
            timeout=60,
        )
        if 200 <= _response.status_code < 300:
            return pydantic.parse_obj_as(Pipeline, _response.json())  # type: ignore
        if _response.status_code == 422:
            raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json()))  # type: ignore
        try:
            _response_json = _response.json()
        except JSONDecodeError:
            raise ApiError(status_code=_response.status_code, body=_response.text)
        raise ApiError(status_code=_response.status_code, body=_response_json)