import json
import os
import tempfile
import uuid
from datetime import datetime
from io import BytesIO
from pathlib import Path
from typing import Any, Optional
from urllib.parse import urlparse
from uuid import UUID

import requests

from shared.api.models import (
    WrappedBooleanResponse,
    WrappedChunksResponse,
    WrappedCollectionsResponse,
    WrappedDocumentResponse,
    WrappedDocumentSearchResponse,
    WrappedDocumentsResponse,
    WrappedEntitiesResponse,
    WrappedGenericMessageResponse,
    WrappedIngestionResponse,
    WrappedRelationshipsResponse,
)

from ..models import IngestionMode, SearchMode, SearchSettings


class DocumentsSDK:
    """SDK for interacting with documents in the v3 API."""

    def __init__(self, client):
        self.client = client

    def create(
        self,
        file_path: Optional[str] = None,
        raw_text: Optional[str] = None,
        chunks: Optional[list[str]] = None,
        id: Optional[str | UUID] = None,
        ingestion_mode: Optional[str] = None,
        collection_ids: Optional[list[str | UUID]] = None,
        metadata: Optional[dict] = None,
        ingestion_config: Optional[dict | IngestionMode] = None,
        run_with_orchestration: Optional[bool] = True,
    ) -> WrappedIngestionResponse:
        """Create a new document from either a file or content.

        Args:
            file_path (Optional[str]): The file to upload, if any
            content (Optional[str]): Optional text content to upload, if no file path is provided
            id (Optional[str | UUID]): Optional ID to assign to the document
            collection_ids (Optional[list[str | UUID]]): Collection IDs to associate with the document. If none are provided, the document will be assigned to the user's default collection.
            metadata (Optional[dict]): Optional metadata to assign to the document
            ingestion_config (Optional[dict]): Optional ingestion configuration to use
            run_with_orchestration (Optional[bool]): Whether to run with orchestration

        Returns:
            WrappedIngestionResponse
        """
        if not file_path and not raw_text and not chunks:
            raise ValueError(
                "Either `file_path`, `raw_text` or `chunks` must be provided"
            )
        if (
            (file_path and raw_text)
            or (file_path and chunks)
            or (raw_text and chunks)
        ):
            raise ValueError(
                "Only one of `file_path`, `raw_text` or `chunks` may be provided"
            )

        data: dict[str, Any] = {}
        files = None

        if id:
            data["id"] = str(id)
        if metadata:
            data["metadata"] = json.dumps(metadata)
        if ingestion_config:
            if isinstance(ingestion_config, IngestionMode):
                ingestion_config = {"mode": ingestion_config.value}
            app_config: dict[str, Any] = (
                {}
                if isinstance(ingestion_config, dict)
                else ingestion_config["app"]
            )
            ingestion_config = dict(ingestion_config)
            ingestion_config["app"] = app_config
            data["ingestion_config"] = json.dumps(ingestion_config)
        if collection_ids:
            collection_ids = [
                str(collection_id) for collection_id in collection_ids
            ]  # type: ignore
            data["collection_ids"] = json.dumps(collection_ids)
        if run_with_orchestration is not None:
            data["run_with_orchestration"] = str(run_with_orchestration)
        if ingestion_mode is not None:
            data["ingestion_mode"] = ingestion_mode
        if file_path:
            # Create a new file instance that will remain open during the request
            file_instance = open(file_path, "rb")
            files = [
                (
                    "file",
                    (file_path, file_instance, "application/octet-stream"),
                )
            ]
            try:
                response_dict = self.client._make_request(
                    "POST",
                    "documents",
                    data=data,
                    files=files,
                    version="v3",
                )
            finally:
                # Ensure we close the file after the request is complete
                file_instance.close()
        elif raw_text:
            data["raw_text"] = raw_text  # type: ignore
            response_dict = self.client._make_request(
                "POST",
                "documents",
                data=data,
                version="v3",
            )
        else:
            data["chunks"] = json.dumps(chunks)
            response_dict = self.client._make_request(
                "POST",
                "documents",
                data=data,
                version="v3",
            )

        return WrappedIngestionResponse(**response_dict)

    def append_metadata(
        self,
        id: str | UUID,
        metadata: list[dict],
    ) -> WrappedDocumentResponse:
        """Append metadata to a document.

        Args:
            id (str | UUID): ID of document to append metadata to
            metadata (list[dict]): Metadata to append

        Returns:
            WrappedDocumentResponse
        """
        data = json.dumps(metadata)
        response_dict = self.client._make_request(
            "PATCH",
            f"documents/{str(id)}/metadata",
            data=data,
            version="v3",
        )

        return WrappedDocumentResponse(**response_dict)

    def replace_metadata(
        self,
        id: str | UUID,
        metadata: list[dict],
    ) -> WrappedDocumentResponse:
        """Replace metadata for a document.

        Args:
            id (str | UUID): ID of document to replace metadata for
            metadata (list[dict]): The metadata that will replace the existing metadata
        """
        data = json.dumps(metadata)
        response_dict = self.client._make_request(
            "PUT",
            f"documents/{str(id)}/metadata",
            data=data,
            version="v3",
        )

        return WrappedDocumentResponse(**response_dict)

    def retrieve(
        self,
        id: str | UUID,
    ) -> WrappedDocumentResponse:
        """Get a specific document by ID.

        Args:
            id (str | UUID): ID of document to retrieve

        Returns:
            WrappedDocumentResponse
        """
        response_dict = self.client._make_request(
            "GET",
            f"documents/{str(id)}",
            version="v3",
        )

        return WrappedDocumentResponse(**response_dict)

    def download(
        self,
        id: str | UUID,
    ) -> BytesIO:
        response = self.client._make_request(
            "GET",
            f"documents/{str(id)}/download",
            version="v3",
        )
        if not isinstance(response, BytesIO):
            raise ValueError("Expected BytesIO response")
        return response

    def download_zip(
        self,
        document_ids: Optional[list[str | UUID]] = None,
        start_date: Optional[datetime] = None,
        end_date: Optional[datetime] = None,
        output_path: Optional[str | Path] = None,
    ) -> BytesIO | None:
        """Download multiple documents as a zip file."""
        params: dict[str, Any] = {}
        if document_ids:
            params["document_ids"] = [str(doc_id) for doc_id in document_ids]
        if start_date:
            params["start_date"] = start_date.isoformat()
        if end_date:
            params["end_date"] = end_date.isoformat()

        response = self.client._make_request(
            "GET",
            "documents/download_zip",
            params=params,
            version="v3",
        )

        if not isinstance(response, BytesIO):
            raise ValueError("Expected BytesIO response")

        if output_path:
            output_path = (
                Path(output_path)
                if isinstance(output_path, str)
                else output_path
            )
            with open(output_path, "wb") as f:
                f.write(response.getvalue())
            return None

        return response

    def export(
        self,
        output_path: str | Path,
        columns: Optional[list[str]] = None,
        filters: Optional[dict] = None,
        include_header: bool = True,
    ) -> None:
        """Export documents to a CSV file, streaming the results directly to
        disk.

        Args:
            output_path (str | Path): Local path where the CSV file should be saved
            columns (Optional[list[str]]): Specific columns to export. If None, exports default columns
            filters (Optional[dict]): Optional filters to apply when selecting documents
            include_header (bool): Whether to include column headers in the CSV (default: True)

        Returns:
            None
        """
        # Convert path to string if it's a Path object
        output_path = (
            str(output_path) if isinstance(output_path, Path) else output_path
        )

        data: dict[str, Any] = {"include_header": include_header}
        if columns:
            data["columns"] = columns
        if filters:
            data["filters"] = filters

        # Stream response directly to file
        with open(output_path, "wb") as f:
            response = self.client.client.post(
                f"{self.client.base_url}/v3/documents/export",
                json=data,
                headers={
                    "Accept": "text/csv",
                    **self.client._get_auth_header(),
                },
            )
            if response.status_code != 200:
                raise ValueError(
                    f"Export failed with status {response.status_code}",
                    response,
                )

            for chunk in response.iter_bytes():
                if chunk:
                    f.write(chunk)

    def export_entities(
        self,
        id: str | UUID,
        output_path: str | Path,
        columns: Optional[list[str]] = None,
        filters: Optional[dict] = None,
        include_header: bool = True,
    ) -> None:
        """Export documents to a CSV file, streaming the results directly to
        disk.

        Args:
            output_path (str | Path): Local path where the CSV file should be saved
            columns (Optional[list[str]]): Specific columns to export. If None, exports default columns
            filters (Optional[dict]): Optional filters to apply when selecting documents
            include_header (bool): Whether to include column headers in the CSV (default: True)

        Returns:
            None
        """
        # Convert path to string if it's a Path object
        output_path = (
            str(output_path) if isinstance(output_path, Path) else output_path
        )

        # Prepare request data
        data: dict[str, Any] = {"include_header": include_header}
        if columns:
            data["columns"] = columns
        if filters:
            data["filters"] = filters

        # Stream response directly to file
        with open(output_path, "wb") as f:
            response = self.client.client.post(
                f"{self.client.base_url}/v3/documents/{str(id)}/entities/export",
                json=data,
                headers={
                    "Accept": "text/csv",
                    **self.client._get_auth_header(),
                },
            )
            if response.status_code != 200:
                raise ValueError(
                    f"Export failed with status {response.status_code}",
                    response,
                )

            for chunk in response.iter_bytes():
                if chunk:
                    f.write(chunk)

    def export_relationships(
        self,
        id: str | UUID,
        output_path: str | Path,
        columns: Optional[list[str]] = None,
        filters: Optional[dict] = None,
        include_header: bool = True,
    ) -> None:
        """Export document relationships to a CSV file, streaming the results
        directly to disk.

        Args:
            output_path (str | Path): Local path where the CSV file should be saved
            columns (Optional[list[str]]): Specific columns to export. If None, exports default columns
            filters (Optional[dict]): Optional filters to apply when selecting documents
            include_header (bool): Whether to include column headers in the CSV (default: True)

        Returns:
            None
        """
        # Convert path to string if it's a Path object
        output_path = (
            str(output_path) if isinstance(output_path, Path) else output_path
        )

        # Prepare request data
        data: dict[str, Any] = {"include_header": include_header}
        if columns:
            data["columns"] = columns
        if filters:
            data["filters"] = filters

        # Stream response directly to file
        with open(output_path, "wb") as f:
            response = self.client.client.post(
                f"{self.client.base_url}/v3/documents/{str(id)}/relationships/export",
                json=data,
                headers={
                    "Accept": "text/csv",
                    **self.client._get_auth_header(),
                },
            )
            if response.status_code != 200:
                raise ValueError(
                    f"Export failed with status {response.status_code}",
                    response,
                )

            for chunk in response.iter_bytes():
                if chunk:
                    f.write(chunk)

    def delete(
        self,
        id: str | UUID,
    ) -> WrappedBooleanResponse:
        """Delete a specific document.

        Args:
            id (str | UUID): ID of document to delete

        Returns:
            WrappedBooleanResponse
        """
        response_dict = self.client._make_request(
            "DELETE",
            f"documents/{str(id)}",
            version="v3",
        )

        return WrappedBooleanResponse(**response_dict)

    def list_chunks(
        self,
        id: str | UUID,
        include_vectors: Optional[bool] = False,
        offset: Optional[int] = 0,
        limit: Optional[int] = 100,
    ) -> WrappedChunksResponse:
        """Get chunks for a specific document.

        Args:
            id (str | UUID): ID of document to retrieve chunks for
            include_vectors (Optional[bool]): Whether to include vector embeddings in the response
            offset (int, optional): Specifies the number of objects to skip. Defaults to 0.
            limit (int, optional): Specifies a limit on the number of objects to return, ranging between 1 and 100. Defaults to 100.

        Returns:
            WrappedChunksResponse
        """
        params = {
            "offset": offset,
            "limit": limit,
            "include_vectors": include_vectors,
        }
        response_dict = self.client._make_request(
            "GET",
            f"documents/{str(id)}/chunks",
            params=params,
            version="v3",
        )

        return WrappedChunksResponse(**response_dict)

    def list_collections(
        self,
        id: str | UUID,
        include_vectors: Optional[bool] = False,
        offset: Optional[int] = 0,
        limit: Optional[int] = 100,
    ) -> WrappedCollectionsResponse:
        """List collections for a specific document.

        Args:
            id (str | UUID): ID of document to retrieve collections for
            offset (int, optional): Specifies the number of objects to skip. Defaults to 0.
            limit (int, optional): Specifies a limit on the number of objects to return, ranging between 1 and 100. Defaults to 100.

        Returns:
            WrappedCollectionsResponse
        """
        params = {
            "offset": offset,
            "limit": limit,
        }

        response_dict = self.client._make_request(
            "GET",
            f"documents/{str(id)}/collections",
            params=params,
            version="v3",
        )

        return WrappedCollectionsResponse(**response_dict)

    def delete_by_filter(
        self,
        filters: dict,
    ) -> WrappedBooleanResponse:
        """Delete documents based on filters.

        Args:
            filters (dict): Filters to apply when selecting documents to delete

        Returns:
            WrappedBooleanResponse
        """
        filters_json = json.dumps(filters)
        response_dict = self.client._make_request(
            "DELETE",
            "documents/by-filter",
            data=filters_json,
            version="v3",
        )

        return WrappedBooleanResponse(**response_dict)

    def extract(
        self,
        id: str | UUID,
        settings: Optional[dict] = None,
        run_with_orchestration: Optional[bool] = True,
    ) -> WrappedGenericMessageResponse:
        """Extract entities and relationships from a document.

        Args:
            id (str, UUID): ID of document to extract from
            settings (Optional[dict]): Settings for extraction process
            run_with_orchestration (Optional[bool]): Whether to run with orchestration

        Returns:
            WrappedGenericMessageResponse
        """
        data: dict[str, Any] = {}
        if settings:
            data["settings"] = json.dumps(settings)
        if run_with_orchestration is not None:
            data["run_with_orchestration"] = str(run_with_orchestration)

        response_dict = self.client._make_request(
            "POST",
            f"documents/{str(id)}/extract",
            params=data,
            version="v3",
        )
        return WrappedGenericMessageResponse(**response_dict)

    def list_entities(
        self,
        id: str | UUID,
        offset: Optional[int] = 0,
        limit: Optional[int] = 100,
        include_embeddings: Optional[bool] = False,
    ) -> WrappedEntitiesResponse:
        """List entities extracted from a document.

        Args:
            id (str | UUID): ID of document to get entities from
            offset (Optional[int]): Number of items to skip
            limit (Optional[int]): Max number of items to return
            include_embeddings (Optional[bool]): Whether to include embeddings

        Returns:
            WrappedEntitiesResponse
        """
        params = {
            "offset": offset,
            "limit": limit,
            "include_embeddings": include_embeddings,
        }
        response_dict = self.client._make_request(
            "GET",
            f"documents/{str(id)}/entities",
            params=params,
            version="v3",
        )

        return WrappedEntitiesResponse(**response_dict)

    def list_relationships(
        self,
        id: str | UUID,
        offset: Optional[int] = 0,
        limit: Optional[int] = 100,
        entity_names: Optional[list[str]] = None,
        relationship_types: Optional[list[str]] = None,
    ) -> WrappedRelationshipsResponse:
        """List relationships extracted from a document.

        Args:
            id (str | UUID): ID of document to get relationships from
            offset (Optional[int]): Number of items to skip
            limit (Optional[int]): Max number of items to return
            entity_names (Optional[list[str]]): Filter by entity names
            relationship_types (Optional[list[str]]): Filter by relationship types

        Returns:
            WrappedRelationshipsResponse
        """
        params: dict[str, Any] = {
            "offset": offset,
            "limit": limit,
        }
        if entity_names:
            params["entity_names"] = entity_names
        if relationship_types:
            params["relationship_types"] = relationship_types

        response_dict = self.client._make_request(
            "GET",
            f"documents/{str(id)}/relationships",
            params=params,
            version="v3",
        )

        return WrappedRelationshipsResponse(**response_dict)

    def list(
        self,
        ids: Optional[list[str | UUID]] = None,
        offset: Optional[int] = 0,
        limit: Optional[int] = 100,
    ) -> WrappedDocumentsResponse:
        """List documents with pagination.

        Args:
            ids (Optional[list[str | UUID]]): Optional list of document IDs to filter by
            offset (int, optional): Specifies the number of objects to skip. Defaults to 0.
            limit (int, optional): Specifies a limit on the number of objects to return, ranging between 1 and 100. Defaults to 100.

        Returns:
            WrappedDocumentsResponse
        """
        params = {
            "offset": offset,
            "limit": limit,
        }
        if ids:
            params["ids"] = [str(doc_id) for doc_id in ids]  # type: ignore

        response_dict = self.client._make_request(
            "GET",
            "documents",
            params=params,
            version="v3",
        )

        return WrappedDocumentsResponse(**response_dict)

    def search(
        self,
        query: str,
        search_mode: Optional[str | SearchMode] = "custom",
        search_settings: Optional[dict | SearchSettings] = None,
    ) -> WrappedDocumentSearchResponse:
        """Conduct a vector and/or graph search.

        Args:
            query (str): The query to search for.
            search_settings (Optional[dict, SearchSettings]]): Vector search settings.

        Returns:
            WrappedDocumentSearchResponse
        """
        if search_settings and not isinstance(search_settings, dict):
            search_settings = search_settings.model_dump()
        data: dict[str, Any] = {
            "query": query,
            "search_settings": search_settings,
        }
        if search_mode:
            data["search_mode"] = search_mode

        response_dict = self.client._make_request(
            "POST",
            "documents/search",
            json=data,
            version="v3",
        )

        return WrappedDocumentSearchResponse(**response_dict)

    def deduplicate(
        self,
        id: str | UUID,
        settings: Optional[dict] = None,
        run_with_orchestration: Optional[bool] = True,
    ) -> WrappedGenericMessageResponse:
        """Deduplicate entities and relationships from a document.

        Args:
            id (str, UUID): ID of document to extract from
            settings (Optional[dict]): Settings for extraction process
            run_with_orchestration (Optional[bool]): Whether to run with orchestration

        Returns:
            dict: Extraction results or cost estimate
        """
        data: dict[str, Any] = {}
        if settings:
            data["settings"] = json.dumps(settings)
        if run_with_orchestration is not None:
            data["run_with_orchestration"] = str(run_with_orchestration)

        response_dict = self.client._make_request(
            "POST",
            f"documents/{str(id)}/deduplicate",
            params=data,
            version="v3",
        )

        return WrappedGenericMessageResponse(**response_dict)

    def create_sample(self, hi_res: bool = False) -> WrappedIngestionResponse:
        """Ingest a sample document into R2R.

        This method downloads a sample file from a predefined URL, saves it
        as a temporary file, and ingests it using the `create` method. The
        temporary file is removed after ingestion.

        Returns:
            WrappedIngestionResponse: The response from the ingestion request.
        """
        # Define the sample file URL
        sample_file_url = "https://raw.githubusercontent.com/SciPhi-AI/R2R/main/py/core/examples/data/DeepSeek_R1.pdf"
        # Parse the URL to extract the filename
        parsed_url = urlparse(sample_file_url)
        filename = os.path.basename(parsed_url.path)
        # Determine whether the file is a PDF (this can affect how we write the file)

        # Create a temporary file.
        # We use binary mode ("wb") for both PDFs and text files because the `create`
        # method will open the file in binary mode.
        temp_file = tempfile.NamedTemporaryFile(
            mode="wb", delete=False, suffix=f"_{filename}"
        )
        try:
            response = requests.get(sample_file_url)
            response.raise_for_status()
            # Write the downloaded content to the temporary file.
            # (For text files, using response.content avoids any potential encoding issues
            # when the file is later opened in binary mode.)
            temp_file.write(response.content)
            temp_file.close()

            # Prepare metadata and generate a stable document ID based on the URL
            metadata = {"title": filename}
            doc_id = str(uuid.uuid5(uuid.NAMESPACE_DNS, sample_file_url))

            # Call the SDK's create method to ingest the file.
            ingestion_response = self.create(
                file_path=temp_file.name,
                metadata=metadata,
                id=doc_id,
                ingestion_mode="hi-res" if hi_res else None,
            )
            return ingestion_response
        finally:
            # Remove the temporary file regardless of whether ingestion succeeded.
            try:
                os.unlink(temp_file.name)
            except Exception:
                pass