# coding: utf-8

"""
    Mixpeek API

    This is the Mixpeek API, providing access to various endpoints for data processing and retrieval.

    The version of the OpenAPI document: 1.3.0
    Contact: info@mixpeek.com
    Generated by OpenAPI Generator (https://openapi-generator.tech)

    Do not edit the class manually.
"""  # noqa: E501


from __future__ import annotations
import pprint
import re  # noqa: F401
import json

from pydantic import BaseModel, ConfigDict, Field
from typing import Any, ClassVar, Dict, List, Optional
from typing_extensions import Annotated
from mixpeek.models.batch_document_update import BatchDocumentUpdate
from mixpeek.models.logical_operator_input import LogicalOperatorInput
from typing import Optional, Set
from typing_extensions import Self

class BatchUpdateDocumentsRequest(BaseModel):
    """
    Request model for batch updating multiple documents by explicit IDs or filters.  Supports TWO modes: 1. Explicit IDs mode: Provide 'updates' array with document_id + update_data for each 2. Filter mode: Provide 'filters' + 'update_data' to update all matching documents  Key difference from BulkUpdateDocumentsRequest: - Batch (this): Can apply DIFFERENT updates to SPECIFIC documents by ID - Bulk: Applies SAME update to ALL documents matching filters  Use Cases:     - Update 5 specific documents with different metadata values     - Update documents by IDs with per-document update control     - Combine with filters for targeted batch updates  Requirements:     - EITHER 'updates' (explicit mode) OR 'filters' + 'update_data' (filter mode)     - NOT BOTH modes simultaneously
    """ # noqa: E501
    updates: Optional[Annotated[List[BatchDocumentUpdate], Field(min_length=1, max_length=1000)]] = Field(default=None, description="OPTIONAL. List of document updates with explicit document IDs. Each entry specifies document_id and update_data. Use this mode when you know exact document IDs and want per-document control. Mutually exclusive with filters + update_data mode. Maximum 1000 documents per batch request.")
    filters: Optional[LogicalOperatorInput] = Field(default=None, description="OPTIONAL. Filter conditions to match documents for update. Must be used with 'update_data' field. Mutually exclusive with 'updates' array. If provided, applies same update_data to all matching documents.")
    update_data: Optional[Dict[str, Any]] = Field(default=None, description="OPTIONAL. Update data to apply when using filters mode. Must be used with 'filters' field. All matched documents receive the same updates. Can update any document field except vectors.")
    __properties: ClassVar[List[str]] = ["updates", "filters", "update_data"]

    model_config = ConfigDict(
        populate_by_name=True,
        validate_assignment=True,
        protected_namespaces=(),
    )


    def to_str(self) -> str:
        """Returns the string representation of the model using alias"""
        return pprint.pformat(self.model_dump(by_alias=True))

    def to_json(self) -> str:
        """Returns the JSON representation of the model using alias"""
        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
        return json.dumps(self.to_dict())

    @classmethod
    def from_json(cls, json_str: str) -> Optional[Self]:
        """Create an instance of BatchUpdateDocumentsRequest from a JSON string"""
        return cls.from_dict(json.loads(json_str))

    def to_dict(self) -> Dict[str, Any]:
        """Return the dictionary representation of the model using alias.

        This has the following differences from calling pydantic's
        `self.model_dump(by_alias=True)`:

        * `None` is only added to the output dict for nullable fields that
          were set at model initialization. Other fields with value `None`
          are ignored.
        """
        excluded_fields: Set[str] = set([
        ])

        _dict = self.model_dump(
            by_alias=True,
            exclude=excluded_fields,
            exclude_none=True,
        )
        # override the default output from pydantic by calling `to_dict()` of each item in updates (list)
        _items = []
        if self.updates:
            for _item_updates in self.updates:
                if _item_updates:
                    _items.append(_item_updates.to_dict())
            _dict['updates'] = _items
        # override the default output from pydantic by calling `to_dict()` of filters
        if self.filters:
            _dict['filters'] = self.filters.to_dict()
        return _dict

    @classmethod
    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
        """Create an instance of BatchUpdateDocumentsRequest from a dict"""
        if obj is None:
            return None

        if not isinstance(obj, dict):
            return cls.model_validate(obj)

        _obj = cls.model_validate({
            "updates": [BatchDocumentUpdate.from_dict(_item) for _item in obj["updates"]] if obj.get("updates") is not None else None,
            "filters": LogicalOperatorInput.from_dict(obj["filters"]) if obj.get("filters") is not None else None,
            "update_data": obj.get("update_data")
        })
        return _obj


