# 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 mixpeek.models.logical_operator_output import LogicalOperatorOutput
from typing import Optional, Set
from typing_extensions import Self

class SourceFiltersOutput(BaseModel):
    """
    Filters applied to source data when processing collections.  Source filters determine which objects (from buckets) or documents (from collections) are processed by this collection. Filters use the same LogicalOperator model as list APIs throughout the system, supporting complex AND/OR/NOT logic.  Use Cases:     - Process only specific content types from mixed-content buckets     - Filter by metadata fields (status, category, tags, dates)     - Create specialized collections from broader sources     - Exclude certain objects or documents from processing  Examples:     Process only video content:         {             \"AND\": [                 {\"field\": \"blobs.type\", \"operator\": \"eq\", \"value\": \"video\"}             ]         }      Process only active, published content:         {             \"AND\": [                 {\"field\": \"metadata.status\", \"operator\": \"eq\", \"value\": \"active\"},                 {\"field\": \"metadata.published\", \"operator\": \"eq\", \"value\": true}             ]         }      Process content from last 30 days:         {             \"AND\": [                 {\"field\": \"created_at\", \"operator\": \"gte\", \"value\": \"2025-10-08T00:00:00Z\"}             ]         }      Process specific brands OR categories:         {             \"OR\": [                 {\"field\": \"brand_name\", \"operator\": \"in\", \"value\": [\"Acme\", \"TechCo\"]},                 {\"field\": \"category\", \"operator\": \"eq\", \"value\": \"premium\"}             ]         }  Filter Operators:     - eq (equals)     - ne (not equals)     - gt (greater than)     - gte (greater than or equal)     - lt (less than)     - lte (less than or equal)     - in (value in list)     - nin (value not in list)     - contains (string contains)     - starts_with (string starts with)     - ends_with (string ends with)  Performance Considerations:     - Filters are evaluated at batch creation time     - Only matching objects/documents are included in processing     - More selective filters = smaller batches = faster processing     - Use indexed fields (metadata, timestamps) for better performance  Relationship to Batch Filters:     - Source filters: Applied at collection definition (consistent across all batches)     - Batch filters: Applied at batch creation (ad-hoc, per-batch basis)     - Both can be used together: source filters + batch filters = intersection
    """ # noqa: E501
    filters: Optional[LogicalOperatorOutput] = Field(default=None, description="Optional logical filters to apply to source data. Uses LogicalOperator model with AND/OR/NOT support. When specified, only objects/documents matching these filters will be processed by this collection. When null, all source data is processed (no filtering). Filters are consistent across all batch runs for this collection.")
    __properties: ClassVar[List[str]] = ["filters"]

    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 SourceFiltersOutput 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 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 SourceFiltersOutput from a dict"""
        if obj is None:
            return None

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

        _obj = cls.model_validate({
            "filters": LogicalOperatorOutput.from_dict(obj["filters"]) if obj.get("filters") is not None else None
        })
        return _obj


