# 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, StrictBool, StrictInt, StrictStr
from typing import Any, ClassVar, Dict, List, Optional
from typing_extensions import Annotated
from typing import Optional, Set
from typing_extensions import Self

class ClusterApplicationConfig(BaseModel):
    """
    Configuration for automatic cluster execution on collection.  Similar to TaxonomyApplicationConfig, this attaches a cluster to a collection and defines when/how it should be automatically executed.  Used in CollectionModel.cluster_applications field.  Supports execution phase ordering for unified post-processing with taxonomies, clusters, and alerts.
    """ # noqa: E501
    cluster_id: StrictStr = Field(description="ID of the cluster to execute (must exist and use this collection as input)")
    auto_execute_on_batch: Optional[StrictBool] = Field(default=True, description="Automatically execute cluster when batch processing completes for this collection. If False, cluster must be executed manually via API.")
    min_document_threshold: Optional[StrictInt] = Field(default=None, description="Minimum number of documents required before executing cluster. If document_count < threshold, clustering is skipped. Useful to avoid clustering on small datasets.")
    cooldown_seconds: Optional[StrictInt] = Field(default=3600, description="Minimum time (in seconds) between automatic cluster executions. Prevents excessive re-clustering on frequent batch completions. Default: 3600 seconds (1 hour).")
    execution_phase: Optional[Annotated[int, Field(le=3, strict=True, ge=1)]] = Field(default=2, description="Which phase this cluster runs in. Default: 2 (CLUSTER phase, after taxonomies). Valid values: 1=TAXONOMY, 2=CLUSTER, 3=ALERT. Lower phases run earlier.")
    priority: Optional[Annotated[int, Field(le=1000, strict=True, ge=0)]] = Field(default=0, description="Priority within the execution phase (higher = runs first)")
    __properties: ClassVar[List[str]] = ["cluster_id", "auto_execute_on_batch", "min_document_threshold", "cooldown_seconds", "execution_phase", "priority"]

    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 ClusterApplicationConfig 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,
        )
        return _dict

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

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

        _obj = cls.model_validate({
            "cluster_id": obj.get("cluster_id"),
            "auto_execute_on_batch": obj.get("auto_execute_on_batch") if obj.get("auto_execute_on_batch") is not None else True,
            "min_document_threshold": obj.get("min_document_threshold"),
            "cooldown_seconds": obj.get("cooldown_seconds") if obj.get("cooldown_seconds") is not None else 3600,
            "execution_phase": obj.get("execution_phase") if obj.get("execution_phase") is not None else 2,
            "priority": obj.get("priority") if obj.get("priority") is not None else 0
        })
        return _obj


