# 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 datetime import datetime
from pydantic import BaseModel, ConfigDict, Field, StrictInt, StrictStr
from typing import Any, ClassVar, Dict, List, Optional
from mixpeek.models.ground_truth_query import GroundTruthQuery
from typing import Optional, Set
from typing_extensions import Self

class EvaluationDataset(BaseModel):
    """
    Complete evaluation dataset with metadata.  An evaluation dataset is a collection of queries with ground truth relevance labels, used to measure retriever quality.
    """ # noqa: E501
    dataset_id: StrictStr = Field(description="Unique dataset identifier")
    dataset_name: StrictStr = Field(description="Human-readable dataset name")
    description: Optional[StrictStr] = Field(default=None, description="Dataset description")
    queries: List[GroundTruthQuery] = Field(description="List of queries with ground truth")
    created_at: datetime = Field(description="When dataset was created")
    updated_at: datetime = Field(description="Last update timestamp")
    namespace_id: StrictStr = Field(description="Namespace this dataset belongs to")
    internal_id: StrictStr = Field(description="Internal organization ID")
    query_count: StrictInt = Field(description="Number of queries in dataset")
    metadata: Optional[Dict[str, Any]] = Field(default=None, description="Additional metadata (e.g., labeling instructions, version info)")
    __properties: ClassVar[List[str]] = ["dataset_id", "dataset_name", "description", "queries", "created_at", "updated_at", "namespace_id", "internal_id", "query_count", "metadata"]

    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 EvaluationDataset 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 queries (list)
        _items = []
        if self.queries:
            for _item_queries in self.queries:
                if _item_queries:
                    _items.append(_item_queries.to_dict())
            _dict['queries'] = _items
        return _dict

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

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

        _obj = cls.model_validate({
            "dataset_id": obj.get("dataset_id"),
            "dataset_name": obj.get("dataset_name"),
            "description": obj.get("description"),
            "queries": [GroundTruthQuery.from_dict(_item) for _item in obj["queries"]] if obj.get("queries") is not None else None,
            "created_at": obj.get("created_at"),
            "updated_at": obj.get("updated_at"),
            "namespace_id": obj.get("namespace_id"),
            "internal_id": obj.get("internal_id"),
            "query_count": obj.get("query_count"),
            "metadata": obj.get("metadata")
        })
        return _obj


