# 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: 0.81.1
    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, StrictStr
from typing import Any, ClassVar, Dict, List, Optional
from mixpeek.models.lint_severity import LintSeverity
from typing import Optional, Set
from typing_extensions import Self

class LintResult(BaseModel):
    """
    A single lint result with actionable suggestion.  Example:     {         \"code\": \"UNUSED_EXTRACTOR\",         \"severity\": \"warning\",         \"message\": \"Feature extractor 'text_extractor' in namespace 'my_ns' is not used by any collection\",         \"location\": \"namespaces[0].feature_extractors[1]\",         \"suggestion\": \"Remove the unused extractor or add a collection that uses it\",         \"fix_example\": \"collections:\\n  - name: text_docs\\n    feature_extractor:\\n      name: text_extractor\"     }
    """ # noqa: E501
    code: StrictStr = Field(description="Lint rule code (e.g., 'UNUSED_EXTRACTOR', 'MISSING_CACHE_CONFIG')")
    severity: LintSeverity = Field(description="Severity level: error, warning, or info")
    message: StrictStr = Field(description="Human-readable description of the issue")
    location: StrictStr = Field(description="Path to the problematic element (e.g., 'retrievers[0].stages[2]')")
    suggestion: StrictStr = Field(description="Actionable suggestion for fixing the issue")
    fix_example: Optional[StrictStr] = Field(default=None, description="Optional YAML example showing the correct configuration")
    __properties: ClassVar[List[str]] = ["code", "severity", "message", "location", "suggestion", "fix_example"]

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

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

        _obj = cls.model_validate({
            "code": obj.get("code"),
            "severity": obj.get("severity"),
            "message": obj.get("message"),
            "location": obj.get("location"),
            "suggestion": obj.get("suggestion"),
            "fix_example": obj.get("fix_example")
        })
        return _obj


