# coding: utf-8

"""
ME TT DataPortal Data Portal API

ME TT DataPortal Data Portal APIs to fetch Gut Microbes Genomes / Genes information.

The version of the OpenAPI document: 1.0.0
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, StrictStr
from typing import Any, ClassVar, Dict, List, Optional, Union
from typing_extensions import Annotated
from typing import Set
from typing_extensions import Self


class ProteomicsSearchQuerySchema(BaseModel):
    """
    Schema for proteomics search endpoint query parameters.
    """  # noqa: E501

    locus_tags: Optional[StrictStr] = None
    uniprot_ids: Optional[StrictStr] = None
    min_coverage: Optional[
        Union[
            Annotated[float, Field(le=100, strict=True, ge=0)],
            Annotated[int, Field(le=100, strict=True, ge=0)],
        ]
    ] = None
    min_unique_peptides: Optional[Annotated[int, Field(strict=True, ge=0)]] = None
    has_evidence: Optional[StrictBool] = None
    __properties: ClassVar[List[str]] = [
        "locus_tags",
        "uniprot_ids",
        "min_coverage",
        "min_unique_peptides",
        "has_evidence",
    ]

    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 ProteomicsSearchQuerySchema 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,
        )
        # set to None if locus_tags (nullable) is None
        # and model_fields_set contains the field
        if self.locus_tags is None and "locus_tags" in self.model_fields_set:
            _dict["locus_tags"] = None

        # set to None if uniprot_ids (nullable) is None
        # and model_fields_set contains the field
        if self.uniprot_ids is None and "uniprot_ids" in self.model_fields_set:
            _dict["uniprot_ids"] = None

        # set to None if min_coverage (nullable) is None
        # and model_fields_set contains the field
        if self.min_coverage is None and "min_coverage" in self.model_fields_set:
            _dict["min_coverage"] = None

        # set to None if min_unique_peptides (nullable) is None
        # and model_fields_set contains the field
        if (
            self.min_unique_peptides is None
            and "min_unique_peptides" in self.model_fields_set
        ):
            _dict["min_unique_peptides"] = None

        # set to None if has_evidence (nullable) is None
        # and model_fields_set contains the field
        if self.has_evidence is None and "has_evidence" in self.model_fields_set:
            _dict["has_evidence"] = None

        return _dict

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

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

        _obj = cls.model_validate(
            {
                "locus_tags": obj.get("locus_tags"),
                "uniprot_ids": obj.get("uniprot_ids"),
                "min_coverage": obj.get("min_coverage"),
                "min_unique_peptides": obj.get("min_unique_peptides"),
                "has_evidence": obj.get("has_evidence"),
            }
        )
        return _obj
