# coding: utf-8

"""
    BSUB.IO API

    REST API for batch processing compute-intensive workloads.  Submit jobs, upload data, and retrieve results through a simple API. Perfect for PDF processing, video transcoding, audio transcription, and more.  ## Authentication  All endpoints require Bearer token authentication using your API key:  ``` Authorization: Bearer <your-api-key> ```  Get your API key from the [Dashboard](/).  ## Job Lifecycle  1. **Create** a job with `POST /v1/jobs` - Returns job ID and upload token 2. **Upload** data with `POST /v1/upload/{jobId}` - Uses upload token 3. **Submit** for processing with `POST /v1/jobs/{jobId}/submit` 4. **Monitor** status with `GET /v1/jobs/{jobId}` 5. **Retrieve** output with `GET /v1/jobs/{jobId}/output`  ## Job States  - `created` - Job created, awaiting data upload - `loaded` - Data uploaded successfully - `pending` - Waiting in queue for a worker - `claimed` - Worker claimed the job - `preparing` - Worker preparing to process - `processing` - Processing started - `finished` - Processing completed successfully - `failed` - Processing failed with error 

    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
from typing import Any, ClassVar, Dict, List, Optional
from bsubio.models.processing_type import ProcessingType
from typing import Optional, Set
from typing_extensions import Self

class GetTypes200Response(BaseModel):
    """
    GetTypes200Response
    """ # noqa: E501
    types: Optional[List[ProcessingType]] = None
    __properties: ClassVar[List[str]] = ["types"]

    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 GetTypes200Response 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 types (list)
        _items = []
        if self.types:
            for _item_types in self.types:
                if _item_types:
                    _items.append(_item_types.to_dict())
            _dict['types'] = _items
        return _dict

    @classmethod
    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
        """Create an instance of GetTypes200Response from a dict"""
        if obj is None:
            return None

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

        _obj = cls.model_validate({
            "types": [ProcessingType.from_dict(_item) for _item in obj["types"]] if obj.get("types") is not None else None
        })
        return _obj


