# 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 typing_extensions import Annotated
from mixpeek.models.pagination_method import PaginationMethod
from typing import Optional, Set
from typing_extensions import Self

class ScrollPaginationParams(BaseModel):
    """
    Scroll-style pagination maintaining server-side context for TTL.  Best for: Bulk exports, batch processing, iterating through all results  How it works: - Server maintains a snapshot of results - First request: scroll_id=null, returns scroll_id - Subsequent requests: use scroll_id from response - Context expires after scroll_ttl seconds - Consistent view of data (point-in-time snapshot)  Tradeoffs: - Requires server-side state (memory/cache) - TTL means sessions can expire - Not suitable for long-lived sessions - Good for background jobs, not user-facing UIs  Use when: - Exporting large datasets - Batch processing all results - Background jobs iterating through results - You need consistent point-in-time view  Example flow: 1. Request: {\"method\": \"scroll\", \"limit\": 100, \"scroll_id\": null} 2. Response: {\"documents\": [...], \"scroll_id\": \"xyz789\", \"has_next\": true} 3. Request: {\"method\": \"scroll\", \"limit\": 100, \"scroll_id\": \"xyz789\"}
    """ # noqa: E501
    method: Optional[PaginationMethod] = Field(default=None, description="Constant identifying scroll pagination (REQUIRED).")
    limit: Optional[Annotated[int, Field(le=1000, strict=True, ge=1)]] = Field(default=100, description="Number of documents to fetch per scroll page (REQUIRED). Default: 100.")
    scroll_id: Optional[StrictStr] = Field(default=None, description="Server-issued scroll session identifier (OPTIONAL). null for first request, then use scroll_id from response")
    scroll_ttl: Optional[Annotated[int, Field(le=3600, strict=True, ge=60)]] = Field(default=300, description="Seconds to keep scroll context alive (REQUIRED). Default: 300 (5 minutes).")
    __properties: ClassVar[List[str]] = ["method", "limit", "scroll_id", "scroll_ttl"]

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

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

        _obj = cls.model_validate({
            "method": obj.get("method"),
            "limit": obj.get("limit") if obj.get("limit") is not None else 100,
            "scroll_id": obj.get("scroll_id"),
            "scroll_ttl": obj.get("scroll_ttl") if obj.get("scroll_ttl") is not None else 300
        })
        return _obj


