from enum import Enum
from typing import List, Optional

from pydantic import BaseModel, Field, field_validator


class InvestmentStage(str, Enum):
    SEED = "SEED"
    SERIES_A = "SERIES_A"
    SERIES_B = "SERIES_B"
    SERIES_C = "SERIES_C"
    GROWTH = "GROWTH"
    LATE_STAGE = "LATE_STAGE"


class SectorSchema(BaseModel):
    """
    Expert parser: Only extract sector information if clearly identifiable.
    Leave name empty if uncertain about the sector classification.
    """

    id: Optional[int] = Field(
        default=None,
        ge=0,
        description="Sector ID, must be 0 or greater. Use 0 if uncertain.",
    )
    name: Optional[str] = Field(
        default=None,
        description="Sector name. Leave empty string if not clearly identifiable from the data.",
    )

    @field_validator("name", mode="before")
    @classmethod
    def empty_string_to_none(cls, v):
        """Convert empty strings to None"""
        if v == "" or (isinstance(v, str) and v.strip() == ""):
            return None
        return v

    @field_validator("id", mode="before")
    @classmethod
    def zero_to_none(cls, v):
        """Convert 0 to None for optional id field"""
        if v == 0:
            return None
        return v

    class Config:
        from_attributes = True


class InvestorMemberSchema(BaseModel):
    """
    Expert parser: Only extract team member information if clearly identifiable.
    Leave fields empty if uncertain about the member details.
    """

    id: Optional[int] = Field(
        default=None,
        ge=0,
        description="Member ID, must be 0 or greater. Use 0 if uncertain.",
    )
    name: Optional[str] = Field(
        default=None,
        description="Team member name. Leave empty string if not clearly identifiable.",
    )
    role: Optional[str] = Field(
        default=None,
        description="Team member role/title. Leave empty string if not clearly identifiable.",
    )
    email: Optional[str] = Field(
        default=None,
        description="Team member email. Leave empty string if not clearly identifiable or not provided.",
    )
    investor_id: Optional[int] = Field(
        default=None,
        ge=0,
        description="Investor ID, must be 0 or greater. Use 0 if uncertain.",
    )

    @field_validator("name", "role", "email", mode="before")
    @classmethod
    def empty_string_to_none(cls, v):
        """Convert empty strings to None"""
        if v == "" or (isinstance(v, str) and v.strip() == ""):
            return None
        return v

    @field_validator("id", "investor_id", mode="before")
    @classmethod
    def zero_to_none(cls, v):
        """Convert 0 to None for optional integer fields"""
        if v == 0:
            return None
        return v

    class Config:
        from_attributes = True


class CompanyMemberSchema(BaseModel):
    """
    Expert parser: Only extract company member information if clearly identifiable.
    Leave fields empty if uncertain about the member details.
    """

    id: Optional[int] = Field(
        default=None,
        ge=0,
        description="Member ID, must be 0 or greater. Use 0 if uncertain.",
    )
    name: Optional[str] = Field(
        default=None,
        description="Company member name. Leave empty if not clearly identifiable.",
    )
    linkedin: Optional[str] = Field(
        default=None,
        description="LinkedIn profile URL. Leave empty if not provided or uncertain.",
    )
    role: Optional[str] = Field(
        default=None,
        description="Company member role/title. Leave empty if not clearly identifiable.",
    )
    company_id: Optional[int] = Field(
        default=None,
        ge=0,
        description="Company ID, must be 0 or greater. Use 0 if uncertain.",
    )

    @field_validator("name", "linkedin", "role", mode="before")
    @classmethod
    def empty_string_to_none(cls, v):
        """Convert empty strings to None"""
        if v == "" or (isinstance(v, str) and v.strip() == ""):
            return None
        return v

    @field_validator("id", "company_id", mode="before")
    @classmethod
    def zero_to_none(cls, v):
        """Convert 0 to None for optional integer fields"""
        if v == 0:
            return None
        return v

    class Config:
        from_attributes = True


class CompanySchema(BaseModel):
    """
    Expert parser: Only extract company information if clearly identifiable.
    Leave optional fields empty if uncertain. Integer values must be 0 or greater.
    """

    id: Optional[int] = Field(
        default=None,
        ge=0,
        description="Company ID, must be 0 or greater. Use 0 if uncertain.",
    )
    name: Optional[str] = Field(
        default=None,
        description="Company name. Leave empty string if not clearly identifiable.",
    )
    industry: Optional[str] = Field(
        default=None,
        description="Company industry/sector. Leave empty string if not clearly identifiable.",
    )
    location: Optional[str] = Field(
        default=None,
        description="Company location/address. Leave empty string if not clearly identifiable.",
    )
    description: Optional[str] = Field(
        default=None,
        description="Company description. Leave empty if not clearly available or uncertain.",
    )
    founded_year: Optional[int] = Field(
        default=None,
        ge=0,
        description="Year company was founded, must be 0 or greater. Leave None if not clearly identifiable or uncertain.",
    )
    website: Optional[str] = Field(
        default=None,
        description="Company website URL. Leave empty if not provided or uncertain.",
    )

    @field_validator(
        "name", "industry", "location", "description", "website", mode="before"
    )
    @classmethod
    def empty_string_to_none(cls, v):
        """Convert empty strings to None"""
        if v == "" or (isinstance(v, str) and v.strip() == ""):
            return None
        return v

    @field_validator("id", "founded_year", mode="before")
    @classmethod
    def zero_to_none(cls, v):
        """Convert 0 to None for founded_year"""
        if v == 0:
            return None
        return v

    @field_validator("founded_year", mode="before")
    @classmethod
    def validate_founded_year(cls, v):
        """Expert parser: Only accept clearly identifiable founding years"""
        if v is None or v == "Not Available" or v == "" or v == "Unknown":
            return None
        if isinstance(v, str):
            try:
                year = int(v)
                return year if year >= 0 else None
            except ValueError:
                return None
        return v if isinstance(v, int) and v >= 0 else None

    class Config:
        from_attributes = True


class InvestorSchema(BaseModel):
    """
    Expert parser: Only extract investor information if clearly identifiable.
    Leave optional fields empty if uncertain. All numeric values must be 0 or greater.
    """

    id: Optional[int] = Field(
        default=None,
        ge=0,
        description="Investor ID, must be 0 or greater. Use 0 if uncertain.",
    )
    name: Optional[str] = Field(
        default=None,
        description="Investor name. Do not return any special characters, Just the name as a string.",
    )
    description: Optional[str] = Field(
        default=None,
        description="Investor description. Leave empty if not clearly available or uncertain.",
    )
    aum: Optional[int] = Field(
        default=None,
        ge=0,
        description="Assets Under Management in USD, must be 0 or greater. Use 0 if not clearly identifiable or uncertain.",
    )
    check_size_lower: Optional[int] = Field(
        default=None,
        ge=0,
        description="Lower bound of typical investment check size in USD, must be 0 or greater. Use 0 if not clearly identifiable.",
    )
    check_size_upper: Optional[int] = Field(
        default=None,
        ge=0,
        description="Upper bound of typical investment check size in USD, must be 0 or greater. Use 0 if not clearly identifiable.",
    )
    geographic_focus: Optional[str] = Field(
        default=None,
        description="Geographic investment focus. Do not return any special characters, Just locations separated by commas. Leave empty if not clearly identifiable.",
    )
    stage_focus: InvestmentStage = Field(
        default=InvestmentStage.SEED,
        description="Investment stage focus. Use SEED as default if uncertain.",
    )
    number_of_investments: Optional[int] = Field(
        default=None,
        ge=0,
        description="Total number of investments made, must be 0 or greater. Use 0 if not clearly identifiable.",
    )

    @field_validator("name", "description", "geographic_focus", mode="before")
    @classmethod
    def empty_string_to_none(cls, v):
        """Convert empty strings to None"""
        if v == "" or (isinstance(v, str) and v.strip() == ""):
            return None
        return v

    @field_validator(
        "id",
        "aum",
        "check_size_lower",
        "check_size_upper",
        "number_of_investments",
        mode="before",
    )
    @classmethod
    def zero_to_none(cls, v):
        """Convert 0 to None for optional integer fields"""
        if v == 0:
            return None
        return v

    class Config:
        from_attributes = True


class InvestorData(BaseModel):
    """
    Expert parser: Comprehensive investor data schema for LLM processing.
    Only populate fields with clearly identifiable information. Leave lists empty if uncertain.
    """

    investor: InvestorSchema = Field(
        description="Core investor information. Only populate with clearly identifiable data."
    )
    portfolio_companies: List[CompanySchema] = Field(
        default=[],
        description="List of portfolio companies. Leave empty if not clearly identifiable.",
    )
    team_members: List[InvestorMemberSchema] = Field(
        default=[],
        description="List of team members. Leave empty if not clearly identifiable.",
    )
    sectors: List[SectorSchema] = Field(
        default=[],
        description="List of investment sectors. Leave empty if not clearly identifiable.",
    )

    class Config:
        from_attributes = True


class CompanyData(BaseModel):
    """
    Expert parser: Comprehensive company data schema for LLM processing.
    Only populate fields with clearly identifiable information. Leave lists empty if uncertain.
    """

    company: CompanySchema = Field(
        description="Core company information. Only populate with clearly identifiable data."
    )
    sectors: List[SectorSchema] = Field(
        default=[],
        description="List of company sectors. Leave empty if not clearly identifiable.",
    )
    members: List[CompanyMemberSchema] = Field(
        default=[],
        description="List of company members. Leave empty if not clearly identifiable.",
    )
    investors: List[InvestorSchema] = Field(
        default=[],
        description="List of investors. Leave empty if not clearly identifiable.",
    )

    class Config:
        from_attributes = True


class InvestorList(BaseModel):
    """Expert parser: List of investors with clearly identifiable information only."""

    investors: List[InvestorData] = Field(
        default=[],
        description="List of investors. Leave empty if no clearly identifiable investors.",
    )