server/tasks.py

"""Hardcoded task definitions with datasets and issue registries."""

import copy
from dataclasses import dataclass, field
from typing import Any, Dict, List, Set


@dataclass
class Issue:
    issue_id: str
    row: int
    column: str
    issue_type: str
    description: str
    # Extra params for validation (e.g. canonical_set, low/high range)
    validation_params: Dict[str, Any] = field(default_factory=dict)
    # For duplicate_row issues, store the original row data
    original_row_data: Dict[str, Any] = field(default_factory=dict)


@dataclass
class TaskDefinition:
    task_id: str
    difficulty: str
    description: str
    columns: List[str]
    data: List[Dict[str, Any]]
    issues: List[Issue]
    max_steps: int
    column_descriptions: Dict[str, str]


# ---------------------------------------------------------------------------
# EASY: Customer Contacts
# ---------------------------------------------------------------------------
_EASY_DATA = [
    {"name": "John Smith", "email": "john.smith@gmail.com", "phone": "555-012-3401", "city": "New York", "signup_date": "2024-01-15"},
    {"name": "Jane Doe", "email": "jane.doe@outlook.com", "phone": "555-012-3402", "city": "Los Angeles", "signup_date": "2024-02-20"},
    {"name": "Bob Johnson", "email": "bob.j@company.com", "phone": "555-012-3403", "city": "Chicago", "signup_date": "2024-03-10"},
    {"name": "Alice Brown", "email": "alice.brown[at]mail.com", "phone": "555-012-3404", "city": "Houston", "signup_date": "2024-04-05"},  # E1: invalid email
    {"name": "Charlie Wilson", "email": "charlie@example.com", "phone": "555-012-3405", "city": "Phoenix", "signup_date": "2024-05-12"},
    {"name": "Diana Davis", "email": "diana@example.com", "phone": "555-012-3406", "city": "", "signup_date": "2024-06-18"},  # E3: empty city
    {"name": "Eve Martinez", "email": "eve@example.com", "phone": "555-012-3407", "city": "Philadelphia", "signup_date": "2024-07-22"},
    {"name": "Frank Taylor", "email": "frank@example.com", "phone": "55A-0B2-34C8", "city": "San Antonio", "signup_date": "2024-08-30"},  # E2: phone has letters
    {"name": "Grace Lee", "email": "grace@example.com", "phone": "555-012-3409", "city": "San Diego", "signup_date": "2024-09-14"},
    {"name": "Hank Moore", "email": "hank@@domain.com", "phone": "555-012-3410", "city": "Dallas", "signup_date": "2024-10-01"},  # E5: double @@
    {"name": "Ivy Clark", "email": "ivy@example.com", "phone": "555-012-3411", "city": "San Jose", "signup_date": "2024-11-18"},
    {"name": "Jack White", "email": "jack@example.com", "phone": "555-012-3412", "city": "Austin", "signup_date": "03/25/2024"},  # E4: wrong date format
    {"name": "Karen Lewis", "email": "karen@example.com", "phone": "555-012-3413", "city": "Jacksonville", "signup_date": "2025-01-03"},
    {"name": "Leo Walker", "email": "leo@example.com", "phone": "555-012-3414", "city": "Columbus", "signup_date": "2025-02-14"},
    {"name": "John Smith", "email": "john.smith@gmail.com", "phone": "555-012-3401", "city": "New York", "signup_date": "2024-01-15"},  # E6: exact duplicate of row 0
]

_EASY_ISSUES = [
    Issue("E1", 3, "email", "invalid_email", "Email uses '[at]' instead of '@'"),
    Issue("E2", 7, "phone", "invalid_phone", "Phone number contains letters"),
    Issue("E3", 5, "city", "missing_value", "City is empty"),
    Issue("E4", 11, "signup_date", "wrong_date_format", "Date is MM/DD/YYYY instead of YYYY-MM-DD"),
    Issue("E5", 9, "email", "invalid_email", "Email has double @@ symbol"),
    Issue(
        "E6", 14, "", "duplicate_row", "Exact duplicate of row 0",
        original_row_data={"name": "John Smith", "email": "john.smith@gmail.com", "phone": "555-012-3401", "city": "New York", "signup_date": "2024-01-15"},
    ),
]

EASY_TASK = TaskDefinition(
    task_id="customer_contacts",
    difficulty="easy",
    description=(
        "You are cleaning a customer contact list for a CRM import. "
        "The data should have valid emails (user@domain.tld), phone numbers "
        "(digits and dashes only, 10+ digits), non-empty cities, dates in "
        "YYYY-MM-DD format, and no duplicate rows. Find and fix all data "
        "quality issues."
    ),
    columns=["name", "email", "phone", "city", "signup_date"],
    data=_EASY_DATA,
    issues=_EASY_ISSUES,
    max_steps=15,
    column_descriptions={
        "name": "Customer full name",
        "email": "Email address (must be user@domain.tld)",
        "phone": "Phone number (digits and dashes, 10+ digits)",
        "city": "City of residence (must not be empty)",
        "signup_date": "Signup date (must be YYYY-MM-DD format)",
    },
)


# ---------------------------------------------------------------------------
# MEDIUM: Sales Records
# ---------------------------------------------------------------------------
_MEDIUM_DATA = [
    {"order_id": "ORD-1001", "customer_name": "Acme Corp", "product": "Widget A", "quantity": 10, "unit_price": 29.99, "order_date": "2024-01-15", "region": "Northeast"},
    {"order_id": "ORD-1002", "customer_name": "Beta Inc", "product": "Widget B", "quantity": 5, "unit_price": 49.99, "order_date": "2024-01-22", "region": "Southeast"},
    {"order_id": "ORD-1003", "customer_name": "Gamma LLC", "product": "Gadget X", "quantity": 20, "unit_price": 15.00, "order_date": "2024-02-03", "region": "Midwest"},
    {"order_id": "ORD-1004", "customer_name": "Delta Co", "product": "Widget A", "quantity": 8, "unit_price": 29.99, "order_date": "2024-02-10", "region": "north-east"},  # M6: inconsistent region
    {"order_id": "ORD-1005", "customer_name": "Epsilon Ltd", "product": "Gadget Y", "quantity": 3, "unit_price": 89.50, "order_date": "Jan 15, 2024", "region": "West"},  # M1: wrong date format
    {"order_id": "ORD-1006", "customer_name": "Zeta Group", "product": "Widget C", "quantity": 12, "unit_price": 35.00, "order_date": "2024-03-01", "region": "Northwest"},
    {"order_id": "", "customer_name": "Eta Partners", "product": "Gadget X", "quantity": 7, "unit_price": 15.00, "order_date": "2024-03-12", "region": "Southeast"},  # M10: missing order_id
    {"order_id": "ORD-1008", "customer_name": "Theta Corp", "product": "Widget B", "quantity": 15, "unit_price": 49.99, "order_date": "2024-03-20", "region": "Midwest"},
    {"order_id": "ORD-1009", "customer_name": "Iota Inc", "product": "Gadget Z", "quantity": -5, "unit_price": 120.00, "order_date": "2024-04-02", "region": "Northeast"},  # M3: negative quantity
    {"order_id": "ORD-1010", "customer_name": "Kappa LLC", "product": "Widget A", "quantity": 25, "unit_price": 29.99, "order_date": "2024-04-15", "region": "West"},
    {"order_id": "ORD-1011", "customer_name": "Lambda Co", "product": "Gadget Y", "quantity": 6, "unit_price": 89.50, "order_date": "2024-04-28", "region": "Southeast"},
    {"order_id": "ORD-1012", "customer_name": "Mu Ltd", "product": "Widget C", "quantity": 30, "unit_price": 35.00, "order_date": "2024-05-05", "region": "Northeast"},
    {"order_id": "ORD-1013", "customer_name": "Nu Group", "product": "Gadget X", "quantity": 4, "unit_price": 15.00, "order_date": "2024/05/18", "region": "Midwest"},  # M2: slash date format
    {"order_id": "ORD-1014", "customer_name": "Xi Partners", "product": "Widget B", "quantity": 9, "unit_price": 49.99, "order_date": "2024-06-01", "region": "Northwest"},
    {"order_id": "ORD-1015", "customer_name": "Omicron Corp", "product": "Gadget Z", "quantity": 2, "unit_price": 29999.99, "order_date": "2024-06-15", "region": "West"},  # M5: price outlier
    {"order_id": "ORD-1016", "customer_name": "Pi Inc", "product": "Widget A", "quantity": 18, "unit_price": 29.99, "order_date": "2024-06-28", "region": "Northeast"},
    {"order_id": "ORD-1017", "customer_name": "Rho LLC", "product": "Gadget Y", "quantity": 11, "unit_price": 89.50, "order_date": "2024-07-10", "region": "Southeast"},
    {"order_id": "ORD-1018", "customer_name": "  Sigma  Co  ", "product": "Widget C", "quantity": 7, "unit_price": 35.00, "order_date": "2024-07-22", "region": "Midwest"},  # M11: excess whitespace
    {"order_id": "ORD-1019", "customer_name": "Tau Group", "product": "Gadget X", "quantity": 14, "unit_price": 15.00, "order_date": "2024-08-05", "region": "Northwest"},
    {"order_id": "ORD-1020", "customer_name": "Upsilon Ltd", "product": "Widget B", "quantity": -12, "unit_price": 49.99, "order_date": "2024-08-18", "region": "Northeast"},  # M4: negative quantity
    {"order_id": "ORD-1021", "customer_name": "Phi Corp", "product": "Gadget Z", "quantity": 8, "unit_price": -15.50, "order_date": "2024-09-01", "region": "West"},  # M12: negative price
    {"order_id": "ORD-1022", "customer_name": "Chi Inc", "product": "Widget A", "quantity": 22, "unit_price": 29.99, "order_date": "2024-09-15", "region": "Southeast"},
    {"order_id": "ORD-1023", "customer_name": "Psi LLC", "product": "Gadget Y", "quantity": 3, "unit_price": 89.50, "order_date": "2024-09-28", "region": "WEST"},  # M7: inconsistent case
    {"order_id": "ORD-1024", "customer_name": "Omega Co", "product": "Widget C", "quantity": 16, "unit_price": 35.00, "order_date": "2024-10-10", "region": "Midwest"},
    {"order_id": "ORD-1025", "customer_name": "Alpha2 Group", "product": "Gadget X", "quantity": 9, "unit_price": 15.00, "order_date": "2024-10-22", "region": "Northwest"},
    {"order_id": "ORD-1011", "customer_name": "Lambda Co", "product": "Gadget Y", "quantity": 6, "unit_price": 89.50, "order_date": "2024-04-28", "region": "Southeast"},  # M9: duplicate of row 10
    {"order_id": "ORD-1027", "customer_name": "Beta2 Inc", "product": "Widget B", "quantity": 13, "unit_price": 49.99, "order_date": "2024-11-15", "region": "Northeast"},
    {"order_id": "ORD-1028", "customer_name": "Gamma2 LLC", "product": "Gadget Z", "quantity": 5, "unit_price": 120.00, "order_date": "2024-11-28", "region": "South East"},  # M8: inconsistent region
    {"order_id": "ORD-1029", "customer_name": "Delta2 Co", "product": "Widget A", "quantity": 20, "unit_price": 29.99, "order_date": "2024-12-10", "region": "West"},
    {"order_id": "ORD-1030", "customer_name": "Epsilon2 Ltd", "product": "Gadget Y", "quantity": 7, "unit_price": 89.50, "order_date": "2024-12-22", "region": "Midwest"},
]

_VALID_REGIONS: Set[str] = {"Northeast", "Southeast", "Midwest", "West", "Northwest"}

_MEDIUM_ISSUES = [
    Issue("M1", 4, "order_date", "wrong_date_format", "Date is 'Jan 15, 2024' instead of YYYY-MM-DD"),
    Issue("M2", 12, "order_date", "wrong_date_format", "Date uses slashes '2024/05/18' instead of YYYY-MM-DD"),
    Issue("M3", 8, "quantity", "negative_number", "Quantity is negative (-5)"),
    Issue("M4", 19, "quantity", "negative_number", "Quantity is negative (-12)"),
    Issue("M5", 14, "unit_price", "outlier", "Price 29999.99 is ~1000x normal range",
          validation_params={"low": 1.0, "high": 500.0}),
    Issue("M6", 3, "region", "inconsistent_format", "Region 'north-east' should match canonical form",
          validation_params={"canonical_set": _VALID_REGIONS}),
    Issue("M7", 22, "region", "inconsistent_format", "Region 'WEST' should match canonical form",
          validation_params={"canonical_set": _VALID_REGIONS}),
    Issue("M8", 27, "region", "inconsistent_format", "Region 'South East' should match canonical form",
          validation_params={"canonical_set": _VALID_REGIONS}),
    Issue(
        "M9", 25, "", "duplicate_row", "Exact duplicate of row 10 (same order_id, customer, product)",
        original_row_data={"order_id": "ORD-1011", "customer_name": "Lambda Co", "product": "Gadget Y", "quantity": 6, "unit_price": 89.50, "order_date": "2024-04-28", "region": "Southeast"},
    ),
    Issue("M10", 6, "order_id", "missing_value", "Order ID is empty"),
    Issue("M11", 17, "customer_name", "excess_whitespace", "Customer name has excess whitespace"),
    Issue("M12", 20, "unit_price", "negative_number", "Price is negative (-15.50)"),
]

MEDIUM_TASK = TaskDefinition(
    task_id="sales_records",
    difficulty="medium",
    description=(
        "You are cleaning sales transaction records for a quarterly report. "
        "Requirements: order_id must be non-empty (format ORD-XXXX), dates must "
        "be YYYY-MM-DD, quantities and prices must be positive numbers, prices "
        "should be in a reasonable range ($1-$500), regions must be one of: "
        "Northeast, Southeast, Midwest, West, Northwest. Names should have no "
        "excess whitespace. No duplicate orders."
    ),
    columns=["order_id", "customer_name", "product", "quantity", "unit_price", "order_date", "region"],
    data=_MEDIUM_DATA,
    issues=_MEDIUM_ISSUES,
    max_steps=25,
    column_descriptions={
        "order_id": "Unique order identifier (format: ORD-XXXX, must not be empty)",
        "customer_name": "Customer/company name (no excess whitespace)",
        "product": "Product name",
        "quantity": "Order quantity (must be positive)",
        "unit_price": "Price per unit in USD (must be positive, reasonable range $1-$500)",
        "order_date": "Order date (must be YYYY-MM-DD)",
        "region": "Sales region (must be: Northeast, Southeast, Midwest, West, or Northwest)",
    },
)


# ---------------------------------------------------------------------------
# HARD: Employee Records
# ---------------------------------------------------------------------------
_HARD_DATA = [
    {"emp_id": "EMP-001", "name": "Sarah Chen", "email": "sarah.chen@company.com", "department": "Engineering", "hire_date": "2020-03-15", "termination_date": "", "salary": 95000, "manager_id": "EMP-010", "performance_score": 8.5},
    {"emp_id": "EMP-002", "name": "James Wilson", "email": "james.w@company.com", "department": "Marketing", "hire_date": "2019-07-01", "termination_date": "", "salary": 78000, "manager_id": "EMP-010", "performance_score": 7.2},
    {"emp_id": "EMP-003", "name": "Maria Garcia", "email": "maria.g@company.com", "department": "Engineering", "hire_date": "2021-01-10", "termination_date": "", "salary": 15000000, "manager_id": "EMP-001", "performance_score": 9.1},  # H5: salary outlier (15M)
    {"emp_id": "EMP-004", "name": "David Kim", "email": "david.kim@company.com", "department": "Sales", "hire_date": "2020-09-20", "termination_date": "", "salary": 72000, "manager_id": "EMP-010", "performance_score": 6.8},
    {"emp_id": "EMP-005", "name": "Emily Patel", "email": "emily.p@company.com", "department": "HR", "hire_date": "2018-04-05", "termination_date": "", "salary": 82000, "manager_id": "EMP-010", "performance_score": 8.0},
    {"emp_id": "EMP-006", "name": "Michael Brown", "email": "michael.b@company.com", "department": "Engineering", "hire_date": "2022-06-15", "termination_date": "", "salary": 88000, "manager_id": "EMP-099", "performance_score": 7.5},  # H1: manager doesn't exist
    {"emp_id": "EMP-007", "name": "  Robert   Williams  ", "email": "robert.w@company.com", "department": "Finance", "hire_date": "2019-11-01", "termination_date": "", "salary": 91000, "manager_id": "EMP-010", "performance_score": 8.3},  # H17: excess whitespace in name
    {"emp_id": "EMP-008", "name": "Lisa Anderson", "email": "lisa.a@company.com", "department": "Sales", "hire_date": "2024-03-15", "termination_date": "2023-01-10", "salary": 68000, "manager_id": "EMP-004", "performance_score": 5.5},  # H3: termination before hire
    {"emp_id": "EMP-009", "name": "Kevin Taylor", "email": "kevin.t@company.com", "department": "Engineering", "hire_date": "2021-08-20", "termination_date": "", "salary": 93000, "manager_id": "EMP-001", "performance_score": 11.5},  # H9: score > 10
    {"emp_id": "EMP-010", "name": "Jennifer Martinez", "email": "jennifer.m@company.com", "department": "Operations", "hire_date": "2017-01-15", "termination_date": "", "salary": 120000, "manager_id": "", "performance_score": 9.0},
    {"emp_id": "EMP-011", "name": "Alice Jones", "email": "alice.jones@", "department": "Marketing", "hire_date": "2022-02-01", "termination_date": "", "salary": 75000, "manager_id": "EMP-002", "performance_score": 7.8},  # H7: incomplete email
    {"emp_id": "EMP-012", "name": "Thomas Lee", "email": "thomas.l@company.com", "department": "Engineering", "hire_date": "2020-10-10", "termination_date": "", "salary": 97000, "manager_id": "EMP-001", "performance_score": 8.7},
    {"emp_id": "EMP-013", "name": "Rachel Green", "email": "rachel.g@company.com", "department": "Engg", "hire_date": "2023-04-15", "termination_date": "", "salary": 85000, "manager_id": "EMP-001", "performance_score": 7.0},  # H11: department abbreviation
    {"emp_id": "EMP-014", "name": "Daniel White", "email": "daniel.w@company.com", "department": "Finance", "hire_date": "2021-05-20", "termination_date": "", "salary": 89000, "manager_id": "EMP-007", "performance_score": 8.1},
    {"emp_id": "EMP-015", "name": "Sophie Clark", "email": "sophie.c@company.com", "department": "HR", "hire_date": "2023-06-01", "termination_date": "2023-05-15", "salary": 71000, "manager_id": "EMP-005", "performance_score": 6.0},  # H4: termination before hire
    {"emp_id": "EMP-016", "name": "Chris Johnson", "email": "chris.j@company.com", "department": "Sales", "hire_date": "2020-12-01", "termination_date": "", "salary": 76000, "manager_id": "EMP-004", "performance_score": 7.3},
    {"emp_id": "EMP-003", "name": "Maria R. Garcia", "email": "maria.g@company.com", "department": "Engineering", "hire_date": "2021-01-10", "termination_date": "", "salary": 15000000, "manager_id": "EMP-001", "performance_score": 9.1},  # H14: semantic dup of row 2 (same emp_id, slightly different name)
    {"emp_id": "EMP-017", "name": "Amanda Davis", "email": "amanda.d@company.com", "department": "Marketing", "hire_date": "2022-09-15", "termination_date": "", "salary": 73000, "manager_id": "EMP-002", "performance_score": 7.6},
    {"emp_id": "EMP-018", "name": "Ryan Thomas", "email": "ryan.t@company.com", "department": "Engineering", "hire_date": "2023-01-20", "termination_date": "", "salary": 90000, "manager_id": "EMP-088", "performance_score": 8.0},  # H2: manager doesn't exist
    {"emp_id": "EMP-019", "name": "Nicole Brown", "email": "nicole.b@company.com", "department": "Finance", "hire_date": "2021-03-01", "termination_date": "", "salary": 87000, "manager_id": "EMP-007", "performance_score": 8.4},
    {"emp_id": "EMP-020", "name": "Jason Miller", "email": "jason.m@company.com", "department": "Operations", "hire_date": "2024-11-15", "termination_date": "2024-06-30", "salary": 79000, "manager_id": "EMP-010", "performance_score": 6.5},  # H16: termination before hire
    {"emp_id": "EMP-021", "name": "Laura Wilson", "email": "laura.w@company.com", "department": "Sales", "hire_date": "2022-11-01", "termination_date": "", "salary": 74000, "manager_id": "EMP-004", "performance_score": 7.1},
    {"emp_id": "EMP-022", "name": "Mark Thompson", "email": "mark.t@company.com", "department": "Engineering", "hire_date": "2019-05-15", "termination_date": "", "salary": 500, "manager_id": "EMP-001", "performance_score": 8.9},  # H6: salary too low (missing zeros)
    {"emp_id": "EMP-023", "name": "Patricia Moore", "email": "patricia.m@company.com", "department": "HR", "hire_date": "2023-08-01", "termination_date": "", "salary": 70000, "manager_id": "EMP-005", "performance_score": 6.2},
    {"emp_id": "EMP-024", "name": "Steven Harris", "email": "steven.h@company.com", "department": "Marketing", "hire_date": "2021-12-10", "termination_date": "", "salary": 77000, "manager_id": "EMP-002", "performance_score": 7.9},
    {"emp_id": "EMP-025", "name": "Angela Martin", "email": "angela.m@company.com", "department": "Finance", "hire_date": "2020-02-20", "termination_date": "", "salary": 92000, "manager_id": "EMP-007", "performance_score": -2.0},  # H10: negative score
    {"emp_id": "EMP-026", "name": "Brian Lewis", "email": "brian.l@company.com", "department": "Operations", "hire_date": "2022-04-01", "termination_date": "", "salary": 81000, "manager_id": "EMP-010", "performance_score": 7.4},
    {"emp_id": "EMP-027", "name": "Michelle Walker", "email": "michelle.w@company.com", "department": "Sales", "hire_date": "2023-10-15", "termination_date": "", "salary": 69000, "manager_id": "EMP-004", "performance_score": 6.7},
    {"emp_id": "EMP-028", "name": "Paul Robinson", "email": "paul.r@company.com", "department": "marketing", "hire_date": "2021-06-20", "termination_date": "", "salary": 76000, "manager_id": "EMP-002", "performance_score": 7.7},  # H12: lowercase department
    {"emp_id": "EMP-029", "name": "Sandra Hall", "email": "sandra.h@company.com", "department": "Engineering", "hire_date": "2020-08-10", "termination_date": "", "salary": 94000, "manager_id": "EMP-001", "performance_score": 8.6},
    {"emp_id": "EMP-030", "name": "Bob Smith", "email": "bob smith@company.com", "department": "Finance", "hire_date": "2022-12-01", "termination_date": "", "salary": 86000, "manager_id": "EMP-007", "performance_score": 7.0},  # H8: space in email
    {"emp_id": "EMP-031", "name": "Diana Scott", "email": "diana.s@company.com", "department": "HR", "hire_date": "2023-03-15", "termination_date": "", "salary": 72000, "manager_id": "EMP-005", "performance_score": 6.9},
    {"emp_id": "EMP-032", "name": "George Adams", "email": "george.a@company.com", "department": "Operations", "hire_date": "2021-09-01", "termination_date": "", "salary": 83000, "manager_id": "EMP-010", "performance_score": 7.8},
    {"emp_id": "EMP-033", "name": "Kevin Taylor", "email": "kevin.t@company.com", "department": "Engineering", "hire_date": "2021-08-20", "termination_date": "", "salary": 93000, "manager_id": "EMP-001", "performance_score": 8.8},  # H15: semantic dup of row 8 (same name+email, diff score)
    {"emp_id": "EMP-034", "name": "Helen King", "email": "helen.k@company.com", "department": "Sales", "hire_date": "2022-07-10", "termination_date": "", "salary": 71000, "manager_id": "EMP-004", "performance_score": 7.2},
    {"emp_id": "EMP-035", "name": "Richard Wright", "email": "richard.w@company.com", "department": "Human Resources", "hire_date": "2020-11-20", "termination_date": "", "salary": 80000, "manager_id": "EMP-005", "performance_score": 8.0},  # H13: non-canonical dept
    {"emp_id": "EMP-036", "name": "Nancy Lopez", "email": "nancy.l@company.com", "department": "Engineering", "hire_date": "2023-05-01", "termination_date": "", "salary": 87000, "manager_id": "EMP-001", "performance_score": 7.3},
    {"emp_id": "EMP-037", "name": "Carl Hill", "email": "carl.h@company.com", "department": "Marketing", "hire_date": "2021-02-15", "termination_date": "", "salary": 74000, "manager_id": "EMP-002", "performance_score": 7.5},
    {"emp_id": "EMP-038", "name": "Betty Young", "email": "betty.y@company.com", "department": "Finance", "hire_date": "2025-13-01", "termination_date": "", "salary": 88000, "manager_id": "EMP-007", "performance_score": 8.2},  # H18: invalid date (month 13)
    {"emp_id": "EMP-039", "name": "Frank Allen", "email": "frank.a@company.com", "department": "Operations", "hire_date": "2022-01-20", "termination_date": "", "salary": 82000, "manager_id": "EMP-010", "performance_score": 7.6},
]

_VALID_DEPARTMENTS: Set[str] = {"Engineering", "Marketing", "Sales", "HR", "Finance", "Operations"}

# Collect all valid emp_ids (excluding known duplicate rows 16 and 33)
_VALID_EMP_IDS: Set[str] = {
    row["emp_id"]
    for i, row in enumerate(_HARD_DATA)
    if i not in (16, 33)  # exclude duplicate rows
}

_HARD_ISSUES = [
    Issue("H1", 5, "manager_id", "referential_integrity", "Manager EMP-099 does not exist in employee list",
          validation_params={"valid_ids": _VALID_EMP_IDS}),
    Issue("H2", 18, "manager_id", "referential_integrity", "Manager EMP-088 does not exist in employee list",
          validation_params={"valid_ids": _VALID_EMP_IDS}),
    Issue("H3", 7, "termination_date", "temporal_inconsistency", "Termination date 2023-01-10 is before hire date 2024-03-15"),
    Issue("H4", 14, "termination_date", "temporal_inconsistency", "Termination date 2023-05-15 is before hire date 2023-06-01"),
    Issue("H5", 2, "salary", "outlier", "Salary 15,000,000 is unreasonably high (expected $20K-$500K)",
          validation_params={"low": 20000, "high": 500000}),
    Issue("H6", 22, "salary", "outlier", "Salary 500 is unreasonably low (expected $20K-$500K)",
          validation_params={"low": 20000, "high": 500000}),
    Issue("H7", 10, "email", "invalid_email", "Email 'alice.jones@' is missing domain"),
    Issue("H8", 30, "email", "invalid_email", "Email 'bob smith@company.com' contains a space"),
    Issue("H9", 8, "performance_score", "score_out_of_range", "Score 11.5 exceeds the 0-10 scale",
          validation_params={"low": 0.0, "high": 10.0}),
    Issue("H10", 25, "performance_score", "score_out_of_range", "Score -2.0 is negative (scale is 0-10)",
          validation_params={"low": 0.0, "high": 10.0}),
    Issue("H11", 12, "department", "inconsistent_format", "Department 'Engg' should match canonical name",
          validation_params={"canonical_set": _VALID_DEPARTMENTS}),
    Issue("H12", 28, "department", "inconsistent_format", "Department 'marketing' has incorrect casing",
          validation_params={"canonical_set": _VALID_DEPARTMENTS}),
    Issue("H13", 35, "department", "inconsistent_format", "Department 'Human Resources' should be canonical 'HR'",
          validation_params={"canonical_set": _VALID_DEPARTMENTS}),
    Issue(
        "H14", 16, "", "duplicate_row", "Semantic duplicate of row 2 (same emp_id EMP-003, slightly different name)",
        original_row_data={"emp_id": "EMP-003", "name": "Maria R. Garcia", "email": "maria.g@company.com", "department": "Engineering", "hire_date": "2021-01-10", "termination_date": "", "salary": 15000000, "manager_id": "EMP-001", "performance_score": 9.1},
    ),
    Issue(
        "H15", 33, "", "duplicate_row", "Semantic duplicate of row 8 (same name and email as Kevin Taylor)",
        original_row_data={"emp_id": "EMP-033", "name": "Kevin Taylor", "email": "kevin.t@company.com", "department": "Engineering", "hire_date": "2021-08-20", "termination_date": "", "salary": 93000, "manager_id": "EMP-001", "performance_score": 8.8},
    ),
    Issue("H16", 20, "termination_date", "temporal_inconsistency",
          "Termination date 2024-06-30 is before hire date 2024-11-15"),
    Issue("H17", 6, "name", "excess_whitespace", "Name '  Robert   Williams  ' has excess whitespace"),
    Issue("H18", 38, "hire_date", "invalid_date", "Date '2025-13-01' has invalid month 13"),
]

HARD_TASK = TaskDefinition(
    task_id="employee_records",
    difficulty="hard",
    description=(
        "You are cleaning employee records for an HR system migration. Requirements: "
        "emp_id must be unique, emails must be valid (user@domain.tld, no spaces), "
        "departments must be one of: Engineering, Marketing, Sales, HR, Finance, Operations. "
        "Dates must be valid YYYY-MM-DD. Termination dates must be after hire dates. "
        "Salaries must be in range $20,000-$500,000. Performance scores must be 0.0-10.0. "
        "Manager IDs must reference existing employees. Names must have no excess whitespace. "
        "Remove duplicate employee entries."
    ),
    columns=["emp_id", "name", "email", "department", "hire_date", "termination_date", "salary", "manager_id", "performance_score"],
    data=_HARD_DATA,
    issues=_HARD_ISSUES,
    max_steps=35,
    column_descriptions={
        "emp_id": "Unique employee ID (format: EMP-XXX)",
        "name": "Full name (no excess whitespace)",
        "email": "Email (valid user@domain.tld, no spaces)",
        "department": "Department (must be: Engineering, Marketing, Sales, HR, Finance, or Operations)",
        "hire_date": "Hire date (valid YYYY-MM-DD)",
        "termination_date": "Termination date (empty if active, must be after hire_date if set)",
        "salary": "Annual salary USD (range: $20,000-$500,000)",
        "manager_id": "Manager's emp_id (must reference existing employee, empty for top-level)",
        "performance_score": "Performance score (0.0 to 10.0)",
    },
)


# ---------------------------------------------------------------------------
# Expert task — Financial Transactions
# ---------------------------------------------------------------------------
_VALID_CATEGORIES: Set[str] = {"Payment", "Refund", "Transfer", "Fee", "Deposit", "Withdrawal"}
_VALID_CURRENCIES: Set[str] = {"USD", "EUR", "GBP", "JPY", "CAD"}
_VALID_STATUSES: Set[str] = {"pending", "approved", "rejected", "flagged"}

_EXPERT_DATA: List[Dict[str, Any]] = [
    {"txn_id": "TXN-1001", "account_id": "ACC-201", "counterparty": "Acme Corp", "amount": 1500.00, "currency": "USD", "txn_date": "2025-01-15", "category": "Payment", "description": "Invoice #4521 payment", "status": "approved", "reviewer_id": "REV-01"},
    {"txn_id": "TXN-1002", "account_id": "ACC-202", "counterparty": "Global Trade Ltd", "amount": -2300.50, "currency": "EUR", "txn_date": "2025-01-16", "category": "Payment", "description": "Quarterly subscription", "status": "approved", "reviewer_id": "REV-02"},
    {"txn_id": "TXN-1003", "account_id": "ACC-203", "counterparty": "Smith & Jones", "amount": 750.00, "currency": "USD", "txn_date": "2025-01-18", "category": "Refund", "description": "Overcharge correction", "status": "approved", "reviewer_id": "REV-01"},
    {"txn_id": "TXN-1004", "account_id": "ACC-204", "counterparty": "TechStart Inc", "amount": 4200.00, "currency": "usd", "txn_date": "2025-01-20", "category": "Payment", "description": "Software license renewal", "status": "approved", "reviewer_id": "REV-03"},
    {"txn_id": "TXN-1005", "account_id": "ACC-205", "counterparty": "DataFlow Systems", "amount": 890.00, "currency": "USD", "txn_date": "01/22/2025", "category": "Payment", "description": "Cloud hosting Jan 2025", "status": "approved", "reviewer_id": "REV-02"},
    {"txn_id": "TXN-1006", "account_id": "ACC-206", "counterparty": "  Mercury Partners  ", "amount": 3100.00, "currency": "USD", "txn_date": "2025-01-23", "category": "Transfer", "description": "Intercompany transfer Q1", "status": "approved", "reviewer_id": "REV-01"},
    {"txn_id": "TXN-1007", "account_id": "ACC-207", "counterparty": "Zenith Solutions", "amount": 5600.00, "currency": "GBP", "txn_date": "2025-01-25", "category": "Payment", "description": "Consulting fees Jan", "status": "approved", "reviewer_id": "REV-03"},
    {"txn_id": "TXN-1008", "account_id": "ACC-208", "counterparty": "Alpha Industries", "amount": 1200.00, "currency": "USD", "txn_date": "2025-01-27", "category": "Pymnt", "description": "Office supplies order", "status": "approved", "reviewer_id": "REV-01"},
    {"txn_id": "", "account_id": "ACC-209", "counterparty": "Beta Services", "amount": 980.00, "currency": "USD", "txn_date": "2025-01-28", "category": "Payment", "description": "Maintenance contract", "status": "approved", "reviewer_id": "REV-02"},
    {"txn_id": "TXN-1010", "account_id": "ACC-210", "counterparty": "Omega Group", "amount": 15000.00, "currency": "EUR", "txn_date": "2025-01-30", "category": "Payment", "description": "Annual license fee", "status": "approved", "reviewer_id": ""},
    {"txn_id": "TXN-1011", "account_id": "ACC-211", "counterparty": "Delta Corp", "amount": 2450.00, "currency": "USD", "txn_date": "2025-02-01", "category": "Payment", "description": "Marketing materials", "status": "pending", "reviewer_id": ""},
    {"txn_id": "TXN-1012", "account_id": "ACC-212", "counterparty": "Pinnacle Tech", "amount": 670.00, "currency": "JPY", "txn_date": "2025-02-03", "category": "Fee", "description": "Processing fee Q1", "status": "approved", "reviewer_id": "REV-01"},
    {"txn_id": "TXN-1013", "account_id": "ACC-213", "counterparty": "Summit Holdings", "amount": 99999.99, "currency": "USD", "txn_date": "2025-02-05", "category": "Payment", "description": "Large equipment purchase", "status": "flagged", "reviewer_id": "REV-03"},
    {"txn_id": "TXN-1014", "account_id": "ACC-214", "counterparty": "Crest Financial", "amount": 3400.00, "currency": "CAD", "txn_date": "2025-02-07", "category": "Transfer", "description": "Cross-border wire", "status": "approved", "reviewer_id": "REV-02"},
    {"txn_id": "TXN-1015", "account_id": "ACC-215", "counterparty": "Nova Analytics", "amount": 1850.00, "currency": "USD", "txn_date": "2025-02-10", "category": "Payment", "description": "Data analytics subscription", "status": "approved", "reviewer_id": "REV-01"},
    {"txn_id": "TXN-1016", "account_id": "ACC-216", "counterparty": "Echo Ventures", "amount": 4700.00, "currency": "Dollars", "txn_date": "2025-02-12", "category": "Payment", "description": "Investment advisory fee", "status": "approved", "reviewer_id": "REV-03"},
    {"txn_id": "TXN-1017", "account_id": "ACC-217", "counterparty": "Vortex Labs", "amount": 560.00, "currency": "USD", "txn_date": "2025-13-14", "category": "Fee", "description": "Lab testing fee", "status": "approved", "reviewer_id": "REV-02"},
    {"txn_id": "TXN-1018", "account_id": "ACC-218", "counterparty": "Horizon Media", "amount": 2100.00, "currency": "USD", "txn_date": "2025-02-16", "category": "Payment", "description": "Ad campaign Feb", "status": "approved", "reviewer_id": "REV-01"},
    {"txn_id": "TXN-1019", "account_id": "ACC-219", "counterparty": "Titan Logistics", "amount": -450.00, "currency": "EUR", "txn_date": "2025-02-18", "category": "Refund", "description": "Shipping overcharge refund", "status": "approved", "reviewer_id": "REV-03"},
    {"txn_id": "TXN-1020", "account_id": "ACC-220", "counterparty": "Quantum Research", "amount": 8200.00, "currency": "USD", "txn_date": "2025-02-20", "category": "Payment", "description": "R&D collaboration Q1", "status": "rejected", "reviewer_id": "REV-02"},
    {"txn_id": "TXN-1021", "account_id": "ACC-221", "counterparty": "Flux Energy", "amount": 1350.00, "currency": "USD", "txn_date": "2025-02-22", "category": "Payment", "description": "Utility bill Feb", "status": "approved", "reviewer_id": "REV-01"},
    {"txn_id": "TXN-1022", "account_id": "ACC-222", "counterparty": "Apex Consulting", "amount": 6300.00, "currency": "GBP", "txn_date": "2025-02-24", "category": "Payment", "description": "Strategy consulting", "status": "approved", "reviewer_id": "REV-03"},
    {"txn_id": "TXN-1023", "account_id": "ACC-223", "counterparty": "Nexus Partners", "amount": 2750.00, "currency": "USD", "txn_date": "2025-02-26", "category": "Transfer", "description": "Partner distribution Q1", "status": "approved", "reviewer_id": "REV-02"},
    {"txn_id": "TXN-1024", "account_id": "ACC-224", "counterparty": "Stellar Dynamics", "amount": 1100.00, "currency": "USD", "txn_date": "2025-02-28", "category": "Payment", "description": "Equipment maintenance", "status": "approved", "reviewer_id": "REV-01"},
    {"txn_id": "TXN-1025", "account_id": "ACC-225", "counterparty": "Cobalt Security", "amount": 4500.00, "currency": "EUR", "txn_date": "2025-03-02", "category": "Payment", "description": "Security audit Q1", "status": "approved", "reviewer_id": "REV-03"},
    {"txn_id": "TXN-1026", "account_id": "ACC-226", "counterparty": "Prism Analytics", "amount": 1750.00, "currency": "USD", "txn_date": "2025-03-04", "category": "Payment", "description": "BI dashboard license", "status": "approved", "reviewer_id": "REV-02"},
    {"txn_id": "TXN-1027", "account_id": "ACC-201", "counterparty": "Acme Corp", "amount": 1500.00, "currency": "USD", "txn_date": "2025-01-15", "category": "Payment", "description": "Invoice #4521 payment", "status": "approved", "reviewer_id": "REV-01"},
    {"txn_id": "TXN-1028", "account_id": "ACC-228", "counterparty": "Iron  Bridge  Capital", "amount": 9200.00, "currency": "USD", "txn_date": "2025-03-08", "category": "Deposit", "description": "Capital injection", "status": "approved", "reviewer_id": "REV-01"},
    {"txn_id": "TXN-1029", "account_id": "ACC-229", "counterparty": "Pulse Health", "amount": 3800.00, "currency": "USD", "txn_date": "2025-03-10", "category": "Payment", "description": "Employee wellness program", "status": "approved", "reviewer_id": "REV-03"},
    {"txn_id": "TXN-1030", "account_id": "ACC-230", "counterparty": "Atlas Freight", "amount": 2200.00, "currency": "US$", "txn_date": "2025-03-12", "category": "Payment", "description": "Freight charges March", "status": "approved", "reviewer_id": "REV-02"},
    {"txn_id": "TXN-1031", "account_id": "ACC-231", "counterparty": "Vertex Design", "amount": 1600.00, "currency": "USD", "txn_date": "2025-03-14", "category": "Payment", "description": "UI/UX redesign phase 1", "status": "approved", "reviewer_id": "REV-01"},
    {"txn_id": "TXN-1032", "account_id": "ACC-232", "counterparty": "Nimbus Cloud", "amount": 5100.00, "currency": "USD", "txn_date": "2025-03-16", "category": "Payment", "description": "Cloud infra March", "status": "approved", "reviewer_id": "REV-03"},
    {"txn_id": "TXN-1033", "account_id": "ACC-233", "counterparty": "Helix Bio", "amount": 7400.00, "currency": "EUR", "txn_date": "2025-03-18", "category": "Payment", "description": "Lab supplies Q1", "status": "flagged", "reviewer_id": ""},
    {"txn_id": "TXN-1034", "account_id": "ACC-234", "counterparty": "Onyx Legal", "amount": 3950.00, "currency": "USD", "txn_date": "2025-03-20", "category": "Fee", "description": "Legal retainer March", "status": "approved", "reviewer_id": "REV-02"},
    {"txn_id": "TXN-1035", "account_id": "ACC-235", "counterparty": "Zephyr Travel", "amount": 2800.00, "currency": "USD", "txn_date": "2025-03-22", "category": "Payment", "description": "Corporate travel Q1", "status": "approved", "reviewer_id": "REV-01"},
    {"txn_id": "TXN-1036", "account_id": "ACC-236", "counterparty": "Ruby Software", "amount": 4100.00, "currency": "USD", "txn_date": "2025-03-24", "category": "Payment", "description": "SaaS licenses March", "status": "approved", "reviewer_id": "REV-03"},
    {"txn_id": "TXN-1037", "account_id": "ACC-237", "counterparty": "Cascade Investments", "amount": 12500.00, "currency": "GBP", "txn_date": "2025-03-26", "category": "Withdrawal", "description": "Dividend distribution", "status": "approved", "reviewer_id": "REV-02"},
    {"txn_id": "TXN-1038", "account_id": "ACC-238", "counterparty": "Lunar Tech", "amount": 890.00, "currency": "USD", "txn_date": "2025-03-28", "category": "Fee", "description": "API usage fee March", "status": "approved", "reviewer_id": "REV-01"},
    {"txn_id": "TXN-1039", "account_id": "ACC-239", "counterparty": "Sapphire HR", "amount": 5500.00, "currency": "USD", "txn_date": "2025-03-30", "category": "Payment", "description": "Recruitment services Q1", "status": "approved", "reviewer_id": "REV-03"},
    {"txn_id": "TXN-1040", "account_id": "ACC-240", "counterparty": "Ember Creative", "amount": 3200.00, "currency": "USD", "txn_date": "2025-04-01", "category": "Payment", "description": "Brand refresh project", "status": "approved", "reviewer_id": "REV-02"},
]

_VALID_REVIEWER_IDS: Set[str] = {"REV-01", "REV-02", "REV-03"}

_EXPERT_ISSUES: List[Issue] = [
    # Negative amounts
    Issue("X1", 1, "amount", "negative_number", "Negative payment amount", {}),
    Issue("X2", 18, "amount", "negative_number", "Negative refund amount (refunds should be positive)", {}),
    # Currency format issues
    Issue("X3", 3, "currency", "inconsistent_format", "Lowercase currency code", {"canonical_set": _VALID_CURRENCIES}),
    Issue("X4", 15, "currency", "inconsistent_format", "Non-standard currency format 'Dollars'", {"canonical_set": _VALID_CURRENCIES}),
    Issue("X5", 29, "currency", "inconsistent_format", "Non-standard currency format 'US$'", {"canonical_set": _VALID_CURRENCIES}),
    # Wrong date formats
    Issue("X6", 4, "txn_date", "wrong_date_format", "Date in MM/DD/YYYY format", {}),
    Issue("X7", 16, "txn_date", "invalid_date", "Invalid date (month 13)", {}),
    # Missing values
    Issue("X8", 8, "txn_id", "missing_value", "Empty transaction ID", {}),
    Issue("X9", 9, "reviewer_id", "cross_column_violation", "Approved status but missing reviewer_id", {}),
    # Inconsistent category
    Issue("X10", 7, "category", "inconsistent_format", "Abbreviated category 'Pymnt' instead of 'Payment'", {"canonical_set": _VALID_CATEGORIES}),
    # Excess whitespace
    Issue("X11", 5, "counterparty", "excess_whitespace", "Leading/trailing whitespace in counterparty", {}),
    Issue("X12", 27, "counterparty", "excess_whitespace", "Double spaces in counterparty name", {}),
    # Duplicate row
    Issue("X13", 26, "txn_id", "duplicate_row", "Exact duplicate of row 0 (TXN-1001)",
          original_row_data={"txn_id": "TXN-1027", "account_id": "ACC-201", "counterparty": "Acme Corp", "amount": 1500.00, "currency": "USD", "txn_date": "2025-01-15", "category": "Payment", "description": "Invoice #4521 payment", "status": "approved", "reviewer_id": "REV-01"}),
    # Cross-column: flagged but no reviewer
    Issue("X14", 32, "reviewer_id", "cross_column_violation", "Flagged status but missing reviewer_id", {}),
    # Outlier amount
    Issue("X15", 12, "amount", "outlier", "Unusually large amount (possible error)", {"low": 0.01, "high": 50000.0}),
]

EXPERT_TASK = TaskDefinition(
    task_id="financial_transactions",
    difficulty="expert",
    description=(
        "You are auditing a financial transactions ledger for compliance review. "
        "The data should have valid transaction IDs, positive amounts, ISO currency codes "
        "(USD, EUR, GBP, JPY, CAD), dates in YYYY-MM-DD format, valid categories "
        "(Payment, Refund, Transfer, Fee, Deposit, Withdrawal), and no duplicate entries. "
        "Approved and flagged transactions must have a reviewer_id. "
        "Fix all data quality issues to pass the audit."
    ),
    columns=["txn_id", "account_id", "counterparty", "amount", "currency", "txn_date", "category", "description", "status", "reviewer_id"],
    data=_EXPERT_DATA,
    issues=_EXPERT_ISSUES,
    max_steps=45,
    column_descriptions={
        "txn_id": "Transaction ID (format: TXN-XXXX, must not be empty)",
        "account_id": "Account ID (format: ACC-XXX)",
        "counterparty": "Counterparty name (no excess whitespace)",
        "amount": "Transaction amount (must be positive)",
        "currency": "ISO currency code (must be: USD, EUR, GBP, JPY, or CAD)",
        "txn_date": "Transaction date (must be valid YYYY-MM-DD)",
        "category": "Transaction category (must be: Payment, Refund, Transfer, Fee, Deposit, or Withdrawal)",
        "description": "Transaction description",
        "status": "Status (pending, approved, rejected, flagged)",
        "reviewer_id": "Reviewer ID (required for approved/flagged transactions)",
    },
)


# ---------------------------------------------------------------------------
# Task registry
# ---------------------------------------------------------------------------
ALL_TASKS: Dict[str, TaskDefinition] = {
    "customer_contacts": EASY_TASK,
    "sales_records": MEDIUM_TASK,
    "employee_records": HARD_TASK,
    "financial_transactions": EXPERT_TASK,
}


def get_task(task_id: str, seed: int | None = None) -> TaskDefinition:
    """Get a task definition. With seed, produces a randomized variant.

    When seed is None, returns the original hardcoded task (deterministic).
    When seed is provided, corrupts random clean rows to create variation
    while keeping the same number of issues and validation rules.
    """
    if task_id not in ALL_TASKS:
        raise ValueError(
            f"Unknown task_id '{task_id}'. Available: {list(ALL_TASKS.keys())}"
        )
    base = copy.deepcopy(ALL_TASKS[task_id])
    if seed is None:
        return base
    return _generate_seeded_task(base, seed)


# ---------------------------------------------------------------------------
# Seed-based procedural data variation
# ---------------------------------------------------------------------------
import random as _random_module


def _corrupt_email(rng: _random_module.Random, value: str) -> str:
    """Corrupt a valid email into an invalid one."""
    corruptions = [
        lambda v: v.replace("@", "[at]"),
        lambda v: v.replace("@", "@@"),
        lambda v: v.split("@")[0],  # missing domain
        lambda v: v.replace(".", " ", 1),
        lambda v: "  " + v + "  ",
    ]
    return rng.choice(corruptions)(value)


def _corrupt_phone(rng: _random_module.Random, value: str) -> str:
    """Inject letters into a phone number."""
    chars = list(value)
    positions = [i for i, c in enumerate(chars) if c.isdigit()]
    if len(positions) >= 3:
        for pos in rng.sample(positions, min(3, len(positions))):
            chars[pos] = rng.choice("ABCDEFX")
    return "".join(chars)


def _corrupt_date(rng: _random_module.Random, value: str) -> tuple[str, str]:
    """Corrupt a YYYY-MM-DD date. Returns (corrupted_value, issue_type)."""
    corruptions = [
        (lambda v: f"{v[5:7]}/{v[8:10]}/{v[:4]}", "wrong_date_format"),  # MM/DD/YYYY
        (lambda v: v.replace("-", "/"), "wrong_date_format"),  # slashes
        (lambda v: v[:5] + "13" + v[7:], "invalid_date"),  # month 13
    ]
    func, issue_type = rng.choice(corruptions)
    return func(value), issue_type


def _corrupt_whitespace(rng: _random_module.Random, value: str) -> str:
    """Add excess whitespace to a string value."""
    corruptions = [
        lambda v: "  " + v + "  ",
        lambda v: v.replace(" ", "  ", 1) if " " in v else "  " + v,
        lambda v: v + "   ",
    ]
    return rng.choice(corruptions)(value)


def _corrupt_canonical(rng: _random_module.Random, value: str, canonical_set: set) -> str:
    """Produce a non-canonical variant of a valid value."""
    corruptions = [
        lambda v: v.lower(),
        lambda v: v.upper(),
        lambda v: v.replace(" ", "-").lower(),
        lambda v: v[:3],  # abbreviation
    ]
    corrupted = rng.choice(corruptions)(value)
    # Make sure it's actually different from all canonical values
    if corrupted in canonical_set:
        corrupted = corrupted + " (typo)"
    return corrupted


def _corrupt_number_negative(rng: _random_module.Random, value: float) -> float:
    """Make a positive number negative."""
    return -abs(value)


def _corrupt_number_outlier(rng: _random_module.Random, value: float, low: float, high: float) -> float:
    """Push a number outside the valid range."""
    if rng.random() < 0.5:
        return high * rng.uniform(10, 1000)  # way above
    else:
        return low * rng.uniform(-10, -0.1)  # way below or negative


def _generate_seeded_task(base: TaskDefinition, seed: int) -> TaskDefinition:
    """Generate a randomized variant of a task using a seed.

    Strategy: For each issue in the base task, pick a different clean row
    to corrupt (when possible) and apply the same type of corruption.
    This keeps issue count and types identical but changes which rows
    are affected and how they're corrupted.
    """
    rng = _random_module.Random(seed)

    # Find which rows have issues in the base task
    issue_rows = {issue.row for issue in base.issues}
    clean_rows = [i for i in range(len(base.data)) if i not in issue_rows]

    # Start with clean versions of all data
    # First, build a "clean" dataset by reverting corrupted cells
    # For simplicity, we'll reuse the base data but re-assign which rows get corrupted
    data = copy.deepcopy(base.data)
    new_issues: List[Issue] = []
    issue_counter = 0

    # Separate non-duplicate issues from duplicate issues
    non_dup_issues = [i for i in base.issues if i.issue_type != "duplicate_row"]
    dup_issues = [i for i in base.issues if i.issue_type == "duplicate_row"]

    # For non-duplicate issues: try to assign to different rows
    available_rows = list(range(len(data)))
    rng.shuffle(available_rows)
    used_rows: set = set()

    for orig_issue in non_dup_issues:
        issue_counter += 1
        issue_id = f"S{seed}-{issue_counter}"
        col = orig_issue.column
        issue_type = orig_issue.issue_type

        # Pick a target row (prefer one not already used)
        candidates = [r for r in available_rows if r not in used_rows and r < len(data)]
        if not candidates:
            candidates = [r for r in range(len(data)) if r not in used_rows]
        if not candidates:
            # All rows used, just reuse the original
            new_issues.append(Issue(
                issue_id=issue_id, row=orig_issue.row, column=col,
                issue_type=issue_type, description=orig_issue.description,
                validation_params=copy.deepcopy(orig_issue.validation_params),
            ))
            continue

        target_row = rng.choice(candidates)
        used_rows.add(target_row)
        original_value = data[target_row].get(col, "")

        # Apply corruption based on issue type
        description = orig_issue.description
        params = copy.deepcopy(orig_issue.validation_params)

        if issue_type == "invalid_email" and original_value:
            if "@" in str(original_value):
                data[target_row][col] = _corrupt_email(rng, str(original_value))
                description = f"Email '{data[target_row][col]}' is invalid"
            else:
                target_row = orig_issue.row  # fallback to original
                description = orig_issue.description

        elif issue_type == "invalid_phone" and original_value:
            data[target_row][col] = _corrupt_phone(rng, str(original_value))
            description = f"Phone contains non-numeric characters"

        elif issue_type in ("wrong_date_format", "invalid_date") and original_value:
            try:
                corrupted, actual_type = _corrupt_date(rng, str(original_value))
                data[target_row][col] = corrupted
                issue_type = actual_type
                description = f"Date '{corrupted}' is not valid YYYY-MM-DD"
            except (IndexError, ValueError):
                target_row = orig_issue.row

        elif issue_type == "missing_value":
            data[target_row][col] = ""
            description = f"Value in column '{col}' is empty"

        elif issue_type == "negative_number" and original_value:
            try:
                val = float(original_value)
                if val > 0:
                    data[target_row][col] = _corrupt_number_negative(rng, val)
                    description = f"Value is negative ({data[target_row][col]})"
                else:
                    target_row = orig_issue.row
            except (ValueError, TypeError):
                target_row = orig_issue.row

        elif issue_type == "outlier" and original_value:
            low = params.get("low", 0)
            high = params.get("high", 100)
            try:
                val = float(original_value)
                data[target_row][col] = round(_corrupt_number_outlier(rng, val, low, high), 2)
                description = f"Value {data[target_row][col]} is outside range [{low}, {high}]"
            except (ValueError, TypeError):
                target_row = orig_issue.row

        elif issue_type == "inconsistent_format" and original_value:
            canonical_set = params.get("canonical_set", set())
            if str(original_value) in canonical_set:
                data[target_row][col] = _corrupt_canonical(rng, str(original_value), canonical_set)
                description = f"Value '{data[target_row][col]}' doesn't match canonical form"
            else:
                target_row = orig_issue.row

        elif issue_type == "excess_whitespace" and original_value:
            data[target_row][col] = _corrupt_whitespace(rng, str(original_value))
            description = f"Excess whitespace in '{col}'"

        elif issue_type == "score_out_of_range" and original_value:
            low = params.get("low", 0)
            high = params.get("high", 10)
            bad_val = rng.choice([rng.uniform(-5, low - 0.1), rng.uniform(high + 0.1, high + 10)])
            data[target_row][col] = round(bad_val, 1)
            description = f"Score {data[target_row][col]} is outside range [{low}, {high}]"

        elif issue_type in ("referential_integrity", "temporal_inconsistency", "cross_column_violation"):
            # Complex types: keep original row assignment
            target_row = orig_issue.row
            description = orig_issue.description

        else:
            # Unknown type or can't corrupt: keep original
            target_row = orig_issue.row
            description = orig_issue.description

        new_issues.append(Issue(
            issue_id=issue_id, row=target_row, column=col,
            issue_type=issue_type, description=description,
            validation_params=params,
        ))

    # Handle duplicate issues: pick a random clean row to duplicate
    for orig_dup in dup_issues:
        issue_counter += 1
        issue_id = f"S{seed}-{issue_counter}"
        # Pick a random row to duplicate at the end
        source_row = rng.randint(0, len(data) - 1)
        dup_data = copy.deepcopy(data[source_row])
        dup_row_idx = len(data)
        data.append(dup_data)
        new_issues.append(Issue(
            issue_id=issue_id, row=dup_row_idx, column="",
            issue_type="duplicate_row",
            description=f"Duplicate of row {source_row}",
            original_row_data=copy.deepcopy(dup_data),
        ))

    # Rebuild the task with seeded data
    base.data = data
    base.issues = new_issues
    base.max_steps = max(base.max_steps, len(new_issues) * 2 + len(base.columns) + 1)
    return base