wehub-resource-sync
316 lines
10 KiB
Python
316 lines
10 KiB
Python
from __future__ import annotations
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import argparse
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import csv
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import sys
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from dataclasses import dataclass
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from pathlib import Path
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from typing import TYPE_CHECKING, TypeAlias
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if __package__ is None or __package__ == "":
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sys.path.insert(0, str(Path(__file__).resolve().parent))
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if TYPE_CHECKING or __package__:
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from .schemas import FinancialMetric, FinancialMetricBatch
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else:
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from schemas import FinancialMetric, FinancialMetricBatch
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MetricKey: TypeAlias = tuple[str, str, str, str | None]
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EXPECTED_SOURCE_METADATA: dict[str, str] = {
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"data/10-k-mdna-overview.txt": (
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"Part II, Item 7. Management's Discussion and Analysis of Financial Condition and "
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"Results of Operations"
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),
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"data/10-k-mdna-liquidity.txt": (
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"Part II, Item 7. Management's Discussion and Analysis of Financial Condition and "
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"Results of Operations"
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),
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"data/10-k-note-segments.txt": ("Part II, Item 8. Financial Statements and Supplementary Data"),
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"data/10-k-note-geography.txt": (
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"Part II, Item 8. Financial Statements and Supplementary Data"
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),
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"data/10-k-note-balance-sheet.txt": (
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"Part II, Item 8. Financial Statements and Supplementary Data"
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),
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"data/10-k-statements-of-operations.pdf": (
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"Part II, Item 8. Financial Statements and Supplementary Data"
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),
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"data/10-k-balance-sheets.pdf": (
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"Part II, Item 8. Financial Statements and Supplementary Data"
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),
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"data/10-k-statements-of-cash-flows.pdf": (
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"Part II, Item 8. Financial Statements and Supplementary Data"
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),
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}
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EXPECTED_ROWS: dict[MetricKey, tuple[float, str]] = {
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("data/10-k-mdna-overview.txt", "Revenue", "FY2025", None): (1284.0, "USD millions"),
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("data/10-k-mdna-overview.txt", "Revenue", "FY2024", None): (1008.0, "USD millions"),
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("data/10-k-mdna-overview.txt", "Gross margin", "FY2025", None): (71.4, "percent"),
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("data/10-k-mdna-overview.txt", "Gross margin", "FY2024", None): (68.2, "percent"),
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("data/10-k-mdna-overview.txt", "Operating income", "FY2025", None): (186.0, "USD millions"),
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("data/10-k-mdna-overview.txt", "Operating income", "FY2024", None): (118.0, "USD millions"),
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(
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"data/10-k-mdna-liquidity.txt",
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"Net cash provided by operating activities",
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"FY2025",
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None,
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): (248.0, "USD millions"),
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(
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"data/10-k-mdna-liquidity.txt",
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"Net cash provided by operating activities",
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"FY2024",
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None,
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): (192.0, "USD millions"),
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("data/10-k-mdna-liquidity.txt", "Capital expenditures", "FY2025", None): (
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86.0,
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"USD millions",
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),
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("data/10-k-mdna-liquidity.txt", "Capital expenditures", "FY2024", None): (
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73.0,
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"USD millions",
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),
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("data/10-k-mdna-liquidity.txt", "Free cash flow", "FY2025", None): (
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162.0,
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"USD millions",
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),
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("data/10-k-mdna-liquidity.txt", "Free cash flow", "FY2024", None): (
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119.0,
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"USD millions",
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),
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("data/10-k-note-segments.txt", "Platform segment revenue", "FY2025", "Platform"): (
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942.0,
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"USD millions",
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),
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("data/10-k-note-segments.txt", "Platform segment revenue", "FY2024", "Platform"): (
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711.0,
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"USD millions",
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),
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("data/10-k-note-segments.txt", "Services segment revenue", "FY2025", "Services"): (
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342.0,
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"USD millions",
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),
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("data/10-k-note-segments.txt", "Services segment revenue", "FY2024", "Services"): (
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297.0,
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"USD millions",
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),
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("data/10-k-note-geography.txt", "Americas revenue", "FY2025", "Americas"): (
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764.0,
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"USD millions",
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),
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("data/10-k-note-geography.txt", "EMEA revenue", "FY2025", "EMEA"): (
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343.0,
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"USD millions",
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),
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("data/10-k-note-geography.txt", "APAC revenue", "FY2025", "APAC"): (
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177.0,
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"USD millions",
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),
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(
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"data/10-k-note-balance-sheet.txt",
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"Cash and cash equivalents",
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"2025-12-31",
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None,
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): (422.0, "USD millions"),
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(
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"data/10-k-note-balance-sheet.txt",
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"Cash and cash equivalents",
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"2024-12-31",
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None,
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): (351.0, "USD millions"),
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("data/10-k-note-balance-sheet.txt", "Deferred revenue", "2025-12-31", None): (
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402.0,
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"USD millions",
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),
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("data/10-k-note-balance-sheet.txt", "Deferred revenue", "2024-12-31", None): (
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337.0,
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"USD millions",
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),
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("data/10-k-statements-of-operations.pdf", "Net revenue", "FY2025", None): (
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1284.0,
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"USD millions",
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),
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("data/10-k-statements-of-operations.pdf", "Net revenue", "FY2024", None): (
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1008.0,
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"USD millions",
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),
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("data/10-k-statements-of-operations.pdf", "Gross profit", "FY2025", None): (
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917.0,
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"USD millions",
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),
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("data/10-k-statements-of-operations.pdf", "Gross profit", "FY2024", None): (
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687.0,
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"USD millions",
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),
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("data/10-k-statements-of-operations.pdf", "Operating income", "FY2025", None): (
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186.0,
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"USD millions",
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),
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("data/10-k-statements-of-operations.pdf", "Operating income", "FY2024", None): (
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118.0,
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"USD millions",
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),
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(
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"data/10-k-balance-sheets.pdf",
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"Cash and cash equivalents",
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"2025-12-31",
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None,
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): (422.0, "USD millions"),
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(
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"data/10-k-balance-sheets.pdf",
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"Cash and cash equivalents",
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"2024-12-31",
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None,
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): (351.0, "USD millions"),
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("data/10-k-balance-sheets.pdf", "Accounts receivable", "2025-12-31", None): (
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211.0,
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"USD millions",
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),
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("data/10-k-balance-sheets.pdf", "Accounts receivable", "2024-12-31", None): (
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187.0,
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"USD millions",
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),
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("data/10-k-balance-sheets.pdf", "Deferred revenue", "2025-12-31", None): (
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402.0,
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"USD millions",
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),
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("data/10-k-balance-sheets.pdf", "Deferred revenue", "2024-12-31", None): (
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337.0,
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"USD millions",
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),
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(
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"data/10-k-statements-of-cash-flows.pdf",
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"Net cash provided by operating activities",
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"FY2025",
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None,
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): (248.0, "USD millions"),
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(
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"data/10-k-statements-of-cash-flows.pdf",
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"Net cash provided by operating activities",
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"FY2024",
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None,
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): (192.0, "USD millions"),
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("data/10-k-statements-of-cash-flows.pdf", "Capital expenditures", "FY2025", None): (
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86.0,
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"USD millions",
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),
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("data/10-k-statements-of-cash-flows.pdf", "Capital expenditures", "FY2024", None): (
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73.0,
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"USD millions",
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),
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("data/10-k-statements-of-cash-flows.pdf", "Free cash flow", "FY2025", None): (
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162.0,
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"USD millions",
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),
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("data/10-k-statements-of-cash-flows.pdf", "Free cash flow", "FY2024", None): (
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119.0,
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"USD millions",
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),
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}
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@dataclass(frozen=True)
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class EvalSummary:
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row_count: int
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def load_metrics(artifact_path: Path) -> FinancialMetricBatch:
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if artifact_path.suffix == ".jsonl":
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metrics = [
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FinancialMetric.model_validate_json(line)
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for line in artifact_path.read_text(encoding="utf-8").splitlines()
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if line.strip()
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]
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return FinancialMetricBatch(metrics=metrics)
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if artifact_path.suffix == ".csv":
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with artifact_path.open(encoding="utf-8", newline="") as input_file:
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reader = csv.DictReader(input_file)
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metrics = []
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for row in reader:
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row["segment"] = row["segment"] or None
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row["value"] = float(row["value"])
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metrics.append(FinancialMetric.model_validate(row))
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return FinancialMetricBatch(metrics=metrics)
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raise ValueError(f"Unsupported artifact type: {artifact_path}")
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def validate_outputs(metrics: FinancialMetricBatch) -> EvalSummary:
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rows = metrics.metrics
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duplicate_keys: list[MetricKey] = []
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seen_keys: set[MetricKey] = set()
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rows_by_key: dict[MetricKey, FinancialMetric] = {
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(
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row.source_file.strip(),
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row.metric_name.strip(),
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row.fiscal_period,
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row.segment.strip() if row.segment else None,
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): row
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for row in rows
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}
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for row in rows:
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row_key = (
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row.source_file.strip(),
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row.metric_name.strip(),
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row.fiscal_period,
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row.segment.strip() if row.segment else None,
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)
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if row_key in seen_keys:
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duplicate_keys.append(row_key)
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seen_keys.add(row_key)
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if duplicate_keys:
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raise AssertionError(f"Duplicate metric rows found: {sorted(set(duplicate_keys))}.")
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if len(rows) != len(EXPECTED_ROWS):
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raise AssertionError(
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f"Expected exactly {len(EXPECTED_ROWS)} metric rows, found {len(rows)}."
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)
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for source_file, expected_section in EXPECTED_SOURCE_METADATA.items():
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source_rows = [row for row in rows if row.source_file.strip() == source_file]
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if not source_rows:
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raise AssertionError(f"Missing rows from {source_file}.")
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bad_sections = {
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row.filing_section for row in source_rows if row.filing_section != expected_section
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}
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if bad_sections:
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raise AssertionError(
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f"{source_file} filing_section mismatch. Expected {expected_section}, found {bad_sections}."
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)
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missing_rows = [
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key
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for key, (expected_value, expected_unit) in EXPECTED_ROWS.items()
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if key not in rows_by_key
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or rows_by_key[key].value != expected_value
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or rows_by_key[key].unit != expected_unit
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]
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if missing_rows:
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observed = sorted(rows_by_key)
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raise AssertionError(
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f"Missing or mismatched expected metric rows: {missing_rows}. Observed keys: {observed}."
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)
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unexpected_rows = sorted(set(rows_by_key) - set(EXPECTED_ROWS))
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if unexpected_rows:
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raise AssertionError(f"Unexpected metric rows found: {unexpected_rows}.")
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return EvalSummary(row_count=len(rows))
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--artifact-path",
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default=str(Path(__file__).resolve().parent / "output" / "financial_metrics.jsonl"),
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help="Path to the generated JSONL or CSV artifact.",
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)
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args = parser.parse_args()
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summary = validate_outputs(load_metrics(Path(args.artifact_path)))
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print(f"Eval checks passed for {summary.row_count} metric row(s).")
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