"""Acceptance corpus for international format standardization.

Stresses the rework's three pillars on a single mixed-locale fixture:
  * Per-row country column drives phone parsing.
  * ``currency_decimal="auto"`` resolves comma-decimal locales.
  * Streaming entry point handles the same content unchanged.
"""

from __future__ import annotations

from pathlib import Path

import pandas as pd
import pytest

from src.core.format_standardize import (
    FieldType,
    StandardizeOptions,
    standardize_dataframe,
    standardize_file,
)

CORPUS = Path(__file__).resolve().parents[1] / "test-cases" / "format-cleaner-corpus" / "international"
FIXTURE = CORPUS / "intl_phones_addresses.csv"


@pytest.fixture(scope="module")
def df():
    return pd.read_csv(FIXTURE, dtype=str, keep_default_na=False)


@pytest.fixture(scope="module")
def options():
    return StandardizeOptions(
        column_types={
            "name": FieldType.NAME,
            "phone": FieldType.PHONE,
            "price": FieldType.CURRENCY,
        },
        phone_country_column="country",
        currency_preserve_code=True,
        currency_decimal="auto",
    )


class TestPhonesByRegion:
    def test_every_row_lands_on_correct_e164_prefix(self, df, options):
        # Each row's country column drives the per-row region used by
        # phonenumbers.parse — the correct + prefix is the acceptance bar.
        res = standardize_dataframe(df, options)
        out = res.standardized_df
        # ISO-2 → expected E.164 country code prefix
        prefix_for_country = {
            "US": "+1", "GB": "+44", "RU": "+7", "ES": "+34",
            "FR": "+33", "JP": "+81", "DE": "+49", "IT": "+39",
            "CN": "+86", "IN": "+91", "EG": "+20", "AU": "+61",
            "BR": "+55", "MX": "+52", "KR": "+82", "TR": "+90",
            "IL": "+972", "PL": "+48", "DK": "+45", "SE": "+46",
        }
        bad: list[tuple[str, str, str]] = []
        for _, row in out.iterrows():
            want = prefix_for_country[row["country"]]
            got = row["phone"]
            if not got.startswith(want):
                bad.append((row["country"], want, got))
        assert not bad, f"phone prefix mismatches: {bad}"


class TestCurrencyByLocale:
    def test_eu_decimal_comma_resolves_under_auto(self, df, options):
        res = standardize_dataframe(df, options)
        # Spain, France, Germany, Italy, Brazil, Sweden all use decimal
        # comma. Verify a clean numeric result post-standardization.
        eu_idx = df.index[df["country"].isin(
            ["ES", "FR", "DE", "IT", "BR", "SE"]
        )]
        for i in eu_idx:
            val = res.standardized_df.loc[i, "price"]
            # Either ``CODE NNN.NN`` or bare ``NNN.NN`` — but the comma
            # in the source must have become a dot in the output.
            assert "," not in val, (
                f"row {i} ({df.loc[i, 'country']}): comma persisted in {val!r}"
            )

    def test_brl_real_prefix_recognised(self, df, options):
        res = standardize_dataframe(df, options)
        br_row = res.standardized_df[res.standardized_df["country"] == "BR"].iloc[0]
        assert "BRL" in br_row["price"]


class TestStreamingMatchesInMemory:
    def test_same_output_via_streaming(self, tmp_path, df, options):
        # Streaming the same fixture through standardize_file should
        # produce a CSV byte-equivalent to the in-memory path.
        in_mem = standardize_dataframe(df, options).standardized_df
        out = tmp_path / "out.csv"
        # Use a chunk size that splits the 20-row fixture mid-way.
        res = standardize_file(FIXTURE, out, options, chunk_size=7)
        assert res.rows_processed == len(df)
        streamed = pd.read_csv(out, dtype=str, keep_default_na=False)
        # Compare typed columns only — others pass through.
        for col in options.column_types:
            assert streamed[col].tolist() == in_mem[col].astype(str).tolist(), (
                f"column {col} differs between in-memory and streaming"
            )