
import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase							):
    assert isinstance(__lowerCAmelCase			,						__lowerCAmelCase							)
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("keep_in_memory"			,						[False, True]							)
def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase							):
    snake_case__  = tmp_path / "cache"
    snake_case__  = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        snake_case__  = CsvDatasetReader(__lowerCAmelCase			,						cache_dir=__lowerCAmelCase			,						keep_in_memory=__lowerCAmelCase							).read()
    _check_csv_dataset(__lowerCAmelCase			,						__lowerCAmelCase							)





@pytest.mark.parametrize(
    "features"			,						[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]			,						)
def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase							):
    snake_case__  = tmp_path / "cache"
    # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
    snake_case__  = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    snake_case__  = features.copy() if features else default_expected_features
    snake_case__  = (
        Features({feature: Value(__lowerCAmelCase							) for feature, dtype in features.items()}							) if features is not None else None
    )
    snake_case__  = CsvDatasetReader(__lowerCAmelCase			,						features=__lowerCAmelCase			,						cache_dir=__lowerCAmelCase							).read()
    _check_csv_dataset(__lowerCAmelCase			,						__lowerCAmelCase							)





@pytest.mark.parametrize("split"			,						[None, NamedSplit("train"							), "train", "test"]							)
def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase							):
    snake_case__  = tmp_path / "cache"
    snake_case__  = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    snake_case__  = CsvDatasetReader(__lowerCAmelCase			,						cache_dir=__lowerCAmelCase			,						split=__lowerCAmelCase							).read()
    _check_csv_dataset(__lowerCAmelCase			,						__lowerCAmelCase							)
    assert dataset.split == split if split else "train"





@pytest.mark.parametrize("path_type"			,						[str, list]							)
def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase							):
    if issubclass(__lowerCAmelCase			,						__lowerCAmelCase							):
        snake_case__  = csv_path
    elif issubclass(__lowerCAmelCase			,						__lowerCAmelCase							):
        snake_case__  = [csv_path]
    snake_case__  = tmp_path / "cache"
    snake_case__  = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    snake_case__  = CsvDatasetReader(__lowerCAmelCase			,						cache_dir=__lowerCAmelCase							).read()
    _check_csv_dataset(__lowerCAmelCase			,						__lowerCAmelCase							)





def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase=("train",)							):
    assert isinstance(__lowerCAmelCase			,						__lowerCAmelCase							)
    for split in splits:
        snake_case__  = dataset_dict[split]
        assert dataset.num_rows == 4
        assert dataset.num_columns == 3
        assert dataset.column_names == ["col_1", "col_2", "col_3"]
        for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("keep_in_memory"			,						[False, True]							)
def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase							):
    snake_case__  = tmp_path / "cache"
    snake_case__  = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        snake_case__  = CsvDatasetReader({"train": csv_path}			,						cache_dir=__lowerCAmelCase			,						keep_in_memory=__lowerCAmelCase							).read()
    _check_csv_datasetdict(__lowerCAmelCase			,						__lowerCAmelCase							)





@pytest.mark.parametrize(
    "features"			,						[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]			,						)
def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase							):
    snake_case__  = tmp_path / "cache"
    # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
    snake_case__  = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    snake_case__  = features.copy() if features else default_expected_features
    snake_case__  = (
        Features({feature: Value(__lowerCAmelCase							) for feature, dtype in features.items()}							) if features is not None else None
    )
    snake_case__  = CsvDatasetReader({"train": csv_path}			,						features=__lowerCAmelCase			,						cache_dir=__lowerCAmelCase							).read()
    _check_csv_datasetdict(__lowerCAmelCase			,						__lowerCAmelCase							)





@pytest.mark.parametrize("split"			,						[None, NamedSplit("train"							), "train", "test"]							)
def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase							):
    if split:
        snake_case__  = {split: csv_path}
    else:
        snake_case__  = {"train": csv_path, "test": csv_path}
    snake_case__  = tmp_path / "cache"
    snake_case__  = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    snake_case__  = CsvDatasetReader(__lowerCAmelCase			,						cache_dir=__lowerCAmelCase							).read()
    _check_csv_datasetdict(__lowerCAmelCase			,						__lowerCAmelCase			,						splits=list(path.keys()							)							)
    assert all(dataset[split].split == split for split in path.keys()							)





def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase							):
    with open(__lowerCAmelCase			,						encoding="utf-8"							) as csvfile:
        yield from csv.reader(__lowerCAmelCase							)





def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase							):
    snake_case__  = tmp_path / "cache"
    snake_case__  = os.path.join(__lowerCAmelCase			,						"tmp.csv"							)
    snake_case__  = CsvDatasetReader({"train": csv_path}			,						cache_dir=__lowerCAmelCase							).read()
    CsvDatasetWriter(dataset["train"]			,						__lowerCAmelCase			,						num_proc=1							).write()

    snake_case__  = iter_csv_file(__lowerCAmelCase							)
    snake_case__  = iter_csv_file(__lowerCAmelCase							)

    for rowa, rowa in zip(__lowerCAmelCase			,						__lowerCAmelCase							):
        assert rowa == rowa





def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase							):
    snake_case__  = tmp_path / "cache"
    snake_case__  = os.path.join(__lowerCAmelCase			,						"tmp.csv"							)
    snake_case__  = CsvDatasetReader({"train": csv_path}			,						cache_dir=__lowerCAmelCase							).read()
    CsvDatasetWriter(dataset["train"]			,						__lowerCAmelCase			,						num_proc=2							).write()

    snake_case__  = iter_csv_file(__lowerCAmelCase							)
    snake_case__  = iter_csv_file(__lowerCAmelCase							)

    for rowa, rowa in zip(__lowerCAmelCase			,						__lowerCAmelCase							):
        assert rowa == rowa





def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase							):
    snake_case__  = tmp_path / "cache"
    snake_case__  = os.path.join(__lowerCAmelCase			,						"tmp.csv"							)
    snake_case__  = CsvDatasetReader({"train": csv_path}			,						cache_dir=__lowerCAmelCase							).read()
    with pytest.raises(__lowerCAmelCase							):
        CsvDatasetWriter(dataset["train"]			,						__lowerCAmelCase			,						num_proc=0							)







'''simple docstring'''






from __future__ import annotations


def 					_snake_case   (     A ,    A       )						->    list[int]:
							lowerCAmelCase__          =	{}
							for index, val in enumerate(A       ):
														lowerCAmelCase__          =	target - val
														if compl in chk_map:
																					return [chk_map[compl], index]
														lowerCAmelCase__          =	index
							return []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f"""{two_sum([2, 7, 11, 15], 9) = }""")