
'''simple docstring'''
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     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    assert isinstance(__A,		__A							)
    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     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Union[str, Any]        =    tmp_path / "cache"
    snake_case_			:     Dict        =    {"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_			:     Optional[int]        =    CsvDatasetReader(__A,		cache_dir=__A,		keep_in_memory=__A							).read()
    _check_csv_dataset(__A,		__A							)

@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     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     str        =    tmp_path / "cache"
    # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
    snake_case_			:     str        =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    snake_case_			:     List[str]        =    features.copy() if features else default_expected_features
    snake_case_			:     Dict        =    (
        Features({feature: Value(__A							) for feature, dtype in features.items()}							) if features is not None else None
    )
    snake_case_			:     Union[str, Any]        =    CsvDatasetReader(__A,		features=__A,		cache_dir=__A							).read()
    _check_csv_dataset(__A,		__A							)

@pytest.mark.parametrize("split",		[None, NamedSplit("train"							), "train", "test"]							)
def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     List[Any]        =    tmp_path / "cache"
    snake_case_			:     Union[str, Any]        =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    snake_case_			:     Union[str, Any]        =    CsvDatasetReader(__A,		cache_dir=__A,		split=__A							).read()
    _check_csv_dataset(__A,		__A							)
    assert dataset.split == split if split else "train"

@pytest.mark.parametrize("path_type",		[str, list]							)
def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if issubclass(__A,		__A							):
        snake_case_			:     Dict        =    csv_path
    elif issubclass(__A,		__A							):
        snake_case_			:     Optional[int]        =    [csv_path]
    snake_case_			:     List[str]        =    tmp_path / "cache"
    snake_case_			:     List[str]        =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    snake_case_			:     int        =    CsvDatasetReader(__A,		cache_dir=__A							).read()
    _check_csv_dataset(__A,		__A							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=("train",)							):
    """simple docstring"""






    assert isinstance(__A,		__A							)
    for split in splits:
        snake_case_			:     int        =    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     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     List[str]        =    tmp_path / "cache"
    snake_case_			:     Tuple        =    {"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_			:     Tuple        =    CsvDatasetReader({"train": csv_path},		cache_dir=__A,		keep_in_memory=__A							).read()
    _check_csv_datasetdict(__A,		__A							)

@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     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     List[str]        =    tmp_path / "cache"
    # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
    snake_case_			:     Tuple        =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    snake_case_			:     Dict        =    features.copy() if features else default_expected_features
    snake_case_			:     Tuple        =    (
        Features({feature: Value(__A							) for feature, dtype in features.items()}							) if features is not None else None
    )
    snake_case_			:     List[str]        =    CsvDatasetReader({"train": csv_path},		features=__A,		cache_dir=__A							).read()
    _check_csv_datasetdict(__A,		__A							)

@pytest.mark.parametrize("split",		[None, NamedSplit("train"							), "train", "test"]							)
def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if split:
        snake_case_			:     str        =    {split: csv_path}
    else:
        snake_case_			:     Union[str, Any]        =    {"train": csv_path, "test": csv_path}
    snake_case_			:     int        =    tmp_path / "cache"
    snake_case_			:     List[Any]        =    {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
    snake_case_			:     List[str]        =    CsvDatasetReader(__A,		cache_dir=__A							).read()
    _check_csv_datasetdict(__A,		__A,		splits=list(path.keys()							)							)
    assert all(dataset[split].split == split for split in path.keys()							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






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

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Optional[int]        =    tmp_path / "cache"
    snake_case_			:     List[str]        =    os.path.join(__A,		"tmp.csv"							)
    snake_case_			:     Optional[int]        =    CsvDatasetReader({"train": csv_path},		cache_dir=__A							).read()
    CsvDatasetWriter(dataset["train"],		__A,		num_proc=1							).write()

    snake_case_			:     Optional[Any]        =    iter_csv_file(__A							)
    snake_case_			:     Dict        =    iter_csv_file(__A							)

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

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Union[str, Any]        =    tmp_path / "cache"
    snake_case_			:     int        =    os.path.join(__A,		"tmp.csv"							)
    snake_case_			:     Tuple        =    CsvDatasetReader({"train": csv_path},		cache_dir=__A							).read()
    CsvDatasetWriter(dataset["train"],		__A,		num_proc=2							).write()

    snake_case_			:     Dict        =    iter_csv_file(__A							)
    snake_case_			:     List[Any]        =    iter_csv_file(__A							)

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


def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     str        =    tmp_path / "cache"
    snake_case_			:     List[Any]        =    os.path.join(__A,		"tmp.csv"							)
    snake_case_			:     Optional[Any]        =    CsvDatasetReader({"train": csv_path},		cache_dir=__A							).read()
    with pytest.raises(__A							):
        CsvDatasetWriter(dataset["train"],		__A,		num_proc=0							)







def 				__a							(       __A			,							__A			,							__A			,							__A						):
    return len_board * len_board_column * row + column





def 				__a							(       __A			,							__A			,							__A			,							__A			,							__A			,							__A			,							):

    if board[row][column] != word[word_index]:
        return False

    if word_index == len(__A						) - 1:
        return True

    SCREAMING_SNAKE_CASE__         =    [(0, 1), (0, -1), (-1, 0), (1, 0)]
    SCREAMING_SNAKE_CASE__         =    len(__A						)
    SCREAMING_SNAKE_CASE__         =    len(board[0]						)
    for direction in traverts_directions:
        SCREAMING_SNAKE_CASE__         =    row + direction[0]
        SCREAMING_SNAKE_CASE__         =    column + direction[1]
        if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
            continue

        SCREAMING_SNAKE_CASE__         =    get_point_key(__A			,							__A			,							__A			,							__A						)
        if key in visited_points_set:
            continue

        visited_points_set.add(__A						)
        if exits_word(__A			,							__A			,							__A			,							__A			,							word_index + 1			,							__A						):
            return True

        visited_points_set.remove(__A						)

    return False





def 				__a							(       __A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    (
        "The board should be a non empty matrix of single chars strings."
    )

    SCREAMING_SNAKE_CASE__         =    len(__A						)
    if not isinstance(__A			,							__A						) or len(__A						) == 0:
        raise ValueError(__A						)

    for row in board:
        if not isinstance(__A			,							__A						) or len(__A						) == 0:
            raise ValueError(__A						)

        for item in row:
            if not isinstance(__A			,							__A						) or len(__A						) != 1:
                raise ValueError(__A						)

    # Validate word
    if not isinstance(__A			,							__A						) or len(__A						) == 0:
        raise ValueError(
            "The word parameter should be a string of length greater than 0."						)

    SCREAMING_SNAKE_CASE__         =    len(board[0]						)
    for i in range(__A						):
        for j in range(__A						):
            if exits_word(
                __A			,							__A			,							__A			,							__A			,							0			,							{get_point_key(__A			,							__A			,							__A			,							__A						)}						):
                return True

    return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()


