



import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		Union[str, Any]:







						"""simple docstring"""

						assert isinstance(__lowercase							,     __lowercase       )
						assert dataset.num_rows == 4
						assert dataset.num_columns == 1
						assert dataset.column_names == ["text"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory"							,     [False, True]       )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		Dict:







						"""simple docstring"""

						A__	:							str						    = tmp_path / "cache"
						A__	:							int						    = {"text": "string"}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												A__	:							Dict						    = TextDatasetReader(__lowercase							,     cache_dir=__lowercase							,     keep_in_memory=__lowercase       ).read()
						_check_text_dataset(__lowercase							,     __lowercase       )
@pytest.mark.parametrize(
    "features"							,     [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]							,     )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		Tuple:







						"""simple docstring"""

						A__	:							List[str]						    = tmp_path / "cache"
						A__	:							str						    = {"text": "string"}
						A__	:							int						    = features.copy() if features else default_expected_features
						A__	:							Any						    = (
						    Features({feature: Value(__lowercase       ) for feature, dtype in features.items()}       ) if features is not None else None
						)
						A__	:							Optional[int]						    = TextDatasetReader(__lowercase							,     features=__lowercase							,     cache_dir=__lowercase       ).read()
						_check_text_dataset(__lowercase							,     __lowercase       )
@pytest.mark.parametrize("split"							,     [None, NamedSplit("train"       ), "train", "test"]       )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		Tuple:







						"""simple docstring"""

						A__	:							Any						    = tmp_path / "cache"
						A__	:							Tuple						    = {"text": "string"}
						A__	:							str						    = TextDatasetReader(__lowercase							,     cache_dir=__lowercase							,     split=__lowercase       ).read()
						_check_text_dataset(__lowercase							,     __lowercase       )
						assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type"							,     [str, list]       )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		int:







						"""simple docstring"""

						if issubclass(__lowercase							,     __lowercase       ):
												A__	:							List[str]						    = text_path
						elif issubclass(__lowercase							,     __lowercase       ):
												A__	:							Union[str, Any]						    = [text_path]
						A__	:							Optional[Any]						    = tmp_path / "cache"
						A__	:							Dict						    = {"text": "string"}
						A__	:							List[Any]						    = TextDatasetReader(__lowercase							,     cache_dir=__lowercase       ).read()
						_check_text_dataset(__lowercase							,     __lowercase       )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase=("train",)       )		->		str:







						"""simple docstring"""

						assert isinstance(__lowercase							,     __lowercase       )
						for split in splits:
												A__	:							str						    = dataset_dict[split]
												assert dataset.num_rows == 4
												assert dataset.num_columns == 1
												assert dataset.column_names == ["text"]
												for feature, expected_dtype in expected_features.items():
																		assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory"							,     [False, True]       )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		str:







						"""simple docstring"""

						A__	:							Optional[int]						    = tmp_path / "cache"
						A__	:							str						    = {"text": "string"}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												A__	:							Dict						    = TextDatasetReader({"train": text_path}							,     cache_dir=__lowercase							,     keep_in_memory=__lowercase       ).read()
						_check_text_datasetdict(__lowercase							,     __lowercase       )
@pytest.mark.parametrize(
    "features"							,     [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]							,     )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		Optional[int]:







						"""simple docstring"""

						A__	:							int						    = tmp_path / "cache"
						# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
						A__	:							List[str]						    = {"text": "string"}
						A__	:							int						    = features.copy() if features else default_expected_features
						A__	:							List[Any]						    = (
						    Features({feature: Value(__lowercase       ) for feature, dtype in features.items()}       ) if features is not None else None
						)
						A__	:							Union[str, Any]						    = TextDatasetReader({"train": text_path}							,     features=__lowercase							,     cache_dir=__lowercase       ).read()
						_check_text_datasetdict(__lowercase							,     __lowercase       )




@pytest.mark.parametrize("split"							,     [None, NamedSplit("train"       ), "train", "test"]       )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		int:







						"""simple docstring"""

						if split:
												A__	:							Dict						    = {split: text_path}
						else:
												A__	:							Dict						    = "train"
												A__	:							int						    = {"train": text_path, "test": text_path}
						A__	:							List[str]						    = tmp_path / "cache"
						A__	:							Any						    = {"text": "string"}
						A__	:							Dict						    = TextDatasetReader(__lowercase							,     cache_dir=__lowercase       ).read()
						_check_text_datasetdict(__lowercase							,     __lowercase							,     splits=list(path.keys()       )       )
						assert all(dataset[split].split == split for split in path.keys()       )




from collections.abc import Callable

import numpy as np
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		np.array:







						"""simple docstring"""

						A__	:							Any						    = int(np.ceil((x_end - xa) / step_size       )       )
						A__	:							Union[str, Any]						    = np.zeros((n + 1,)       )
						A__	:							Any						    = ya
						A__	:							Union[str, Any]						    = xa

						for k in range(__lowercase       ):
												A__	:							Any						    = y[k] + step_size * ode_func(__lowercase							,     y[k]       )
												A__	:							Any						    = y[k] + (
												    (step_size / 2) * (ode_func(__lowercase							,     y[k]       ) + ode_func(x + step_size							,     __lowercase       ))
												)
												x += step_size

						return y


if __name__ == "__main__":
		import doctest

		doctest.testmod()