



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 			A_      (	__a     :				Any ,		__a     :				int		):

				"""simple docstring"""
				assert isinstance(_lowerCamelCase ,		_lowerCamelCase		)
				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 			A_      (	__a     :				Tuple ,		__a     :				int ,		__a     :				List[Any]		):

				"""simple docstring"""
				a__				      =		tmp_path / "cache"
				a__				      =		{"text": "string"}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								a__				      =		TextDatasetReader(_lowerCamelCase ,		cache_dir=_lowerCamelCase ,		keep_in_memory=_lowerCamelCase		).read()
				_check_text_dataset(_lowerCamelCase ,		_lowerCamelCase		)

@pytest.mark.parametrize(
    """features""" ,		[
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ] ,		)
def 			A_      (	__a     :				str ,		__a     :				List[str] ,		__a     :				Optional[int]		):

				"""simple docstring"""
				a__				      =		tmp_path / "cache"
				a__				      =		{"text": "string"}
				a__				      =		features.copy() if features else default_expected_features
				a__				      =		(
				    Features({feature: Value(_lowerCamelCase		) for feature, dtype in features.items()}		) if features is not None else None
				)
				a__				      =		TextDatasetReader(_lowerCamelCase ,		features=_lowerCamelCase ,		cache_dir=_lowerCamelCase		).read()
				_check_text_dataset(_lowerCamelCase ,		_lowerCamelCase		)

@pytest.mark.parametrize("""split""" ,		[None, NamedSplit("""train"""		), """train""", """test"""]		)
def 			A_      (	__a     :				List[Any] ,		__a     :				List[Any] ,		__a     :				Any		):

				"""simple docstring"""
				a__				      =		tmp_path / "cache"
				a__				      =		{"text": "string"}
				a__				      =		TextDatasetReader(_lowerCamelCase ,		cache_dir=_lowerCamelCase ,		split=_lowerCamelCase		).read()
				_check_text_dataset(_lowerCamelCase ,		_lowerCamelCase		)
				assert dataset.split == split if split else "train"

@pytest.mark.parametrize("""path_type""" ,		[str, list]		)
def 			A_      (	__a     :				Union[str, Any] ,		__a     :				Dict ,		__a     :				str		):

				"""simple docstring"""
				if issubclass(_lowerCamelCase ,		_lowerCamelCase		):
								a__				      =		text_path
				elif issubclass(_lowerCamelCase ,		_lowerCamelCase		):
								a__				      =		[text_path]
				a__				      =		tmp_path / "cache"
				a__				      =		{"text": "string"}
				a__				      =		TextDatasetReader(_lowerCamelCase ,		cache_dir=_lowerCamelCase		).read()
				_check_text_dataset(_lowerCamelCase ,		_lowerCamelCase		)

def 			A_      (	__a     :				List[str] ,		__a     :				Dict ,		__a     :				Dict=("train",)		):

				"""simple docstring"""
				assert isinstance(_lowerCamelCase ,		_lowerCamelCase		)
				for split in splits:
								a__				      =		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 			A_      (	__a     :				Union[str, Any] ,		__a     :				str ,		__a     :				str		):

				"""simple docstring"""
				a__				      =		tmp_path / "cache"
				a__				      =		{"text": "string"}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								a__				      =		TextDatasetReader({"""train""": text_path} ,		cache_dir=_lowerCamelCase ,		keep_in_memory=_lowerCamelCase		).read()
				_check_text_datasetdict(_lowerCamelCase ,		_lowerCamelCase		)

@pytest.mark.parametrize(
    """features""" ,		[
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ] ,		)
def 			A_      (	__a     :				List[Any] ,		__a     :				Dict ,		__a     :				str		):

				"""simple docstring"""
				a__				      =		tmp_path / "cache"
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				a__				      =		{"text": "string"}
				a__				      =		features.copy() if features else default_expected_features
				a__				      =		(
				    Features({feature: Value(_lowerCamelCase		) for feature, dtype in features.items()}		) if features is not None else None
				)
				a__				      =		TextDatasetReader({"""train""": text_path} ,		features=_lowerCamelCase ,		cache_dir=_lowerCamelCase		).read()
				_check_text_datasetdict(_lowerCamelCase ,		_lowerCamelCase		)




@pytest.mark.parametrize("""split""" ,		[None, NamedSplit("""train"""		), """train""", """test"""]		)
def 			A_      (	__a     :				str ,		__a     :				str ,		__a     :				Any		):

				"""simple docstring"""
				if split:
								a__				      =		{split: text_path}
				else:
								a__				      =		"train"
								a__				      =		{"train": text_path, "test": text_path}
				a__				      =		tmp_path / "cache"
				a__				      =		{"text": "string"}
				a__				      =		TextDatasetReader(_lowerCamelCase ,		cache_dir=_lowerCamelCase		).read()
				_check_text_datasetdict(_lowerCamelCase ,		_lowerCamelCase ,		splits=list(path.keys()		)		)
				assert all(dataset[split].split == split for split in path.keys()		)











from __future__ import annotations

UpperCAmelCase							 =					8.988E9  # units = N * m^s * C^-2

def 			A_      (	__a     :				float ,		__a     :				float ,		__a     :				float ,		__a     :				float		):

				"""simple docstring"""
				a__				      =		abs(chargea * chargea		)

				if (force, chargea, chargea, distance).count(0		) != 1:
								raise ValueError("""One and only one argument must be 0"""		)
				if distance < 0:
								raise ValueError("""Distance cannot be negative"""		)
				if force == 0:
								a__				      =		COULOMBS_CONSTANT * charge_product / (distance**2)
								return {"force": force}
				elif chargea == 0:
								a__				      =		abs(__a		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge1": chargea}
				elif chargea == 0:
								a__				      =		abs(__a		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge2": chargea}
				elif distance == 0:
								a__				      =		(COULOMBS_CONSTANT * charge_product / abs(__a		)) ** 0.5
								return {"distance": distance}
				raise ValueError("""Exactly one argument must be 0"""		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

