



'''simple docstring'''

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 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			Union[str, Any]:
	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 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			Tuple:
	_lowercase					:       Optional[Any]       =		tmp_path / 'cache'
	_lowercase					:       Any       =		{'text': 'string'}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		_lowercase					:       List[Any]       =		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 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			Tuple:
	_lowercase					:       List[str]       =		tmp_path / 'cache'
	_lowercase					:       Optional[int]       =		{'text': 'string'}
	_lowercase					:       int       =		features.copy() if features else default_expected_features
	_lowercase					:       List[str]       =		(
	    Features({feature: Value(_lowercase					) for feature, dtype in features.items()}					) if features is not None else None
	)
	_lowercase					:       Tuple       =		TextDatasetReader(_lowercase,				features=_lowercase,				cache_dir=_lowercase					).read()
	_check_text_dataset(_lowercase,				_lowercase					)







@pytest.mark.parametrize('split',				[None, NamedSplit('train'					), 'train', 'test']					)
def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			Optional[Any]:
	_lowercase					:       Optional[Any]       =		tmp_path / 'cache'
	_lowercase					:       Any       =		{'text': 'string'}
	_lowercase					:       Tuple       =		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 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			int:
	if issubclass(_lowercase,				_lowercase					):
		_lowercase					:       Any       =		text_path
	elif issubclass(_lowercase,				_lowercase					):
		_lowercase					:       Any       =		[text_path]
	_lowercase					:       List[Any]       =		tmp_path / 'cache'
	_lowercase					:       Any       =		{'text': 'string'}
	_lowercase					:       List[str]       =		TextDatasetReader(_lowercase,				cache_dir=_lowercase					).read()
	_check_text_dataset(_lowercase,				_lowercase					)







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase=("train",)					) ->			Optional[Any]:
	assert isinstance(_lowercase,				_lowercase					)
	for split in splits:
		_lowercase					:       Dict       =		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 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			str:
	_lowercase					:       Any       =		tmp_path / 'cache'
	_lowercase					:       Union[str, Any]       =		{'text': 'string'}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		_lowercase					:       List[Any]       =		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 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			List[Any]:
	_lowercase					:       str       =		tmp_path / 'cache'
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	_lowercase					:       Optional[int]       =		{'text': 'string'}
	_lowercase					:       Union[str, Any]       =		features.copy() if features else default_expected_features
	_lowercase					:       List[str]       =		(
	    Features({feature: Value(_lowercase					) for feature, dtype in features.items()}					) if features is not None else None
	)
	_lowercase					:       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 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			int:
	if split:
		_lowercase					:       Dict       =		{split: text_path}
	else:
		_lowercase					:       Union[str, Any]       =		'train'
		_lowercase					:       Union[str, Any]       =		{'train': text_path, 'test': text_path}
	_lowercase					:       Tuple       =		tmp_path / 'cache'
	_lowercase					:       Union[str, Any]       =		{'text': 'string'}
	_lowercase					:       Optional[int]       =		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()					)





'''simple docstring'''

_A				:    Dict     						='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

_A				:    Dict     						=[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
_A				:    Dict     						={
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}
