





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


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


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







_lowercase		:  Optional[Any]			       ="""
# 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
"""

_lowercase		:  Union[str, Any]			       =[{"""type""": """code""", """content""": INSTALL_CONTENT}]
_lowercase		:  List[Any]			       ={
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}
