

"""simple docstring"""






snake_case_        =       {
    """Pillow""": """Pillow""",
    """accelerate""": """accelerate>=0.11.0""",
    """compel""": """compel==0.1.8""",
    """black""": """black~=23.1""",
    """datasets""": """datasets""",
    """filelock""": """filelock""",
    """flax""": """flax>=0.4.1""",
    """hf-doc-builder""": """hf-doc-builder>=0.3.0""",
    """huggingface-hub""": """huggingface-hub>=0.13.2""",
    """requests-mock""": """requests-mock==1.10.0""",
    """importlib_metadata""": """importlib_metadata""",
    """invisible-watermark""": """invisible-watermark""",
    """isort""": """isort>=5.5.4""",
    """jax""": """jax>=0.2.8,!=0.3.2""",
    """jaxlib""": """jaxlib>=0.1.65""",
    """Jinja2""": """Jinja2""",
    """k-diffusion""": """k-diffusion>=0.0.12""",
    """torchsde""": """torchsde""",
    """note_seq""": """note_seq""",
    """librosa""": """librosa""",
    """numpy""": """numpy""",
    """omegaconf""": """omegaconf""",
    """parameterized""": """parameterized""",
    """protobuf""": """protobuf>=3.20.3,<4""",
    """pytest""": """pytest""",
    """pytest-timeout""": """pytest-timeout""",
    """pytest-xdist""": """pytest-xdist""",
    """ruff""": """ruff>=0.0.241""",
    """safetensors""": """safetensors""",
    """sentencepiece""": """sentencepiece>=0.1.91,!=0.1.92""",
    """scipy""": """scipy""",
    """onnx""": """onnx""",
    """regex""": """regex!=2019.12.17""",
    """requests""": """requests""",
    """tensorboard""": """tensorboard""",
    """torch""": """torch>=1.4""",
    """torchvision""": """torchvision""",
    """transformers""": """transformers>=4.25.1""",
    """urllib3""": """urllib3<=2.0.0""",
}





"""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   lowercase_	(    _lowercase       :		Optional[int]							,      _lowercase       :		int	):




					'''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   lowercase_	(    _lowercase       :		Union[str, Any]							,      _lowercase       :		Optional[int]							,      _lowercase       :		Dict	):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				tmp_path / "cache"
					UpperCAmelCase			:   str      =				{"text": "string"}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										UpperCAmelCase			:   Tuple      =				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   lowercase_	(    _lowercase       :		Dict							,      _lowercase       :		Union[str, Any]							,      _lowercase       :		Optional[int]	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				tmp_path / "cache"
					UpperCAmelCase			:   Any      =				{"text": "string"}
					UpperCAmelCase			:   List[str]      =				features.copy() if features else default_expected_features
					UpperCAmelCase			:   Union[str, Any]      =				(
					    Features({feature: Value(_lowercase	) for feature, dtype in features.items()}	) if features is not None else None
					)
					UpperCAmelCase			:   str      =				TextDatasetReader(_lowercase							,      features=_lowercase							,      cache_dir=_lowercase	).read()
					_check_text_dataset(_lowercase							,      _lowercase	)



@pytest.mark.parametrize("split"							,      [None, NamedSplit("train"	), "train", "test"]	)
def   lowercase_	(    _lowercase       :		int							,      _lowercase       :		Tuple							,      _lowercase       :		List[str]	):




					'''simple docstring'''






					UpperCAmelCase			:   str      =				tmp_path / "cache"
					UpperCAmelCase			:   Any      =				{"text": "string"}
					UpperCAmelCase			:   List[Any]      =				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   lowercase_	(    _lowercase       :		Optional[int]							,      _lowercase       :		Any							,      _lowercase       :		Union[str, Any]	):




					'''simple docstring'''






					if issubclass(_lowercase							,      _lowercase	):
										UpperCAmelCase			:   List[str]      =				text_path
					elif issubclass(_lowercase							,      _lowercase	):
										UpperCAmelCase			:   List[Any]      =				[text_path]
					UpperCAmelCase			:   Union[str, Any]      =				tmp_path / "cache"
					UpperCAmelCase			:   List[Any]      =				{"text": "string"}
					UpperCAmelCase			:   Tuple      =				TextDatasetReader(_lowercase							,      cache_dir=_lowercase	).read()
					_check_text_dataset(_lowercase							,      _lowercase	)



def   lowercase_	(    _lowercase       :		Dict							,      _lowercase       :		int							,      _lowercase       :		Optional[Any]=("train",)	):




					'''simple docstring'''






					assert isinstance(_lowercase							,      _lowercase	)
					for split in splits:
										UpperCAmelCase			:   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   lowercase_	(    _lowercase       :		Dict							,      _lowercase       :		str							,      _lowercase       :		Dict	):




					'''simple docstring'''






					UpperCAmelCase			:   str      =				tmp_path / "cache"
					UpperCAmelCase			:   Union[str, Any]      =				{"text": "string"}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										UpperCAmelCase			:   Tuple      =				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   lowercase_	(    _lowercase       :		List[Any]							,      _lowercase       :		Union[str, Any]							,      _lowercase       :		int	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				tmp_path / "cache"
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					UpperCAmelCase			:   List[str]      =				{"text": "string"}
					UpperCAmelCase			:   str      =				features.copy() if features else default_expected_features
					UpperCAmelCase			:   Optional[int]      =				(
					    Features({feature: Value(_lowercase	) for feature, dtype in features.items()}	) if features is not None else None
					)
					UpperCAmelCase			:   Optional[int]      =				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   lowercase_	(    _lowercase       :		int							,      _lowercase       :		Tuple							,      _lowercase       :		Optional[Any]	):




					'''simple docstring'''






					if split:
										UpperCAmelCase			:   Optional[Any]      =				{split: text_path}
					else:
										UpperCAmelCase			:   str      =				"train"
										UpperCAmelCase			:   Union[str, Any]      =				{"train": text_path, "test": text_path}
					UpperCAmelCase			:   Any      =				tmp_path / "cache"
					UpperCAmelCase			:   List[Any]      =				{"text": "string"}
					UpperCAmelCase			:   Any      =				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()	)


