






"""simple docstring"""



import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def    _lowerCAmelCase						(							UpperCAmelCase__				:    Any,  UpperCAmelCase__				:    List[Any]					)						->Optional[Any]:
						assert isinstance(UpperCAmelCase__,  UpperCAmelCase__					)
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory""",  [False, True]					)
def    _lowerCAmelCase						(							UpperCAmelCase__				:    Union[str, Any],  UpperCAmelCase__				:    List[str],  UpperCAmelCase__				:    int					)						->Union[str, Any]:
						A__  :     Union[str, Any]						     =     tmp_path / "cache"
						A__  :     Dict						     =     {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												A__  :     Optional[Any]						     =     CsvDatasetReader(UpperCAmelCase__,  cache_dir=UpperCAmelCase__,  keep_in_memory=UpperCAmelCase__					).read()
						_check_csv_dataset(UpperCAmelCase__,  UpperCAmelCase__					)




@pytest.mark.parametrize(
    """features""",  [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ],  )
def    _lowerCAmelCase						(							UpperCAmelCase__				:    Union[str, Any],  UpperCAmelCase__				:    Dict,  UpperCAmelCase__				:    Dict					)						->str:
						A__  :     Any						     =     tmp_path / "cache"
						# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
						A__  :     Optional[Any]						     =     {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
						A__  :     Any						     =     features.copy() if features else default_expected_features
						A__  :     List[Any]						     =     (
						    Features({feature: Value(UpperCAmelCase__					) for feature, dtype in features.items()}					) if features is not None else None
						)
						A__  :     Tuple						     =     CsvDatasetReader(UpperCAmelCase__,  features=UpperCAmelCase__,  cache_dir=UpperCAmelCase__					).read()
						_check_csv_dataset(UpperCAmelCase__,  UpperCAmelCase__					)




@pytest.mark.parametrize("""split""",  [None, NamedSplit("""train"""					), """train""", """test"""]					)
def    _lowerCAmelCase						(							UpperCAmelCase__				:    Tuple,  UpperCAmelCase__				:    Tuple,  UpperCAmelCase__				:    Dict					)						->Tuple:
						A__  :     Dict						     =     tmp_path / "cache"
						A__  :     Dict						     =     {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
						A__  :     List[Any]						     =     CsvDatasetReader(UpperCAmelCase__,  cache_dir=UpperCAmelCase__,  split=UpperCAmelCase__					).read()
						_check_csv_dataset(UpperCAmelCase__,  UpperCAmelCase__					)
						assert dataset.split == split if split else "train"




@pytest.mark.parametrize("""path_type""",  [str, list]					)
def    _lowerCAmelCase						(							UpperCAmelCase__				:    Tuple,  UpperCAmelCase__				:    str,  UpperCAmelCase__				:    int					)						->Union[str, Any]:






						if issubclass(UpperCAmelCase__,  UpperCAmelCase__					):
												A__  :     Tuple						     =     csv_path
						elif issubclass(UpperCAmelCase__,  UpperCAmelCase__					):
												A__  :     int						     =     [csv_path]
						A__  :     Any						     =     tmp_path / "cache"
						A__  :     Optional[int]						     =     {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
						A__  :     Tuple						     =     CsvDatasetReader(UpperCAmelCase__,  cache_dir=UpperCAmelCase__					).read()
						_check_csv_dataset(UpperCAmelCase__,  UpperCAmelCase__					)




def    _lowerCAmelCase						(							UpperCAmelCase__				:    Dict,  UpperCAmelCase__				:    Tuple,  UpperCAmelCase__				:    List[Any]=("train",)					)						->Optional[int]:
						assert isinstance(UpperCAmelCase__,  UpperCAmelCase__					)
						for split in splits:
												A__  :     str						     =     dataset_dict[split]
												assert dataset.num_rows == 4
												assert dataset.num_columns == 3
												assert dataset.column_names == ["col_1", "col_2", "col_3"]
												for feature, expected_dtype in expected_features.items():
																		assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory""",  [False, True]					)
def    _lowerCAmelCase						(							UpperCAmelCase__				:    Any,  UpperCAmelCase__				:    Tuple,  UpperCAmelCase__				:    List[Any]					)						->List[Any]:
						A__  :     Union[str, Any]						     =     tmp_path / "cache"
						A__  :     Union[str, Any]						     =     {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												A__  :     Optional[Any]						     =     CsvDatasetReader({"""train""": csv_path},  cache_dir=UpperCAmelCase__,  keep_in_memory=UpperCAmelCase__					).read()
						_check_csv_datasetdict(UpperCAmelCase__,  UpperCAmelCase__					)




@pytest.mark.parametrize(
    """features""",  [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ],  )
def    _lowerCAmelCase						(							UpperCAmelCase__				:    Tuple,  UpperCAmelCase__				:    Tuple,  UpperCAmelCase__				:    List[str]					)						->Optional[Any]:
						A__  :     int						     =     tmp_path / "cache"
						# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
						A__  :     Any						     =     {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
						A__  :     Optional[Any]						     =     features.copy() if features else default_expected_features
						A__  :     List[Any]						     =     (
						    Features({feature: Value(UpperCAmelCase__					) for feature, dtype in features.items()}					) if features is not None else None
						)
						A__  :     Optional[Any]						     =     CsvDatasetReader({"""train""": csv_path},  features=UpperCAmelCase__,  cache_dir=UpperCAmelCase__					).read()
						_check_csv_datasetdict(UpperCAmelCase__,  UpperCAmelCase__					)




@pytest.mark.parametrize("""split""",  [None, NamedSplit("""train"""					), """train""", """test"""]					)
def    _lowerCAmelCase						(							UpperCAmelCase__				:    Dict,  UpperCAmelCase__				:    List[Any],  UpperCAmelCase__				:    Any					)						->int:






						if split:
												A__  :     Dict						     =     {split: csv_path}
						else:
												A__  :     str						     =     {"train": csv_path, "test": csv_path}
						A__  :     Optional[Any]						     =     tmp_path / "cache"
						A__  :     Dict						     =     {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
						A__  :     Tuple						     =     CsvDatasetReader(UpperCAmelCase__,  cache_dir=UpperCAmelCase__					).read()
						_check_csv_datasetdict(UpperCAmelCase__,  UpperCAmelCase__,  splits=list(path.keys()					)					)
						assert all(dataset[split].split == split for split in path.keys()					)




def    _lowerCAmelCase						(							UpperCAmelCase__				:    Dict					)						->List[Any]:






						with open(UpperCAmelCase__,  encoding="""utf-8"""					) as csvfile:
												yield from csv.reader(UpperCAmelCase__					)




def    _lowerCAmelCase						(							UpperCAmelCase__				:    Union[str, Any],  UpperCAmelCase__				:    Optional[Any]					)						->str:
						A__  :     List[Any]						     =     tmp_path / "cache"
						A__  :     Optional[int]						     =     os.path.join(UpperCAmelCase__,  """tmp.csv"""					)
						A__  :     Optional[int]						     =     CsvDatasetReader({"""train""": csv_path},  cache_dir=UpperCAmelCase__					).read()
						CsvDatasetWriter(dataset["""train"""],  UpperCAmelCase__,  num_proc=1					).write()

						A__  :     Optional[Any]						     =     iter_csv_file(UpperCAmelCase__					)
						A__  :     Optional[Any]						     =     iter_csv_file(UpperCAmelCase__					)

						for rowa, rowa in zip(UpperCAmelCase__,  UpperCAmelCase__					):
												assert rowa == rowa




def    _lowerCAmelCase						(							UpperCAmelCase__				:    Optional[Any],  UpperCAmelCase__				:    int					)						->Dict:
						A__  :     Union[str, Any]						     =     tmp_path / "cache"
						A__  :     List[Any]						     =     os.path.join(UpperCAmelCase__,  """tmp.csv"""					)
						A__  :     List[str]						     =     CsvDatasetReader({"""train""": csv_path},  cache_dir=UpperCAmelCase__					).read()
						CsvDatasetWriter(dataset["""train"""],  UpperCAmelCase__,  num_proc=2					).write()

						A__  :     Dict						     =     iter_csv_file(UpperCAmelCase__					)
						A__  :     Tuple						     =     iter_csv_file(UpperCAmelCase__					)

						for rowa, rowa in zip(UpperCAmelCase__,  UpperCAmelCase__					):
												assert rowa == rowa




def    _lowerCAmelCase						(							UpperCAmelCase__				:    List[Any],  UpperCAmelCase__				:    Optional[int]					)						->str:
						A__  :     Optional[Any]						     =     tmp_path / "cache"
						A__  :     Union[str, Any]						     =     os.path.join(UpperCAmelCase__,  """tmp.csv"""					)
						A__  :     List[str]						     =     CsvDatasetReader({"""train""": csv_path},  cache_dir=UpperCAmelCase__					).read()
						with pytest.raises(UpperCAmelCase__					):
												CsvDatasetWriter(dataset["""train"""],  UpperCAmelCase__,  num_proc=0					)














import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  assert isinstance(lowercase  ,   lowercase )
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"  ,   [False, True] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase  ,   keep_in_memory=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


@pytest.mark.parametrize(
    "features"  ,   [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]  ,   )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Any		  =			tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Any		  =			features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			(
      Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
  )
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   features=lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


@pytest.mark.parametrize("split"  ,   [None, NamedSplit("train" ), "train", "test"] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Dict		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase  ,   split=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )
  assert dataset.split == split if split else "train"


@pytest.mark.parametrize("path_type"  ,   [str, list] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  if issubclass(lowercase  ,   lowercase ):
    SCREAMING_SNAKE_CASE				:					Tuple		  =			csv_path
  elif issubclass(lowercase  ,   lowercase ):
    SCREAMING_SNAKE_CASE				:					int		  =			[csv_path]
  SCREAMING_SNAKE_CASE				:					Any		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase=("train",) ):

  """simple docstring"""






  assert isinstance(lowercase  ,   lowercase )
  for split in splits:
    SCREAMING_SNAKE_CASE				:					str		  =			dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"  ,   [False, True] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase  ,   keep_in_memory=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase )


@pytest.mark.parametrize(
    "features"  ,   [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]  ,   )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					int		  =			tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  SCREAMING_SNAKE_CASE				:					Any		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			(
      Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
  )
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader({"train": csv_path}  ,   features=lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase )


@pytest.mark.parametrize("split"  ,   [None, NamedSplit("train" ), "train", "test"] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  if split:
    SCREAMING_SNAKE_CASE				:					Dict		  =			{split: csv_path}
  else:
    SCREAMING_SNAKE_CASE				:					str		  =			{"train": csv_path, "test": csv_path}
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase  ,   splits=list(path.keys() ) )
  assert all(dataset[split].split == split for split in path.keys() )


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  with open(lowercase  ,   encoding="utf-8" ) as csvfile:
    yield from csv.reader(lowercase )


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					List[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=1 ).write()

  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			iter_csv_file(lowercase )
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			iter_csv_file(lowercase )

  for rowa, rowa in zip(lowercase  ,   lowercase ):
    assert rowa == rowa


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=2 ).write()

  SCREAMING_SNAKE_CASE				:					Dict		  =			iter_csv_file(lowercase )
  SCREAMING_SNAKE_CASE				:					Tuple		  =			iter_csv_file(lowercase )

  for rowa, rowa in zip(lowercase  ,   lowercase ):
    assert rowa == rowa


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  with pytest.raises(lowercase ):
    CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=0 )






