
"""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 					a__ ( a :					Optional[Any]							,  a :					Any		):

  """simple docstring"""







  assert isinstance(a							,  a		)
  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 					a__ ( a :					int							,  a :					Union[str, Any]							,  a :					Dict		):

  """simple docstring"""







  _snake_case  :    List[Any]            = tmp_path / "cache"
  _snake_case  :    Tuple            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _snake_case  :    Optional[int]            = CsvDatasetReader(a							,  cache_dir=a							,  keep_in_memory=a		).read()
  _check_csv_dataset(a							,  a		)
@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 					a__ ( a :					List[Any]							,  a :					str							,  a :					Any		):

  """simple docstring"""







  _snake_case  :    Optional[int]            = tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  _snake_case  :    str            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  _snake_case  :    str            = features.copy() if features else default_expected_features
  _snake_case  :    List[Any]            = (
      Features({feature: Value(a		) for feature, dtype in features.items()}		) if features is not None else None
  )
  _snake_case  :    List[str]            = CsvDatasetReader(a							,  features=a							,  cache_dir=a		).read()
  _check_csv_dataset(a							,  a		)
@pytest.mark.parametrize("split"							,  [None, NamedSplit("train"		), "train", "test"]		)
def 					a__ ( a :					int							,  a :					List[str]							,  a :					Any		):

  """simple docstring"""







  _snake_case  :    Dict            = tmp_path / "cache"
  _snake_case  :    Optional[int]            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  _snake_case  :    Any            = CsvDatasetReader(a							,  cache_dir=a							,  split=a		).read()
  _check_csv_dataset(a							,  a		)
  assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type"							,  [str, list]		)
def 					a__ ( a :					int							,  a :					Union[str, Any]							,  a :					Optional[Any]		):

  """simple docstring"""







  if issubclass(a							,  a		):
    _snake_case  :    Any            = csv_path
  elif issubclass(a							,  a		):
    _snake_case  :    Optional[int]            = [csv_path]
  _snake_case  :    Optional[Any]            = tmp_path / "cache"
  _snake_case  :    Tuple            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  _snake_case  :    int            = CsvDatasetReader(a							,  cache_dir=a		).read()
  _check_csv_dataset(a							,  a		)
def 					a__ ( a :					List[str]							,  a :					int							,  a :					int=("train",)		):

  """simple docstring"""







  assert isinstance(a							,  a		)
  for split in splits:
    _snake_case  :    Tuple            = 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 					a__ ( a :					Optional[int]							,  a :					str							,  a :					str		):

  """simple docstring"""







  _snake_case  :    int            = tmp_path / "cache"
  _snake_case  :    Optional[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():
    _snake_case  :    Any            = CsvDatasetReader({"train": csv_path}							,  cache_dir=a							,  keep_in_memory=a		).read()
  _check_csv_datasetdict(a							,  a		)
@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 					a__ ( a :					Any							,  a :					int							,  a :					Optional[int]		):

  """simple docstring"""







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

  """simple docstring"""







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

  """simple docstring"""







  with open(a							,  encoding="utf-8"		) as csvfile:
    yield from csv.reader(a		)
def 					a__ ( a :					List[str]							,  a :					Optional[Any]		):

  """simple docstring"""







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

  _snake_case  :    List[Any]            = iter_csv_file(a		)
  _snake_case  :    Any            = iter_csv_file(a		)

  for rowa, rowa in zip(a							,  a		):
    assert rowa == rowa
def 					a__ ( a :					Optional[int]							,  a :					Optional[int]		):

  """simple docstring"""







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

  _snake_case  :    int            = iter_csv_file(a		)
  _snake_case  :    Optional[Any]            = iter_csv_file(a		)

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



def 					a__ ( a :					Tuple							,  a :					Tuple		):

  """simple docstring"""







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




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





@pytest.mark.parametrize("""split"""       , [None, NamedSplit("""train"""      ), """train""", """test"""]      )
def 			_snake_case  (     UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       int      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	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 			_snake_case  (     UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Dict      ):
     if issubclass(UpperCAmelCase_       , UpperCAmelCase_      ):
          A__				      =	csv_path
     elif issubclass(UpperCAmelCase_       , UpperCAmelCase_      ):
          A__				      =	[csv_path]
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	CsvDatasetReader(UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       Optional[Any]       , UpperCAmelCase_	:       Dict       , UpperCAmelCase_	:       int=("train",)      ):
     assert isinstance(UpperCAmelCase_       , UpperCAmelCase_      )
     for split in splits:
          A__				      =	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 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       List[str]      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""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__				      =	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 			_snake_case  (     UpperCAmelCase_	:       Dict       , UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       Optional[Any]      ):
     A__				      =	tmp_path / """cache"""
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	features.copy() if features else default_expected_features
     A__				      =	(
         Features({feature: Value(UpperCAmelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
     )
     A__				      =	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 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       List[str]       , UpperCAmelCase_	:       Optional[int]      ):
     if split:
          A__				      =	{split: csv_path}
     else:
          A__				      =	{"""train""": csv_path, """test""": csv_path}
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	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 			_snake_case  (     UpperCAmelCase_	:       Optional[Any]      ):
     with open(UpperCAmelCase_       , encoding="""utf-8"""      ) as csvfile:
          yield from csv.reader(UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       str       , UpperCAmelCase_	:       int      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	os.path.join(UpperCAmelCase_       , """tmp.csv"""      )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_      ).read()
     CsvDatasetWriter(dataset["""train"""]       , UpperCAmelCase_       , num_proc=1      ).write()

     A__				      =	iter_csv_file(UpperCAmelCase_      )
     A__				      =	iter_csv_file(UpperCAmelCase_      )

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





def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       List[Any]      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	os.path.join(UpperCAmelCase_       , """tmp.csv"""      )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_      ).read()
     CsvDatasetWriter(dataset["""train"""]       , UpperCAmelCase_       , num_proc=2      ).write()

     A__				      =	iter_csv_file(UpperCAmelCase_      )
     A__				      =	iter_csv_file(UpperCAmelCase_      )

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





def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       Any      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	os.path.join(UpperCAmelCase_       , """tmp.csv"""      )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_      ).read()
     with pytest.raises(UpperCAmelCase_      ):
          CsvDatasetWriter(dataset["""train"""]       , UpperCAmelCase_       , num_proc=0      )






