
"""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"""
SCREAMING_SNAKE_CASE					:       List[Any]							   =							1000






def 						lowercase				(	_snake_case		: float	)	->float:
	"""simple docstring"""





	return x * x






def 						lowercase				(	_snake_case		: Union[str, Any]	,  _snake_case		: float	,  _snake_case		: float	,  _snake_case		: int = 4	)	->float:
	"""simple docstring"""





	assert callable(
	    _snake_case	), f"""the function(object) passed should be callable your input : {function}"""
	assert isinstance(_snake_case	,  (float, int)	), f"""a should be float or integer your input : {a}"""
	assert isinstance(function(_snake_case	)	,  (float, int)	), (
	    "the function should return integer or float return type of your function, "
	    f"""{type(_snake_case	)}"""
	)
	assert isinstance(_snake_case	,  (float, int)	), f"""b should be float or integer your input : {b}"""
	assert (
	    isinstance(_snake_case	,  _snake_case	) and precision > 0
	), f"""precision should be positive integer your input : {precision}"""

	# just applying the formula of simpson for approximate integration written in
	# mentioned article in first comment of this file and above this function

	__snake_case      :		Tuple         =							(b - a) / N_STEPS
	__snake_case      :		Union[str, Any]         =							function(_snake_case	) + function(_snake_case	)

	for i in range(1	,  _snake_case	):
		__snake_case      :		List[str]         =							a + h * i
		result += function(_snake_case	) * (4 if i % 2 else 2)

	result *= h / 3
	return round(_snake_case	,  _snake_case	)


if __name__ == "__main__":
			import doctest

			doctest.testmod()





