


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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	assert isinstance(lowerCAmelCase_      ,					lowerCAmelCase_)
	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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


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







@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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							List[Any]      =       tmp_path / "cache"
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	lowerCamelCase_			:							Tuple      =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
	lowerCamelCase_			:							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_			:							Any      =       CsvDatasetReader(lowerCAmelCase_      ,					features=lowerCAmelCase_      ,					cache_dir=lowerCAmelCase_).read()
	_check_csv_dataset(lowerCAmelCase_      ,					lowerCAmelCase_)







@pytest.mark.parametrize("split"      ,					[None, NamedSplit("train"), "train", "test"])
def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


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







@pytest.mark.parametrize("path_type"      ,					[str, list])
def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


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







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_=("train",)):
	'''simple docstring'''


	assert isinstance(lowerCAmelCase_      ,					lowerCAmelCase_)
	for split in splits:
		lowerCamelCase_			:							Any      =       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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


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







@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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


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







@pytest.mark.parametrize("split"      ,					[None, NamedSplit("train"), "train", "test"])
def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


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







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


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







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


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

	lowerCamelCase_			:							List[Any]      =       iter_csv_file(lowerCAmelCase_)
	lowerCamelCase_			:							int      =       iter_csv_file(lowerCAmelCase_)

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







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


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

	lowerCamelCase_			:							Optional[int]      =       iter_csv_file(lowerCAmelCase_)
	lowerCamelCase_			:							str      =       iter_csv_file(lowerCAmelCase_)

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







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


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





from __future__ import annotations





class    lowerCAmelCase__				:






						"""simple docstring"""




						def __init__(   self				,							a_     ):
							lowerCamelCase_			:							List[str]      =       value
							lowerCamelCase_			:							Node | None      =       None
							lowerCamelCase_			:							Node | None      =       None





class    lowerCAmelCase__				:






						"""simple docstring"""
						__UpperCAmelCase   :		int




						def __init__(   self     ):
							lowerCamelCase_			:							str      =       0




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							if node is None:
								return

							if path_sum == self.target:
								self.paths += 1

							if node.left:
								self.depth_first_search(node.left				,							path_sum + node.left.value     )
							if node.right:
								self.depth_first_search(node.right				,							path_sum + node.right.value     )




						def 					_UpperCamelCase			(   self				,							a_				,							a_ = None     ):
							if node is None:
								return 0
							if target is not None:
								lowerCamelCase_			:							List[str]      =       target

							self.depth_first_search(a_				,							node.value     )
							self.path_sum(node.left     )
							self.path_sum(node.right     )

							return self.paths


if __name__ == "__main__":
							import doctest

							doctest.testmod()

