

"""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_	(   __UpperCamelCase     :				int       ,	__UpperCamelCase     :				List[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 		A_	(   __UpperCamelCase     :				Optional[int]       ,	__UpperCamelCase     :				List[str]       ,	__UpperCamelCase     :				Any				):
		lowercase										=							tmp_path / '''cache'''
		lowercase										=							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				lowercase										=							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 		A_	(   __UpperCamelCase     :				int       ,	__UpperCamelCase     :				Any       ,	__UpperCamelCase     :				Optional[int]				):
		lowercase										=							tmp_path / '''cache'''
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		lowercase										=							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
		lowercase										=							features.copy() if features else default_expected_features
		lowercase										=							(
		    Features({feature: Value(__UpperCamelCase				) for feature, dtype in features.items()}				) if features is not None else None
		)
		lowercase										=							CsvDatasetReader(__UpperCamelCase       ,	features=__UpperCamelCase       ,	cache_dir=__UpperCamelCase				).read()
		_check_csv_dataset(__UpperCamelCase       ,	__UpperCamelCase				)
@pytest.mark.parametrize('''split'''       ,	[None, NamedSplit('''train'''				), '''train''', '''test''']				)
def 		A_	(   __UpperCamelCase     :				Union[str, Any]       ,	__UpperCamelCase     :				int       ,	__UpperCamelCase     :				Optional[int]				):
		lowercase										=							tmp_path / '''cache'''
		lowercase										=							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
		lowercase										=							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 		A_	(   __UpperCamelCase     :				Tuple       ,	__UpperCamelCase     :				Union[str, Any]       ,	__UpperCamelCase     :				int				):
		if issubclass(__UpperCamelCase       ,	__UpperCamelCase				):
				lowercase										=							csv_path
		elif issubclass(__UpperCamelCase       ,	__UpperCamelCase				):
				lowercase										=							[csv_path]
		lowercase										=							tmp_path / '''cache'''
		lowercase										=							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
		lowercase										=							CsvDatasetReader(__UpperCamelCase       ,	cache_dir=__UpperCamelCase				).read()
		_check_csv_dataset(__UpperCamelCase       ,	__UpperCamelCase				)
def 		A_	(   __UpperCamelCase     :				str       ,	__UpperCamelCase     :				Optional[int]       ,	__UpperCamelCase     :				List[str]=("train",)				):
		assert isinstance(__UpperCamelCase       ,	__UpperCamelCase				)
		for split in splits:
				lowercase										=							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_	(   __UpperCamelCase     :				List[str]       ,	__UpperCamelCase     :				Dict       ,	__UpperCamelCase     :				str				):
		lowercase										=							tmp_path / '''cache'''
		lowercase										=							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				lowercase										=							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 		A_	(   __UpperCamelCase     :				List[Any]       ,	__UpperCamelCase     :				str       ,	__UpperCamelCase     :				Dict				):
		lowercase										=							tmp_path / '''cache'''
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		lowercase										=							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
		lowercase										=							features.copy() if features else default_expected_features
		lowercase										=							(
		    Features({feature: Value(__UpperCamelCase				) for feature, dtype in features.items()}				) if features is not None else None
		)
		lowercase										=							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 		A_	(   __UpperCamelCase     :				int       ,	__UpperCamelCase     :				Any       ,	__UpperCamelCase     :				List[Any]				):
		if split:
				lowercase										=							{split: csv_path}
		else:
				lowercase										=							{'''train''': csv_path, '''test''': csv_path}
		lowercase										=							tmp_path / '''cache'''
		lowercase										=							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
		lowercase										=							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 		A_	(   __UpperCamelCase     :				List[str]				):
		with open(__UpperCamelCase       ,	encoding='''utf-8'''				) as csvfile:
				yield from csv.reader(__UpperCamelCase				)
def 		A_	(   __UpperCamelCase     :				List[str]       ,	__UpperCamelCase     :				Dict				):
		lowercase										=							tmp_path / '''cache'''
		lowercase										=							os.path.join(__UpperCamelCase       ,	'''tmp.csv'''				)
		lowercase										=							CsvDatasetReader({'''train''': csv_path}       ,	cache_dir=__UpperCamelCase				).read()
		CsvDatasetWriter(dataset['''train''']       ,	__UpperCamelCase       ,	num_proc=1				).write()

		lowercase										=							iter_csv_file(__UpperCamelCase				)
		lowercase										=							iter_csv_file(__UpperCamelCase				)

		for rowa, rowa in zip(__UpperCamelCase       ,	__UpperCamelCase				):
				assert rowa == rowa
def 		A_	(   __UpperCamelCase     :				List[Any]       ,	__UpperCamelCase     :				List[Any]				):
		lowercase										=							tmp_path / '''cache'''
		lowercase										=							os.path.join(__UpperCamelCase       ,	'''tmp.csv'''				)
		lowercase										=							CsvDatasetReader({'''train''': csv_path}       ,	cache_dir=__UpperCamelCase				).read()
		CsvDatasetWriter(dataset['''train''']       ,	__UpperCamelCase       ,	num_proc=2				).write()

		lowercase										=							iter_csv_file(__UpperCamelCase				)
		lowercase										=							iter_csv_file(__UpperCamelCase				)

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



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


"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCAmelCase				      =   {'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
							from .tokenization_phobert import PhobertTokenizer

else:
							import sys

							__lowerCAmelCase				      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)