


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


@pytest.mark.parametrize('''split''' ,    [None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def 					_snake_case   (     A ,    A ,    A       )						->    Union[str, Any]:
							lowerCAmelCase__          =	tmp_path / '''cache'''
							lowerCAmelCase__          =	{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							lowerCAmelCase__          =	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 					_snake_case   (     A ,    A ,    A       )						->    Any:
							if issubclass(A ,    A       ):
														lowerCAmelCase__          =	csv_path
							elif issubclass(A ,    A       ):
														lowerCAmelCase__          =	[csv_path]
							lowerCAmelCase__          =	tmp_path / '''cache'''
							lowerCAmelCase__          =	{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							lowerCAmelCase__          =	CsvDatasetReader(A ,    cache_dir=A       ).read()
							_check_csv_dataset(A ,    A       )


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


def 					_snake_case   (     A ,    A       )						->    Optional[int]:
							lowerCAmelCase__          =	tmp_path / '''cache'''
							lowerCAmelCase__          =	os.path.join(A ,    '''tmp.csv'''       )
							lowerCAmelCase__          =	CsvDatasetReader({'''train''': csv_path} ,    cache_dir=A       ).read()
							CsvDatasetWriter(dataset['''train'''] ,    A ,    num_proc=1       ).write()

							lowerCAmelCase__          =	iter_csv_file(A       )
							lowerCAmelCase__          =	iter_csv_file(A       )

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


def 					_snake_case   (     A ,    A       )						->    Union[str, Any]:
							lowerCAmelCase__          =	tmp_path / '''cache'''
							lowerCAmelCase__          =	os.path.join(A ,    '''tmp.csv'''       )
							lowerCAmelCase__          =	CsvDatasetReader({'''train''': csv_path} ,    cache_dir=A       ).read()
							CsvDatasetWriter(dataset['''train'''] ,    A ,    num_proc=2       ).write()

							lowerCAmelCase__          =	iter_csv_file(A       )
							lowerCAmelCase__          =	iter_csv_file(A       )

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






def 					_snake_case   (     A ,    A       )						->    Optional[int]:
							lowerCAmelCase__          =	tmp_path / '''cache'''
							lowerCAmelCase__          =	os.path.join(A ,    '''tmp.csv'''       )
							lowerCAmelCase__          =	CsvDatasetReader({'''train''': csv_path} ,    cache_dir=A       ).read()
							with pytest.raises(A       ):
														CsvDatasetWriter(dataset['''train'''] ,    A ,    num_proc=0       )



'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase    				=							{
    '''configuration_switch_transformers''': [
        '''SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwitchTransformersConfig''',
        '''SwitchTransformersOnnxConfig''',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    				=							[
		    '''SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SwitchTransformersEncoderModel''',
		    '''SwitchTransformersForConditionalGeneration''',
		    '''SwitchTransformersModel''',
		    '''SwitchTransformersPreTrainedModel''',
		    '''SwitchTransformersTop1Router''',
		    '''SwitchTransformersSparseMLP''',
		]


if TYPE_CHECKING:
		from .configuration_switch_transformers import (
		    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SwitchTransformersConfig,
		    SwitchTransformersOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_switch_transformers import (
				    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SwitchTransformersEncoderModel,
				    SwitchTransformersForConditionalGeneration,
				    SwitchTransformersModel,
				    SwitchTransformersPreTrainedModel,
				    SwitchTransformersSparseMLP,
				    SwitchTransformersTopaRouter,
				)


else:
		import sys

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