





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





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





@pytest.mark.parametrize('''split'''       ,						[None, NamedSplit('''train'''   ), '''train''', '''test''']   )
def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					List[str]     =    tmp_path / '''cache'''
						_UpperCamelCase			:					Dict     =    {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						_UpperCamelCase			:					Dict     =    CsvDatasetReader(_lowercase       ,						cache_dir=_lowercase       ,						split=_lowercase   ).read()
						_check_csv_dataset(_lowercase       ,						_lowercase   )
						assert dataset.split == split if split else "train"





@pytest.mark.parametrize('''path_type'''       ,						[str, list]   )
def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						if issubclass(_lowercase       ,						_lowercase   ):
												_UpperCamelCase			:					Tuple     =    csv_path
						elif issubclass(_lowercase       ,						_lowercase   ):
												_UpperCamelCase			:					List[Any]     =    [csv_path]
						_UpperCamelCase			:					Any     =    tmp_path / '''cache'''
						_UpperCamelCase			:					Optional[Any]     =    {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						_UpperCamelCase			:					int     =    CsvDatasetReader(_lowercase       ,						cache_dir=_lowercase   ).read()
						_check_csv_dataset(_lowercase       ,						_lowercase   )





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase=("train",)   ):
						assert isinstance(_lowercase       ,						_lowercase   )
						for split in splits:
												_UpperCamelCase			:					List[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        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					int     =    tmp_path / '''cache'''
						_UpperCamelCase			:					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():
												_UpperCamelCase			:					Optional[Any]     =    CsvDatasetReader({'''train''': csv_path}       ,						cache_dir=_lowercase       ,						keep_in_memory=_lowercase   ).read()
						_check_csv_datasetdict(_lowercase       ,						_lowercase   )





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





@pytest.mark.parametrize('''split'''       ,						[None, NamedSplit('''train'''   ), '''train''', '''test''']   )
def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						if split:
												_UpperCamelCase			:					List[Any]     =    {split: csv_path}
						else:
												_UpperCamelCase			:					List[str]     =    {'''train''': csv_path, '''test''': csv_path}
						_UpperCamelCase			:					int     =    tmp_path / '''cache'''
						_UpperCamelCase			:					Any     =    {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						_UpperCamelCase			:					int     =    CsvDatasetReader(_lowercase       ,						cache_dir=_lowercase   ).read()
						_check_csv_datasetdict(_lowercase       ,						_lowercase       ,						splits=list(path.keys()   )   )
						assert all(dataset[split].split == split for split in path.keys()   )





def        a_				(							_lowercase   ):
						with open(_lowercase       ,						encoding='''utf-8'''   ) as csvfile:
												yield from csv.reader(_lowercase   )





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					int     =    tmp_path / '''cache'''
						_UpperCamelCase			:					str     =    os.path.join(_lowercase       ,						'''tmp.csv'''   )
						_UpperCamelCase			:					Tuple     =    CsvDatasetReader({'''train''': csv_path}       ,						cache_dir=_lowercase   ).read()
						CsvDatasetWriter(dataset['''train''']       ,						_lowercase       ,						num_proc=1   ).write()

						_UpperCamelCase			:					Optional[Any]     =    iter_csv_file(_lowercase   )
						_UpperCamelCase			:					List[str]     =    iter_csv_file(_lowercase   )

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





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Union[str, Any]     =    tmp_path / '''cache'''
						_UpperCamelCase			:					Any     =    os.path.join(_lowercase       ,						'''tmp.csv'''   )
						_UpperCamelCase			:					Dict     =    CsvDatasetReader({'''train''': csv_path}       ,						cache_dir=_lowercase   ).read()
						CsvDatasetWriter(dataset['''train''']       ,						_lowercase       ,						num_proc=2   ).write()

						_UpperCamelCase			:					Tuple     =    iter_csv_file(_lowercase   )
						_UpperCamelCase			:					Dict     =    iter_csv_file(_lowercase   )

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






def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					List[Any]     =    tmp_path / '''cache'''
						_UpperCamelCase			:					Optional[int]     =    os.path.join(_lowercase       ,						'''tmp.csv'''   )
						_UpperCamelCase			:					Any     =    CsvDatasetReader({'''train''': csv_path}       ,						cache_dir=_lowercase   ).read()
						with pytest.raises(_lowercase   ):
												CsvDatasetWriter(dataset['''train''']       ,						_lowercase       ,						num_proc=0   )










"""simple docstring"""




import torch
from torch import autograd




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float, lowerCAmelCase__		:    bool							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    inputs.numel()
										_UpperCamelCase			:					int     =    int(0.005 * nb_elems							) + 1
										if sigmoid:
																_UpperCamelCase			:					Dict     =    (torch.sigmoid(lowerCAmelCase__							) > threshold).type(inputs.type()							)
										else:
																_UpperCamelCase			:					Optional[int]     =    (inputs > threshold).type(inputs.type()							)
										if mask.sum() < nb_min:
																# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
																_UpperCamelCase			:					int     =    inputs.flatten().kthvalue(max(nb_elems - nb_min, 1							)							).values
																_UpperCamelCase			:					List[str]     =    (inputs > k_threshold).type(inputs.type()							)
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    Optional[int]							)	->      Union[str, Any]:




										'''simple docstring'''



										return gradOutput, None, None




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Union[str, Any]     =    inputs.flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    int(threshold * inputs.numel()							)

										# flat_out and mask access the same memory.
										_UpperCamelCase			:					Tuple     =    mask.flatten()
										_UpperCamelCase			:					int     =    0
										_UpperCamelCase			:					Tuple     =    1
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    Union[str, Any]							)	->      int:




										'''simple docstring'''



										return gradOutput, None




class  _a						(      _lowerCAmelCase				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Optional[int]     =    inputs.abs().flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					List[Any]     =    int(threshold * inputs.numel()							)

										# flat_out and mask access the same memory.
										_UpperCamelCase			:					List[str]     =    mask.flatten()
										_UpperCamelCase			:					List[Any]     =    0
										_UpperCamelCase			:					str     =    1
										return mask



