






"""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_				:					Optional[int]  ,						UpperCamelCase_				:					Tuple		)	->		Optional[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[Any]  ,						UpperCamelCase_				:					Any		)	->		List[str]:
 __A						:							Tuple      =						tmp_path / 'cache'
 __A						:							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():
  __A						:							List[Any]      =						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_				:					Optional[int]  ,						UpperCamelCase_				:					Optional[Any]  ,						UpperCamelCase_				:					str		)	->		Any:
 __A						:							Any      =						tmp_path / 'cache'
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 __A						:							List[Any]      =						{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
 __A						:							Optional[int]      =						features.copy() if features else default_expected_features
 __A						:							Any      =						(
     Features({feature: Value(UpperCamelCase_		) for feature, dtype in features.items()}		) if features is not None else None
 )
 __A						:							Any      =						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_				:					int  ,						UpperCamelCase_				:					Any  ,						UpperCamelCase_				:					Dict		)	->		str:
 __A						:							Tuple      =						tmp_path / 'cache'
 __A						:							Any      =						{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
 __A						:							Optional[int]      =						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_				:					Dict  ,						UpperCamelCase_				:					str  ,						UpperCamelCase_				:					Tuple		)	->		Optional[int]:
 if issubclass(UpperCamelCase_  ,						UpperCamelCase_		):
  __A						:							List[Any]      =						csv_path
 elif issubclass(UpperCamelCase_  ,						UpperCamelCase_		):
  __A						:							int      =						[csv_path]
 __A						:							int      =						tmp_path / 'cache'
 __A						:							List[str]      =						{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
 __A						:							Any      =						CsvDatasetReader(UpperCamelCase_  ,						cache_dir=UpperCamelCase_		).read()
 _check_csv_dataset(UpperCamelCase_  ,						UpperCamelCase_		)




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




def 					a			(  UpperCamelCase_				:					Any  ,						UpperCamelCase_				:					List[Any]		)	->		Dict:
 __A						:							Dict      =						tmp_path / 'cache'
 __A						:							Any      =						os.path.join(UpperCamelCase_  ,						'tmp.csv'		)
 __A						:							Optional[int]      =						CsvDatasetReader({'train': csv_path}  ,						cache_dir=UpperCamelCase_		).read()
 CsvDatasetWriter(dataset['train']  ,						UpperCamelCase_  ,						num_proc=1		).write()

 __A						:							Tuple      =						iter_csv_file(UpperCamelCase_		)
 __A						:							Optional[Any]      =						iter_csv_file(UpperCamelCase_		)

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




def 					a			(  UpperCamelCase_				:					List[Any]  ,						UpperCamelCase_				:					List[Any]		)	->		Optional[Any]:
 __A						:							int      =						tmp_path / 'cache'
 __A						:							int      =						os.path.join(UpperCamelCase_  ,						'tmp.csv'		)
 __A						:							int      =						CsvDatasetReader({'train': csv_path}  ,						cache_dir=UpperCamelCase_		).read()
 CsvDatasetWriter(dataset['train']  ,						UpperCamelCase_  ,						num_proc=2		).write()

 __A						:							Dict      =						iter_csv_file(UpperCamelCase_		)
 __A						:							Dict      =						iter_csv_file(UpperCamelCase_		)

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




def 					a			(  UpperCamelCase_				:					Tuple  ,						UpperCamelCase_				:					Union[str, Any]		)	->		int:
 __A						:							Any      =						tmp_path / 'cache'
 __A						:							Optional[Any]      =						os.path.join(UpperCamelCase_  ,						'tmp.csv'		)
 __A						:							List[str]      =						CsvDatasetReader({'train': csv_path}  ,						cache_dir=UpperCamelCase_		).read()
 with pytest.raises(UpperCamelCase_		):
  CsvDatasetWriter(dataset['train']  ,						UpperCamelCase_  ,						num_proc=0		)













"""simple docstring"""


import torch
from torch import autograd






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		Optional[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float					,      lowercase_	:		bool )		->				List[str]:
   __A						:							str      =						inputs.numel()
   __A						:							str      =						int(0.005 * nb_elems ) + 1
   if sigmoid:
    __A						:							Dict      =						(torch.sigmoid(lowercase_ ) > threshold).type(inputs.type() )
   else:
    __A						:							Tuple      =						(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
    __A						:							Any      =						inputs.flatten().kthvalue(max(nb_elems - nb_min					,      1 ) ).values
    __A						:							Union[str, Any]      =						(inputs > k_threshold).type(inputs.type() )
   return mask



  @staticmethod
  def     A_					(lowercase_	:		str					,      lowercase_	:		Any )		->				int:
   return gradOutput, None, None






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		List[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				Dict:
   __A						:							List[Any]      =						inputs.clone()
   __A     ,       __A						:							Optional[int]      =						inputs.flatten().sort(descending=lowercase_ )
   __A						:							Tuple      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							Optional[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Any      =						1
   return mask



  @staticmethod
  def     A_					(lowercase_	:		Tuple					,      lowercase_	:		Optional[Any] )		->				str:
   return gradOutput, None






class __a ( snake_case_					):



  @staticmethod
  def     A_					(lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				List[str]:
   __A						:							List[str]      =						inputs.clone()
   __A     ,       __A						:							List[Any]      =						inputs.abs().flatten().sort(descending=lowercase_ )
   __A						:							Dict      =						int(threshold * inputs.numel() )

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





