


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	(					SCREAMING_SNAKE_CASE						:					Tuple     ,			SCREAMING_SNAKE_CASE						:					List[str]  ):







 """simple docstring"""



 assert isinstance(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )
 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	(					SCREAMING_SNAKE_CASE						:					List[str]     ,			SCREAMING_SNAKE_CASE						:					int     ,			SCREAMING_SNAKE_CASE						:					Tuple  ):







 """simple docstring"""



 UpperCamelCase__       :     Optional[Any] 							= 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__       :     List[str] 							= CsvDatasetReader(SCREAMING_SNAKE_CASE     ,			cache_dir=SCREAMING_SNAKE_CASE     ,			keep_in_memory=SCREAMING_SNAKE_CASE  ).read()
 _check_csv_dataset(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )

@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	(					SCREAMING_SNAKE_CASE						:					Dict     ,			SCREAMING_SNAKE_CASE						:					Optional[int]     ,			SCREAMING_SNAKE_CASE						:					Any  ):







 """simple docstring"""



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

@pytest.mark.parametrize('''split'''     ,			[None, NamedSplit('''train'''  ), '''train''', '''test''']  )
def       _a	(					SCREAMING_SNAKE_CASE						:					Union[str, Any]     ,			SCREAMING_SNAKE_CASE						:					List[Any]     ,			SCREAMING_SNAKE_CASE						:					Any  ):







 """simple docstring"""



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

@pytest.mark.parametrize('''path_type'''     ,			[str, list]  )
def       _a	(					SCREAMING_SNAKE_CASE						:					List[str]     ,			SCREAMING_SNAKE_CASE						:					Dict     ,			SCREAMING_SNAKE_CASE						:					int  ):







 """simple docstring"""



 if issubclass(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  ):
  UpperCamelCase__       :     List[str] 							= csv_path
 elif issubclass(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  ):
  UpperCamelCase__       :     int 							= [csv_path]
 UpperCamelCase__       :     Union[str, Any] 							= tmp_path / '''cache'''
 UpperCamelCase__       :     List[Any] 							= {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 UpperCamelCase__       :     str 							= CsvDatasetReader(SCREAMING_SNAKE_CASE     ,			cache_dir=SCREAMING_SNAKE_CASE  ).read()
 _check_csv_dataset(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )

def       _a	(					SCREAMING_SNAKE_CASE						:					int     ,			SCREAMING_SNAKE_CASE						:					Tuple     ,			SCREAMING_SNAKE_CASE						:					Union[str, Any]=("train",)  ):







 """simple docstring"""



 assert isinstance(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )
 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	(					SCREAMING_SNAKE_CASE						:					Dict     ,			SCREAMING_SNAKE_CASE						:					Dict     ,			SCREAMING_SNAKE_CASE						:					Optional[Any]  ):







 """simple docstring"""



 UpperCamelCase__       :     Optional[int] 							= tmp_path / '''cache'''
 UpperCamelCase__       :     Union[str, 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__       :     Union[str, Any] 							= CsvDatasetReader({'''train''': csv_path}     ,			cache_dir=SCREAMING_SNAKE_CASE     ,			keep_in_memory=SCREAMING_SNAKE_CASE  ).read()
 _check_csv_datasetdict(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )

@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	(					SCREAMING_SNAKE_CASE						:					Optional[int]     ,			SCREAMING_SNAKE_CASE						:					List[Any]     ,			SCREAMING_SNAKE_CASE						:					Tuple  ):







 """simple docstring"""



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

@pytest.mark.parametrize('''split'''     ,			[None, NamedSplit('''train'''  ), '''train''', '''test''']  )
def       _a	(					SCREAMING_SNAKE_CASE						:					int     ,			SCREAMING_SNAKE_CASE						:					Union[str, Any]     ,			SCREAMING_SNAKE_CASE						:					List[str]  ):







 """simple docstring"""



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

def       _a	(					SCREAMING_SNAKE_CASE						:					Any  ):







 """simple docstring"""



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

def       _a	(					SCREAMING_SNAKE_CASE						:					Optional[Any]     ,			SCREAMING_SNAKE_CASE						:					List[str]  ):







 """simple docstring"""



 UpperCamelCase__       :     Union[str, Any] 							= tmp_path / '''cache'''
 UpperCamelCase__       :     Optional[int] 							= os.path.join(SCREAMING_SNAKE_CASE     ,			'''tmp.csv'''  )
 UpperCamelCase__       :     Union[str, Any] 							= CsvDatasetReader({'''train''': csv_path}     ,			cache_dir=SCREAMING_SNAKE_CASE  ).read()
 CsvDatasetWriter(dataset['''train''']     ,			SCREAMING_SNAKE_CASE     ,			num_proc=1  ).write()

 UpperCamelCase__       :     Dict 							= iter_csv_file(SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     Optional[Any] 							= iter_csv_file(SCREAMING_SNAKE_CASE  )

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

def       _a	(					SCREAMING_SNAKE_CASE						:					int     ,			SCREAMING_SNAKE_CASE						:					List[Any]  ):







 """simple docstring"""



 UpperCamelCase__       :     Dict 							= tmp_path / '''cache'''
 UpperCamelCase__       :     List[Any] 							= os.path.join(SCREAMING_SNAKE_CASE     ,			'''tmp.csv'''  )
 UpperCamelCase__       :     Optional[int] 							= CsvDatasetReader({'''train''': csv_path}     ,			cache_dir=SCREAMING_SNAKE_CASE  ).read()
 CsvDatasetWriter(dataset['''train''']     ,			SCREAMING_SNAKE_CASE     ,			num_proc=2  ).write()

 UpperCamelCase__       :     str 							= iter_csv_file(SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     Optional[Any] 							= iter_csv_file(SCREAMING_SNAKE_CASE  )

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






def       _a	(					SCREAMING_SNAKE_CASE						:					Optional[int]     ,			SCREAMING_SNAKE_CASE						:					List[Any]  ):







 """simple docstring"""



 UpperCamelCase__       :     Union[str, Any] 							= tmp_path / '''cache'''
 UpperCamelCase__       :     Optional[Any] 							= os.path.join(SCREAMING_SNAKE_CASE     ,			'''tmp.csv'''  )
 UpperCamelCase__       :     Union[str, Any] 							= CsvDatasetReader({'''train''': csv_path}     ,			cache_dir=SCREAMING_SNAKE_CASE  ).read()
 with pytest.raises(SCREAMING_SNAKE_CASE  ):
  CsvDatasetWriter(dataset['''train''']     ,			SCREAMING_SNAKE_CASE     ,			num_proc=0  )






import numpy as np

def       _a	(					SCREAMING_SNAKE_CASE						:					Tuple  ):







 """simple docstring"""



 UpperCamelCase__       :     str 							= np.exp(SCREAMING_SNAKE_CASE  )

 # Add up the all the exponentials
 UpperCamelCase__       :     Optional[int] 							= np.sum(SCREAMING_SNAKE_CASE  )

 # Divide every exponent by the sum of all exponents
 UpperCamelCase__       :     Optional[Any] 							= exponent_vector / sum_of_exponents

 return softmax_vector


if __name__ == "__main__":
       print(softmax((0,)))


