




'''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_(			lowerCamelCase ,					lowerCamelCase   )					->Optional[Any]:






       '''simple docstring'''




       assert isinstance(lowerCamelCase ,					lowerCamelCase   )
       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_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )					->List[Any]:






       '''simple docstring'''




       lowercase	:		str    =   tmp_path / '''cache'''
       lowercase	:		List[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():
              lowercase	:		Any    =   CsvDatasetReader(lowerCamelCase ,					cache_dir=lowerCamelCase ,					keep_in_memory=lowerCamelCase   ).read()
       _check_csv_dataset(lowerCamelCase ,					lowerCamelCase   )




@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_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )					->Optional[Any]:






       '''simple docstring'''




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




@pytest.mark.parametrize('''split''' ,					[None, NamedSplit('''train'''   ), '''train''', '''test''']   )
def 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )					->Optional[int]:






       '''simple docstring'''




       lowercase	:		Optional[int]    =   tmp_path / '''cache'''
       lowercase	:		Optional[Any]    =   {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       lowercase	:		Dict    =   CsvDatasetReader(lowerCamelCase ,					cache_dir=lowerCamelCase ,					split=lowerCamelCase   ).read()
       _check_csv_dataset(lowerCamelCase ,					lowerCamelCase   )
       assert dataset.split == split if split else "train"




@pytest.mark.parametrize('''path_type''' ,					[str, list]   )
def 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )					->List[Any]:






       '''simple docstring'''




       if issubclass(lowerCamelCase ,					lowerCamelCase   ):
              lowercase	:		List[str]    =   csv_path
       elif issubclass(lowerCamelCase ,					lowerCamelCase   ):
              lowercase	:		int    =   [csv_path]
       lowercase	:		Dict    =   tmp_path / '''cache'''
       lowercase	:		Tuple    =   {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       lowercase	:		Tuple    =   CsvDatasetReader(lowerCamelCase ,					cache_dir=lowerCamelCase   ).read()
       _check_csv_dataset(lowerCamelCase ,					lowerCamelCase   )




def 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase=("train",)   )					->Optional[int]:






       '''simple docstring'''




       assert isinstance(lowerCamelCase ,					lowerCamelCase   )
       for split in splits:
              lowercase	:		str    =   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_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )					->Optional[int]:






       '''simple docstring'''




       lowercase	:		List[Any]    =   tmp_path / '''cache'''
       lowercase	:		Tuple    =   {'''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	:		Optional[Any]    =   CsvDatasetReader({'''train''': csv_path} ,					cache_dir=lowerCamelCase ,					keep_in_memory=lowerCamelCase   ).read()
       _check_csv_datasetdict(lowerCamelCase ,					lowerCamelCase   )




@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_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )					->Optional[int]:






       '''simple docstring'''




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




@pytest.mark.parametrize('''split''' ,					[None, NamedSplit('''train'''   ), '''train''', '''test''']   )
def 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )					->str:






       '''simple docstring'''




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




def 		a_(			lowerCamelCase   )					->Dict:






       '''simple docstring'''




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




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Dict:






       '''simple docstring'''




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

       lowercase	:		Optional[int]    =   iter_csv_file(lowerCamelCase   )
       lowercase	:		str    =   iter_csv_file(lowerCamelCase   )

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




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->int:






       '''simple docstring'''




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

       lowercase	:		List[Any]    =   iter_csv_file(lowerCamelCase   )
       lowercase	:		str    =   iter_csv_file(lowerCamelCase   )

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





def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Tuple:






       '''simple docstring'''




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










'''simple docstring'''






from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

lowercase						:   Union[str, Any]      						=							TypeVar('''T''', bound=bool)

class 	SCREAMING_SNAKE_CASE_		(							Generic[T]					):



 def __init__(	self ,		A			):






        '''simple docstring'''




        lowercase	:		T    =   value
        lowercase	:		SkewNode[T] | None    =   None
        lowercase	:		SkewNode[T] | None    =   None



 @property
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        return self._value





 @staticmethod
 def  SCREAMING_SNAKE_CASE(	A ,		A			):






        '''simple docstring'''




        if not roota:
               return roota

        if not roota:
               return roota

        if roota.value > roota.value:
               lowercase,  lowercase	:		Union[str, Any]    =   roota, roota

        lowercase	:		Optional[int]    =   roota
        lowercase	:		Tuple    =   roota.right
        lowercase	:		Union[str, Any]    =   roota.left
        lowercase	:		Any    =   SkewNode.merge(A ,		A			)

        return result






class 	SCREAMING_SNAKE_CASE_		(							Generic[T]					):



 def __init__(	self ,		A = ()			):






        '''simple docstring'''




        lowercase	:		SkewNode[T] | None    =   None
        if data:
               for item in data:
                      self.insert(A			)



 def __bool__(	self			):






        '''simple docstring'''




        return self._root is not None



 def __iter__(	self			):






        '''simple docstring'''




        lowercase	:		list[Any]    =   []
        while self:
               result.append(self.pop()			)

        # Pushing items back to the heap not to clear it.
        for item in result:
               self.insert(A			)

        return iter(A			)



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        lowercase	:		List[str]    =   SkewNode.merge(self._root ,		SkewNode(A			)			)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		List[Any]    =   self.top()
        lowercase	:		int    =   (
            SkewNode.merge(self._root.left ,		self._root.right			) if self._root else None
        )

        return result



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        if not self._root:
               raise IndexError('''Can\'t get top element for the empty heap.'''			)
        return self._root.value





 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Optional[Any]    =   None


if __name__ == "__main__":
      import doctest

      doctest.testmod()




